@Article{info:doi/10.2196/56877, author="Cvijanovic, Dane and Grubor, Nikola and Rajovic, Nina and Vucevic, Mira and Miltenovic, Svetlana and Laban, Marija and Mostic, Tatjana and Tasic, Radica and Matejic, Bojana and Milic, Natasa", title="Assessing COVID-19 Mortality in Serbia's Capital: Model-Based Analysis of Excess Deaths", journal="JMIR Public Health Surveill", year="2025", month="Apr", day="17", volume="11", pages="e56877", keywords="COVID-19", keywords="COVID-19 impact", keywords="SARS-Cov-2", keywords="coronavirus", keywords="respiratory", keywords="infectious disease", keywords="pulmonary", keywords="pandemic", keywords="excess mortality", keywords="death rate", keywords="death toll", keywords="centralized health care", keywords="urban", keywords="Serbia", keywords="dense population", keywords="public health", keywords="surveillance", abstract="Background: Concerns have been raised about discrepancies in COVID-19 mortality data, particularly between preliminary and final datasets of vital statistics in Serbia. In the original preliminary dataset, released daily during the ongoing pandemic, there was an underestimation of deaths in contrast to those reported in the subsequently released yearly dataset of vital statistics. Objective: This study aimed to assess the accuracy of the final mortality dataset and justify its use in further analyses. In addition, we quantified the relative impact of COVID-19 on the death rate in the Serbian capital's population. In the process, we aimed to explore whether any evidence of cause-of-death misattribution existed in the final published datasets. Methods: Data were sourced from the electronic databases of the Statistical Office of the Republic of Serbia. The dataset included yearly recorded deaths and the causes of death of all citizens currently living in the territory of Belgrade, the capital of the Republic of Serbia, from 2015 to 2021. Standardization and modeling techniques were utilized to quantify the direct impact of COVID-19 and to estimate excess deaths. To account for year-to-year trends, we used a mixed-effects hierarchical Poisson generalized linear regression model to predict mortality for 2020 and 2021. The model was fitted to the mortality data observed from 2015 to 2019 and used to generate mortality predictions for 2020 and 2021. Actual death rates were then compared to the obtained predictions and used to generate excess mortality estimates. Results: The total number of excess deaths, calculated from model estimates, was 3175 deaths (99\% CI 1715-4094) for 2020 and 8321 deaths (99\% CI 6975-9197) for 2021. The ratio of estimated excess deaths to reported COVID-19 deaths was 1.07. The estimated increase in mortality during 2020 and 2021 was 12.93\% (99\% CI 15.74\%-17.33\%) and 39.32\% (99\% CI 35.91\%-39.32\%) from the expected values, respectively. Those aged 0?19 years experienced an average decrease in mortality of 22.43\% and 23.71\% during 2020 and 2021, respectively. For those aged up to 39 years, there was a slight increase in mortality (4.72\%) during 2020. However, in 2021, even those aged 20?39 years had an estimated increase in mortality of 32.95\%. For people aged 60?79 years, there was an estimated increase in mortality of 16.95\% and 38.50\% in 2020 and 2021, respectively. For those aged >80 years, the increase was estimated at 11.50\% and 34.14\% in 2020 and 2021, respectively. The model-predicted deaths matched the non-COVID-19 deaths recorded in the territory of Belgrade. This concordance between the predicted and recorded non-COVID-19 deaths provides evidence that the cause-of-death misattribution did not occur in the territory of Belgrade. Conclusions: The finalized mortality dataset for Belgrade can be safely used in COVID-19 impact analysis. Belgrade experienced a significant increase in mortality during 2020 and 2021, with most of the excess mortality attributable to SARS-CoV-2. Concerns about increased mortality from causes other than COVID-19 in Belgrade seem misplaced as their impact appears negligible. ", doi="10.2196/56877", url="https://publichealth.jmir.org/2025/1/e56877" } @Article{info:doi/10.2196/67368, author="Marian, Marian and P{\'e}rez, L. Ramona and Reed, Elizabeth and Hurst, Samantha and Lundgren, Rebecka and McClain, C. Amanda and Barker, M. Kathryn", title="Exploring the Use of Social Media for Activism by Mexican Nongovernmental Organizations Using Posts From the 16 Days of Activism Against Gender-Based Violence Campaign: Thematic Content Analysis", journal="JMIR Infodemiology", year="2025", month="Apr", day="17", volume="5", pages="e67368", keywords="gender-based violence", keywords="Mexico", keywords="hashtag activism", keywords="feminist social activism", keywords="hashtag feminism", keywords="Twitter", keywords="X", keywords="nongovernmental organization", keywords="social media", abstract="Background: In the past decade, hashtag feminism has emerged in Mexico as a prevalent strategy to build social movements against gender-based violence (GBV). For example, during the global ``16 Days of Activism Against GBV'' campaign held between November 25 and December 10 each year, Mexico-based nongovernmental organizations (NGOs) turn to X (formerly known as Twitter) to share messages. Despite this prevalence, there is limited research on the type of information shared by these NGO activists on social media and the public's engagement with these messages. Objective: This study aims to explore the themes covered by Mexican NGOs on X and examine what types of messages related to GBV potentially resonated more with the public. Methods: We collated and reviewed posts (commonly known as tweets) published in Spanish on the platform X by Mexico-based NGOs between November 25 and December 10 of 2020, 2021, and 2022, a period when digital interactions increased during the COVID-19 pandemic. We then extracted posts using the following 4 hashtags: \#16d{\'i}as, \#16DiasdeActivismo, or \#16D{\'i}asdeActivismo; \#25N or \#25Noviembre; \#DiaNaranja or \#D{\'i}aNaranja; and \#PintaElMundoDeNaranja. We subsequently assessed the number of likes each post had and retained the top 200 posts from each year with the highest number of likes. We used the iterative content analysis process and the inductive 6-step qualitative thematic analysis method in NVivo software to code and analyze the final 600 posts. Results: Five themes emerged from the 16 Days of Activism Against GBV campaigns, covering both knowledge-sharing and activism-generating messages as follows: (1) activism and how to be an activist, (2) types of GBV most commonly highlighted in posts, (3) changing public discourse surrounding GBV, (4) GBV as a violation of human rights, and (5) the COVID-19 pandemic's impact on GBV. Most of the messages on these posts exclusively mentioned women and younger girls, while a few included adolescents. Gaps in the representation of vulnerable populations were also found. Conclusions: The posts from this campaign that were highly liked by the public reflect some of the most significant societal issues currently present in the country. Our results could help guide further GBV campaigns. Still, further research related to hashtag feminism by Mexico-based NGOs on GBV is needed to understand the population that NGOs reach and how the messages shared on these campaigns translate into activism on online and offline social media platforms. ", doi="10.2196/67368", url="https://infodemiology.jmir.org/2025/1/e67368" } @Article{info:doi/10.2196/67646, author="Lin, Sheng-Hsuan and Su, Kuan-Pin and Tsou, Hsiao-Hui and Hsia, Pei-Hsuan and Lin, Yu-Hsuan", title="Search Volume of Insomnia and Suicide as Digital Footprints of Global Mental Health During the COVID-19 Pandemic: 3-Year Infodemiology Study", journal="J Med Internet Res", year="2025", month="Apr", day="17", volume="27", pages="e67646", keywords="mediation analysis", keywords="internet searches", keywords="stay-at-home measures", keywords="insomnia", keywords="suicide", keywords="COVID-19", abstract="Background: The global COVID-19 pandemic's mental health impact was primarily studied in the initial year of lockdowns but remained underexplored in subsequent years despite evolving conditions. This study aimed to address this gap by investigating how COVID-19--related factors, including nationwide COVID-19 deaths and incidence rates, influenced mental health indicators over time. Objective: This study aimed to examine the interplay among national COVID-19 pandemic deaths, incidence rates, stay-at-home behaviors, and mental health indicators across different income-level countries. Specifically, we assessed the mediating role of stay-at-home behaviors in the relationship between the COVID-19 pandemic deaths and mental health indicators. Methods: We analyzed data from 45 countries spanning March 2020 to October 2022. COVID-19--related factors included national COVID-19 pandemic deaths and incidence rates, obtained from publicly available datasets. Stay-at-home behaviors were assessed using Google Location History data, which captured residence-based cell phone activity as a proxy for mobility patterns. Mental health indicators were evaluated through Google Trends data, measuring changes in search volumes for ``insomnia'' and ``suicide.'' The interplay among these variables was assessed using mediation analysis to quantify the proportion mediated by stay-at-home behaviors in the association between COVID-19 deaths and mental health indicators. Results: In high-income countries, during the first pandemic year (March 2020 to February 2021), a higher monthly COVID-19 death count was associated with increased searches for ``insomnia,'' with a total effect estimate of 2.1{\texttimes}10-4 (95\% CI 4.3{\texttimes}10-5 to 3.9{\texttimes}10-4; P=.01). Stay-at-home behaviors mediated 31.9\% of this effect (95\% CI 9.8\% to 127.5\%, P=.02). This association weakened and became nonsignificant in the second and third years (P=.25 and P=.54, respectively). For middle-income countries, a different pattern emerged regarding ``suicide'' searches. Higher COVID-19 death counts were linked to a decline in ``suicide'' searches in the first (estimate: --3.5{\texttimes}10-4, 95\% CI --6.1{\texttimes}10-4 to --9.8{\texttimes}10-5; P=.006) and second years (P=.01). Mediation analysis indicated that this effect was not significantly explained by stay-at-home behaviors, suggesting the influence of other societal factors. In high-income countries, no significant association between COVID-19 deaths and ``suicide'' searches was observed in the first year (P=.86). However, a positive association emerged in the second year, approaching statistical significance (estimate: 2.2{\texttimes}10-4, 95\% CI --9.5{\texttimes}10-7 to 4.2{\texttimes}10-4; P=.05), and became significant in the third year (estimate: 5.0{\texttimes}10-4, 95\% CI 5.0{\texttimes}10-5 to 1.0{\texttimes}10-3; P=.03,), independent of stay-at-home behaviors. Conclusions: Our findings highlight how the mental health impact of the pandemic varied across income groups and evolved over time. The mediating effect of stay-at-home behaviors was significant in the early phases but diminished in later stages, particularly in high-income countries. Meanwhile, middle-income countries exhibited unique patterns that suggest alternative protective factors. These insights can inform tailored mental health interventions and policy strategies in future public health crises. ", doi="10.2196/67646", url="https://www.jmir.org/2025/1/e67646" } @Article{info:doi/10.2196/59076, author="Maharjan, Julina and Zhu, Jianfeng and King, Jennifer and Phan, NhatHai and Kenne, Deric and Jin, Ruoming", title="Large-Scale Deep Learning--Enabled Infodemiological Analysis of Substance Use Patterns on Social Media: Insights From the COVID-19 Pandemic", journal="JMIR Infodemiology", year="2025", month="Apr", day="17", volume="5", pages="e59076", keywords="substance use", keywords="social media", keywords="deep learning", keywords="Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach", keywords="human-in-the-loop", keywords="COVID-19", abstract="Background: The COVID-19 pandemic intensified the challenges associated with mental health and substance use (SU), with societal and economic upheavals leading to heightened stress and increased reliance on drugs as a coping mechanism. Centers for Disease Control and Prevention data from June 2020 showed that 13\% of Americans used substances more frequently due to pandemic-related stress, accompanied by an 18\% rise in drug overdoses early in the year. Simultaneously, a significant increase in social media engagement provided unique insights into these trends. Our study analyzed social media data from January 2019 to December 2021 to identify changes in SU patterns across the pandemic timeline, aiming to inform effective public health interventions. Objective: This study aims to analyze SU from large-scale social media data during the COVID-19 pandemic, including the prepandemic and postpandemic periods as baseline and consequence periods. The objective was to examine the patterns related to a broader spectrum of drug types with underlying themes, aiming to provide a more comprehensive understanding of SU trends during the COVID-19 pandemic. Methods: We leveraged a deep learning model, Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach (RoBERTa), to analyze 1.13 billion Twitter (subsequently rebranded X) posts from January 2019 to December 2021, aiming to identify SU posts. The model's performance was enhanced by a human-in-the-loop strategy that subsequently enriched the annotated data used during the fine-tuning phase. To gain insights into SU trends over the study period, we applied a range of statistical techniques, including trend analysis, k-means clustering, topic modeling, and thematic analysis. In addition, we integrated the system into a real-time application designed for monitoring and preventing SU within specific geographic locations. Results: Our research identified 9 million SU posts in the studied period. Compared to 2019 and 2021, the most substantial display of SU-related posts occurred in 2020, with a sharp 21\% increase within 3 days of the global COVID-19 pandemic declaration. Alcohol and cannabinoids remained the most discussed substances throughout the research period. The pandemic particularly influenced the rise in nonillicit substances, such as alcohol, prescription medication, and cannabinoids. In addition, thematic analysis highlighted COVID-19, mental health, and economic stress as the leading issues that contributed to the influx of substance-related posts during the study period. Conclusions: This study demonstrates the potential of leveraging social media data for real-time detection of SU trends during global crises. By uncovering how factors such as mental health and economic stress drive SU spikes, particularly in alcohol and prescription medication, we offer crucial insights for public health strategies. Our approach paves the way for proactive, data-driven interventions that will help mitigate the impact of future crises on vulnerable populations. ", doi="10.2196/59076", url="https://infodemiology.jmir.org/2025/1/e59076", url="http://www.ncbi.nlm.nih.gov/pubmed/40244656" } @Article{info:doi/10.2196/56080, author="Liu, Xiao and Susarla, Anjana and Padman, Rema", title="Promoting Health Literacy With Human-in-the-Loop Video Understandability Classification of YouTube Videos: Development and Evaluation Study", journal="J Med Internet Res", year="2025", month="Apr", day="8", volume="27", pages="e56080", keywords="patient education", keywords="video analysis", keywords="video understandability", keywords="machine learning", keywords="cotraining", keywords="human-in-the-loop", keywords="augmented intelligence", keywords="artificial intelligence", keywords="AI", abstract="Background: An estimated 93\% of adults in the United States access the internet, with up to 80\% looking for health information. However, only 12\% of US adults are proficient enough in health literacy to interpret health information and make informed health care decisions meaningfully. With the vast amount of health information available in multimedia formats on social media platforms such as YouTube and Facebook, there is an urgent need and a unique opportunity to design an automated approach to curate online health information using multiple criteria to meet the health literacy needs of a diverse population. Objective: This study aimed to develop an automated approach to assessing the understandability of patient educational videos according to the Patient Education Materials Assessment Tool (PEMAT) guidelines and evaluating the impact of video understandability on viewer engagement. We also offer insights for content creators and health care organizations on how to improve engagement with these educational videos on user-generated content platforms. Methods: We developed a human-in-the-loop, augmented intelligence approach that explicitly focused on the human-algorithm interaction, combining PEMAT-based patient education constructs mapped to features extracted from the videos, annotations of the videos by domain experts, and cotraining methods from machine learning to assess the understandability of videos on diabetes and classify them. We further examined the impact of understandability on several dimensions of viewer engagement with the videos. Results: We collected 9873 YouTube videos on diabetes using search keywords extracted from a patient-oriented forum and reviewed by a medical expert. Our machine learning methods achieved a weighted precision of 0.84, a weighted recall of 0.79, and an F1-score of 0.81 in classifying video understandability and could effectively identify patient educational videos that medical experts would like to recommend for patients. Videos rated as highly understandable had an average higher view count (average treatment effect [ATE]=2.55; P<.001), like count (ATE=2.95; P<.001), and comment count (ATE=3.10; P<.001) than less understandable videos. In addition, in a user study, 4 medical experts recommended 72\% (144/200) of the top 10 videos ranked by understandability compared to 40\% (80/200) of the top 10 videos ranked by YouTube's default algorithm for 20 ramdomly selected search keywords. Conclusions: We developed a human-in-the-loop, scalable algorithm to assess the understandability of health information on YouTube. Our method optimally combines expert input with algorithmic support, enhancing engagement and aiding medical experts in recommending educational content. This solution also guides health care organizations in creating effective patient education materials for underserved health topics. ", doi="10.2196/56080", url="https://www.jmir.org/2025/1/e56080" } @Article{info:doi/10.2196/59231, author="Avery, Atiya and Baker, White Elizabeth and Wright, Brittany and Avery, Ishmael and Gomez, Dream", title="Media Framing and Portrayals of Ransomware Impacts on Informatics, Employees, and Patients: Systematic Media Literature Review", journal="J Med Internet Res", year="2025", month="Apr", day="8", volume="27", pages="e59231", keywords="cybersecurity", keywords="media frames", keywords="medical informatics", keywords="practitioners", keywords="health care provider", keywords="systematic review", keywords="employees", keywords="patient", keywords="mortality", keywords="morbidity", keywords="news media", keywords="ransomware", keywords="health information system", keywords="database", keywords="health care service", abstract="Background: Ransomware attacks on health care provider information systems have the potential to impact patient mortality and morbidity, and event details are relayed publicly through news stories. Despite this, little research exists on how these events are depicted in the media and the subsequent impacts of these events. Objective: This study used collaborative qualitative analysis to understand how news media frames and portrays the impacts of ransomware attacks on health informatic systems, employees, and patients. Methods: We developed and implemented a systematic search protocol across academic news databases, which included (1) the Associated Press Newswires, (2) Newspaper Source, and (3) Access World News (Newsbank), using the search string ``(hospital OR healthcare OR clinic OR medical) AND (ransomware OR denial of service OR cybersecurity).'' In total, 4 inclusion and 4 exclusion criteria were applied as part of the search protocol. For articles included in the study, we performed an inductive and deductive analysis of the news articles, which included their article characteristics, impact portrayals, media framings, and discussions of the core functions outlined in the National Institute of Standards and Technologies (NIST) Cybersecurity Framework 2.0. Results: The search returned 2195 articles, among which 48 news articles published from 2009 to 2023 were included in the study. First, an analysis of the geographic prevalence showed that the United States (34/48, 71\%), followed to a lesser extent by India (4/48, 8\%) and Canada (3/48, 6\%), featured more prominently in our sample. Second, there were no apparent year-to-year patterns in the occurrence of reported events of ransomware attacks on health care provider information systems. Third, ransomware attacks on health care provider information systems appeared to cascade from a single point of failure. Fourth, media frames regarding ``human interest'' and ``responsibility'' were equally representative in the sample. The ``response'' function of the NIST Cybersecurity Framework 2.0 was noted in 36 of the 48 (75\%) articles. Finally, we noted that 17 (14\%) of the articles assessed for eligibility were excluded from this study as they promoted a product or service or spoke hypothetically about ransomware events among health care providers. Conclusions: Organizational response represented a substantial aspect of the news articles in our corpus. To address the perception of health care providers' management of ransomware attacks, they should take measures to influence perceptions of (1) health care service continuity, despite a lack of availability of health informatics; (2) responsibility for the patient experience; and (3) acknowledgment of the strain on health care practitioners and patients through a public declaration of support and gratitude. Furthermore, the media portrayals revealed a prevalence of single points of failure in the health informatics system, thus providing guidance for the implementation of safety protocols that could significantly reduce cascading impacts. ", doi="10.2196/59231", url="https://www.jmir.org/2025/1/e59231" } @Article{info:doi/10.2196/54650, author="Al-Mansoori, Alghalia and Al Hayk, Ola and Qassmi, Sharifa and Aziz, M. Sarah and Haouari, Fatima and Chivese, Tawanda and Tamimi, Faleh and Daud, Alaa", title="Infoveillance of COVID-19 Infections in Dentistry Using Platform X: Descriptive Study", journal="J Med Internet Res", year="2025", month="Apr", day="3", volume="27", pages="e54650", keywords="COVID-19", keywords="dentistry", keywords="infection", keywords="patient", keywords="infoveillance", keywords="platform X", keywords="Twitter", abstract="Background: The effect of the COVID-19 pandemic on the well-being of dental professionals and patients has been difficult to track and quantify. X (formerly known as Twitter) proved to be a useful infoveillance tool for tracing the impact of the COVID-19 pandemic worldwide. Objective: This study aims to investigate the use of X to track COVID-19 infections and deaths associated with dental practices. Methods: English Tweets reporting infections or deaths associated with the dental practice were collected from January 1, 2020, to March 31, 2021. Tweets were searched manually using the X Pro search engine (previously known as TweetDeck [X Corp], Twitter Inc, and TweetDeck Ltd) and automatically using a tweet crawler on the X Academic Research application programming interface. Queries included keywords on infection or death of dental staff and patients caused by COVID-19. Tweets registering events on infection or death of dentists, dental staff, and patients as part of their conversation were included. Results: A total of 5641 eligible tweets were retrieved. Of which 1583 (28.1\%) were deemed relevant after applying the inclusion and exclusion criteria. Of the relevant tweets, 311 (19.6\%) described infections at dental practices, where 1168 (86.9\%) infection cases were reported among dentists, 134 (9.9\%) dental staff, and 41 (3.1\%) patients. The majority of reported infections occurred in the United States, India, and Canada, affecting individuals aged 20-51 years. Among the 600 documented deaths, 253 (42.2\%) were dentists, 22 (3.7\%) were dental staff, and 7 (1.2\%) were patients. The countries with the highest number of deaths were the United States, Pakistan, and India, with an affected age range of 23-83 years. Conclusions: The data suggest that analyses of X information in populations of affected areas may provide useful information regarding the impact of a pandemic on the dental profession and demonstrate a correlation with suspected and confirmed infection or death cases. Platform X shows potential as an early predictor for disease spread. However, further research is required to confirm its validity. ", doi="10.2196/54650", url="https://www.jmir.org/2025/1/e54650" } @Article{info:doi/10.2196/67361, author="Amoozegar, B. Jacqueline and Williams, Peyton and Giombi, C. Kristen and Richardson, Courtney and Shenkar, Ella and Watkins, L. Rebecca and O'Donoghue, C. Amie and Sullivan, W. Helen", title="Consumer Engagement With Risk Information on Prescription Drug Social Media Pages: Findings From In-Depth Interviews", journal="J Med Internet Res", year="2025", month="Mar", day="25", volume="27", pages="e67361", keywords="social media", keywords="prescription drugs", keywords="risk information", keywords="safety information", keywords="Facebook", keywords="Instagram", keywords="prescription", keywords="risk", keywords="information", keywords="safety", keywords="interview", keywords="consumer engagement", keywords="digital", keywords="drug promotion", keywords="user experience", keywords="promotion", abstract="Background: The volume of digital drug promotion has grown over time, and social media has become a source of information about prescription drugs for many consumers. Pharmaceutical companies currently present risk information about prescription drugs they promote in a variety of ways within and across social media platforms. There is scarce research on consumers' interactions with prescription drug promotion on social media, particularly on which features may facilitate or inhibit consumers' ability to find, review, and comprehend drug information. This is concerning because it is critical for consumers to know and weigh drug benefits and risks to be able to make informed decisions regarding medical treatment. Objective: We aimed to develop an understanding of the user interface (UI) and user experience (UX) of social media pages and posts created by pharmaceutical companies to promote drugs and how UI or UX design features impact consumers' interactions with drug information. Methods: We conducted in-person interviews with 54 consumers segmented into groups by device type (laptop or mobile phone), social media platform (Facebook or Instagram), and age. Interviewers asked participants to navigate to and review a series of 4 pages and 3 posts on their assigned device and platform. Interviewers encouraged participants to ``think aloud,'' as they interacted with the stimuli during a brief observation period. Following each observation period, participants were asked probing questions. An analyst reviewed video recordings of the observation periods to abstract quantitative interaction data on whether a participant clicked on or viewed risk information at each location it appeared on each page. Participants' responses were organized in a metamatrix, which we used to conduct thematic analysis. Results: Observational data revealed that 59\% of participants using Facebook and 70\% of participants using Instagram viewed risk information in at least 1 possible location on average across all pages tested during the observation period. There was not a single location across the Facebook pages that participants commonly clicked on to view risk information. However, a video with scrolling risk information attracted more views than other features. On Instagram, at least half of the participants consistently clicked on the highlighted story with risk information across the pages. Although thematic analysis showed that most participants were able to identify the official pages and risk information for each drug, auto-scrolling text and text size posed barriers to identification and comprehensive review for some participants. Participants generally found it more difficult to identify the drugs' indications than risks. Participants using Instagram more frequently reported challenges identifying risks and indications compared to those using Facebook. Conclusions: UI or UX design features can facilitate or pose barriers to users' identification, review, and comprehension of the risk information provided on prescription drugs' social media pages and posts. ", doi="10.2196/67361", url="https://www.jmir.org/2025/1/e67361" } @Article{info:doi/10.2196/57084, author="Eguchi, Kana and Kubota, Takeaki and Koyanagi, Tomoyoshi and Muto, Manabu", title="Real-World Data on Alcohol Consumption Behavior Among Smartphone Health Care App Users in Japan: Retrospective Study", journal="Online J Public Health Inform", year="2025", month="Mar", day="25", volume="17", pages="e57084", keywords="alcohol consumption", keywords="individual behavior", keywords="mobile health", keywords="mobile health app", keywords="mobile health care app log-based survey", keywords="real-world data", keywords="RWD", keywords="RWD analysis", keywords="smartphone health care app", keywords="surveillance system", keywords="health care app", abstract="Background: Although many studies have used smartphone apps to examine alcohol consumption, none have clearly delineated long-term (>1 year) consumption among the general population. Objective: The objective of our study is to elucidate in detail the alcohol consumption behavior of alcohol drinkers in Japan using individual real-world data. During the state of emergency associated with the COVID-19 outbreak, the government requested that people restrict social gatherings and stay at home, so we hypothesize that alcohol consumption among Japanese working people decreased during this period due to the decrease in occasions for alcohol consumption. This analysis was only possible with individual real-world data. We also aimed to clarify the effects of digital interventions based on notifications about daily alcohol consumption. Methods: We conducted a retrospective study targeting 5-year log data from January 1, 2018, to December 31, 2022, obtained from a commercial smartphone health care app (CALO mama Plus). First, to investigate the possible size of the real-world data, we investigated the rate of active users of this commercial smartphone app. Second, to validate the individual real-world data recorded in the app, we compared individual real-world data from 9991 randomly selected users with government-provided open data on the number of daily confirmed COVID-19 cases in Japan and with nationwide alcohol consumption data. To clarify the effects of digital interventions, we investigated the relationship between 2 types of notification records (ie, ``good'' and ``bad'') and a 3-day daily alcohol consumption log following the notification. The protocol of this retrospective study was approved by the Ethics Committee of the Kyoto University Graduate School and Faculty of Medicine (R4699). ", doi="10.2196/57084", url="https://ojphi.jmir.org/2025/1/e57084", url="http://www.ncbi.nlm.nih.gov/pubmed/40131328" } @Article{info:doi/10.2196/73062, author="Flaherty, Thomas Gerard and Mangan, Michael Ryan", title="Impact of Social Media Influencers on Amplifying Positive Public Health Messages", journal="J Med Internet Res", year="2025", month="Mar", day="21", volume="27", pages="e73062", keywords="social media", keywords="COVID-19", keywords="vaccination", keywords="personal brands", keywords="public health", keywords="wellness", keywords="global health", keywords="pandemic", keywords="Twitter", keywords="tweets", keywords="vaccine", keywords="longitudinal design", keywords="wellness influencers", keywords="hand annotation", keywords="antivaccination", keywords="infodemiology", doi="10.2196/73062", url="https://www.jmir.org/2025/1/e73062" } @Article{info:doi/10.2196/64679, author="Grimes, Robert David and Gorski, H. David", title="Quantifying Public Engagement With Science and Malinformation on COVID-19 Vaccines: Cross-Sectional Study", journal="J Med Internet Res", year="2025", month="Mar", day="21", volume="27", pages="e64679", keywords="misinformation", keywords="altmetrics", keywords="disinformation", keywords="malinformation", keywords="public engagement", keywords="medical journals", keywords="medicoscientific", keywords="public health", keywords="altmetric analysis", keywords="comparative analysis", keywords="social media", keywords="Twitter", keywords="vaccine", keywords="digital health", keywords="mHealth", keywords="mobile health", keywords="health informatics", abstract="Background: Medical journals are critical vanguards of research, and previous years have seen increasing public interest in and engagement with medicoscientific findings. How findings propagate and are understood and what harms erroneous claims might cause to public health remain unclear, especially on publicly contentious topics like COVID-19 vaccines. Gauging the engagement of the public with medical science and quantifying propagation patterns of medicoscientific papers are thus important undertakings. In contrast to misinformation and disinformation, which pivot on falsehood, the more nuanced issue of malinformation, where ostensibly true information is presented out of context or selectively curated to cause harm and misconception, has been less researched. As findings and facts can be selectively marshaled to present a misleading picture, it is crucial to consider this issue and its potential ramifications. Objective: This study aims to quantify patterns of public engagement with medical research and the vectors of propagation taken by a high-profile incidence of medical malinformation. Methods: In this work, we undertook an analysis of all altmetric engagements over a decade for 5 leading general-purpose medical journals, constituting approximately 9.8 million engagements with 84,529 papers. We identify and examine the proliferation of sentiment concerning a high-profile publication containing vaccine-negative malinformation. Engagement with this paper, with the highest altmetric score of any paper in an academic journal ever released, was tracked across media outlets worldwide and in social media users on Twitter (subsequently rebranded as X). Vectoring media sources were analyzed, and manual sentiment analysis on high-engagement Twitter shares of the paper was undertaken, contrasted with users' prior vaccine sentiment. Results: Results of this analysis suggested that this COVID-19 scientific malinformation was much more likely to be engaged and amplified with negative by vaccine-negative Twitter accounts than neutral ones (odds ratio 58.2, 95\% CI 9.7-658.0; P<.001), often alluding to the ostensible prestige of medical journals. Malinformation was frequently invoked by conspiracy theory websites and non-news sources (71/181 citations, 39.2\%) on the internet to cast doubt on the efficacy of vaccination, many of whom tended to cite the paper repeatedly (51/181, 28.2\%). Conclusions: Our findings suggest growing public interest in medical science and present evidence that medical and scientific journals need to be aware of not only the potential overt misinformation but also the more insidious impact of malinformation. Also, we discuss how journals and scientific communicators can reduce the influence of malinformation on public understanding. ", doi="10.2196/64679", url="https://www.jmir.org/2025/1/e64679" } @Article{info:doi/10.2196/66010, author="Watkins, Lea Shannon and Snodgrass, Katherine and Fahrion, Lexi and Shaw, Emily", title="Contextualizing Changes in e-Cigarette Use During the Early COVID-19 Pandemic and Accompanying Infodemic (``So Much Contradictory Evidence''): Qualitative Document Analysis of Reddit Forums", journal="J Med Internet Res", year="2025", month="Mar", day="20", volume="27", pages="e66010", keywords="vaping", keywords="nicotine", keywords="tobacco", keywords="health communication", keywords="social media", keywords="new media", abstract="Background: Understanding how social media platforms facilitate information exchange and influence behavior during health crises can enhance public health responses during times of uncertainty. While some risk factors for COVID-19 susceptibility and severity (eg, old age) were clear, whether e-cigarette use increased risk was not clear. People who used e-cigarettes had to navigate both the COVID-19 infodemic and a conflicting, politicized, and changing information environment about the interaction between COVID-19 and e-cigarette use. Objective: This study aims to characterize and contextualize e-cigarette--related behavior changes during the early COVID-19 pandemic and illuminate the role that social media played in decision-making. Methods: We conducted a qualitative analysis of COVID-19--related e-cigarette discussions on 3 Reddit forums about e-cigarettes. We collected 189 relevant discussion threads made in the first 6 months of the pandemic (collected from June 27, 2020, to July 3, 2020). Threads included 3155 total comments (mean 17 comments) from approximately 1200 unique Redditors. We developed and applied emergent codes related to e-cigarette perceptions and behaviors (eg, the role of nicotine in COVID-19 and do-it-yourself narratives) and web-based community interactions (eg, advice), identified thematic patterns across codes, and developed a model to synthesize the socioecological context of e-cigarette behaviors. Results: e-Cigarette subreddits provided a platform for Redditors to discuss perceptions and experiences with e-cigarettes, make sense of information, and provide emotional support. Discussions reflected an array of e-cigarette--related behavioral responses, including increases and decreases in use intensity, changes in purchasing practices (eg, stockpiling), and changes in vaping practices (eg, reusing disposable pods). This study presented a theoretically and empirically informed model of how circumstances created by the pandemic (eg, changes in activity space and product shortages) compelled behavior changes. Redditors drew from their existing perceptions, intentions, and experiences with nicotine and tobacco products; their personal pandemic experiences; and their participation on Reddit to decide whether and how to change their e-cigarette behaviors during the early pandemic. Forums reflected uncertainty, stress, and debate about the rapidly evolving and complicated public health information. Consumption and discussion of media (eg, news articles and peer-reviewed publications) on Reddit informed e-cigarette perceptions and behaviors. Decisions were complicated by distrust of the media. Conclusions: Variations in individual traits and environmental circumstances during the early COVID-19 pandemic provide context for why there was no unified direction of e-cigarette behavior change during this period. Information and discussion on Reddit also informed risk perceptions and decisions during the pandemic. Social media is an effective and important place to communicate public health information, particularly during crisis or disaster situations. Moving forward, transparent, accurate, and specific message development should consider the stress, struggles, and stigma of people who use e-cigarettes and address the roles mistrust and misinformation play in decisions. ", doi="10.2196/66010", url="https://www.jmir.org/2025/1/e66010" } @Article{info:doi/10.2196/56116, author="Zhao, Xiyu and Yang, Victor and Menta, Arjun and Blum, Jacob and Ranasinghe, Padmini", title="Exploring the Use of Social Media for Medical Problem Solving by Analyzing the Subreddit r/medical\_advice: Quantitative Analysis", journal="JMIR Infodemiology", year="2025", month="Mar", day="20", volume="5", pages="e56116", keywords="online health information", keywords="medical advice", keywords="Reddit", keywords="r/medical\_advice", keywords="health information--seeking behavior", keywords="user-generated content", keywords="subreddits", keywords="patient education", keywords="virtual environments", keywords="information quality", keywords="social media", keywords="medical problem", keywords="quantitative analyses", keywords="cross-sectional study", keywords="user interactions", keywords="online health", keywords="decision-making", keywords="social news", keywords="health information", abstract="Background: The advent of the internet has transformed the landscape of health information acquisition and sharing. Reddit has become a hub for such activities, such as the subreddit r/medical\_advice, affecting patients' knowledge and decision-making. While the popularity of these platforms is recognized, research into the interactions and content within these communities remains sparse. Understanding the dynamics of these platforms is crucial for improving online health information quality. Objective: This study aims to quantitatively analyze the subreddit r/medical\_advice to characterize the medical questions posed and the demographics of individuals providing answers. Insights into the subreddit's user engagement, information-seeking behavior, and the quality of shared information will contribute to the existing body of literature on health information seeking in the digital era. Methods: A cross-sectional study was conducted, examining all posts and top comments from r/medical\_advice since its creation on October 1, 2011. Data were collected on March 2, 2023, from pushhift.io, and the analysis included post and author flairs, scores, and engagement metrics. Statistical analyses were performed using RStudio and GraphPad Prism 9.0. Results: From October 2011 to March 2023, a total of 201,680 posts and 721,882 comments were analyzed. After excluding autogenerated posts and comments, 194,678 posts and 528,383 comments remained for analysis. A total of 41\% (77,529/194,678) of posts had no user flairs, while only 0.1\% (108/194,678) of posts were made by verified medical professionals. The average engagement per post was a score of 2 (SD 7.03) and 3.32 (SD 4.89) comments. In period 2, urgent questions and those with level-10 pain reported higher engagement, with significant differences in scores and comments based on flair type (P<.001). Period 3 saw the highest engagement in posts related to pregnancy and the lowest in posts about bones, joints, or ligaments. Media inclusion significantly increased engagement, with video posts receiving the highest interaction (P<.001). Conclusions: The study reveals a significant engagement with r/medical\_advice, with user interactions influenced by the type of query and the inclusion of visual media. High engagement with posts about pregnancy and urgent medical queries reflects a focused public interest and the subreddit's role as a preliminary health information resource. The predominance of nonverified medical professionals providing information highlights a shift toward community-based knowledge exchange, though it raises questions about the reliability of the information. Future research should explore cross-platform behaviors and the impact of misinformation on public health. Effective moderation and the involvement of verified medical professionals are recommended to enhance the subreddit's role as a reliable health information resource. ", doi="10.2196/56116", url="https://infodemiology.jmir.org/2025/1/e56116" } @Article{info:doi/10.2196/53399, author="Alhazzaa, Linah and Curcin, Vasa", title="Profiling Generalized Anxiety Disorder on Social Networks: Content and Behavior Analysis", journal="J Med Internet Res", year="2025", month="Mar", day="20", volume="27", pages="e53399", keywords="generalized anxiety disorder", keywords="mental health", keywords="Twitter", keywords="social media analysis", keywords="natural language processing", abstract="Background: Despite a dramatic increase in the number of people with generalized anxiety disorder (GAD), a substantial number still do not seek help from health professionals, resulting in reduced quality of life. With the growth in popularity of social media platforms, individuals have become more willing to express their emotions through these channels. Therefore, social media data have become valuable for identifying mental health status. Objective: This study investigated the social media posts and behavioral patterns of people with GAD, focusing on language use, emotional expression, topics discussed, and engagement to identify digital markers of GAD, such as anxious patterns and behaviors. These insights could help reveal mental health indicators, aiding in digital intervention development. Methods: Data were first collected from Twitter (subsequently rebranded as X) for the GAD and control groups. Several preprocessing steps were performed. Three measurements were defined based on Linguistic Inquiry and Word Count for linguistic analysis. GuidedLDA was also used to identify the themes present in the tweets. Additionally, users' behaviors were analyzed using Twitter metadata. Finally, we studied the correlation between the GuidedLDA-based themes and users' behaviors. Results: The linguistic analysis indicated differences in cognitive style, personal needs, and emotional expressiveness between people with and without GAD. Regarding cognitive style, there were significant differences (P<.001) for all features, such as insight (Cohen d=1.13), causation (Cohen d=1.03), and discrepancy (Cohen d=1.16). Regarding personal needs, there were significant differences (P<.001) in most personal needs categories, such as curiosity (Cohen d=1.05) and communication (Cohen d=0.64). Regarding emotional expressiveness, there were significant differences (P<.001) for most features, including anxiety (Cohen d=0.62), anger (Cohen d=0.72), sadness (Cohen d=0.48), and swear words (Cohen d=2.61). Additionally, topic modeling identified 4 primary themes (ie, symptoms, relationships, life problems, and feelings). We found that all themes were significantly more prevalent for people with GAD than for those without GAD (P<.001), along with significant effect sizes (Cohen d>0.50; P<.001) for most themes. Moreover, studying users' behaviors, including hashtag participation, volume, interaction pattern, social engagement, and reactive behaviors, revealed some digital markers of GAD, with most behavior-based features, such as the hashtag (Cohen d=0.49) and retweet (Cohen d=0.69) ratios, being statistically significant (P<.001). Furthermore, correlations between the GuidedLDA-based themes and users' behaviors were also identified. Conclusions: Our findings revealed several digital markers of GAD on social media. These findings are significant and could contribute to developing an assessment tool that clinicians could use for the initial diagnosis of GAD or the detection of an early signal of worsening in people with GAD via social media posts. This tool could provide ongoing support and personalized coping strategies. However, one limitation of using social media for mental health assessment is the lack of a demographic representativeness analysis. ", doi="10.2196/53399", url="https://www.jmir.org/2025/1/e53399" } @Article{info:doi/10.2196/63772, author="Zheng, Yuwen and Tian, Meirong and Chen, Jingjing and Zhang, Lei and Gao, Jia and Li, Xiang and Wen, Jin and Qu, Xing", title="Public Attitudes Toward Violence Against Doctors: Sentiment Analysis of Chinese Users", journal="JMIR Med Inform", year="2025", month="Mar", day="20", volume="13", pages="e63772", keywords="doctor-patient conflict", keywords="sentiment analysis", keywords="latent Dirichlet allocation", keywords="LDA", keywords="social media analysis", keywords="public health crisis", abstract="Background: Violence against doctors attracts the public's attention both online and in the real world. Understanding how public sentiment evolves during such crises is essential for developing strategies to manage emotions and rebuild trust. Objective: This study aims to quantify the difference in public sentiment based on the public opinion life cycle theory and describe how public sentiment evolved during a high-profile crisis involving violence against doctors in China. Methods: This study used the term frequency-inverse document frequency (TF-IDF) algorithm to extract key terms and create keyword clouds from textual comments. The latent Dirichlet allocation (LDA) topic model was used to analyze the thematic trends and shifts within public sentiment. The integrated Chinese Sentiment Lexicon was used to analyze sentiment trajectories in the collected data. Results: A total of 12,775 valid comments were collected on Sina Weibo about public opinion related to a doctor-patient conflict. Thematic and sentiment analyses showed that the public's sentiments were highly negative during the outbreak period (disgust: 10,201/30,433, 33.52\%; anger: 6792/30,433, 22.32\%) then smoothly changed to positive and negative during the spread period (sorrow: 2952/8569, 34.45\%; joy: 2782/8569, 32.47\%) and tended to be rational and peaceful during the decline period (joy: 4757/14,543, 32.71\%; sorrow: 4070/14,543, 27.99\%). However, no matter how emotions changed, each period's leading tone contained many negative sentiments. Conclusions: This study simultaneously examined the dynamics of theme change and sentiment evolution in crises involving violence against doctors. It discovered that public sentiment evolved alongside thematic changes, with the dominant negative tone from the initial stage persisting throughout. This finding, distinguished from prior research, underscores the lasting influence of early public sentiment. The results offer valuable insights for medical institutions and authorities, suggesting the need for tailored risk communication strategies responsive to the evolving themes and sentiments at different stages of a crisis. ", doi="10.2196/63772", url="https://medinform.jmir.org/2025/1/e63772", url="http://www.ncbi.nlm.nih.gov/pubmed/40111382" } @Article{info:doi/10.2196/59687, author="Parveen, Sana and Pereira, Garcia Agustin and Garzon-Orjuela, Nathaly and McHugh, Patricia and Surendran, Aswathi and Vornhagen, Heike and Vellinga, Akke", title="COVID-19 Public Health Communication on X (Formerly Twitter): Cross-Sectional Study of Message Type, Sentiment, and Source", journal="JMIR Form Res", year="2025", month="Mar", day="19", volume="9", pages="e59687", keywords="public health communication", keywords="surveillance", keywords="COVID-19", keywords="SARS-CoV-2", keywords="coronavirus", keywords="respiratory", keywords="infectious", keywords="pulmonary", keywords="pandemic", keywords="public health messaging", keywords="healthcare information", keywords="social media", keywords="tweets", keywords="text mining", keywords="data mining", keywords="social marketing", keywords="infoveillance", keywords="intervention planning", abstract="Background: Social media can be used to quickly disseminate focused public health messages, increasing message reach and interaction with the public. Social media can also be an indicator of people's emotions and concerns. Social media data text mining can be used for disease forecasting and understanding public awareness of health-related concerns. Limited studies explore the impact of type, sentiment and source of tweets on engagement. Thus, it is crucial to research how the general public reacts to various kinds of messages from different sources. Objective: The objective of this paper was to determine the association between message type, user (source) and sentiment of tweets and public engagement during the COVID-19 pandemic. Methods: For this study, 867,485 tweets were extracted from January 1, 2020 to March 31, 2022 from Ireland and the United Kingdom. A 4-step analytical process was undertaken, encompassing sentiment analysis, bio-classification (user), message classification and statistical analysis. A combination of manual content analysis with abductive coding and machine learning models were used to categorize sentiment, user category and message type for every tweet. A zero-inflated negative binomial model was applied to explore the most engaging content mix. Results: Our analysis resulted in 12 user categories, 6 message categories, and 3 sentiment classes. Personal stories and positive messages have the most engagement, even though not for every user group; known persons and influencers have the most engagement with humorous tweets. Health professionals receive more engagement with advocacy, personal stories/statements and humor-based tweets. Health institutes observe higher engagement with advocacy, personal stories/statements, and tweets with a positive sentiment. Personal stories/statements are not the most often tweeted category (22\%) but have the highest engagement (27\%). Messages centered on shock/disgust/fear-based (32\%) have a 21\% engagement. The frequency of informative/educational communications is high (33\%) and their engagement is 16\%. Advocacy message (8\%) receive 9\% engagement. Humor and opportunistic messages have engagements of 4\% and 0.5\% and low frequenciesof 5\% and 1\%, respectively. This study suggests the optimum mix of message type and sentiment that each user category should use to get more engagement. Conclusions: This study provides comprehensive insight into Twitter (rebranded as X in 2023) users' responses toward various message type and sources. Our study shows that audience engages with personal stories and positive messages the most. Our findings provide valuable guidance for social media-based public health campaigns in developing messages for maximum engagement. ", doi="10.2196/59687", url="https://formative.jmir.org/2025/1/e59687" } @Article{info:doi/10.2196/59944, author="Wang, Yijun and Zheng, Han and Zhou, Yuxin and Chukwusa, Emeka and Koffman, Jonathan and Curcin, Vasa", title="Promoting Public Engagement in Palliative and End-of-Life Care Discussions on Chinese Social Media: Model Development and Analysis", journal="J Med Internet Res", year="2025", month="Mar", day="18", volume="27", pages="e59944", keywords="palliative care", keywords="end-of-life care", keywords="health promotion", keywords="social media", keywords="China", keywords="Weibo", keywords="public engagement", keywords="elaboration likelihood model", keywords="ELM", abstract="Background: In Chinese traditional culture, discussions surrounding death are often considered taboo, leading to a poor quality of death, and limited public awareness and knowledge about palliative and end-of-life care (PEoLC). However, the increasing prevalence of social media in health communication in China presents an opportunity to promote and educate the public about PEoLC through online discussions. Objective: This study aimed to examine the factors influencing public engagement in PEoLC discussions on a Chinese social media platform and develop practice recommendations to promote such engagement. Methods: We gathered 30,811 PEoLC-related posts on Weibo, the largest social media platform in China. Guided by the elaboration likelihood model, our study examined factors across 4 dimensions: content theme, mood, information richness, and source credibility. Content theme was examined using thematic analysis, while sentiment analysis was used to determine the mood of the posts. The impact of potential factors on post engagement was quantified using negative binomial regression. Results: Organizational accounts exhibited lower engagement compared to individual accounts (incidence rate ratio [IRR]<1; P<.001), suggesting an underuse of organizational accounts in advocating for PEoLC on Weibo. Posts centered on PEoLC-related entertainment (films, television shows, and books; IRR=1.37; P<.001) or controversial social news (IRR=1.64; P<.001) garnered more engagement, primarily published by individual accounts. An interaction effect was observed between content theme and post mood, with posts featuring more negative sentiment generally attracting higher public engagement, except for educational-related posts (IRR=2.68; P<.001). Conclusions: Overall, organizations faced challenges in capturing public attention and involving the public when promoting PEoLC on Chinese social media platforms. It is imperative to move beyond a traditional mode to incorporate cultural elements of social media, such as engaging influencers, leveraging entertainment content and social news, or using visual elements, which can serve as effective catalysts in attracting public attention. The strategies developed in this study are particularly pertinent to nonprofit organizations and academics aiming to use social media for PEoLC campaigns, fundraising efforts, or research dissemination. ", doi="10.2196/59944", url="https://www.jmir.org/2025/1/e59944" } @Article{info:doi/10.2196/49464, author="Shah, Ali Hurmat and Househ, Mowafa", title="Understanding Loneliness Through Analysis of Twitter and Reddit Data: Comparative Study", journal="Interact J Med Res", year="2025", month="Mar", day="14", volume="14", pages="e49464", keywords="health informatics", keywords="loneliness informatics", keywords="loneliness theory", keywords="health effects", keywords="loneliness interventions", keywords="social media", keywords="lonely", keywords="loneliness", keywords="isolation", keywords="mental health", keywords="natural language processing", keywords="tweet", keywords="tweets", keywords="comparative analysis", abstract="Background: Loneliness is a global public health issue contributing to a variety of mental and physical health issues. It increases the risk of life-threatening conditions and contributes to the?burden on the economy in terms of the number of productive days lost. Loneliness is a highly varied concept, which is associated with multiple factors. Objective: This study aimed to understand loneliness through a comparative analysis of loneliness data on Twitter and Reddit, which are popular social media platforms. These platforms differ in terms of their use, as Twitter allows only short posts, while Reddit allows long posts in a forum setting. Methods: We collected global data on loneliness in October 2022. Twitter posts containing the words ``lonely,'' ``loneliness,'' ``alone,'' ``solitude,'' and ``isolation'' were collected. Reddit posts were extracted in March 2023. Using natural language processing techniques (valence aware dictionary for sentiment reasoning [VADER] tool from the natural language toolkit [NLTK]), the study identified and extracted relevant keywords and phrases related to loneliness from user-generated content on both platforms. The study used both sentiment analysis and the number of occurrences of a topic. Quantitative analysis was performed to determine the number of occurrences of a topic in tweets and posts, and overall meaningful topics were reported under a category. Results: The extracted data were subjected to comparative analysis to identify common themes and trends related to loneliness across Twitter and Reddit. A total of 100,000 collected tweets and 10,000 unique Reddit posts, including comments, were analyzed. The results of the study revealed the relationships of various social, political, and personal-emotional themes with the expression of loneliness on social media. Both platforms showed similar patterns in terms of themes and categories of discussion in conjunction with loneliness-related content. Both Reddit and Twitter addressed loneliness, but they differed in terms of focus. Reddit discussions were predominantly centered on personal-emotional themes, with a higher occurrence of these topics. Twitter, while still emphasizing personal-emotional themes, included a broader range of categories. Both platforms aligned with psychological linguistic features related to the self-expression of mental health issues. The key difference was in the range of topics, with Twitter having a wider variety of topics and Reddit having more focus on personal-emotional aspects. Conclusions: Reddit posts provide detailed insights into data about the expression of loneliness, although at the cost of the diversity of themes and categories, which can be inferred from the data. These insights can guide future research using social media data to understand loneliness. The findings provide the basis for further comparative investigation of the expression of loneliness on different social media platforms and online platforms. ", doi="10.2196/49464", url="https://www.i-jmr.org/2025/1/e49464" } @Article{info:doi/10.2196/66054, author="Tieu, Vivian and Kim, Sungjin and Seok, Minji and Ballas, Leslie and Kamrava, Mitchell and Atkins, M. Katelyn", title="Gender Differences in X (Formerly Twitter) Use Among Oncology Physicians at National Cancer Institute--Designated Cancer Centers: Cross-Sectional Study", journal="J Med Internet Res", year="2025", month="Mar", day="11", volume="27", pages="e66054", keywords="social media", keywords="gender disparities", keywords="gender differences", keywords="cross-sectional study", keywords="twitter", keywords="oncology", doi="10.2196/66054", url="https://www.jmir.org/2025/1/e66054" } @Article{info:doi/10.2196/58855, author="Liu, Jinpei and Qiu, Yifan and Liu, Yilong and Xu, Wenping and Ning, Weichen and Shi, Peimei and Yuan, Zongli and Wang, Fang and Shi, Yihai", title="The Reliability and Quality of Videos as Guidance for Gastrointestinal Endoscopy: Cross-Sectional Study", journal="J Med Internet Res", year="2025", month="Mar", day="11", volume="27", pages="e58855", keywords="gastrointestinal endoscopy", keywords="YouTube", keywords="patient education", keywords="social media gastrointestinal", keywords="large language model", keywords="LLM", keywords="reliability", keywords="quality", keywords="video", keywords="cross-sectional study", keywords="endoscopy-related videos", keywords="health information", keywords="endoscopy", keywords="gastroscopy", keywords="colonoscopy", abstract="Background: Gastrointestinal endoscopy represents a useful tool for the diagnosis and treatment of gastrointestinal diseases. Video platforms for spreading endoscopy-related knowledge may help patients understand the pros and cons of endoscopy on the premise of ensuring accuracy. However, videos with misinformation may lead to adverse consequences. Objective: This study aims to evaluate the quality of gastrointestinal endoscopy-related videos on YouTube and to assess whether large language models (LLMs) can help patients obtain information from videos more efficiently. Methods: We collected information from YouTube videos about 3 commonly used gastrointestinal endoscopes (gastroscopy, colonoscopy, and capsule endoscopy) and assessed their quality (rated by the modified DISCERN Tool, mDISCERN), reliability (rated by the Journal of the American Medical Association), and recommendation (rated by the Global Quality Score). We tasked LLM with summarizing the video content and assessed it from 3 perspectives: accuracy, completeness, and readability. Results: A total of 167 videos were included. According to the indicated scoring, the quality, reliability, and recommendation of the 3 gastrointestinal endoscopy-related videos on YouTube were overall unsatisfactory, and the quality of the videos released by patients was particularly poor. Capsule endoscopy yielded a significantly lower Global Quality Score than did gastroscopy and colonoscopy. LLM-based summaries yielded accuracy scores of 4 (IQR 4-5), completeness scores of 4 (IQR 4-5), and readability scores of 2 (IQR 1-2). Conclusions: The quality of gastrointestinal endoscope-related videos currently on YouTube is poor. Moreover, additional regulatory and improvement strategies are needed in the future. LLM may be helpful in generalizing video-related information, but there is still room for improvement in its ability. ", doi="10.2196/58855", url="https://www.jmir.org/2025/1/e58855" } @Article{info:doi/10.2196/58882, author="Kramer, L. Melissa and Polo, Medina Jose and Kumar, Nishant and Mulgirigama, Aruni and Benkiran, Amina", title="Living With and Managing Uncomplicated Urinary Tract Infection: Mixed Methods Analysis of Patient Insights From Social Media", journal="J Med Internet Res", year="2025", month="Mar", day="11", volume="27", pages="e58882", keywords="acute cystitis", keywords="bladder infection", keywords="HCP interactions", keywords="urology", keywords="patient experience", keywords="patient insights", keywords="social media", keywords="uncomplicated urinary tract infection", keywords="urinary tract infection", keywords="urinary", keywords="women", keywords="quality of life", keywords="disease management", keywords="cystitis", keywords="healthcare professional", keywords="self-management", keywords="patient behavior", keywords="UTI", abstract="Background: Uncomplicated urinary tract infections (uUTIs) affect more than half of women in their lifetime and can impact on quality of life. We analyzed social media posts discussing uUTIs to gather insights into the patient experience, including aspects of their disease management journey and associated opinions and concerns. Objective: This study aims to gather patient experience insights by analyzing social media posts that discussed uUTI. Methods: A search string (``urinary tract infection'' [UTI] or ``bladder infection'' or ``cystitis'' or ``UTI'' not ``interstitial cystitis'') was used to identify posts from public blogs and patient forums (June 2021 to June 2023). Posts were excluded if they were not written in English or discussed complicated UTI (posts that mentioned ``pregnancy'' or ``pregnant'' or ``trimester'' or ``catheter'' or ``interstitial''). Posts were limited to publicly available sources and anonymized. The primary objective was to gather patient perspectives on key elements of the uUTI experience, including health care professional (HCP) interactions, diagnosis, treatment, and recurrence. Results: In total, more than 42,000 unique posts were identified (mostly from reddit.com; 29,506/42,265, 70\%) and >3600 posts were analyzed. Posts were most commonly from users in the United States (6707/11,180, 60\%), the United Kingdom (2261/11,180, 20\%), Canada (509/11,180, 5\%), Germany (356/11,180, 3\%), or India (320/11,180, 3\%). Six main themes were identified: symptom awareness and information seeking, HCP interactions, diagnosis and management challenges, management with antibiotics, self-management, and challenges with recurrent UTI. Most posts highlighted the importance of seeking professional medical advice, while some patients raised concerns regarding their HCP interactions and lack of shared decision-making. Patients searched for advice and guidance on the web prior to consulting an HCP, described their symptoms, and discussed lifestyle adjustments. Most patients tried self-management and shared their experiences with nonprescribed treatment options. There was general agreement among posts that antibiotics are necessary to cure UTIs and prevent associated complications. Conclusions: Social media posts provide valuable insight into the experiences and opinions of patients with uUTIs in Canada, Germany, India, the United Kingdom, and the United States. The insights from this study provide a more complete picture of patient behaviors and highlight the potential for HCP and patient education, as well as better communication through shared decision-making to improve care. ", doi="10.2196/58882", url="https://www.jmir.org/2025/1/e58882" } @Article{info:doi/10.2196/51804, author="Portela, Diana and Freitas, Alberto and Costa, El{\'i}sio and Giovannini, Mattia and Bousquet, Jean and Almeida Fonseca, Jo{\~a}o and Sousa-Pinto, Bernardo", title="Impact of Demographic and Clinical Subgroups in Google Trends Data: Infodemiology Case Study on Asthma Hospitalizations", journal="J Med Internet Res", year="2025", month="Mar", day="10", volume="27", pages="e51804", keywords="infodemiology", keywords="asthma", keywords="administrative databases", keywords="multimorbidity", keywords="co-morbidity", keywords="respiratory", keywords="pulmonary", keywords="Google Trends", keywords="correlation", keywords="hospitalization", keywords="admissions", keywords="autoregressive", keywords="information seeking", keywords="searching", keywords="searches", keywords="forecasting", abstract="Background: Google Trends (GT) data have shown promising results as a complementary tool to classical surveillance approaches. However, GT data are not necessarily provided by a representative sample of patients and may be skewed toward demographic and clinical groups that are more likely to use the internet to search for their health. Objective: In this study, we aimed to assess whether GT-based models perform differently in distinct population subgroups. To assess that, we analyzed a case study on asthma hospitalizations. Methods: We analyzed all hospitalizations with a main diagnosis of asthma occurring in 3 different countries (Portugal, Spain, and Brazil) for a period of approximately 5 years (January 1, 2012-December 17, 2016). Data on web-based searches on common cold for the same countries and time period were retrieved from GT. We estimated the correlation between GT data and the weekly occurrence of asthma hospitalizations (considering separate asthma admissions data according to patients' age, sex, ethnicity, and presence of comorbidities). In addition, we built autoregressive models to forecast the weekly number of asthma hospitalizations (for the different aforementioned subgroups) for a period of 1 year (June 2015-June 2016) based on admissions and GT data from the 3 previous years. Results: Overall, correlation coefficients between GT on the pseudo-influenza syndrome topic and asthma hospitalizations ranged between 0.33 (in Portugal for admissions with at least one Charlson comorbidity group) and 0.86 (for admissions in women and in White people in Brazil). In the 3 assessed countries, forecasted hospitalizations for 2015-2016 correlated more strongly with observed admissions of older versus younger individuals (Portugal: Spearman $\rho$=0.70 vs $\rho$=0.56; Spain: $\rho$=0.88 vs $\rho$=0.76; Brazil: $\rho$=0.83 vs $\rho$=0.82). In Portugal and Spain, forecasted hospitalizations had a stronger correlation with admissions occurring for women than men (Portugal: $\rho$=0.75 vs $\rho$=0.52; Spain: $\rho$=0.83 vs $\rho$=0.51). In Brazil, stronger correlations were observed for admissions of White than of Black or Brown individuals ($\rho$=0.92 vs $\rho$=0.87). In Portugal, stronger correlations were observed for admissions of individuals without any comorbidity compared with admissions of individuals with comorbidities ($\rho$=0.68 vs $\rho$=0.66). Conclusions: We observed that the models based on GT data may perform differently in demographic and clinical subgroups of participants, possibly reflecting differences in the composition of internet users' health-seeking behaviors. ", doi="10.2196/51804", url="https://www.jmir.org/2025/1/e51804" } @Article{info:doi/10.2196/64307, author="Shao, Anqi and Chen, Kaiping and Johnson, Branden and Miranda, Shaila and Xing, Qidi", title="Ubiquitous News Coverage and Its Varied Effects in Communicating Protective Behaviors to American Adults in Infectious Disease Outbreaks: Time-Series and Longitudinal Panel Study", journal="J Med Internet Res", year="2025", month="Mar", day="10", volume="27", pages="e64307", keywords="risk communication", keywords="panel study", keywords="computational method", keywords="intermedia agenda setting", keywords="protective behaviors", keywords="infectious disease", abstract="Background: Effective communication is essential for promoting preventive behaviors during infectious disease outbreaks like COVID-19. While consistent news can better inform the public about these health behaviors, the public may not adopt them. Objective: This study aims to explore the role of different media platforms in shaping public discourse on preventive measures to infectious diseases such as quarantine and vaccination, and how media exposure influences individuals' intentions to adopt these behaviors in the United States. Methods: This study uses data from 3 selected top national newspapers in the United States, Twitter discussions, and a US nationwide longitudinal panel survey from February 2020 to April 2021. We used the Intermedia Agenda-Setting Theory and the Protective Action Decision Model to develop the theoretical framework. Results: We found a 2-way agenda flow between selected national newspapers and the social media platform Twitter, particularly in controversial topics like vaccination (F1,426=16.39; P<.001 for newspapers; F1,426=44.46; P<.001 for Twitter). Exposure to media coverage increased individuals' perceived benefits of certain behaviors like vaccination but did not necessarily translate into behavioral adoption. For example, while individuals' media exposure increased perceived benefits of mask-wearing ($\beta$=.057; P<.001 for household benefits; $\beta$=.049; P<.001 for community benefits), it was not consistently linked to higher intentions to wear masks ($\beta$=--.026; P=.04). Conclusions: This study integrates media flow across platforms with US national panel survey data, offering a comprehensive view of communication dynamics during the early stage of an infectious disease outbreak. The findings caution against a one-size-fits-all approach in communicating different preventive behaviors, especially where individual and community benefits may not always align. ", doi="10.2196/64307", url="https://www.jmir.org/2025/1/e64307" } @Article{info:doi/10.2196/64672, author="DuPont-Reyes, J. Melissa and Villatoro, P. Alice and Tang, Lu", title="Health Information Scanning and Seeking in Diverse Language, Cultural and Technological Media Among Latinx Adolescents: Cross-Sectional Study", journal="J Med Internet Res", year="2025", month="Mar", day="5", volume="27", pages="e64672", keywords="adolescent behaviors", keywords="mental health", keywords="Latino", keywords="social media", keywords="adolescent", keywords="media use", keywords="internet use", keywords="health information seeking", keywords="health information scanning", keywords="mobile phone", abstract="Background: Continuous scientific and policy debate regarding the potential harm and/or benefit of media and social media on adolescent health has resulted, in part, from a deficiency in robust scientific evidence. Even with a lack of scientific consensus, public attitudes, and sweeping social media prohibitions have swiftly ensued. A focus on the diversity of adolescents around the world and their diverse use of language, culture, and social media is absent from these discussions. Objective: This study aims to guide communication policy and practice, including those addressing access to social media by adolescent populations. This study assesses physical and mental health information scanning and seeking behaviors across diverse language, cultural, and technological media and social media among Latinx adolescent residents in the United States. This study also explores how Latinx adolescents with mental health concerns use media and social media for support. Methods: In 2021, a cross-sectional survey was conducted among 701 US-based Latinx adolescents aged 13-20 years to assess their health-related media use. Assessments ascertained the frequency of media use and mental and physical health information scanning and seeking across various media technologies (eg, TV, podcasts, and social media) and language and cultural types (ie, Spanish, Latinx-tailored English, and general English). Linear regression models were used to estimate adjusted predicted means of mental and physical health information scanning and seeking across diverse language and cultural media types, net personal and family factors, in the full sample and by subsamples of mental health symptoms (moderate-high vs none-mild). Results: Among Latinx adolescents, media and social media use was similar across mental health symptoms. However, Latinx adolescents with moderate-high versus none-mild symptoms more often scanned general English media and social media for mental health information (P<.05), although not for physical health information. Also, Latinx adolescents with moderate-high versus none-mild symptoms more often sought mental health information on Latinx-tailored and general English media, and social media (P<.05); a similar pattern was found for physical health information seeking. In addition, Latinx adolescents with moderate-high versus none-mild symptoms often sought help from family and friends for mental and physical health problems and health care providers for mental health only (P<.05). Conclusions: While media and social media usage was similar across mental health, Latinx adolescents with moderate-high symptoms more often encountered mental health content in general English media and social media and turned to general English- and Latinx-tailored media and social media more often for their health concerns. Together these study findings suggest more prevalent and available mental health content in general English versus Spanish language and Latinx-tailored media and underscore the importance of providing accessible, quality health information across diverse language, cultural, and technological media and social networks as a viable opportunity to help improve adolescent health. ", doi="10.2196/64672", url="https://www.jmir.org/2025/1/e64672", url="http://www.ncbi.nlm.nih.gov/pubmed/40053766" } @Article{info:doi/10.2196/63755, author="Li, Wanxin and Hua, Yining and Zhou, Peilin and Zhou, Li and Xu, Xin and Yang, Jie", title="Characterizing Public Sentiments and Drug Interactions in the COVID-19 Pandemic Using Social Media: Natural Language Processing and Network Analysis", journal="J Med Internet Res", year="2025", month="Mar", day="5", volume="27", pages="e63755", keywords="COVID-19", keywords="natural language processing", keywords="drugs", keywords="social media", keywords="pharmacovigilance", keywords="public health", abstract="Background: While the COVID-19 pandemic has induced massive discussion of available medications on social media, traditional studies focused only on limited aspects, such as public opinions, and endured reporting biases, inefficiency, and long collection times. Objective: Harnessing drug-related data posted on social media in real-time can offer insights into how the pandemic impacts drug use and monitor misinformation. This study aimed to develop a natural language processing (NLP) pipeline tailored for the analysis of social media discourse on COVID-19--related drugs. Methods: This study constructed a full pipeline for COVID-19--related drug tweet analysis, using pretrained language model--based NLP techniques as the backbone. This pipeline is architecturally composed of 4 core modules: named entity recognition and normalization to identify medical entities from relevant tweets and standardize them to uniform medication names for time trend analysis, target sentiment analysis to reveal sentiment polarities associated with the entities, topic modeling to understand underlying themes discussed by the population, and drug network analysis to dig potential adverse drug reactions (ADR) and drug-drug interactions (DDI). The pipeline was deployed to analyze tweets related to the COVID-19 pandemic and drug therapies between February 1, 2020, and April 30, 2022. Results: From a dataset comprising 169,659,956 COVID-19--related tweets from 103,682,686 users, our named entity recognition model identified 2,124,757 relevant tweets sourced from 1,800,372 unique users, and the top 5 most-discussed drugs: ivermectin, hydroxychloroquine, remdesivir, zinc, and vitamin D. Time trend analysis revealed that the public focused mostly on repurposed drugs (ie, hydroxychloroquine and ivermectin), and least on remdesivir, the only officially approved drug among the 5. Sentiment analysis of the top 5 most-discussed drugs revealed that public perception was predominantly shaped by celebrity endorsements, media hot spots, and governmental directives rather than empirical evidence of drug efficacy. Topic analysis obtained 15 general topics of overall drug-related tweets, with ``clinical treatment effects of drugs'' and ``physical symptoms'' emerging as the most frequently discussed topics. Co-occurrence matrices and complex network analysis further identified emerging patterns of DDI and ADR that could be critical for public health surveillance like better safeguarding public safety in medicines use. Conclusions: This study shows that an NLP-based pipeline can be a robust tool for large-scale public health monitoring and can offer valuable supplementary data for traditional epidemiological studies concerning DDI and ADR. The framework presented here aspires to serve as a cornerstone for future social media--based public health analytics. ", doi="10.2196/63755", url="https://www.jmir.org/2025/1/e63755", url="http://www.ncbi.nlm.nih.gov/pubmed/40053730" } @Article{info:doi/10.2196/64667, author="Biliotti, Carolina and Fraccaroli, Nicol{\`o} and Puliga, Michelangelo and Bargagli-Stoffi, J. Falco and Riccaboni, Massimo", title="The Impact of Stay-At-Home Mandates on Uncertainty and Sentiments: Quasi-Experimental Study", journal="J Med Internet Res", year="2025", month="Mar", day="4", volume="27", pages="e64667", keywords="lockdown policy", keywords="sentiment analysis", keywords="uncertainty", keywords="social media", keywords="quasi-experiment", abstract="Background: As the spread of the SARS-CoV-2 virus coincided with lockdown measures, it is challenging to distinguish public reactions to lockdowns from responses to COVID-19 itself. Beyond the direct impact on health, lockdowns may have worsened public sentiment toward politics and the economy or even heightened dissatisfaction with health care, imposing a significant cost on both the public and policy makers. Objective: This study aims to analyze the causal effect of COVID-19 lockdown policies on various dimensions of sentiment and uncertainty, using the Italian lockdown of February 2020 as a quasi-experiment. At the time of implementation, communities inside and just outside the lockdown area were equally exposed to COVID-19, enabling a quasi-random distribution of the lockdown. Additionally, both areas had similar socioeconomic and demographic characteristics before the lockdown, suggesting that the delineation of the strict lockdown zone approximates a randomized experiment. This approach allows us to isolate the causal effects of the lockdown on public emotions, distinguishing the impact of the policy itself from changes driven by the virus's spread. Methods: We used Twitter data (N=24,261), natural language models, and a difference-in-differences approach to compare changes in sentiment and uncertainty inside (n=1567) and outside (n=22,694) the lockdown areas before and after the lockdown began. By fine-tuning the AlBERTo (Italian BERT optimized) pretrained model, we analyzed emotions expressed in tweets from 1124 unique users. Additionally, we applied dictionary-based methods to categorize tweets into 4 dimensions---economy, health, politics, and lockdown policy---to assess the corresponding emotional reactions. This approach enabled us to measure the direct impact of local policies on public sentiment using geo-referenced social media and can be easily adapted for other policy impact analyses. Results: Our analysis shows that the lockdown had no significant effect on economic uncertainty (b=0.005, SE 0.007, t125=0.70; P=.48) or negative economic sentiment (b=--0.011, SE 0.0089, t125=--1.32; P=.19). However, it increased uncertainty about health (b=0.036, SE 0.0065, t125=5.55; P<.001) and lockdown policy (b=0.026, SE 0.006, t125=4.47; P<.001), as well as negative sentiment toward politics (b=0.025, SE 0.011, t125=2.33; P=.02), indicating that lockdowns have broad externalities beyond health. Our key findings are confirmed through a series of robustness checks. Conclusions: Our findings reveal that lockdowns have broad externalities extending beyond health. By heightening health concerns and negative political sentiment, policy makers have struggled to secure explicit public support for government measures, which may discourage future leaders from implementing timely stay-at-home policies. These results highlight the need for authorities to leverage such insights to enhance future policies and communication strategies, reducing uncertainty and mitigating social panic. ", doi="10.2196/64667", url="https://www.jmir.org/2025/1/e64667", url="http://www.ncbi.nlm.nih.gov/pubmed/40053818" } @Article{info:doi/10.2196/64020, author="Takahashi, Noriaki and Nakao, Mutsuhiro and Nakayama, Tomio and Yamazaki, Tsutomu", title="Breast Cancer Screening Participation and Internet Search Activity in a Japanese Population: Decade-Long Time-Series Study", journal="JMIR Cancer", year="2025", month="Mar", day="4", volume="11", pages="e64020", keywords="breast cancer", keywords="cancer screening", keywords="internet use", keywords="mass media", keywords="public health surveillance", keywords="health belief model", keywords="mammography", keywords="awareness", keywords="Japanese", keywords="Google", abstract="Background: Breast cancer is a major health concern in various countries. Routine mammography screening has been shown to reduce breast cancer mortality, and Japan has set national targets to improve screening participation and increase public attention. However, collecting nationwide data on public attention and activity is not easy. Google Trends can reveal changes in societal interest, yet there are no reports on the relationship between internet search volume and nationwide participation rates in Japan. Objective: This study aims to reveal and discuss the relationship between public awareness and actual behavior in breast cancer screening by examining trends in internet search volume for the keyword ``breast cancer screening'' and participation rates over a decade-long period. Methods: This time-series study evaluated the association between internet search volume and breast cancer screening participation behavior among women aged 60?69 years in Japan from 2009 to 2019. Relative search volume (RSV) data for the search term ``breast cancer screening (nyuugan-kenshin)'' were extracted from Google Trends as internet search volume. Breast cancer screening and further assessment participation rates were based on government municipal screening data. Joinpoint regression analyses were conducted with weighted BIC to evaluate the time trends. An ethics review was not required because all data were open. Results: The RSV for ``breast cancer screening (nyuugan-kenshin)'' peaked in June 2017 (100) and showed clear spikes in June 2016 (94), September (69), and October (77) 2015. No RSVs above 60 were observed except around these three specific periods, and the average RSV for the entire period was 30.7 (SD 16.2). Two statistically significant joinpoints were detected, rising in December 2013 and falling in June 2017. Screening participation rates showed a temporary increase in 2015 in a slowly decreasing trend, and no joinpoints were detected. Further assessment participation rates showed a temporary spike in 2015 in the middle of an increasing trend, with a statistically significant point of slowing increase detected in 2015. Post hoc manual searches revealed that Japanese celebrities' breast cancer diagnoses were announced on the relevant dates, and many Japanese media reports were found. Conclusions: This study found a notable association between internet search activity and celebrity cancer media reports and a temporal association with screening participation in breast cancer screening in Japan. Celebrity cancer media reports triggered internet searches for cancer screening, but this did not lead to long-term changes in screening participation behavior. This finding suggests what information needs to be provided to citizens to encourage participation in screening. ", doi="10.2196/64020", url="https://cancer.jmir.org/2025/1/e64020" } @Article{info:doi/10.2196/67515, author="Stimpson, P. Jim and Srivastava, Aditi and Tamirisa, Ketan and Kaholokula, Keawe?aimoku Joseph and Ortega, N. Alexander", title="Crisis Communication About the Maui Wildfires on TikTok: Content Analysis of Engagement With Maui Wildfire--Related Posts Over 1 Year", journal="JMIR Form Res", year="2025", month="Mar", day="4", volume="9", pages="e67515", keywords="social media", keywords="public health", keywords="disasters", keywords="Hawaii", keywords="media", keywords="post", keywords="communication", keywords="disaster", keywords="disaster communication", keywords="wildfire", keywords="information", keywords="dissemination", keywords="engagement", keywords="content analysis", keywords="content", keywords="metrics", keywords="misinformation", keywords="community", keywords="support", abstract="Background: The August 2023 wildfire in the town of L?hain? on the island of Maui in Hawai?i caused catastrophic damage, affecting thousands of residents, and killing 102 people. Social media platforms, particularly TikTok, have become essential tools for crisis communication during disasters, providing real-time crisis updates, mobilizing relief efforts, and addressing misinformation. Understanding how disaster-related content is disseminated and engaged with on these platforms can inform strategies for improving emergency communication and community resilience. Objective: Guided by Social-Mediated Crisis Communication theory, this study examined TikTok posts related to the Maui wildfires to assess content themes, public engagement, and the effectiveness of social media in disseminating disaster-related information. Methods: TikTok posts related to the Maui wildfires were collected from August 8, 2023, to August 9, 2024. Using TikTok's search functionality, we identified and reviewed public posts that contained relevant hashtags. Posts were categorized into 3 periods: during the disaster (August 8 to August 31, 2023), the immediate aftermath (September 1 to December 31, 2023), and the long-term recovery (January 1 to August 9, 2024). Two researchers independently coded the posts into thematic categories, achieving an interrater reliability of 87\%. Engagement metrics (likes and shares) were analyzed to assess public interaction with different themes. Multivariable linear regression models were used to examine the associations between log-transformed likes and shares and independent variables, including time intervals, video length, the inclusion of music or effects, content themes, and hashtags. Results: A total of 275 TikTok posts were included in the analysis. Most posts (132/275, 48\%) occurred in the immediate aftermath, while 76 (27.6\%) were posted during the long-term recovery phase, and 24.4\% (n=67) were posted during the event. Posts during the event garnered the highest average number of likes (mean 75,092, SD 252,759) and shares (mean 10,928, SD 55,308). Posts focused on ``Impact \& Damage'' accounted for the highest engagement, representing 36.8\% (4,090,574/11,104,031) of total likes and 61.2\% (724,848/1,184,049) of total shares. ``Tourism Impact'' (2,172,991/11,104,031, 19.6\% of likes; 81,372/1,184,049, 6.9\% of shares) and ``Relief Efforts'' (509,855/11,104,031, 4.6\% of likes; 52,587/1,184,049, 4.4\% of shares) were also prominent themes. Regression analyses revealed that videos with ``Misinformation \& Fake News'' themes had the highest engagement per post, with a 4.55 coefficient for log-shares (95\% CI 2.44-6.65), while videos about ``Tourism Impact'' and ``Relief Efforts'' also showed strong engagement (coefficients for log-likes: 2.55 and 1.76, respectively). Conclusions: TikTok is an influential tool for disaster communication, amplifying both critical disaster updates and misinformation, highlighting the need for strategic content moderation and evidence-based messaging to enhance the platform's role in crisis response. Public health officials, emergency responders, and policy makers can leverage TikTok's engagement patterns to optimize communication strategies, improve real-time risk messaging, and support long-term community resilience. ", doi="10.2196/67515", url="https://formative.jmir.org/2025/1/e67515" } @Article{info:doi/10.2196/54847, author="Huang, Mengxia Nova and Wong, Ze Liang and Ho, S. Shirley and Timothy, Bryan", title="Understanding Challenges and Emotions of Informal Caregivers of General Older Adults and People With Alzheimer Disease and Related Dementia: Comparative Study", journal="J Med Internet Res", year="2025", month="Feb", day="28", volume="27", pages="e54847", keywords="informal caregivers", keywords="older adults", keywords="Alzheimer disease and related dementia", keywords="online support communities", keywords="Reddit", abstract="Background: Faced with multiple challenges, informal caregivers often turn to online support communities for information and support. While scholarly attention has focused on experiences expressed by informal caregivers in these communities, how caregivers' challenges and emotional expressions vary across different health contexts remains understudied. Objective: We aimed to examine and compare the challenges discussed by informal caregivers of general older adults and those of patients with Alzheimer disease and related dementia, as well as their emotional expressions, on Reddit. In addition, we examined how informal caregivers expressed their emotions in response to various challenges. Methods: We collected posts from 6 subreddits, including 3 subreddits on caregiving for older adults and 3 on caregiving for patients with Alzheimer disease and related dementia. Using topic modeling, we identified topics discussed by caregivers in the collected posts. We further used deep reading to contextualize these topics and understand the challenges behind them, conducted sentiment analysis to investigate their emotional expressions, and used Spearman rank-order correlation to examine the relationship between the obtained topics and emotions. Results: In total, 3028 posts were retrieved, including 1552 from older adult--related subreddits and 1476 from Alzheimer disease--related subreddits; 18 key topics were identified, with the most frequent topics being expressing feelings (2178/3028, 71.93\%) and seeking advice and support (1982/3028, 65.46\%). Other topics covered various challenges in caregiving, such as duration of medical care (1954/3028, 64.53\%), sleep and incontinence (1536/3028, 50.73\%), financial issues (1348/3028, 44.52\%), and nursing home (1221/3028, 40.32\%). There was a positive, negligible correlation between expressing feelings and seeking advice and support ($\rho$=0.09, P<.001). Other topics also showed positive, negligible or weak correlations with these 2 topics but in distinct patterns. Posts from older adult--related subreddits were more focused on practical caregiving issues and seeking advice and support, whereas posts from Alzheimer disease--related subreddits emphasized health- and medical-related topics and expressing feelings. Caregivers in both contexts predominantly expressed negative emotions (older adults: 1263/1552, 81.38\%; Alzheimer disease: 1247/1476, 84.49\%), with caregivers in Alzheimer disease--related subreddits exhibiting slightly greater fear and sadness (P<.001). Specific challenges were significantly correlated with negative emotions: duration of medicalcare was positively, weakly correlated with anger ($\rho$=0.25, P<.001), fear ($\rho$=0.25, P<.001), and sadness ($\rho$=0.22, P<.001). Medical appointments were positively, negligibly correlated with anger ($\rho$=0.10, P<.001), fear ($\rho$=0.09, P<.001), and sadness ($\rho$=0.06, P<.001). Sleep and incontinence ($\rho$=0.14, P<.001) and finances ($\rho$=0.24, P<.001) were positively, weakly correlated with anger. Conclusions: By identifying the challenges and feelings expressed by caregivers for general older adults and caregivers for patients with Alzheimer disease and related dementia, our findings could inform health practitioners and policy makers in developing more targeted support interventions for informal caregivers in different contexts. ", doi="10.2196/54847", url="https://www.jmir.org/2025/1/e54847", url="http://www.ncbi.nlm.nih.gov/pubmed/40053723" } @Article{info:doi/10.2196/62680, author="Zhu, Jianfeng and Zhang, Xinyu and Jin, Ruoming and Jiang, Hailong and Kenne, R. Deric", title="Probing Public Perceptions of Antidepressants on Social Media: Mixed Methods Study", journal="JMIR Form Res", year="2025", month="Feb", day="26", volume="9", pages="e62680", keywords="antidepressant", keywords="AskaPatient", keywords="natural language processing", keywords="BERTopic", keywords="large language models", keywords="Reddit", abstract="Background: Antidepressants are crucial for managing major depressive disorders; however, nonadherence remains a widespread challenge, driven by concerns over side effects, fear of dependency, and doubts about efficacy. Understanding patients' experiences is essential for improving patient-centered care and enhancing adherence, which prioritizes individual needs in treatment. Objective: This study aims to gain a deeper understanding of patient experiences with antidepressants, providing insights that health care providers, families, and communities can develop into personalized treatment strategies. By integrating patient-centered care, these processes may improve satisfaction and adherence with antidepressants. Methods: Data were collected from AskaPatient and Reddit, analyzed using natural language processing and large language models. Analytical techniques included sentiment analysis, emotion detection, personality profiling, and topic modeling. Furthermore, demographic variations in patient experiences were also examined to offer a comprehensive understanding of discussions around antidepressants. Results: Sentiment and emotion analysis revealed that the majority of discussions (21,499/36,253, 59.3\%) expressed neutral sentiments, with negative sentiments following closely (13,922/36,253, 38.4\%). The most common emotions were fear (16,196/36,253, 44.66\%) and sadness (12,507/36,253, 34.49\%). The largest topic, ``Mental Health and Relationships,'' accounted for 11.69\% (3755/36,253) of the discussions, which indicated a significant focus on managing mental health conditions. Discussions around nonadherence were marked by fear, followed by sadness, while self-care discussions showed a notable trend of sadness. Conclusions: These psychological insights into public perceptions of antidepressants provide a foundation for developing tailored, patient-centered treatment approaches that align with individual needs, enhancing both effectiveness and empathy of care. ", doi="10.2196/62680", url="https://formative.jmir.org/2025/1/e62680" } @Article{info:doi/10.2196/59875, author="Cerit, Merve and Lee, Y. Angela and Hancock, Jeffrey and Miner, Adam and Cho, Mu-Jung and Muise, Daniel and Garr{\`o}n Torres, Anna-Angelina and Haber, Nick and Ram, Nilam and Robinson, N. Thomas and Reeves, Byron", title="Person-Specific Analyses of Smartphone Use and Mental Health: Intensive Longitudinal Study", journal="JMIR Form Res", year="2025", month="Feb", day="26", volume="9", pages="e59875", keywords="media use", keywords="mental health", keywords="mHealth", keywords="uHealth", keywords="digital health", keywords="precision mental health", keywords="idiographic analysis", keywords="person-specific modeling", keywords="p-technique", keywords="longitudinal study", keywords="precision interventions", keywords="smartphones", keywords="idiosyncrasy", keywords="psychological well-being", keywords="canonical correlation analysis", keywords="United States", abstract="Background: Contrary to popular concerns about the harmful effects of media use on mental health, research on this relationship is ambiguous, stalling advances in theory, interventions, and policy. Scientific explorations of the relationship between media and mental health have mostly been found null or have small associations, with the results often blamed on the use of cross-sectional study designs or imprecise measures of media use and mental health. Objective: This exploratory empirical demonstration aims to answer whether mental health effects are associated with media use experiences by (1) redirecting research investments to granular and intensive longitudinal recordings of digital experiences to build models of media use and mental health for single individuals over the course of 1 year, (2) using new metrics of fragmented media use to propose explanations of mental health effects that will advance person-specific theorizing in media psychology, and (3) identifying combinations of media behaviors and mental health symptoms that may be more useful for studying media effects than single measures of dosage and affect or assessments of clinical symptoms related to specific disorders. Methods: The activity on individuals' smartphone screens was recorded every 5 seconds when devices were in use over 1 year, resulting in a dataset of 6,744,013 screenshots and 123 fortnightly surveys from 5 adult participants. Each participant contributed between 0.8 and 2.7 million screens. Six media use metrics were derived from smartphone metadata. Fortnightly surveys captured symptoms of depression, attention-deficit/hyperactivity disorder, state anxiety, and positive affect. Idiographic filter models (p-technique canonical correlation analyses) were applied to explore person-specific associations. Results: Canonical correlations revealed substantial person-specific associations between media use and mental health, ranging from r=0.82 (P=.008) to r=0.92 (P=.03). The specific combinations of media use metrics and mental health dimensions were different for each person, reflecting significant individual variability. For instance, the media use canonical variate for 1 participant was characterized by higher loadings for app-switching, which, in combination with other behaviors, correlated strongly with a mental health variate emphasizing anxiety symptoms. For another, prolonged screen time, alongside other media use behaviors, contributed to a mental health variate weighted more heavily toward depression symptoms. These within-person correlations are among the strongest reported in this literature. Conclusions: Results suggest that the relationships between media use and mental health are highly individualized, with implications for the development of personalized models and precision smartphone-informed interventions in mental health. We discuss how our approach can be extended generally, while still emphasizing the importance of idiographic approaches. This study highlights the potential for granular, longitudinal data to reveal person-specific patterns that can inform theory development, personalized screening, diagnosis, and interventions in mental health. ", doi="10.2196/59875", url="https://formative.jmir.org/2025/1/e59875", url="http://www.ncbi.nlm.nih.gov/pubmed/39808832" } @Article{info:doi/10.2196/70108, author="Wilhelm, Elisabeth and Vivilaki, Victoria and Calleja-Agius, Jean and Petelos, Elena and Tzeli, Maria and Giaxi, Paraskevi and Triantiafyllou, Elena and Asimaki, Eleni and Alevizou, Faye and Purnat, D. Tina", title="Effects of the Modern Digital Information Environment on Maternal Health Care Professionals, the Role of Midwives, and the People in Their Care: Scoping Review", journal="J Med Internet Res", year="2025", month="Feb", day="25", volume="27", pages="e70108", keywords="digital health", keywords="midwifery", keywords="misinformation", keywords="information-seeking midwifery", keywords="health information seeking", keywords="social media", keywords="medical misinformation", abstract="Background: The digital information environment poses challenges for pregnant women and other people seeking care, as well as for their midwives and other health care professionals (HCPs). They can encounter questions, concerns, information gaps, and misinformation, which can influence health care decisions. Objective: This scoping review examines how HCPs are affected by the modern digital information environment including health misinformation, its effects on the women and people they care for, and its implications for care provision. Methods: English-language peer-reviewed literature, published from January 1, 2020, to May 31, 2024, with keywords related to midwifery, misinformation, and health equity collected and analyzed by a team of midwives and maternal care professionals and mapped onto a patient-centered conceptual model. Results: A total of 105 studies were ultimately included. Further, 95 papers identified specific digital information environment issues that affected clients; 58 specifically highlighted digital information environment issues impacting HCPs; 91 papers identified specific topics of common questions, concerns, misinformation, information voids, or narratives; 57 papers identified patient or population vulnerability; and 75 included mentions of solutions or recommendations for addressing a digital information environment issue around clients seeking care from midwives and other HCPs. When mapped onto the Journey to Health model, the most prominent barrier was access to care and information. Individual-level issues dominate the step related to knowledge, awareness, and belief, with more social norms and wider engagement appearing at steps related to intent. Client-specific themes dominate the left-hand side of the model and provider-specific issues dominate the right-hand side of the model. Conclusions: Misinformation, information voids, unaddressed questions and concerns, and lack of access to high-quality health information are worldwide prevalent barriers that affect both patients and HCPs. We identified individual, provider-level, health systems, and societal-level strategies that can be used to promote healthier digital information environments. ", doi="10.2196/70108", url="https://www.jmir.org/2025/1/e70108", url="http://www.ncbi.nlm.nih.gov/pubmed/39998875" } @Article{info:doi/10.2196/64838, author="Hwang, Jeong Hee and Kim, Nara and You, Yun Jeong and Ryu, Ri Hye and Kim, Seo-Young and Yoon Park, Han Jung and Lee, Won Ki", title="Harnessing Social Media Data to Understand Information Needs About Kidney Diseases and Emotional Experiences With Disease Management: Topic and Sentiment Analysis", journal="J Med Internet Res", year="2025", month="Feb", day="25", volume="27", pages="e64838", keywords="kidney diseases", keywords="online health communities", keywords="topic modeling", keywords="sentiment analysis", keywords="disease management", keywords="patient support", abstract="Background: Kidney diseases encompass a variety of conditions, including chronic kidney disease, acute kidney injury, glomerulonephritis, and polycystic kidney disease. These diseases significantly impact patients' quality of life and health care costs, often necessitating substantial lifestyle changes, especially regarding dietary management. However, patients frequently receive ambiguous or conflicting dietary advice from health care providers, leading them to seek information and support from online health communities. Objective: This study aimed to analyze social media data to better understand the experiences, challenges, and concerns of patients with kidney disease and their caregivers in South Korea. Specifically, it explored how online communities assist in disease management and examined the sentiment surrounding dietary management. Methods: Data were collected from KidneyCafe, a prominent South Korean online community for patients with kidney disease hosted on the Naver platform. A total of 124,211 posts from 10 disease-specific boards were analyzed using latent Dirichlet allocation for topic modeling and Bidirectional Encoder Representations From Transformers--based sentiment analysis. In addition, Efficiently Learning an Encoder That Classifies Token Replacements Accurately--based classification was used to further analyze posts related to disease management. Results: The analysis identified 6 main topics within the community: family health and support, medication and side effects, examination and diagnosis, disease management, surgery for dialysis, and costs and insurance. Sentiment analysis revealed that posts related to the medication and side effects and surgery for dialysis topics predominantly expressed negative sentiments. Both significant negative sentiments concerning worries about kidney transplantation among family members and positive sentiments regarding physical improvements after transplantation were expressed in posts about family health and support. For disease management, 7 key subtopics were identified, with inquiries about dietary management being the leading subtopic. Conclusions: The findings highlight the critical role of online communities in providing support and information for patients with kidney disease and their caregivers. The insights gained from this study can inform health care providers, policy makers, and support organizations to better address the needs of patients with kidney disease, particularly in areas related to dietary management and emotional support. ", doi="10.2196/64838", url="https://www.jmir.org/2025/1/e64838", url="http://www.ncbi.nlm.nih.gov/pubmed/39998877" } @Article{info:doi/10.2196/59387, author="Rivera, M. Yonaira and Corpuz, Kathryna and Karver, Sanchez Tahilin", title="Engagement With and Use of Health Information on Social Media Among US Latino Individuals: National Cross-Sectional Survey Study", journal="J Med Internet Res", year="2025", month="Feb", day="24", volume="27", pages="e59387", keywords="Latinos", keywords="health misinformation", keywords="engagement", keywords="utilization", keywords="social media", keywords="health information", keywords="United States", keywords="national", keywords="trends", keywords="survey", keywords="pandemic", keywords="non-Latino whites", abstract="Background: During the COVID-19 pandemic, US Latino individuals were more likely to report accessing coronavirus information on social media than other groups, despite copious amounts of health misinformation documented on these platforms. Among the existing literature on factors associated with engagement and use of health information, racial minority status has been associated with greater susceptibility to health misinformation. However, literature to date has not reported national trends on how Latino individuals engage with or use health information on social media compared to non-Latino White (NLW) individuals, nor whether perceptions of the amount of health misinformation on social media influence health information engagement and usage. Objective: This study aimed to examine differences in engagement with and use of health information on social media among Latino and NLW individuals in the United States. Methods: We examined a nationally representative cross-sectional sample of Latino (n=827) and NLW (n=2563) respondents of the 2022 Health Information National Trends Survey who used social media in 2022 to assess differences in engagement with and use of health information. Items related to the perceived quantity of health misinformation on social media, social media use frequency, health information engagement (sharing content; watching videos), and health information usage (health decision-making; discussions with health care providers) were selected to conduct weighted bivariate analyses and logistic regressions. Results: Latino individuals perceive lower amounts of health misinformation on social media (28.9\% perceived little to no misinformation vs 13.6\% NLW individuals, P<.001). Latino audiences also reported higher health information engagement compared to NLW individuals (20\% vs 10.2\% shared information several times a month or more, P<.001; 42.4\% vs 27.2\% watched videos several times a month or more, P<.001), as well as higher information usage for health decision-making (22.8\% vs 13.7\%, P=.003). When controlling for ethnicity and other sociodemographic variables, perceiving lower amounts of health misinformation on social media was associated with higher odds of watching videos more frequently, making health decisions, and discussing health-related content with a health care provider (P<.001). Furthermore, Latino audiences were 1.85 times more likely to watch videos (P<.001), when controlling for the perceived amount of health misinformation and other sociodemographic variables. Finally, when compared to NLW individuals perceiving little to no health misinformation, Latino audiences perceiving little to no health misinformation were 2.91 times more likely to watch videos (P<.001). Conclusions: The findings suggest that Latino individuals engage with visual health (mis)information at higher rates. Digital health literacy interventions should consider video formats and preferred social media platforms among Latino individuals. Further research is warranted to understand sociocultural factors important to Latino social media users when consuming health information, as these may impact the success of digital media literacy interventions that teach users how to navigate misinformation online. ", doi="10.2196/59387", url="https://www.jmir.org/2025/1/e59387" } @Article{info:doi/10.2196/65610, author="Uchiyama, Ashley Misa and Bekki, Hirofumi and McMann, Tiana and Li, Zhuoran and Mackey, Tim", title="Characterizing Experiences With Hikikomori Syndrome on Twitter Among Japanese-Language Users: Qualitative Infodemiology Content Analysis", journal="JMIR Infodemiology", year="2025", month="Feb", day="24", volume="5", pages="e65610", keywords="hikikomori", keywords="social withdrawal", keywords="hikikomori syndrome", keywords="mental health", keywords="social isolation", abstract="Background: Hikikomori syndrome is a form of severe social withdrawal prevalent in Japan but is also a worldwide psychiatric issue. Twitter (subsequently rebranded X) offers valuable insights into personal experiences with mental health conditions, particularly among isolated individuals or hard-to-reach populations. Objective: This study aimed to examine trends in firsthand and secondhand experiences reported on Twitter between 2021 and 2023 in the Japanese language. Methods: Tweets were collected using the Twitter academic research application programming interface filtered for the following keywords: ``\#?????,'' ``\#?????,'' ``\#hikikomori,'' ``\#???,'' ``\#??????,'' ``\#???,'' and ``\#?????.'' The Bidirectional Encoder Representations From Transformers language model was used to analyze all Japanese-language posts collected. Themes and subthemes were then inductively coded for in-depth exploration of topic clusters relevant to first- and secondhand experiences with hikikomori syndrome. Results: We collected 2,018,822 tweets, which were narrowed down to 379,265 (18.79\%) tweets in Japanese from January 2021 to January 2023. After examining the topic clusters output by the Bidirectional Encoder Representations From Transformers model, 4 topics were determined to be relevant to the study aims. A total of 400 of the most highly interacted with tweets from these topic clusters were manually annotated for inclusion and exclusion, of which 148 (37\%) tweets from 89 unique users were identified as relevant to hikikomori experiences. Of these 148 relevant tweets, 71 (48\%) were identified as firsthand accounts, and 77 (52\%) were identified as secondhand accounts. Within firsthand reports, the themes identified included seeking social support, personal anecdotes, debunking misconceptions, and emotional ranting. Within secondhand reports, themes included seeking social support, personal anecdotes, seeking and giving advice, and advocacy against the negative stigma of hikikomori. Conclusions: This study provides new insights into experiences reported by web-based users regarding hikikomori syndrome specific to Japanese-speaking populations. Although not yet found in diagnostic manuals classifying mental disorders, the rise of web-based lifestyles as a consequence of the COVID-19 pandemic has increased the importance of discussions regarding hikikomori syndrome in web-based spaces. The results indicate that social media platforms may represent a web-based space for those experiencing hikikomori syndrome to engage in social interaction, advocacy against stigmatization, and participation in a community that can be maintained through a web-based barrier and minimized sense of social anxiety. ", doi="10.2196/65610", url="https://infodemiology.jmir.org/2025/1/e65610" } @Article{info:doi/10.2196/51154, author="Nagpal, Meghan and Jalali, Niloofar and Sherifali, Diana and Morita, Plinio and Cafazzo, A. Joseph", title="Investigating Reddit Data on Type 2 Diabetes Management During the COVID-19 Pandemic Using Latent Dirichlet Allocation Topic Modeling and Valence Aware Dictionary for Sentiment Reasoning Analysis: Content Analysis", journal="JMIR Form Res", year="2025", month="Feb", day="21", volume="9", pages="e51154", keywords="diabetes", keywords="diabetes mellitus", keywords="DM", keywords="COVID-19", keywords="pandemics", keywords="social media", keywords="health behavior", keywords="health knowledge", keywords="attitudes", keywords="practice", keywords="self-management", keywords="patient-generated health data", keywords="perspective", keywords="T2DM", abstract="Background: Type 2 diabetes (T2D) is a chronic disease that can be partially managed through healthy behaviors. However, the COVID-19 pandemic impacted how people managed T2D due to work and school closures and social isolation. Moreover, individuals with T2D were at increased risk of complications from COVID-19 and experienced worsened mental health due to stress and anxiety. Objective: This study aims to synthesize emerging themes related to the health behaviors of people living with T2D, and how they were affected during the early stages of the COVID-19 pandemic by examining Reddit forums dedicated to people living with T2D. Methods: Data from Reddit forums related to T2D, from January 2018 to early March 2021, were downloaded using the Pushshift API; support vector machines were used to classify whether a post was made in the context of the pandemic. Latent Dirichlet allocation topic modelling was performed to identify topics of discussion across the entire dataset and a subsequent iteration was performed to identify topics specific to the COVID-19 pandemic. Sentiment analysis using the VADER (Valence Aware Dictionary for Sentiment Reasoning) algorithm was performed to assess attitudes towards the pandemic. Results: From all posts, the identified topics of discussion were classified into the following themes: managing lifestyle (sentiment score 0.25, 95\% CI 0.25-0.26), managing blood glucose (sentiment score 0.19, 95\% CI 0.18-0.19), obtaining diabetes care (sentiment score 0.19, 95\% CI 0.18-0.20), and coping and receiving support (sentiment score 0.34, 95\% CI 0.33-0.35). Among the COVID-19--specific posts, the topics of discussion were coping with poor mental health (sentiment score 0.04, 95\% CI ?0.01 to0.11), accessing doctor and medications and controlling blood glucose (sentiment score 0.14, 95\% CI 0.09-0.20), changing food habits during the pandemic (sentiment score 0.25, 95\% CI 0.20-0.31), impact of stress on blood glucose levels (sentiment score 0.03, 95\% CI ?0.03 to 0.08), changing status of employment and insurance (sentiment score 0.17, 95\% CI 0.13-0.22), and risk of COVID-19 complications (sentiment score 0.09, 95\% CI 0.03-0.14). Overall, posts classified as COVID-19--related (0.12, 95\% CI 0.01-0.15) were associated with a lower sentiment score than those classified as nonCOVID (0.25, 95\% CI 0.24-0.25). This study was limited due to the lack of a method for assessing the demographics of users and verifying whether users had T2D. Conclusions: Themes identified from Reddit data suggested that the COVID-19 pandemic significantly influenced how people with T2D managed their disease, particularly in terms of accessing care and dealing with the complications of the virus. Overall, the early stages of the pandemic negatively impacted the attitudes of people living with T2D. This study demonstrates that social media data can be a qualitative data source for understanding patient perspectives. ", doi="10.2196/51154", url="https://formative.jmir.org/2025/1/e51154" } @Article{info:doi/10.2196/63190, author="Xie, Jiacheng and Zhang, Ziyang and Zeng, Shuai and Hilliard, Joel and An, Guanghui and Tang, Xiaoting and Jiang, Lei and Yu, Yang and Wan, Xiufeng and Xu, Dong", title="Leveraging Large Language Models for Infectious Disease Surveillance---Using a Web Service for Monitoring COVID-19 Patterns From Self-Reporting Tweets: Content Analysis", journal="J Med Internet Res", year="2025", month="Feb", day="20", volume="27", pages="e63190", keywords="COVID-19", keywords="self-reporting data", keywords="large language model", keywords="Twitter", keywords="social media analysis", keywords="natural language processing", keywords="machine learning", abstract="Background: The emergence of new SARS-CoV-2 variants, the resulting reinfections, and post--COVID-19 condition continue to impact many people's lives. Tracking websites like the one at Johns Hopkins University no longer report the daily confirmed cases, posing challenges to accurately determine the true extent of infections. Many COVID-19 cases with mild symptoms are self-assessed at home and reported on social media, which provides an opportunity to monitor and understand the progression and evolving trends of the disease. Objective: We aim to build a publicly available database of COVID-19--related tweets and extracted information about symptoms and recovery cycles from self-reported tweets. We have presented the results of our analysis of infection, reinfection, recovery, and long-term effects of COVID-19 on a visualization website that refreshes data on a weekly basis. Methods: We used Twitter (subsequently rebranded as X) to collect COVID-19--related data, from which 9 native English-speaking annotators annotated a training dataset of COVID-19--positive self-reporters. We then used large language models to identify positive self-reporters from other unannotated tweets. We used the Hibert transform to calculate the lead of the prediction curve ahead of the reported curve. Finally, we presented our findings on symptoms, recovery, reinfections, and long-term effects of COVID-19 on the Covlab website. Results: We collected 7.3 million tweets related to COVID-19 between January 1, 2020, and April 1, 2024, including 262,278 self-reported cases. The predicted number of infection cases by our model is 7.63 days ahead of the official report. In addition to common symptoms, we identified some symptoms that were not included in the list from the US Centers for Disease Control and Prevention, such as lethargy and hallucinations. Repeat infections were commonly occurring, with rates of second and third infections at 7.49\% (19,644/262,278) and 1.37\% (3593/262,278), respectively, whereas 0.45\% (1180/262,278) also reported that they had been infected >5 times. We identified 723 individuals who shared detailed recovery experiences through tweets, indicating a substantially reduction in recovery time over the years. Specifically, the average recovery period decreased from around 30 days in 2020 to approximately 12 days in 2023. In addition, geographic information collected from confirmed individuals indicates that the temporal patterns of confirmed cases in states such as California and Texas closely mirror the overall trajectory observed across the United States. Conclusions: Although with some biases and limitations, self-reported tweet data serves as a valuable complement to clinical data, especially in the postpandemic era dominated by mild cases. Our web-based analytic platform can play a significant role in continuously tracking COVID-19, finding new uncommon symptoms, detecting and monitoring the manifestation of long-term effects, and providing necessary insights to the public and decision-makers. ", doi="10.2196/63190", url="https://www.jmir.org/2025/1/e63190" } @Article{info:doi/10.2196/59338, author="Zhang, Xinyu and Zhu, Jianfeng and Kenne, R. Deric and Jin, Ruoming", title="Teenager Substance Use on Reddit: Mixed Methods Computational Analysis of Frames and Emotions", journal="J Med Internet Res", year="2025", month="Feb", day="19", volume="27", pages="e59338", keywords="teenager", keywords="substance use", keywords="Reddit", keywords="emotional analysis", keywords="bidirectional encoder representations from transformers", keywords="BERT", keywords="frame approach", abstract="Background: Adolescent substance use disorder is a pressing public health issue, with increasing prevalence as individuals age. Social media platforms like Reddit (Reddit Inc) serve as significant venues for teenagers to discuss and navigate substance use. Social media platforms, such as Reddit, serve as increasingly important spaces where teenagers discuss, share, and navigate their experiences with substance use, presenting unique opportunities and challenges for understanding and addressing this issue. Objective: This study aims to explore how teenagers frame substance-use discussions on the r/teenagers subreddit, focusing on their personal interpretations, causal attributions, and the social and psychological contexts that shape these online support groups. By identifying these interpretive frames, we aimed to better understand the complex drivers of adolescent substance use behavior and their potential interventions. Methods: Using natural language processing techniques, we analyzed 32,674 substance use--related posts from 2018 to 2022. A framing approach was used to identify and categorize prevalent themes, supplemented by emotional profiling using the EmoLLaMA-chat-13B model developed by Liu and colleagues. Results: In total, 7 primary frames emerged: normalization, risk awareness, social integration, autonomy and rebellion, coping mechanisms, media influence, and stigmatization. These frames varied in prevalence and were associated with distinct emotional profiles, highlighting the complex interplay between substance use and adolescent experiences. We observed that, for example, the normalization frame was often associated with a mix of sadness and anxiety, while the coping frame exhibited elevated levels of anger, sadness, and anxiety. These distinctive emotional landscapes associated with each frame reveal unique insights into the mental state of adolescents navigating substance use. Conclusions: The findings underscore the multifaceted nature of adolescent substance-use discussions on social media. Interventions must address underlying emotional and social factors as well as identity to effectively mitigate substance use disorder among adolescents. By understanding the frames teenagers use to interpret substance use, we can pave the way for more effective and personalized public health campaigns, and support services designed to resonate with adolescents' unique lived experiences. ", doi="10.2196/59338", url="https://www.jmir.org/2025/1/e59338" } @Article{info:doi/10.2196/65031, author="Joshi, Aditya and Kaune, Federico Diego and Leff, Phillip and Fraser, Emily and Lee, Sarah and Harrison, Morgan and Hazin, Moustafa", title="Self-Reported Side Effects Associated With Selective Androgen Receptor Modulators: Social Media Data Analysis", journal="J Med Internet Res", year="2025", month="Feb", day="18", volume="27", pages="e65031", keywords="selective androgen receptor modulator", keywords="SARM", keywords="liver toxicity", keywords="social media", keywords="data analysis", keywords="anabolic", keywords="muscle", keywords="bone", keywords="toxicities", keywords="self-report", keywords="side effect", keywords="retrospective analysis", keywords="public post", keywords="Reddit", keywords="androgen receptor ligands", keywords="drug", doi="10.2196/65031", url="https://www.jmir.org/2025/1/e65031" } @Article{info:doi/10.2196/60040, author="Obeng-Nyarko, Charissa and Barrera, Tatiana and Ogunleye, Temitayo and Taylor, Susan", title="A Google Trends Analysis of Search Interest for Tender-Headedness and Scalp-Related Concerns", journal="JMIR Dermatol", year="2025", month="Feb", day="13", volume="8", pages="e60040", keywords="tender-headedness", keywords="tender-headed", keywords="scalp tenderness", keywords="dermatologists", keywords="Google Trends", keywords="Black patients", doi="10.2196/60040", url="https://derma.jmir.org/2025/1/e60040" } @Article{info:doi/10.2196/66696, author="Chen, Sihui and Ngai, Bik Cindy Sing and Cheng, Cecilia and Hu, Yangna", title="Analyzing Themes, Sentiments, and Coping Strategies Regarding Online News Coverage of Depression in Hong Kong: Mixed Methods Study", journal="J Med Internet Res", year="2025", month="Feb", day="13", volume="27", pages="e66696", keywords="online news coverage", keywords="depression", keywords="natural language processing", keywords="NLP", keywords="latent Dirichlet allocation", keywords="LDA", keywords="sentiment", keywords="coping strategies", keywords="content analysis", abstract="Background: Depression, a highly prevalent global mental disorder, has prompted significant research concerning its association with social media use and its impact during Hong Kong's social unrest and COVID-19 pandemic. However, other mainstream media, specifically online news, has been largely overlooked. Despite extensive research conducted in countries, such as the United States, Australia, and Canada, to investigate the latent subthemes, sentiments, and coping strategies portrayed in depression-related news, the landscape in Hong Kong remains unexplored. Objective: This study aims to uncover the latent subthemes presented in the online news coverage of depression in Hong Kong, examine the sentiment conveyed in the news, and assess whether coping strategies have been provided in the news for individuals experiencing depression. Methods: This study used natural language processing (NLP) techniques, namely the latent Dirichlet allocation topic modeling and the Valence Aware Dictionary and Sentiment Reasoner (VADER) sentiment analysis, to fulfill the first and second objectives. Coping strategies were rigorously assessed and manually labeled with designated categories by content analysis. The online news was collected from February 2019 to May 2024 from Hong Kong mainstream news websites to examine the latest portrayal of depression, particularly during and after the social unrest and the COVID-19 pandemic. Results: In total, 2435 news articles were retained for data analysis after the news screening process. A total of 7 subthemes were identified based on the topic modeling results. Societal system, law enforcement, global recession, lifestyle, leisure, health issues, and US politics were the latent subthemes. Moreover, the overall news exhibited a slightly positive sentiment. The correlations between the sentiment scores and the latent subthemes indicated that the societal system, law enforcement, health issues, and US politics revealed negative tendencies, while the remainder leaned toward a positive sentiment. The coping strategies for depression were substantially lacking; however, the categories emphasizing information on skills and resources and individual adjustment to cope with depression emerged as the priority focus. Conclusions: This pioneering study used a mixed methods approach where NLP was used to investigate latent subthemes and underlying sentiment in online news. Content analysis was also performed to examine available coping strategies. The findings of this research enhance our understanding of how depression is portrayed through online news in Hong Kong and the preferable coping strategies being used to mitigate depression. The potential impact on readers was discussed. Future research is encouraged to address the mentioned implications and limitations, with recommendations to apply advanced NLP techniques to a new mental health issue case or language. ", doi="10.2196/66696", url="https://www.jmir.org/2025/1/e66696", url="http://www.ncbi.nlm.nih.gov/pubmed/39946170" } @Article{info:doi/10.2196/68881, author="Liu, Junyu and Niu, Qian and Nagai-Tanima, Momoko and Aoyama, Tomoki", title="Understanding Human Papillomavirus Vaccination Hesitancy in Japan Using Social Media: Content Analysis", journal="J Med Internet Res", year="2025", month="Feb", day="11", volume="27", pages="e68881", keywords="human papillomavirus", keywords="HPV", keywords="HPV vaccine", keywords="vaccine confidence", keywords="large language model", keywords="stance analysis", keywords="topic modeling", abstract="Background: Despite the reinstatement of proactive human papillomavirus (HPV) vaccine recommendations in 2022, Japan continues to face persistently low HPV vaccination rates, which pose significant public health challenges. Misinformation, complacency, and accessibility issues have been identified as key factors undermining vaccine uptake. Objective: This study aims to examine the evolution of public attitudes toward HPV vaccination in Japan by analyzing social media content. Specifically, we investigate the role of misinformation, public health events, and cross-vaccine attitudes (eg, COVID-19 vaccines) in shaping vaccine hesitancy over time. Methods: We collected tweets related to the HPV vaccine from 2011 to 2021. Natural language processing techniques and large language models (LLMs) were used for stance analysis of the collected data. Time series analysis and latent Dirichlet allocation topic modeling were used to identify shifts in public sentiment and topic trends over the decade. Misinformation within opposed-stance tweets was detected using LLMs. Furthermore, we analyzed the relationship between attitudes toward HPV and COVID-19 vaccines through logic analysis. Results: Among the tested models, Gemini 1.0 pro (Google) achieved the highest accuracy (0.902) for stance analysis, improving to 0.968 with hyperparameter tuning. Time series analysis identified significant shifts in public stance in 2013, 2016, and 2020, corresponding to key public health events and policy changes. Topic modeling revealed that discussions around vaccine safety peaked in 2015 before declining, while topics concerning vaccine effectiveness exhibited an opposite trend. Misinformation in topic ``Scientific Warnings and Public Health Risk'' in the sopposed-stance tweets reached a peak of 2.84\% (47/1656) in 2012 and stabilized at approximately 0.5\% from 2014 onward. The volume of tweets using HPV vaccine experiences to argue stances on COVID-19 vaccines was significantly higher than the reverse. Conclusions: Based on observation on the public attitudes toward HPV vaccination from social media contents over 10 years, our findings highlight the need for targeted public health interventions to address vaccine hesitancy in Japan. Although vaccine confidence has increased slowly, sustained efforts are necessary to ensure long-term improvements. Addressing misinformation, reducing complacency, and enhancing vaccine accessibility are key strategies for improving vaccine uptake. Some evidence suggests that confidence in one vaccine may positively influence perceptions of other vaccines. This study also demonstrated the use of LLMs in providing a comprehensive understanding of public health attitudes. Future public health strategies can benefit from these insights by designing effective interventions to boost vaccine confidence and uptake. ", doi="10.2196/68881", url="https://www.jmir.org/2025/1/e68881" } @Article{info:doi/10.2196/58227, author="Ivanitskaya, V. Lana and Erzikova, Elina", title="Visualizing YouTube Commenters' Conceptions of the US Health Care System: Semantic Network Analysis Method for Evidence-Based Policy Making", journal="JMIR Infodemiology", year="2025", month="Feb", day="11", volume="5", pages="e58227", keywords="social media", keywords="semantic network", keywords="health system", keywords="health policy", keywords="ideology", keywords="VOSviewer", keywords="health care reform", keywords="health services", keywords="health care workforce", keywords="health insurance", abstract="Background: The challenge of extracting meaningful patterns from the overwhelming noise of social media to guide decision-makers remains largely unresolved. Objective: This study aimed to evaluate the application of a semantic network method for creating an interactive visualization of social media discourse surrounding the US health care system. Methods: Building upon bibliometric approaches to conducting health studies, we repurposed the VOSviewer software program to analyze 179,193 YouTube comments about the US health care system. Using the overlay-enhanced semantic network method, we mapped the contents and structure of the commentary evoked by 53 YouTube videos uploaded in 2014 to 2023 by right-wing, left-wing, and centrist media outlets. The videos included newscasts, full-length documentaries, political satire, and stand-up comedy. We analyzed term co-occurrence network clusters, contextualized with custom-built information layers called overlays, and performed tests of the semantic network's robustness, representativeness, structural relevance, semantic accuracy, and usefulness for decision support. We examined how the comments mentioning 4 health system design concepts---universal health care, Medicare for All, single payer, and socialized medicine---were distributed across the network terms. Results: Grounded in the textual data, the macrolevel network representation unveiled complex discussions about illness and wellness; health services; ideology and society; the politics of health care agendas and reforms, market regulation, and health insurance; the health care workforce; dental care; and wait times. We observed thematic alignment between the network terms, extracted from YouTube comments, and the videos that elicited these comments. Discussions about illness and wellness persisted across time, as well as international comparisons of costs of ambulances, specialist care, prescriptions, and appointment wait times. The international comparisons were linked to commentaries with a higher concentration of British-spelled words, underscoring the global nature of the US health care discussion, which attracted domestic and global YouTube commenters. Shortages of nurses, nurse burnout, and their contributing factors (eg, shift work, nurse-to-patient staffing ratios, and corporate greed) were covered in comments with many likes. Comments about universal health care had much higher use of ideological terms than comments about single-payer health systems. Conclusions: YouTube users addressed issues of societal and policy relevance: social determinants of health, concerns for populations considered vulnerable, health equity, racism, health care quality, and access to essential health services. Versatile and applicable to health policy studies, the method presented and evaluated in our study supports evidence-based decision-making and contextualized understanding of diverse viewpoints. Interactive visualizations can help to uncover large-scale patterns and guide strategic use of analytical resources to perform qualitative research. ", doi="10.2196/58227", url="https://infodemiology.jmir.org/2025/1/e58227", url="http://www.ncbi.nlm.nih.gov/pubmed/39932770" } @Article{info:doi/10.2196/63824, author="Saito, Ryuichi and Tsugawa, Sho", title="Understanding Citizens' Response to Social Activities on Twitter in US Metropolises During the COVID-19 Recovery Phase Using a Fine-Tuned Large Language Model: Application of AI", journal="J Med Internet Res", year="2025", month="Feb", day="11", volume="27", pages="e63824", keywords="COVID-19", keywords="restriction", keywords="United States", keywords="X", keywords="Twitter", keywords="sentiment analysis", keywords="large language model", keywords="LLM", keywords="GPT-3.5", keywords="fine-tuning", abstract="Background: The COVID-19 pandemic continues to hold an important place in the collective memory as of 2024. As of March 2024, >676 million cases, 6 million deaths, and 13 billion vaccine doses have been reported. It is crucial to evaluate sociopsychological impacts as well as public health indicators such as these to understand the effects of the COVID-19 pandemic. Objective: This study aimed to explore the sentiments of residents of major US cities toward restrictions on social activities in 2022 during the transitional phase of the COVID-19 pandemic, from the peak of the pandemic to its gradual decline. By illuminating people's susceptibility to COVID-19, we provide insights into the general sentiment trends during the recovery phase of the pandemic. Methods: To analyze these trends, we collected posts (N=119,437) on the social media platform Twitter (now X) created by people living in New York City, Los Angeles, and Chicago from December 2021 to December 2022, which were impacted by the COVID-19 pandemic in similar ways. A total of 47,111 unique users authored these posts. In addition, for privacy considerations, any identifiable information, such as author IDs and usernames, was excluded, retaining only the text for analysis. Then, we developed a sentiment estimation model by fine-tuning a large language model on the collected data and used it to analyze how citizens' sentiments evolved throughout the pandemic. Results: In the evaluation of models, GPT-3.5 Turbo with fine-tuning outperformed GPT-3.5 Turbo without fine-tuning and Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach (RoBERTa)--large with fine-tuning, demonstrating significant accuracy (0.80), recall (0.79), precision (0.79), and F1-score (0.79). The findings using GPT-3.5 Turbo with fine-tuning reveal a significant relationship between sentiment levels and actual cases in all 3 cities. Specifically, the correlation coefficient for New York City is 0.89 (95\% CI 0.81-0.93), for Los Angeles is 0.39 (95\% CI 0.14-0.60), and for Chicago is 0.65 (95\% CI 0.47-0.78). Furthermore, feature words analysis showed that COVID-19--related keywords were replaced with non--COVID-19-related keywords in New York City and Los Angeles from January 2022 onward and Chicago from March 2022 onward. Conclusions: The results show a gradual decline in sentiment and interest in restrictions across all 3 cities as the pandemic approached its conclusion. These results are also ensured by a sentiment estimation model fine-tuned on actual Twitter posts. This study represents the first attempt from a macro perspective to depict sentiment using a classification model created with actual data from the period when COVID-19 was prevalent. This approach can be applied to the spread of other infectious diseases by adjusting search keywords for observational data. ", doi="10.2196/63824", url="https://www.jmir.org/2025/1/e63824", url="http://www.ncbi.nlm.nih.gov/pubmed/39932775" } @Article{info:doi/10.2196/60948, author="Wu, Xingyue and Lam, Sing Chun and Hui, Ho Ka and Loong, Ho-fung Herbert and Zhou, Rui Keary and Ngan, Chun-Kit and Cheung, Ting Yin", title="Perceptions in 3.6 Million Web-Based Posts of Online Communities on the Use of Cancer Immunotherapy: Data Mining Using BERTopic", journal="J Med Internet Res", year="2025", month="Feb", day="10", volume="27", pages="e60948", keywords="social media", keywords="cancer", keywords="immunotherapy", keywords="perceptions", keywords="data mining", keywords="oncology", keywords="web-based", keywords="lifestyle", keywords="therapeutic intervention", keywords="leukemia", keywords="lymphoma", keywords="survival", keywords="treatment", keywords="health information", keywords="decision-making", keywords="online community", keywords="machine learning", abstract="Background: Immunotherapy has become a game changer in cancer treatment. The internet has been used by patients as a platform to share personal experiences and seek medical guidance. Despite the increased utilization of immunotherapy in clinical practice, few studies have investigated the perceptions about its use by analyzing social media data. Objective: This study aims to use BERTopic (a topic modeling technique that is an extension of the Bidirectional Encoder Representation from Transformers machine learning model) to explore the perceptions of online cancer communities regarding immunotherapy. Methods: A total of 4.9 million posts were extracted from Facebook, Twitter, Reddit, and 16 online cancer-related forums. The textual data were preprocessed by natural language processing. BERTopic modeling was performed to identify topics from the posts. The effectiveness of isolating topics from the posts was evaluated using 3 metrics: topic diversity, coherence, and quality. Sentiment analysis was performed to determine the polarity of each topic and categorize them as positive or negative. Based on the topics generated through topic modeling, thematic analysis was conducted to identify themes associated with immunotherapy. Results: After data cleaning, 3.6 million posts remained for modeling. The highest overall topic quality achieved by BERTopic was 70.47\% (topic diversity: 87.86\%; topic coherence: 80.21\%). BERTopic generated 14 topics related to the perceptions of immunotherapy. The sentiment score of around 0.3 across the 14 topics suggested generally positive sentiments toward immunotherapy within the online communities. Six themes were identified, primarily covering (1) hopeful prospects offered by immunotherapy, (2) perceived effectiveness of immunotherapy, (3) complementary therapies or self-treatments, (4) financial and mental impact of undergoing immunotherapy, (5) impact on lifestyle and time schedules, and (6) side effects due to treatment. Conclusions: This study provides an overview of the multifaceted considerations essential for the application of immunotherapy as a therapeutic intervention. The topics and themes identified can serve as supporting information to facilitate physician-patient communication and the decision-making process. Furthermore, this study also demonstrates the effectiveness of BERTopic in analyzing large amounts of data to identify perceptions underlying social media and online communities. ", doi="10.2196/60948", url="https://www.jmir.org/2025/1/e60948" } @Article{info:doi/10.2196/70071, author="BinHamdan, Hamdan Rahaf and Alsadhan, Abdulrahman Salwa and Gazzaz, Zohair Arwa and AlJameel, Hassan AlBandary", title="Social Media Use and Oral Health--Related Misconceptions in Saudi Arabia: Cross-Sectional Study", journal="JMIR Form Res", year="2025", month="Feb", day="10", volume="9", pages="e70071", keywords="social media", keywords="oral health", keywords="health misinformation", keywords="digital health", keywords="Saudi Arabia", keywords="public health", keywords="Instagram", keywords="Snapchat", keywords="TikTok", keywords="Twitter", abstract="Background: Social media has become a central tool in health communication, offering both opportunities and challenges. In Saudi Arabia, where platforms like WhatsApp, Snapchat, and Instagram are widely used, the quality and credibility of oral health information shared digitally remain critical issues. Misconceptions about oral health can negatively influence individuals' behaviors and oral health outcomes. Objective: This study aimed to describe the patterns of social media use and estimate the prevalence of oral health--related misconceptions among adults in Saudi Arabia. Additionally, it assessed the associations between engagement with oral health information, self-reported oral health, and the presence and count of these misconceptions. Methods: A cross-sectional survey was conducted over 10 weeks, targeting adults aged 15 years and older in Saudi Arabia. Data were collected from a total sample size (n=387) via a questionnaire distributed through targeted advertisements on Instagram, TikTok, Snapchat, and X (Twitter). The prevalence of oral health--related misconceptions was estimated using descriptive statistics, including counts and percentages. Chi-square tests described sociodemographic, social media engagement, and self-reported oral health. Logistic and Poisson regression analyses were used to assess associations between engagement and self-reported oral health with misconceptions. Logistic regression models provided odds ratios and adjusted odds ratios with 95\% CI to assess the presence of oral health misconceptions. Poisson regression was used to calculate mean ratios and adjusted mean ratios (AMRs) for the count of misconceptions. Results: WhatsApp (n=344, 89.8\%) and Instagram (n=304, 78.9\%) were the most frequently used social media platforms daily. Common oral health misconceptions included beliefs that ``Pregnancy causes calcium loss in teeth'' (n=337, 87\%) and ``Dental treatment should be avoided during pregnancy'' (n=245, 63.3\%). Following dental-specific accounts was significantly associated with lower odds of having any misconceptions (adjusted odds ratio 0.41, 95\% CI 0.22-0.78) and a lower count of misconceptions (AMR 0.87, 95\% CI 0.77-0.98). Conversely, trust in social media as a source of oral health information was associated with a higher count of misconceptions (AMR 1.16, 95\% CI 1.02-1.31). Conclusions: Social media platforms are essential yet double-edged tools for oral health information dissemination in Saudi Arabia. Participants who followed dental-specific accounts had significantly lower misconceptions, while trust in social media as a source of information was linked to higher counts of misconceptions. These findings highlight the importance of promoting credible content from verified sources to combat misconceptions. Strategic collaborations with dental professionals are necessary to enhance the dissemination of accurate oral health information and public awareness and reduce the prevalence of oral health--related misconceptions. ", doi="10.2196/70071", url="https://formative.jmir.org/2025/1/e70071" } @Article{info:doi/10.2196/66446, author="Zhang, Zhongmin and Xu, Hengyi and Pan, Jing and Song, Fujian and Chen, Ting", title="Spatiotemporal Characteristics and Influential Factors of Electronic Cigarette Web-Based Attention in Mainland China: Time Series Observational Study", journal="J Med Internet Res", year="2025", month="Feb", day="10", volume="27", pages="e66446", keywords="electronic cigarettes", keywords="Baidu index", keywords="web-based attention", keywords="spatiotemporal characteristics", keywords="China", abstract="Background: The popularity of electronic cigarettes (e-cigarettes) has steadily increased, prompting a considerable number of individuals to search for relevant information on them. Previous e-cigarette infodemiology studies have focused on assessing the quality and reliability of website content and quantifying the impact of policies. In reality, most low-income countries and low- and middle-income countries have not yet conducted e-cigarette use surveillance. Data sourced from web-based search engines related to e-cigarettes have the potential to serve as cost-effective supplementary means to traditional monitoring approaches. Objective: This study aimed to analyze the spatiotemporal distribution characteristics and associated sociodemographic factors of e-cigarette searches using trends from the Baidu search engine. Methods: The query data related to e-cigarettes for 31 provinces in mainland China were retrieved from the Baidu index database from January 1, 2015, to December 31, 2022. Concentration ratio methods and spatial autocorrelation analysis were applied to analyze the temporal aggregation and spatial aggregation of the e-cigarette Baidu index, respectively. A variance inflation factor test was performed to avoid multicollinearity. A spatial panel econometric model was developed to assess the determinants of e-cigarette web-based attention. Results: The daily average Baidu index for e-cigarettes increased from 53,234.873 in 2015 to 85,416.995 in 2021 and then declined to 52,174.906 in 2022. This index was concentrated in the southeastern coastal region, whereas the hot spot shifted to the northwestern region after adjusting for population size. Positive spatial autocorrelation existed in the per capita Baidu index of e-cigarettes from 2015 to 2022. The results of the local Moran's I showed that there were mainly low-low cluster areas of the per capita Baidu index, especially in the central region. Furthermore, the male-female ratio, the proportion of high school and above education, and the per capita gross regional domestic product were positively correlated with the per capita Baidu index for e-cigarettes. A higher urbanization rate was associated with a reduced per capita Baidu index. Conclusions: With the increasing popularity of web-based searches for e-cigarettes, a targeted e-cigarette health education program for individuals in the northwest, males, rural populations, high school and above educated individuals, and high-income groups is warranted. ", doi="10.2196/66446", url="https://www.jmir.org/2025/1/e66446", url="http://www.ncbi.nlm.nih.gov/pubmed/39928402" } @Article{info:doi/10.2196/64739, author="Oono, Fumi and Matsumoto, Mai and Ogata, Risa and Suga, Mizuki and Murakami, Kentaro", title="Description of Weight-Related Content and Recommended Dietary Behaviors for Weight Loss Frequently Reposted on X (Twitter) in English and Japanese: Content Analysis", journal="J Med Internet Res", year="2025", month="Feb", day="7", volume="27", pages="e64739", keywords="social networking service", keywords="X, Twitter", keywords="web-based health information", keywords="dieting", keywords="weight loss", keywords="content analysis", keywords="digital health", keywords="weight control", keywords="weight", keywords="social media", keywords="diet", keywords="dietary behavior", keywords="obesity", keywords="eating disorders", keywords="public perceptions", abstract="Background: Both obesity and underweight are matters of global concern. Weight-related content frequently shared on social media can reflect public recognition and affect users' behaviors and perceptions. Although X (Twitter) is a popular social media platform, few studies have revealed the content of weight-related posts or details of dietary behaviors for weight loss shared on X. Objective: This study aims to describe body weight--related content frequently reposted on X, with a particular focus on dietary behaviors for weight loss, in English and Japanese. Methods: We collected English and Japanese X posts related to human body weight having over 100 reposts in July 2023 using an application programming interface tool. Two independent researchers categorized the contents of the posts into 7 main categories and then summarized recommended weight loss strategies. Results: We analyzed 815 English and 1213 Japanese posts. The most popular main category of the content was ``how to change weight'' in both languages. The Japanese posts were more likely to mention ``how to change weight'' (n=571, 47.1\%) and ``recipes to change weight'' (n=114, 9.4\%) than the English posts (n=195, 23.9\% and n=10, 1.2\%, respectively), whereas the English posts were more likely to mention ``will or experience to change weight'' (n=167, 20.5\%), ``attitudes toward weight status'' (n=78, 9.6\%), and ``public health situation'' (n=44, 5.4\%) than Japanese posts. Among 146 English and 541 Japanese posts about weight loss strategies, the predominant strategies were diet (n=76, 52.1\% in English and n=170, 31.4\% in Japanese) and physical activities (n=56, 38.4\% and n=295, 54.5\%, respectively). The proportion of posts mentioning both diet and physical activity was smaller in Japanese (n=62, 11.5\%) than in English (n=31, 21.2\%). Among 76 English and 170 Japanese posts about dietary behaviors for weight loss, more than 60\% of posts recommended increasing intakes of specific nutrients or food groups in both languages. The most popular dietary component recommended to increase was vegetables in both English (n=31, 40.8\%) and Japanese (n=48, 28.2\%), followed by protein and fruits in English and grains or potatoes and legumes in Japanese. Japanese posts were less likely to mention reducing energy intake; meal timing or eating frequency; or reducing intakes of specific nutrients or food groups than the English posts. The most popular dietary component recommended to decrease was alcohol in English and confectioneries in Japanese. Conclusions: This study characterized user interest in weight management and suggested the potential of X as an information source for weight management. Although weight loss strategies related to diet and physical activity were popular in both English and Japanese, some differences in the details of the strategies were present, indicating that X users are exposed to different information in English and Japanese. ", doi="10.2196/64739", url="https://www.jmir.org/2025/1/e64739", url="http://www.ncbi.nlm.nih.gov/pubmed/39918849" } @Article{info:doi/10.2196/59872, author="Ahmed, Wasim and Hardey, Mariann and Vidal-Alaball, Josep", title="Organ Donation Conversations on X and Development of the OrgReach Social Media Marketing Strategy: Social Network Analysis", journal="J Med Internet Res", year="2025", month="Feb", day="6", volume="27", pages="e59872", keywords="organ donation", keywords="organ transplant", keywords="social media", keywords="health", keywords="social network analysis", keywords="marketing strategy", keywords="awareness", keywords="public health", keywords="health information", keywords="qualitative", keywords="thematic analysis", keywords="NodeXL Pro", keywords="algorithm", keywords="elite tier", keywords="digital health", keywords="United Kingdom", keywords="X", abstract="Background: The digital landscape has become a vital platform for public health discourse, particularly concerning important topics like organ donation. With a global rise in organ transplant needs, fostering public understanding and positive attitudes toward organ donation is critical. Social media platforms, such as X, contain conversations from the public, and key stakeholders maintain an active presence on the platform. Objective: The goal is to develop insights into organ donation discussions on a popular social media platform (X) and understand the context in which users discussed organ donation advocacy. We investigate the influence of prominent profiles on X and meta-level accounts, including those seeking health information. We use credibility theory to explore the construction and impact of credibility within social media contexts in organ donation discussions. Methods: Data were retrieved from X between October 2023 and May 2024, covering a 7-month period. The study was able to retrieve a dataset with 20,124 unique users and 33,830 posts. The posts were analyzed using social network analysis and qualitative thematic analysis. NodeXL Pro was used to retrieve and analyze the data, and a network visualization was created by drawing upon the Clauset-Newman-Moore cluster algorithm and the Harel-Koren Fast Multiscale layout algorithm. Results: This analysis reveals an ``elite tier'' shaping the conversation, with themes reflecting existing societal sensitivities around organ donation. We demonstrate how prominent social media profiles act as information intermediaries, navigating the tension between open dialogue and negative perceptions. We use our findings, social credibility theory, and review of existing literature to develop the OrgReach Social Media Marketing Strategy for Organ Donation Awareness. The OrgReach strategy developed is based on 5 C's (Create, Connect, Collaborate, Correct, and Curate), 2 A's (Access and Analyse), and 3 R's (Recognize, Respond, and Reevaluate). Conclusions: The study highlights the crucial role of analyzing social media data by drawing upon social networks and topic analysis to understand influence and network communication patterns. By doing so, the study proposes the OrgReach strategy that can feed into the marketing strategies for organ donation outreach and awareness. ", doi="10.2196/59872", url="https://www.jmir.org/2025/1/e59872" } @Article{info:doi/10.2196/66072, author="Xiong, Xin and Xiang, Linghui and Chang, Litao and Wu, XY Irene and Deng, Shuzhen", title="Forecasting the Incidence of Mumps Based on the Baidu Index and Environmental Data in Yunnan, China: Deep Learning Model Study", journal="J Med Internet Res", year="2025", month="Feb", day="6", volume="27", pages="e66072", keywords="mumps", keywords="deep learning", keywords="baidu index", keywords="forecasting", keywords="incidence prediction", keywords="time series analysis", keywords="Yunnan", keywords="China", abstract="Background: Mumps is a viral respiratory disease characterized by facial swelling and transmitted through respiratory secretions. Despite the availability of an effective vaccine, mumps outbreaks have reemerged globally, including in China, where it remains a significant public health issue. In Yunnan province, China, the incidence of mumps has fluctuated markedly and is higher than that in mainland China, underscoring the need for improved outbreak prediction methods. Traditional surveillance methods, however, may not be sufficient for timely and accurate outbreak prediction. Objective: Our study aims to leverage the Baidu search index, representing search volumes from China's most popular search engine, along with environmental data to develop a predictive model for mumps incidence in Yunnan province. Methods: We analyzed mumps incidence in Yunnan Province from 2014 to 2023, and used time series data, including mumps incidence, Baidu search index, and environmental factors, from 2016 to 2023, to develop predictive models based on long short-term memory networks. Feature selection was conducted using Pearson correlation analysis, and lag correlations were explored through a distributed nonlinear lag model (DNLM). We constructed four models with different combinations of predictors: (1) model BE, combining the Baidu index and environmental factors data; (2) model IB, combining mumps incidence and Baidu index data; (3) model IE, combining mumps incidence and environmental factors; and (4) model IBE, integrating all 3 data sources. Results: The incidence of mumps in Yunnan showed significant variability, peaking at 37.5 per 100,000 population in 2019. From 2014 to 2023, the proportion of female patients ranged from 41.3\% in 2015 to 45.7\% in 2020, consistently lower than that of male patients. After excluding variables with a Pearson correlation coefficient of <0.10 or P values of <.05, we included 3 Baidu index search term groups (disease name, symptoms, and treatment) and 6 environmental factors (maximum temperature, minimum temperature, sulfur dioxide, carbon monoxide, particulate matter with a diameter of 2.5 {\textmu}m or less, and particulate matter with a diameter of 10 {\textmu}m or less) for model development. DNLM analysis revealed that the relative risks consistently increased with rising Baidu index values, while nonlinear associations between temperature and mumps incidence were observed. Among the 4 models, model IBE exhibited the best performance, achieving the coefficient of determination of 0.72, with mean absolute error, mean absolute percentage error, and root-mean-square error values of 0.33, 15.9\%, and 0.43, respectively, in the test set. Conclusions: Our study developed model IBE to predict the incidence of mumps in Yunnan province, offering a potential tool for early detection of mumps outbreaks. The performance of model IBE underscores the potential of integrating search engine data and environmental factors to enhance mumps incidence forecasting. This approach offers a promising tool for improving public health surveillance and enabling rapid responses to mumps outbreaks. ", doi="10.2196/66072", url="https://www.jmir.org/2025/1/e66072" } @Article{info:doi/10.2196/63910, author="Rao, Ashwin and Sabri, Nazanin and Guo, Siyi and Raschid, Louiqa and Lerman, Kristina", title="Public Health Messaging on Twitter During the COVID-19 Pandemic: Observational Study", journal="J Med Internet Res", year="2025", month="Feb", day="5", volume="27", pages="e63910", keywords="public health", keywords="public health messaging", keywords="COVID-19", keywords="Twitter", keywords="emotions", keywords="moral foundations", keywords="polarization", abstract="Background: Effective communication is crucial during health crises, and social media has become a prominent platform for public health experts (PHEs) to share information and engage with the public. At the same time, social media also provides a platform for pseudoexperts who may spread contrarian views. Despite the importance of social media, key elements of communication, such as the use of moral or emotional language and messaging strategy, particularly during the emergency phase of the COVID-19 pandemic, have not been explored. Objective: This study aimed to analyze how PHEs and pseudoexperts communicated with the public during the emergency phase of the COVID-19 pandemic. We focused on the emotional and moral language used in their messages on various COVID-19 pandemic--related topics. We also analyzed their interactions with political elites and the public's engagement with PHEs to gain a deeper understanding of their influence on public discourse. Methods: For this observational study, we gathered a dataset of >539,000 original posts or reposts from 489 PHEs and 356 pseudoexperts on Twitter (subsequently rebranded X) from January 2020 to January 2021, along with the replies to the original posts from the PHEs. We identified the key issues that PHEs and pseudoexperts prioritized. We also determined the emotional and moral language in both the original posts and the replies. This allows us to characterize priorities for PHEs and pseudoexperts as well as differences in messaging strategy between these 2 groups. We also evaluated the influence of PHEs' language and strategy on the public response. Results: Our analyses revealed that PHEs focused more on masking, health care, education, and vaccines, whereas pseudoexperts discussed therapeutics and lockdowns more frequently (P<.001). PHEs typically used positive emotional language across all issues (P<.001), expressing optimism and joy. Pseudoexperts often used negative emotions of pessimism and disgust, while limiting positive emotional language to origins and therapeutics (P<.001). Along the dimensions of moral language, PHEs and pseudoexperts differed on care versus harm and authority versus subversion across different issues. Negative emotional and moral language tends to boost engagement in COVID-19 discussions across all issues. However, the use of positive language by PHEs increases the use of positive language in the public responses. PHEs act as liberal partisans: they express more positive affect in their posts directed at liberals and more negative affect in their posts directed at conservative elites. In contrast, pseudoexperts act as conservative partisans. These results provide nuanced insights into the elements that have polarized the COVID-19 discourse. Conclusions: Understanding the nature of the public response to PHEs' messages on social media is essential for refining communication strategies during health crises. Our findings underscore the importance of using moral-emotional language strategically to reduce polarization and build trust. ", doi="10.2196/63910", url="https://www.jmir.org/2025/1/e63910" } @Article{info:doi/10.2196/64722, author="Tawfik, Daniel and Shanafelt, D. Tait and Bayati, Mohsen and Profit, Jochen", title="Electronic Health Record Use Patterns Among Well-Being Survey Responders and Nonresponders: Longitudinal Observational Study", journal="JMIR Med Inform", year="2025", month="Feb", day="4", volume="13", pages="e64722", keywords="electronic health record metadata", keywords="electronic health record", keywords="EHR", keywords="electronic medical record", keywords="patient record", keywords="health records", keywords="personal health record", keywords="use", keywords="response bias", keywords="well-being", keywords="burnout", keywords="physicians", keywords="experiences", keywords="surveys", keywords="longitudinal studies", keywords="observational studies", abstract="Background: Physician surveys provide indispensable insights into physician experience, but the question of whether responders are representative can limit confidence in conclusions. Ubiquitously collected electronic health record (EHR) use data may improve understanding of the experiences of survey nonresponders in relation to responders, providing clues regarding their well-being. Objective: The aim of the study was to identify EHR use measures corresponding with physician survey responses and examine methods to estimate population-level survey results among physicians. Methods: This longitudinal observational study was conducted from 2019 through 2020 among academic and community primary care physicians. We quantified EHR use using vendor-derived and investigator-derived measures, quantified burnout symptoms using emotional exhaustion and interpersonal disengagement subscales of the Stanford Professional Fulfillment Index, and used an ensemble of response propensity-weighted penalized linear regressions to develop a burnout symptom prediction model. Results: Among 697 surveys from 477 physicians with a response rate of 80.5\% (697/866), always responders were similar to nonresponders in gender (204/340, 60\% vs 38/66, 58\% women; P=.78) and age (median 50, IQR 40?60 years vs median 50, IQR 37.5?57.5 years; P=.88) but with higher clinical workload (median 121.5, IQR 58.5?184 vs median 34.5, IQR 0?115 appointments; P<.001), efficiency (median 5.2, IQR 4.0-6.2 vs median 4.3, IQR 0?5.6; P<.001), and proficiency (median 7.0, IQR 5.4?8.5 vs median 3.1, IQR 0?6.3; P<.001). Survey response status prediction showed an out-of-sample area under the receiver operating characteristics curve of 0.88 (95\% CI 0.77-0.91). Burnout symptom prediction showed an out-of-sample area under the receiver operating characteristics curve of 0.63 (95\% CI 0.57-0.70). The predicted burnout prevalence among nonresponders was 52\%, higher than the observed prevalence of 28\% among responders, resulting in an estimated population burnout prevalence of 31\%. Conclusions: EHR use measures showed limited utility for predicting burnout symptoms but allowed discrimination between responders and nonresponders. These measures may enable qualitative interpretations of the effects of nonresponders and may inform survey response maximization efforts. ", doi="10.2196/64722", url="https://medinform.jmir.org/2025/1/e64722" } @Article{info:doi/10.2196/55642, author="Asaad, Chaimae and Khaouja, Imane and Ghogho, Mounir and Ba{\"i}na, Karim", title="When Infodemic Meets Epidemic: Systematic Literature Review", journal="JMIR Public Health Surveill", year="2025", month="Feb", day="3", volume="11", pages="e55642", keywords="epidemics", keywords="social media", keywords="epidemic surveillance", keywords="misinformation", keywords="mental health", abstract="Background: Epidemics and outbreaks present arduous challenges, requiring both individual and communal efforts. The significant medical, emotional, and financial burden associated with epidemics creates feelings of distrust, fear, and loss of control, making vulnerable populations prone to exploitation and manipulation through misinformation, rumors, and conspiracies. The use of social media sites has increased in the last decade. As a result, significant amounts of public data can be leveraged for biosurveillance. Social media sites can also provide a platform to quickly and efficiently reach a sizable percentage of the population; therefore, they have a potential role in various aspects of epidemic mitigation. Objective: This systematic literature review aimed to provide a methodical overview of the integration of social media in 3 epidemic-related contexts: epidemic monitoring, misinformation detection, and the relationship with mental health. The aim is to understand how social media has been used efficiently in these contexts, and which gaps need further research efforts. Methods: Three research questions, related to epidemic monitoring, misinformation, and mental health, were conceptualized for this review. In the first PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) stage, 13,522 publications were collected from several digital libraries (PubMed, IEEE Xplore, ScienceDirect, SpringerLink, MDPI, ACM, and ACL) and gray literature sources (arXiv and ProQuest), spanning from 2010 to 2022. A total of 242 (1.79\%) papers were selected for inclusion and were synthesized to identify themes, methods, epidemics studied, and social media sites used. Results: Five main themes were identified in the literature, as follows: epidemic forecasting and surveillance, public opinion understanding, fake news identification and characterization, mental health assessment, and association of social media use with psychological outcomes. Social media data were found to be an efficient tool to gauge public response, monitor discourse, identify misleading and fake news, and estimate the mental health toll of epidemics. Findings uncovered a need for more robust applications of lessons learned from epidemic ``postmortem documentation.'' A vast gap exists between retrospective analysis of epidemic management and result integration in prospective studies. Conclusions: Harnessing the full potential of social media in epidemic-related tasks requires streamlining the results of epidemic forecasting, public opinion understanding, and misinformation detection, all while keeping abreast of potential mental health implications. Proactive prevention has thus become vital for epidemic curtailment and containment. ", doi="10.2196/55642", url="https://publichealth.jmir.org/2025/1/e55642" } @Article{info:doi/10.2196/63864, author="Tong, Chau and Margolin, Drew and Niederdeppe, Jeff and Chunara, Rumi and Liu, Jiawei and Jih-Vieira, Lea and King, J. Andy", title="Colorectal Cancer Racial Equity Post Volume, Content, and Exposure: Observational Study Using Twitter Data", journal="J Med Internet Res", year="2025", month="Feb", day="3", volume="27", pages="e63864", keywords="racial equity information", keywords="information exposure", keywords="health disparities", keywords="colorectal cancer", keywords="cancer communication", keywords="Twitter", keywords="X", abstract="Background: Racial inequity in health outcomes, particularly in colorectal cancer (CRC), remains one of the most pressing issues in cancer communication and public health. Social media platforms like Twitter (now X) provide opportunities to disseminate health equity information widely, yet little is known about the availability, content, and reach of racial health equity information related to CRC on these platforms. Addressing this gap is essential to leveraging social media for equitable health communication. Objective: This study aims to analyze the volume, content, and exposure of CRC racial health equity tweets from identified CRC equity disseminator accounts on Twitter. These accounts were defined as those actively sharing information related to racial equity in CRC outcomes. By examining the behavior and impact of these disseminators, this study provides insights into how health equity content is shared and received on social media. Methods: We identified accounts that posted CRC-related content on Twitter between 2019 and 2021. Accounts were classified as CRC equity disseminators (n=798) if they followed at least 2 CRC racial equity organization accounts. We analyzed the volume and content of racial equity--related CRC tweets (n=1134) from these accounts and categorized them by account type (experts vs nonexperts). Additionally, we evaluated exposure by analyzing follower reach (n=6,266,269) and the role of broker accounts---accounts serving as unique sources of CRC racial equity information to their followers. Results: Among 19,559 tweets posted by 798 CRC equity disseminators, only 5.8\% (n=1134) mentioned racially and ethnically minoritized groups. Most of these tweets (641/1134, 57\%) addressed disparities in outcomes, while fewer emphasized actionable content, such as symptoms (11/1134, 1\%) or screening procedures (159/1134, 14\%). Expert accounts (n=479; 716 tweets) were more likely to post CRC equity tweets compared with nonexpert accounts (n=319; 418 tweets). Broker accounts (n=500), or those with a substantial portion of followers relying on them for equity-related information, demonstrated the highest capacity for exposing followers to CRC equity content, thereby extending the reach of these critical messages to underserved communities. Conclusions: This study emphasizes the critical roles played by expert and broker accounts in disseminating CRC racial equity information on social media. Despite the limited volume of equity-focused content, broker accounts were crucial in reaching otherwise unexposed audiences. Public health practitioners should focus on encouraging equity disseminators to share more actionable information, such as symptoms and screening benefits, and implement measures to amplify the reach of such content on social media. Strengthening these efforts could help bridge disparities in cancer outcomes among racially minoritized groups. ", doi="10.2196/63864", url="https://www.jmir.org/2025/1/e63864" } @Article{info:doi/10.2196/58539, author="Arifi, Dorian and Resch, Bernd and Santillana, Mauricio and Guan, Wendy Weihe and Knoblauch, Steffen and Lautenbach, Sven and Jaenisch, Thomas and Morales, Ivonne and Havas, Clemens", title="Geosocial Media's Early Warning Capabilities Across US County-Level Political Clusters: Observational Study", journal="JMIR Infodemiology", year="2025", month="Jan", day="30", volume="5", pages="e58539", keywords="spatiotemporal epidemiology", keywords="geo-social media data", keywords="digital disease surveillance", keywords="political polarization", keywords="epidemiological early warning", keywords="digital early warning", abstract="Background: The novel coronavirus disease (COVID-19) sparked significant health concerns worldwide, prompting policy makers and health care experts to implement nonpharmaceutical public health interventions, such as stay-at-home orders and mask mandates, to slow the spread of the virus. While these interventions proved essential in controlling transmission, they also caused substantial economic and societal costs and should therefore be used strategically, particularly when disease activity is on the rise. In this context, geosocial media posts (posts with an explicit georeference) have been shown to provide a promising tool for anticipating moments of potential health care crises. However, previous studies on the early warning capabilities of geosocial media data have largely been constrained by coarse spatial resolutions or short temporal scopes, with limited understanding of how local political beliefs may influence these capabilities. Objective: This study aimed to assess how the epidemiological early warning capabilities of geosocial media posts for COVID-19 vary over time and across US counties with differing political beliefs. Methods: We classified US counties into 3 political clusters, democrat, republican, and swing counties, based on voting data from the last 6 federal election cycles. In these clusters, we analyzed the early warning capabilities of geosocial media posts across 6 consecutive COVID-19 waves (February 2020-April 2022). We specifically examined the temporal lag between geosocial media signals and surges in COVID-19 cases, measuring both the number of days by which the geosocial media signals preceded the surges in COVID-19 cases (temporal lag) and the correlation between their respective time series. Results: The early warning capabilities of geosocial media data differed across political clusters and COVID-19 waves. On average, geosocial media posts preceded COVID-19 cases by 21 days in republican counties compared with 14.6 days in democrat counties and 24.2 days in swing counties. In general, geosocial media posts were preceding COVID-19 cases in 5 out of 6 waves across all political clusters. However, we observed a decrease over time in the number of days that posts preceded COVID-19 cases, particularly in democrat and republican counties. Furthermore, a decline in signal strength and the impact of trending topics presented challenges for the reliability of the early warning signals. Conclusions: This study provides valuable insights into the strengths and limitations of geosocial media data as an epidemiological early warning tool, particularly highlighting how they can change across county-level political clusters. Thus, these findings indicate that future geosocial media based epidemiological early warning systems might benefit from accounting for political beliefs. In addition, the impact of declining geosocial media signal strength over time and the role of trending topics for signal reliability in early warning systems need to be assessed in future research. ", doi="10.2196/58539", url="https://infodemiology.jmir.org/2025/1/e58539" } @Article{info:doi/10.2196/54601, author="Trevena, William and Zhong, Xiang and Alvarado, Michelle and Semenov, Alexander and Oktay, Alp and Devlin, Devin and Gohil, Yogesh Aarya and Chittimouju, Harsha Sai", title="Using Large Language Models to Detect and Understand Drug Discontinuation Events in Web-Based Forums: Development and Validation Study", journal="J Med Internet Res", year="2025", month="Jan", day="30", volume="27", pages="e54601", keywords="natural language processing", keywords="large language models", keywords="ChatGPT", keywords="drug discontinuation events", keywords="zero-shot classification", keywords="artificial intelligence", keywords="AI", abstract="Background: The implementation of large language models (LLMs), such as BART (Bidirectional and Auto-Regressive Transformers) and GPT-4, has revolutionized the extraction of insights from unstructured text. These advancements have expanded into health care, allowing analysis of social media for public health insights. However, the detection of drug discontinuation events (DDEs) remains underexplored. Identifying DDEs is crucial for understanding medication adherence and patient outcomes. Objective: The aim of this study is to provide a flexible framework for investigating various clinical research questions in data-sparse environments. We provide an example of the utility of this framework by identifying DDEs and their root causes in an open-source web-based forum, MedHelp, and by releasing the first open-source DDE datasets to aid further research in this domain. Methods: We used several LLMs, including GPT-4 Turbo, GPT-4o, DeBERTa (Decoding-Enhanced Bidirectional Encoder Representations from Transformer with Disentangled Attention), and BART, among others, to detect and determine the root causes of DDEs in user comments posted on MedHelp. Our study design included the use of zero-shot classification, which allows these models to make predictions without task-specific training. We split user comments into sentences and applied different classification strategies to assess the performance of these models in identifying DDEs and their root causes. Results: Among the selected models, GPT-4o performed the best at determining the root causes of DDEs, predicting only 12.9\% of root causes incorrectly (hamming loss). Among the open-source models tested, BART demonstrated the best performance in detecting DDEs, achieving an F1-score of 0.86, a false positive rate of 2.8\%, and a false negative rate of 6.5\%, all without any fine-tuning. The dataset included 10.7\% (107/1000) DDEs, emphasizing the models' robustness in an imbalanced data context. Conclusions: This study demonstrated the effectiveness of open- and closed-source LLMs, such as GPT-4o and BART, for detecting DDEs and their root causes from publicly accessible data through zero-shot classification. The robust and scalable framework we propose can aid researchers in addressing data-sparse clinical research questions. The launch of open-access DDE datasets has the potential to stimulate further research and novel discoveries in this field. ", doi="10.2196/54601", url="https://www.jmir.org/2025/1/e54601", url="http://www.ncbi.nlm.nih.gov/pubmed/39883487" } @Article{info:doi/10.2196/63634, author="Rojas, K. Natalia and Martin, Sam and Cortina-Borja, Mario and Shafran, Roz and Fox-Smith, Lana and Stephenson, Terence and Ching, F. Brian C. and d'Oelsnitz, Ana{\"i}s and Norris, Tom and Xu, Yue and McOwat, Kelsey and Dalrymple, Emma and Heyman, Isobel and Ford, Tamsin and Chalder, Trudie and Simmons, Ruth and and Pinto Pereira, M. Snehal", title="Health and Experiences During the COVID-19 Pandemic Among Children and Young People: Analysis of Free-Text Responses From the Children and Young People With Long COVID Study", journal="J Med Internet Res", year="2025", month="Jan", day="28", volume="27", pages="e63634", keywords="children and young people", keywords="text mining", keywords="free-text responses", keywords="experiences", keywords="COVID-19", keywords="long COVID", keywords="InfraNodus", keywords="sentiment analysis", keywords="discourse analysis", keywords="AI", keywords="artificial intelligence", abstract="Background: The literature is equivocal as to whether the predicted negative mental health impact of the COVID-19 pandemic came to fruition. Some quantitative studies report increased emotional problems and depression; others report improved mental health and well-being. Qualitative explorations reveal heterogeneity, with themes ranging from feelings of loss to growth and development. Objective: This study aims to analyze free-text responses from children and young people participating in the Children and Young People With Long COVID study to get a clearer understanding of how young people were feeling during the pandemic. Methods: A total of 8224 free-text responses from children and young people were analyzed using InfraNodus, an artificial intelligence--powered text network analysis tool, to determine the most prevalent topics. A random subsample of 411 (5\%) of the 8224 responses underwent a manual sentiment analysis; this was reweighted to represent the general population of children and young people in England. Results: Experiences fell into 6 main overlapping topical clusters: school, examination stress, mental health, emotional impact of the pandemic, social and family support, and physical health (including COVID-19 symptoms). Sentiment analysis showed that statements were largely negative (314/411, 76.4\%), with a small proportion being positive (57/411, 13.9\%). Those reporting negative sentiment were mostly female (227/314, 72.3\%), while those reporting positive sentiment were mostly older (170/314, 54.1\%). There were significant observed associations between sentiment and COVID-19 status as well as sex (P=.001 and P<.001, respectively) such that the majority of the responses, regardless of COVID-19 status or sex, were negative; for example, 84.1\% (227/270) of the responses from female individuals and 61.7\% (87/141) of those from male individuals were negative. There were no observed associations between sentiment and all other examined demographics. The results were broadly similar when reweighted to the general population of children and young people in England: 78.52\% (negative), 13.23\% (positive), and 8.24\% (neutral). Conclusions: We used InfraNodus to analyze free-text responses from a large sample of children and young people. The majority of responses (314/411, 76.4\%) were negative, and many of the children and young people reported experiencing distress across a range of domains related to school, social situations, and mental health. Our findings add to the literature, highlighting the importance of specific considerations for children and young people when responding to national emergencies. ", doi="10.2196/63634", url="https://www.jmir.org/2025/1/e63634" } @Article{info:doi/10.2196/58656, author="Kahlawi, Adham and Masri, Firas and Ahmed, Wasim and Vidal-Alaball, Josep", title="Cross-Cultural Sense-Making of Global Health Crises: A Text Mining Study of Public Opinions on Social Media Related to the COVID-19 Pandemic in Developed and Developing Economies", journal="J Med Internet Res", year="2025", month="Jan", day="27", volume="27", pages="e58656", keywords="COVID-19", keywords="SARS-CoV-2", keywords="pandemic", keywords="citizen opinion", keywords="text mining", keywords="LDA", keywords="health crisis", keywords="developing economies", keywords="Italy", keywords="Egypt", keywords="UK", keywords="dataset", keywords="content analysis", keywords="social media", keywords="twitter", keywords="tweet", keywords="sentiment", keywords="attitude", keywords="perception", keywords="perspective", keywords="machine learning", keywords="latent Dirichlet allocation", keywords="vaccine", keywords="vaccination", keywords="public health", keywords="infectious", abstract="Background: The COVID-19 pandemic reshaped social dynamics, fostering reliance on social media for information, connection, and collective sense-making. Understanding how citizens navigate a global health crisis in varying cultural and economic contexts is crucial for effective crisis communication. Objective: This study examines the evolution of citizen collective sense-making during the COVID-19 pandemic by analyzing social media discourse across Italy, the United Kingdom, and Egypt, representing diverse economic and cultural contexts. Methods: A total of 755,215 social media posts from X (formerly Twitter) were collected across 3 time periods: the virus' emergence (February 15 to March 31, 2020), strict lockdown (April 1 to May 30, 2020), and the vaccine rollout (December 1, 2020 to January 15, 2021). In total, 284,512 posts from Italy, 261,978 posts from the United Kingdom, and 209,725 posts from Egypt were analyzed using the latent Dirichlet allocation algorithm to identify key thematic topics and track shifts in discourse across time and regions. Results: The analysis revealed significant regional and temporal differences in collective sense-making during the pandemic. In Italy and the United Kingdom, public discourse prominently addressed pragmatic health care measures and government interventions, reflecting higher institutional trust. By contrast, discussions in Egypt were more focused on religious and political themes, highlighting skepticism toward governmental capacity and reliance on alternative frameworks for understanding the crisis. Over time, all 3 countries displayed a shift in discourse toward vaccine-related topics during the later phase of the pandemic, highlighting its global significance. Misinformation emerged as a recurrent theme across regions, demonstrating the need for proactive measures to ensure accurate information dissemination. These findings emphasize the role of cultural, economic, and institutional factors in shaping public responses during health crises. Conclusions: Crisis communication is influenced by cultural, economic, and institutional contexts, as evidenced by regional variations in citizen engagement. Transparent and culturally adaptive communication strategies are essential to combat misinformation and build public trust. This study highlights the importance of tailoring crisis responses to local contexts to improve compliance and collective resilience. ", doi="10.2196/58656", url="https://www.jmir.org/2025/1/e58656" } @Article{info:doi/10.2196/65631, author="Kuo, Hsin-Yu and Chen, Su-Yen", title="Predicting User Engagement in Health Misinformation Correction on Social Media Platforms in Taiwan: Content Analysis and Text Mining Study", journal="J Med Internet Res", year="2025", month="Jan", day="23", volume="27", pages="e65631", keywords="health misinformation", keywords="misinformation correction", keywords="fact-checking", keywords="content analysis", keywords="text mining", keywords="fuzzy-trace theory", keywords="social media", keywords="large language models", keywords="user engagement", keywords="health communication", abstract="Background: Health misinformation undermines responses to health crises, with social media amplifying the issue. Although organizations work to correct misinformation, challenges persist due to reasons such as the difficulty of effectively sharing corrections and information being overwhelming. At the same time, social media offers valuable interactive data, enabling researchers to analyze user engagement with health misinformation corrections and refine content design strategies. Objective: This study aimed to identify the attributes of correction posts and user engagement and investigate (1) the trend of user engagement with health misinformation correction during 3 years of the COVID-19 pandemic; (2) the relationship between post attributes and user engagement in sharing and reactions; and (3) the content generated by user comments serving as additional information attached to the post, affecting user engagement in sharing and reactions. Methods: Data were collected from the Facebook pages of a fact-checking organization and a health agency from January 2020 to December 2022. A total of 1424 posts and 67,378 corresponding comments were analyzed. The posts were manually annotated by developing a research framework based on the fuzzy-trace theory, categorizing information into ``gist'' and ``verbatim'' representations. Three types of gist representations were examined: risk (risks associated with misinformation), awareness (awareness of misinformation), and value (value in health promotion). Furthermore, 3 types of verbatim representations were identified: numeric (numeric and statistical bases for correction), authority (authority from experts, scholars, or institutions), and facts (facts with varying levels of detail). The basic metrics of user engagement included shares, reactions, and comments as the primary dependent variables. Moreover, this study examined user comments and classified engagement as cognitive (knowledge-based, critical, and bias-based) or emotional (positive, negative, and neutral). Statistical analyses were performed to explore the impact of post attributes on user engagement. Results: On the basis of the results of the regression analysis, risk ($\beta$=.07; P=.001), awareness ($\beta$=.09; P<.001), and facts ($\beta$=.14; P<.001) predicted higher shares; awareness ($\beta$=.07; P=.001) and facts ($\beta$=.24; P<.001) increased reactions; and awareness ($\beta$=.06; P=.005), numeric representations ($\beta$=.06; P=.02), and facts ($\beta$=.19; P<.001) increased comments. All 3 gist representations significantly predicted shares (risk: $\beta$=.08; P<.001, awareness: $\beta$=.08; P<.001, and value: $\beta$=.06; P<.001) and reactions (risk: $\beta$=.04; P=.007, awareness: $\beta$=.06; P<.001, and value: $\beta$=.05; P<.001) when considering comment content. In addition, comments with bias-based engagement ($\beta$=--.11; P=.001) negatively predicted shares. Generally, posts providing gist attributes, especially awareness of misinformation, were beneficial for user engagement in misinformation correction. Conclusions: This study enriches the theoretical understanding of the relationship between post attributes and user engagement within web-based communication efforts to correct health misinformation. These findings provide a foundation for designing more effective content approaches to combat misinformation and strengthen public health communication. ", doi="10.2196/65631", url="https://www.jmir.org/2025/1/e65631", url="http://www.ncbi.nlm.nih.gov/pubmed/39847418" } @Article{info:doi/10.2196/65434, author="Bouktif, Salah and Khanday, Din Akib Mohi Ud and Ouni, Ali", title="Explainable Predictive Model for Suicidal Ideation During COVID-19: Social Media Discourse Study", journal="J Med Internet Res", year="2025", month="Jan", day="17", volume="27", pages="e65434", keywords="COVID-19", keywords="suicide", keywords="social networking sites", keywords="deep learning", keywords="explainable artificial intelligence", keywords="suicidal ideation", keywords="artificial intelligence", keywords="AI", keywords="social media", keywords="predictive model", keywords="mental health", keywords="pandemic", keywords="natural language processing", keywords="NLP", keywords="suicidal thought", keywords="deep neural network approach", abstract="Background: Studying the impact of COVID-19 on mental health is both compelling and imperative for the health care system's preparedness development. Discovering how pandemic conditions and governmental strategies and measures have impacted mental health is a challenging task. Mental health issues, such as depression and suicidal tendency, are traditionally explored through psychological battery tests and clinical procedures. To address the stigma associated with mental illness, social media is used to examine language patterns in posts related to suicide. This strategy enhances the comprehension and interpretation of suicidal ideation. Despite easy expression via social media, suicidal thoughts remain sensitive and complex to comprehend and detect. Suicidal ideation captures the new suicidal statements used during the COVID-19 pandemic that represents a different context of expressions. Objective: In this study, our aim was to detect suicidal ideation by mining textual content extracted from social media by leveraging state-of-the-art natural language processing (NLP) techniques. Methods: The work was divided into 2 major phases, one to classify suicidal ideation posts and the other to extract factors that cause suicidal ideation. We proposed a hybrid deep learning--based neural network approach (Bidirectional Encoder Representations from Transformers [BERT]+convolutional neural network [CNN]+long short-term memory [LSTM]) to classify suicidal and nonsuicidal posts. Two state-of-the-art deep learning approaches (CNN and LSTM) were combined based on features (terms) selected from term frequency--inverse document frequency (TF-IDF), Word2vec, and BERT. Explainable artificial intelligence (XAI) was used to extract key factors that contribute to suicidal ideation in order to provide a reliable and sustainable solution. Results: Of 348,110 records, 3154 (0.9\%) were selected, resulting in 1338 (42.4\%) suicidal and 1816 (57.6\%) nonsuicidal instances. The CNN+LSTM+BERT model achieved superior performance, with a precision of 94\%, a recall of 95\%, an F1-score of 94\%, and an accuracy of 93.65\%. Conclusions: Considering the dynamic nature of suicidal behavior posts, we proposed a fused architecture that captures both localized and generalized contextual information that is important for understanding the language patterns and predict the evolution of suicidal ideation over time. According to Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) XAI algorithms, there was a drift in the features during and before COVID-19. Due to the COVID-19 pandemic, new features have been added, which leads to suicidal tendencies. In the future, strategies need to be developed to combat this deadly disease. ", doi="10.2196/65434", url="https://www.jmir.org/2025/1/e65434" } @Article{info:doi/10.2196/62993, author="Duggal, Keenan", title="Patterns of Public Interest in Lipomas and Lipoma-Removal Procedures: Google Trends Analysis", journal="JMIR Dermatol", year="2025", month="Jan", day="17", volume="8", pages="e62993", keywords="lipoma", keywords="fatty tumor", keywords="adipocyte", keywords="public interest", keywords="Google Trends", abstract="Background: Lipomas are benign tumors composed of encapsulated adipocytes. Although relatively common, uncertainty remains about the population-level prevalence, the etiology, and the degree of public interest in lipomas and associated removal procedures. Objective: The spatiotemporal patterns of public interest in lipomas and lipoma removal procedures were characterized. Methods: Google Trends data that report the relative search volume (RSV) of Google queries pertaining to lipomas and their removal procedures at national and international levels were analyzed. To contextualize these trends, the RSV for lipomas was compared to that of several other common dermatological conditions in the United States. Results: In the United States, lipomas have consistently generated lower levels of public interest than other common dermatological conditions, but interest in the condition has been rising since the mid-2010s. Across the world, public interest in lipomas appears to be the highest in pockets of Eastern Europe, whereas in the United States, relative interest has been higher in Midwestern and Southern states. In addition, the interest in lipoma removal procedures has risen steadily from 2004 to the present, with particularly high RSVs coming from Southwestern states Conclusions: Dermatologists and plastic surgeons should be aware of the increasing public interest in lipomas and lipoma-removal procedures. Clinical awareness is especially important in states with an elevated interest in lipomas and their associated removal procedures. ", doi="10.2196/62993", url="https://derma.jmir.org/2025/1/e62993" } @Article{info:doi/10.2196/59352, author="Taira, Kazuya and Shiomi, Misa and Nakabe, Takayo and Imanaka, Yuichi", title="The Association Between COVID-19 Vaccination Uptake and Information-Seeking Behaviors Using the Internet: Nationwide Cross-Sectional Study", journal="J Med Internet Res", year="2025", month="Jan", day="14", volume="27", pages="e59352", keywords="COVID-19 vaccines", keywords="internet use", keywords="information seeking behavior", keywords="Japan", keywords="vaccine", keywords="COVID-19", keywords="behavior", keywords="panel study", keywords="longitudinal", keywords="survey", keywords="regression analysis", keywords="chi-square test", keywords="adult", keywords="epidemiology", keywords="health informatics", abstract="Background: The COVID-19 pandemic, declared in March 2020, profoundly affected global health, societal, and economic frameworks. Vaccination became a crucial tactic in combating the virus. Simultaneously, the pandemic likely underscored the internet's role as a vital resource for seeking health information. The proliferation of misinformation on social media was observed, potentially influencing vaccination decisions and timing. Objective: This study aimed to explore the relationship between COVID-19 vaccination rates, including the timing of vaccination, and reliance on internet-based information sources in Japan. Methods: Using a cross-sectional study design using a subset of panel data, this nationwide survey was conducted in 7 waves. A total of 10,000 participants were randomly selected through an internet survey firm, narrowing down to 8724 after applying inclusion and exclusion criteria. The primary outcome was the COVID-19 vaccination date, divided into vaccinated versus unvaccinated and early versus late vaccination groups. The main exposure variable was the use of internet-based information sources. Control variables included gender, family structure, education level, employment status, household income, eligibility for priority COVID-19 vaccination due to pre-existing medical conditions, and a health literacy scale score. Two regression analyses using generalized estimating equations accounted for prefecture-specific correlations, focusing on vaccination status and timing. In addition, chi-square tests assessed the relationship between each information source and vaccination rates. Results: Representing a cross-section of the Japanese population, the regression analysis found a significant association between internet information seeking and higher vaccination rates (adjusted odds ratio [aOR] 1.42 for those younger than 65 years; aOR 1.66 for those aged 65 years and older). However, no significant link was found regarding vaccination timing. Chi-square tests showed positive associations with vaccination for television, government web pages, and web news, whereas blogs and some social networking sites were negatively correlated. Conclusions: Internet-based information seeking is positively linked to COVID-19 vaccination rates in Japan, underscoring the significant influence of online information on public health decisions. Nonetheless, certain online information sources, including blogs and some social networks, negatively affected vaccination rates, warranting caution in their use and recognition. The study highlights the critical role of credible online sources in public health communication and the challenge of combating misinformation on less regulated platforms. This research sheds light on how the digital information landscape influences health behaviors, stressing the importance of accurate and trustworthy health information amidst global health emergencies. ", doi="10.2196/59352", url="https://www.jmir.org/2025/1/e59352", url="http://www.ncbi.nlm.nih.gov/pubmed/39808493" } @Article{info:doi/10.2196/50021, author="Suarez-Lledo, Victor and Ortega-Martin, Esther and Carretero-Bravo, Jesus and Ramos-Fiol, Bego{\~n}a and Alvarez-Galvez, Javier", title="Unraveling the Use of Disinformation Hashtags by Social Bots During the COVID-19 Pandemic: Social Networks Analysis", journal="JMIR Infodemiology", year="2025", month="Jan", day="9", volume="5", pages="e50021", keywords="social media", keywords="misinformation", keywords="COVID-19", keywords="bot", keywords="hashtags", keywords="disinformation", keywords="network analysis", keywords="community detection", keywords="dissemination", keywords="decision-making", keywords="social bot", keywords="infodemics", keywords="tweets", keywords="social media network", abstract="Background: During the COVID-19 pandemic, social media platforms have been a venue for the exchange of messages, including those related to fake news. There are also accounts programmed to disseminate and amplify specific messages, which can affect individual decision-making and present new challenges for public health. Objective: This study aimed to analyze how social bots use hashtags compared to human users on topics related to misinformation during the outbreak of the COVID-19 pandemic. Methods: We selected posts on specific topics related to infodemics such as vaccines, hydroxychloroquine, military, conspiracy, laboratory, Bill Gates, 5G, and UV. We built a network based on the co-occurrence of hashtags and classified the posts based on their source. Using network analysis and community detection algorithms, we identified hashtags that tend to appear together in messages. For each topic, we extracted the most relevant subtopic communities, which are groups of interconnected hashtags. Results: The distribution of bots and nonbots in each of these communities was uneven, with some sets of hashtags being more common among accounts classified as bots or nonbots. Hashtags related to the Trump and QAnon social movements were common among bots, and specific hashtags with anti-Asian sentiments were also identified. In the subcommunities most populated by bots in the case of vaccines, the group of hashtags including \#billgates, \#pandemic, and \#china was among the most common. Conclusions: The use of certain hashtags varies depending on the source, and some hashtags are used for different purposes. Understanding these patterns may help address the spread of health misinformation on social media networks. ", doi="10.2196/50021", url="https://infodemiology.jmir.org/2025/1/e50021" } @Article{info:doi/10.2196/58902, author="Cummins, A. Jack and Gottlieb, J. Daniel and Sofer, Tamar and Wallace, A. Danielle", title="Applying Natural Language Processing Techniques to Map Trends in Insomnia Treatment Terms on the r/Insomnia Subreddit: Infodemiology Study", journal="J Med Internet Res", year="2025", month="Jan", day="9", volume="27", pages="e58902", keywords="insomnia", keywords="natural language processing", keywords="NLP", keywords="social media", keywords="cognitive behavioral therapy", keywords="CBT", keywords="sleep initiation", keywords="sleep disorder", keywords="easly awakening", keywords="sleep aids", keywords="benzodiazepines", keywords="trazodone", keywords="antidepressants", keywords="melatonin", keywords="treatment", abstract="Background: People share health-related experiences and treatments, such as for insomnia, in digital communities. Natural language processing tools can be leveraged to understand the terms used in digital spaces to discuss insomnia and insomnia treatments. Objective: The aim of this study is to summarize and chart trends of insomnia treatment terms on a digital insomnia message board. Methods: We performed a natural language processing analysis of the r/insomnia subreddit. Using Pushshift, we obtained all r/insomnia subreddit comments from 2008 to 2022. A bag of words model was used to identify the top 1000 most frequently used terms, which were manually reduced to 35 terms related to treatment and medication use. Regular expression analysis was used to identify and count comments containing specific words, followed by sentiment analysis to estimate the tonality (positive or negative) of comments. Data from 2013 to 2022 were visually examined for trends. Results: There were 340,130 comments on r/insomnia from 2008, the beginning of the subreddit, to 2022. Of the 35 top treatment and medication terms that were identified, melatonin, cognitive behavioral therapy for insomnia (CBT-I), and Ambien were the most frequently used (n=15,005, n=13,461, and n=11,256 comments, respectively). When the frequency of individual terms was compared over time, terms related to CBT-I increased over time (doubling from approximately 2\% in 2013-2014 to a peak of over 5\% of comments in 2018); in contrast, terms related to nonprescription over-the-counter (OTC) sleep aids (such as Benadryl or melatonin) decreased over time. CBT-I--related terms also had the highest positive sentiment and showed a spike in frequency in 2017. Terms with the most positive sentiment included ``hygiene'' (median sentiment 0.47, IQR 0.31-0.88), ``valerian'' (median sentiment 0.47, IQR 0-0.85), and ``CBT'' (median sentiment 0.42, IQR 0.14-0.82). Conclusions: The Reddit r/insomnia discussion board provides an alternative way to capture trends in both prescription and nonprescription sleep aids among people experiencing sleeplessness and using social media. This analysis suggests that language related to CBT-I (with a spike in 2017, perhaps following the 2016 recommendations by the American College of Physicians for CBT-I as a treatment for insomnia), benzodiazepines, trazodone, and antidepressant medication use has increased from 2013 to 2022. The findings also suggest that the use of OTC or other alternative therapies, such as melatonin and cannabis, among r/insomnia Reddit contributors is common and has also exhibited fluctuations over time. Future studies could consider incorporating alternative data sources in addition to prescription medication to track trends in prescription and nonprescription sleep aid use. Additionally, future prospective studies of insomnia should consider collecting data on the use of OTC or other alternative therapies, such as cannabis. More broadly, digital communities such as r/insomnia may be useful in understanding how social and societal factors influence sleep health. ", doi="10.2196/58902", url="https://www.jmir.org/2025/1/e58902" } @Article{info:doi/10.2196/57395, author="Gandy, M. Lisa and Ivanitskaya, V. Lana and Bacon, L. Leeza and Bizri-Baryak, Rodina", title="Public Health Discussions on Social Media: Evaluating Automated Sentiment Analysis Methods", journal="JMIR Form Res", year="2025", month="Jan", day="8", volume="9", pages="e57395", keywords="ChatGPT", keywords="VADER", keywords="valence aware dictionary for sentiment reasoning", keywords="LIWC-22", keywords="machine learning", keywords="social media", keywords="sentiment analysis", keywords="public health", keywords="population health", keywords="opioids", keywords="drugs", keywords="pharmacotherapy", keywords="pharmaceuticals", keywords="medications", keywords="YouTube", abstract="Background: Sentiment analysis is one of the most widely used methods for mining and examining text. Social media researchers need guidance on choosing between manual and automated sentiment analysis methods. Objective: Popular sentiment analysis tools based on natural language processing (NLP; VADER [Valence Aware Dictionary for Sentiment Reasoning], TEXT2DATA [T2D], and Linguistic Inquiry and Word Count [LIWC-22]), and a large language model (ChatGPT 4.0) were compared with manually coded sentiment scores, as applied to the analysis of YouTube comments on videos discussing the opioid epidemic. Sentiment analysis methods were also examined regarding ease of programming, monetary cost, and other practical considerations. Methods: Evaluation methods included descriptive statistics, receiver operating characteristic (ROC) curve analysis, confusion matrices, Cohen $\kappa$, accuracy, specificity, precision, sensitivity (recall), F1-score harmonic mean, and the Matthews correlation coefficient. An inductive, iterative approach to content analysis of the data was used to obtain manual sentiment codes. Results: A subset of comments were analyzed by a second coder, producing good agreement between the 2 coders' judgments ($\kappa$=0.734). YouTube social media about the opioid crisis had many more negative comments (4286/4871, 88\%) than positive comments (79/662, 12\%), making it possible to evaluate the performance of sentiment analysis models in an unbalanced dataset. The tone summary measure from LIWC-22 performed better than other tools for estimating the prevalence of negative versus positive sentiment. According to the ROC curve analysis, VADER was best at classifying manually coded negative comments. A comparison of Cohen $\kappa$ values indicated that NLP tools (VADER, followed by LIWC's tone and T2D) showed only fair agreement with manual coding. In contrast, ChatGPT 4.0 had poor agreement and failed to generate binary sentiment scores in 2 out of 3 attempts. Variations in accuracy, specificity, precision, sensitivity, F1-score, and MCC did not reveal a single superior model. F1-score harmonic means were 0.34-0.38 (SD 0.02) for NLP tools and very low (0.13) for ChatGPT 4.0. None of the MCCs reached a strong correlation level. Conclusions: Researchers studying negative emotions, public worries, or dissatisfaction with social media face unique challenges in selecting models suitable for unbalanced datasets. We recommend VADER, the only cost-free tool we evaluated, due to its excellent discrimination, which can be further improved when the comments are at least 100 characters long. If estimating the prevalence of negative comments in an unbalanced dataset is important, we recommend the tone summary measure from LIWC-22. Researchers using T2D must know that it may only score some data and, compared with other methods, be more time-consuming and cost-prohibitive. A general-purpose large language model, ChatGPT 4.0, has yet to surpass the performance of NLP models, at least for unbalanced datasets with highly prevalent (7:1) negative comments. ", doi="10.2196/57395", url="https://formative.jmir.org/2025/1/e57395" } @Article{info:doi/10.2196/60446, author="Kim, Jungok and Yun, Kyoung Eun", title="Topic Modeling of Nursing Issues in the Media During 4 Emerging Infectious Disease Epidemics in South Korea: Descriptive Analysis", journal="J Med Internet Res", year="2025", month="Jan", day="6", volume="27", pages="e60446", keywords="topic modeling", keywords="news articles", keywords="nursing issues", keywords="text analysis", keywords="emerging infectious disease", abstract="Background: Emerging infectious disease disasters receive extensive media coverage and public attention. Nurse burnout and attrition peak during health crises such as pandemics. However, there is limited research on nursing issues related to repeated emerging infectious disease crises over time. Objective: The purpose of this study was to analyze and draw implications from changes in key nursing issues reported by the news media during the outbreaks of severe acute respiratory syndrome (SARS; 2003), influenza A (2009), Middle East respiratory syndrome (MERS; 2015), and COVID-19 (2020) in Korea using topic modeling. Methods: A total of 51,489 news articles were extracted by searching for the keywords ``nursing'' or ``nurse'' in the title or body of articles published from April 2003 to May 2021 (during new infectious disease outbreaks) in the open integrated database. The selected news articles were preprocessed then analyzed for text and structure using a 3-step keyword analysis method, latent Dirichlet allocation topic modeling, and keyword network analysis. Results: Among the 51,489 news articles collected with the search terms ``nursing'' and ``nurse,'' 17,285 (33.6\%) were selected based on the eligibility criteria and used in the final analysis. Using topic modeling, we derived 5 topics each for SARS, influenza A, and MERS and 6 topics for COVID-19. The themes commonly identified through topic modeling and keyword network analysis across the 4 epidemics were ``response to emerging infectious diseases in Korea,'' ``demand for nurses,'' ``vulnerability in the work environment,'' and ``roles and responsibilities of nurses.'' Although the topic names were the same, the meanings implied by the comprehensive keywords for each epidemic varied depending on the epidemic and the times. Conclusions: Analysis of the identified themes and associated keyword network revealed that issues related to nurse shortages, working conditions, and poor treatment were not unique to the COVID-19 pandemic but rather recurring themes from previous epidemics. Our findings can be used to inform strategies to improve the professional roles, work environment, and treatment of nurses during health crises. Suggestions for future nursing-related policy impact and change research are also provided. ", doi="10.2196/60446", url="https://www.jmir.org/2025/1/e60446" } @Article{info:doi/10.2196/59230, author="Sasaki, Kenji and Ikeda, Yoichi and Nakano, Takashi", title="Quantifying the Regional Disproportionality of COVID-19 Spread: Modeling Study", journal="JMIR Form Res", year="2025", month="Jan", day="3", volume="9", pages="e59230", keywords="infectious disease", keywords="COVID-19", keywords="epidemiology", keywords="public health", keywords="SARS-CoV-2", keywords="pandemic", keywords="inequality measure", keywords="information theory", keywords="Kullback-Leibler divergence", abstract="Background: The COVID-19 pandemic has caused serious health, economic, and social consequences worldwide. Understanding how infectious diseases spread can help mitigate these impacts. The Theil index, a measure of inequality rooted in information theory, is useful for identifying geographic disproportionality in COVID-19 incidence across regions. Objective: This study focused on capturing the degrees of regional disproportionality in incidence rates of infectious diseases over time. Using the Theil index, we aim to assess regional disproportionality in the spread of COVID-19 and detect epicenters where the number of infected individuals was disproportionately concentrated. Methods: To quantify the degree of disproportionality in the incidence rates, we applied the Theil index to the publicly available data of daily confirmed COVID-19 cases in the United States over a 1100-day period. This index measures relative disproportionality by comparing daily regional case distributions with population proportions, thereby identifying regions where infections are disproportionately concentrated. Results: Our analysis revealed a dynamic pattern of regional disproportionality in the confirmed cases by monitoring variations in regional contributions to the Theil index as the pandemic progressed. Over time, the index reflected a transition from localized outbreaks to widespread transmission, with high values corresponding to concentrated cases in some regions. We also found that the peaks in the Theil index often preceded surges in confirmed cases, suggesting its potential utility as an early warning signal. Conclusions: This study demonstrated that the Theil index is one of the effective indices for quantifying regional disproportionality in COVID-19 incidence rates. Although the Theil index alone cannot fully capture all aspects of pandemic dynamics, it serves as a valuable tool when used alongside other indicators such as infection and hospitalization rates. This approach allows policy makers to monitor regional disproportionality efficiently, offering insights for early intervention and targeted resource allocation. ", doi="10.2196/59230", url="https://formative.jmir.org/2025/1/e59230" } @Article{info:doi/10.2196/59786, author="Mendez, R. Samuel and Munoz-Najar, Sebastian and Emmons, M. Karen and Viswanath, Kasisomayajula", title="US State Public Health Agencies' Use of Twitter From 2012 to 2022: Observational Study", journal="J Med Internet Res", year="2025", month="Jan", day="3", volume="27", pages="e59786", keywords="social media", keywords="health communication", keywords="Twitter", keywords="tweet", keywords="public health", keywords="state government", keywords="government agencies", keywords="information technology", keywords="data science", keywords="communication tool", keywords="COVID-19 pandemic", keywords="data collection", keywords="theoretical framework", keywords="message", keywords="interaction", abstract="Background: Twitter (subsequently rebranded as X) is acknowledged by US health agencies, including the US Centers for Disease Control and Prevention (CDC), as an important public health communication tool. However, there is a lack of data describing its use by state health agencies over time. This knowledge is important amid a changing social media landscape in the wake of the COVID-19 pandemic. Objective: The study aimed to describe US state health agencies' use of Twitter from 2012 through 2022. Furthermore, we organized our data collection and analysis around the theoretical framework of the networked public to contribute to the broader literature on health communication beyond a single platform. Methods: We used Twitter application programming interface data as indicators of state health agencies' engagement with the 4 key qualities of communication in a networked public: scalability, persistence, replicability, and searchability. To assess scalability, we calculated tweet volume and audience engagement metrics per tweet. To assess persistence, we calculated the portion of tweets that were manual retweets or included an account mention. To assess replicability, we calculated the portion of tweets that were retweets or quote tweets. To assess searchability, we calculated the portion of tweets using at least 1 hashtag. Results: We observed a COVID-19 pandemic--era shift in state health agency engagement with scalability. The overall volume of tweets increased suddenly from less than 50,000 tweets in 2019 to over 94,000 in 2020, resulting in an average of 5.3 per day. Though mean tweets per day fell in 2021 and 2022, this COVID-19 pandemic--era low was still higher than the pre--COVID-19 pandemic peak. We also observed a more fragmented approach to searchability aligning with the start of the COVID-19 pandemic. More state-specific hashtags were among the top 10 during the COVID-19 pandemic, compared with more general hashtags related to disease outbreaks and natural disasters in years before. We did not observe such a clear COVID-19 pandemic--era shift in engagement with replicability. The portion of tweets mentioning a CDC account gradually rose and fell around a peak of 7.0\% in 2018. Similarly, the rate of retweets of a CDC account rose and fell gradually around a peak of 5.4\% in 2018. We did not observe a clear COVID-19 pandemic--era shift in persistence. The portion of tweets mentioning any account reached a maximum of 21\% in 2013. It oscillated for much of the study period before dropping off in 2021 and reaching a minimum of 10\% in 2022. Before 2018, the top 10 mentioned accounts included at least 2 non-CDC or corporate accounts. From 2018 onward, state agencies were much more prominent. Conclusions: Overall, we observed a more fragmented approach to state health agency communication on Twitter during the pandemic, prioritizing volume over searchability, formally replicating existing messages, and leaving traces of interactions with other accounts. ", doi="10.2196/59786", url="https://www.jmir.org/2025/1/e59786", url="http://www.ncbi.nlm.nih.gov/pubmed/39752190" } @Article{info:doi/10.2196/54506, author="Montoya, Alana and Mao, Lingchao and Drewnowski, Adam and Chen, Joshua and Shi, Ella and Liang, Aileen and Weiner, J. Bryan and Su, Yanfang", title="Influencers in Policy Fields on Social Media: Global Longitudinal Study of Dietary Sodium Reduction Posts, 2006-2022", journal="J Med Internet Res", year="2024", month="Dec", day="30", volume="26", pages="e54506", keywords="policy field", keywords="sodium intake", keywords="sodium consumption", keywords="cardiovascular disease", keywords="social media", keywords="health education", keywords="health promotion", keywords="dissemination", keywords="influence", keywords="Twitter", keywords="X", keywords="activity", keywords="priority", keywords="originality", keywords="popularity", abstract="Background: Excessive sodium intake is a major concern for global public health. Despite multiple dietary guidelines, population sodium intakes are above recommended levels. Lack of health literacy could be one contributing issue and contemporary health literacy is largely shaped by social media. Objective: This study aims to quantify the posting behaviors and influence patterns on dietary sodium--related content by influencers in the policy field on X (formerly Twitter) across time. Methods: We first identified X users with a scope of work related to dietary sodium and retrieved their posts (formerly Tweets) from 2006 to 2022. Users were categorized into the policy groups of outer-setting organization, inner-setting organization, or individual, based on their role in the conceptual policy field. Network analysis was used to analyze interactions among users and identify the top influencers in each policy group. A 4D influence framework was applied to measure the overall influence, activity, priority, originality, and popularity scores. These measures were used to reveal the user-level, group-level, and temporal patterns of sodium-related influence. Results: We identified 78 users with content related to dietary sodium, with 1,099,605 posts in total and 14,732 dietary sodium posts. There was an increasing volume of sodium posts from 2010 to 2015; however, the trend has been decreasing since 2016, especially among outer-setting organizations. The top influencers from the three policy groups were the World Health Organization (WHO), the American Heart Association, and Tom Frieden. Simon Capewell and the WHO ranked the highest in activity; the World Action on Salt, Sugar, and Health and Action on Salt had the highest priority for dietary sodium content; General Mills and Tom Frieden had the highest originality; and WHO, Harvard University School of Medicine, and Tom Frieden received the highest popularity. Outer-setting organizations tend to interact with more users in the network compared to inner-setting organizations and individuals, while inner-setting organizations tend to receive more engagements from other users in the network than the other two groups. Monthly patterns showed a significant peak in the number of sodium posts in March compared with other months. Conclusions: Despite the increased use of social media, recent trends of sodium intake education on social media are decreasing and the priority of sodium among other topics is low. To improve policy implementation effectiveness and meet recommended dietary targets, there is an increasing need for health leaders to consistently and collectively advocate for sodium intake reduction on social media. ", doi="10.2196/54506", url="https://www.jmir.org/2024/1/e54506" } @Article{info:doi/10.2196/49567, author="Luo, Waylon and Jin, Ruoming and Kenne, Deric and Phan, NhatHai and Tang, Tang", title="An Analysis of the Prevalence and Trends in Drug-Related Lyrics on Twitter (X): Quantitative Approach", journal="JMIR Form Res", year="2024", month="Dec", day="30", volume="8", pages="e49567", keywords="Twitter (X)", keywords="popular music", keywords="big data analysis", keywords="music", keywords="lyrics", keywords="big data", keywords="substance abuse", keywords="tweet", keywords="social media", keywords="drug", keywords="alcohol", abstract="Background: The pervasiveness of drug culture has become evident in popular music and social media. Previous research has examined drug abuse content in both social media and popular music; however, to our knowledge, the intersection of drug abuse content in these 2 domains has not been explored. To address the ongoing drug epidemic, we analyzed drug-related content on Twitter (subsequently rebranded X), with a specific focus on lyrics. Our study provides a novel finding on the prevalence of drug abuse by defining a new subcategory of X content: ``tweets that reference established drug lyrics.'' Objective: We aim to investigate drug trends in popular music on X, identify and classify popular drugs, and analyze related artists' gender, genre, and popularity. Based on the collected data, our goal is to create a prediction model for future drug trends and gain a deeper understanding of the characteristics of users who cite drug lyrics on X. Methods: X data were collected from 2015 to 2017 through the X streaming application programming interface (API). Drug lyrics were obtained from the Genius lyrics database using the Genius API based on drug keywords. The Smith-Waterman text-matching algorithm is used to detect the drug lyrics in posts. We identified famous drugs in lyrics that were posted. Consequently, the analysis was extended to related artists, songs, genres, and popularity on X. The frequency of drug-related lyrics on X was aggregated into a time-series, which was then used to create prediction models using linear regression, Facebook Prophet, and NIXTLA TimeGPT-1. In addition, we analyzed the number of followers of users posting drug-related lyrics to explore user characteristics. Results: We analyzed over 1.97 billion publicly available posts from 2015 to 2017, identifying more than 157 million that matched drug-related keywords. Of these, 150,746 posts referenced drug-related lyrics. Cannabinoids, opioids, stimulants, and hallucinogens were the most cited drugs in lyrics on X. Rap and hip-hop dominated, with 91.98\% of drug-related lyrics from these genres and 84.21\% performed by male artists. Predictions from all 3 models, linear regression, Facebook Prophet, and NIXTLA TimeGPT-1, indicate a slight decline in the prevalence of drug-related lyrics on X over time. Conclusions: Our study revealed 2 significant findings. First, we identified a previously unexamined subset of drug-related content on X: drug lyrics, which could play a critical role in models predicting the surge in drug-related incidents. Second, we demonstrated the use of cutting-edge time-series forecasting tools, including Facebook Prophet and NIXTLA TimeGPT-1, in accurately predicting these trends. These insights contribute to our understanding of how social media shapes public behavior and sentiment toward drug use. ", doi="10.2196/49567", url="https://formative.jmir.org/2024/1/e49567" } @Article{info:doi/10.2196/65521, author="Bragazzi, Luigi Nicola and Garbarino, Sergio", title="Understanding and Combating Misinformation: An Evolutionary Perspective", journal="JMIR Infodemiology", year="2024", month="Dec", day="27", volume="4", pages="e65521", keywords="misinformation", keywords="infodemics", keywords="evolutionary theory", keywords="fake news", keywords="spoof news", keywords="fact-checking", keywords="digital platform", keywords="behavioral research", keywords="social cohesion", keywords="extrapolation", keywords="deformation", keywords="fabrication", keywords="disinformation", keywords="evolutionary paradox", keywords="adaptive qualities", keywords="strategic deception", keywords="intrapolation", keywords="health information", keywords="public health", doi="10.2196/65521", url="https://infodemiology.jmir.org/2024/1/e65521", url="http://www.ncbi.nlm.nih.gov/pubmed/39466077" } @Article{info:doi/10.2196/53696, author="Khoshnaw, Sara and Panzarasa, Pietro and De Simoni, Anna", title="Metaphor Diffusion in Online Health Communities: Infodemiology Study in a Stroke Online Health Community", journal="JMIR Cardio", year="2024", month="Dec", day="17", volume="8", pages="e53696", keywords="online health community", keywords="social capital", keywords="metaphor", keywords="stroke", keywords="OHC", keywords="novelty", keywords="passive analysis", keywords="stroke survivor", keywords="self-promotion", keywords="post-stroke", keywords="information diffusion", abstract="Background: Online health communities (OHCs) enable patients to create social ties with people with similar health conditions outside their existing social networks. Harnessing mechanisms of information diffusion in OHCs has attracted attention for its ability to improve illness self-management without the use of health care resources. Objective: We aimed to analyze the novelty of a metaphor used for the first time in an OHC, assess how it can facilitate self-management of post-stroke symptoms, describe its appearance over time, and classify its diffusion mechanisms. Methods: We conducted a passive analysis of posts written by UK stroke survivors and their family members in an online stroke community between 2004 and 2011. Posts including the term ``legacy of stroke'' were identified. Information diffusion was classified according to self-promotion or viral spread mechanisms and diffusion depth (the number of users the information spreads out to). Linguistic analysis was performed through the British National Corpus and the Google search engine. Results: Post-stroke symptoms were referred to as ``legacy of stroke.'' This metaphor was novel and appeared for the first time in the OHC in the second out of a total of 3459 threads. The metaphor was written by user A, who attributed it to a stroke consultant explaining post-stroke fatigue. This user was a ``superuser'' (ie, a user with high posting activity) and self-promoted the metaphor throughout the years in response to posts written by other users, in 51 separate threads. In total, 7 users subsequently used the metaphor, contributing to its viral diffusion, of which 3 were superusers themselves. Superusers achieved the higher diffusion depths (maximum of 3). Of the 7 users, 3 had been part of threads where user A mentioned the metaphor, while 2 users had been part of discussion threads in unrelated conversations. In total, 2 users had not been part of threads with any of the other users, suggesting that the metaphor was acquired through prior lurking activity. Conclusions: Metaphors that are considered helpful by patients with stroke to come to terms with their symptoms can diffuse in OHCs through both self-promotion and social (or viral) spreading, with the main driver of diffusion being the superuser trait. Lurking activity (the most common behavior in OHCs) contributed to the diffusion of information. As an increasing number of patients with long-term conditions join OHCs to find others with similar health-related concerns, improving clinicians' and researchers' awareness of the diffusion of metaphors that facilitate self-management in health social media may be beneficial beyond the individual patient. ", doi="10.2196/53696", url="https://cardio.jmir.org/2024/1/e53696" } @Article{info:doi/10.2196/63146, author="Nie, Jia and Huang, Tian and Sun, Yuhong and Peng, Zutong and Dong, Wenlong and Chen, Jiancheng and Zheng, Di and Guo, Fuyin and Shi, Wenhui and Ling, Yuewei and Zhao, Weijia and Yang, Haijun and Shui, Tiejun and Yan, Xiangyu", title="Influence of the Enterovirus 71 Vaccine and the COVID-19 Pandemic on Hand, Foot, and Mouth Disease in China Based on Counterfactual Models: Observational Study", journal="JMIR Public Health Surveill", year="2024", month="Dec", day="17", volume="10", pages="e63146", keywords="hand, foot, and mouth disease", keywords="vaccination", keywords="enterovirus 71", keywords="COVID-19", keywords="epidemical trend", keywords="HFMD", keywords="EV71", abstract="Background: Hand, foot, and mouth disease (HFMD) is a highly contagious viral illness. Understanding the long-term trends of HFMD incidence and its epidemic characteristics under the circumstances of the enterovirus 71 (EV71) vaccination program and the outbreak of COVID-19 is crucial for effective disease surveillance and control. Objective: We aim to give an overview of the trends of HFMD over the past decades and evaluate the impact of the EV71 vaccination program and the COVID-19 pandemic on the epidemic trends of HFMD. Methods: Using official surveillance data from the Yunnan Province, China, we described long-term incidence trends and severity rates of HFMD as well as the variation of enterovirus proportions among cases. We conducted the autoregressive integrated moving average (ARIMA) of time series analyses to predict monthly incidences based on given subsets. The difference between the actual incidences and their counterfactual predictions was compared using absolute percentage errors (APEs) for periods after the EV71 vaccination program and the COVID-19 pandemic, respectively. Results: The annual incidence of HFMD fluctuated between 25.62 cases per 100,000 people in 2008 and 221.52 cases per 100,000 people in 2018. The incidence for men ranged from 30 to 250 cases per 100,000 people from 2008 to 2021, which was constantly higher than that for women. The annual incidence for children aged 1 to 2 years old ranged from 54.54 to 630.06 cases per 100,000 people, which was persistently higher than that for other age groups. For monthly incidences, semiannual peaks were observed for each year. All actual monthly incidences of 2014 to 2015 fell within the predicted 95\% CI by the ARIMA(1,0,1)(1,1,0)[12] model. The average APE was 19\% for a 2-year prediction. After the EV71 vaccination program, the actual monthly incidence of HFMD was consistently lower than the counterfactual predictions by ARIMA(1,0,1)(1,1,0)[12], with negative APEs ranging from ?11\% to ?229\% from January 2017 to April 2018. In the meantime, the proportion of EV71 among the enteroviruses causing HFMD decreased significantly, and the proportion was highly correlated (r=0.73, P=.004) with the severity rate. After the onset of the COVID-19 pandemic in 2020, the actual monthly incidence of HFMD consistently maintained a lower magnitude compared to the counterfactual predictions---ARIMA(1,0,1)(0,1,0)[12]---from February to September 2020, with considerable negative APEs (ranging from ?31\% to ?2248\%). Conclusions: EV71 vaccination alleviated severe HFMD cases and altered epidemiological trends. The HFMD may also benefit from nonpharmaceutical interventions during outbreaks such as the COVID-19 pandemic. Further development of a multivalent virus vaccine is crucial for effectively controlling HFMD outbreaks. Policymakers should implement nonpharmaceutical interventions and emphasize personal hygiene for routine prevention when appropriate. ", doi="10.2196/63146", url="https://publichealth.jmir.org/2024/1/e63146" } @Article{info:doi/10.2196/64577, author="Lemieux, Mackenzie and Zhou, Cyrus and Cary, Caroline and Kelly, Jeannie", title="Changes in Reproductive Health Information-Seeking Behaviors After the Dobbs Decision: Systematic Search of the Wikimedia Database", journal="JMIR Infodemiology", year="2024", month="Dec", day="16", volume="4", pages="e64577", keywords="abortion", keywords="Dobbs", keywords="internet", keywords="viewer trends", keywords="Wikipedia", keywords="women's health", keywords="contraception", keywords="contraceptive", keywords="trend", keywords="information seeking", keywords="page view", keywords="reproductive", keywords="reproduction", abstract="Background: After the US Supreme Court overturned Roe v. Wade, confusion followed regarding the legality of abortion in different states across the country. Recent studies found increased Google searches for abortion-related terms in restricted states after the Dobbsv. Jackson Women's Health Organization decision was leaked. As patients and providers use Wikipedia (Wikimedia Foundation) as a predominant medical information source, we hypothesized that changes in reproductive health information-seeking behavior could be better understood by examining Wikipedia article traffic. Objective: This study aimed to examine trends in Wikipedia usage for abortion and contraception information before and after the Dobbs decision. Methods: Page views of abortion- and contraception-related Wikipedia pages were scraped. Temporal changes in page views before and after the Dobbs decision were then analyzed to explore changes in baseline views, differences in views for abortion-related information in states with restrictive abortion laws versus nonrestrictive states, and viewer trends on contraception-related pages. Results: Wikipedia articles related to abortion topics had significantly increased page views following the leaked and final Dobbs decision. There was a 103-fold increase in the page views for the Wikipedia article Roe v. Wade following the Dobbs decision leak (mean 372,654, SD 135,478 vs mean 3614, SD 248; P<.001) and a 67-fold increase in page views following the release of the final Dobbs decision (mean 8942, SD 402 vs mean 595,871, SD 178,649; P<.001). Articles about abortion in the most restrictive states had a greater increase in page views (mean 40.6, SD 12.7; 18/51, 35\% states) than articles about abortion in states with some restrictions or protections (mean 26.8, SD 7.3; 24/51, 47\% states; P<.001) and in the most protective states (mean 20.6, SD 5.7; 8/51, 16\% states; P<.001). Finally, views to pages about common contraceptive methods significantly increased after the Dobbs decision. ``Vasectomy'' page views increased by 183\% (P<.001), ``IUD'' (intrauterine device) page views increased by 80\% (P<.001), ``Combined oral contraceptive pill'' page views increased by 24\% (P<.001), ``Emergency Contraception'' page views increased by 224\% (P<.001), and ``Tubal ligation'' page views increased by 92\% (P<.001). Conclusions: People sought information on Wikipedia about abortion and contraception at increased rates after the Dobbs decision. Increased traffic to abortion-related Wikipedia articles correlated to the restrictiveness of state abortion policies. Increased interest in contraception-related pages reflects the increased demand for contraceptives observed after the Dobbs decision. Our work positions Wikipedia as an important source of reproductive health information and demands increased attention to maintain and improve Wikipedia as a reliable source of health information after the Dobbs decision. ", doi="10.2196/64577", url="https://infodemiology.jmir.org/2024/1/e64577" } @Article{info:doi/10.2196/63476, author="Ahn, Seong-Ho and Yim, Kwangil and Won, Hyun-Sik and Kim, Kang-Min and Jeong, Dong-Hwa", title="Discovering Time-Varying Public Interest for COVID-19 Case Prediction in South Korea Using Search Engine Queries: Infodemiology Study", journal="J Med Internet Res", year="2024", month="Dec", day="16", volume="26", pages="e63476", keywords="COVID-19", keywords="confirmed case prediction", keywords="search engine queries", keywords="query expansion", keywords="word embedding", keywords="public health", keywords="case prediction", keywords="South Korea", keywords="search engine", keywords="infodemiology", keywords="infodemiology study", keywords="policy", keywords="lifestyle", keywords="machine learning", keywords="machine learning techniques", keywords="utilization", keywords="temporal variation", keywords="novel framework", keywords="temporal", keywords="web-based search", keywords="temporal semantics", keywords="prediction model", keywords="model", abstract="Background: The number of confirmed COVID-19 cases is a crucial indicator of policies and lifestyles. Previous studies have attempted to forecast cases using machine learning techniques that use a previous number of case counts and search engine queries predetermined by experts. However, they have limitations in reflecting temporal variations in queries associated with pandemic dynamics. Objective: This study aims to propose a novel framework to extract keywords highly associated with COVID-19, considering their temporal occurrence. We aim to extract relevant keywords based on pandemic variations using query expansion. Additionally, we examine time-delayed web-based search behavior related to public interest in COVID-19 and adjust for better prediction performance. Methods: To capture temporal semantics regarding COVID-19, word embedding models were trained on a news corpus, and the top 100 words related to ``Corona'' were extracted over 4-month windows. Time-lagged cross-correlation was applied to select optimal time lags correlated to confirmed cases from the expanded queries. Subsequently, ElasticNet regression models were trained after reducing the feature dimensions using principal component analysis of the time-lagged features to predict future daily case counts. Results: Our approach successfully extracted relevant keywords depending on the pandemic phase, encompassing keywords directly related to COVID-19, such as its symptoms, and its societal impact. Specifically, during the first outbreak, keywords directly linked to COVID-19 and past infectious disease outbreaks similar to those of COVID-19 exhibited a high positive correlation. In the second phase of the pandemic, as community infections emerged, keywords related to the government's pandemic control policies were frequently observed with a high positive correlation. In the third phase of the pandemic, during the delta variant outbreak, keywords such as ``economic crisis'' and ``anxiety'' appeared, reflecting public fatigue. Consequently, prediction models trained by the extracted queries over 4-month windows outperformed previous methods for most predictions 1-14 days ahead. Notably, our approach showed significantly higher Pearson correlation coefficients than models based solely on the number of past cases for predictions 9-11 days ahead (P=.02, P<.01, and P<.01), in contrast to heuristic- and symptom-based query sets. Conclusions: This study proposes a novel COVID-19 case-prediction model that automatically extracts relevant queries over time using word embedding. The model outperformed previous methods that relied on static symptom-based or heuristic queries, even without prior expert knowledge. The results demonstrate the capability of our approach to track temporal shifts in public interest regarding changes in the pandemic. ", doi="10.2196/63476", url="https://www.jmir.org/2024/1/e63476" } @Article{info:doi/10.2196/57748, author="Bragazzi, Luigi Nicola and Garbarino, Sergio", title="The Complex Interaction Between Sleep-Related Information, Misinformation, and Sleep Health: Call for Comprehensive Research on Sleep Infodemiology and Infoveillance", journal="JMIR Infodemiology", year="2024", month="Dec", day="13", volume="4", pages="e57748", keywords="sleep health", keywords="sleep-related clinical public health", keywords="sleep information", keywords="health information", keywords="infodemiology", keywords="infoveillance", keywords="social media", keywords="myth", keywords="misconception", keywords="circadian", keywords="chronobiology", keywords="insomnia", keywords="eHealth", keywords="digital health", keywords="public health informatics", keywords="sleep data", keywords="health data", keywords="well-being", keywords="patient information", keywords="lifestyle", doi="10.2196/57748", url="https://infodemiology.jmir.org/2024/1/e57748", url="http://www.ncbi.nlm.nih.gov/pubmed/39475424" } @Article{info:doi/10.2196/52997, author="Fan, Lizhou and Li, Lingyao and Hemphill, Libby", title="Toxicity on Social Media During the 2022 Mpox Public Health Emergency: Quantitative Study of Topical and Network Dynamics", journal="J Med Internet Res", year="2024", month="Dec", day="12", volume="26", pages="e52997", keywords="social media", keywords="network analysis", keywords="pandemic risk", keywords="health care analytics", keywords="infodemiology", keywords="infoveillance", keywords="health communication", keywords="mpox", abstract="Background: Toxicity on social media, encompassing behaviors such as harassment, bullying, hate speech, and the dissemination of misinformation, has become a pressing social concern in the digital age. Its prevalence intensifies during periods of social crises and unrest, eroding a sense of safety and community. Such toxic environments can adversely impact the mental well-being of those exposed and further deepen societal divisions and polarization. The 2022 mpox outbreak, initially called ``monkeypox'' but later renamed to reduce stigma and address societal concerns, provides a relevant context for this issue. Objective: In this study, we conducted a comprehensive analysis of the toxic online discourse surrounding the 2022 mpox outbreak. We aimed to dissect its origins, characterize its nature and content, trace its dissemination patterns, and assess its broader societal implications, with the goal of providing insights that can inform strategies to mitigate such toxicity in future crises. Methods: We collected >1.6 million unique tweets and analyzed them with 5 dimensions: context, extent, content, speaker, and intent. Using topic modeling based on bidirectional encoder representations from transformers and social network community clustering, we delineated the toxic dynamics on Twitter. Results: By categorizing topics, we identified 5 high-level categories in the toxic online discourse on Twitter, including disease (20,281/43,521, 46.6\%), health policy and health care (8400/43,521, 19.3\%), homophobia (10,402/43,521, 23.9\%), politics (2611/43,521, 6\%), and racism (1784/43,521, 4.1\%). Across these categories, users displayed negativity or controversial views on the mpox outbreak, highlighting the escalating political tensions and the weaponization of stigma during this infodemic. Through the toxicity diffusion networks of mentions (17,437 vertices with 3628 clusters), retweets (59,749 vertices with 3015 clusters), and the top users with the highest in-degree centrality, we found that retweets of toxic content were widespread, while influential users rarely engaged with or countered this toxicity through retweets. Conclusions: Our study introduces a comprehensive workflow that combines topical and network analyses to decode emerging social issues during crises. By tracking topical dynamics, we can track the changing popularity of toxic content on the internet, providing a better understanding of societal challenges. Network dynamics highlight key social media influencers and their intentions, suggesting that engaging with these central figures in toxic discourse can improve crisis communication and guide policy making. ", doi="10.2196/52997", url="https://www.jmir.org/2024/1/e52997" } @Article{info:doi/10.2196/60033, author="Niu, Zheyu and Hao, Yijie and Yang, Faji and Jiang, Qirong and Jiang, Yupeng and Zhang, Shizhe and Song, Xie and Chang, Hong and Zhou, Xu and Zhu, Huaqiang and Gao, Hengjun and Lu, Jun", title="Quality of Pancreatic Neuroendocrine Tumor Videos Available on TikTok and Bilibili: Content Analysis", journal="JMIR Form Res", year="2024", month="Dec", day="11", volume="8", pages="e60033", keywords="pancreatic neuroendocrine tumors", keywords="short videos", keywords="quality analysis", keywords="TikTok", keywords="Bilibili", keywords="social media", abstract="Background: Disseminating disease knowledge through concise videos on various platforms is an innovative and efficient approach. However, it remains uncertain whether pancreatic neuroendocrine tumor (pNET)-related videos available on current short video platforms can effectively convey accurate and impactful information to the general public. Objective: Our study aims to extensively analyze the quality of pNET-related videos on TikTok and Bilibili, intending to enhance the development of pNET-related social media content to provide the general public with more comprehensive and suitable avenues for accessing pNET-related information. Methods: A total of 168 qualifying videos pertaining to pNETs were evaluated from the video-sharing platforms Bilibili and TikTok. Initially, the fundamental information conveyed in the videos was documented. Subsequently, we discerned the source and content type of each video. Following that, the Global Quality Scale (GQS) and modified DISCERN (mDISCERN) scale were employed to appraise the educational value and quality of each video. A comparative evaluation was conducted on the videos obtained from these two platforms. Results: The number of pNET-related videos saw a significant increase since 2020, with 9 videos in 2020, 19 videos in 2021, 29 videos in 2022, and 106 videos in 2023. There were no significant improvements in the mean GQS or mDISCERN scores from 2020 to 2023, which were 3.22 and 3.00 in 2020, 3.33 and 2.94 in 2021, 2.83 and 2.79 in 2022, and 2.78 and 2.94 in 2023, respectively. The average quality scores of the videos on Bilibili and Tiktok were comparable, with GQS and mDISCERN scores of 2.98 on Bilibili versus 2.77 on TikTok and 2.82 on Bilibili versus 3.05 on TikTok, respectively. The source and format of the videos remained independent factors affecting the two quality scores. Videos that were uploaded by professionals (hazard ratio=7.02, P=.002) and recorded in specialized popular science formats (hazard ratio=12.45, P<.001) tended to exhibit superior quality. Conclusions: This study demonstrates that the number of short videos on pNETs has increased in recent years, but video quality has not improved significantly. This comprehensive analysis shows that the source and format of videos are independent factors affecting video quality, which provides potential measures for improving the quality of short videos. ", doi="10.2196/60033", url="https://formative.jmir.org/2024/1/e60033" } @Article{info:doi/10.2196/54321, author="Walsh, Julia and Cave, Jonathan and Griffiths, Frances", title="Combining Topic Modeling, Sentiment Analysis, and Corpus Linguistics to Analyze Unstructured Web-Based Patient Experience Data: Case Study of Modafinil Experiences", journal="J Med Internet Res", year="2024", month="Dec", day="11", volume="26", pages="e54321", keywords="unstructured text", keywords="natural language processing", keywords="NLP", keywords="topic modeling", keywords="sentiment analysis", keywords="corpus linguistics", keywords="social media data", keywords="patient experience", keywords="unsupervised", keywords="modafinil", abstract="Background: Patient experience data from social media offer patient-centered perspectives on disease, treatments, and health service delivery. Current guidelines typically rely on systematic reviews, while qualitative health studies are often seen as anecdotal and nongeneralizable. This study explores combining personal health experiences from multiple sources to create generalizable evidence. Objective: The study aims to (1) investigate how combining unsupervised natural language processing (NLP) and corpus linguistics can explore patient perspectives from a large unstructured dataset of modafinil experiences, (2) compare findings with Cochrane meta-analyses on modafinil's effectiveness, and (3) develop a methodology for analyzing such data. Methods: Using 69,022 posts from 790 sources, we used a variety of NLP and corpus techniques to analyze the data, including data cleaning techniques to maximize post context, Python for NLP techniques, and Sketch Engine for linguistic analysis. We used multiple topic mining approaches, such as latent Dirichlet allocation, nonnegative matrix factorization, and word-embedding methods. Sentiment analysis used TextBlob and Valence Aware Dictionary and Sentiment Reasoner, while corpus methods including collocation, concordance, and n-gram generation. Previous work had mapped topic mining to themes, such as health conditions, reasons for taking modafinil, symptom impacts, dosage, side effects, effectiveness, and treatment comparisons. Results: Key findings of the study included modafinil use across 166 health conditions, most frequently narcolepsy, multiple sclerosis, attention-deficit disorder, anxiety, sleep apnea, depression, bipolar disorder, chronic fatigue syndrome, fibromyalgia, and chronic disease. Word-embedding topic modeling mapped 70\% of posts to predefined themes, while sentiment analysis revealed 65\% positive responses, 6\% neutral responses, and 28\% negative responses. Notably, the perceived effectiveness of modafinil for various conditions strongly contrasts with the findings of existing randomized controlled trials and systematic reviews, which conclude insufficient or low-quality evidence of effectiveness. Conclusions: This study demonstrated the value of combining NLP with linguistic techniques for analyzing large unstructured text datasets. Despite varying opinions, findings were methodologically consistent and challenged existing clinical evidence. This suggests that patient-generated data could potentially provide valuable insights into treatment outcomes, potentially improving clinical understanding and patient care. ", doi="10.2196/54321", url="https://www.jmir.org/2024/1/e54321" } @Article{info:doi/10.2196/51701, author="Almenara, A. Carlos and Gulec, Hayriye", title="Uncovering the Top Nonadvertising Weight Loss Websites on Google: A Data-Mining Approach", journal="JMIR Infodemiology", year="2024", month="Dec", day="11", volume="4", pages="e51701", keywords="consumer health informatics", keywords="cyberattack risk", keywords="data mining", keywords="Google", keywords="information seeking", keywords="weight loss", keywords="online health information", keywords="website analysis", keywords="digital health", keywords="internet search", abstract="Background: Online weight loss information is commonly sought by internet users, and it may impact their health decisions and behaviors. Previous studies examined a limited number of Google search queries and relied on manual approaches to retrieve online weight loss websites. Objective: This study aimed to identify and describe the characteristics of the top weight loss websites on Google. Methods: This study gathered 432 Google search queries collected from Google autocomplete suggestions, ``People Also Ask'' featured questions, and Google Trends data. A data-mining software tool was developed to retrieve the search results automatically, setting English and the United States as the default criteria for language and location, respectively. Domain classification and evaluation technologies were used to categorize the websites according to their content and determine their risk of cyberattack. In addition, the top 5 most frequent websites in nonadvertising (ie, nonsponsored) search results were inspected for quality. Results: The results revealed that the top 5 nonadvertising websites were healthline.com, webmd.com, verywellfit.com, mayoclinic.org, and womenshealthmag.com. All provided accuracy statements and author credentials. The domain categorization taxonomy yielded a total of 101 unique categories. After grouping the websites that appeared less than 5 times, the most frequent categories involved ``Health'' (104/623, 16.69\%), ``Personal Pages and Blogs'' (91/623, 14.61\%), ``Nutrition and Diet'' (48/623, 7.7\%), and ``Exercise'' (34/623, 5.46\%). The risk of being a victim of a cyberattack was low. Conclusions: The findings suggested that while quality information is accessible, users may still encounter less reliable content among various online resources. Therefore, better tools and methods are needed to guide users toward trustworthy weight loss information. ", doi="10.2196/51701", url="https://infodemiology.jmir.org/2024/1/e51701" } @Article{info:doi/10.2196/58581, author="Mora Pinzon, Maria and Hills, Ornella and Levy, George and James, T. Taryn and Benitez, Ashley and Lawrence, Sacheen and Ellis, Tiffany and Washington, Venus and Solorzano, Lizbeth and Tellez-Giron, Patricia and Cano Ospina, Fernando and Metoxen, F. Melissa and Gleason, E. Carey", title="Implementation of a Social Media Strategy for Public Health Promotion in Black, American Indian or Alaska Native, and Hispanic or Latino Communities During the COVID-19 Pandemic: Cross-Sectional Study", journal="J Med Internet Res", year="2024", month="Dec", day="10", volume="26", pages="e58581", keywords="health communications", keywords="social media", keywords="Hispanic", keywords="Latino", keywords="Black", keywords="American Indian", keywords="Alaska Native", keywords="minority health", keywords="health disparities", keywords="COVID-19", abstract="Background: Individuals identifying as Black, American Indian or Alaska Native, or Hispanic or Latino lack access to culturally appropriate accurate information and are the target of disinformation campaigns, which create doubt in science and health care providers and might play a role in sustaining health disparities related to the COVID-19 pandemic. Objective: This study aims to create and disseminate culturally and medically appropriate social media messages for Black, Latino, and American Indian or Alaska Native communities in Wisconsin and evaluate their reach and effectiveness in addressing the information needs of these communities. Methods: Our team identified relevant COVID-19 topics based on feedback from their respective community, developed lay format materials, and translated materials into culturally appropriate social media messages that community advocates delivered across their respective communities. Social media metrics (reach, engagement, and impressions) were collected using Sprout Social and Facebook Analytics. We hosted 9 focus groups with community members to learn about their social media use. These data were analyzed using an inductive approach, using NVivo software (release 1.7) to code content. Results: Between August 2021 and January 2023, we created 980 unique social media posts that reached 88,790 individuals and gathered >6700 engagements. Average reach per post was similar across the 3 communities, despite differences in the number of posts and followers on each page: 119.46 (Latino individuals), 111.74 (Black individuals), and 113.11 (Oneida Nation members). The type of posts that had higher engagement rate per reached person (ERR) varied across communities and platforms, with the highest being live videos for the Latino community on Facebook (ERR 9.4\%), videos for the Black community on Facebook (ERR 19.53\%), and social media messages for the Oneida Nation community (ERR 59.01\%). Conclusions: Our project presents a unique and effective model for health messages and highlights the need for tailoring social media messages and approaches for minoritized audiences (eg, age, gender, race, and ethnicity). Further research studies are needed to explore how specific types of information affect the dissemination of information and the implications for health communications. ", doi="10.2196/58581", url="https://www.jmir.org/2024/1/e58581" } @Article{info:doi/10.2196/60283, author="Muenster, Mika Roxana and Gangi, Kai and Margolin, Drew", title="Alternative Health and Conventional Medicine Discourse About Cancer on TikTok: Computer Vision Analysis of TikTok Videos", journal="J Med Internet Res", year="2024", month="Dec", day="9", volume="26", pages="e60283", keywords="misinformation", keywords="social media", keywords="TikTok", keywords="alternative health", keywords="cancer", keywords="computer vision", abstract="Background: Health misinformation is abundant online and becoming an increasingly pressing concern for both oncology practitioners and patients with cancer. On social media platforms, including the popular audiovisual app TikTok, the flourishing alternative health industry is further contributing to the spread of misleading and often harmful information, endangering patients' health and outcomes and sowing distrust of the medical community. The prevalence of false and potentially dangerous treatments on a platform that is used as a quasi--search engine by young people poses a serious risk to the health of patients with cancer. Objective: This study seeks to examine how cancer discourse on TikTok differs between alternative health and conventional medicine videos. It aims to look beyond mere facts and falsehoods that TikTok users may utter to understand the visual language and format used in the support of both misleading and truthful narratives, as well as other messages. Methods: Using computer vision analysis and subsequent qualitative close reading of 831 TikTok videos, this study examined how alternative health and conventional medicine videos on cancer differ with regard to the visual language used. Videos were examined for the length of time and prominence in which faces are displayed, as well as for the background setting, location, and dominant color scheme. Results: The results show that the alt-health and conventional health samples made different use of the audiovisual affordances of TikTok. First, videos from the alternative health sample were more likely to contain a single face that was prominently featured (making up at least 7.5\% of the image) for a substantial period of time (35\% of the shots), with these testimonial-style videos making up 28.5\% (93/326) of the sample compared to 18.6\% (94/505) of the conventional medicine sample. Alternative health videos predominantly featured cool tones (P<.001) and were significantly more likely to be filmed outdoors (P<.001), whereas conventional medicine videos were more likely to be shot indoors and feature warm tones such as red, orange, or yellow. Conclusions: The findings of this study contribute to an increased understanding of misinformation as not merely a matter of individual falsehoods but also a phenomenon whose effects might be transported through emotive as well as rational means. They also point to influencer practices and style being an important contributing factor in the declining health of the information environment around cancer and its treatment. The results suggest that public health efforts must extend beyond correcting false statements by injecting factual information into the online cancer discourse and look toward incorporating both visual and rational strategies. ", doi="10.2196/60283", url="https://www.jmir.org/2024/1/e60283" } @Article{info:doi/10.2196/59425, author="AbuRaed, Tawfiq Ahmed Ghassan and Prikryl, Azuma Emil and Carenini, Giuseppe and Janjua, Zafar Naveed", title="Long COVID Discourse in Canada, the United States, and Europe: Topic Modeling and Sentiment Analysis of Twitter Data", journal="J Med Internet Res", year="2024", month="Dec", day="9", volume="26", pages="e59425", keywords="long COVID", keywords="topic modeling", keywords="sentiment analysis", keywords="Twitter", keywords="public perception", keywords="social media analysis", keywords="public health", abstract="Background: Social media serves as a vast repository of data, offering insights into public perceptions and emotions surrounding significant societal issues. Amid the COVID-19 pandemic, long COVID (formally known as post--COVID-19 condition) has emerged as a chronic health condition, profoundly impacting numerous lives and livelihoods. Given the dynamic nature of long COVID and our evolving understanding of it, effectively capturing people's sentiments and perceptions through social media becomes increasingly crucial. By harnessing the wealth of data available on social platforms, we can better track the evolving narrative surrounding long COVID and the collective efforts to address this pressing issue. Objective: This study aimed to investigate people's perceptions and sentiments around long COVID in Canada, the United States, and Europe, by analyzing English-language tweets from these regions using advanced topic modeling and sentiment analysis techniques. Understanding regional differences in public discourse can inform tailored public health strategies. Methods: We analyzed long COVID--related tweets from 2021. Contextualized topic modeling was used to capture word meanings in context, providing coherent and semantically meaningful topics. Sentiment analysis was conducted in a zero-shot manner using Llama 2, a large language model, to classify tweets into positive, negative, or neutral sentiments. The results were interpreted in collaboration with public health experts, comparing the timelines of topics discussed across the 3 regions. This dual approach enabled a comprehensive understanding of the public discourse surrounding long COVID. We used metrics such as normalized pointwise mutual information for coherence and topic diversity for diversity to ensure robust topic modeling results. Results: Topic modeling identified five main topics: (1) long COVID in people including children in the context of vaccination, (2) duration and suffering associated with long COVID, (3) persistent symptoms of long COVID, (4) the need for research on long COVID treatment, and (5) measuring long COVID symptoms. Significant concern was noted across all regions about the duration and suffering associated with long COVID, along with consistent discussions on persistent symptoms and calls for more research and better treatments. In particular, the topic of persistent symptoms was highly prevalent, reflecting ongoing challenges faced by individuals with long COVID. Sentiment analysis showed a mix of positive and negative sentiments, fluctuating with significant events and news related to long COVID. Conclusions: Our study combines natural language processing techniques, including contextualized topic modeling and sentiment analysis, along with domain expert input, to provide detailed insights into public health monitoring and intervention. These findings highlight the importance of tracking public discourse on long COVID to inform public health strategies, address misinformation, and provide support to affected individuals. The use of social media analysis in understanding public health issues is underscored, emphasizing the role of emerging technologies in enhancing public health responses. ", doi="10.2196/59425", url="https://www.jmir.org/2024/1/e59425" } @Article{info:doi/10.2196/57687, author="Orr, Noreen and Rogers, Morwenna and Stein, Abigail and Thompson Coon, Jo and Stein, Kenneth", title="Reviewing the Evidence Base for Topical Steroid Withdrawal Syndrome in the Research Literature and Social Media Platforms: An Evidence Gap Map", journal="J Med Internet Res", year="2024", month="Dec", day="6", volume="26", pages="e57687", keywords="topical steroid withdrawal syndrome", keywords="evidence gap map", keywords="social media", keywords="blogs", keywords="Instagram", keywords="Reddit", keywords="topical corticosteroids", abstract="Background: Within the dermatological community, topical steroid withdrawal syndrome (TSWS) is a medically contested condition with a limited research base. Published studies on TSWS indicate that it is a distinct adverse effect of prolonged use of topical corticosteroids, but there is a paucity of high-quality research evidence. Among the ``patient community,'' awareness has been increasing, with rapid growth in social media posts on TSWS and the introduction of online communities such as the International Topical Steroid Awareness Network. This evidence gap map (EGM) was developed in response to recent calls for research to better understand TSWS and aims to be an important resource to guide both researchers and clinicians in the prioritization of research topics for further research. Objective: This study aims to identify the range, extent, and type of evidence on TSWS in the research literature and social media platforms using an EGM. Methods: The MEDLINE and Embase (Ovid), CINAHL (EBSCOhost), and ProQuest Dissertations \& Theses and Conference Proceedings Citation Index (CPCI-Science and CPCI-Social Science \& Humanities via Web of Science) databases were searched. The final search was run in November 2023. Study titles, abstracts, and full texts were screened by 2 reviewers, and a third was consulted to resolve any differences. Blogging sites WordPress, Medium, and Blogspot and Google were searched; Instagram and Reddit were searched for the 100 most recent posts on specific dates in February 2023. Blog titles, Instagram posts, and Reddit posts were screened for relevance by 2 reviewers. A data extraction tool was developed on EPPI-Reviewer, and data extraction was undertaken by one reviewer and checked by a second; any inconsistencies were resolved through discussion. We did not undertake quality appraisal of the included studies. EPPI-Reviewer and EPPI-Mapper were used to generate the interactive EGM. Results: Overall, 81 academic publications and 223 social media posts were included in the EGM. The research evidence mainly addressed the physical symptoms of TSWS (skin), treatments, and, to a lesser extent, risk factors and disease mechanisms. The social media evidence primarily focused on the physical symptoms (skin and nonskin), mental health symptoms, relationships, activities of everyday living, beliefs and attitudes, and treatments. Conclusions: The EGM shows that research evidence is growing on TSWS but remains lacking in several important areas: longer-term prospective observational studies to assess the safety of prolonged use of topical corticosteroids and to prevent addiction; qualitative research to understand the lived experience of TSWS; and longitudinal research on the patient's ``TSWS journey'' to healing. The inclusion of social media evidence is a methodological innovation in EGMs, recognizing the increased presence of \#topicalsteroidwithdrawal on social media and how it can be used to better understand the patient perspective and ultimately, provide better care for people with TSWS. ", doi="10.2196/57687", url="https://www.jmir.org/2024/1/e57687" } @Article{info:doi/10.2196/49927, author="Kaminsky, Zachary and McQuaid, J. Robyn and Hellemans, GC Kim and Patterson, R. Zachary and Saad, Mysa and Gabrys, L. Robert and Kendzerska, Tetyana and Abizaid, Alfonso and Robillard, Rebecca", title="Machine Learning--Based Suicide Risk Prediction Model for Suicidal Trajectory on Social Media Following Suicidal Mentions: Independent Algorithm Validation", journal="J Med Internet Res", year="2024", month="Dec", day="5", volume="26", pages="e49927", keywords="suicide", keywords="prediction", keywords="social media", keywords="machine learning", keywords="suicide risk model", keywords="validation", keywords="natural language processing", keywords="suicide risk", keywords="Twitter", keywords="suicidal ideation", keywords="suicidal mention", abstract="Background: Previous efforts to apply machine learning--based natural language processing to longitudinally collected social media data have shown promise in predicting suicide risk. Objective: Our primary objective was to externally validate our previous machine learning algorithm, the Suicide Artificial Intelligence Prediction Heuristic (SAIPH), against external survey data in 2 independent cohorts. A second objective was to evaluate the efficacy of SAIPH as an indicator of changing suicidal ideation (SI) over time. The tertiary objective was to use SAIPH to evaluate factors important for improving or worsening suicidal trajectory on social media following suicidal mention. Methods: Twitter (subsequently rebranded as X) timeline data from a student survey cohort and COVID-19 survey cohort were scored using SAIPH and compared to SI questions on the Beck Depression Inventory and the Self-Report version of the Quick Inventory of Depressive Symptomatology in 159 and 307 individuals, respectively. SAIPH was used to evaluate changing SI trajectory following suicidal mentions in 2 cohorts collected using the Twitter application programming interface. Results: An interaction of the mean SAIPH score derived from 12 days of Twitter data before survey completion and the average number of posts per day was associated with quantitative SI metrics in each cohort (student survey cohort interaction $\beta$=.038, SD 0.014; F4,94=3.3, P=.01; and COVID-19 survey cohort interaction $\beta$=.0035, SD 0.0016; F4,493=2.9, P=.03). The slope of average daily SAIPH scores was associated with the change in SI scores within longitudinally followed individuals when evaluating periods of 2 weeks or less ($\rho$=0.27, P=.04). Using SAIPH as an indicator of changing SI, we evaluated SI trajectory in 2 cohorts with suicidal mentions, which identified that those with responses within 72 hours exhibit a significant negative association of the SAIPH score with time in the 3 weeks following suicidal mention ($\rho$=--0.52, P=.02). Conclusions: Taken together, our results not only validate the association of SAIPH with perceived stress, SI, and changing SI over time but also generate novel methods to evaluate the effects of social media interactions on changing suicidal trajectory. ", doi="10.2196/49927", url="https://www.jmir.org/2024/1/e49927" } @Article{info:doi/10.2196/52551, author="Pierce, Joni and Conway, Mike and Grace, Kathryn and Mikal, Jude", title="Identifying Factors Associated With Heightened Anxiety During Breast Cancer Diagnosis Through the Analysis of Social Media Data on Reddit: Mixed Methods Study", journal="JMIR Cancer", year="2024", month="Dec", day="5", volume="10", pages="e52551", keywords="breast cancer", keywords="anxiety", keywords="NLP", keywords="natural language processing", keywords="mixed methods study", keywords="cancer diagnosis", keywords="social media apps", keywords="descriptive analysis", keywords="diagnostic progression", keywords="patient-centered care", abstract="Background: More than 85\% of patients report heightened levels of anxiety following breast cancer diagnosis. Anxiety may become amplified during the early stages of breast cancer diagnosis when ambiguity is high. High levels of anxiety can negatively impact patients by reducing their ability to function physically, make decisions, and adhere to treatment plans, with all these elements combined serving to diminish the quality of life. Objective: This study aimed to use individual social media posts about breast cancer experiences from Reddit (r/breastcancer) to understand the factors associated with breast cancer--related anxiety as individuals move from suspecting to confirming cancer diagnosis. Methods: We used a mixed method approach by combining natural language processing--based computational methods with descriptive analysis. Our team coded the entire corpus of 2170 unique posts from the r/breastcancer subreddit with respect to key variables, including whether the post was related to prediagnosis, diagnosis, or postdiagnosis concerns. We then used Linguistic Inquiry and Word Count (LIWC) to rank-order the codified posts as low, neutral, or high anxiety. High-anxiety posts were then retained for deep descriptive analysis to identify key themes relative to diagnostic progression. Results: After several iterations of data analysis and classification through both descriptive and computational methods, we identified a total of 448 high-anxiety posts across the 3 diagnostic categories. Our analyses revealed that individuals experience higher anxiety before a confirmed cancer diagnosis. Analysis of the high-anxiety posts revealed that the factors associated with anxiety differed depending on an individual's stage in the diagnostic process. Prediagnosis anxiety was associated with physical symptoms, cancer-related risk factors, communication, and interpreting medical information. During the diagnosis period, high anxiety was associated with physical symptoms, cancer-related risk factors, communication, and difficulty navigating the health care system. Following diagnosis, high-anxiety posts generally discussed topics related to treatment options, physical symptoms, emotional distress, family, and financial issues. Conclusions: This study has practical, theoretical, and methodological implications for cancer research. Content analysis reveals several possible drivers of anxiety at each stage (prediagnosis, during diagnosis, and postdiagnosis) and provides key insights into how clinicians can help to alleviate anxiety at all stages of diagnosis. Findings provide insights into cancer-related anxiety as a process beginning before engagement with the health care system: when an individual first notices possible cancer symptoms. Uncertainty around physical symptoms and risk factors suggests the need for increased education and improved access to trained medical staff who can assist patients with questions and concerns during the diagnostic process. Assistance in understanding technical reports, scheduling, and patient-centric clinician behavior may pinpoint opportunities for improved communication between patients and providers. ", doi="10.2196/52551", url="https://cancer.jmir.org/2024/1/e52551" } @Article{info:doi/10.2196/53218, author="Wu, A. Scott and Soetikno, G. Alan and Ozer, A. Egon and Welch, B. Sarah and Liu, Yingxuan and Havey, J. Robert and Murphy, L. Robert and Hawkins, Claudia and Mason, Maryann and Post, A. Lori and Achenbach, J. Chad and Lundberg, L. Alexander", title="Updated Surveillance Metrics and History of the COVID-19 Pandemic (2020-2023) in Canada: Longitudinal Trend Analysis", journal="JMIR Public Health Surveill", year="2024", month="Dec", day="5", volume="10", pages="e53218", keywords="SARS-CoV-2", keywords="COVID-19", keywords="Canada", keywords="pandemic", keywords="surveillance", keywords="transmission", keywords="acceleration", keywords="deceleration", keywords="dynamic panel", keywords="generalized method of moments", keywords="GMM", keywords="Arellano-Bond", keywords="7-day lag", keywords="k", keywords="metrics", keywords="epidemiology", keywords="dynamic", keywords="genomic", keywords="historical context", keywords="outbreak threshold", abstract="Background: This study provides an update on the status of the COVID-19 pandemic in Canada, building upon our initial analysis conducted in 2020 by incorporating an additional 2 years of data. Objective: This study aims to (1) summarize the status of the pandemic in Canada when the World Health Organization (WHO) declared the end of the public health emergency for the COVID-19 pandemic on May 5, 2023; (2) use dynamic and genomic surveillance methods to describe the history of the pandemic in Canada and situate the window of the WHO declaration within the broader history; and (3) provide historical context for the course of the pandemic in Canada. Methods: This longitudinal study analyzed trends in traditional surveillance data and dynamic panel estimates for COVID-19 transmissions and deaths in Canada from June 2020 to May 2023. We also used sequenced SARS-CoV-2 variants from the Global Initiative on Sharing All Influenza Data (GISAID) to identify the appearance and duration of variants of concern. For these sequences, we used Nextclade nomenclature to collect clade designations and Pangolin nomenclature for lineage designations of SARS-CoV-2. We used 1-sided t tests of dynamic panel regression coefficients to measure the persistence of COVID-19 transmissions around the WHO declaration. Finally, we conducted a 1-sided t test for whether provincial and territorial weekly speed was greater than an outbreak threshold of 10. We ran the test iteratively with 6 months of data across the sample period. Results: Canada's speed remained below the outbreak threshold for 8 months by the time of the WHO declaration ending the COVID-19 emergency of international concern. Acceleration and jerk were also low and stable. While the 1-day persistence coefficient remained statistically significant and positive (1.074; P<.001), the 7-day coefficient was negative and small in magnitude (--0.080; P=.02). Furthermore, shift parameters for either of the 2 most recent weeks around May 5, 2023, were negligible (0.003 and 0.018, respectively, with P values of .75 and .31), meaning the clustering effect of new COVID-19 cases had remained stable in the 2 weeks around the WHO declaration. From December 2021 onward, Omicron was the predominant variant of concern in sequenced viral samples. The rolling 1-sided t test of speed equal to 10 became entirely insignificant from mid-October 2022 onward. Conclusions: While COVID-19 continues to circulate in Canada, the rate of transmission remained well below the threshold of an outbreak for 8 months ahead of the WHO declaration. Both standard and enhanced surveillance metrics confirm that the pandemic had largely ended in Canada by the time of the WHO declaration. These results can inform future public health interventions and strategies in Canada, as well as contribute to the global understanding of the trajectory of the COVID-19 pandemic. ", doi="10.2196/53218", url="https://publichealth.jmir.org/2024/1/e53218", url="http://www.ncbi.nlm.nih.gov/pubmed/39471286" } @Article{info:doi/10.2196/52871, author="B{\'e}chard, Beno{\^i}t and Gramaccia, A. Julie and Gagnon, Dominique and Laouan-Sidi, Anassour Elhadji and Dub{\'e}, {\`E}ve and Ouimet, Mathieu and de Hemptinne, Delphine and Tremblay, S{\'e}bastien", title="The Resilience of Attitude Toward Vaccination: Web-Based Randomized Controlled Trial on the Processing of Misinformation", journal="JMIR Form Res", year="2024", month="Dec", day="4", volume="8", pages="e52871", keywords="attitude toward vaccination", keywords="misinformation", keywords="reinformation", keywords="confidence", keywords="perceived tentativeness", keywords="vaccine hesitancy", keywords="COVID-19", abstract="Background: Before the COVID-19 pandemic, it was already recognized that internet-based misinformation and disinformation could influence individuals to refuse or delay vaccination for themselves, their families, or their children. Reinformation, which refers to hyperpartisan and ideologically biased content, can propagate polarizing messages on vaccines, thereby contributing to vaccine hesitancy even if it is not outright disinformation. Objective: This study aimed to evaluate the impact of reinformation on vaccine hesitancy. Specifically, the goal was to investigate how misinformation presented in the style and layout of a news article could influence the perceived tentativeness (credibility) of COVID-19 vaccine information and confidence in COVID-19 vaccination. Methods: We conducted a web-based randomized controlled trial by recruiting English-speaking Canadians aged 18 years and older from across Canada through the Qualtrics (Silver Lake) paid opt-in panel system. Participants were randomly assigned to 1 of 4 distinct versions of a news article on COVID-19 vaccines, each featuring variations in writing style and presentation layout. After reading the news article, participants self-assessed the tentativeness of the information provided, their confidence in COVID-19 vaccines, and their attitude toward vaccination in general. Results: The survey included 537 participants, with 12 excluded for not meeting the task completion time. The final sample comprised 525 participants distributed about equally across the 4 news article versions. Chi-square analyses revealed a statistically significant association between general attitude toward vaccination and the perceived tentativeness of the information about COVID-19 vaccines included in the news article ($\chi$21=37.8, P<.001). The effect size was small to moderate, with Cramer V=0.27. An interaction was found between vaccine attitude and writing style ($\chi$21=6.2, P=.01), with a small effect size, Cramer V=0.11. In addition, a Pearson correlation revealed a significant moderate to strong correlation between perceived tentativeness and confidence in COVID-19 vaccination, r(523)=0.48, P<.001. The coefficient of determination (r2) was 0.23, indicating that 23\% of the variance in perceived tentativeness was explained by confidence in COVID-19 vaccines. In comparing participants exposed to a journalistic-style news article with those exposed to an ideologically biased article, Cohen d was calculated to be 0.38, indicating a small to medium effect size for the difference in the perceived tentativeness between these groups. Conclusions: Exposure to a news article conveying misinformation may not be sufficient to change an individual's level of vaccine hesitancy. The study reveals that the predominant factor in shaping individuals' perceptions of COVID-19 vaccines is their attitude toward vaccination in general. This attitude also moderates the influence of writing style on perceived tentativeness; the stronger one's opposition to vaccines, the less pronounced the impact of writing style on perceived tentativeness. International Registered Report Identifier (IRRID): RR2-10.2196/41012 ", doi="10.2196/52871", url="https://formative.jmir.org/2024/1/e52871" } @Article{info:doi/10.2196/63035, author="Xie, Zidian and Liu, Xinyi and Lou, Xubin and Li, Dongmei", title="Public Perceptions of Very Low Nicotine Content on Twitter: Observational Study", journal="JMIR Form Res", year="2024", month="Dec", day="4", volume="8", pages="e63035", keywords="very low nicotine", keywords="Twitter", keywords="public perception", keywords="observational study", keywords="content analysis", abstract="Background: Nicotine is a highly addictive agent in tobacco products. On June 21, 2022, the US Food and Drug Administration (FDA) announced a plan to propose a rule to establish a maximum nicotine level in cigarettes and other combusted tobacco products. Objective: This study aimed to understand public perception and discussion of very low nicotine content (VLNC) on Twitter (rebranded as X in July 2023). Methods: From December 12, 2021, to January 1, 2023, we collected Twitter data using relevant keywords such as ``vln,'' ``low nicotine,'' and ``reduced nicotine.'' After a series of preprocessing steps (such as removing duplicates, retweets, and commercial tweets), we identified 3270 unique noncommercial tweets related to VLNC. We used an inductive method to assess the public perception and discussion of VLNC on Twitter. To establish a codebook, we randomly selected 300 tweets for hand-coding, including the attitudes (positive, neutral, and negative) toward VLNC (including its proposed rule) and major topics (13 topics). The Cohen $\kappa$ statistic between the 2 human coders reached over 70\%, indicating a substantial interrater agreement. The rest of the tweets were single-coded according to the codebook. Results: We observed a significant peak in the discussion of VLNC on Twitter within 4 days of the FDA's announcement of the proposed rule on June 21, 2022. The proportion of tweets with a negative attitude toward VLNC was significantly lower than those with a positive attitude, 24.5\% (801/3270) versus 37.09\% (1213/3270) with P<.001 from the 2-proportion z test. Among tweets with a positive attitude, the topic ``Reduce cigarette consumption or help smoking cessation'' was dominant (1097/1213, 90.44\%). Among tweets with a negative attitude, the topic ``VLNC leads to more smoking'' was the most popular topic (227/801, 28.34\%), followed by ``Similar toxicity of VLNC as a regular cigarette'' (223/801, 27.84\%), and ``VLNC is not a good method for quitting smoking'' (211/801, 26.34\%). Conclusions: There is a more positive attitude toward VLNC than a negative attitude on Twitter, resulting from different opinions about VLNC. Discussions around VLNC mainly focused on whether VLNC could help people quit smoking. ", doi="10.2196/63035", url="https://formative.jmir.org/2024/1/e63035" } @Article{info:doi/10.2196/56651, author="O'Brien, Gabrielle and Ganjigunta, Ronith and Dhillon, S. Paramveer", title="Wellness Influencer Responses to COVID-19 Vaccines on Social Media: A Longitudinal Observational Study", journal="J Med Internet Res", year="2024", month="Nov", day="27", volume="26", pages="e56651", keywords="social media, COVID-19, vaccination", keywords="personal brands", keywords="public health", keywords="wellness", keywords="global health", keywords="pandemic", keywords="Twitter", keywords="tweets", keywords="vaccine", keywords="longitudinal design", keywords="wellness influencers", keywords="hand-annotation", keywords="anti-vaccination", keywords="infodemiology", abstract="Background: Online wellness influencers (individuals dispensing unregulated health and wellness advice over social media) may have incentives to oppose traditional medical authorities. Their messaging may decrease the overall effectiveness of public health campaigns during global health crises like the COVID-19 pandemic. Objective: This study aimed to probe how wellness influencers respond to a public health campaign; we examined how a sample of wellness influencers on Twitter (rebranded as X in 2023) identified before the COVID-19 pandemic on Twitter took stances on the COVID-19 vaccine during 2020-2022. We evaluated the prevalence of provaccination messaging among wellness influencers compared with a control group, as well as the rhetorical strategies these influencers used when supporting or opposing vaccination. Methods: Following a longitudinal design, wellness influencer accounts were identified on Twitter from a random sample of tweets posted in 2019. Accounts were identified using a combination of topic modeling and hand-annotation for adherence to influencer criteria. Their tweets from 2020-2022 containing vaccine keywords were collected and labeled as pro- or antivaccination stances using a language model. We compared their stances to a control group of noninfluencer accounts that discussed similar health topics before the pandemic using a generalized linear model with mixed effects and a nearest-neighbors classifier. We also used topic modeling to locate key themes in influencer's pro- and antivaccine messages. Results: Wellness influencers (n=161) had lower rates of provaccination stances in their on-topic tweets (20\%, 614/3045) compared with controls (n=242 accounts, with 42\% or 3201/7584 provaccination tweets). Using a generalized linear model of tweet stance with mixed effects to model tweets from the same account, the main effect of the group was significant ($\beta$1=--2.2668, SE=0.2940; P<.001). Covariate analysis suggests an association between antivaccination tweets and accounts representing individuals ($\beta$=--0.9591, SE=0.2917; P=.001) but not social network position. A complementary modeling exercise of stance within user accounts showed a significant difference in the proportion of antivaccination users by group ($\chi$21[N=321]=36.1, P<.001). While nearly half of the influencer accounts were labeled by a K-nearest neighbor classifier as predominantly antivaccination (48\%, 58/120), only 16\% of control accounts were labeled this way (33/201). Topic modeling of influencer tweets showed that the most prevalent antivaccination themes were protecting children, guarding against government overreach, and the corruption of the pharmaceutical industry. Provaccination messaging tended to encourage followers to take action or emphasize the efficacy of the vaccine. Conclusions: Wellness influencers showed higher rates of vaccine opposition compared with other accounts that participated in health discourse before the pandemic. This pattern supports the theory that unregulated wellness influencers have incentives to resist messaging from establishment authorities such as public health agencies. ", doi="10.2196/56651", url="https://www.jmir.org/2024/1/e56651" } @Article{info:doi/10.2196/59742, author="Yip, Yee Yan and Makmor-Bakry, Mohd and Chong, Wen Wei", title="Elements Influencing User Engagement in Social Media Posts on Lifestyle Risk Factors: Systematic Review", journal="J Med Internet Res", year="2024", month="Nov", day="22", volume="26", pages="e59742", keywords="chronic disease", keywords="health promotion", keywords="internet", keywords="primary prevention", keywords="social media", keywords="systematic reviews", keywords="health care professional", keywords="health personnel", keywords="user engagement", keywords="lifestyle", keywords="risk", abstract="Background: The high prevalence of noncommunicable diseases and the growing importance of social media have prompted health care professionals (HCPs) to use social media to deliver health information aimed at reducing lifestyle risk factors. Previous studies have acknowledged that the identification of elements that influence user engagement metrics could help HCPs in creating engaging posts toward effective health promotion on social media. Nevertheless, few studies have attempted to comprehensively identify a list of elements in social media posts that could influence user engagement metrics. Objective: This systematic review aimed to identify elements influencing user engagement metrics in social media posts by HCPs aimed to reduce lifestyle risk factors. Methods: Relevant studies in English, published between January 2006 and June 2023 were identified from MEDLINE or OVID, Scopus, Web of Science, and CINAHL databases. Included studies were those that examined social media posts by HCPs aimed at reducing the 4 key lifestyle risk factors. Additionally, the studies also outlined elements in social media posts that influenced user engagement metrics. The titles, abstracts, and full papers were screened and reviewed for eligibility. Following data extraction, narrative synthesis was performed. All investigated elements in the included studies were categorized. The elements in social media posts that influenced user engagement metrics were identified. Results: A total of 19 studies were included in this review. Investigated elements were grouped into 9 categories, with 35 elements found to influence user engagement. The 3 predominant categories of elements influencing user engagement were communication using supportive or emotive elements, communication aimed toward behavioral changes, and the appearance of posts. In contrast, the source of post content, social media platform, and timing of post had less than 3 studies with elements influencing user engagement. Conclusions: Findings demonstrated that supportive or emotive communication toward behavioral changes and post appearance could increase postlevel interactions, indicating a favorable response from the users toward posts made by HCPs. As social media continues to evolve, these elements should be constantly evaluated through further research. ", doi="10.2196/59742", url="https://www.jmir.org/2024/1/e59742" } @Article{info:doi/10.2196/59225, author="Owen, David and Lynham, J. Amy and Smart, E. Sophie and Pardi{\~n}as, F. Antonio and Camacho Collados, Jose", title="AI for Analyzing Mental Health Disorders Among Social Media Users: Quarter-Century Narrative Review of Progress and Challenges", journal="J Med Internet Res", year="2024", month="Nov", day="15", volume="26", pages="e59225", keywords="mental health", keywords="depression", keywords="anxiety", keywords="schizophrenia", keywords="social media", keywords="natural language processing", keywords="narrative review", abstract="Background: Mental health disorders are currently the main contributor to poor quality of life and years lived with disability. Symptoms common to many mental health disorders lead to impairments or changes in the use of language, which are observable in the routine use of social media. Detection of these linguistic cues has been explored throughout the last quarter century, but interest and methodological development have burgeoned following the COVID-19 pandemic. The next decade may see the development of reliable methods for predicting mental health status using social media data. This might have implications for clinical practice and public health policy, particularly in the context of early intervention in mental health care. Objective: This study aims to examine the state of the art in methods for predicting mental health statuses of social media users. Our focus is the development of artificial intelligence--driven methods, particularly natural language processing, for analyzing large volumes of written text. This study details constraints affecting research in this area. These include the dearth of high-quality public datasets for methodological benchmarking and the need to adopt ethical and privacy frameworks acknowledging the stigma experienced by those with a mental illness. Methods: A Google Scholar search yielded peer-reviewed articles dated between 1999 and 2024. We manually grouped the articles by 4 primary areas of interest: datasets on social media and mental health, methods for predicting mental health status, longitudinal analyses of mental health, and ethical aspects of the data and analysis of mental health. Selected articles from these groups formed our narrative review. Results: Larger datasets with precise dates of participants' diagnoses are needed to support the development of methods for predicting mental health status, particularly in severe disorders such as schizophrenia. Inviting users to donate their social media data for research purposes could help overcome widespread ethical and privacy concerns. In any event, multimodal methods for predicting mental health status appear likely to provide advancements that may not be achievable using natural language processing alone. Conclusions: Multimodal methods for predicting mental health status from voice, image, and video-based social media data need to be further developed before they may be considered for adoption in health care, medical support, or as consumer-facing products. Such methods are likely to garner greater public confidence in their efficacy than those that rely on text alone. To achieve this, more high-quality social media datasets need to be made available and privacy concerns regarding the use of these data must be formally addressed. A social media platform feature that invites users to share their data upon publication is a possible solution. Finally, a review of literature studying the effects of social media use on a user's depression and anxiety is merited. ", doi="10.2196/59225", url="https://www.jmir.org/2024/1/e59225" } @Article{info:doi/10.2196/56006, author="Soshnikov, Sergey and Bekker, Svetlana and Idrisov, Bulat and Vlassov, Vasiliy", title="Association of Drugs for Sale on the Internet and Official Health Indicators: Darknet Parsing and Correlational Study", journal="JMIR Form Res", year="2024", month="Nov", day="15", volume="8", pages="e56006", keywords="darknet", keywords="Internet black market", keywords="illicit drugs", keywords="Hydra", keywords="marketplace", keywords="cannabis", keywords="opiates", keywords="zakladka", keywords="Bitcoin", keywords="crypto", keywords="public health", keywords="overdose", keywords="harmful drug use", keywords="drug availability", keywords="drug use", abstract="Background: Studying illicit drug circulation and its effects on population health is complicated due to the criminalization of trade and consumption. Illicit drug markets have evolved with IT, moving digital to the ``darknet.'' Previous research has analyzed darknet market listings and customer reviews. Research tools include public health surveys and medical reports but lack neutral data on drugs' spread and impact. This study fills this gap with an analysis of the volume of drugs traded on the darknet market. Objective: We aimed to use the dark web data and officially published indicators to identify the most vulnerable regions of Russia and the correlations between the pairs of variables to measure how illicit drug trade can affect population well-being. Methods: We web-parsed the Hydra darknet drug marketplace using Python code. The dataset encompassed 3045 individual sellers marketing 6721 unique products via 58,563 distinct postings, each representing specific quantities sold in different Russian regions during 2019. In the second stage, we collected 31 variables from official sources to compare officially collected data with darknet data about amounts and types of selling drugs in every 85 regions of Russia. The health-related data were obtained from official published sources---statistical yearbooks. Maps, diagrams, correlation matrixes, and applied observational statistical methods were used. Results: In 2019, a minimum of 124 kilograms of drugs circulated daily in small batches on the Russian darknet. Cannabis dominated the market, being 10 times more prevalent than opiates, and cannabis products' higher availability in the region is correlated with a lower incidence of opiate overdoses. The ``grams of opiates in the region'' variable is significantly correlated with drug overdose deaths (r=.41; P=.003), HIV-positive cases due to drug use (r=.51; P=.002), and drug court convictions in Russia (r=.39; P=.004). The study identified significant correlations between opiate sales on the darknet and higher rates of HIV among injection drug users (r=.47; P=.003). Conversely, regions with higher cannabis sales exhibited significant negative correlations with indicators of harmful drug use (r=--.52; P=.002) and its prevalence (r=--.49; P=.001). These findings suggest regional variations in drug sales on the darknet may be associated with differing public health outcomes. These indicators accurately reflect regional drug issues, though some official statistics may be incomplete or biased. Conclusions: Our findings point to varying levels of risk associated with different types of drugs sold on the darknet, but further research is needed to explore these relationships in greater depth. The study's findings highlight the importance of considering regional variations in darknet drug sales when developing public health strategies. The significant correlations between drug sales data and public health indicators suggest that region-specific interventions could be more effective in addressing the diverse challenges posed by illicit drug use. ", doi="10.2196/56006", url="https://formative.jmir.org/2024/1/e56006" } @Article{info:doi/10.2196/57967, author="Lu, Fangcao and Tu, Caixie", title="The Impact of Comment Slant and Comment Tone on Digital Health Communication Among Polarized Publics: A Web-Based Survey Experiment", journal="J Med Internet Res", year="2024", month="Nov", day="15", volume="26", pages="e57967", keywords="comments slant", keywords="incivility", keywords="social media", keywords="influence of presumed influence", keywords="health compliance", keywords="mask wearing", keywords="web survey", abstract="Background: Public attitudes toward health issues are becoming increasingly polarized, as seen in social media comments, which vary from supportive to oppositional and frequently include uncivil language. The combined effects of comment slant and comment tone on health behavior among a polarized public need further examination. Objective: This study aims to examine how social media users' prior attitudes toward mask wearing and their exposure to a mask-wearing--promoting post, synchronized with polarized and hostile discussions, affect their compliance with mask wearing. Methods: The study was a web-based survey experiment with participants recruited from Amazon Mechanical Turk. A total of 522 participants provided consent and completed the study. Participants were assigned to read a fictitious mask-wearing--promoting social media post with either civil anti--mask-wearing comments (130/522, 24.9\%), civil pro--mask-wearing comments (129/522, 24.7\%), uncivil anti--mask-wearing comments (131/522, 25.1\%), or uncivil pro--mask-wearing comments (132/522, 25.3\%). Following this, the participants were asked to complete self-assessed questionnaires. The PROCESS macro in SPSS (model 12; IBM Corp) was used to test the 3-way interaction effects between comment slant, comment tone, and prior attitudes on participants' presumed influence from the post and their behavioral intention to comply with mask-wearing. Results: Anti--mask-wearing comments led social media users to presume less influence about others' acceptance of masks (B=1.49; P<.001; 95\% CI 0.98-2.00) and resulted in decreased mask-wearing intention (B=0.07; P=.03; 95\% CI 0.01-0.13). Comment tone with incivility also reduced compliance with mask-wearing (B=--0.44; P=.02; 95\% CI --0.81 to --0.07). Furthermore, polarized attitudes had a direct impact (B=0.86; P<.001; 95\% CI 0.45-1.26) and also interacted with both the slant and tone of comments, influencing mask-wearing intention. Conclusions: Pro--mask-wearing comments enhanced presumed influence and compliance of mask-wearing, but incivility in the comments hindered this positive impact. Antimaskers showed increased compliance when they were unable to find civil support for their opinion in the social media environment. The findings suggest the need to correct and moderate uncivil language and misleading information in online comment sections while encouraging the posting of supportive and civil comments. In addition, information literacy programs are needed to prevent the public from being misled by polarized comments. ", doi="10.2196/57967", url="https://www.jmir.org/2024/1/e57967" } @Article{info:doi/10.2196/51870, author="Ranker, R. Lynsie and Tofu, Assefa David and Lu, Manyuan and Wu, Jiaxi and Bhatnagar, Aruni and Robertson, Marie Rose and Wijaya, Derry and Hong, Traci and Fetterman, L. Jessica and Xuan, Ziming", title="Concurrent Mentions of Vaping and Alcohol on Twitter: Latent Dirichlet Analysis", journal="J Med Internet Res", year="2024", month="Nov", day="12", volume="26", pages="e51870", keywords="e-cigarettes", keywords="alcohol", keywords="social media", keywords="vape", keywords="tweet", keywords="vaping", keywords="alcohol use", keywords="co-use", keywords="substance use disorder", keywords="social networking site", keywords="insight", keywords="regulation", keywords="youth", keywords="vaping policy", abstract="Background: Co-use of alcohol and e-cigarettes (often called vaping) has been linked with long-term health outcomes, including increased risk for substance use disorder. Co-use may have been exacerbated by the COVID-19 pandemic. Social networking sites may offer insights into current perspectives on polysubstance use. Objective: The aims of this study were to investigate concurrent mentions of vaping and alcohol on Twitter (subsequently rebranded X) during a time of changing vaping regulations in the United States and the emergence of the COVID-19 pandemic. Methods: Tweets including both vape- and alcohol-related terms posted between October 2019 and September 2020 were analyzed using latent Dirichlet allocation modeling. Distinct topics were identified and described. Results: Three topics were identified across 6437 tweets: (1) flavors and flavor ban (n=3334, 51.8\% of tweets), (2) co-use discourse (n=1119, 17.4\%), and (3) availability and access regulation (n=1984, 30.8\%). Co-use discussions often portrayed co-use as positive and prosocial. Tweets focused on regulation often used alcohol regulations for comparison. Some focused on the perceived overregulation of vaping (compared to alcohol), while others supported limiting youth access but not at the expense of adult access (eg, stronger age verification over product bans). Across topics, vaping was typically portrayed as less harmful than alcohol use. The benefits of flavors for adult smoking cessation were also discussed. The distribution of topics across time varied across both pre-- and post--regulatory change and pre-- and post--COVID-19 pandemic declaration periods, suggesting shifts in topic focus salience across time. Conclusions: Co-use discussions on social media during this time of regulatory change and social upheaval typically portrayed both vaping and alcohol use in a positive light. It also included debates surrounding the differences in regulation of the 2 substances---particularly as it related to limiting youth access. Emergent themes from the analysis suggest that alcohol was perceived as more harmful but less regulated and more accessible to underage youth than vaping products. Frequent discussions and comparisons of the 2 substances as it relates to their regulation emphasize the still-evolving vaping policy landscape. Social media content analyses during times of change may help regulators and policy makers to better understand and respond to common concerns and potential misconceptions surrounding drug-related policies and accessibility. ", doi="10.2196/51870", url="https://www.jmir.org/2024/1/e51870" } @Article{info:doi/10.2196/64555, author="Yang, Si Myung and Taira, Kazuya", title="Predicting Prefecture-Level Well-Being Indicators in Japan Using Search Volumes in Internet Search Engines: Infodemiology Study", journal="J Med Internet Res", year="2024", month="Nov", day="11", volume="26", pages="e64555", keywords="well-being", keywords="spatial indicator", keywords="infodemiology", keywords="search engine", keywords="public health", keywords="health policy", keywords="policy-making", keywords="Google", keywords="Japan", abstract="Background: In recent years, the adoption of well-being indicators by national governments and international organizations has emerged as an important tool for evaluating state governance and societal progress. Traditionally, well-being has been gauged primarily through economic metrics such as gross domestic product, which fall short of capturing multifaceted well-being, including socioeconomic inequalities, life satisfaction, and health status. Current well-being indicators, including both subjective and objective measures, offer a broader evaluation but face challenges such as high survey costs and difficulties in evaluating at regional levels within countries. The emergence of web log data as an alternative source of well-being indicators offers the potential for more cost-effective, timely, and less biased assessments. Objective: This study aimed to develop a model using internet search data to predict well-being indicators at the regional level in Japan, providing policy makers with a more accessible and cost-effective tool for assessing public well-being and making informed decisions. Methods: This study used the Regional Well-Being Index (RWI) for Japan, which evaluates prefectural well-being across 47 prefectures for the years 2010, 2013, 2016, and 2019, as the outcome variable. The RWI includes a comprehensive approach integrating both subjective and objective indicators across 11 domains, including income, job, and life satisfaction. Predictor variables included z score--normalized relative search volume (RSV) data from Google Trends for words relevant to each domain. Unrelated words were excluded from the analysis to ensure relevance. The Elastic Net methodology was applied to predict RWI using RSVs, with $\alpha$ balancing ridge and lasso effects and $\lambda$ regulating their strengths. The model was optimized by cross-validation, determining the best mix and strength of regularization parameters to minimize prediction error. Root mean square errors (RMSE) and coefficients of determination (R2) were used to assess the model's predictive accuracy and fit. Results: An analysis of Google Trends data yielded 275 words related to the RWI domains, and RSVs were collected for 211 words after filtering out irrelevant terms. The mean search frequencies for these words during 2010, 2013, 2016, and 2019 ranged from ?1.587 to 3.902, with SDs between 3.025 and 0.053. The best Elastic Net model ($\alpha$=0.1, $\lambda$=0.906, RMSE=1.290, and R2=0.904) was built using 2010-2016 training data and 2-13 variables per domain. Applied to 2019 test data, it yielded an RMSE of 2.328 and R2 of 0.665. Conclusions: This study demonstrates the effectiveness of using internet search log data through the Elastic Net machine learning method to predict the RWI in Japanese prefectures with high accuracy, offering a rapid and cost-efficient alternative to traditional survey approaches. This study highlights the potential of this methodology to provide foundational data for evidence-based policy making aimed at enhancing well-being. ", doi="10.2196/64555", url="https://www.jmir.org/2024/1/e64555" } @Article{info:doi/10.2196/59395, author="Uemura, Kosuke and Miyagami, Taiju and Saita, Mizue and Uchida, Takuro and Yuasa, Shun and Kondo, Keita and Miura, Shun and Matsushita, Mizuki and Shirai, Yuka and Misawa, Baku Richard and Naito, Toshio", title="Trends in Exercise-Related Internet Search Keywords by Sex, Age, and Lifestyle: Infodemiological Study", journal="JMIR Form Res", year="2024", month="Nov", day="11", volume="8", pages="e59395", keywords="exercise prescriptions", keywords="sex", keywords="age", keywords="lifestyle", keywords="internet search keywords", keywords="infodemiology", keywords="demographic", keywords="physical activity", abstract="Background: Exercise prescription by physicians is beneficial for initiating or intensifying physical activity. However, providing specific exercise prescriptions is challenging; therefore, few physicians prescribe exercise. Objective: This infodemiological study aimed to understand trends in exercise-related internet search keywords based on sex, age, and environmental factors to help doctors prescribe exercise more easily. Methods: Search keyword volume was collected from Yahoo! JAPAN for 2022. Ten exercise-related terms were analyzed to assess exercise interest. Total search activities were analyzed by sex and age. Characteristic scores were based on the Japanese prefecture. By performing hierarchical cluster analysis, regional features were examined, and Kruskal-Wallis tests were used to assess relationships with population and industry data. Results: The top-searched term was ``Pilates'' (266,000 queries). Male individuals showed higher interest in activities such as ``running'' (25,400/40,700, 62.4\%), ``muscle training'' (65,800/111,000, 59.3\%), and ``hiking'' (23,400/40,400, 57.9\%) than female individuals. Female individuals exhibited higher interest in ``Pilates'' (199,000/266,000, 74.8\%), ``yoga'' (86,200/117,000, 73.7\%), and ``tai chi'' (45,300/65,900, 68.7\%) than male individuals. Based on age, search activity was highest in the 40-49 years age group for both male and female individuals across most terms. For male individuals, 7 of the 10 searched terms' volume peaked for those in their 40s; ``stretch'' was most popular among those in their 50s; and ``tai chi'' and ``radio calisthenics'' had the highest search volume for those in their 70s. Female individuals in their 40s led the search volume for 9 of the 10 terms, with the exception of ``tai chi,'' which peaked for those in their 70s. Hierarchical cluster analysis using a characteristic score as a variable classified prefectures into 4 clusters. The characteristics of these clusters were as follows: cluster 1 had the largest population and a thriving tertiary industry, and individuals tended to search for Pilates and yoga. Following cluster 1, cluster 2, with its substantial population, had a thriving secondary industry, with searches for radio calisthenics and exercise bike. Cluster 4 had a small population, a thriving primary industry, and the lowest search volume for any term. Cluster 3 had a similar population to that of cluster 4 but had a larger secondary industry. Conclusions: Male individuals show more interest in individual activities, such as running, whereas female individuals are interested in group activities, such as Pilates. Despite the high search volume among individuals in their 40s, actual exercise habits are low among those in their 30s to 50s. Search volumes for instructor-led exercises are higher in cluster 1 than in other cluster areas, and the total number of searches decreases as the community size decreases. These results suggest that trends in search behavior depending on sex, age, and environment factors are essential when prescribing exercise for effective behavioral change. ", doi="10.2196/59395", url="https://formative.jmir.org/2024/1/e59395" } @Article{info:doi/10.2196/58176, author="Zhang, Pengfei and Kamitaki, K. Brad and Do, Phu Thien", title="Crowdsourcing Adverse Events Associated With Monoclonal Antibodies Targeting Calcitonin Gene--Related Peptide Signaling for Migraine Prevention: Natural Language Processing Analysis of Social Media", journal="JMIR Form Res", year="2024", month="Nov", day="8", volume="8", pages="e58176", keywords="internet", keywords="patient reported outcome", keywords="headache", keywords="health information", keywords="Reddit", keywords="registry", keywords="monoclonal antibody", keywords="crowdsourcing", keywords="postmarketing", keywords="safety", keywords="surveillance", keywords="migraine", keywords="preventives", keywords="prevention", keywords="self-reported", keywords="calcitonin gene--related peptide", keywords="calcitonin", keywords="therapeutics", keywords="social media", keywords="medication-related", keywords="posts", keywords="propranolol", keywords="topiramate", keywords="erenumab", keywords="fremanezumab", keywords="cross-sectional", keywords="surveys", abstract="Background: Clinical trials demonstrate the efficacy and tolerability of medications targeting calcitonin gene--related peptide (CGRP) signaling for migraine prevention. However, these trials may not accurately reflect the real-world experiences of more diverse and heterogeneous patient populations, who often have higher disease burden and more comorbidities. Therefore, postmarketing safety surveillance is warranted. Regulatory organizations encourage marketing authorization holders to screen digital media for suspected adverse reactions, applying the same requirements as for spontaneous reports. Real-world data from social media platforms constitute a potential venue to capture diverse patient experiences and help detect treatment-related adverse events. However, while social media holds promise for this purpose, its use in pharmacovigilance is still in its early stages. Computational linguistics, which involves the automatic manipulation and quantitative analysis of oral or written language, offers a potential method for exploring this content. Objective: This study aims to characterize adverse events related to monoclonal antibodies targeting CGRP signaling on Reddit, a large online social media forum, by using computational linguistics. Methods: We examined differences in word frequencies from medication-related posts on the Reddit subforum r/Migraine over a 10-year period (2010-2020) using computational linguistics. The study had 2 phases: a validation phase and an application phase. In the validation phase, we compared posts about propranolol and topiramate, as well as posts about each medication against randomly selected posts, to identify known and expected adverse events. In the application phase, we analyzed posts discussing 2 monoclonal antibodies targeting CGRP signaling---erenumab and fremanezumab---to identify potential adverse events for these medications. Results: From 22,467 Reddit r/Migraine posts, we extracted 402 (2\%) propranolol posts, 1423 (6.33\%) topiramate posts, 468 (2.08\%) erenumab posts, and 73 (0.32\%) fremanezumab posts. Comparing topiramate against propranolol identified several expected adverse events, for example, ``appetite,'' ``weight,'' ``taste,'' ``foggy,'' ``forgetful,'' and ``dizziness.'' Comparing erenumab against a random selection of terms identified ``constipation'' as a recurring keyword. Comparing erenumab against fremanezumab identified ``constipation,'' ``depression,'' ``vomiting,'' and ``muscle'' as keywords. No adverse events were identified for fremanezumab. Conclusions: The validation phase of our study accurately identified common adverse events for oral migraine preventive medications. For example, typical adverse events such as ``appetite'' and ``dizziness'' were mentioned in posts about topiramate. When we applied this methodology to monoclonal antibodies targeting CGRP or its receptor---fremanezumab and erenumab, respectively---we found no definite adverse events for fremanezumab. However, notable flagged words for erenumab included ``constipation,'' ``depression,'' and ``vomiting.'' In conclusion, computational linguistics applied to social media may help identify potential adverse events for novel therapeutics. While social media data show promise for pharmacovigilance, further work is needed to improve its reliability and usability. ", doi="10.2196/58176", url="https://formative.jmir.org/2024/1/e58176" } @Article{info:doi/10.2196/55555, author="Tran, Thao Thi Phuong and Vu, Trang Thu and Li, Yachao and Popova, Lucy", title="Tobacco and Alcohol Content in Top Vietnamese YouTube Music Videos: Content Analysis", journal="J Med Internet Res", year="2024", month="Nov", day="8", volume="26", pages="e55555", keywords="risk", keywords="risk factor", keywords="tobacco content", keywords="alcohol content", keywords="tobacco", keywords="alcohol", keywords="tobacco portrayal", keywords="alcohol portrayal", keywords="music video", keywords="Vietnam", keywords="Vietnamese", keywords="YouTube", keywords="social media", keywords="socials", keywords="youth", keywords="adolescent", keywords="teen", keywords="teenager", keywords="young adult", abstract="Background: Seeing portrayals of tobacco and alcohol in music videos (MVs) may reduce perceived risks, increase susceptibility, and lead to the initiation of tobacco and alcohol use among adolescents and young adults. Previous studies have predominantly concentrated on assessing tobacco and alcohol contents in English-language MVs within Western countries. However, many other countries have not only been influenced by the English music market but have also produced music in their native languages, and this content remains underexamined. Objective: This study aims to investigate the prevalence of tobacco- and alcohol-related content in top Vietnamese MVs on YouTube from 2013 to 2021, to describe how tobacco and alcohol are portrayed in these MVs, and to examine associations between these portrayals and MV characteristics. Methods: A total of 410 Vietnamese MVs, including the top 40 or 50 most viewed released each year between 2013 and 2021, were analyzed. General information, such as the song name, its release date and ranking, age restriction, musical genre, and type of MV, was collected. We examined tobacco and alcohol content in the MVs, with specific details such as tobacco types, their brands, as well as the number, age, sex, and roles of individuals smoking or drinking. Results: Among the 410 MVs, 36 (8.8\%) contained tobacco-related content and 136 (33.2\%) featured alcohol-related content. Additionally, 28 (6.8\%) out of 410 MVs included both tobacco and alcohol content. The prevalence of videos with tobacco and alcohol content fluctuated over the years. In MVs with tobacco-related content, a higher proportion of hip-hop or rap songs contained tobacco-related content (n=6, 30\%) compared to other music genres. In MVs with tobacco-related content, cigarettes were the most frequently shown product (n=28, 77.8\%), and smoking scenes were often depicted at parties (n=13, 36.1\%) and during dancing and singing scenes (n=12, 33.3\%). Among the 31 MVs portraying actual tobacco use, tobacco use was typically depicted with 1 person, often a young adult male, while 38.7\% (n=12) showed singer(s) smoking. For MVs with alcohol-related content, there was a high proportion showing alcohol images at parties, bars, or pubs (n=96, 70.6\%). Among 87 MVs containing drinking scenes, 60.9\% (n=53) involved groups of young adults of both sexes, and 64\% (n=56) depicted singers drinking. Additionally, only 2 (5.6\%) MVs included health warnings about tobacco harm, and 2 MVs (1.5\%) included warnings about drinking restricted to individuals 18 years and above. Conclusions: The notable prevalence of tobacco and alcohol content in leading Vietnamese YouTube MVs raises concerns, especially as most of this content is portrayed without any warnings. The study underscores a regulatory gap in addressing such content on the internet, emphasizing the urgent need for stricter regulations and age restrictions on platforms such as YouTube. ", doi="10.2196/55555", url="https://www.jmir.org/2024/1/e55555" } @Article{info:doi/10.2196/51594, author="Zhou, Xinyi and Hao, Xinyu and Chen, Yuhang and Deng, Hui and Fang, Ling and Zhang, Lingyun and Yan, Xiaotao and Zheng, Pinpin and Wang, Fan", title="Social Media Marketing Strategies for Electronic Cigarettes: Content Analysis of Chinese Weibo Accounts", journal="J Med Internet Res", year="2024", month="Nov", day="7", volume="26", pages="e51594", keywords="e-cigarette", keywords="marketing strategy", keywords="social media", keywords="teenagers", keywords="content analysis", abstract="Background: E-cigarettes have gained popularity among teenagers due to extensive marketing strategies on social media platforms. This widespread promotion is a risk factor, as it fosters more positive attitudes toward e-cigarette use among teenagers and increases the perception that using e-cigarettes is normal. Therefore, the marketing of e-cigarettes on social media is a serious global health concern, and its strategies and impact should be clearly identified. Objective: This study examined how e-cigarette companies popularize their products via Weibo and identified the specific strategies influencing the effectiveness of their marketing. Methods: In phase 1, we conducted a search on Qcc.com and identified 32 e-cigarette brands with active Weibo accounts between October 1 and December 31, 2020, along with 863 Weibo posts. The data were investigated through content analysis. The codebook was developed into four categories: (1) product and features, (2) sales and promotions, (3) social contact and interaction, and (4) restrictions and warnings. To further understand the factors influencing e-cigarette brand marketing, we conducted a multiple linear regression analysis. Results: Marketing tactics by e-cigarette companies on Chinese social media were documented, including emphasizing attractive product features, using trendy characters, implicit promotions, downplaying health concerns, and engaging with Weibo users in various ways. Out of 863 posts, 449 (52\%) mentioned product characteristics. In 313 (36.3\%) posts, visible figures were used to attract attention. Product promotion was absent in 762 (88.3\%) posts, and purchase channels were not mentioned in 790 (98.3\%) posts. Social interaction--related posts received attention (n=548, 63.5\%), particularly those featuring hashtag content (n=538, 62.3\%). Most posts did not include claims for restrictions on teenagers' purchases or use (n=687, 79.6\%) or information on health warnings (n=839, 97.2\%). Multiple linear regression analysis identified marketing strategies that effectively increase the exposure of e-cigarette posts on Weibo. Posts including engagement via posts encouraging reposts, comments, and likes (P<.001) and engagement topics related to e-cigarette brands were positively correlated with the number of reposts (P=.009). Posts highlighting nonmonetary incentives (P=.004), posts with age restriction statements (P<.001), engaging via stories and idea collection (P<.001), and engagement topics related to products (P<.001) and current affairs (P=.002) had a positive effect on the number of comments. Engagement topics related to brands (P<.001) or interactive sweepstakes (P<.001) had a positive effect on the number of likes. Conclusions: E-cigarette posts on Weibo that focus on product features and social interaction attract public attention, especially from teenagers. Stricter regulations and monitoring should be adopted to restrict the social media marketing of e-cigarettes. ", doi="10.2196/51594", url="https://www.jmir.org/2024/1/e51594" } @Article{info:doi/10.2196/52738, author="Gong, Jie and Gu, Dandan and Dong, Suyun and Shen, Wangqin and Yan, Haiou and Xie, Juan", title="Effects of Message Framing on Human Papillomavirus Vaccination: Systematic Review", journal="J Med Internet Res", year="2024", month="Nov", day="7", volume="26", pages="e52738", keywords="message framing", keywords="gain-loss framing", keywords="human?papillomavirus", keywords="vaccination", keywords="attitude", keywords="intention", keywords="behavior", keywords="systematic review", keywords="PRISMA", abstract="Background: With the advancement of cervical cancer elimination strategies, promoting human papillomavirus (HPV) vaccination is essential to achieving this goal. The issue of how to structure and develop message content to promote HPV vaccination is a debatable issue. Objective: The efficacy of gain-loss framing in vaccination contexts is disputed. Our study aimed to elucidate the consequences of message framing on attitudes, intentions, and behavioral tendencies toward HPV vaccination, with the objective of refining message framing strategies and their elements. Methods: This systematic review adhered strictly to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guideline reporting standards to comprehensively retrieve, extract, and integrate data. We searched databases, including PubMed, Embase, Scopus, and Web of Science, for literature published from database construction to August 15, 2023. Literature screening, data extraction, and quality evaluation were performed by 2 researchers. Intervention studies published in English, conducted with populations with children eligible for HPV vaccination, and involving message framing were included. Attitudes, intentions, and behaviors served as outcome evaluation criteria. Results: A total of 19 intervention studies were included. Gain-loss framing had no clear effect on vaccination attitudes nor intentions. Loss framing showed a weak advantage at improving HPV vaccination attitudes or intentions, but the evidence was not strong enough to draw definitive conclusions. The impact of gain-loss framing on HPV vaccination behaviors could not be determined due to the limited number of studies and the qualitative nature of the analysis. Conclusions: Combining gain-loss framing with other message framing approaches may be an effective way to enhance the effect of message framing. More high-quality message framing content and exploring alternative moderator or mediator variables are required to support the conclusion. Trial Registration: CRD42023451612; https://www.crd.york.ac.uk/PROSPERO/display\_record.php?RecordID=451612 ", doi="10.2196/52738", url="https://www.jmir.org/2024/1/e52738" } @Article{info:doi/10.2196/49761, author="Woolard, Alix and Paciente, Rigel and Munro, Emily and Wickens, Nicole and Wells, Gabriella and Ta, Daniel and Mandzufas, Joelie and Lombardi, Karen", title="\#TraumaTok---TikTok Videos Relating to Trauma: Content Analysis", journal="JMIR Form Res", year="2024", month="Nov", day="7", volume="8", pages="e49761", keywords="trauma", keywords="traumatic events", keywords="traumatic stress", keywords="TikTok", keywords="public health", keywords="social media", keywords="content analysis", abstract="Background: Experiencing a traumatic event can significantly impact mental and emotional well-being. Social media platforms offer spaces for sharing stories, seeking support, and accessing psychoeducation. TikTok (ByteDance), a rapidly growing social media platform, is increasingly used for advice, validation, and information, although the content of this requires further study. Research is particularly needed to better understand TikTok content relating to trauma and the potential implications for young viewers, considering the distressing nature of the subject and the possibility of users experiencing vicarious trauma through exposure to these videos. Objective: This study aims to explore the content of trauma-related videos on TikTok, focusing on hashtags related to trauma. Specifically, this study analyzes how TikTok videos present information, advice, stories, and support relating to trauma. Methods: A quantitative cross-sectional descriptive content analysis was performed on TikTok in December 2022. A total of 5 hashtags related to trauma were selected: \#trauma, \#traumatized, \#traumatok, \#traumatic, and \#traumabond, with the top 50 videos from each hashtag analyzed (total N=250 videos). A standardized codebook was developed inductively to analyze the content of the videos, while an existing generic codebook was used to collect the video features (eg, age of people in the video) and metadata (likes, comments, and shares) for each video. Results: A total of 2 major content themes were identified, which were instructional videos (54/250, 21.6\%) and videos disclosing personal stories (168/250, 67.3\%). The videos garnered significant engagement, with a total of 296.6 million likes, 2.3 million comments, and 4.6 million shares, indicating that users find this content engaging and useful. Alarmingly, only 3.7\% (9/250) of videos included a trigger warning, despite many featuring highly distressing stories that young people and those with trauma may be exposed to. Conclusions: The study highlights the potential risks of vicarious trauma due to trauma dumping without trigger warnings on TikTok, and the need for further research to assess the accuracy of advice and information in these videos. However, it also underscores the platform's potential to foster social connections, provide validation, and reduce stigma around mental health issues. Public health professionals should leverage social media to disseminate accurate mental health information, while promoting user education and content moderation to mitigate potential harms. People often use social media, such as TikTok to share advice, stories, and support around mental health, including their experiences with trauma. Out of 250 videos, most were either giving advice (54/250, 21.6\%) or sharing personal experiences (168/250, 67.3\%). The study found many videos lacked warnings about upsetting content, which could potentially harm young viewers or people suffering from trauma. While TikTok can help people feel connected and reduce the stigma around mental health, it is important to seek support from professionals when needed. ", doi="10.2196/49761", url="https://formative.jmir.org/2024/1/e49761" } @Article{info:doi/10.2196/65440, author="Ashraf, Reza Amir and Mackey, Ken Tim and Vida, Gy{\"o}rgy R{\'o}bert and Kulcs{\'a}r, Gy?z? and Schmidt, J{\'a}nos and Bal{\'a}zs, Orsolya and Domi{\'a}n, M{\'a}rk B{\'a}lint and Li, Jiawei and Cs{\'a}k{\'o}, Ibolya and Fittler, Andr{\'a}s", title="Multifactor Quality and Safety Analysis of Semaglutide Products Sold by Online Sellers Without a Prescription: Market Surveillance, Content Analysis, and Product Purchase Evaluation Study", journal="J Med Internet Res", year="2024", month="Nov", day="7", volume="26", pages="e65440", keywords="semaglutide", keywords="Ozempic", keywords="Wegovy", keywords="search engines", keywords="online pharmacies", keywords="patient safety", keywords="medication safety", keywords="nondelivery schemes", keywords="counterfeit", keywords="substandard and falsified medical products", abstract="Background: Over the past 4 decades, obesity has escalated into a global epidemic, with its worldwide prevalence nearly tripling. Pharmacological treatments have evolved with the recent development of glucagon-like peptide 1 agonists, such as semaglutide. However, off-label use of drugs such as Ozempic for cosmetic weight loss has surged in popularity, raising concerns about potential misuse and the emergence of substandard and falsified products in the unregulated supply chain. Objective: This study aims to conduct a multifactor investigation of product quality and patient safety risks associated with the unregulated online sale of semaglutide by examining product availability and vendor characteristics and assessing product quality through test purchases. Methods: We used a complex risk and quality assessment methodology combining online market surveillance, search engine results page analysis, website content assessment, domain traffic analytics, conducting targeted product test purchases, visual quality inspection of product packaging, microbiological sterility and endotoxin contamination evaluation, and quantitative sample analysis using liquid chromatography coupled with mass spectrometry. Results: We collected and evaluated 1080 links from search engine results pages and identified 317 (29.35\%) links belonging to online pharmacies, of which 183 (57.7\%) led to legal pharmacies and 134 (42.3\%) directed users to 59 unique illegal online pharmacy websites.?Web traffic data for the period between July and September 2023 revealed that the top 30 domains directly or indirectly affiliated with illegal online pharmacies accumulated over 4.7 million visits.?Test purchases were completed from?6 illegal online pharmacies with the highest number of links offering semaglutide products for sale without prescription at the lowest price range. Three injection vial purchases were delivered; none of the 3 Ozempic prefilled injection pens were received due to nondelivery e-commerce scams.?All purchased vials were considered probable substandard and falsified products, as visual inspection indicated noncompliance in more than half (59\%-63\%) of the evaluated criteria. The semaglutide content of samples substantially exceeded labeled amounts by 28.56\%-38.69\%, although no peptide-like impurities were identified. The lyophilized peptide samples were devoid of viable microorganisms at the time of testing; however, endotoxin was detected in all samples with levels ranging between 2.1645 EU/mg and 8.9511 EU/mg. Furthermore, the measured semaglutide purity was significantly low, ranging between 7.7\% and 14.37\% and deviating from the 99\% claimed on product labels by manufacturers. Conclusions: Glucagon-like peptide 1 agonist drugs promoted for weight loss, similar to erectile dysfunction medications more than 2 decades ago, are becoming the new blockbuster lifestyle medications for the illegal online pharmacy market. Protecting the pharmaceutical supply chain from substandard and falsified weight loss products and raising awareness regarding online medication safety must be a public health priority for regulators and technology platforms alike. ", doi="10.2196/65440", url="https://www.jmir.org/2024/1/e65440" } @Article{info:doi/10.2196/50343, author="Bhagavathula, Srikanth Akshaya and Dobbs, D. Page", title="Online Interest in Elf Bar in the United States: Google Health Trends Analysis", journal="J Med Internet Res", year="2024", month="Nov", day="5", volume="26", pages="e50343", keywords="e-cigarettes", keywords="Elf Bar", keywords="JUUL", keywords="tobacco", keywords="Google Trends", keywords="Google Health Trends", abstract="Background: Despite the popularity of JUUL e-cigarettes, other brands (eg, Elf Bar) may be gaining digital attention. Objective: This study compared Google searches for Elf Bar and JUUL from 2022 to 2023 using Google Health Trends Application Programming Interface data. Methods: Using an infodemiology approach, we examined weekly trends in Google searches (per 10 million) for ``Elf Bar'' and ``JUUL'' at the US national and state levels from January 1, 2022, to December 31, 2023. Joinpoint regression was used to assess statistically significant trends in the search probabilities for ``Elf Bar'' and ``JUUL'' during the study period. Results: Elf Bar had less online interest than JUUL at the beginning of 2022. When the US Food and Drug Administration denied JUUL marketing authority on June 23, 2022, JUUL searches peaked at 2609.3 {\texttimes} 107 and fell to 83.9 {\texttimes} 107 on September 3, 2023. Elf Bar searches surpassed JUUL on July 10, 2022, and steadily increased, reaching 523.2 {\texttimes} 107 on December 4, 2022. Overall, Elf Bar's weekly search probability increased by 1.6\% (95\% CI 1.5\%-1.7\%; P=.05) from January 2022 to December 2023, with the greatest increase between May 29 and June 19, 2022 (87.7\%, 95\% CI 35.9\%-123.9\%; P=.001). Elf Bar searches increased after JUUL's suspension in Pennsylvania (1010\%), Minnesota (872.5\%), Connecticut (803.5\%), New York (738.1\%), and New Jersey (702.9\%). Conclusions: Increasing trends in Google searches for Elf Bar indicate that there was a growing online interest in this brand in the United States in 2022. ", doi="10.2196/50343", url="https://www.jmir.org/2024/1/e50343" } @Article{info:doi/10.2196/59345, author="Vivion, Maryline and Trottier, Val{\'e}rie and Bouh{\^e}lier, {\`E}ve and Goupil-Sormany, Isabelle and Diallo, Thierno", title="Misinformation About Climate Change and Related Environmental Events on Social Media: Protocol for a Scoping Review", journal="JMIR Res Protoc", year="2024", month="Oct", day="31", volume="13", pages="e59345", keywords="misinformation", keywords="disinformation", keywords="infodemiology", keywords="infoveillance", keywords="climate change", keywords="global warming", keywords="greenhouse effect", keywords="social media", keywords="online social network", keywords="environmental health", keywords="public support", keywords="global challenges", keywords="Google", keywords="health policy", abstract="Background: Climate change and related environmental events represent major global challenges and are often accompanied by the spread of misinformation on social media. According to previous reviews, the dissemination of this misinformation on various social media platforms requires deeper exploration. Moreover, the findings reported applied mainly to the context of the United States, limiting the possibility of extending the results to other settings. Objective: This study aims to assess the current state of knowledge about misinformation concerning climate change and related environmental events that are circulating on social media. More specifically, we will explore past and current themes, actors, and sources, and the dissemination of this misinformation within the Canadian context. Methods: This scoping review protocol follows the methodological approach developed by Arksey and O'Malley and advanced by Levac, complemented by the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) checklist and the best practice guidance for the development of scoping review protocols. Following the identification of the research questions and assisted by a specialized librarian, we developed search strategies for selected bibliographic databases (MEDLINE, Embase, Web of Science, and GreenFILE) and for gray literature (Google and pertinent databases) searches. Bibliographic and gray literature will be searched to identify relevant publications. In total, 2 members of our team will use the review software Covidence (Veritas Health Innovation) to independently select publications to include in the review. Publications specifically addressing our research questions, peer-reviewed, evidence-based, and published from January 1, 2000, in the full-text version in English or French will be included. Data will be extracted from the included publications to chart, among other items, the years of publication, geographic areas, themes, actors, and sources of the climate change--related misinformation and conclusions reported. Our team will then synthesize the extracted data to articulate the current state of knowledge relating to our research inquiries. Results: The research questions were identified in January 2024. The search strategies were developed from January to March 2024 for MEDLINE, Embase, and Web of Science and in July 2024 for GreenFILE and gray literature. MEDLINE, Embase, and Web of Science searches were launched on March 26, 2024. The first of 2 rounds of selection of publications identified through these databases was achieved in April 2024. Conclusions: This protocol will enable us to identify the evolution of themes, actors, and sources of misinformation regarding climate change and related environmental events on social media, including the latest platforms, and to potentially identify a context particular to Canada. As misinformation is known to undermine actions and public support in the fight against climate change, we intend to facilitate the targeting of efforts to combat misinformation related to climate change in an up-to-date and contextualized manner. International Registered Report Identifier (IRRID): DERR1-10.2196/59345 ", doi="10.2196/59345", url="https://www.researchprotocols.org/2024/1/e59345" } @Article{info:doi/10.2196/55059, author="Ravaut, Mathieu and Zhao, Ruochen and Phung, Duy and Qin, Mengqi Vicky and Milovanovic, Dusan and Pienkowska, Anita and Bojic, Iva and Car, Josip and Joty, Shafiq", title="Targeting COVID-19 and Human Resources for Health News Information Extraction: Algorithm Development and Validation", journal="JMIR AI", year="2024", month="Oct", day="30", volume="3", pages="e55059", keywords="COVID-19", keywords="SARS-CoV-2", keywords="summary", keywords="summarize", keywords="news articles", keywords="deep learning", keywords="classification", keywords="summarization", keywords="machine learning", keywords="extract", keywords="extraction", keywords="news", keywords="media", keywords="NLP", keywords="natural language processing", abstract="Background: Global pandemics like COVID-19 put a high amount of strain on health care systems and health workers worldwide. These crises generate a vast amount of news information published online across the globe. This extensive corpus of articles has the potential to provide valuable insights into the nature of ongoing events and guide interventions and policies. However, the sheer volume of information is beyond the capacity of human experts to process and analyze effectively. Objective: The aim of this study was to explore how natural language processing (NLP) can be leveraged to build a system that allows for quick analysis of a high volume of news articles. Along with this, the objective was to create a workflow comprising human-computer symbiosis to derive valuable insights to support health workforce strategic policy dialogue, advocacy, and decision-making. Methods: We conducted a review of open-source news coverage from January 2020 to June 2022 on COVID-19 and its impacts on the health workforce from the World Health Organization (WHO) Epidemic Intelligence from Open Sources (EIOS) by synergizing NLP models, including classification and extractive summarization, and human-generated analyses. Our DeepCovid system was trained on 2.8 million news articles in English from more than 3000 internet sources across hundreds of jurisdictions. Results: Rules-based classification with hand-designed rules narrowed the data set to 8508 articles with high relevancy confirmed in the human-led evaluation. DeepCovid's automated information targeting component reached a very strong binary classification performance of 98.98 for the area under the receiver operating characteristic curve (ROC-AUC) and 47.21 for the area under the precision recall curve (PR-AUC). Its information extraction component attained good performance in automatic extractive summarization with a mean Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score of 47.76. DeepCovid's final summaries were used by human experts to write reports on the COVID-19 pandemic. Conclusions: It is feasible to synergize high-performing NLP models and human-generated analyses to benefit open-source health workforce intelligence. The DeepCovid approach can contribute to an agile and timely global view, providing complementary information to scientific literature. ", doi="10.2196/55059", url="https://ai.jmir.org/2024/1/e55059" } @Article{info:doi/10.2196/58518, author="Liu, Min and Yuan, Shuo and Li, Bingyan and Zhang, Yuxi and Liu, Jia and Guan, Cuixia and Chen, Qingqing and Ruan, Jiayi and Xie, Lunfang", title="Chinese Public Attitudes and Opinions on Health Policies During Public Health Emergencies: Sentiment and Topic Analysis", journal="J Med Internet Res", year="2024", month="Oct", day="28", volume="26", pages="e58518", keywords="public health emergencies", keywords="nucleic acid testing", keywords="governance strategies", keywords="sentiment analysis", keywords="LDA", keywords="social media", keywords="COVID-19", keywords="opinion analysis", abstract="Background: By the end of 2021, the new wave of COVID-19 sparked by the Omicron variant spread rapidly due to its highly contagious nature, affecting more than 170 countries worldwide. Nucleic acid testing became the gold standard for diagnosing novel coronavirus infections. As of July 2022, numerous cities and regions in China have implemented regular nucleic acid testing policies, which have had a significant impact on socioeconomics and people's lives. This policy has garnered widespread attention on social media platforms. Objective: This study took the newly issued regular nucleic acid testing policy during the COVID-19 pandemic as an example to explore the sentiment responses and fluctuations of netizens toward new policies during public health emergencies. It aimed to propose strategies for managing public opinion on the internet and provide recommendations for policy making and public opinion control. Methods: We collected blog posts related to nucleic acid testing on Weibo from April 1, 2022, to July 31, 2022. We used the topic modeling technique latent Dirichlet allocation (LDA) to identify the most common topics posted by users. We used Bidirectional Encoder Representations from Transformers (BERT) to calculate the sentiment score of each post. We used an autoregressive integrated moving average (ARIMA) model to examine the relationship between sentiment scores and changes over time. We compared the differences in sentiment scores across various topics, as well as the changes in sentiment before and after the announcement of the nucleic acid price reduction policy (May 22) and the lifting of the lockdown policy in Shanghai (June 1). Results: We collected a total of 463,566 Weibo posts, with an average of 3799.72 (SD 1296.06) posts published daily. The LDA topic extraction identified 8 topics, with the most numerous being the Shanghai outbreak, nucleic acid testing price, and transportation. The average sentiment score of the posts was 0.64 (SD 0.31), indicating a predominance of positive sentiment. For all topics, posts with positive sentiment consistently outnumbered those with negative sentiment ($\chi$27=24,844.4, P<.001). The sentiment scores of posts related to ``nucleic acid testing price'' decreased after May 22 compared with before (t120=3.882, P<.001). Similarly, the sentiment scores of posts related to the ``Shanghai outbreak'' decreased after June 1 compared with before (t120=11.943, P<.001). Conclusions: During public health emergencies, the topics of public concern were diverse. Public sentiment toward the regular nucleic acid testing policy was generally positive, but fluctuations occurred following the announcement of key policies. To understand the primary concerns of the public, the government needs to monitor social media posts by citizens. By promptly sharing information on media platforms and engaging in effective communication, the government can bridge the information gap between the public and government agencies, fostering a positive public opinion environment. ", doi="10.2196/58518", url="https://www.jmir.org/2024/1/e58518", url="http://www.ncbi.nlm.nih.gov/pubmed/39466313" } @Article{info:doi/10.2196/58257, author="Oan?, Iulian and H{\^a}ncean, Marian-Gabriel and Perc, Matja? and Lerner, J{\"u}rgen and Mih?il?, Bianca-Elena and Geant?, Marius and Molina, Luis Jos{\'e} and Tinc?, Isabela and Espina, Carolina", title="Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study", journal="J Med Internet Res", year="2024", month="Oct", day="25", volume="26", pages="e58257", keywords="vaccine hesitancy", keywords="online media", keywords="social media", keywords="assortative mixing", keywords="personal network analysis", keywords="social network analysis", keywords="Romania", keywords="vaccination", keywords="health information", keywords="COVID-19", abstract="Background: Most studies assessing the impact of online media and social media use on COVID-19 vaccine hesitancy predominantly rely on survey data, which often fail to capture the clustering of health opinions and behaviors within real-world networks. In contrast, research using social network analysis aims to uncover the diverse communities and discourse themes related to vaccine support and hesitancy within social media platforms. Despite these advancements, there is a gap in the literature on how a person's social circle affects vaccine acceptance, wherein an important part of social influence stems from offline interactions. Objective: We aimed to examine how online media consumption influences vaccination decisions within real-world social networks by analyzing unique quantitative network data collected from Romania, an Eastern European state and member of the European Union. Methods: We conducted 83 face-to-face interviews with participants from a living lab in Lere?ti, a small rural community in Romania, using a personal network analysis framework. This approach involved gathering data on both the respondents and individuals within their social circles (referred to as alters). After excluding cases with missing data, our analysis proceeded with 73\% (61/83) of the complete personal networks. To examine the hierarchical structure of alters nested within ego networks, we used a mixed multilevel logistic regression model with random intercepts. The model aimed to predict vaccination status among alters, with the focal independent variable being the respondents' preferred source of health and prevention information. This variable was categorized into 3 types: traditional media, online media (including social media), and a combination of both, with traditional media as the reference category. Results: In this study, we analyzed 61 personal networks, encompassing between 15 and 25 alters each, totaling 1280 alters with valid data across all variables of interest. Our primary findings indicate that alters within personal networks, whose respondents rely solely on online media for health information, exhibit lower vaccination rates (odds ratio [OR] 0.37, 95\% CI 0.15-0.92; P=.03). Conversely, the transition from exclusive traditional media use to a combination of both traditional and online media does not significantly impact vaccination rate odds (OR 0.75, 95\% CI 0.32-1.78; P=.52). In addition, our analysis revealed that alters in personal networks of respondents who received the vaccine are more likely to have received the vaccine themselves (OR 3.75, 95\% CI 1.79-7.85; P<.001). Conclusions: Real-world networks combine diverse human interactions and attributes along with consequences on health opinions and behaviors. As individuals' vaccination status is influenced by how their social alters use online media and vaccination behavior, further insights are needed to create tailored communication campaigns and interventions regarding vaccination in areas with low levels of digital health literacy and vaccination rates, as Romania exposes. ", doi="10.2196/58257", url="https://www.jmir.org/2024/1/e58257" } @Article{info:doi/10.2196/52129, author="Kierstead, Elexis and Silver, Nathan and Amato, Michael", title="Examining Quitting Experiences on Quit Vaping Subreddits From 2015 to 2021: Content Analysis", journal="J Med Internet Res", year="2024", month="Oct", day="25", volume="26", pages="e52129", keywords="quitting vaping, social media, tobacco policy", keywords="cessation", keywords="e-cigarette", keywords="electronic cigarette", keywords="smoking", keywords="vaping", keywords="cessation programs", keywords="social support", keywords="peer support", abstract="Background: Despite the prevalence of vaping nicotine, most nicotine cessation research remains focused on smoking cigarettes. However, the lived experience of quitting smoking is different from quitting vaping. As a result, research examining the unique experiences of those quitting vaping can better inform quitting resources and cessation programs specific to e-cigarette use. Examining Reddit forums (ie, subreddits) dedicated to the topics of quitting vaping nicotine can provide insight into the discussion around experiences on quitting vaping. Prior literature examining limited discussions around quitting vaping on Reddit has identified the sharing of barriers and facilitators for quitting, but more research is needed to investigate the content comprehensively across all subreddits. Objective: The objective of this study is to examine content across quit vaping subreddits since their inception to better understand quitting vaping within the context of the expanding nicotine market. Methods: All posts from January 2015 to October 2021 were scraped from all quit vaping subreddits: r/QuittingJuul, r/QuitVaping, r/quit\_vaping, and r/stopvaping (N=7110). Rolling weekly average post volume was calculated. A codebook informed by a latent Dirichlet allocation topic model was developed to characterize themes in a subsample of 695 randomly selected posts. Frequencies and percentages of posts containing each coded theme were assessed along with the number of upvotes and comments. Results: Post volume increased across all subreddits over time, spiking from August -- September of 2019 when vaping lung injury emerged. Just over 52\% of posts discussed seeking social support and 16.83\% discussed providing social support. Posts providing support received the most positive engagements (i.e. upvotes) of all coded categories. Posts also discussed physical and psychological symptoms of withdrawal (30.65\% and 18.85\%, respectively), strategies for quitting including: quitting cold turkey (38.33\%), using alternative nicotine products (17\%), and tapering down nicotine content (10.50\%). Most posts shared a personal narrative (92.37\%) and some discussed quit motivation (28.20\%) and relapse (14.99\%). Conclusions: This work identifies a desire for peer-to-peer support for quitting vaping, which reinforces existing literature and highlights characteristics of quitting vaping specific to a changing nicotine product environment. Given that posts providing social support were the most upvoted, this suggests that subreddit contributors are seeking support from their peers when discussing quitting vaping. Additionally, this analysis shows the sharing of barriers and facilitators for quitting, supporting findings from prior exploration of quit vaping subreddits. Finally, quitting vaping in an ever-growing nicotine market has led to the evolution of vaping-specific quit methods such as tapering down nicotine content. These findings have direct implications for quit vaping product implementation and development. ", doi="10.2196/52129", url="https://www.jmir.org/2024/1/e52129" } @Article{info:doi/10.2196/51909, author="Kolis, Jessica and Brookmeyer, Kathryn and Chuvileva, Yulia and Voegeli, Christopher and Juma, Sarina and Ishizumi, Atsuyoshi and Renfro, Katy and Wilhelm, Elisabeth and Tice, Hannah and Fogarty, Hannah and Kocer, Irma and Helms, Jordan and Verma, Anisha", title="Infodemics and Vaccine Confidence: Protocol for Social Listening and Insight Generation to Inform Action", journal="JMIR Public Health Surveill", year="2024", month="Oct", day="24", volume="10", pages="e51909", keywords="infodemic", keywords="infodemic management", keywords="vaccine confidence", keywords="vaccine demand", keywords="misinformation", keywords="disinformation", keywords="infodemiology", keywords="mixed methods", keywords="thematic analysis", keywords="COVID-19", abstract="Background: In the fall of 2020, the COVID-19 infodemic began to affect public confidence in and demand for COVID-19 vaccines in the United States. While polls indicated what consumers felt regarding COVID-19 vaccines, they did not provide an understanding of why they felt that way or the social and informational influences that factored into vaccine confidence and uptake. It was essential for us to better understand how information ecosystems were affecting the confidence in and demand for COVID-19 vaccines in the United States. Objective: The US Centers for Disease Control and Prevention (CDC) established an Insights Unit within the COVID-19 Response's Vaccine Task Force in January 2021 to assist the agency in acting more swiftly to address the questions, concerns, perceptions, and misinformation that appeared to be affecting uptake of COVID-19 vaccines. We established a novel methodology to rapidly detect and report on trends in vaccine confidence and demand to guide communication efforts and improve programmatic quality in near real time. Methods: We identified and assessed data sources for inclusion through an informal landscape analysis using a snowball method. Selected data sources provided an expansive look at the information ecosystem of the United States regarding COVID-19 vaccines. The CDC's Vaccinate with Confidence framework and the World Health Organization's behavioral and social drivers for vaccine decision-making framework were selected as guiding principles for interpreting generated insights and their impact. We used qualitative thematic analysis methods and a consensus-building approach to identify prevailing and emerging themes, assess their potential threat to vaccine confidence, and propose actions to increase confidence and demand. Results: As of August 2022, we have produced and distributed 34 reports to >950 recipients within the CDC and externally. State and local health departments, nonprofit organizations, professional associations, and congressional committees have referenced and used the reports for learning about COVID-19 vaccine confidence and demand, developing communication strategies, and demonstrating how the CDC monitored and responded to misinformation. A survey of the reports' end users found that nearly 75\% (40/53) of respondents found them ``very'' or ``extremely'' relevant and 52\% (32/61) used the reports to inform communication strategies. In addition, our methodology underwent continuous process improvement to increase the rigor of the research process, the validity of the findings, and the usability of the reports. Conclusions: This methodology can serve as a diagnostic technique for rapidly identifying opportunities for public health interventions and prevention. As the methodology itself is adaptable, it could be leveraged and scaled for use in a variety of public health settings. Furthermore, it could be considered beyond acute public health crises to support adherence to guidance and recommendations and could be considered within routine monitoring and surveillance systems. ", doi="10.2196/51909", url="https://publichealth.jmir.org/2024/1/e51909" } @Article{info:doi/10.2196/53938, author="Kim, Minji and Vassey, Julia and Li, Dongmei and Galimov, Artur and Han, Eileen and Kirkpatrick, G. Matthew and Stanton, A. Cassandra and Ozga, E. Jenny and Lee, Sarah and Unger, B. Jennifer", title="Discussion of Heated Tobacco Products on Twitter Following IQOS's Modified-Risk Tobacco Product Authorization and US Import Ban: Content Analysis", journal="J Med Internet Res", year="2024", month="Oct", day="24", volume="26", pages="e53938", keywords="heated tobacco products", keywords="IQOS", keywords="social media", keywords="Twitter", keywords="tobacco control", keywords="modified-risk tobacco product authorization", keywords="MRTP authorization", keywords="tobacco regulatory science", keywords="import ban", keywords="observational study", keywords="public opinion", keywords="content analysis", abstract="Background: Understanding public opinions about emerging tobacco products is important to inform future interventions and regulatory decisions. Heated tobacco products (HTPs) are an emerging tobacco product category promoted by the tobacco industry as a ``better alternative'' to combustible cigarettes. Philip Morris International's IQOS is leading the global HTP market and recently has been subject to important policy events, including the US Food and Drug Administration's (FDA) modified-risk tobacco product (MRTP) authorization (July 2020) and the US import ban (November 2021). Although limited in their legal implications outside the United States, these policy events have been quoted in global news outlets and Philip Morris International's promotional communications, showing how they may potentially impact global tobacco regulation. Given the impending return of IQOS to the US market, understanding how the policy events were received through social media discourse will provide valuable insights to inform global tobacco control policy. Objective: This study aims to examine HTP-related social media discourse around important policy events. Methods: We analyzed HTP-related posts on Twitter during the time period that included IQOS's MRTP authorization in the United States and the US import ban, examining personal testimonial, news/information, and direct marketing/retail tweets separately. We also examined how the tweets discussed health and policy. A total of 10,454 public English tweets (posted from June 2020 to December 2021) were collected using HTP-related keywords. We randomly sampled 2796 (26.7\%) tweets and conducted a content analysis. We used pairwise co-occurrence analyses to evaluate connections across themes. Results: Tweet volumes peaked around IQOS-related policy events. Among all tweets, personal testimonials were the most common (1613/2796, 57.7\%), followed by news/information (862/2796, 30.8\%) and direct marketing/retail (321/2796, 11\%). Among personal testimonials, more tweets were positive (495/1613, 30.7\%) than negative (372/1613, 23.1\%), often comparing the health risks of HTPs with cigarettes (402/1613, 24.9\%) or vaping products (252/1613, 15.6\%). Approximately 10\% (31/321) of the direct marketing/retail tweets promoted international delivery, suggesting cross-border promotion. More than a quarter of tweets (809/2796, 28.9\%) discussed US and global policy, including misinterpretation about IQOS being a ``safer'' tobacco product after the US FDA's MRTP authorization. Neutral testimonials mentioning the IQOS brand (634/1613, 39.3\%) and discussing policy (378/1613, 23.4\%) showed the largest pairwise co-occurrence. Conclusions: Results suggest the need for careful communication about the meaning of MRTP authorizations and relative risks of tobacco products. Many tweets expressed HTP-favorable opinions referring to reduced health risks, even though the US FDA has denied marketing of the HTP with reduced risk claims. The popularity of social media as an information source with global reach poses unique challenges in health communication and health policies. While many countries restrict tobacco marketing via the web, our results suggest that retailers may circumvent such regulations by operating overseas. ", doi="10.2196/53938", url="https://www.jmir.org/2024/1/e53938", url="http://www.ncbi.nlm.nih.gov/pubmed/39446431" } @Article{info:doi/10.2196/50099, author="Acero, Nicole and Herrero, Emma and Foncham, Juanita and McIlvaine, Jamie and Kayaalp, Emre and Figueora, Melissa and Oladipo, Francis Antonia", title="Accuracy, Quality, and Misinformation of YouTube Abortion Procedural Videos: Cross-Sectional Study", journal="J Med Internet Res", year="2024", month="Oct", day="22", volume="26", pages="e50099", keywords="abortion", keywords="YouTube", keywords="social media", keywords="accuracy", keywords="quality", keywords="misinformation", keywords="reliability", keywords="obstetrics", keywords="women's health", keywords="reproductive", keywords="patient education", keywords="health information", keywords="prochoice", abstract="Background: The internet is often the first source patients turn to for medical information. YouTube is a commonly used internet-based resource for patients seeking to learn about medical procedures, including their risks, benefits, and safety profile. Abortion is a common yet polarizing medical procedure. People interested in obtaining an abortion are likely to use the internet to learn more about abortion procedures and may encounter misinformed and biased information. This is troubling as information found on the internet can significantly alter perceptions and understanding of these procedures. There is no current research that evaluates the accuracy, quality, and misinformation of instructional abortion videos available to patients. Objective: The purpose of this study was to assess if any given video can deliver accurate and quality information about this topic in an unbiased manner and to assess the level of factually incorrect, distorted, or medically irrelevant information in any given video. Methods: Procedural methods of abortion were queried on YouTube on August 22, 2022. The videos were screened with strict exclusion criteria. Videos were categorized into ``video slants'' based on the language and attitudes expressed in each video. Video accuracy was calculated using the Surgical Curriculum in Obstetrics and Gynecology (SCOG) checklist for each corresponding procedure. Video quality was calculated using the Laparoscopic Surgery Video Educational Guidelines (LAP-VEGaS) criteria. The level of misinformation was assessed with the evidence-based Anti-Choice Rubric, which scores the amount of factually incorrect, distorted, or medically irrelevant information in each video. Results: A total of 32 videos were analyzed and categorized into 3 ``video slant'' groups: neutral (n=23, 72\%), antichoice (n=4, 12\%), and prochoice (n=5, 16\%). Using the SCOG checklist, neutral videos had the highest median accuracy (45.9\%), followed by antichoice videos (24.6\%) and prochoice videos (18.5\%). None of the videos met the LAP-VEGaS quality control criteria, (score>11, indicating adequate quality). Neutral videos had a median score of 8.8 out of 18, with antichoice videos scoring 10.75 and prochoice videos scoring 6.2. Using the Anti-Choice Rubric, neutral videos mentioned only 1 factually incorrect piece of information. Antichoice videos mentioned 12 factually incorrect pieces of information, 8 distortions, and 3 medically irrelevant pieces of information. Prochoice videos did not mention any of the 3 themes. Conclusions: Using the SCOG checklist, the accuracy of instructional videos were inconsistent across the 3 identified ``video slants.'' Using LAP-VEGaS criteria, the quality of educational videos were also inconsistent across the 3 ``video slants.'' Prochoice videos had the lowest level of misinformation, with no mentions of any of the 3 themes. Antichoice videos had the highest levels of misinformation, with mentions in all 3 themes. Health care professionals should consider this when counseling patients who may watch YouTube videos for information regarding abortion procedures. ", doi="10.2196/50099", url="https://www.jmir.org/2024/1/e50099" } @Article{info:doi/10.2196/58309, author="P{\'e}rez-P{\'e}rez, Mart{\'i}n and Fernandez Gonzalez, Mar{\'i}a and Rodriguez-Rajo, Javier Francisco and Fdez-Riverola, Florentino", title="Tracking the Spread of Pollen on Social Media Using Pollen-Related Messages From Twitter: Retrospective Analysis", journal="J Med Internet Res", year="2024", month="Oct", day="21", volume="26", pages="e58309", keywords="pollen", keywords="respiratory allergies", keywords="Twitter", keywords="large language model", keywords="LLM", keywords="knowledge reconstruction", keywords="text mining", abstract="Background: Allergy disorders caused by biological particles, such as the proteins in some airborne pollen grains, are currently considered one of the most common chronic diseases, and European Academy of Allergy and Clinical Immunology forecasts indicate that within 15 years 50\% of Europeans will have some kind of allergy as a consequence of urbanization, industrialization, pollution, and climate change. Objective: The aim of this study was to monitor and analyze the dissemination of information about pollen symptoms from December 2006 to January 2022. By conducting a comprehensive evaluation of public comments and trends on Twitter, the research sought to provide valuable insights into the impact of pollen on sensitive individuals, ultimately enhancing our understanding of how pollen-related information spreads and its implications for public health awareness. Methods: Using a blend of large language models, dimensionality reduction, unsupervised clustering, and term frequency--inverse document frequency, alongside visual representations such as word clouds and semantic interaction graphs, our study analyzed Twitter data to uncover insights on respiratory allergies. This concise methodology enabled the extraction of significant themes and patterns, offering a deep dive into public knowledge and discussions surrounding respiratory allergies on Twitter. Results: The months between March and August had the highest volume of messages. The percentage of patient tweets appeared to increase notably during the later years, and there was also a potential increase in the prevalence of symptoms, mainly in the morning hours, indicating a potential rise in pollen allergies and related discussions on social media. While pollen allergy is a global issue, specific sociocultural, political, and economic contexts mean that patients experience symptomatology at a localized level, needing appropriate localized responses. Conclusions: The interpretation of tweet information represents a valuable tool to take preventive measures to mitigate the impact of pollen allergy on sensitive patients to achieve equity in living conditions and enhance access to health information and services. ", doi="10.2196/58309", url="https://www.jmir.org/2024/1/e58309", url="http://www.ncbi.nlm.nih.gov/pubmed/39432897" } @Article{info:doi/10.2196/51710, author="Arai, Takahiro and Tsubaki, Hiroe and Wakano, Ayako and Shimizu, Yasuyuki", title="Association Between School-Related Google Trends Search Volume and Suicides Among Children and Adolescents in Japan During 2016-2020: Retrospective Observational Study With a Time-Series Analysis", journal="J Med Internet Res", year="2024", month="Oct", day="21", volume="26", pages="e51710", keywords="adolescent", keywords="children", keywords="COVID-19", keywords="Google Trends", keywords="internet", keywords="Japan", keywords="monitoring", keywords="suicide", keywords="surveillance", keywords="time series analysis", abstract="Background: Suicide is the leading cause of death among children and adolescents in Japan. Internet search volume may be useful in detecting suicide risk. However, few studies have shown an association between suicides attempted by children and adolescents and their internet search volume. Objective: This study aimed to examine the relationship between the number of suicides and the volume of school-related internet searches to identify the search terms that could serve as the leading indicators of suicide prevention among children and adolescents. Methods: We used data on weekly suicides attempted by elementary, middle, and high school students in Japan from 2016 to 2020, provided by the National Police Agency. Internet search volume was weekly data for 20 school-related terms obtained from Google Trends. Granger causality and cross-correlation analysis were performed to estimate the temporal back-and-forth and lag between suicide deaths and search volume for the related terms. Results: The search queries ``I do not want to go to school'' and ``study'' showed Granger causality with suicide incidences. The cross-correlation analysis showed significant positive correlations in the range of --2 to 2 for ``I do not want to go to school'' (highest value at time lag 0, r=0.28), and --1 to 2 for ``study'' (highest value at time lag --1, r=0.18), indicating that the search volume increased as the number of suicides increased. Furthermore, during the COVID-19 pandemic period (January-December 2020), the search trend for ``I do not want to go to school,'' unlike ``study,'' was highly associated with suicide frequency. Conclusions: Monitoring the volume of internet searches for ``I do not want to go to school'' could be useful for the early detection of suicide risk among children and adolescents and for optimizing web-based helpline displays. ", doi="10.2196/51710", url="https://www.jmir.org/2024/1/e51710" } @Article{info:doi/10.2196/50408, author="Kang, Jeemin and Szeto, D. Mindy and Suh, Lois and Olayinka, T. Jadesola and Dellavalle, P. Robert", title="Popular Skin-of-Color Dermatology Social Media Hashtags on TikTok From 2021 to 2022: Content Analysis", journal="JMIR Dermatol", year="2024", month="Oct", day="18", volume="7", pages="e50408", keywords="dermatology", keywords="dermatologist", keywords="social media", keywords="TikTok", keywords="skin of color", keywords="hashtag", keywords="content analysis", keywords="education", keywords="influencers", keywords="diversity", keywords="inclusion", keywords="disparities", doi="10.2196/50408", url="https://derma.jmir.org/2024/1/e50408" } @Article{info:doi/10.2196/57720, author="Zhang, Baolu and Kalampakorn, Surintorn and Powwattana, Arpaporn and Sillabutra, Jutatip and Liu, Gang", title="Oral Diabetes Medication Videos on Douyin: Analysis of Information Quality and User Comment Attitudes", journal="JMIR Form Res", year="2024", month="Oct", day="18", volume="8", pages="e57720", keywords="diabetes", keywords="oral diabetes medication", keywords="information quality", keywords="user comment attitude", keywords="video analysis", keywords="Douyin", abstract="Background: Oral diabetes medications are important for glucose management in people with diabetes. Although there are many health-related videos on Douyin (the Chinese version of TikTok), the quality of information and the effects on user comment attitudes are unclear. Objective: The purpose of this study was to analyze the quality of information and user comment attitudes related to oral diabetes medication videos on Douyin. Methods: The key phrase ``oral diabetes medications'' was used to search Douyin on July 24, 2023, and the final samples included 138 videos. The basic information in the videos and the content of user comments were captured using Python. Each video was assigned a sentiment category based on the predominant positive, neutral, or negative attitude, as analyzed using the Weiciyun website. Two independent raters assessed the video content and information quality using the DISCERN (a tool for assessing health information quality) and PEMAT-A/V (Patient Education Materials Assessment Tool for Audiovisual Materials) instruments. Results: Doctors were the main source of the videos (136/138, 98.6\%). The overall information quality of the videos was acceptable (median 3, IQR 1). Videos on Douyin showed relatively high understandability (median 75\%, IQR 16.6\%) but poor actionability (median 66.7\%, IQR 48\%). Most content on oral diabetes medications on Douyin related to the mechanism of action (75/138, 54.3\%), precautions (70/138, 50.7\%), and advantages (68/138, 49.3\%), with limited content on indications (19/138, 13.8\%) and contraindications (14/138, 10.1\%). It was found that 10.1\% (14/138) of the videos contained misinformation, of which 50\% (7/14) were about the method of administration. Regarding user comment attitudes, the majority of videos garnered positive comments (81/138, 58.7\%), followed by neutral comments (46/138, 33.3\%) and negative comments (11/138, 8\%). Multinomial logistic regression revealed 2 factors influencing a positive attitude: user comment count (adjusted odds ratio [OR] 1.00, 95\% CI 1.00-1.00; P=.02) and information quality of treatment choices (adjusted OR 1.49, 95\% CI 1.09-2.04; P=.01). Conclusions: Despite most videos on Douyin being posted by doctors, with generally acceptable information quality and positive user comment attitudes, some content inaccuracies and poor actionability remain. Users show more positive attitudes toward videos with high-quality information about treatment choices. This study suggests that health care providers should ensure the accuracy and actionability of video content, enhance the information quality of treatment choices of oral diabetes medications to foster positive user attitudes, help users access accurate health information, and promote medication adherence. ", doi="10.2196/57720", url="https://formative.jmir.org/2024/1/e57720" } @Article{info:doi/10.2196/57698, author="Dhakal, Smita and Merani, Shermeen and Ahluwalia, Vandana and Battistella, Marisa and Borkhoff, M. Cornelia and Hazlewood, Stewart Glen and Lofters, Aisha and Marshall, A. Deborah and MacKay, Crystal and Gagliardi, R. Anna", title="The Quality and Cultural Safety of Online Osteoarthritis Information for Affected Persons and Health Care Professionals: Content Analysis", journal="J Med Internet Res", year="2024", month="Oct", day="18", volume="26", pages="e57698", keywords="osteoarthritis", keywords="women's health", keywords="equity", keywords="educational materials", keywords="internet", keywords="content analysis", keywords="Canada", keywords="persons living with osteoarthritis", keywords="healthcare professionals", keywords="OA care", keywords="ethno-culturally women", keywords="immigrant women", keywords="diverse women", keywords="online materials", keywords="health information", keywords="prevention", keywords="management", keywords="misinformation", keywords="cultural safety", abstract="Background: Osteoarthritis is more prevalent and severe among women than among men, but women are less likely to access early diagnosis and first-line management, particularly racialized immigrant women. Previous research advocated for greater access to culturally safe osteoarthritis information for both diverse women and health care professionals. The internet can reduce disparities by facilitating access to health information, but online materials can vary in quality. Objective: This study aimed to assess the quality and cultural safety of online osteoarthritis materials for persons affected by osteoarthritis and health care professionals. Methods: Content analysis was used to describe publicly available materials on osteoarthritis first-line management developed by Canadian organizations for affected persons or health care professionals. Searching, screening, and data extraction were performed in triplicate. We identified materials by searching Google, MEDLINE, and references of osteoarthritis-relevant guidelines and policies, and consulting our research team and collaborators. We assessed quality using DISCERN (University of Oxford) and a compiled framework for affected persons and health care professionals. We compiled frameworks to assess cultural safety. We derived an overall score, categorized as low (<50\%), moderate (50\%-69\%), or high (?70\%+) for criteria met. Results: After screening 176 items and eliminating 129, we included 47 osteoarthritis materials published between 2013 and 2023. Of those, 43 were for persons with osteoarthritis, most were developed by charities (n=31, 72.1\%), based on expert advice (n=16, 55.2\%), and in the format of booklets (n=15, 34.9\%) or text on web pages (n=10, 23.3\%). Of those, 23.3\% (10/43) low, 46.5\% (20/43) moderate, and 30.2\% (13/43) high scored quality; and 25.6\% (11/43), 48.8\% (21/43), and 25.6\% (11/43) were rated low, moderate, and high cultural safety, respectively. Of the 47 included osteoarthritis materials, 4 were for health care professionals. They were developed by a consortium (2/4, 50\%), a charity (1/4, 25\%), and a professional society (1/4, 25\%), and largely based on expert advice (3/4, 75\%). The format included infographics (3/4, 75\%) and text on web pages (1/4, 25\%). Of those, 25\% (1/4), 25\% (1/4), and 50\% (2/4) were rated low, moderate, and high quality, respectively; and all were rated low for cultural safety. Quality and cultural safety did not appear to be associated with the characteristics of osteoarthritis materials (eg, type of developer, development method, and format). Conclusions: Overall, included osteoarthritis materials for persons with osteoarthritis and health care professionals were of low to moderate quality and cultural safety. These findings reveal the need for further efforts to improve existing or develop new osteoarthritis materials for both affected persons, including ethnoculturally diverse immigrant women, and health care professionals. Further research is needed to assess the quality and cultural safety of osteoarthritis materials developed by organizations outside of Canada and to establish a framework or instrument to assess cultural safety in the osteoarthritis context. ", doi="10.2196/57698", url="https://www.jmir.org/2024/1/e57698", url="http://www.ncbi.nlm.nih.gov/pubmed/39422989" } @Article{info:doi/10.2196/53488, author="Deng, Tianjie and Urbaczewski, Andrew and Lee, Jin Young and Barman-Adhikari, Anamika and Dewri, Rinku", title="Identifying Marijuana Use Behaviors Among Youth Experiencing Homelessness Using a Machine Learning--Based Framework: Development and Evaluation Study", journal="JMIR AI", year="2024", month="Oct", day="17", volume="3", pages="e53488", keywords="machine learning", keywords="youth experiencing homelessness", keywords="natural language processing", keywords="infodemiology", keywords="social good", keywords="digital intervention", abstract="Background: Youth experiencing homelessness face substance use problems disproportionately compared to other youth. A study found that 69\% of youth experiencing homelessness meet the criteria for dependence on at least 1 substance, compared to 1.8\% for all US adolescents. In addition, they experience major structural and social inequalities, which further undermine their ability to receive the care they need. Objective: The goal of this study was to develop a machine learning--based framework that uses the social media content (posts and interactions) of youth experiencing homelessness to predict their substance use behaviors (ie, the probability of using marijuana). With this framework, social workers and care providers can identify and reach out to youth experiencing homelessness who are at a higher risk of substance use. Methods: We recruited 133 young people experiencing homelessness at a nonprofit organization located in a city in the western United States. After obtaining their consent, we collected the participants' social media conversations for the past year before they were recruited, and we asked the participants to complete a survey on their demographic information, health conditions, sexual behaviors, and substance use behaviors. Building on the social sharing of emotions theory and social support theory, we identified important features that can potentially predict substance use. Then, we used natural language processing techniques to extract such features from social media conversations and reactions and built a series of machine learning models to predict participants' marijuana use. Results: We evaluated our models based on their predictive performance as well as their conformity with measures of fairness. Without predictive features from survey information, which may introduce sex and racial biases, our machine learning models can reach an area under the curve of 0.72 and an accuracy of 0.81 using only social media data when predicting marijuana use. We also evaluated the false-positive rate for each sex and age segment. Conclusions: We showed that textual interactions among youth experiencing homelessness and their friends on social media can serve as a powerful resource to predict their substance use. The framework we developed allows care providers to allocate resources efficiently to youth experiencing homelessness in the greatest need while costing minimal overhead. It can be extended to analyze and predict other health-related behaviors and conditions observed in this vulnerable community. ", doi="10.2196/53488", url="https://ai.jmir.org/2024/1/e53488" } @Article{info:doi/10.2196/50057, author="Wu, Manli and Yan, Jun and Qiao, Chongming and Yan, Chu", title="Impact of Concurrent Media Exposure on Professional Identity: Cross-Sectional Study of 1087 Medical Students During Long COVID", journal="J Med Internet Res", year="2024", month="Oct", day="17", volume="26", pages="e50057", keywords="COVID-19", keywords="media exposure", keywords="social support", keywords="professional identity", keywords="medical students", keywords="Stimulus-Organism-Response framework", abstract="Background: Long COVID has widened the health gap across society and highlighted the vulnerabilities and risks faced by health care systems. For instance, the global trend of medical workers resigning has become a prominent topic on social media. In response to this severe social problem in global public health within the digital society, it is urgent to investigate how the professional identity of medical students, who are digital natives and the future workforce of medical practitioners, is affected by the media environment. Objective: This study aims to examine how media exposure relates to medical students' perceptions of informational and emotional support, and how these perceptions further influence the development of their professional identity. Methods: Building on the Stimulus-Organism-Response (SOR) framework, this study develops a theoretical model to illustrate how media exposure affects medical students' professional identity through the mediation of social support. Specifically, media exposure was assessed through online news media and social media exposure; social support was evaluated in terms of informational and emotional support; and professional identity was measured through medical students' sense of belonging and professional commitment. A survey was conducted at a medical school in China, yielding 1087 valid responses that were analyzed using SmartPLS 4.0. Results: Consistent with our expectations, online news media exposure was positively associated with both informational support ($\beta$=.163; P<.001) and emotional support ($\beta$=.084; P=.007). Similarly, social media exposure showed positive associations with informational support ($\beta$=.122; P<.001) and emotional support ($\beta$=.235; P<.001). Thereafter, informational support ($\beta$=.228; P<.001) and emotional support ($\beta$=.344; P<.001) were positively associated with students' sense of belonging. Meanwhile, both informational support ($\beta$=.245; P<.001) and emotional support ($\beta$=.412; P<.001) positively impacted medical students' professional commitment. In addition, a mediation test was conducted. The results confirmed that informational support and emotional support partially mediated the effect of online news media, while fully mediating the effect of social media on medical students' sense of belonging and professional commitment. Conclusions: This study finds that exposure to online news media and social media can enhance medical students' sense of belonging and professional commitment through the formation of informational and emotional support. It expands the discussion on the role of media in providing social support and facilitating the development of medical students' professional identity. This is a valuable contribution to addressing complex public health crises through effective media governance in the network era. ", doi="10.2196/50057", url="https://www.jmir.org/2024/1/e50057", url="http://www.ncbi.nlm.nih.gov/pubmed/39418080" } @Article{info:doi/10.2196/52354, author="Kelsall, Clancy Nora and Gimbrone, Catherine and Olfson, Mark and Gould, Madelyn and Shaman, Jeffrey and Keyes, Katherine", title="Internet Search Activity for Intentional Self-Harm Forums After a High-Profile News Publication: Interrupted Time Series Analysis", journal="J Med Internet Res", year="2024", month="Oct", day="15", volume="26", pages="e52354", keywords="suicide risk", keywords="suicide", keywords="journalism", keywords="media", keywords="self-harm", keywords="Google Trends", keywords="websites", keywords="mental health", keywords="depression", keywords="quality of life", keywords="harmful information", doi="10.2196/52354", url="https://www.jmir.org/2024/1/e52354", url="http://www.ncbi.nlm.nih.gov/pubmed/39405095" } @Article{info:doi/10.2196/53505, author="Germani, Federico and Spitale, Giovanni and Biller-Andorno, Nikola", title="The Dual Nature of AI in Information Dissemination: Ethical Considerations", journal="JMIR AI", year="2024", month="Oct", day="15", volume="3", pages="e53505", keywords="AI", keywords="bioethics", keywords="infodemic management", keywords="disinformation", keywords="artificial intelligence", keywords="ethics", keywords="ethical", keywords="infodemic", keywords="infodemics", keywords="public health", keywords="misinformation", keywords="information dissemination", keywords="information literacy", doi="10.2196/53505", url="https://ai.jmir.org/2024/1/e53505", url="http://www.ncbi.nlm.nih.gov/pubmed/39405099" } @Article{info:doi/10.2196/52142, author="Correia, C{\'e}sar Jorge and Ahmad, Shaharyar Sarmad and Waqas, Ahmed and Meraj, Hafsa and Pataky, Zoltan", title="Exploring Public Emotions on Obesity During the COVID-19 Pandemic Using Sentiment Analysis and Topic Modeling: Cross-Sectional Study", journal="J Med Internet Res", year="2024", month="Oct", day="11", volume="26", pages="e52142", keywords="obesity", keywords="Twitter", keywords="infodemic", keywords="attitude", keywords="opinion", keywords="perception", keywords="perspective", keywords="obese", keywords="weight", keywords="overweight", keywords="social media", keywords="tweet", keywords="sentiment", keywords="topic modeling", keywords="BERT", keywords="Bidirectional Encoder Representations from Transformers", keywords="NLP", keywords="natural language processing", keywords="general public", keywords="celebrities", abstract="Background: Obesity is a chronic, multifactorial, and relapsing disease, affecting people of all ages worldwide, and is directly related to multiple complications. Understanding public attitudes and perceptions toward obesity is essential for developing effective health policies, prevention strategies, and treatment approaches. Objective: This study investigated the sentiments of the general public, celebrities, and important organizations regarding obesity using social media data, specifically from Twitter (subsequently rebranded as X). Methods: The study analyzes a dataset of 53,414 tweets related to obesity posted on Twitter during the COVID-19 pandemic, from April 2019 to December 2022. Sentiment analysis was performed using the XLM-RoBERTa-base model, and topic modeling was conducted using the BERTopic library. Results: The analysis revealed that tweets regarding obesity were predominantly negative. Spikes in Twitter activity correlated with significant political events, such as the exchange of obesity-related comments between US politicians and criticism of the United Kingdom's obesity campaign. Topic modeling identified 243 clusters representing various obesity-related topics, such as childhood obesity; the US President's obesity struggle; COVID-19 vaccinations; the UK government's obesity campaign; body shaming; racism and high obesity rates among Black American people; smoking, substance abuse, and alcohol consumption among people with obesity; environmental risk factors; and surgical treatments. Conclusions: Twitter serves as a valuable source for understanding obesity-related sentiments and attitudes among the public, celebrities, and influential organizations. Sentiments regarding obesity were predominantly negative. Negative portrayals of obesity by influential politicians and celebrities were shown to contribute to negative public sentiments, which can have adverse effects on public health. It is essential for public figures to be mindful of their impact on public opinion and the potential consequences of their statements. ", doi="10.2196/52142", url="https://www.jmir.org/2024/1/e52142" } @Article{info:doi/10.2196/52977, author="Baek, Kwangyeol and Jeong, Jake and Kim, Hyun-Woo and Shin, Dong-Hyeon and Kim, Jiyoung and Lee, Gha-Hyun and Cho, Wook Jae", title="Seasonal and Weekly Patterns of Korean Adolescents' Web Search Activity on Insomnia: Retrospective Study", journal="JMIR Form Res", year="2024", month="Oct", day="11", volume="8", pages="e52977", keywords="insomnia", keywords="sleep", keywords="internet search", keywords="adolescents", keywords="school", keywords="seasonal", keywords="weekly", keywords="NAVER", keywords="infodemiology", keywords="inforveillance", abstract="Background: Sleep deprivation in adolescents is a common but serious public health issue. Adolescents often have a progressive circadian delay and suffer from insufficient sleep during weekdays due to the school schedule. Temporal patterns in internet search activity data can provide relevant information for understanding the characteristic sleep problems of the adolescent population. Objective: We aimed to reveal whether adolescents exhibit distinct temporal seasonal and weekly patterns in internet search activity on insomnia compared to adults. Methods: We hypothesized that adolescents exhibit larger variations in the internet search volume for insomnia, particularly in association with the school schedule (e.g., academic vacations and weekends). We extracted the daily search volume for insomnia in South Korean adolescents (13-18 years old), adults (19-59 years old), and young adults (19-24 years old) during the years 2016-2019 using NAVER DataLab, the most popular search engine in South Korea. The daily search volume data for each group were normalized with the annual median of each group. The time series of the search volume was decomposed into slow fluctuation (over a year) and fast fluctuation (within a week) using fast Fourier transform. Next, we compared the normalized search volume across months in a year (slow fluctuation) and days in a week (fast fluctuation). Results: In the annual trend, 2-way ANOVA revealed a significant (group) {\texttimes} (month) interaction (P<.001). Adolescents exhibited much greater seasonal variations across a year than the adult population (coefficient of variation=0.483 for adolescents vs 0.131 for adults). The search volume for insomnia in adolescents was notably higher in January, February, and August, which are academic vacation periods in South Korea (P<.001). In the weekly pattern, 2-way ANOVA revealed a significant (group) {\texttimes} (day) interaction (P<.001). Adolescents showed a considerably increased search volume on Sunday and Monday (P<.001) compared to adults. In contrast, young adults demonstrated seasonal and weekly patterns similar to adults. Conclusions: Adolescents demonstrate distinctive seasonal and weekly patterns in internet searches on insomnia (ie, increased search in vacation months and weekend--weekday transitions), which are closely associated with the school schedule. Adolescents' sleep concerns might be potentially affected by the disrupted daily routine and the delayed sleep phase during vacations and weekends. As we demonstrated, comparing various age groups in infodemiology and infoveillance data might be helpful in identifying distinctive features in vulnerable age groups. ", doi="10.2196/52977", url="https://formative.jmir.org/2024/1/e52977", url="http://www.ncbi.nlm.nih.gov/pubmed/39311496" } @Article{info:doi/10.2196/56354, author="Zenone, Marco and van Schalkwyk, May and Hartwell, Greg and Caulfield, Timothy and Maani, Nason", title="Selling Misleading ``Cancer Cure'' Books on Amazon: Systematic Search on Amazon.com and Thematic Analysis", journal="J Med Internet Res", year="2024", month="Oct", day="8", volume="26", pages="e56354", keywords="cancer", keywords="Amazon", keywords="misinformation", keywords="e-commerce", keywords="cancer cure", keywords="cancer misinformation", keywords="misleading", keywords="cancer information", keywords="treatment", keywords="cancer treatment", keywords="thematic analysis", keywords="online information", abstract="Background: While the evidence base on web-based cancer misinformation continues to develop, relatively little is known about the extent of such information on the world's largest e-commerce website, Amazon. Multiple media reports indicate that Amazon may host on its platform questionable cancer-related products for sale, such as books on purported cancer cures. This context suggests an urgent need to evaluate Amazon.com for cancer misinformation. Objective: This study sought to (1) examine to what extent are misleading cancer cure books for sale on Amazon.com and (2) determine how cancer cure books on Amazon.com provide misleading cancer information. Methods: We searched ``cancer cure'' on Amazon.com and retrieved the top 1000 English-language book search results. We reviewed the books' descriptions and titles to determine whether the books provided misleading cancer cure or treatment information. We considered a book to be misleading if it suggested scientifically unsupported cancer treatment approaches to cure or meaningfully treat cancer. Among books coded as misleading, we conducted an inductive latent thematic analysis to determine the informational value the books sought to offer. Results: Nearly half (494/1000, 49.4\%) of the sampled ``cancer cure'' books for sale on Amazon.com appeared to contain misleading cancer treatment and cure information. Overall, 17 (51.5\%) out of 33 Amazon.com results pages had 50\% or more of the books coded as misleading. The first search result page had the highest percentage of misleading books (23/33, 69.7\%). Misleading books (n=494) contained eight themes: (1) claims of efficacious cancer cure strategies (n=451, 91.3\%), (2) oversimplifying cancer and cancer treatment (n=194, 39.3\%), (3) falsely justifying ineffective treatments as science based (n=189, 38.3\%), (4) discrediting conventional cancer treatments (n=169, 34.2\%), (5) finding the true cause of cancer (n=133, 26.9\%), (6) homogenizing cancer (n=132, 26.7\%), (7) discovery of new cancer treatments (n=119, 24.1\%), and (8) cancer cure suppression (n=82, 16.6\%). Conclusions: The results demonstrate that misleading cancer cure books are for sale, visible, and prevalent on Amazon.com, with prominence in initial search hits. These misleading books for sale on Amazon can be conceived of as forming part of a wider, cross-platform, web-based information environment in which misleading cancer cures are often given prominence. Our results suggest that greater enforcement is needed from Amazon and that cancer-focused organizations should engage in preemptive misinformation debunking. ", doi="10.2196/56354", url="https://www.jmir.org/2024/1/e56354" } @Article{info:doi/10.2196/56034, author="Ahmed, Wasim and Hardey, Mariann and Winters, David Bradford and Sarwal, Aarti", title="Racial Biases Associated With Pulse Oximetry: Longitudinal Social Network Analysis of Social Media Advocacy Impact", journal="J Med Internet Res", year="2024", month="Oct", day="8", volume="26", pages="e56034", keywords="social media", keywords="X", keywords="racial biases", keywords="pulse oximetry", keywords="advocacy", keywords="impact", keywords="awareness", keywords="racial", keywords="bias", keywords="biases", keywords="longitudinal study", keywords="information", keywords="dissemination", keywords="disparity", keywords="disparities", keywords="accuracy", keywords="social network analysis", keywords="Academic Track application programming interface", keywords="API", abstract="Background: Pulse oximetry is a noninvasive method widely used in critical care and various clinical settings to monitor blood oxygen saturation. During the COVID-19 pandemic, its application for at-home oxygen saturation monitoring became prevalent. Further investigations found that pulse oximetry devices show decreased accuracy when used on individuals with darker skin tones. This study aimed to investigate the influence of X (previously known as Twitter) on the dissemination of information and the extent to which it raised health care sector awareness regarding racial disparities in pulse oximetry. Objective: This study aimed to explore the impact of social media, specifically X, on increasing awareness of racial disparities in the accuracy of pulse oximetry and to map this analysis against the evolution of published literature on this topic. Methods: We used social network analysis drawing upon Network Overview Discovery and Exploration for Excel Pro (NodeXL Pro; Social Media Research Foundation) to examine the impact of X conversations concerning pulse oximetry devices. Searches were conducted using the Twitter Academic Track application programming interface (as it was known then). These searches were performed each year (January to December) from 2012 to 2022 to cover 11 years with up to 52,052 users, generating 188,051 posts. We identified the nature of influencers in this field and monitored the temporal dissemination of information about social events and regulatory changes. Furthermore, our social media analysis was mapped against the evolution of published literature on this topic, which we located using PubMed. Results: Conversations on X increased health care awareness of racial bias in pulse oximetry. They also facilitated the rapid dissemination of information, attaining a substantial audience within a compressed time frame, which may have impacted regulatory action announced concerning the investigation of racial biases in pulse oximetry. This increased awareness led to a surge in scientific research on the subject, highlighting a growing recognition of the necessity to understand and address these disparities in medical technology and its usage. Conclusions: Social media platforms such as X enabled researchers, health experts, patients, and the public to rapidly share information, increasing awareness of potential racial bias. These platforms also helped connect individuals interested in these topics and facilitated discussions that spurred further research. Our research provides a basis for understanding the role of X and other social media platforms in spreading health-related information about potential biases in medical devices such as pulse oximeters. ", doi="10.2196/56034", url="https://www.jmir.org/2024/1/e56034" } @Article{info:doi/10.2196/52735, author="Wu, Dezhi and Ng, Minnie and Gupta, Sen Saborny and Raynor, Phyllis and Tao, Youyou and Ren, Yang and Hung, Peiyin and Qiao, Shan and Zhang, Jiajia and Fillo, Jennifer and Li, Xiaoming and Guille, Constance and Eichelberger, Kacey and Olatosi, Bankole", title="Disclosure Patterns of Opioid Use Disorders in Perinatal Care During the Opioid Epidemic on X From 2019 to 2021: Thematic Analysis", journal="JMIR Pediatr Parent", year="2024", month="Oct", day="7", volume="7", pages="e52735", keywords="X", keywords="Twitter", keywords="opioid use disorder", keywords="thematic analysis", keywords="pregnancy", keywords="perinatal care", keywords="women and child health", keywords="maternal health", keywords="COVID-19", keywords="opioid epidemic", abstract="Background: In 2021, the United States experienced a 14\% rise in fatal drug overdoses totaling 106,699 deaths, driven by harmful opioid use, particularly among individuals in the perinatal period who face increased risks associated with opioid use disorders (OUDs). Increased concerns about the impacts of escalating harmful opioid use among pregnant and postpartum persons are rising. Most of the current limited perinatal OUD studies were conducted using traditional methods, such as interviews and randomized controlled trials to understand OUD treatment, risk factors, and associated adverse effects. However, little is known about how social media data, such as X, formerly known as Twitter, can be leveraged to explore and identify broad perinatal OUD trends, disclosure and communication patterns, and public health surveillance about OUD in the perinatal period. Objective: The objective is 3-fold: first, we aim to identify key themes and trends in perinatal OUD discussions on platform X. Second, we explore user engagement patterns, including replying and retweeting behaviors. Third, we investigate computational methods that could potentially streamline and scale the labor-intensive manual annotation effort. Methods: We extracted 6 million raw perinatal-themed tweets posted by global X users during the opioid epidemic from May 2019 to October 2021. After data cleaning and sampling, we used 500 tweets related to OUD in the perinatal period by US X users for a thematic analysis using NVivo (Lumivero) software. Results: Seven major themes emerged from our thematic analysis: (1) political views related to harmful opioid and other substance use, (2) perceptions of others' substance use, (3) lived experiences of opioid and other substance use, (4) news reports or papers related to opioid and other substance use, (5) health care initiatives, (6) adverse effects on children's health due to parental substance use, and (7) topics related to nonopioid substance use. Among these 7 themes, our user engagement analysis revealed that themes 4 and 5 received the highest average retweet counts, and theme 3 received the highest average tweet reply count. We further found that different computational methods excel in analyzing different themes. Conclusions: Social media platforms such as X can serve as a valuable tool for analyzing real-time discourse and exploring public perceptions, opinions, and behaviors related to maternal substance use, particularly, harmful opioid use in the perinatal period. More health promotion strategies can be carried out on social media platforms to provide educational support for the OUD perinatal population. ", doi="10.2196/52735", url="https://pediatrics.jmir.org/2024/1/e52735", url="http://www.ncbi.nlm.nih.gov/pubmed/39374068" } @Article{info:doi/10.2196/47357, author="Thulin, J. Elyse and Walton, A. Maureen and Bonar, E. Erin and Fernandez, Anne", title="Examining the Popularity, Content, and Intersections With the Substance Abuse and Mental Health Services Administration's Definition of Recovery in a Nonclinical Online Cannabis Cessation Community: Infodemiology Study of Reddit Posts", journal="J Med Internet Res", year="2024", month="Sep", day="27", volume="26", pages="e47357", keywords="cannabis use disorder", keywords="online community", keywords="cannabis", keywords="human-computer interaction", keywords="mobile phone", abstract="Background: Cannabis consumption has increased in recent years, as has cannabis use disorder. While researchers have explored public online community discussions of active cannabis use, less is known about the popularity and content of publicly available online communities intended to support cannabis cessation. Objective: This study aims to examine the level of engagement and dominant content of an online community for cannabis cessation through 3 specific aims. First, we examine the use of a subreddit cannabis cessation community (r/leaves) over time to evaluate the popularity of this type of resource for individuals who want to stop using cannabis. Second, we examine the content of posts in the community to identify popular topics related to cessation. Third, we compare the thematic findings relative to the 4 domains of recovery defined by the Substance Abuse and Mental Health Services Administration (SAMHSA). By examining these 3 gaps, we take the initial steps toward understanding the experiences being shared online among individuals interested in cannabis cessation and compare them with the principles outlined in the SAMHSA definition of recovery. Methods: Using the Pushshift application programming interface, we collected the count of posts by year between 2011 and 2021 and the narrative of the 100 posts with the most comments per year in a popular cannabis cessation--focused subreddit (r/leaves). A linear model and a nonlinear model were compared to evaluate change in the number of posts by year. Mixed natural language processing and qualitative analyses were applied to identify top terms, phrases, and themes present in posts over time. Overlap between themes and the 4 SAMHSA domains of recovery (health, purpose, community, and home) were examined. Results: The number of annual posts in r/leaves increased from 420 in 2011 to 34,841 in 2021 (83-fold increase), with exponential growth since 2018. The term that was the most common across posts was ``smoke'' (2019 posts). Five major themes were identified, and a narrative arc was represented, from motivations and perceived benefits of cannabis use to the negative consequences of use, strategies to change behaviors, and the positive and negative consequences of change. There was substantial overlap between these 5 themes and 3 of SAMHSA's 4 domains of recovery: health, purpose, and community. However, the domain of home was less commonly identified. Conclusions: Engagement in this online cannabis support community appears to be increasing. Individuals using this forum discussed several topics, including multiple aspects of recovery defined by the SAMHSA. Online communities, such as this one may, serve as an important pathway for individuals seeking to reduce or cease their consumption of cannabis. ", doi="10.2196/47357", url="https://www.jmir.org/2024/1/e47357", url="http://www.ncbi.nlm.nih.gov/pubmed/39331460" } @Article{info:doi/10.2196/60678, author="Haupt, Robert Michael and Yang, Luning and Purnat, Tina and Mackey, Tim", title="Evaluating the Influence of Role-Playing Prompts on ChatGPT's Misinformation Detection Accuracy: Quantitative Study", journal="JMIR Infodemiology", year="2024", month="Sep", day="26", volume="4", pages="e60678", keywords="large language models", keywords="ChatGPT", keywords="artificial intelligence", keywords="AI", keywords="experiment", keywords="prompt engineering", keywords="role-playing", keywords="social identity", keywords="misinformation detection", keywords="COVID-19", abstract="Background: During the COVID-19 pandemic, the rapid spread of misinformation on social media created significant public health challenges. Large language models (LLMs), pretrained on extensive textual data, have shown potential in detecting misinformation, but their performance can be influenced by factors such as prompt engineering (ie, modifying LLM requests to assess changes in output). One form of prompt engineering is role-playing, where, upon request, OpenAI's ChatGPT imitates specific social roles or identities. This research examines how ChatGPT's accuracy in detecting COVID-19--related misinformation is affected when it is assigned social identities in the request prompt. Understanding how LLMs respond to different identity cues can inform messaging campaigns, ensuring effective use in public health communications. Objective: This study investigates the impact of role-playing prompts on ChatGPT's accuracy in detecting misinformation. This study also assesses differences in performance when misinformation is explicitly stated versus implied, based on contextual knowledge, and examines the reasoning given by ChatGPT for classification decisions. Methods: Overall, 36 real-world tweets about COVID-19 collected in September 2021 were categorized into misinformation, sentiment (opinions aligned vs unaligned with public health guidelines), corrections, and neutral reporting. ChatGPT was tested with prompts incorporating different combinations of multiple social identities (ie, political beliefs, education levels, locality, religiosity, and personality traits), resulting in 51,840 runs. Two control conditions were used to compare results: prompts with no identities and those including only political identity. Results: The findings reveal that including social identities in prompts reduces average detection accuracy, with a notable drop from 68.1\% (SD 41.2\%; no identities) to 29.3\% (SD 31.6\%; all identities included). Prompts with only political identity resulted in the lowest accuracy (19.2\%, SD 29.2\%). ChatGPT was also able to distinguish between sentiments expressing opinions not aligned with public health guidelines from misinformation making declarative statements. There were no consistent differences in performance between explicit and implicit misinformation requiring contextual knowledge. While the findings show that the inclusion of identities decreased detection accuracy, it remains uncertain whether ChatGPT adopts views aligned with social identities: when assigned a conservative identity, ChatGPT identified misinformation with nearly the same accuracy as it did when assigned a liberal identity. While political identity was mentioned most frequently in ChatGPT's explanations for its classification decisions, the rationales for classifications were inconsistent across study conditions, and contradictory explanations were provided in some instances. Conclusions: These results indicate that ChatGPT's ability to classify misinformation is negatively impacted when role-playing social identities, highlighting the complexity of integrating human biases and perspectives in LLMs. This points to the need for human oversight in the use of LLMs for misinformation detection. Further research is needed to understand how LLMs weigh social identities in prompt-based tasks and explore their application in different cultural contexts. ", doi="10.2196/60678", url="https://infodemiology.jmir.org/2024/1/e60678" } @Article{info:doi/10.2196/53899, author="Schneller-Najm, M. Liane and Xie, Zidian and Chen, Jiarui and Lee, Sarah and Xu, Emily and Li, Dongmei", title="Public Perception of the Tobacco 21 Amendment on Twitter in the United States: Observational Study", journal="JMIR Infodemiology", year="2024", month="Sep", day="25", volume="4", pages="e53899", keywords="tobacco policy", keywords="tobacco regulation", keywords="social media", keywords="tobacco use", keywords="tobacco", keywords="health belief", keywords="sentiment analysis", keywords="smoking", keywords="cigarettes", keywords="social media analysis", keywords="vaping", keywords="e-cigarettes", keywords="health behavior", keywords="public opinion", abstract="Background: Following the signing of the Tobacco 21 Amendment (T21) in December 2019 to raise the minimum legal age for the sale of tobacco products from 18 to 21 years in the United States, there is a need to monitor public responses and potential unintended consequences. Social media platforms, such as Twitter (subsequently rebranded as X), can provide rich data on public perceptions. Objective: This study contributes to the literature using Twitter data to assess the knowledge and beliefs of T21. Methods: Twitter data were collected from November 2019 to February 2021 using the Twitter streaming application programming interface with keywords related to vaping or e-cigarettes, such as ``vape,'' ``ecig,'' etc. The temporal trend of the T21 discussion on Twitter was examined using the mean number of daily T21-related tweets. Inductive methods were used to manually code the tweets into different sentiment groups (positive, neutral, and negative) based on the attitude expressed toward the policy by 3 coders with high interrater reliability. Topics discussed were examined within each sentiment group through theme analyses. Results: Among the collected 3197 tweets, 2169 tweets were related to T21, of which 444 tweets (20.5\%) showed a positive attitude, 736 (33.9\%) showed a negative attitude, and 989 (45.6\%) showed a neutral attitude. The temporal trend showed a clear peak in the number of tweets around January 2020, following the enactment of this legislation. For positive tweets, the most frequent topics were ``avoidance of further regulation'' (120/444, 27\%), ``Enforce T21'' (110/444, 24.8\%), and ``health benefits'' (81/444, 18.2\%). For negative tweets, the most frequent topics were ``general disagreement or frustration'' (207/736, 28.1\%) and ``will still use tobacco'' (188/736, 25.5\%). Neutral tweets were primarily ``public service announcements (PSA) or news posts'' (782/989, 79.1\%). Conclusions: Overall, we find that one-third of tweets displayed a negative attitude toward T21 during the study period. Many were frustrated with T21 and reported that underage consumers could still obtain products. Social media data provide a timely opportunity to monitor public perceptions and responses to regulatory actions. Continued monitoring can inform enforcement efforts and potential unintended consequences of T21. ", doi="10.2196/53899", url="https://infodemiology.jmir.org/2024/1/e53899" } @Article{info:doi/10.2196/53171, author="Wu, Dezhi and Shead, Hannah and Ren, Yang and Raynor, Phyllis and Tao, Youyou and Villanueva, Harvey and Hung, Peiyin and Li, Xiaoming and Brookshire, G. Robert and Eichelberger, Kacey and Guille, Constance and Litwin, H. Alain and Olatosi, Bankole", title="Uncovering the Complexity of Perinatal Polysubstance Use Disclosure Patterns on X: Mixed Methods Study", journal="J Med Internet Res", year="2024", month="Sep", day="20", volume="26", pages="e53171", keywords="polysubstance use", keywords="prenatal care", keywords="perinatal care", keywords="pregnant care", keywords="social media", keywords="Twitter", keywords="sentiment analysis", abstract="Background: According to the Morbidity and Mortality Weekly Report, polysubstance use among pregnant women is prevalent, with 38.2\% of those who consume alcohol also engaging in the use of one or more additional substances. However, the underlying mechanisms, contexts, and experiences of polysubstance use are unclear. Organic information is abundant on social media such as X (formerly Twitter). Traditional quantitative and qualitative methods, as well as natural language processing techniques, can be jointly used to derive insights into public opinions, sentiments, and clinical and public health policy implications. Objective: Based on perinatal polysubstance use (PPU) data that we extracted on X from May 1, 2019, to October 31, 2021, we proposed two primary research questions: (1) What is the overall trend and sentiment of PPU discussions on X? (2) Are there any distinct patterns in the discussion trends of PPU-related tweets? If so, what are the implications for perinatal care and associated public health policies? Methods: We used X's application programming interface to extract >6 million raw tweets worldwide containing ?2 prenatal health- and substance-related keywords provided by our clinical team. After removing all non--English-language tweets, non-US tweets, and US tweets without disclosed geolocations, we obtained 4848 PPU-related US tweets. We then evaluated them using a mixed methods approach. The quantitative analysis applied frequency, trend analysis, and several natural language processing techniques such as sentiment analysis to derive statistics to preview the corpus. To further understand semantics and clinical insights among these tweets, we conducted an in-depth thematic content analysis with a random sample of 500 PPU-related tweets with a satisfying $\kappa$ score of 0.7748 for intercoder reliability. Results: Our quantitative analysis indicates the overall trends, bigram and trigram patterns, and negative sentiments were more dominant in PPU tweets (2490/4848, 51.36\%) than in the non-PPU sample (1323/4848, 27.29\%). Paired polysubstance use (4134/4848, 85.27\%) was the most common, with the combination alcohol and drugs identified as the most mentioned. From the qualitative analysis, we identified 3 main themes: nonsubstance, single substance, and polysubstance, and 4 subthemes to contextualize the rationale of underlying PPU behaviors: lifestyle, perceptions of others' drug use, legal implications, and public health. Conclusions: This study identified underexplored, emerging, and important topics related to perinatal PPU, with significant stigmas and legal ramifications discussed on X. Overall, public sentiments on PPU were mixed, encompassing negative (2490/4848, 51.36\%), positive (1884/4848, 38.86\%), and neutral (474/4848, 9.78\%) sentiments. The leading substances in PPU were alcohol and drugs, and the normalization of PPU discussed on X is becoming more prevalent. Thus, this study provides valuable insights to further understand the complexity of PPU and its implications for public health practitioners and policy makers to provide proper access and support to individuals with PPU. ", doi="10.2196/53171", url="https://www.jmir.org/2024/1/e53171" } @Article{info:doi/10.2196/58378, author="Chimukuche, Samanthia Rujeko and Ndlazi, Julia and Mtolo, Thembani Lucky and Bird, Kristien and Seeley, Janet", title="Evaluation of Drug and Herbal Medicinal Promotions on Social Media During the COVID-19 Pandemic in Relation to World Health Organization Ethical Criteria and South African Health Products Regulatory Authority Guidelines in South Africa: Cross-Sectional Content Analysis", journal="Online J Public Health Inform", year="2024", month="Sep", day="18", volume="16", pages="e58378", keywords="drug advertising", keywords="internet", keywords="social media", keywords="ethical guidelines", keywords="traditional medicine", keywords="COVID-19", abstract="Background: Consideration of ethics in the promotion of medications is essential to safeguard the health of consumers, particularly during health crises. The World Health Organization (WHO) and the South African Health Products Regulatory Authority (SAHPRA) have established stringent standards to ensure the integrity of pharmaceutical promotions and safeguard public health, including advertisements on the internet and social media platforms. However, the dynamic nature of online advertising poses challenges for monitoring and enforcing ethical standards. Objective: The study aimed (1) to examine the COVID-19 drug and medicinal promotions across online platforms and social media from 2020 to 2022 in South Africa and (2) to ensure that drug promotions adhere to ethical guidelines outlined by the WHO and SAPHRA. Methods: A cross-sectional content analysis was conducted to assess drug and medicinal advertisements across various internet and social media platforms. A systematic approach was used to identify and analyze promotional content, focusing on adherence to ethical guidelines outlined by WHO and SAPHRA. Data were collected and analyzed to determine the extent of compliance and identify any potential violations or areas for improvement. Results: A total of 14 online drug advertisements were included in this analysis. Our findings show that most of the drugs advertised did not meet the regulations and guidelines provided by WHO and SAHPRA. There were omissions about active ingredients, proprietary names, adverse drug responses, precautions, and overdosage and adverse drug reactions. Traditional medicines were not fully consistent with the approved WHO ethical criteria data sheet. Conclusions: Our analysis highlights the critical importance of ensuring compliance with ethical guidelines in drug promotions on the internet and social media platforms. There is a need for continued vigilance and enforcement efforts to uphold ethical standards and protect the health of the public. Ongoing monitoring and collaboration between national drug regulatory agencies, pharmaceutical companies, and online platforms will be essential for promoting responsible advertising. In addition, safety monitoring and pharmacovigilance systems for herbal medicinal products are yet to be established. ", doi="10.2196/58378", url="https://ojphi.jmir.org/2024/1/e58378", url="http://www.ncbi.nlm.nih.gov/pubmed/39293046" } @Article{info:doi/10.2196/56854, author="Ho, S. Shirley and Chuah, F. Agnes S. and Ho, S. Vanessa and Rosenthal, Sonny and Kim, Kyung Hye and Soh, H. Shannon S.", title="Crisis and Emergency Risk Communication and Emotional Appeals in COVID-19 Public Health Messaging: Quantitative Content Analysis", journal="J Med Internet Res", year="2024", month="Sep", day="17", volume="26", pages="e56854", keywords="COVID-19", keywords="crisis and emergency risk communication", keywords="CERC", keywords="emotional appeal", keywords="content analysis", keywords="public health", keywords="Facebook", keywords="social media", keywords="Singapore", abstract="Background: Although COVID-19 is no longer a global health emergency, it remains pervasive in Singapore, a city-state situated in Southeast Asia, with periodic waves of infection. In addition to disease management, strong communication strategies are critical in the government's response to the pandemic to keep the public updated and equip them in protecting themselves. Objective: Grounded in the crisis and emergency risk communication (CERC) framework and emotional appeals, this study aimed to analyze public health communication strategies in Singapore during the COVID-19 pandemic. Methods: Quantitative content analysis was conducted on 696 Facebook (Meta Platforms Inc) posts and 83 website articles published by Singapore-based public health institutions between January 2020 and September 2022. Results: The results showed that increasing communication on message themes, such as inquisitive messaging and clarification, can enhance communication strategies. The use of emotional appeals also varies with time and should be carefully used as they are context-specific. Conclusions: Theoretically, this study contributes to advancements in the CERC framework and concepts of emotional appeals by exploring the applications and changes of CERC message types and emotional appeals at different phases. The findings can provide practical guidance for authorities and communication practitioners in developing effective communication strategies. ", doi="10.2196/56854", url="https://www.jmir.org/2024/1/e56854" } @Article{info:doi/10.2196/51156, author="Almeida, Alexandra and Patton, Thomas and Conway, Mike and Gupta, Amarnath and Strathdee, A. Steffanie and B{\'o}rquez, Annick", title="The Use of Natural Language Processing Methods in Reddit to Investigate Opioid Use: Scoping Review", journal="JMIR Infodemiology", year="2024", month="Sep", day="13", volume="4", pages="e51156", keywords="opioid", keywords="Reddit", keywords="natural language processing", keywords="NLP", keywords="machine learning", abstract="Background: The growing availability of big data spontaneously generated by social media platforms allows us to leverage natural language processing (NLP) methods as valuable tools to understand the opioid crisis. Objective: We aimed to understand how NLP has been applied to Reddit (Reddit Inc) data to study opioid use. Methods: We systematically searched for peer-reviewed studies and conference abstracts in PubMed, Scopus, PsycINFO, ACL Anthology, IEEE Xplore, and Association for Computing Machinery data repositories up to July 19, 2022. Inclusion criteria were studies investigating opioid use, using NLP techniques to analyze the textual corpora, and using Reddit as the social media data source. We were specifically interested in mapping studies' overarching goals and findings, methodologies and software used, and main limitations. Results: In total, 30 studies were included, which were classified into 4 nonmutually exclusive overarching goal categories: methodological (n=6, 20\% studies), infodemiology (n=22, 73\% studies), infoveillance (n=7, 23\% studies), and pharmacovigilance (n=3, 10\% studies). NLP methods were used to identify content relevant to opioid use among vast quantities of textual data, to establish potential relationships between opioid use patterns or profiles and contextual factors or comorbidities, and to anticipate individuals' transitions between different opioid-related subreddits, likely revealing progression through opioid use stages. Most studies used an embedding technique (12/30, 40\%), prediction or classification approach (12/30, 40\%), topic modeling (9/30, 30\%), and sentiment analysis (6/30, 20\%). The most frequently used programming languages were Python (20/30, 67\%) and R (2/30, 7\%). Among the studies that reported limitations (20/30, 67\%), the most cited was the uncertainty regarding whether redditors participating in these forums were representative of people who use opioids (8/20, 40\%). The papers were very recent (28/30, 93\%), from 2019 to 2022, with authors from a range of disciplines. Conclusions: This scoping review identified a wide variety of NLP techniques and applications used to support surveillance and social media interventions addressing the opioid crisis. Despite the clear potential of these methods to enable the identification of opioid-relevant content in Reddit and its analysis, there are limits to the degree of interpretive meaning that they can provide. Moreover, we identified the need for standardized ethical guidelines to govern the use of Reddit data to safeguard the anonymity and privacy of people using these forums. ", doi="10.2196/51156", url="https://infodemiology.jmir.org/2024/1/e51156" } @Article{info:doi/10.2196/48257, author="Alasmari, Ashwag and Zhou, Lina", title="Quality Measurement of Consumer Health Questions: Content and Language Perspectives", journal="J Med Internet Res", year="2024", month="Sep", day="12", volume="26", pages="e48257", keywords="question quality", keywords="quality measurement", keywords="health questions", keywords="", keywords="information needs", keywords="information behavior", keywords="information sharing", keywords="consumer", keywords="health information", keywords="health information consumers", keywords="quality", abstract="Background: Health information consumers increasingly rely on question-and-answer (Q\&A) communities to address their health concerns. However, the quality of questions posted significantly impacts the likelihood and relevance of received answers. Objective: This study aims to improve our understanding of the quality of health questions within web-based Q\&A communities. Methods: We develop a novel framework for defining and measuring question quality within web-based health communities, incorporating content- and language-based variables. This framework leverages k-means clustering and establishes automated metrics to assess overall question quality. To validate our framework, we analyze questions related to kidney disease from expert-curated and community-based Q\&A platforms. Expert evaluations confirm the validity of our quality construct, while regression analysis helps identify key variables. Results: High-quality questions were more likely to include demographic and medical information than lower-quality questions (P<.001). In contrast, asking questions at the various stages of disease development was less likely to reflect high-quality questions (P<.001). Low-quality questions were generally shorter with lengthier sentences than high-quality questions (P<.01). Conclusions: Our findings empower consumers to formulate more effective health information questions, ultimately leading to better engagement and more valuable insights within web-based Q\&A communities. Furthermore, our findings provide valuable insights for platform developers and moderators seeking to enhance the quality of user interactions and foster a more trustworthy and informative environment for health information exchange. ", doi="10.2196/48257", url="https://www.jmir.org/2024/1/e48257" } @Article{info:doi/10.2196/47562, author="Fari{\v c}, Nu{\vs}a and Potts, WW Henry and Heilman, M. James", title="Quality of Male and Female Medical Content on English-Language Wikipedia: Quantitative Content Analysis", journal="J Med Internet Res", year="2024", month="Sep", day="12", volume="26", pages="e47562", keywords="Wikipedia", keywords="wikis", keywords="writing", keywords="internet", keywords="health information", keywords="sex", keywords="sex bias", keywords="consumer health information", keywords="health communication", keywords="public education", keywords="social media", abstract="Background: Wikipedia is the largest free online encyclopedia and the seventh most visited website worldwide, containing >45,000 freely accessible English-language medical articles accessed nearly 1.6 billion times annually. Concerns have been expressed about the balance of content related to biological sex on Wikipedia. Objective: This study aims to categorize the top 1000 most-read (most popular) English-language Wikipedia health articles for June 2019 according to the relevance of the article topic to each sex and quality. Methods: In the first step, Wikipedia articles were identified using WikiProject Medicine Popular Pages. These were analyzed on 13 factors, including total views, article quality, and total number of references. In the second step, 2 general medical textbooks were used as comparators to assess whether Wikipedia's spread of articles was typical compared to the general medical coverage. According to the article's content, we proposed criteria with 5 categories: 1=``exclusively female,'' 2=``predominantly female but can also affect male individuals,'' 3=``not sex specific or neutral,'' 4=predominantly male but can affect female individuals,'' and 5=``exclusively male.'' Results: Of the 1000 Wikipedia health articles, 933 (93.3\%) were not sex specific and 67 (6.7\%) were sex specific. There was no statistically significant difference in the number of reads per month between the sex-specific and non--sex-specific articles (P=.29). Coverage of female topics was higher (50/1000, 5\%) than male topics (17/1000, 1.7\%; this difference was also observed for the 2 medical textbooks, in which 90.2\% (2330/2584) of content was not sex specific, female topics accounted for 8.1\% (209/2584), and male topics for accounted for 1.7\% (45/2584; statistically significant difference; Fisher exact test P=.03). Female-category articles were ranked higher on the Wikipedia medical topic importance list (top, high, or mid importance) than male-category articles (borderline statistical significance; Fisher exact test P=.05). Female articles had a higher number of total and unique references; a slightly higher number of page watchers, pictures, and available languages; and lower number of edits than male articles (all were statistically nonsignificant). Conclusions: Across several metrics, a sample of popular Wikipedia health-related articles for both sexes had comparable quality. Wikipedia had a lower number of female articles and a higher number of neutral articles relative to the 2 medical textbooks. These differences were small, but statistically significant. Higher exclusively female coverage, compared to exclusively male coverage, in Wikipedia articles was similar to the 2 medical textbooks and can be explained by inclusion of sections on obstetrics and gynecology. This is unlike the imbalance seen among biographies of living people, in which approximately 77.6\% pertain to male individuals. Although this study included a small sample of articles, the spread of Wikipedia articles may reflect the readership and the population's content consumption at a given time. Further study of a larger sample of Wikipedia articles would be valuable. ", doi="10.2196/47562", url="https://www.jmir.org/2024/1/e47562" } @Article{info:doi/10.2196/55591, author="Jung, Sungwon and Murthy, Dhiraj and Bateineh, S. Bara and Loukas, Alexandra and Wilkinson, V. Anna", title="The Normalization of Vaping on TikTok Using Computer Vision, Natural Language Processing, and Qualitative Thematic Analysis: Mixed Methods Study", journal="J Med Internet Res", year="2024", month="Sep", day="11", volume="26", pages="e55591", keywords="electronic cigarettes", keywords="vaping", keywords="social media", keywords="natural language processing", keywords="computer vision", abstract="Background: Social media posts that portray vaping in positive social contexts shape people's perceptions and serve to normalize vaping. Despite restrictions on depicting or promoting controlled substances, vape-related content is easily accessible on TikTok. There is a need to understand strategies used in promoting vaping on TikTok, especially among susceptible youth audiences. Objective: This study seeks to comprehensively describe direct (ie, explicit promotional efforts) and indirect (ie, subtler strategies) themes promoting vaping on TikTok using a mixture of computational and qualitative thematic analyses of social media posts. In addition, we aim to describe how these themes might play a role in normalizing vaping behavior on TikTok for youth audiences, thereby informing public health communication and regulatory policies regarding vaping endorsements on TikTok. Methods: We collected 14,002 unique TikTok posts using 50 vape-related hashtags (eg, \#vapetok and \#boxmod). Using the k-means unsupervised machine learning algorithm, we identified clusters and then categorized posts qualitatively based on themes. Next, we organized all videos from the posts thematically and extracted the visual features of each theme using 3 machine learning--based model architectures: residual network (ResNet) with 50 layers (ResNet50), Visual Geometry Group model with 16 layers, and vision transformer. We chose the best-performing model, ResNet50, to thoroughly analyze the image clustering output. To assess clustering accuracy, we examined 4.01\% (441/10,990) of the samples from each video cluster. Finally, we randomly selected 50 videos (5\% of the total videos) from each theme, which were qualitatively coded and compared with the machine-derived classification for validation. Results: We successfully identified 5 major themes from the TikTok posts. Vape product marketing (1160/10,990, 8.28\%) reflected direct marketing, while the other 4 themes reflected indirect marketing: TikTok influencer (3775/14,002, 26.96\%), general vape (2741/14,002, 19.58\%), vape brands (2042/14,002, 14.58\%), and vaping cessation (1272/14,002, 9.08\%). The ResNet50 model successfully classified clusters based on image features, achieving an average F1-score of 0.97, the highest among the 3 models. Qualitative content analyses indicated that vaping was depicted as a normal, routine part of daily life, with TikTok influencers subtly incorporating vaping into popular culture (eg, gaming, skateboarding, and tattooing) and social practices (eg, shopping sprees, driving, and grocery shopping). Conclusions: The results from both computational and qualitative analyses of text and visual data reveal that vaping is normalized on TikTok. Our identified themes underscore how everyday conversations, promotional content, and the influence of popular figures collectively contribute to depicting vaping as a normal and accepted aspect of daily life on TikTok. Our study provides valuable insights for regulatory policies and public health initiatives aimed at tackling the normalization of vaping on social media platforms. ", doi="10.2196/55591", url="https://www.jmir.org/2024/1/e55591" } @Article{info:doi/10.2196/53050, author="Wei, Hanxue and Hswen, Yulin and Merchant, S. Junaid and Drew, B. Laura and Nguyen, C. Quynh and Yue, Xiaohe and Mane, Heran and Nguyen, T. Thu", title="From Tweets to Streets: Observational Study on the Association Between Twitter Sentiment and Anti-Asian Hate Crimes in New York City from 2019 to 2022", journal="J Med Internet Res", year="2024", month="Sep", day="9", volume="26", pages="e53050", keywords="anti-Asian", keywords="hate crime", keywords="Twitter", keywords="racism", keywords="social media, machine learning, sentiment analysis", abstract="Background: Anti-Asian hate crimes escalated during the COVID-19 pandemic; however, limited research has explored the association between social media sentiment and hate crimes toward Asian communities. Objective: This study aims to investigate the relationship between Twitter (rebranded as X) sentiment data and the occurrence of anti-Asian hate crimes in New York City from 2019 to 2022, a period encompassing both before and during COVID-19 pandemic conditions. Methods: We used a hate crime dataset from the New York City Police Department. This dataset included detailed information on the occurrence of anti-Asian hate crimes at the police precinct level from 2019 to 2022. We used Twitter's application programming interface for Academic Research to collect a random 1\% sample of publicly available Twitter data in New York State, including New York City, that included 1 or more of the selected Asian-related keywords and applied support vector machine to classify sentiment. We measured sentiment toward the Asian community using the rates of negative and positive sentiment expressed in tweets at the monthly level (N=48). We used negative binomial models to explore the associations between sentiment levels and the number of anti-Asian hate crimes in the same month. We further adjusted our models for confounders such as the unemployment rate and the emergence of the COVID-19 pandemic. As sensitivity analyses, we used distributed lag models to capture 1- to 2-month lag times. Results: A point increase of 1\% in negative sentiment rate toward the Asian community in the same month was associated with a 24\% increase (incidence rate ratio [IRR] 1.24; 95\% CI 1.07-1.44; P=.005) in the number of anti-Asian hate crimes. The association was slightly attenuated after adjusting for unemployment and COVID-19 emergence (ie, after March 2020; P=.008). The positive sentiment toward Asian tweets with a 0-month lag was associated with a 12\% decrease (IRR 0.88; 95\% CI 0.79-0.97; P=.002) in expected anti-Asian hate crimes in the same month, but the relationship was no longer significant after adjusting for the unemployment rate and the emergence of COVID-19 pandemic (P=.11). Conclusions: A higher negative sentiment level was associated with more hate crimes specifically targeting the Asian community in the same month. The findings highlight the importance of monitoring public sentiment to predict and potentially mitigate hate crimes against Asian individuals. ", doi="10.2196/53050", url="https://www.jmir.org/2024/1/e53050", url="http://www.ncbi.nlm.nih.gov/pubmed/39250221" } @Article{info:doi/10.2196/46531, author="Zahroh, Islamiah Rana and Cheong, Marc and Hazfiarini, Alya and Vazquez Corona, Martha and Ekawati, Murriya Fitriana and Emilia, Ova and Homer, SE Caroline and Betr{\'a}n, Pilar Ana and Bohren, A. Meghan", title="The Portrayal of Cesarean Section on Instagram: Mixed Methods Social Media Analysis", journal="JMIR Form Res", year="2024", month="Sep", day="6", volume="8", pages="e46531", keywords="cesarean section", keywords="social media analysis", keywords="maternal health", keywords="childbirth", keywords="mode of birth", keywords="instagram", abstract="Background: Cesarean section (CS) rates in Indonesia are rapidly increasing for both sociocultural and medical reasons. However, there is limited understanding of the role that social media plays in influencing preferences regarding mode of birth (vaginal or CS). Social media provides a platform for users to seek and exchange information, including information on the mode of birth, which may help unpack social influences on health behavior. Objective: This study aims to explore how CS is portrayed on Instagram in Indonesia. Methods: We downloaded public Instagram posts from Indonesia containing CS hashtags and extracted their attributes (image, caption, hashtags, and objects and texts within images). Posts were divided into 2 periods---before COVID-19 and during COVID-19---to examine changes in CS portrayal during the pandemic. We used a mixed methods approach to analysis using text mining, descriptive statistics, and qualitative content analysis. Results: A total of 9978 posts were analyzed quantitatively, and 720 (7.22\%) posts were sampled and analyzed qualitatively. The use of text (527/5913, 8.91\% vs 242/4065, 5.95\%; P<.001) and advertisement materials (411/5913, 6.95\% vs 83/4065, 2.04\%; P<.001) increased during the COVID-19 pandemic compared to before the pandemic, indicating growth of information sharing on CS over time. Posts with CS hashtags primarily promoted herbal medicine for faster recovery and services for choosing auspicious childbirth dates, encouraging elective CS. Some private health facilities offered discounts on CS for special events such as Mother's Day and promoted techniques such as enhanced recovery after CS for comfortable, painless birth, and faster recovery after CS. Hashtags related to comfortable or painless birth (2358/5913, 39.88\% vs 278/4065, 6.84\%; P<.001), enhanced recovery after CS (124/5913, 2.1\% vs 0\%; P<.001), feng shui services (110/5913, 1.86\% vs 56/4065, 1.38\%; P=.03), names of health care providers (2974/5913, 50.3\% vs 304/4065, 7.48\%; P<.001), and names of hospitals (1460/5913, 24.69\% vs 917/4065, 22.56\%; P=.007) were more prominent during compared to before the pandemic. Conclusions: This study highlights the necessity of enforcing advertisement regulations regarding birth-related medical services in the commercial and private sectors. Enhanced health promotion efforts are crucial to ensure that women receive accurate, balanced, and appropriate information about birth options. Continuous and proactive health information dissemination from government organizations is essential to counteract biases favoring CS over vaginal birth. ", doi="10.2196/46531", url="https://formative.jmir.org/2024/1/e46531" } @Article{info:doi/10.2196/45858, author="Necaise, Aaron and Amon, Jean Mary", title="Peer Support for Chronic Pain in Online Health Communities: Quantitative Study on the Dynamics of Social Interactions in a Chronic Pain Forum", journal="J Med Internet Res", year="2024", month="Sep", day="5", volume="26", pages="e45858", keywords="social media", keywords="chronic pain", keywords="peer support", keywords="sentiment analysis", keywords="wavelet analysis", keywords="nonlinear dynamics", keywords="growth curve modeling", keywords="online health communities", keywords="affective synchrony", abstract="Background: Peer support for chronic pain is increasingly taking place on social media via social networking communities. Several theories on the development and maintenance of chronic pain highlight how rumination, catastrophizing, and negative social interactions can contribute to poor health outcomes. However, little is known regarding the role web-based health discussions play in the development of negative versus positive health attitudes relevant to chronic pain. Objective: This study aims to investigate how participation in online peer-to-peer support communities influenced pain expressions by examining how the sentiment of user language evolved in response to peer interactions. Methods: We collected the comment histories of 199 randomly sampled Reddit (Reddit, Inc) users who were active in a popular peer-to-peer chronic pain support community over 10 years. A total of 2 separate natural language processing methods were compared to calculate the sentiment of user comments on the forum (N=73,876). We then modeled the trajectories of users' language sentiment using mixed-effects growth curve modeling and measured the degree to which users affectively synchronized with their peers using bivariate wavelet analysis. Results: In comparison to a shuffled baseline, we found evidence that users entrained their language sentiment to match the language of community members they interacted with (t198=4.02; P<.001; Cohen d=0.40). This synchrony was most apparent in low-frequency sentiment changes unfolding over hundreds of interactions as opposed to reactionary changes occurring from comment to comment (F2,198=17.70; P<.001). We also observed a significant trend in sentiment across all users ($\beta$=--.02; P=.003), with users increasingly using more negative language as they continued to interact with the community. Notably, there was a significant interaction between affective synchrony and community tenure ($\beta$=.02; P=.02), such that greater affective synchrony was associated with negative sentiment trajectories among short-term users and positive sentiment trajectories among long-term users. Conclusions: Our results are consistent with the social communication model of pain, which describes how social interactions can influence the expression of pain symptoms. The difference in long-term versus short-term affective synchrony observed between community members suggests a process of emotional coregulation and social learning. Participating in health discussions on Reddit appears to be associated with both negative and positive changes in sentiment depending on how individual users interacted with their peers. Thus, in addition to characterizing the sentiment dynamics existing within online chronic pain communities, our work provides insight into the potential benefits and drawbacks of relying on support communities organized on social media platforms. ", doi="10.2196/45858", url="https://www.jmir.org/2024/1/e45858", url="http://www.ncbi.nlm.nih.gov/pubmed/39235845" } @Article{info:doi/10.2196/54874, author="Yan, XiangYu and Li, Zhuo and Cao, Chunxia and Huang, Longxin and Li, Yongjie and Meng, Xiangbin and Zhang, Bo and Yu, Maohe and Huang, Tian and Chen, Jiancheng and Li, Wei and Hao, Linhui and Huang, Dongsheng and Yi, Bin and Zhang, Ming and Zha, Shun and Yang, Haijun and Yao, Jian and Qian, Pengjiang and Leung, Kai Chun and Fan, Haojun and Jiang, Pei and Shui, Tiejun", title="Characteristics, Influence, Prevention, and Control Measures of the Mpox Infodemic: Scoping Review of Infodemiology Studies", journal="J Med Internet Res", year="2024", month="Aug", day="30", volume="26", pages="e54874", keywords="mpox", keywords="infodemic", keywords="infodemiology", keywords="information search volume", keywords="content topic", keywords="digital health", abstract="Background: The mpox pandemic has caused widespread public concern around the world. The spread of misinformation through the internet and social media could lead to an infodemic that poses challenges to mpox control. Objective: This review aims to summarize mpox-related infodemiology studies to determine the characteristics, influence, prevention, and control measures of the mpox infodemic and propose prospects for future research. Methods: The scoping review was conducted based on a structured 5-step methodological framework. A comprehensive search for mpox-related infodemiology studies was performed using PubMed, Web of Science, Embase, and Scopus, with searches completed by April 30, 2024. After study selection and data extraction, the main topics of the mpox infodemic were categorized and summarized in 4 aspects, including a trend analysis of online information search volume, content topics of mpox-related online posts and comments, emotional and sentiment characteristics of online content, and prevention and control measures for the mpox infodemic. Results: A total of 1607 articles were retrieved from the databases according to the keywords, and 61 studies were included in the final analysis. After the World Health Organization's declaration of an mpox public health emergency of international concern in July 2022, the number of related studies began growing rapidly. Google was the most widely used search engine platform (9/61, 15\%), and Twitter was the most used social media app (32/61, 52\%) for researchers. Researchers from 33 countries were concerned about mpox infodemic--related topics. Among them, the top 3 countries for article publication were the United States (27 studies), India (9 studies), and the United Kingdom (7 studies). Studies of online information search trends showed that mpox-related online search volume skyrocketed at the beginning of the mpox outbreak, especially when the World Health Organization provided important declarations. There was a large amount of misinformation with negative sentiment and discriminatory and hostile content against gay, bisexual, and other men who have sex with men. Given the characteristics of the mpox infodemic, the studies provided several positive prevention and control measures, including the timely and active publishing of professional, high-quality, and easy-to-understand information online; strengthening surveillance and early warning for the infodemic based on internet data; and taking measures to protect key populations from the harm of the mpox infodemic. Conclusions: This comprehensive summary of evidence from previous mpox infodemiology studies is valuable for understanding the characteristics of the mpox infodemic and for formulating prevention and control measures. It is essential for researchers and policy makers to establish prediction and early warning approaches and targeted intervention methods for dealing with the mpox infodemic in the future. ", doi="10.2196/54874", url="https://www.jmir.org/2024/1/e54874" } @Article{info:doi/10.2196/48389, author="Haddad, Firas and Abou Shahla, William and Saade, Dana", title="Investigating Topical Steroid Withdrawal Videos on TikTok: Cross-Sectional Analysis of the Top 100 Videos", journal="JMIR Form Res", year="2024", month="Aug", day="29", volume="8", pages="e48389", keywords="steroid withdrawal", keywords="medical dermatology", keywords="drug response", keywords="social media", keywords="videos", keywords="TikTok", keywords="steroids", keywords="content analysis", keywords="information quality", keywords="skin", keywords="topical", keywords="dermatology", keywords="misinformation", abstract="Background: Social media platforms like TikTok are a very popular source of information, especially for skin diseases. Topical steroid withdrawal (TSW) is a condition that is yet to be fully defined and understood. This did not stop the hashtag \#topicalsteroidwithdrawal from amassing more than 600 million views on TikTok. It is of utmost importance to assess the quality and content of TikTok videos on TSW to prevent the spread of misinformation. Objective: This study aims to assess the quality and content of the top 100 videos dedicated to the topic of TSW on TikTok. Methods: This observational study assesses the content and quality of the top 100 videos about TSW on TikTok. A total of 3 independent scoring systems: DISCERN, Journal of the American Medical Association, and Global Quality Scale were used to assess the video quality. The content of the videos was coded by 2 reviewers and analyzed for recurrent themes and topics. Results: This study found that only 10.0\% (n=10) of the videos clearly defined what TSW is. Videos were predominantly posted by White, middle-aged, and female creators. Neither cause nor mechanism of the disease were described in the videos. The symptoms suggested itching, peeling, and dryness which resembled the symptoms of atopic dermatitis. The videos fail to mention important information regarding the use of steroids such as the reason it was initially prescribed, the name of the drug, concentration, mechanism of usage, and method of discontinuation. Management techniques varied from hydration methods approved for treatment of atopic dermatitis to treatment options without scientific evidence. Overall, the videos had immense reach with over 200 million views, 45 million likes, 90,000 comments, and 100,000 shares. Video quality was poor with an average DISCERN score of 1.63 (SD 0.56)/5. Video length, total view count, and views/day were all associated with increased quality, indicating that patients were interacting more with higher quality videos. However, videos were created exclusively by personal accounts, highlighting the absence of dermatologists on the platform to discuss this topic. Conclusions: The videos posted on TikTok are of low quality and lack pertinent information. The content is varied and not consistent. Health care professionals, including dermatologists and residents in the field, need to be more active on the topic, to spread proper information and prevent an increase in steroid phobia. Health care professionals are encouraged to ride the wave and produce high-quality videos discussing what is known about TSW to avoid the spread of misinformation. ", doi="10.2196/48389", url="https://formative.jmir.org/2024/1/e48389" } @Article{info:doi/10.2196/51328, author="Watson, Sara and Benning, J. Tyler and Marcon, R. Alessandro and Zhu, Xuan and Caulfield, Timothy and Sharp, R. Richard and Master, Zubin", title="Descriptions of Scientific Evidence and Uncertainty of Unproven COVID-19 Therapies in US News: Content Analysis Study", journal="JMIR Infodemiology", year="2024", month="Aug", day="29", volume="4", pages="e51328", keywords="COVID-19", keywords="COVID-19 drug treatment", keywords="information dissemination", keywords="health communication", keywords="uncertainty", keywords="content analysis", keywords="information sources", keywords="therapy", keywords="misinformation", keywords="communication", keywords="scientific evidence", keywords="media analysis", keywords="news report", keywords="COVID-19 therapy", keywords="treatment", keywords="public awareness", keywords="public trepidation", keywords="therapeutic", keywords="therapeutics", keywords="vaccine", keywords="vaccines", keywords="pandemic", keywords="United States", keywords="safety", keywords="efficacy", keywords="evidence", keywords="news", keywords="report", keywords="reports", abstract="Background: Politicization and misinformation or disinformation of unproven COVID-19 therapies have resulted in communication challenges in presenting science to the public, especially in times of heightened public trepidation and uncertainty. Objective: This study aims to examine how scientific evidence and uncertainty were portrayed in US news on 3 unproven COVID-19 therapeutics, prior to the development of proven therapeutics and vaccines. Methods: We conducted a media analysis of unproven COVID-19 therapeutics in early 2020. A total of 479 discussions of unproven COVID-19 therapeutics (hydroxychloroquine, remdesivir, and convalescent plasma) in traditional and online US news reports from January 1, 2020, to July 30, 2020, were systematically analyzed for theme, scientific evidence, evidence details and limitations, safety, efficacy, and sources of authority. Results: The majority of discussions included scientific evidence (n=322, 67\%) although only 24\% (n=116) of them mentioned publications. ``Government'' was the most frequently named source of authority for safety and efficacy claims on remdesivir (n=43, 35\%) while ``expert'' claims were mostly mentioned for convalescent plasma (n=22, 38\%). Most claims on hydroxychloroquine (n=236, 79\%) were offered by a ``prominent person,'' of which 97\% (n=230) were from former US President Trump. Despite the inclusion of scientific evidence, many claims of the safety and efficacy were made by nonexperts. Few news reports expressed scientific uncertainty in discussions of unproven COVID-19 therapeutics as limitations of evidence were infrequently included in the body of news reports (n=125, 26\%) and rarely found in headlines (n=2, 2\%) or lead paragraphs (n=9, 9\%; P<.001). Conclusions: These results highlight that while scientific evidence is discussed relatively frequently in news reports, scientific uncertainty is infrequently reported and rarely found in prominent headlines and lead paragraphs. ", doi="10.2196/51328", url="https://infodemiology.jmir.org/2024/1/e51328" } @Article{info:doi/10.2196/54072, author="Aboalshamat, Khalid", title="Assessment of the Quality and Readability of Web-Based Arabic Health Information on Halitosis: Infodemiological Study", journal="J Med Internet Res", year="2024", month="Aug", day="28", volume="26", pages="e54072", keywords="halitosis", keywords="bad breath", keywords="malodor, Arabic web-based", keywords="infodemiological study", keywords="oral malodor", keywords="readability", keywords="infodemiology", keywords="health information", keywords="Arabic mouth medical information", keywords="reliable information", keywords="odor treatment", abstract="Background: Halitosis, characterized by an undesirable mouth odor, represents a common concern. Objective: This study aims to assess the quality and readability of web-based Arabic health information on halitosis as the internet is becoming a prominent global source of medical information. Methods: A total of 300 Arabic websites were retrieved from Google using 3 commonly used phrases for halitosis in Arabic. The quality of the websites was assessed using benchmark criteria established by the Journal of the American Medical Association, the DISCERN tool, and the presence of the Health on the Net Foundation Code of Conduct (HONcode). The assessment of readability (Flesch Reading Ease [FRE], Simple Measure of Gobbledygook, and Flesch-Kincaid Grade Level [FKGL]) was conducted using web-based readability indexes. Results: A total of 127 websites were examined. Regarding quality assessment, 87.4\% (n=111) of websites failed to fulfill any Journal of the American Medical Association requirements, highlighting a lack of authorship (authors' contributions), attribution (references), disclosure (sponsorship), and currency (publication date). The DISCERN tool had a mean score of 34.55 (SD 7.46), with the majority (n=72, 56.6\%) rated as moderate quality, 43.3\% (n=55) as having a low score, and none receiving a high DISCERN score, indicating a general inadequacy in providing quality health information to make decisions and treatment choices. No website had HONcode certification, emphasizing the concern over the credibility and trustworthiness of these resources. Regarding readability assessment, Arabic halitosis websites had high readability scores, with 90.5\% (n=115) receiving an FRE score ?80, 98.4\% (n=125) receiving a Simple Measure of Gobbledygook score <7, and 67.7\% (n=86) receiving an FKGL score <7. There were significant correlations between the DISCERN scores and the quantity of words (P<.001) and sentences (P<.001) on the websites. Additionally, there was a significant relationship (P<.001) between the number of sentences and FKGL and FRE scores. Conclusions: While readability was found to be very good, indicating that the information is accessible to the public, the quality of Arabic halitosis websites was poor, reflecting a significant gap in providing reliable and comprehensive health information. This highlights the need for improving the availability of high-quality materials to ensure Arabic-speaking populations have access to reliable information about halitosis and its treatment options, tying quality and availability together as critical for effective health communication. ", doi="10.2196/54072", url="https://www.jmir.org/2024/1/e54072", url="http://www.ncbi.nlm.nih.gov/pubmed/39196637" } @Article{info:doi/10.2196/57885, author="Rao, K. Varun and Valdez, Danny and Muralidharan, Rasika and Agley, Jon and Eddens, S. Kate and Dendukuri, Aravind and Panth, Vandana and Parker, A. Maria", title="Digital Epidemiology of Prescription Drug References on X (Formerly Twitter): Neural Network Topic Modeling and Sentiment Analysis", journal="J Med Internet Res", year="2024", month="Aug", day="23", volume="26", pages="e57885", keywords="digital epidemiology", keywords="BERTtopic", keywords="Valence Aware Dictionary and Sentiment Reasoner", keywords="VADER", keywords="sentiment analysis", keywords="social media", keywords="prescription drugs", keywords="prescription", keywords="prescriptions", keywords="drug", keywords="drugs", keywords="drug use", keywords="platform X", keywords="Twitter", keywords="tweet", keywords="tweets", keywords="latent Dirichlet allocation", keywords="machine-driven", keywords="natural language processing", keywords="NLP", keywords="brand name", keywords="logistic regression", keywords="machine learning", keywords="health informatics", abstract="Background: Data from the social media platform X (formerly Twitter) can provide insights into the types of language that are used when discussing drug use. In past research using latent Dirichlet allocation (LDA), we found that tweets containing ``street names'' of prescription drugs were difficult to classify due to the similarity to other colloquialisms and lack of clarity over how the terms were used. Conversely, ``brand name'' references were more amenable to machine-driven categorization. Objective: This study sought to use next-generation techniques (beyond LDA) from natural language processing to reprocess X data and automatically cluster groups of tweets into topics to differentiate between street- and brand-name data sets. We also aimed to analyze the differences in emotional valence between the 2 data sets to study the relationship between engagement on social media and sentiment. Methods: We used the Twitter application programming interface to collect tweets that contained the street and brand name of a prescription drug within the tweet. Using BERTopic in combination with Uniform Manifold Approximation and Projection and k-means, we generated topics for the street-name corpus (n=170,618) and brand-name corpus (n=245,145). Valence Aware Dictionary and Sentiment Reasoner (VADER) scores were used to classify whether tweets within the topics had positive, negative, or neutral sentiments. Two different logistic regression classifiers were used to predict the sentiment label within each corpus. The first model used a tweet's engagement metrics and topic ID to predict the label, while the second model used those features in addition to the top 5000 tweets with the largest term-frequency--inverse document frequency score. Results: Using BERTopic, we identified 40 topics for the street-name data set and 5 topics for the brand-name data set, which we generalized into 8 and 5 topics of discussion, respectively. Four of the general themes of discussion in the brand-name corpus referenced drug use, while 2 themes of discussion in the street-name corpus referenced drug use. From the VADER scores, we found that both corpora were inclined toward positive sentiment. Adding the vectorized tweet text increased the accuracy of our models by around 40\% compared with the models that did not incorporate the tweet text in both corpora. Conclusions: BERTopic was able to classify tweets well. As with LDA, the discussion using brand names was more similar between tweets than the discussion using street names. VADER scores could only be logically applied to the brand-name corpus because of the high prevalence of non--drug-related topics in the street-name data. Brand-name tweets either discussed drugs positively or negatively, with few posts having a neutral emotionality. From our machine learning models, engagement alone was not enough to predict the sentiment label; the added context from the tweets was needed to understand the emotionality of a tweet. ", doi="10.2196/57885", url="https://www.jmir.org/2024/1/e57885", url="http://www.ncbi.nlm.nih.gov/pubmed/39178036" } @Article{info:doi/10.2196/56931, author="Kisa, Sezer and Kisa, Adnan", title="A Comprehensive Analysis of COVID-19 Misinformation, Public Health Impacts, and Communication Strategies: Scoping Review", journal="J Med Internet Res", year="2024", month="Aug", day="21", volume="26", pages="e56931", keywords="communication strategies", keywords="COVID-19", keywords="infodemic", keywords="misinformation", keywords="public health", abstract="Background: The COVID-19 pandemic was marked by an infodemic, characterized by the rapid spread of both accurate and false information, which significantly affected public health. This infodemic led to confusion, mistrust in health authorities, noncompliance with health guidelines, and engagement in risky health behaviors. Understanding the dynamics of misinformation during the pandemic is crucial for developing effective public health communication strategies. Objective: This comprehensive analysis aimed to examine the complexities of COVID-19 misinformation. Specifically, it sought to identify the sources and themes of misinformation, the target audiences most affected, and the effectiveness of various public health communication strategies in mitigating misinformation. Methods: This scoping review used the MEDLINE (PubMed), Embase, and Scopus databases to identify relevant studies. An established, methodical framework for scoping reviews was used to review literature published between December 2019 and September 2023. The inclusion criteria focused on peer-reviewed studies published in English that address COVID-19 misinformation and its sources, themes, and target audiences, as well as the effectiveness of public health communication strategies. Results: The scoping review identified that misinformation significantly impacted mental health, vaccine hesitancy, and health care decision-making. Social media and traditional media were major conduits for spreading misinformation. Key misinformation themes included the origins of the virus, ineffective treatments, and misunderstandings about public health measures. Misinformation sources ranged from social media platforms to traditional media outlets and informal networks. The impact of misinformation was found to vary across different regions and demographic groups, with vulnerable populations being disproportionately affected. Effective strategies to counter misinformation included enhancing health literacy; using digital technology; promoting clear, authoritative communication; and implementing fact-checking mechanisms. In addition, community engagement and targeted health campaigns played a crucial role in addressing misinformation. Conclusions: The review emphasizes the critical need for accurate and consistent messaging to combat misinformation. Cooperative efforts among policy makers, health professionals, and communication experts are essential for developing effective interventions. Addressing the infodemic is vital for building a well-informed, health-literate society capable of handling misinformation in future global health crises. The study provides valuable insights into the dynamics of misinformation and highlights the importance of robust public health communication strategies. These findings can guide future efforts to mitigate the impact of misinformation during health emergencies. ", doi="10.2196/56931", url="https://www.jmir.org/2024/1/e56931" } @Article{info:doi/10.2196/55403, author="Cui, Nannan and Lu, Yuting and Cao, Yelin and Chen, Xiaofan and Fu, Shuiqiao and Su, Qun", title="Quality Assessment of TikTok as a Source of Information About Mitral Valve Regurgitation in China: Cross-Sectional Study", journal="J Med Internet Res", year="2024", month="Aug", day="20", volume="26", pages="e55403", keywords="mitral valve regurgitation", keywords="video quality", keywords="TikTok", keywords="Journal of American Medical Association", keywords="JAMA", keywords="Global Quality Score", keywords="GQS", keywords="PEMAT- A/V", keywords="Spearman correlation analysis", keywords="Poisson regression analysis", abstract="Background: In China, mitral valve regurgitation (MR) is the most common cardiovascular valve disease. However, patients in China typically experience a high incidence of this condition, coupled with a low level of health knowledge and a relatively low rate of surgical treatment. TikTok hosts a vast amount of content related to diseases and health knowledge, providing viewers with access to relevant information. However, there has been no investigation or evaluation of the quality of videos specifically addressing MR. Objective: This study aims to assess the quality of videos about MR on TikTok in China. Methods: A cross-sectional study was conducted on the Chinese version of TikTok on September 9, 2023. The top 100 videos on MR were included and evaluated using quantitative scoring tools such as the modified DISCERN (mDISCERN), the Journal of the American Medical Association (JAMA) benchmark criteria, the Global Quality Score (GQS), and the Patient Education Materials Assessment Tool for Audio-Visual Content (PEMAT-A/V). Correlation and stepwise regression analyses were performed to examine the relationships between video quality and various characteristics. Results: We obtained 88 valid video files, of which most (n=81, 92\%) were uploaded by certified physicians, primarily cardiac surgeons, and cardiologists. News agencies/organizations and physicians had higher GQS scores compared with individuals (news agencies/organizations vs individuals, P=.001; physicians vs individuals, P=.03). Additionally, news agencies/organizations had higher PEMAT understandability scores than individuals (P=.01). Videos focused on disease knowledge scored higher in GQS (P<.001), PEMAT understandability (P<.001), and PEMAT actionability (P<.001) compared with videos covering surgical cases. PEMAT actionability scores were higher for outpatient cases compared with surgical cases (P<.001). Additionally, videos focused on surgical techniques had lower PEMAT actionability scores than those about disease knowledge (P=.04). The strongest correlations observed were between thumbs up and comments (r=0.92, P<.001), thumbs up and favorites (r=0.89, P<.001), thumbs up and shares (r=0.87, P<.001), comments and favorites (r=0.81, P<.001), comments and shares (r=0.87, P<.001), and favorites and shares (r=0.83, P<.001). Stepwise regression analysis identified ``length (P<.001),'' ``content (P<.001),'' and ``physicians (P=.004)'' as significant predictors of GQS. The final model (model 3) explained 50.1\% of the variance in GQSs. The predictive equation for GQS is as follows: GQS = 3.230 ? 0.294 {\texttimes} content ? 0.274 {\texttimes} physicians + 0.005 {\texttimes} length. This model was statistically significant (P=.004) and showed no issues with multicollinearity or autocorrelation. Conclusions: Our study reveals that while most MR-related videos on TikTok were uploaded by certified physicians, ensuring professional and scientific content, the overall quality scores were suboptimal. Despite the educational value of these videos, the guidance provided was often insufficient. The predictive equation for GQS developed from our analysis offers valuable insights but should be applied with caution beyond the study context. It suggests that creators should focus on improving both the content and presentation of their videos to enhance the quality of health information shared on social media. ", doi="10.2196/55403", url="https://www.jmir.org/2024/1/e55403", url="http://www.ncbi.nlm.nih.gov/pubmed/39163110" } @Article{info:doi/10.2196/38786, author="Kbaier, Dhouha and Kane, Annemarie and McJury, Mark and Kenny, Ian", title="Prevalence of Health Misinformation on Social Media---Challenges and Mitigation Before, During, and Beyond the COVID-19 Pandemic: Scoping Literature Review", journal="J Med Internet Res", year="2024", month="Aug", day="19", volume="26", pages="e38786", keywords="health misinformation", keywords="online health communities", keywords="vaccine hesitancy", keywords="social media", keywords="health professionals", keywords="public health", keywords="COVID-19", keywords="intervention", keywords="antivaxxers", abstract="Background: This scoping review accompanies our research study ``The Experience of Health Professionals With Misinformation and Its Impact on Their Job Practice: Qualitative Interview Study.'' It surveys online health misinformation and is intended to provide an understanding of the communication context in which health professionals must operate. Objective: Our objective was to illustrate the impact of social media in introducing additional sources of misinformation that impact health practitioners' ability to communicate effectively with their patients. In addition, we considered how the level of knowledge of practitioners mitigated the effect of misinformation and additional stress factors associated with dealing with outbreaks, such as the COVID-19 pandemic, that affect communication with patients. Methods: This study used a 5-step scoping review methodology following Arksey and O'Malley's methodology to map relevant literature published in English between January 2012 and March 2024, focusing on health misinformation on social media platforms. We defined health misinformation as a false or misleading health-related claim that is not based on valid evidence or scientific knowledge. Electronic searches were performed on PubMed, Scopus, Web of Science, and Google Scholar. We included studies on the extent and impact of health misinformation in social media, mitigation strategies, and health practitioners' experiences of confronting health misinformation. Our independent reviewers identified relevant articles for data extraction. Results: Our review synthesized findings from 70 sources on online health misinformation. It revealed a consensus regarding the significant problem of health misinformation disseminated on social network platforms. While users seek trustworthy sources of health information, they often lack adequate health and digital literacies, which is exacerbated by social and economic inequalities. Cultural contexts influence the reception of such misinformation, and health practitioners may be vulnerable, too. The effectiveness of online mitigation strategies like user correction and automatic detection are complicated by malicious actors and politicization. The role of health practitioners in this context is a challenging one. Although they are still best placed to combat health misinformation, this review identified stressors that create barriers to their abilities to do this well. Investment in health information management at local and global levels could enhance their capacity for effective communication with patients. Conclusions: This scoping review underscores the significance of addressing online health misinformation, particularly in the postpandemic era. It highlights the necessity for a collaborative global interdisciplinary effort to ensure equitable access to accurate health information, thereby empowering health practitioners to effectively combat the impact of online health misinformation. Academic research will need to be disseminated into the public domain in a way that is accessible to the public. Without equipping populations with health and digital literacies, the prevalence of online health misinformation will continue to pose a threat to global public health efforts. ", doi="10.2196/38786", url="https://www.jmir.org/2024/1/e38786" } @Article{info:doi/10.2196/49924, author="Henegan, Patricia and Koczara, Jack and Bluhm, Robyn and Cabrera, Y. Laura", title="Public Perceptions of Treating Opioid Use Disorder With Deep Brain Stimulation: Comment Analysis Study", journal="Online J Public Health Inform", year="2024", month="Aug", day="16", volume="16", pages="e49924", keywords="deep brain stimulation", keywords="DBS", keywords="comment analysis", keywords="refractory opioid use disorder", keywords="substance abuse", keywords="opioid addiction", keywords="opioid", keywords="substance use", keywords="opioid use", keywords="treatment", keywords="addiction", keywords="mental health", keywords="therapeutic", keywords="psychiatric disorder", abstract="Background: The number of opioid-related deaths in the United States has more than tripled over the past 7 years, with a steep increase beginning at the same time as the COVID-19 pandemic. There is an urgent need for novel treatment options that can help alleviate the individual and social effects of refractory opioid use disorder (OUD). Deep brain stimulation (DBS), an intervention that involves implanting electrodes in the brain to deliver electrical impulses, is one potential treatment. Currently in clinical trials for many psychiatric conditions, including OUD, DBS's use for psychiatric indications is not without controversy. Several studies have examined ethical issues raised by using DBS to counter treatment-resistant depression, obsessive-compulsive disorder, and eating disorders. In contrast, there has been limited literature regarding the use of DBS for OUD. Objective: This study aims to gain empirical neuroethical insights into public perceptions regarding the use of DBS for OUD, specifically via the analysis of web-based comments on news media stories about the topic. Methods: Qualitative thematic content analysis was performed on 2 Washington Post newspaper stories that described a case of DBS being used to treat OUD. A total of 292 comments were included in the analysis, 146 comments from each story, to identify predominant themes raised by commenters. Results: Predominant themes raised by commenters across the 2 samples included the hopes and expectations with treatment outcomes, whether addiction is a mental health disorder, and issues related to resource allocation. Controversial comments regarding DBS as a treatment method for OUD seemingly decreased when comparing the first printed newspaper story to the second. In comparison, the number of comments relating to therapeutic need increased over time. Conclusions: The general public's perspectives on DBS as a treatment method for OUD elucidated themes via this qualitative thematic content analysis that include overarching sociopolitical issues, positions on the use of technology, and technological and scientific issues. A better understanding of the public perceptions around the use of DBS for OUD can help address misinformation and misperceptions about the use of DBS for OUD, and identify similarities and differences regarding ethical concerns when DBS is used specifically for OUD compared to other psychiatric disorders. ", doi="10.2196/49924", url="https://ojphi.jmir.org/2024/1/e49924" } @Article{info:doi/10.2196/50353, author="Ma, Ning and Yu, Guang and Jin, Xin", title="Investigation of Public Acceptance of Misinformation Correction in Social Media Based on Sentiment Attributions: Infodemiology Study Using Aspect-Based Sentiment Analysis", journal="J Med Internet Res", year="2024", month="Aug", day="16", volume="26", pages="e50353", keywords="misinformation correction", keywords="sentiment attribution", keywords="public acceptance", keywords="public sentiments", keywords="aspect-based sentiment analysis", keywords="pretraining model", abstract="Background: The proliferation of misinformation on social media is a significant concern due to its frequent occurrence and subsequent adverse social consequences. Effective interventions for and corrections of misinformation have become a focal point of scholarly inquiry. However, exploration of the underlying causes that affect the public acceptance of misinformation correction is still important and not yet sufficient. Objective: This study aims to identify the critical attributions that influence public acceptance of misinformation correction by using attribution analysis of aspects of public sentiment, as well as investigate the differences and similarities in public sentiment attributions in different types of misinformation correction. Methods: A theoretical framework was developed for analysis based on attribution theory, and public sentiment attributions were divided into 6 aspects and 11 dimensions. The correction posts for the 31 screened misinformation events comprised 33,422 Weibo posts, and the corresponding Weibo comments amounted to 370,218. A pretraining model was used to assess public acceptance of misinformation correction from these comments, and the aspect-based sentiment analysis method was used to identify the attributions of public sentiment response. Ultimately, this study revealed the causality between public sentiment attributions and public acceptance of misinformation correction through logistic regression analysis. Results: The findings were as follows: First, public sentiments attributed to external attribution had a greater impact on public acceptance than those attributed to internal attribution. The public associated different aspects with correction depending on the type of misinformation. The accuracy of the correction and the entity responsible for carrying it out had a significant impact on public acceptance of misinformation correction. Second, negative sentiments toward the media significantly increased, and public trust in the media significantly decreased. The collapse of media credibility had a detrimental effect on the actual effectiveness of misinformation correction. Third, there was a significant difference in public attitudes toward the official government and local governments. Public negative sentiments toward local governments were more pronounced. Conclusions: Our findings imply that public acceptance of misinformation correction requires flexible communication tailored to public sentiment attribution. The media need to rebuild their image and regain public trust. Moreover, the government plays a central role in public acceptance of misinformation correction. Some local governments need to repair trust with the public. Overall, this study offered insights into practical experience and a theoretical foundation for controlling various types of misinformation based on attribution analysis of public sentiment. ", doi="10.2196/50353", url="https://www.jmir.org/2024/1/e50353" } @Article{info:doi/10.2196/52401, author="Wu, Gloria and Lee, A. David and Zhao, Weichen and Wong, Adrial and Jhangiani, Rohan and Kurniawan, Sri", title="ChatGPT and Google Assistant as a Source of Patient Education for Patients With Amblyopia: Content Analysis", journal="J Med Internet Res", year="2024", month="Aug", day="15", volume="26", pages="e52401", keywords="ChatGPT", keywords="Google Assistant", keywords="amblyopia", keywords="health literacy", keywords="American Association for Pediatric Ophthalmology and Strabismus", keywords="pediatric", keywords="ophthalmology", keywords="patient education", keywords="education", keywords="ophthalmologist", keywords="Google", keywords="monitoring", abstract="Background: We queried ChatGPT (OpenAI) and Google Assistant about amblyopia and compared their answers with the keywords found on the American Association for Pediatric Ophthalmology and Strabismus (AAPOS) website, specifically the section on amblyopia. Out of the 26 keywords chosen from the website, ChatGPT included 11 (42\%) in its responses, while Google included 8 (31\%). Objective: Our study investigated the adherence of ChatGPT-3.5 and Google Assistant to the guidelines of the AAPOS for patient education on amblyopia. Methods: ChatGPT-3.5 was used. The four questions taken from the AAPOS website, specifically its glossary section for amblyopia, are as follows: (1) What is amblyopia? (2) What causes amblyopia? (3) How is amblyopia treated? (4) What happens if amblyopia is untreated? Approved and selected by ophthalmologists (GW and DL), the keywords from AAPOS were words or phrases that deemed significant for the education of patients with amblyopia. The ``Flesch-Kincaid Grade Level'' formula, approved by the US Department of Education, was used to evaluate the reading comprehension level for the responses from ChatGPT, Google Assistant, and AAPOS. Results: In their responses, ChatGPT did not mention the term ``ophthalmologist,'' whereas Google Assistant and AAPOS both mentioned the term once and twice, respectively. ChatGPT did, however, use the term ``eye doctors'' once. According to the Flesch-Kincaid test, the average reading level of AAPOS was 11.4 (SD 2.1; the lowest level) while that of Google was 13.1 (SD 4.8; the highest required reading level), also showing the greatest variation in grade level in its responses. ChatGPT's answers, on average, scored 12.4 (SD 1.1) grade level. They were all similar in terms of difficulty level in reading. For the keywords, out of the 4 responses, ChatGPT used 42\% (11/26) of the keywords, whereas Google Assistant used 31\% (8/26). Conclusions: ChatGPT trains on texts and phrases and generates new sentences, while Google Assistant automatically copies website links. As ophthalmologists, we should consider including ``see an ophthalmologist'' on our websites and journals. While ChatGPT is here to stay, we, as physicians, need to monitor its answers. ", doi="10.2196/52401", url="https://www.jmir.org/2024/1/e52401", url="http://www.ncbi.nlm.nih.gov/pubmed/39146013" } @Article{info:doi/10.2196/55937, author="Zhao, Keyang and Li, Xiaojing and Li, Jingyang", title="Cancer Prevention and Treatment on Chinese Social Media: Machine Learning--Based Content Analysis Study", journal="J Med Internet Res", year="2024", month="Aug", day="14", volume="26", pages="e55937", keywords="social media", keywords="cancer information", keywords="text mining", keywords="supervised machine learning", keywords="content analysis", abstract="Background: Nowadays, social media plays a crucial role in disseminating information about cancer prevention and treatment. A growing body of research has focused on assessing access and communication effects of cancer information on social media. However, there remains a limited understanding of the comprehensive presentation of cancer prevention and treatment methods across social media platforms. Furthermore, research comparing the differences between medical social media (MSM) and common social media (CSM) is also lacking. Objective: Using big data analytics, this study aims to comprehensively map the characteristics of cancer treatment and prevention information on MSM and CSM. This approach promises to enhance cancer coverage and assist patients in making informed treatment decisions. Methods: We collected all posts (N=60,843) from 4 medical WeChat official accounts (accounts with professional medical backgrounds, classified as MSM in this paper) and 5 health and lifestyle WeChat official accounts (accounts with nonprofessional medical backgrounds, classified as CSM in this paper). We applied latent Dirichlet allocation topic modeling to extract cancer-related posts (N=8427) and identified 6 cancer themes separately in CSM and MSM. After manually labeling posts according to our codebook, we used a neural-based method for automated labeling. Specifically, we framed our task as a multilabel task and utilized different pretrained models, such as Bidirectional Encoder Representations from Transformers (BERT) and Global Vectors for Word Representation (GloVe), to learn document-level semantic representations for labeling. Results: We analyzed a total of 4479 articles from MSM and 3948 articles from CSM related to cancer. Among these, 35.52\% (2993/8427) contained prevention information and 44.43\% (3744/8427) contained treatment information. Themes in CSM were predominantly related to lifestyle, whereas MSM focused more on medical aspects. The most frequently mentioned prevention measures were early screening and testing, healthy diet, and physical exercise. MSM mentioned vaccinations for cancer prevention more frequently compared with CSM. Both types of media provided limited coverage of radiation prevention (including sun protection) and breastfeeding. The most mentioned treatment measures were surgery, chemotherapy, and radiotherapy. Compared with MSM (1137/8427, 13.49\%), CSM (2993/8427, 35.52\%) focused more on prevention. Conclusions: The information about cancer prevention and treatment on social media revealed a lack of balance. The focus was primarily limited to a few aspects, indicating a need for broader coverage of prevention measures and treatments in social media. Additionally, the study's findings underscored the potential of applying machine learning to content analysis as a promising research approach for mapping key dimensions of cancer information on social media. These findings hold methodological and practical significance for future studies and health promotion. ", doi="10.2196/55937", url="https://www.jmir.org/2024/1/e55937" } @Article{info:doi/10.2196/52018, author="Snyder, Jeremy and Zenone, Marco and Grewal, Ashmita and Caulfield, Timothy", title="Crowdfunding for Complementary and Alternative Cancer Treatments in Tijuana, Mexico: Content Analysis", journal="JMIR Cancer", year="2024", month="Aug", day="14", volume="10", pages="e52018", keywords="cancer", keywords="crowdfunding", keywords="Tijuana", keywords="CAM", keywords="patient", keywords="patients", keywords="insurance", keywords="crowdfunding platforms", keywords="GoFundMe", keywords="GiveSendGo", keywords="cancer clinic", keywords="Mexico", keywords="campaigns", keywords="cancer treatment", keywords="medical intervention", keywords="CAM cancer treatments", keywords="misinformation", keywords="alternate care", keywords="women's health", keywords="internet research", keywords="international medical tourism", keywords="alternative cancer therapy", keywords="financial toxicity", abstract="Background: Complementary and alternative (CAM) cancer treatment is often expensive and not covered by insurance. As a result, many people turn to crowdfunding to access this treatment. Objective: The aim of this study is to identify the rationales of patients with cancer seeking CAM treatment abroad by looking specifically at crowdfunding campaigns to support CAM cancer treatment in Tijuana, Mexico. Methods: We scraped the GoFundMe.com and GiveSendGo.com crowdfunding platforms for campaigns referencing CAM cancer clinics in Tijuana, initiated between January 1, 2022, and February 28, 2023. The authors created a coding framework to identify rationales for seeking CAM treatment in Tijuana. To supplement campaign metadata, we coded the beneficiary's cancer stage, type, age, specific treatment sought, whether the beneficiary died, gender, and race. Results: Patients sought CAM cancer treatment in Tijuana because the (1) treatment offers the greatest efficacy (29.9\%); (2) treatment offered domestically was not curative (23.2\%); (3) the clinic treats the whole person, and addresses the spiritual dimension of the person (20.1\%); (4) treatments are nontoxic, natural, or less invasive (18.2\%); and (5) clinic offers the newest technology (8.5\%). Campaigns raised US \$5,275,268.37 and most campaign beneficiaries were women (69.7\%) or White individuals (71.1\%). Conclusions: These campaigns spread problematic misinformation about the likely efficacy of CAM treatments, funnel money and endorsements to CAM clinics in Tijuana, and leave many campaigners short of the money needed to pay for CAM treatments while costing beneficiaries and their loved one's time, privacy, and dignity. This study affirms that Tijuana, Mexico, is a very popular destination for CAM cancer treatment. ", doi="10.2196/52018", url="https://cancer.jmir.org/2024/1/e52018" } @Article{info:doi/10.2196/51325, author="Denis-Robichaud, Jos{\'e} and Rees, E. Erin and Daley, Patrick and Zarowsky, Christina and Diouf, Assane and Nasri, R. Bouchra and de Montigny, Simon and Carabin, H{\'e}l{\`e}ne", title="Linking Opinions Shared on Social Media About COVID-19 Public Health Measures to Adherence: Repeated Cross-Sectional Surveys of Twitter Use in Canada", journal="J Med Internet Res", year="2024", month="Aug", day="13", volume="26", pages="e51325", keywords="adherence to mask wearing", keywords="adherence to vaccination", keywords="social media", keywords="sociodemographic characteristics", keywords="Twitter", keywords="COVID-19", keywords="survey data", abstract="Background: The effectiveness of public health measures (PHMs) depends on population adherence. Social media were suggested as a tool to assess adherence, but representativeness and accuracy issues have been raised. Objective: The objectives of this repeated cross-sectional study were to compare self-reported PHM adherence and sociodemographic characteristics between people who used Twitter (subsequently rebranded X) and people who did not use Twitter. Methods: Repeated Canada-wide web-based surveys were conducted every 14 days from September 2020 to March 2022. Weighted proportions were calculated for descriptive variables. Using Bayesian logistic regression models, we investigated associations between Twitter use, as well as opinions in tweets, and self-reported adherence with mask wearing and vaccination. Results: Data from 40,230 respondents were analyzed. As self-reported, Twitter was used by 20.6\% (95\% CI 20.1\%-21.2\%) of Canadians, of whom 29.9\% (95\% CI 28.6\%-31.3\%) tweeted about COVID-19. The sociodemographic characteristics differed across categories of Twitter use and opinions. Overall, 11\% (95\% CI 10.6\%-11.3\%) of Canadians reported poor adherence to mask-wearing, and 10.8\% (95\% CI 10.4\%-11.2\%) to vaccination. Twitter users who tweeted about COVID-19 reported poorer adherence to mask wearing than nonusers, which was modified by the age of the respondents and their geographical region (odds ratio [OR] 0.79, 95\% Bayesian credibility interval [BCI] 0.18-1.69 to OR 4.83, 95\% BCI 3.13-6.86). The odds of poor adherence to vaccination of Twitter users who tweeted about COVID-19 were greater than those of nonusers (OR 1.76, 95\% BCI 1.48-2.07). English- and French-speaking Twitter users who tweeted critically of PHMs were more likely (OR 4.07, 95\% BCI 3.38-4.80 and OR 7.31, 95\% BCI 4.26-11.03, respectively) to report poor adherence to mask wearing than non--Twitter users, and those who tweeted in support were less likely (OR 0.47, 95\% BCI 0.31-0.64 and OR 0.96, 95\% BCI 0.18-2.33, respectively) to report poor adherence to mask wearing than non--Twitter users. The OR of poor adherence to vaccination for those tweeting critically about PHMs and for those tweeting in support of PHMs were 4.10 (95\% BCI 3.40-4.85) and 0.20 (95\% BCI 0.10-0.32), respectively, compared to non--Twitter users. Conclusions: Opinions shared on Twitter can be useful to public health authorities, as they are associated with adherence to PHMs. However, the sociodemographics of social media users do not represent the general population, calling for caution when using tweets to assess general population-level behaviors. ", doi="10.2196/51325", url="https://www.jmir.org/2024/1/e51325", url="http://www.ncbi.nlm.nih.gov/pubmed/39137009" } @Article{info:doi/10.2196/59193, author="Wang, Yunwen and O'Connor, Karen and Flores, Ivan and Berdahl, T. Carl and Urbanowicz, J. Ryan and Stevens, Robin and Bauermeister, A. Jos{\'e} and Gonzalez-Hernandez, Graciela", title="Mpox Discourse on Twitter by Sexual Minority Men and Gender-Diverse Individuals: Infodemiological Study Using BERTopic", journal="JMIR Public Health Surveill", year="2024", month="Aug", day="13", volume="10", pages="e59193", keywords="mpox", keywords="monkeypox", keywords="social media", keywords="sexual minority", keywords="SMMGD", keywords="sexual minority men and gender diverse", keywords="emerging infectious disease", keywords="infectious disease outbreak", keywords="health activism", keywords="health promotion", keywords="health stigma", keywords="stigma prevention", keywords="health equity", keywords="natural language processing", keywords="BERTopic", abstract="Background: The mpox outbreak resulted in 32,063 cases and 58 deaths in the United States and 95,912 cases worldwide from May 2022 to March 2024 according to the US Centers for Disease Control and Prevention (CDC). Like other disease outbreaks (eg, HIV) with perceived community associations, mpox can create the risk of stigma, exacerbate homophobia, and potentially hinder health care access and social equity. However, the existing literature on mpox has limited representation of the perspective of sexual minority men and gender-diverse (SMMGD) individuals. Objective: To fill this gap, this study aimed to synthesize themes of discussions among SMMGD individuals and listen to SMMGD voices for identifying problems in current public health communication surrounding mpox to improve inclusivity, equity, and justice. Methods: We analyzed mpox-related posts (N=8688) posted between October 2020 and September 2022 by 2326 users who self-identified on Twitter/X as SMMGD and were geolocated in the United States. We applied BERTopic (a topic-modeling technique) on the tweets, validated the machine-generated topics through human labeling and annotations, and conducted content analysis of the tweets in each topic. Geographic analysis was performed on the size of the most prominent topic across US states in relation to the University of California, Los Angeles (UCLA) lesbian, gay, and bisexual (LGB) social climate index. Results: BERTopic identified 11 topics, which annotators labeled as mpox health activism (n=2590, 29.81\%), mpox vaccination (n=2242, 25.81\%), and adverse events (n=85, 0.98\%); sarcasm, jokes, and emotional expressions (n=1220, 14.04\%); COVID-19 and mpox (n=636, 7.32\%); government or public health response (n=532, 6.12\%); mpox symptoms (n=238, 2.74\%); case reports (n=192, 2.21\%); puns on the naming of the virus (ie, mpox; n=75, 0.86\%); media publicity (n=59, 0.68\%); and mpox in children (n=58, 0.67\%). Spearman rank correlation indicated significant negative correlation ($\rho$=--0.322, P=.03) between the topic size of health activism and the UCLA LGB social climate index at the US state level. Conclusions: Discussions among SMMGD individuals on mpox encompass both utilitarian (eg, vaccine access, case reports, and mpox symptoms) and emotionally charged (ie, promoting awareness, advocating against homophobia, misinformation/disinformation, and health stigma) themes. Mpox health activism is more prevalent in US states with lower LGB social acceptance, suggesting a resilient communicative pattern among SMMGD individuals in the face of public health oppression. Our method for social listening could facilitate future public health efforts, providing a cost-effective way to capture the perspective of impacted populations. This study illuminates SMMGD engagement with the mpox discourse, underscoring the need for more inclusive public health programming. Findings also highlight the social impact of mpox: health stigma. Our findings could inform interventions to optimize the delivery of informational and tangible health resources leveraging computational mixed-method analyses (eg, BERTopic) and big data. ", doi="10.2196/59193", url="https://publichealth.jmir.org/2024/1/e59193", url="http://www.ncbi.nlm.nih.gov/pubmed/39137013" } @Article{info:doi/10.2196/50125, author="Marley, Gifty and Dako-Gyeke, Phyllis and Nepal, Prajwol and Rajgopal, Rohini and Koko, Evelyn and Chen, Elizabeth and Nuamah, Kwabena and Osei, Kingsley and Hofkirchner, Hubertus and Marks, Michael and Tucker, D. Joseph and Eggo, Rosalind and Ampofo, William and Sylvia, Sean", title="Collective Intelligence--Based Participatory COVID-19 Surveillance in Accra, Ghana: Pilot Mixed Methods Study", journal="JMIR Infodemiology", year="2024", month="Aug", day="12", volume="4", pages="e50125", keywords="information markets", keywords="participatory disease surveillance", keywords="collective intelligence", keywords="community engagement", keywords="the wisdom of the crowds", keywords="Ghana", keywords="mobile phone", abstract="Background: Infectious disease surveillance is difficult in many low- and middle-income countries. Information market (IM)--based participatory surveillance is a crowdsourcing method that encourages individuals to actively report health symptoms and observed trends by trading web-based virtual ``stocks'' with payoffs tied to a future event. Objective: This study aims to assess the feasibility and acceptability of a tailored IM surveillance system to monitor population-level COVID-19 outcomes in Accra, Ghana. Methods: We designed and evaluated a prediction markets IM system from October to December 2021 using a mixed methods study approach. Health care workers and community volunteers aged ?18 years living in Accra participated in the pilot trading. Participants received 10,000 virtual credits to trade on 12 questions on COVID-19--related outcomes. Payoffs were tied to the cost estimation of new and cumulative cases in the region (Greater Accra) and nationwide (Ghana) at specified future time points. Questions included the number of new COVID-19 cases, the number of people likely to get the COVID-19 vaccination, and the total number of COVID-19 cases in Ghana by the end of the year. Phone credits were awarded based on the tally of virtual credits left and the participant's percentile ranking. Data collected included age, occupation, and trading frequency. In-depth interviews explored the reasons and factors associated with participants' user journey experience, barriers to system use, and willingness to use IM systems in the future. Trading frequency was assessed using trend analysis, and ordinary least squares regression analysis was conducted to determine the factors associated with trading at least once. Results: Of the 105 eligible participants invited, 21 (84\%) traded at least once on the platform. Questions estimating the national-level number of COVID-19 cases received 13 to 19 trades, and obtaining COVID-19--related information mainly from television and radio was associated with less likelihood of trading (marginal effect: ?0.184). Individuals aged <30 years traded 7.5 times more and earned GH {\textcent}134.1 (US \$11.7) more in rewards than those aged >30 years (marginal effect: 0.0135). Implementing the IM surveillance was feasible; all 21 participants who traded found using IM for COVID-19 surveillance acceptable. Active trading by friends with communal discussion and a strong onboarding process facilitated participation. The lack of bidirectional communication on social media and technical difficulties were key barriers. Conclusions: Using an IM system for disease surveillance is feasible and acceptable in Ghana. This approach shows promise as a cost-effective source of information on disease trends in low- and middle-income countries where surveillance is underdeveloped, but further studies are needed to optimize its use. ", doi="10.2196/50125", url="https://infodemiology.jmir.org/2024/1/e50125" } @Article{info:doi/10.2196/49197, author="Ramadan, Majed and Aboalola, Doaa and Aouabdi, Sihem and Alghamdi, Tariq and Alsolami, Mona and Samkari, Alaa and Alsiary, Rawiah", title="Influence of Breast Cancer Awareness Month on Public Interest of Breast Cancer in High-Income Countries Between 2012 and 2022: Google Trends Analysis", journal="JMIR Cancer", year="2024", month="Aug", day="12", volume="10", pages="e49197", keywords="Google Trends", keywords="breast cancer", keywords="pandemic", keywords="awareness", keywords="public interest", keywords="cancer", keywords="cancer awareness", keywords="women", keywords="mortality rate", keywords="detection", keywords="treatment", keywords="social media", keywords="tool", keywords="education", keywords="support", keywords="internet users", abstract="Background: Breast cancer is the most common cancer among women worldwide. High-income countries have a greater incidence and mortality rate of breast cancer than low-income countries. As a result, raising awareness about breast cancer is crucial in increasing the chances of early detection and treatment. Social media has evolved into an essential tool for Breast Cancer Awareness Month campaigns, allowing people to share their breast cancer stories and experiences while also providing a venue for education and support. Objective: The aim of this study was to assess the level of public interest in searches linked to breast cancer among a sample of high-income nations with a sizable internet user base from 2012 to 2022. We also sought to compare the proportional search volume for breast cancer during Breast Cancer Awareness Month with that during other months of the year. Methods: Google Trends was used to retrieve data on internet user search behaviors in the context of breast cancer from 2012 to 2022. Seven countries were evaluated in this study: Australia, Canada, Ireland, New Zealand, the United Kingdom, Saudi Arabia, and the United States, in addition to global data. Breast cancer relative search volume trends were analyzed annually, monthly, and weekly from 2012 to 2022. The annual percent change (APC) was calculated for each country and worldwide. Monthly and weekly data were used to identify potential trends. Results: A fluctuating pattern in APC rates was observed, with a notable increase in 2018 and a significant decrease in 2020, particularly in Saudi Arabia. Monthly analysis revealed a consistent peak in search volume during October (Breast Cancer Awareness Month) each year. Weekly trends over a 20-year period indicated significant decreases in Australia, Canada, New Zealand, and the United States, while increases were noted in Ireland. Heatmap analysis further highlighted a consistent elevation in median search volume during October across all countries. Conclusions: These findings underscore the impact of Breast Cancer Awareness Month and suggest potential influences of governmental COVID-19 pandemic control measures in 2020 on internet search behavior. ", doi="10.2196/49197", url="https://cancer.jmir.org/2024/1/e49197" } @Article{info:doi/10.2196/55104, author="Yin, Dean-Chen Jason", title="Vaccine Hesitancy in Taiwan: Temporal, Multilayer Network Study of Echo Chambers Shaped by Influential Users", journal="Online J Public Health Inform", year="2024", month="Aug", day="9", volume="16", pages="e55104", keywords="network analysis", keywords="infodemiology", keywords="vaccine hesitancy", keywords="Taiwan", keywords="multiplex network", keywords="echo chambers", keywords="influential users", keywords="information dissemination", keywords="health communication", keywords="Taiwanese data set", keywords="multilayer network model", keywords="vaccine hesitant", keywords="antivaccination", keywords="infoveillance", keywords="disease surveillance", keywords="public health", abstract="Background: Vaccine hesitancy is a growing global health threat that is increasingly studied through the monitoring and analysis of social media platforms. One understudied area is the impact of echo chambers and influential users on disseminating vaccine information in social networks. Assessing the temporal development of echo chambers and the influence of key users on their growth provides valuable insights into effective communication strategies to prevent increases in vaccine hesitancy. This also aligns with the World Health Organization's (WHO) infodemiology research agenda, which aims to propose new methods for social listening. Objective: Using data from a Taiwanese forum, this study aims to examine how engagement patterns of influential users, both within and across different COVID-19 stances, contribute to the formation of echo chambers over time. Methods: Data for this study come from a Taiwanese forum called PTT. All vaccine-related posts on the ``Gossiping'' subforum were scraped from January 2021 to December 2022 using the keyword ``vaccine.'' A multilayer network model was constructed to assess the existence of echo chambers. Each layer represents either provaccination, vaccine hesitant, or antivaccination posts based on specific criteria. Layer-level metrics, such as average diversity and Spearman rank correlations, were used to measure chambering. To understand the behavior of influential users---or key nodes---in the network, the activity of high-diversity and hardliner nodes was analyzed. Results: Overall, the provaccination and antivaccination layers are strongly polarized. This trend is temporal and becomes more apparent after November 2021. Diverse nodes primarily participate in discussions related to provaccination topics, both receiving comments and contributing to them. Interactions with the antivaccination layer are comparatively minimal, likely due to its smaller size, suggesting that the forum is a ``healthy community.'' Overall, diverse nodes exhibit cross-cutting engagement. By contrast, hardliners in the vaccine hesitant and antivaccination layers are more active in commenting within their own communities. This trend is temporal, showing an increase during the Omicron outbreak. Hardliner activity potentially reinforces their stances over time. Thus, there are opposing forces of chambering and cross-cutting. Conclusions: Efforts should be made to moderate hardliner and influential nodes in the antivaccination layer and to support provaccination users engaged in cross-cutting exchanges. There are several limitations to this study. One is the bias of the platform used, and another is the lack of a comprehensive definition of ``influence.'' To address these issues, comparative studies across different platforms can be conducted, and various metrics of influence should be explored. Additionally, examining the impact of influential users on network structure and chambering through network simulations and regression analysis provides more robust insights. The study also lacks an explanation for the reasons behind chambering trends. Conducting content analysis can help to understand the nature of engagement and inform interventions to address echo chambers. These approaches align with and further the WHO infodemic research agenda. ", doi="10.2196/55104", url="https://ojphi.jmir.org/2024/1/e55104" } @Article{info:doi/10.2196/55151, author="Paradise Vit, Abigail and Magid, Avi", title="Differences in Fear and Negativity Levels Between Formal and Informal Health-Related Websites: Analysis of Sentiments and Emotions", journal="J Med Internet Res", year="2024", month="Aug", day="9", volume="26", pages="e55151", keywords="emotions", keywords="sentiment", keywords="health websites", keywords="fear", abstract="Background: Searching for web-based health-related information is frequently performed by the public and may affect public behavior regarding health decision-making. Particularly, it may result in anxiety, erroneous, and harmful self-diagnosis. Most searched health-related topics are cancer, cardiovascular diseases, and infectious diseases. A health-related web-based search may result in either formal or informal medical website, both of which may evoke feelings of fear and negativity. Objective: Our study aimed to assess whether there is a difference in fear and negativity levels between information appearing on formal and informal health-related websites. Methods: A web search was performed to retrieve the contents of websites containing symptoms of selected diseases, using selected common symptoms. Retrieved websites were classified into formal and informal websites. Fear and negativity of each content were evaluated using 3 transformer models. A fourth transformer model was fine-tuned using an existing emotion data set obtained from a web-based health community. For formal and informal websites, fear and negativity levels were aggregated. t tests were conducted to evaluate the differences in fear and negativity levels between formal and informal websites. Results: In this study, unique websites (N=1448) were collected, of which 534 were considered formal and 914 were considered informal. There were 1820 result pages from formal websites and 1494 result pages from informal websites. According to our findings, fear levels were statistically higher (t2753=3.331; P<.001) on formal websites (mean 0.388, SD 0.177) than on informal websites (mean 0.366, SD 0.168). The results also show that the level of negativity was statistically higher (t2753=2.726; P=.006) on formal websites (mean 0.657, SD 0.211) than on informal websites (mean 0.636, SD 0.201). Conclusions: Positive texts may increase the credibility of formal health websites and increase their usage by the general public and the public's compliance to the recommendations. Increasing the usage of natural language processing tools before publishing health-related information to achieve a more positive and less stressful text to be disseminated to the public is recommended. ", doi="10.2196/55151", url="https://www.jmir.org/2024/1/e55151", url="http://www.ncbi.nlm.nih.gov/pubmed/39120928" } @Article{info:doi/10.2196/54967, author="Gan, Ting and Liu, Yunning and Bambrick, Hilary and Zhou, Maigeng and Hu, Wenbiao", title="Liver Cancer Mortality Disparities at a Fine Scale Among Subpopulations in China: Nationwide Analysis of Spatial and Temporal Trends", journal="JMIR Public Health Surveill", year="2024", month="Aug", day="8", volume="10", pages="e54967", keywords="liver cancer", keywords="mortality", keywords="year of life lost", keywords="spatial distribution", keywords="temporal trend", abstract="Background: China has the highest number of liver cancers worldwide, and liver cancer is at the forefront of all cancers in China. However, current research on liver cancer in China primarily relies on extrapolated data or relatively lagging data, with limited focus on subregions and specific population groups. Objective: The purpose of this study is to identify geographic disparities in liver cancer by exploring the spatial and temporal trends of liver cancer mortality and the years of life lost (YLL) caused by it within distinct geographical regions, climate zones, and population groups in China. Methods: Data from the National Death Surveillance System between 2013 and 2020 were used to calculate the age-standardized mortality rate of liver cancer (LASMR) and YLL from liver cancer in China. The spatial distribution and temporal trends of liver cancer were analyzed in subgroups by sex, age, region, and climate classification. Estimated annual percentage change was used to describe liver cancer trends in various regions, and partial correlation was applied to explore associations between LASMR and latitude. Results: In China, the average LASMR decreased from 28.79 in 2013 to 26.38 per 100,000 in 2020 among men and 11.09 to 9.83 per 100,000 among women. This decline in mortality was consistent across all age groups. Geographically, Guangxi had the highest LASMR for men in China, with a rate of 50.15 per 100,000, while for women, it was Heilongjiang, with a rate of 16.64 per 100,000. Within these regions, the LASMR among men in most parts of Guangxi ranged from 32.32 to 74.98 per 100,000, whereas the LASMR among women in the majority of Heilongjiang ranged from 13.72 to 21.86 per 100,000. The trend of LASMR varied among regions. For both men and women, Guizhou showed an increasing trend in LASMR from 2013 to 2020, with estimated annual percentage changes ranging from 10.05\% to 29.07\% and from 10.09\% to 21.71\%, respectively. Both men and women observed an increase in LASMR with increasing latitude below the 40th parallel. However, overall, LASMR in men was positively correlated with latitude (R=0.225; P<.001), while in women, it showed a negative correlation (R=0.083; P=.04). High LASMR areas among men aligned with subtropical zones, like Cwa and Cfa. The age group 65 years and older, the southern region, and the Cwa climate zone had the highest YLL rates at 4850.50, 495.50, and 440.17 per 100,000, respectively. However, the overall trends in these groups showed a decline over the period. Conclusions: Despite the declining overall trend of liver cancer in China, there are still marked disparities between regions and populations. Future prevention and control should focus on high-risk regions and populations to further reduce the burden of liver cancer in China. ", doi="10.2196/54967", url="https://publichealth.jmir.org/2024/1/e54967" } @Article{info:doi/10.2196/49073, author="Nagpal, S. Meghan and Jalali, Niloofar and Sherifali, Diana and Morita, P. Plinio and Cafazzo, A. Joseph", title="Managing Type 2 Diabetes During the COVID-19 Pandemic: Scoping Review and Qualitative Study Using Systematic Literature Review and Reddit", journal="Interact J Med Res", year="2024", month="Aug", day="8", volume="13", pages="e49073", keywords="type 2 diabetes", keywords="social media", keywords="patient-generated health data", keywords="big data", keywords="machine learning", keywords="natural language processing", keywords="COVID-19, COVID-19 stress syndrome, health behaviors", keywords="Reddit", keywords="qualitative", keywords="analysis", keywords="diabetes", keywords="scoping review", abstract="Background: The COVID-19 pandemic impacted how people accessed health services and likely how they managed chronic conditions such as type 2 diabetes (T2D). Social media forums present a source of qualitative data to understand how adaptation might have occurred from the perspective of the patient. Objective: Our objective is to understand how the care-seeking behaviors and attitudes of people living with T2D were impacted during the early part of the pandemic by conducting a scoping literature review. A secondary objective is to compare the findings of the scoping review to those presented on a popular social media platform Reddit. Methods: A scoping review was conducted in 2021. Inclusion criteria were population with T2D, studies are patient-centered, and study objectives are centered around health behaviors, disease management, or mental health outcomes during the COVID-19 pandemic. Exclusion criteria were populations with other noncommunicable diseases, examining COVID-19 as a comorbidity to T2D, clinical treatments for COVID-19 among people living with T2D, genetic expressions of COVID-19 among people living with T2D, gray literature, or studies not published in English. Bias was mitigated by reviewing uncertainties with other authors. Data extracted from the studies were classified into thematic categories. These categories reflect the findings of this study as per our objective. Data from the Reddit forums related to T2D from March 2020 to early March 2021 were downloaded, and support vector machines were used to classify if a post was published in the context of the pandemic. Latent Dirichlet allocation topic modeling was performed to gather topics of discussion specific to the COVID-19 pandemic. Results: A total of 26 studies conducted between February and September 2020, consisting of 13,673 participants, were included in this scoping literature review. The studies were qualitative and relied mostly on qualitative data from surveys or questionnaires. Themes found from the literature review were ``poorer glycemic control,'' ``increased consumption of unhealthy foods,'' ``decreased physical activity,'' ``inability to access medical appointments,'' and ``increased stress and anxiety.'' Findings from latent Dirichlet allocation topic modeling of Reddit forums were ``Coping With Poor Mental Health,'' ``Accessing Doctor \& Medications and Controlling Blood Glucose,'' ``Changing Food Habits During Pandemic,'' ``Impact of Stress on Blood Glucose Levels,'' ``Changing Status of Employment \& Insurance,'' and ``Risk of COVID Complications.'' Conclusions: Topics of discussion gauged from the Reddit forums provide a holistic perspective of the impact of the pandemic on people living with T2D, which were found to be comparable to the findings of the literature review. The study was limited by only having 1 reviewer for the literature review, but biases were mitigated by consulting authors when there were uncertainties. Qualitative analysis of Reddit forms can supplement traditional qualitative studies of the behaviors of people living with T2D. ", doi="10.2196/49073", url="https://www.i-jmr.org/2024/1/e49073" } @Article{info:doi/10.2196/57823, author="Erbas, Ege Mert and Ziehfreund, Stefanie and Wecker, Hannah and Biedermann, Tilo and Zink, Alexander", title="Digital Media Usage Behavior and Its Impact on the Physician-Patient Relationship: Cross-Sectional Study Among Individuals Affected by Psoriasis in Germany", journal="J Med Internet Res", year="2024", month="Aug", day="7", volume="26", pages="e57823", keywords="psoriasis", keywords="dermatology", keywords="digital health", keywords="digital media", keywords="internet use", keywords="questionnaire", keywords="physician-patient relationship", abstract="Background: Psoriasis is a chronic skin disorder with a high burden of disease. People affected with psoriasis increasingly use the internet for health-related reasons, especially those with younger age, higher education, and higher disease severity. Despite advantages such as enhancing the individuals' knowledge with the use of digital media for health-related issues, disadvantages were also present such as quality control, and variability in the individuals' health information literacy. While patients with psoriasis within medical settings generally trust physicians over digital media, they commonly withhold their web-based research findings from health care providers. Objective: The study aims to (1) identify further factors associated with regular psoriasis-related internet use, (2) rank specific digital media platforms used, and (3) examine digital media within the physician-patient relationship among individuals with and without dermatological treatment. Methods: A cross-sectional, questionnaire-based study was conducted among individuals with self-reported psoriasis in Germany between September 2021 and February 2022. Participants were recruited via digital media platforms and in person at a University Hospital Department of Dermatology in southern Germany. The questionnaire asked about demographic and medical information, individual psoriasis-related digital media use, and the impact of digital media on the physician-patient relationship. Data were analyzed descriptively, and logistic regression models were performed to assess the factors associated with regular psoriasis-related internet use. Results: Among 321 individuals with a median age of 53 (IQR 41-61) years (nonnormally distributed; females: 195/321), female sex, shorter disease duration, moderate mental burden of disease, and good self-assessed psoriasis-related knowledge were associated with regular psoriasis-related internet use. Of the 188 participants with a mean age of 51.2 (SD 13.9) years (normally distributed) who used digital media 106 (56.4\%) usually searched for information on psoriasis-based websites and 98 (52.1\%) on search engines, primarily for obtaining information about the disease and therapy options, while social media were less frequently used (49/188, 26.1\%). Nearly two-thirds of internet users (125/188) claimed that their physicians did not recommend digital media platforms. About 44\% (82/188) of the individuals reported to seek for additional information due to the insufficient information provided by their physician. Conclusions: This study revealed the importance of digital media in the context of psoriasis, especially among women, individuals with shorter disease duration, and moderate mental disease severity. The lack of physicians' digital media recommendations despite their patients' desire to receive such and being more involved in health-related decisions seems to be a shortcoming within the physician-patient relationships. Physicians should guide their patients on digital media by recommending platforms with evidence-based information, thereby potentially creating an adequate framework for shared decision-making. Future research should focus on strategies to prevent the spread of false information on digital media and address the needs of patients and physicians to enhance health-related digital media offerings. ", doi="10.2196/57823", url="https://www.jmir.org/2024/1/e57823" } @Article{info:doi/10.2196/48284, author="Gwon, Nahyun and Jeong, Wonjeong and Kim, Hyun Jee and Oh, Hee Kyoung and Jun, Kwan Jae", title="Effects of Intervention Timing on Health-Related Fake News: Simulation Study", journal="JMIR Form Res", year="2024", month="Aug", day="7", volume="8", pages="e48284", keywords="disinformation", keywords="fenbendazole", keywords="cancer information", keywords="simulation", keywords="fake news", keywords="online social networking", keywords="misinformation", keywords="lung cancer", abstract="Background: Fake health-related news has spread rapidly through the internet, causing harm to individuals and society. Despite interventions, a fenbendazole scandal recently spread among patients with lung cancer in South Korea. It is crucial to intervene appropriately to prevent the spread of fake news. Objective: This study investigated the appropriate timing of interventions to minimize the side effects of fake news. Methods: A simulation was conducted using the susceptible-infected-recovered (SIR) model, which is a representative model of the virus spread mechanism. We applied this model to the fake news spread mechanism. The parameters were set similarly to those in the digital environment, where the fenbendazole scandal occurred. NetLogo, an agent-based model, was used as the analytical tool. Results: Fake news lasted 278 days in the absence of interventions. As a result of adjusting and analyzing the timing of the intervention in response to the fenbendazole scandal, we found that faster intervention leads to a shorter duration of fake news (intervention at 54 days = fake news that lasted for 210 days; intervention at 16 days = fake news that lasted for 187 days; and intervention at 10 days = fake news that lasted for 157 days). However, no significant differences were observed when the intervention was performed within 10 days. Conclusions: Interventions implemented within 10 days were effective in reducing the duration of the spread of fake news. Our findings suggest that timely intervention is critical for preventing the spread of fake news in the digital environment. Additionally, a monitoring system that can detect fake news should be developed for a rapid response ", doi="10.2196/48284", url="https://formative.jmir.org/2024/1/e48284" } @Article{info:doi/10.2196/51317, author="Postma, J. Doerine and Heijkoop, A. Magali L. and De Smet, M. Peter A. G. and Notenboom, Kim and Leufkens, M. Hubert G. and Mantel-Teeuwisse, K. Aukje", title="Identifying Medicine Shortages With the Twitter Social Network: Retrospective Observational Study", journal="J Med Internet Res", year="2024", month="Aug", day="6", volume="26", pages="e51317", keywords="medicine shortages", keywords="signal detection", keywords="social media", keywords="Twitter social network", keywords="drug shortage", keywords="Twitter", abstract="Background: Early identification is critical for mitigating the impact of medicine shortages on patients. The internet, specifically social media, is an emerging source of health data. Objective: This study aimed to explore whether a routine analysis of data from the Twitter social network can detect signals of a medicine shortage and serve as an early warning system and, if so, for which medicines or patient groups. Methods: Medicine shortages between January 31 and December 1, 2019, were collected from the Dutch pharmacists' society's national catalog Royal Dutch Pharmacists Association (KNMP) Farmanco. Posts on these shortages were collected by searching for the name, the active pharmaceutical ingredient, or the first word of the brand name of the medicines in shortage. Posts were then selected based on relevant keywords that potentially indicated a shortage and the percentage of shortages with at least 1 post was calculated. The first posts per shortage were analyzed for their timing (median number of days, including the IQR) versus the national catalog, also stratified by disease and medicine characteristics. The content of the first post per shortage was analyzed descriptively for its reporting stakeholder and the nature of the post. Results: Of the 341 medicine shortages, 102 (29.9\%) were mentioned on Twitter. Of these 102 shortages, 18 (5.3\% of the total) were mentioned prior to or simultaneous to publication by KNMP Farmanco. Only 4 (1.2\%) of these were mentioned on Twitter more than 14 days before. On average, posts were published with a median delay of 37 (IQR 7-81) days to publication by KNMP Farmanco. Shortages mentioned on Twitter affected a greater number of patients and lasted longer than those that were not mentioned. We could not conclusively relate either the presence or absence on Twitter to a disease area or route of administration of the medicine in shortage. The first posts on the 102 shortages were mainly published by patients (n=51, 50.0\%) and health care professionals (n=46, 45.1\%). We identified 8 categories of nature of content. Sharing personal experience (n=44, 43.1\%) was the most common category. Conclusions: The Twitter social network is not a suitable early warning system for medicine shortages. Twitter primarily echoes already-known information rather than spreads new information. However, Twitter or potentially any other social media platform provides the opportunity for future qualitative research in the increasingly important field of medicine shortages that investigates how a larger population of patients is affected by shortages. ", doi="10.2196/51317", url="https://www.jmir.org/2024/1/e51317" } @Article{info:doi/10.2196/52058, author="Cho, HyunYi and Li, Wenbo and Lopez, Rachel", title="A Multidimensional Approach for Evaluating Reality in Social Media: Mixed Methods Study", journal="J Med Internet Res", year="2024", month="Aug", day="6", volume="26", pages="e52058", keywords="fake", keywords="fact", keywords="misinformation", keywords="reality", keywords="social media", keywords="scale", keywords="measure", keywords="instrument", keywords="user-centric", keywords="tailoring", keywords="digital media literacy", abstract="Background: Misinformation is a threat to public health. The effective countering of misinformation may require moving beyond the binary classification of fake versus fact to capture the range of schemas that users employ to evaluate social media content. A more comprehensive understanding of user evaluation schemas is necessary. Objective: The goal of this research was to advance the current understanding of user evaluations of social media information and to develop and validate a measurement instrument for assessing social media realism. Methods: This research involved a sequence of 2 studies. First, we used qualitative focus groups (n=48). Second, building on the first study, we surveyed a national sample of social media users (n=442). The focus group data were analyzed using the constant comparison approach. The survey data were analyzed using confirmatory factor analyses and ordinary least squares regression. Results: The findings showed that social media reality evaluation involves 5 dimensions: falsity, naturality, authenticity, resonance, and social assurance. These dimensions were differentially mapped onto patterns of social media use. Authenticity was strongly associated with the existing global measure of social media realism (P<.001). Naturality, or the willingness to accept artificiality and engineered aspects of social media representations, was linked to hedonic enjoyment (P<.001). Resonance predicted reflective thinking (P<.001), while social assurance was strongly related to addictive use (P<.001). Falsity, the general belief that much of what is on social media is not real, showed a positive association with both frequency (P<.001) and engagement with (P=.003) social media. These results provide preliminary validity data for a social media reality measure that encompasses multiple evaluation schemas for social media content. Conclusions: The identification of divergent schemas expands the current focus beyond fake versus fact, while the goals, contexts, and outcomes of social media use associated with these schemas can guide future digital media literacy efforts. Specifically, the social media reality measure can be used to develop tailored digital media literacy interventions for addressing diverse public health issues. ", doi="10.2196/52058", url="https://www.jmir.org/2024/1/e52058", url="http://www.ncbi.nlm.nih.gov/pubmed/39106092" } @Article{info:doi/10.2196/56594, author="Wang, Qinqin and Liu, Lingjun and Li, Hong and Zhang, Qiao and Ma, Qianli", title="Quality of Chronic Obstructive Pulmonary Disease Information on the Chinese Internet: Website Evaluation Study", journal="JMIR Form Res", year="2024", month="Aug", day="1", volume="8", pages="e56594", keywords="chronic obstructive pulmonary disease", keywords="internet", keywords="information quality", keywords="DISCERN", keywords="websites", keywords="health information", keywords="DISCERN instrument", keywords="pulmonary disease", keywords="chronic pulmonary disease", keywords="cross-sectional study", keywords="website information", keywords="treatment", keywords="COPD", keywords="China", keywords="evaluation", keywords="pulmonary", keywords="chronic", abstract="Background: The development of internet technology has greatly increased the ability of patients with chronic obstructive pulmonary disease (COPD) to obtain health information, giving patients more initiative in the patient-physician decision-making process. However, concerns about the quality of website health information will affect the enthusiasm of patients' website search behavior. Therefore, it is necessary to evaluate the current situation of Chinese internet information on COPD. Objective: This study aims to evaluate the quality of COPD treatment information on the Chinese internet. Methods: Using the standard disease name ``????????'' (``chronic obstructive pulmonary disease'' in Chinese) and the commonly used public search terms ``???'' (``COPD'') and ``???'' (``emphysema'') combined with the keyword ``??'' (``treatment''), we searched the PC client web page of Baidu, Sogou, and 360 search engines and screened the first 50 links of the website from July to August 2021. The language was restricted to Chinese for all the websites. The DISCERN tool was used to evaluate the websites. Results: A total of 96 websites were included and analyzed. The mean overall DISCERN score for all websites was 30.4 (SD 10.3; range 17.3-58.7; low quality), no website reached the maximum DISCERN score of 75, and the mean score for each item was 2.0 (SD 0.7; range 1.2-3.9). There were significant differences in mean DISCERN scores between terms, with ``chronic obstructive pulmonary disease'' having the highest mean score. Conclusions: The quality of COPD information on the Chinese internet is poor, which is mainly reflected in the low reliability and relevance of COPD treatment information, which can easily lead consumers to make inappropriate treatment choices. The term ``chronic obstructive pulmonary disease'' has the highest DISCERN score among commonly used disease search terms. It is recommended that consumers use standard disease names when searching for website information, as the information obtained is relatively reliable. ", doi="10.2196/56594", url="https://formative.jmir.org/2024/1/e56594" } @Article{info:doi/10.2196/50871, author="Pascual-Ferr{\'a}, Paola and Alperstein, Neil and Burleson, Julia and Jamison, M. Amelia and Bhaktaram, Ananya and Rath, Sidharth and Ganjoo, Rohini and Mohanty, Satyanarayan and Barnett, J. Daniel and Rimal, N. Rajiv", title="Assessing Message Deployment During Public Health Emergencies Through Social Media: Empirical Test of Optimizing Content for Effective Dissemination", journal="J Med Internet Res", year="2024", month="Jul", day="26", volume="26", pages="e50871", keywords="message testing", keywords="web-based communication", keywords="user engagement", keywords="vaccine communication", keywords="methodology", keywords="Meta", keywords="Facebook", keywords="advertising", keywords="infodemic", keywords="communication", keywords="infodemiology", keywords="social media advertising tool", keywords="social media", keywords="audience", keywords="engagement", keywords="rapid message testing at scale", keywords="mobile phone", abstract="Background: During an infodemic, timely, reliable, and accessible information is crucial to combat the proliferation of health misinformation. While message testing can provide vital information to make data-informed decisions, traditional methods tend to be time- and resource-intensive. Recognizing this need, we developed the rapid message testing at scale (RMTS) approach to allow communicators to repurpose existing social media advertising tools and understand the full spectrum of audience engagement. Objective: We had two main objectives: (1) to demonstrate the use of the RMTS approach for message testing, especially when resources and time are limited, and (2) to propose and test the efficacy of an outcome variable that measures engagement along a continuum of viewing experience. Methods: We developed 12 versions of a single video created for a vaccine confidence project in India. We manipulated video length, aspect ratio, and use of subtitles. The videos were tested across 4 demographic groups (women or men, younger or older). We assessed user engagement along a continuum of viewing experience: obtaining attention, sustaining attention, conveying the message, and inspiring action. These were measured by the percentage of video watched and clicks on the call-to-action link. Results: The video advertisements were placed on Facebook for over 4 consecutive days at the cost of US \$450 and garnered a total of 3.34 million impressions. Overall, we found that the best-performing video was the shorter version in portrait aspect ratio and without subtitles. There was a significant but small association between the length of the video and users' level of engagement at key points along the continuum of viewing experience (N=1,032,888; $\chi$24=48,261.97; P<.001; V=.22). We found that for the longer video, those with subtitles held viewers longer after 25\% video watch time than those without subtitles (n=15,597; $\chi$21=7.33; P=.007; V=.02). While we found some significant associations between the aspect ratio, the use of subtitles, and the number of users watching the video and clicking on the call-to-action link, the effect size for those were extremely small. Conclusions: This test served as a proof of concept for the RMTS approach. We obtained rapid feedback on formal message attributes from a very large sample. The results of this test reinforce the need for platform-specific tailoring of communications. While our data showed a general preference for a short video in portrait orientation and without subtitles among our target audiences on Facebook, that may not necessarily be the case in other social media platforms such as YouTube or TikTok, where users go primarily to watch videos. RMTS testing highlights nuances that communication professionals can address instead of being limited to a ``one size fits all'' approach. ", doi="10.2196/50871", url="https://www.jmir.org/2024/1/e50871", url="http://www.ncbi.nlm.nih.gov/pubmed/38861266" } @Article{info:doi/10.2196/53404, author="Kelsall, Clancy Nora and Gimbrone, Catherine and Olfson, Mark and Gould, S. Madelyn and Shaman, Jeffrey and Keyes, Katherine", title="Association Between Prosuicide Website Searches Through Google and Suicide Death in the United States From 2010 to 2021: Lagged Time-Series Analysis", journal="J Med Internet Res", year="2024", month="Jul", day="26", volume="26", pages="e53404", keywords="pro-suicide forum", keywords="suicide", keywords="google search", keywords="social media", keywords="online forum", keywords="internet search", keywords="death", keywords="United States", keywords="suicide death", keywords="forum", keywords="analysis", keywords="association", keywords="poisoning", keywords="suffocation", abstract="Background: ?The rate of suicide death has been increasing, making understanding risk factors of growing importance. While exposure to explicit suicide-related media, such as description of means in news reports or sensationalized fictional portrayal, is known to increase population suicide rates, it is not known whether prosuicide website forums, which often promote or facilitate information about fatal suicide means, are related to change in suicide deaths overall or by specific means. Objective: ?This study aimed to estimate the association of the frequency of Google searches of known prosuicide web forums and content with death by suicide over time in the United States, by age, sex, and means of death. Methods: ?National monthly Google search data for names of common prosuicide websites between January 2010 and December 2021 were extracted from Google Health Trends API (application programming interface). Suicide deaths were identified using the CDC (Centers for Disease Control and Prevention) National Vital Statistics System (NVSS), and 3 primary means of death were identified (poisoning, suffocation, and firearm). Distributed lag nonlinear models (DLNMs) were then used to estimate the lagged association between the number of Google searches on suicide mortality, stratified by age, sex, and means, and adjusted for month. Sensitivity analyses, including using autoregressive integrated moving average (ARIMA) modeling approaches, were also conducted. Results: ?Months in the United States in which search rates for prosuicide websites increased had more documented deaths by intentional poisoning and suffocation among both adolescents and adults. For example, the risk of poisoning suicide among youth and young adults (age 10-24 years) was 1.79 (95\% CI 1.06-3.03) times higher in months with 22 searches per 10 million as compared to 0 searches. The risk of poisoning suicide among adults aged 25-64 was 1.10 (95\% CI 1.03-1.16) times higher 1 month after searches reached 9 per 10 million compared with 0 searches. We also observed that increased search rates were associated with fewer youth suicide deaths by firearms with a 3-month time lag for adolescents. These models were robust to sensitivity tests. Conclusions: ?Although more analysis is needed, the findings are suggestive of an association between increased prosuicide website access and increased suicide deaths, specifically deaths by poisoning and suffocation. These findings emphasize the need to further investigate sites containing potentially dangerous information and their associations with deaths by suicide, as they may affect vulnerable individuals. ", doi="10.2196/53404", url="https://www.jmir.org/2024/1/e53404" } @Article{info:doi/10.2196/53904, author="Roschke, Kristy and Koskan, M. Alexis and Sivanandam, Shalini and Irby, Jonathan", title="Partisan Media, Trust, and Media Literacy: Regression Analysis of Predictors of COVID-19 Knowledge", journal="JMIR Form Res", year="2024", month="Jul", day="24", volume="8", pages="e53904", keywords="COVID-19", keywords="misinformation", keywords="media literacy", keywords="news consumption", keywords="institutional trust", keywords="media", keywords="trust", keywords="prevention", keywords="control", keywords="health care professional", keywords="health care", abstract="Background: The COVID-19 pandemic was a devastating public health event that spurred an influx of misinformation. The increase in questionable health content was aided by the speed and scale of digital and social media and certain news agencies' and politicians' active dissemination of misinformation about the virus. The popularity of certain COVID-19 myths created confusion about effective health protocols and impacted trust in the health care and government sectors deployed to manage the pandemic. Objective: This study explored how people's information habits, their level of institutional trust, the news media outlets they consume and the technologies in which they access it, and their media literacy skills influenced their COVID-19 knowledge. Methods: We administered a web-based survey using Amazon Mechanical Turk (MTurk) to assess US adults' (n=1498) COVID-19 knowledge, media and news habits, media literacy skills, and trust in government and health-related institutions. The data were analyzed using a hierarchical linear regression to examine the association between trust, media literacy, news use, and COVID-19 knowledge. Results: The regression model of demographic variables, political affiliation, trust in institutions, media literacy, and the preference for watching Fox or CNN was statistically significant (R2=0.464; F24,1434=51.653; P<.001; adjusted R2=0.455) in predicting COVID-19 knowledge scores. People who identified as politically conservative, watched Fox News, and reported lower levels of institutional trust and media literacy, scored lower on COVID-19 knowledge questions than those who identified as politically liberal, did not watch Fox News and reported higher levels of institutional trust and media literacy. Conclusions: This study suggests that the media outlets people turn to, their trust in institutions, and their perceived degree of agency to discern credible information can impact people's knowledge of COVID-19, which has potential implications for managing communication in other public health events. ", doi="10.2196/53904", url="https://formative.jmir.org/2024/1/e53904" } @Article{info:doi/10.2196/55797, author="Vargas Meza, Xanat and Oikawa, Masanori", title="Japanese Perception of Organ Donation and Implications for New Medical Technologies: Quantitative and Qualitative Social Media Analyses", journal="JMIR Form Res", year="2024", month="Jul", day="19", volume="8", pages="e55797", keywords="Japan", keywords="organ donation", keywords="social media", keywords="multidimensional analysis", keywords="Twitter/X", keywords="YouTube", abstract="Background: The Rapid Autopsy Program (RAP) is a valuable procedure for studying human biology and diseases such as cancer. However, implementing the RAP in Japan necessitates a thorough understanding of concepts such as good death and the integration of sociocultural aspects. By revising perceptions of organ donation on social media, we bring attention to the challenges associated with implementing new medical research procedures such as the RAP. Objective: This study aims to examine YouTube and Twitter/X to identify stakeholders, evaluate the quality of organ donation communication, and analyze sociocultural aspects associated with organ donation. Based on our findings, we propose recommendations for the implementation of new medical research procedures. Methods: Using the term ``????'' (organ donation), we collected data from YouTube and Twitter/X, categorizing them into 5 dimensions: time, individuality, place, activity, and relationships. We utilized a scale to evaluate the quality of organ donation information and categorized YouTube videos into 3 groups to analyze their differences using statistical methods. Additionally, we conducted a text-based analysis to explore narratives associated with organ donation. Results: Most YouTube videos were uploaded in 2021 (189/638, 29.6\%) and 2022 (165/638, 25.9\%), while tweets about organ donation peaked between 2019 and 2022. Citizens (184/770, 23.9\%), media (170/770, 22.0\%), and unknown actors (121/770, 15.7\%) were the primary uploaders of videos on organ donation. In a sample of average retweeted and liked tweets, citizens accounted for the majority of identified users (64/91, 70\%, and 65/95, 68\%, respectively). Regarding Japanese regions, there were numerous information videos about organ donation in Hokkaido (F2.46,147.74=--5.28, P=.005) and Kyushu and Okinawa (F2.46,147.74=--5.28, P=.005). On Twitter/X, Japan and China were the most frequently mentioned countries in relation to organ donation discussions. Information videos often focused on themes such as borrowed life and calls to register as donors, whereas videos categorized as no information and misinformation frequently included accusations of organ trafficking, often propagated by Chinese-American media. Tweets primarily centered around statements of donation intention and discussions about family consent. The majority of video hyperlinks directed users to YouTube and Twitter/X platforms, while Twitter/X hyperlinks predominantly led to news reports from Japanese media outlets. Conclusions: There is significant potential to implement new medical research procedures such as the RAP in Japan. Recommendations include conceptualizing research data as borrowed data, implementing horizontally diversified management of donation programs, and addressing issues related to science misinformation and popular culture trends. ", doi="10.2196/55797", url="https://formative.jmir.org/2024/1/e55797", url="http://www.ncbi.nlm.nih.gov/pubmed/39028549" } @Article{info:doi/10.2196/59794, author="Jaiswal, Aditi and Shah, Aekta and Harjadi, Christopher and Windgassen, Erik and Washington, Peter", title="Ethics of the Use of Social Media as Training Data for AI Models Used for Digital Phenotyping", journal="JMIR Form Res", year="2024", month="Jul", day="17", volume="8", pages="e59794", keywords="social media analytics", keywords="machine learning", keywords="ethics", keywords="research ethics", keywords="consent", keywords="scientific integrity", doi="10.2196/59794", url="https://formative.jmir.org/2024/1/e59794" } @Article{info:doi/10.2196/59546, author="Cho, Hyeongchan and Kim, Kyu-Min and Kim, Jee-Young and Youn, Bo-Young", title="Twitter Discussions on \#digitaldementia: Content and Sentiment Analysis", journal="J Med Internet Res", year="2024", month="Jul", day="16", volume="26", pages="e59546", keywords="digital dementia", keywords="dementia", keywords="public health", keywords="Twitter", keywords="social media", keywords="mobile phone", abstract="Background: Digital dementia is a term that describes a possible decline in cognitive abilities, especially memory, attributed to the excessive use of digital technology such as smartphones, computers, and tablets. This concept has gained popularity in public discourse and media lately. With the increasing use of social media platforms such as Twitter (subsequently rebranded as X), discussions about digital dementia have become more widespread, which offer a rich source of information to understand public perceptions, concerns, and sentiments regarding this phenomenon. Objective: The aim of this research was to delve into a comprehensive content and sentiment analysis of Twitter discussions regarding digital dementia using the hashtag \#digitaldementia. Methods: Retrospectively, publicly available English-language tweets with hashtag combinations related to the topic of digital dementia were extracted from Twitter. The tweets were collected over a period of 15 years, from January 1, 2008, to December 31, 2022. Content analysis was used to identify major themes within the tweets, and sentiment analysis was conducted to understand the positive and negative emotions associated with these themes in order to gain a better understanding of the issues surrounding digital dementia. A one-way ANOVA was performed to gather detailed statistical insights regarding the selected tweets from influencers within each theme. Results: This study was conducted on 26,290 tweets over 15 years by 5123 Twitter users, mostly female users in the United States. The influencers had followers ranging from 20,000 to 1,195,000 and an average of 214,878 subscribers. The study identified four themes regarding digital dementia after analyzing tweet content: (1) cognitive decline, (2) digital dependency, (3) technology overload, and (4) coping strategies. Categorized according to Glaser and Strauss's classifications, most tweets (14,492/26,290, 55.12\%) fell under the categories of wretched (purely negative) or bad (mostly negative). However, only a small proportion of tweets (3122/26,290, 11.86\%) were classified as great (purely positive) or swell sentiment (mostly positive). The ANOVA results showed significant differences in mean sentiment scores among the themes (F3,3581=29.03; P<.001). The mean sentiment score was --0.1072 (SD 0.4276). Conclusions: Various negative tweets have raised concerns about the link between excessive use of digital devices and cognitive decline, often known as digital dementia. Of particular concern is the rapid increase in digital device use. However, some positive tweets have suggested coping strategies. Engaging in digital detox activities, such as increasing physical exercise and participating in yoga and meditation, could potentially help prevent cognitive decline. ", doi="10.2196/59546", url="https://www.jmir.org/2024/1/e59546", url="http://www.ncbi.nlm.nih.gov/pubmed/39012679" } @Article{info:doi/10.2196/51327, author="Liu, Pinxin and Lou, Xubin and Xie, Zidian and Shang, Ce and Li, Dongmei", title="Public Perceptions and Discussions of the US Food and Drug Administration's JUUL Ban Policy on Twitter: Observational Study", journal="JMIR Form Res", year="2024", month="Jul", day="11", volume="8", pages="e51327", keywords="e-cigarettes", keywords="JUUL", keywords="Twitter", keywords="deep learning", keywords="FDA", keywords="Food and Drug Administration", keywords="vape", keywords="vaping", keywords="smoking", keywords="social media", keywords="regulation", abstract="Background: On June 23, 2022, the US Food and Drug Administration announced a JUUL ban policy, to ban all vaping and electronic cigarette products sold by Juul Labs. Objective: This study aims to understand public perceptions and discussions of this policy using Twitter (subsequently rebranded as X) data. Methods: Using the Twitter streaming application programming interface, 17,007 tweets potentially related to the JUUL ban policy were collected between June 22, 2022, and July 25, 2022. Based on 2600 hand-coded tweets, a deep learning model (RoBERTa) was trained to classify all tweets into propolicy, antipolicy, neutral, and irrelevant categories. A deep learning model (M3 model) was used to estimate basic demographics (such as age and gender) of Twitter users. Furthermore, major topics were identified using latent Dirichlet allocation modeling. A logistic regression model was used to examine the association of different Twitter users with their attitudes toward the policy. Results: Among 10,480 tweets related to the JUUL ban policy, there were similar proportions of propolicy and antipolicy tweets (n=2777, 26.5\% vs n=2666, 25.44\%). Major propolicy topics included ``JUUL causes youth addition,'' ``market surge of JUUL,'' and ``health effects of JUUL.'' In contrast, major antipolicy topics included ``cigarette should be banned instead of JUUL,'' ``against the irrational policy,'' and ``emotional catharsis.'' Twitter users older than 29 years were more likely to be propolicy (have a positive attitude toward the JUUL ban policy) than those younger than 29 years. Conclusions: Our study showed that the public showed different responses to the JUUL ban policy, which varies depending on the demographic characteristics of Twitter users. Our findings could provide valuable information to the Food and Drug Administration for future electronic cigarette and other tobacco product regulations. ", doi="10.2196/51327", url="https://formative.jmir.org/2024/1/e51327", url="http://www.ncbi.nlm.nih.gov/pubmed/38990633" } @Article{info:doi/10.2196/49879, author="Foriest, C. Jasmine and Mittal, Shravika and Kim, Eugenia and Carmichael, Andrea and Lennon, Natalie and Sumner, A. Steven and De Choudhury, Munmun", title="News Media Framing of Suicide Circumstances and Gender: Mixed Methods Analysis", journal="JMIR Ment Health", year="2024", month="Jul", day="3", volume="11", pages="e49879", keywords="suicide", keywords="framing", keywords="disparities", keywords="reporting guidelines", keywords="gender", keywords="stigma", keywords="glorification", keywords="glorify", keywords="glorifying", keywords="suicidal", keywords="self harm", keywords="suicides", keywords="stigmatizing", keywords="stigmatization", keywords="reporting", keywords="news", keywords="journalist", keywords="journalists", keywords="journalism", keywords="machine learning", keywords="NLP", keywords="natural language processing", keywords="LLM", keywords="LLMs", keywords="language model", keywords="language models", keywords="linguistic", keywords="linguistics", keywords="reporter", keywords="reporters", keywords="digital mental health", keywords="mHealth", keywords="media", abstract="Background: Suicide is a leading cause of death worldwide. Journalistic reporting guidelines were created to curb the impact of unsafe reporting; however, how suicide is framed in news reports may differ by important characteristics such as the circumstances and the decedent's gender. Objective: This study aimed to examine the degree to which news media reports of suicides are framed using stigmatized or glorified language and differences in such framing by gender and circumstance of suicide. Methods: We analyzed 200 news articles regarding suicides and applied the validated Stigma of Suicide Scale to identify stigmatized and glorified language. We assessed linguistic similarity with 2 widely used metrics, cosine similarity and mutual information scores, using a machine learning--based large language model. Results: News reports of male suicides were framed more similarly to stigmatizing (P<.001) and glorifying (P=.005) language than reports of female suicides. Considering the circumstances of suicide, mutual information scores indicated that differences in the use of stigmatizing or glorifying language by gender were most pronounced for articles attributing legal (0.155), relationship (0.268), or mental health problems (0.251) as the cause. Conclusions: Linguistic differences, by gender, in stigmatizing or glorifying language when reporting suicide may exacerbate suicide disparities. ", doi="10.2196/49879", url="https://mental.jmir.org/2024/1/e49879" } @Article{info:doi/10.2196/52992, author="Huo, Weixue and He, Mengwei and Zeng, Zhaoxiang and Bao, Xianhao and Lu, Ye and Tian, Wen and Feng, Jiaxuan and Feng, Rui", title="Impact Analysis of COVID-19 Pandemic on Hospital Reviews on Dianping Website in Shanghai, China: Empirical Study", journal="J Med Internet Res", year="2024", month="Jul", day="2", volume="26", pages="e52992", keywords="patient satisfaction", keywords="physician-patient relationship", keywords="ChatGPT", keywords="patient concern", keywords="COVID-19", abstract="Background: In the era of the internet, individuals have increasingly accustomed themselves to gathering necessary information and expressing their opinions on public web-based platforms. The health care sector is no exception, as these comments, to a certain extent, influence people's health care decisions. During the onset of the COVID-19 pandemic, how the medical experience of Chinese patients and their evaluations of hospitals have changed remains to be studied. Therefore, we plan to collect patient medical visit data from the internet to reflect the current status of medical relationships under specific circumstances. Objective: This study aims to explore the differences in patient comments across various stages (during, before, and after) of the COVID-19 pandemic, as well as among different types of hospitals (children's hospitals, maternity hospitals, and tumor hospitals). Additionally, by leveraging ChatGPT (OpenAI), the study categorizes the elements of negative hospital evaluations. An analysis is conducted on the acquired data, and potential solutions that could improve patient satisfaction are proposed. This study is intended to assist hospital managers in providing a better experience for patients who are seeking care amid an emergent public health crisis. Methods: Selecting the top 50 comprehensive hospitals nationwide and the top specialized hospitals (children's hospitals, tumor hospitals, and maternity hospitals), we collected patient reviews from these hospitals on the Dianping website. Using ChatGPT, we classified the content of negative reviews. Additionally, we conducted statistical analysis using SPSS (IBM Corp) to examine the scoring and composition of negative evaluations. Results: A total of 30,317 pieces of effective comment information were collected from January 1, 2018, to August 15, 2023, including 7696 pieces of negative comment information. Manual inspection results indicated that ChatGPT had an accuracy rate of 92.05\%. The F1-score was 0.914. The analysis of this data revealed a significant correlation between the comments and ratings received by hospitals during the pandemic. Overall, there was a significant increase in average comment scores during the outbreak (P<.001). Furthermore, there were notable differences in the composition of negative comments among different types of hospitals (P<.001). Children's hospitals received sensitive feedback regarding waiting times and treatment effectiveness, while patients at maternity hospitals showed a greater concern for the attitude of health care providers. Patients at tumor hospitals expressed a desire for timely examinations and treatments, especially during the pandemic period. Conclusions: The COVID-19 pandemic had some association with patient comment scores. There were variations in the scores and content of comments among different types of specialized hospitals. Using ChatGPT to analyze patient comment content represents an innovative approach for statistically assessing factors contributing to patient dissatisfaction. The findings of this study could provide valuable insights for hospital administrators to foster more harmonious physician-patient relationships and enhance hospital performance during public health emergencies. ", doi="10.2196/52992", url="https://www.jmir.org/2024/1/e52992" } @Article{info:doi/10.2196/58040, author="Okuhara, Tsuyoshi and Terada, Marina and Okada, Hiroko and Kiuchi, Takahiro", title="Experiences of Governments and Public Health Agencies Regarding Crisis Communication During the COVID-19 Pandemic in the Digital Age: Protocol for a Systematic Review of Qualitative Studies", journal="JMIR Res Protoc", year="2024", month="Jun", day="27", volume="13", pages="e58040", keywords="COVID-19", keywords="health communication", keywords="infodemic", keywords="misinformation", keywords="social media", keywords="SARS-CoV-2", keywords="coronavirus", keywords="pandemic", keywords="infectious", keywords="digital age", keywords="systematic review", keywords="internet", keywords="public health", keywords="government", keywords="governments", keywords="crisis communication", keywords="qualitative", keywords="methodology", keywords="disinformation", keywords="eHealth", keywords="digital health", keywords="medical informatics", abstract="Background: Governments and public health agencies worldwide experienced difficulties with social media--mediated infodemics on the internet during the COVID-19 pandemic. Existing public health crisis communication strategies need to be updated. However, crisis communication experiences of governments and public health agencies worldwide during the COVID-19 pandemic have not been systematically compiled, necessitating updated crisis communication strategies. Objective: This systematic review aims to collect and organize the crisis communication experiences of senders (ie, governments and public health agencies) during the COVID-19 pandemic. Our focus is on exploring the difficulties that governments and public health agencies experienced, best practices in crisis communication by governments and public health agencies during the COVID-19 pandemic in times of infodemic, and challenges that should be overcome in future public health crises. Methods: We plan to begin the literature search on May 1, 2024. We will search PubMed, MEDLINE, CINAHL, PsycINFO, PsycARTICLES, Communication Abstracts, and Web of Science. We will filter our database searches to search from the year 2020 and beyond. We will use a combination of keywords by referring to the SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, and Research type) tool to search the abstracts in databases. We intend to include qualitative studies on crisis communication by governments and public health agencies (eg, officials, staff, health professionals, and researchers) to the public. Quantitative data--based studies will be excluded. Only papers written in English will be included. Data on study characteristics, study aim, participant characteristics, methodology, theoretical framework, object of crisis communication, and key results will be extracted. The methodological quality of eligible studies will be assessed using the Joanna Briggs Institute critical appraisal checklist for qualitative research. A total of 2 independent reviewers will share responsibility for screening publications, data extraction, and quality assessment. Disagreement will be resolved through discussion, and the third reviewer will be consulted, if necessary. The findings will be summarized in a table and a conceptual diagram and synthesized in a descriptive and narrative review. Results: The results will be systematically integrated and presented in a way that corresponds to our research objectives and interests. We expect the results of this review to be submitted for publication by the end of 2024. Conclusions: To our knowledge, this will be the first systematic review of the experiences of governments and public health agencies regarding their crisis communication to the public during the COVID-19 pandemic. This review will contribute to the future improvement of the guidelines for crisis communication by governments and public health agencies to the public. Trial Registration: PROSPERO CRD42024528975; https://tinyurl.com/4fjmd8te International Registered Report Identifier (IRRID): PRR1-10.2196/58040 ", doi="10.2196/58040", url="https://www.researchprotocols.org/2024/1/e58040", url="http://www.ncbi.nlm.nih.gov/pubmed/38935414" } @Article{info:doi/10.2196/50453, author="Szeto, D. Mindy and Hook Sobotka, Michelle and Woolhiser, Emily and Parmar, Pritika and Wu, Jieying and Alhanshali, Lina and Dellavalle, P. Robert", title="PatientsLikeMe and Online Patient Support Communities in Dermatology", journal="JMIR Dermatol", year="2024", month="Jun", day="26", volume="7", pages="e50453", keywords="PatientsLikeMe", keywords="PLM", keywords="online support communities", keywords="social media", keywords="forums", keywords="discussion boards", keywords="internet", keywords="misinformation", keywords="community engagement", keywords="representation", keywords="demographics", keywords="lived experience", keywords="atopic dermatitis", keywords="prevalence", doi="10.2196/50453", url="https://derma.jmir.org/2024/1/e50453" } @Article{info:doi/10.2196/49077, author="Terada, Marina and Okuhara, Tsuyoshi and Yokota, Rie and Kiuchi, Takahiro and Murakami, Kentaro", title="Nutrients and Foods Recommended for Blood Pressure Control on Twitter in Japan: Content Analysis", journal="J Med Internet Res", year="2024", month="Jun", day="20", volume="26", pages="e49077", keywords="Twitter", keywords="food", keywords="nutrition", keywords="misinformation", keywords="salt", keywords="content analysis", keywords="hypertension", keywords="blood pressure", keywords="sodium", keywords="salt reduction", abstract="Background: Management and prevention of hypertension are important public health issues. Healthy dietary habits are one of the modifiable factors. As Twitter (subsequently rebranded X) is a digital platform that can influence public eating behavior, there is a knowledge gap regarding the information about foods and nutrients recommended for blood pressure control and who disseminates them on Twitter. Objective: This study aimed to investigate the nature of the information people are exposed to on Twitter regarding nutrients and foods for blood pressure control. Methods: A total of 147,898 Japanese tweets were extracted from January 1, 2022, to December 31, 2022. The final sample of 2347 tweets with at least 1 retweet was manually coded into categories of food groups, nutrients, user characteristics, and themes. The number and percentage of tweets, retweets, and themes in each category were calculated. Results: Of the 2347 tweets, 80\% (n=1877) of tweets mentioned foods, which were categorized into 17 different food groups. Seasonings and spices, including salt, were most frequently mentioned (1356/1877, 72.2\%). This was followed by vegetable and fruit groups. The 15 kinds of nutrients were mentioned in 1566 tweets, with sodium being the largest proportion at 83.1\% (n=1301), followed by potassium at 8.4\% (n=132). There was misinformation regarding salt intake for hypertension, accounting for 40.8\% (n=531) of tweets referring to salt, including recommendations for salt intake to lower blood pressure. In total, 75\% (n=21) of tweets from ``doctors'' mentioned salt reduction is effective for hypertension control, while 31.1\% (n=74) of tweets from ``health, losing weight, and beauty-related users,'' 25.9\% (n=429) of tweets from ``general public,'' and 23.5\% (n=4) tweets from ``dietitian or registered dietitian'' denied salt reduction for hypertension. The antisalt reduction tweets accounted for 31.5\% (n=106) of the most disseminated tweets related to nutrients and foods for blood pressure control. Conclusions: The large number of tweets in this study indicates a high interest in nutrients and foods for blood pressure control. Misinformation asserting antisalt reduction was posted primarily by the general public and self-proclaimed health experts. The number of tweets from nutritionists, registered dietitians, and doctors who were expected to correct misinformation and promote salt reduction was relatively low, and their messages were not always positive toward salt reduction. There is a need for communication strategies to combat misinformation, promote correct information on salt reduction, and train health care professionals to effectively communicate evidence-based information on this topic. ", doi="10.2196/49077", url="https://www.jmir.org/2024/1/e49077" } @Article{info:doi/10.2196/58056, author="Frennesson, Felicia Nessie and Barnett, Julie and Merouani, Youssouf and Attwood, Angela and Zuccolo, Luisa and McQuire, Cheryl", title="Analyzing Questions About Alcohol in Pregnancy Using Web-Based Forum Topics: Qualitative Content Analysis", journal="JMIR Infodemiology", year="2024", month="Jun", day="20", volume="4", pages="e58056", keywords="social media", keywords="web-based forum", keywords="alcohol", keywords="pregnancy", keywords="prenatal health", keywords="prenatal alcohol exposure", abstract="Background: Prenatal alcohol exposure represents a substantial public health concern as it may lead to detrimental outcomes, including pregnancy complications and fetal alcohol spectrum disorder. Although UK national guidance recommends abstaining from alcohol if pregnant or planning a pregnancy, evidence suggests that confusion remains on this topic among members of the public, and little is known about what questions people have about consumption of alcohol in pregnancy outside of health care settings. Objective: This study aims to assess what questions and topics are raised on alcohol in pregnancy on a web-based UK-based parenting forum and how these correspond to official public health guidelines with respect to 2 critical events: the implementation of the revised UK Chief Medical Officers' (CMO) low-risk drinking guidelines (2016) and the first COVID-19 pandemic lockdown (2020). Methods: All thread starts mentioning alcohol in the ``Pregnancy'' forum were collected from Mumsnet for the period 2002 to 2022 and analyzed using qualitative content analysis. Descriptive statistics were used to characterize the number and proportion of thread starts for each topic over the whole study period and for the periods corresponding to the change in CMO guidance and the COVID-19 pandemic. Results: A total of 395 thread starts were analyzed, and key topics included ``Asking for advice on whether it is safe to consume alcohol'' or on ``safe limits'' and concerns about having consumed alcohol before being aware of a pregnancy. In addition, the Mumsnet thread starts included discussions and information seeking on ``Research, guidelines, and official information about alcohol in pregnancy.'' Topics discussed on Mumsnet regarding alcohol in pregnancy remained broadly similar between 2002 and 2022, although thread starts disclosing prenatal alcohol use were more common before the introduction of the revised CMO guidance than in later periods. Conclusions: Web-based discussions within a UK parenting forum indicated that users were often unclear on guidance and risks associated with prenatal alcohol use and that they used this platform to seek information and reassurance from peers. ", doi="10.2196/58056", url="https://infodemiology.jmir.org/2024/1/e58056", url="http://www.ncbi.nlm.nih.gov/pubmed/38900536" } @Article{info:doi/10.2196/51094, author="Raber, Margaret and Allen, Haley and Huang, Sophia and Vazquez, Maria and Warner, Echo and Thompson, Debbe", title="Mediterranean Diet Information on TikTok and Implications for Digital Health Promotion Research: Social Media Content Analysis", journal="JMIR Form Res", year="2024", month="Jun", day="19", volume="8", pages="e51094", keywords="misinformation", keywords="social media", keywords="Mediterranean Diet", keywords="content analysis", keywords="health communication", keywords="communication", keywords="TikTok", keywords="diet", keywords="cardiometabolic disease", keywords="cardiometabolic", keywords="consumer", keywords="eating", keywords="quality", keywords="mHealth", keywords="mobile health", keywords="digital health", keywords="promotion research", keywords="nutrition therapy", keywords="healthy diet", abstract="Background: The Mediterranean diet has been linked to reduced risk for several cardiometabolic diseases. The lack of a clear definition of the Mediterranean diet in the scientific literature and the documented proliferation of nutrition misinformation on the internet suggest the potential for confusion among consumers seeking web-based Mediterranean diet information. Objective: We conducted a social media content analysis of information about the Mediterranean diet on the influential social media platform, TikTok, to examine public discourse about the diet and identify potential areas of misinformation. We then analyzed these findings in the context of health promotion to identify potential challenges and opportunities for the use of TikTok in promoting the Mediterranean diet for healthy living. Methods: The first-appearing 202 TikTok posts that resulted from a search of the hashtag \#mediterraneandiet were downloaded and qualitatively examined. Post features and characteristics, poster information, and engagement metrics were extracted and synthesized across posts. Posts were categorized as those created by health professionals and those created by nonhealth professionals based on poster-reported credentials. In addition to descriptive statistics of the entire sample, we compared posts created by professionals and nonprofessionals for content using chi-square tests. Results: TikTok posts varied in content, but posts that were developed by health professionals versus nonprofessionals were more likely to offer a definition of the Mediterranean diet (16/106, 15.1\% vs 2/96, 2.1\%; P=.001), use scientific citations to support claims (26/106, 24.5\% vs 0/96, 0\%; P<.001), and discuss specific nutrients (33/106, 31.1\% vs 6/96, 6.3\%; P<.001) and diseases related to the diet (27/106, 25.5\% vs 5/96, 5.2\%; P<.001) compared to posts created by nonhealth professionals. Conclusions: Social media holds promise as a venue to promote the Mediterranean diet, but the variability in information found in this study highlights the need to create clear definitions about the diet and its components when developing Mediterranean diet interventions that use new media structures. ", doi="10.2196/51094", url="https://formative.jmir.org/2024/1/e51094" } @Article{info:doi/10.2196/46176, author="Karapetiantz, Pierre and Audeh, Bissan and Redjdal, Akram and Tiffet, Th{\'e}ophile and Bousquet, C{\'e}dric and Jaulent, Marie-Christine", title="Monitoring Adverse Drug Events in Web Forums: Evaluation of a Pipeline and Use Case Study", journal="J Med Internet Res", year="2024", month="Jun", day="18", volume="26", pages="e46176", keywords="pharmacovigilance", keywords="social media", keywords="scraper", keywords="natural language processing", keywords="signal detection", keywords="graphical user interface", abstract="Background: To mitigate safety concerns, regulatory agencies must make informed decisions regarding drug usage and adverse drug events (ADEs). The primary pharmacovigilance data stem from spontaneous reports by health care professionals. However, underreporting poses a notable challenge within the current system. Explorations into alternative sources, including electronic patient records and social media, have been undertaken. Nevertheless, social media's potential remains largely untapped in real-world scenarios. Objective: The challenge faced by regulatory agencies in using social media is primarily attributed to the absence of suitable tools to support decision makers. An effective tool should enable access to information via a graphical user interface, presenting data in a user-friendly manner rather than in their raw form. This interface should offer various visualization options, empowering users to choose representations that best convey the data and facilitate informed decision-making. Thus, this study aims to assess the potential of integrating social media into pharmacovigilance and enhancing decision-making with this novel data source. To achieve this, our objective was to develop and assess a pipeline that processes data from the extraction of web forum posts to the generation of indicators and alerts within a visual and interactive environment. The goal was to create a user-friendly tool that enables regulatory authorities to make better-informed decisions effectively. Methods: To enhance pharmacovigilance efforts, we have devised a pipeline comprising 4 distinct modules, each independently editable, aimed at efficiently analyzing health-related French web forums. These modules were (1) web forums' posts extraction, (2) web forums' posts annotation, (3) statistics and signal detection algorithm, and (4) a graphical user interface (GUI). We showcase the efficacy of the GUI through an illustrative case study involving the introduction of the new formula of Levothyrox in France. This event led to a surge in reports to the French regulatory authority. Results: Between January 1, 2017, and February 28, 2021, a total of 2,081,296 posts were extracted from 23 French web forums. These posts contained 437,192 normalized drug-ADE couples, annotated with the Anatomical Therapeutic Chemical (ATC) Classification and Medical Dictionary for Regulatory Activities (MedDRA). The analysis of the Levothyrox new formula revealed a notable pattern. In August 2017, there was a sharp increase in posts related to this medication on social media platforms, which coincided with a substantial uptick in reports submitted by patients to the national regulatory authority during the same period. Conclusions: We demonstrated that conducting quantitative analysis using the GUI is straightforward and requires no coding. The results aligned with prior research and also offered potential insights into drug-related matters. Our hypothesis received partial confirmation because the final users were not involved in the evaluation process. Further studies, concentrating on ergonomics and the impact on professionals within regulatory agencies, are imperative for future research endeavors. We emphasized the versatility of our approach and the seamless interoperability between different modules over the performance of individual modules. Specifically, the annotation module was integrated early in the development process and could undergo substantial enhancement by leveraging contemporary techniques rooted in the Transformers architecture. Our pipeline holds potential applications in health surveillance by regulatory agencies or pharmaceutical companies, aiding in the identification of safety concerns. Moreover, it could be used by research teams for retrospective analysis of events. ", doi="10.2196/46176", url="https://www.jmir.org/2024/1/e46176", url="http://www.ncbi.nlm.nih.gov/pubmed/38888956" } @Article{info:doi/10.2196/53574, author="Pienkowska, Anita and Ravaut, Mathieu and Mammadova, Maleyka and Ang, Chin-Siang and Wang, Hanyu and Ong, Chwen Qi and Bojic, Iva and Qin, Mengqi Vicky and Sumsuzzman, Md Dewan and Ajuebor, Onyema and Boniol, Mathieu and Bustamante, Paola Juana and Campbell, James and Cometto, Giorgio and Fitzpatrick, Siobhan and Kane, Catherine and Joty, Shafiq and Car, Josip", title="Understanding COVID-19 Impacts on the Health Workforce: AI-Assisted Open-Source Media Content Analysis", journal="JMIR Form Res", year="2024", month="Jun", day="13", volume="8", pages="e53574", keywords="World Health Organization", keywords="WHO", keywords="public surveillance", keywords="natural language processing", keywords="NLP", keywords="artificial intelligence", keywords="AI", keywords="COVID-19", keywords="SARS-COV-2", keywords="COVID-19 pandemic", keywords="human-generated analysis", keywords="decision-making", keywords="strategic policy", keywords="health workforce", keywords="news article", keywords="media content analysis", keywords="news coverage", keywords="health care worker", keywords="mental health", keywords="death risk", keywords="intervention", keywords="efficiency", keywords="public health", keywords="surveillance", keywords="innovation", keywords="innovative method", abstract="Background: To investigate the impacts of the COVID-19 pandemic on the health workforce, we aimed to develop a framework that synergizes natural language processing (NLP) techniques and human-generated analysis to reduce, organize, classify, and analyze a vast volume of publicly available news articles to complement scientific literature and support strategic policy dialogue, advocacy, and decision-making. Objective: This study aimed to explore the possibility of systematically scanning intelligence from media that are usually not captured or best gathered through structured academic channels and inform on the impacts of the COVID-19 pandemic on the health workforce, contributing factors to the pervasiveness of the impacts, and policy responses, as depicted in publicly available news articles. Our focus was to investigate the impacts of the COVID-19 pandemic and, concurrently, assess the feasibility of gathering health workforce insights from open sources rapidly. Methods: We conducted an NLP-assisted media content analysis of open-source news coverage on the COVID-19 pandemic published between January 2020 and June 2022. A data set of 3,299,158 English news articles on the COVID-19 pandemic was extracted from the World Health Organization Epidemic Intelligence through Open Sources (EIOS) system. The data preparation phase included developing rules-based classification, fine-tuning an NLP summarization model, and further data processing. Following relevancy evaluation, a deductive-inductive approach was used for the analysis of the summarizations. This included data extraction, inductive coding, and theme grouping. Results: After processing and classifying the initial data set comprising 3,299,158 news articles and reports, a data set of 5131 articles with 3,007,693 words was devised. The NLP summarization model allowed for a reduction in the length of each article resulting in 496,209 words that facilitated agile analysis performed by humans. Media content analysis yielded results in 3 sections: areas of COVID-19 impacts and their pervasiveness, contributing factors to COVID-19--related impacts, and responses to the impacts. The results suggest that insufficient remuneration and compensation packages have been key disruptors for the health workforce during the COVID-19 pandemic, leading to industrial actions and mental health burdens. Shortages of personal protective equipment and occupational risks have increased infection and death risks, particularly at the pandemic's onset. Workload and staff shortages became a growing disruption as the pandemic progressed. Conclusions: This study demonstrates the capacity of artificial intelligence--assisted media content analysis applied to open-source news articles and reports concerning the health workforce. Adequate remuneration packages and personal protective equipment supplies should be prioritized as preventive measures to reduce the initial impact of future pandemics on the health workforce. Interventions aimed at lessening the emotional toll and workload need to be formulated as a part of reactive measures, enhancing the efficiency and maintainability of health delivery during a pandemic. ", doi="10.2196/53574", url="https://formative.jmir.org/2024/1/e53574", url="http://www.ncbi.nlm.nih.gov/pubmed/38869940" } @Article{info:doi/10.2196/48491, author="Wang, Hanjing and Li, Yupeng and Ning, Xuan", title="News Coverage of the COVID-19 Pandemic on Social Media and the Public's Negative Emotions: Computational Study", journal="J Med Internet Res", year="2024", month="Jun", day="6", volume="26", pages="e48491", keywords="web news coverage", keywords="emotions", keywords="social media", keywords="Facebook", keywords="COVID-19", abstract="Background: Social media has become an increasingly popular and critical tool for users to digest diverse information and express their perceptions and attitudes. While most studies endeavor to delineate the emotional responses of social media users, there is limited research exploring the factors associated with the emergence of emotions, particularly negative ones, during news consumption. Objective: We aim to first depict the web coverage by news organizations on social media and then explore the crucial elements of news coverage that trigger the public's negative emotions. Our findings can act as a reference for responsible parties and news organizations in times of crisis. Methods: We collected 23,705 Facebook posts with 1,019,317 comments from the public pages of representative news organizations in Hong Kong. We used text mining techniques, such as topic models and Bidirectional Encoder Representations from Transformers, to analyze news components and public reactions. Beyond descriptive analysis, we used regression models to shed light on how news coverage on social media is associated with the public's negative emotional responses. Results: Our results suggest that occurrences of issues regarding pandemic situations, antipandemic measures, and supportive actions are likely to reduce the public's negative emotions, while comments on the posts mentioning the central government and the Government of Hong Kong reveal more negativeness. Negative and neutral media tones can alleviate the rage and interact with the subjects and issues in the news to affect users' negative emotions. Post length is found to have a curvilinear relationship with users' negative emotions. Conclusions: This study sheds light on the impacts of various components of news coverage (issues, subjects, media tone, and length) on social media on the public's negative emotions (anger, fear, and sadness). Our comprehensive analysis provides a reference framework for efficient crisis communication for similar pandemics at present or in the future. This research, although first extending the analysis between the components of news coverage and negative user emotions to the scenario of social media, echoes previous studies drawn from traditional media and its derivatives, such as web newspapers. Although the era of COVID-19 pandemic gradually brings down the curtain, the commonality of this research and previous studies also contributes to establishing a clearer territory in the field of health crises. ", doi="10.2196/48491", url="https://www.jmir.org/2024/1/e48491", url="http://www.ncbi.nlm.nih.gov/pubmed/38843521" } @Article{info:doi/10.2196/49450, author="Li, Weicong and Tang, Maggie Liyaning and Montayre, Jed and Harris, B. Celia and West, Sancia and Antoniou, Mark", title="Investigating Health and Well-Being Challenges Faced by an Aging Workforce in the Construction and Nursing Industries: Computational Linguistic Analysis of Twitter Data", journal="J Med Internet Res", year="2024", month="Jun", day="5", volume="26", pages="e49450", keywords="social media", keywords="construction", keywords="nursing", keywords="aging", keywords="health and well-being", keywords="Twitter", abstract="Background: Construction and nursing are critical industries. Although both careers involve physically and mentally demanding work, the risks to workers during the COVID-19 pandemic are not well understood. Nurses (both younger and older) are more likely to experience the ill effects of burnout and stress than construction workers, likely due to accelerated work demands and increased pressure on nurses during the COVID-19 pandemic. In this study, we analyzed a large social media data set using advanced natural language processing techniques to explore indicators of the mental status of workers across both industries before and during the COVID-19 pandemic. Objective: This social media analysis aims to fill a knowledge gap by comparing the tweets of younger and older construction workers and nurses to obtain insights into any potential risks to their mental health due to work health and safety issues. Methods: We analyzed 1,505,638 tweets published on Twitter (subsequently rebranded as X) by younger and older (aged <45 vs >45 years) construction workers and nurses. The study period spanned 54 months, from January 2018 to June 2022, which equates to approximately 27 months before and 27 months after the World Health Organization declared COVID-19 a global pandemic on March 11, 2020. The tweets were analyzed using big data analytics and computational linguistic analyses. Results: Text analyses revealed that nurses made greater use of hashtags and keywords (both monograms and bigrams) associated with burnout, health issues, and mental health compared to construction workers. The COVID-19 pandemic had a pronounced effect on nurses' tweets, and this was especially noticeable in younger nurses. Tweets about health and well-being contained more first-person singular pronouns and affect words, and health-related tweets contained more affect words. Sentiment analyses revealed that, overall, nurses had a higher proportion of positive sentiment in their tweets than construction workers. However, this changed markedly during the COVID-19 pandemic. Since early 2020, sentiment switched, and negative sentiment dominated the tweets of nurses. No such crossover was observed in the tweets of construction workers. Conclusions: The social media analysis revealed that younger nurses had language use patterns consistent with someone experiencing the ill effects of burnout and stress. Older construction workers had more negative sentiments than younger workers, who were more focused on communicating about social and recreational activities rather than work matters. More broadly, these findings demonstrate the utility of large data sets enabled by social media to understand the well-being of target populations, especially during times of rapid societal change. ", doi="10.2196/49450", url="https://www.jmir.org/2024/1/e49450", url="http://www.ncbi.nlm.nih.gov/pubmed/38838308" } @Article{info:doi/10.2196/56899, author="Nickel, Brooke and Heiss, Raffael and Shih, Patti and Gram, Grundtvig Emma and Copp, Tessa and Taba, Melody and Moynihan, Ray and Zadro, Joshua", title="Social Media Promotion of Health Tests With Potential for Overdiagnosis or Overuse: Protocol for a Content Analysis", journal="JMIR Res Protoc", year="2024", month="Jun", day="4", volume="13", pages="e56899", keywords="social media", keywords="influencers", keywords="tests", keywords="overdiagnosis", keywords="overuse", keywords="evidence-based medicine", keywords="promotion", abstract="Background: In recent years, social media have emerged as important spaces for commercial marketing of health tests, which can be used for the screening and diagnosis of otherwise generally healthy people. However, little is known about how health tests are promoted on social media, whether the information provided is accurate and balanced, and if there is transparency around conflicts of interest. Objective: This study aims to understand and quantify how social media is being used to discuss or promote health tests with the potential for overdiagnosis or overuse to generally healthy people. Methods: Content analysis of social media posts on the anti-Mullerian hormone test, whole-body magnetic resonance imaging scan, multicancer early detection, testosterone test, and gut microbe test from influential international social media accounts on Instagram and TikTok. The 5 tests have been identified as having the following criteria: (1) there are evidence-based concerns about overdiagnosis or overuse, (2) there is evidence or concerns that the results of tests do not lead to improved health outcomes for generally healthy people and may cause harm or waste, and (3) the tests are being promoted on social media to generally healthy people. English language text-only posts, images, infographics, articles, recorded videos including reels, and audio-only posts are included. Posts from accounts with <1000 followers as well as stories, live videos, and non-English posts are excluded. Using keywords related to the test, the top posts were searched and screened until there were 100 eligible posts from each platform for each test (total of 1000 posts). Data from the caption, video, and on-screen text are being summarized and extracted into a Microsoft Excel (Microsoft Corporation) spreadsheet and included in the analysis. The analysis will take a combined inductive approach when generating key themes and a deductive approach using a prespecified framework. Quantitative data will be analyzed in Stata SE (version 18.0; Stata Corp). Results: Data on Instagram and TikTok have been searched and screened. Analysis has now commenced. The findings will be disseminated via publications in peer-reviewed international medical journals and will also be presented at national and international conferences in late 2024 and 2025. Conclusions: This study will contribute to the limited evidence base on the nature of the relationship between social media and the problems of overdiagnosis and overuse of health care services. This understanding is essential to develop strategies to mitigate potential harm and plan solutions, with the aim of helping to protect members of the public from being marketed low-value tests, becoming patients unnecessarily, and taking resources away from genuine needs within the health system. International Registered Report Identifier (IRRID): DERR1-10.2196/56899 ", doi="10.2196/56899", url="https://www.researchprotocols.org/2024/1/e56899", url="http://www.ncbi.nlm.nih.gov/pubmed/38833693" } @Article{info:doi/10.2196/54334, author="Huang, Ching-Yuan and Lee, Po-Chun and Chen, Long-Hui", title="Exploring Consumers' Negative Electronic Word-of-Mouth of 5 Military Hospitals in Taiwan Through SERVQUAL and Flower of Services: Web Scraping Analysis", journal="JMIR Form Res", year="2024", month="May", day="29", volume="8", pages="e54334", keywords="electronic word-of-mouth", keywords="eWOM", keywords="service quality", keywords="SERVQUAL scale", keywords="Flower of Services", keywords="health care service quality", keywords="military hospitals", abstract="Background: In recent years, with the widespread use of the internet, the influence of electronic word-of-mouth (eWOM) has been increasingly recognized, particularly the significance of negative eWOM, which has surpassed positive eWOM in importance. Such reviews play a pivotal role in research related to service industry management, particularly in intangible service sectors such as hospitals, where they have become a reference point for improving service quality. Objective: This study comprehensively collected negative eWOM from 5 military hospitals in Taiwan that were at or above the level of regional teaching hospitals. It aimed to investigate service quality issues before and after the pandemic. The findings provide important references for formulating strategies to improve service quality. Methods: In this study, we used web scraping techniques to gather 1259 valid negative eWOM, covering the period from the inception of the first review to December 31, 2022. These reviews were categorized using content analysis based on the modified Parasuraman, Zeithaml, and Berry service quality (PZB SERVQUAL) scale and Flower of Services. Statistical data analysis was conducted to investigate the performance of service quality. Results: The annual count of negative reviews for each hospital has exhibited a consistent upward trajectory over the years, with a more pronounced increase following the onset of the pandemic. In the analysis, among the 5 dimensions of PZB SERVQUAL framework, the ``Assurance'' dimension yielded the least favorable results, registering a negative review rate as high as 58.3\%. Closely trailing, the ``Responsiveness'' dimension recorded a negative review rate of 34.2\%. When evaluating the service process, the subitem ``In Service: Diagnosis/Examination/Medical/Hospitalization'' exhibited the least satisfactory performance, with a negative review rate of 46.2\%. This was followed by the subitem ``In Service: Pre-diagnosis Waiting,'' which had a negative review rate of 20.2\%. To evaluate the average scores of negative reviews before and during the onset of the COVID-19 pandemic, independent sample t tests (2-tailed) were used. The analysis revealed statistically significant differences (P<.001). Furthermore, an ANOVA was conducted to investigate whether the length of the negative reviews impacted their ratings, which also showed significant differences (P=.01). Conclusions: Before and during the pandemic, there were significant differences in evaluating hospital services, and a higher word count in negative reviews indicated greater dissatisfaction with the service. Therefore, it is recommended that hospitals establish more comprehensive service quality management mechanisms, carefully respond to negative reviews, and categorize significant service deficiencies as critical events to prevent a decrease in overall service quality. Furthermore, during the service process, customers are particularly concerned about the attitude and responsiveness of health care personnel in the treatment process. Therefore, hospitals should enhance training and management in this area. ", doi="10.2196/54334", url="https://formative.jmir.org/2024/1/e54334", url="http://www.ncbi.nlm.nih.gov/pubmed/38809602" } @Article{info:doi/10.2196/54023, author="Hakariya, Hayase and Yokoyama, Natsuki and Lee, Jeonse and Hakariya, Arisa and Ikejiri, Tatsuki", title="Illicit Trade of Prescription Medications Through X (Formerly Twitter) in Japan: Cross-Sectional Study", journal="JMIR Form Res", year="2024", month="May", day="28", volume="8", pages="e54023", keywords="illegal trading", keywords="pharmacovigilance", keywords="social networking service", keywords="SNS", keywords="overdose", keywords="social support", keywords="antipsychotics", keywords="Japan", keywords="prescription medication", keywords="cross-sectional study", keywords="prescription drug", keywords="social networking", keywords="medication", keywords="pharmaceutical", keywords="pharmaceutical drugs", keywords="Japanese", keywords="psychiatric", keywords="support", abstract="Background: Nonmedical use of prescription drugs can cause overdose; this represents a serious public health crisis globally. In this digital era, social networking services serve as viable platforms for illegal acquisition of excessive amounts of medications, including prescription medications. In Japan, such illegal drug transactions have been conducted through popular flea market applications, social media, and auction websites, with most of the trades being over-the-counter (OTC) medications. Recently, an emerging unique black market, where individuals trade prescription medications---predominantly nervous system drugs---using a specific keyword (``Okusuri Mogu Mogu''), has emerged on X (formerly Twitter). Hence, these dynamic methods of illicit trading should routinely be monitored to encourage the appropriate use of medications. Objective: This study aimed to specify the characteristics of medications traded on X using the search term ``Okusuri Mogu Mogu'' and analyze individual behaviors associated with X posts, including the types of medications traded and hashtag usage. Methods: We conducted a cross-sectional study with publicly available posts on X between September 18 and October 1, 2022. Posts that included the term ``Okusuri Mogu Mogu'' during this period were scrutinized. Posts were categorized on the basis of their contents: buying, selling, self-administration, heads-up, and others. Among posts categorized as buying, selling, and self-administration, medication names were systematically enumerated and categorized using the Anatomical Therapeutic Chemical (ATC) classification. Additionally, hashtags in all the analyzed posts were counted and classified into 6 categories: medication name, mental disorder, self-harm, buying and selling, community formation, and others. Results: Out of 961 identified posts, 549 were included for analysis. Of these posts, 119 (21.7\%) referenced self-administration, and 237 (43.2\%; buying: n=67, 12.2\%; selling: n=170, 31.0\%) referenced transactions. Among these 237 posts, 1041 medication names were mentioned, exhibiting a >5-fold increase from the study in March 2021. Categorization based on the ATC classification predominantly revealed nervous system drugs, representing 82.1\% (n=855) of the mentioned medications, consistent with the previous survey. Of note, the diversity of medications has expanded to include medications that have not been approved by the Japanese government. Interestingly, OTC medications were frequently mentioned in self-administration posts (odds ratio 23.6, 95\% CI 6.93-80.15). Analysis of hashtags (n=866) revealed efforts to foster community connections among users. Conclusions: This study highlighted the escalating complexity of trading of illegal prescription medication facilitated by X posts. Regulatory measures to enhance public awareness should be considered to prevent illegal transactions, which may ultimately lead to misuse or abuse such as overdose. Along with such pharmacovigilance measures, social approaches that could direct individuals to appropriate medical or psychiatric resources would also be beneficial as our hashtag analysis shed light on the formation of a cohesive or closed community among users. ", doi="10.2196/54023", url="https://formative.jmir.org/2024/1/e54023", url="http://www.ncbi.nlm.nih.gov/pubmed/38805262" } @Article{info:doi/10.2196/51991, author="Sun, Yehao and Prabhu, Prital and Rahman, Ryan and Li, Dongmei and McIntosh, Scott and Rahman, Irfan", title="e-Cigarette Tobacco Flavors, Public Health, and Toxicity: Narrative Review", journal="Online J Public Health Inform", year="2024", month="May", day="27", volume="16", pages="e51991", keywords="vaping", keywords="e-cigarettes", keywords="tobacco flavors", keywords="toxicity", keywords="regulation", keywords="tobacco", keywords="public health", keywords="smoking", keywords="menthol", keywords="social media", keywords="nicotine", keywords="symptoms", keywords="symptom", keywords="risk", keywords="risks", keywords="toxicology", keywords="health risk", abstract="Background: Recently, the US Food and Drug Administration implemented enforcement priorities against all flavored, cartridge-based e-cigarettes other than menthol and tobacco flavors. This ban undermined the products' appeal to vapers, so e-cigarette manufacturers added flavorants of other attractive flavors into tobacco-flavored e-cigarettes and reestablished appeal. Objective: This review aims to analyze the impact of the addition of other flavorants in tobacco-flavored e-cigarettes on both human and public health issues and to propose further research as well as potential interventions. Methods: Searches for relevant literature published between 2018 and 2023 were performed. Cited articles about the toxicity of e-cigarette chemicals included those published before 2018, and governmental websites and documents were also included for crucial information. Results: Both the sales of e-cigarettes and posts on social media suggested that the manufacturers' strategy was successful. The reestablished appeal causes not only a public health issue but also threats to the health of individual vapers. Research has shown an increase in toxicity associated with the flavorants commonly used in flavored e-cigarettes, which are likely added to tobacco-flavored e-cigarettes based on tobacco-derived and synthetic tobacco-free nicotine, and these other flavors are associated with higher clinical symptoms not often induced solely by natural, traditional tobacco flavors. Conclusions: The additional health risks posed by the flavorants are pronounced even without considering the toxicological interactions of the different tobacco flavorants, and more research should be done to understand the health risks thoroughly and to take proper actions accordingly for the regulation of these emerging products. ", doi="10.2196/51991", url="https://ojphi.jmir.org/2024/1/e51991", url="http://www.ncbi.nlm.nih.gov/pubmed/38801769" } @Article{info:doi/10.2196/47154, author="Comer, Leigha and Donelle, Lorie and Hiebert, Bradley and Smith, J. Maxwell and Kothari, Anita and Stranges, Saverio and Gilliland, Jason and Long, Jed and Burkell, Jacquelyn and Shelley, J. Jacob and Hall, Jodi and Shelley, James and Cooke, Tommy and Ngole Dione, Marionette and Facca, Danica", title="Short- and Long-Term Predicted and Witnessed Consequences of Digital Surveillance During the COVID-19 Pandemic: Scoping Review", journal="JMIR Public Health Surveill", year="2024", month="May", day="24", volume="10", pages="e47154", keywords="digital surveillance", keywords="COVID-19", keywords="public health", keywords="scoping review", keywords="pandemic", keywords="digital technologies", abstract="Background: The COVID-19 pandemic has prompted the deployment of digital technologies for public health surveillance globally. The rapid development and use of these technologies have curtailed opportunities to fully consider their potential impacts (eg, for human rights, civil liberties, privacy, and marginalization of vulnerable groups). Objective: We conducted a scoping review of peer-reviewed and gray literature to identify the types and applications of digital technologies used for surveillance during the COVID-19 pandemic and the predicted and witnessed consequences of digital surveillance. Methods: Our methodology was informed by the 5-stage methodological framework to guide scoping reviews: identifying the research question; identifying relevant studies; study selection; charting the data; and collating, summarizing, and reporting the findings. We conducted a search of peer-reviewed and gray literature published between December 1, 2019, and December 31, 2020. We focused on the first year of the pandemic to provide a snapshot of the questions, concerns, findings, and discussions emerging from peer-reviewed and gray literature during this pivotal first year of the pandemic. Our review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) reporting guidelines. Results: We reviewed a total of 147 peer-reviewed and 79 gray literature publications. Based on our analysis of these publications, we identified a total of 90 countries and regions where digital technologies were used for public health surveillance during the COVID-19 pandemic. Some of the most frequently used technologies included mobile phone apps, location-tracking technologies, drones, temperature-scanning technologies, and wearable devices. We also found that the literature raised concerns regarding the implications of digital surveillance in relation to data security and privacy, function creep and mission creep, private sector involvement in surveillance, human rights, civil liberties, and impacts on marginalized groups. Finally, we identified recommendations for ethical digital technology design and use, including proportionality, transparency, purpose limitation, protecting privacy and security, and accountability. Conclusions: A wide range of digital technologies was used worldwide to support public health surveillance during the COVID-19 pandemic. The findings of our analysis highlight the importance of considering short- and long-term consequences of digital surveillance not only during the COVID-19 pandemic but also for future public health crises. These findings also demonstrate the ways in which digital surveillance has rendered visible the shifting and blurred boundaries between public health surveillance and other forms of surveillance, particularly given the ubiquitous nature of digital surveillance. International Registered Report Identifier (IRRID): RR2-https://doi.org/10.1136/bmjopen-2021-053962 ", doi="10.2196/47154", url="https://publichealth.jmir.org/2024/1/e47154", url="http://www.ncbi.nlm.nih.gov/pubmed/38788212" } @Article{info:doi/10.2196/54663, author="Kirkpatrick, E. Ciera and Lawrie, L. LaRissa", title="TikTok as a Source of Health Information and Misinformation for Young Women in the United States: Survey Study", journal="JMIR Infodemiology", year="2024", month="May", day="21", volume="4", pages="e54663", keywords="credibility perceptions", keywords="health information", keywords="health misinformation", keywords="information seeking", keywords="misinformation perceptions", keywords="public health", keywords="social media", keywords="strategic communication", keywords="third-person effect", keywords="TikTok", abstract="Background: TikTok is one of the most-used and fastest-growing social media platforms in the world, and recent reports indicate that it has become an increasingly popular source of news and information in the United States. These trends have important implications for public health because an abundance of health information exists on the platform. Women are among the largest group of TikTok users in the United States and may be especially affected by the dissemination of health information on TikTok. Prior research has shown that women are not only more likely to look for information on the internet but are also more likely to have their health-related behaviors and perceptions affected by their involvement with social media. Objective: We conducted a survey of young women in the United States to better understand their use of TikTok for health information as well as their perceptions of TikTok's health information and health communication sources. Methods: A web-based survey of US women aged 18 to 29 years (N=1172) was conducted in April-May 2023. The sample was recruited from a Qualtrics research panel and 2 public universities in the United States. Results: The results indicate that the majority of young women in the United States who have used TikTok have obtained health information from the platform either intentionally (672/1026, 65.5\%) or unintentionally (948/1026, 92.4\%). Age (959/1026, 93.47\%; r=0.30; P<.001), education (959/1026, 93.47\%; $\rho$=0.10; P=.001), and TikTok intensity (ie, participants' emotional connectedness to TikTok and TikTok's integration into their daily lives; 959/1026, 93.47\%; r=0.32; P<.001) were positively correlated with overall credibility perceptions of the health information. Nearly the entire sample reported that they think that misinformation is prevalent on TikTok to at least some extent (1007/1026, 98.15\%), but a third-person effect was found because the young women reported that they believe that other people are more susceptible to health misinformation on TikTok than they personally are (t1025=21.16; P<.001). Both health professionals and general users were common sources of health information on TikTok: 93.08\% (955/1026) of the participants indicated that they had obtained health information from a health professional, and 93.86\% (963/1026) indicated that they had obtained health information from a general user. The respondents showed greater preference for health information from health professionals (vs general users; t1025=23.75; P<.001); the respondents also reported obtaining health information from health professionals more often than from general users (t1025=8.13; P<.001), and they were more likely to act on health information from health professionals (vs general users; t1025=12.74; P<.001). Conclusions: The findings suggest that health professionals and health communication scholars need to proactively consider using TikTok as a platform for disseminating health information to young women because young women are obtaining health information from TikTok and prefer information from health professionals. ", doi="10.2196/54663", url="https://infodemiology.jmir.org/2024/1/e54663", url="http://www.ncbi.nlm.nih.gov/pubmed/38772020" } @Article{info:doi/10.2196/53968, author="Zhu, Jianfeng and Jin, Ruoming and Kenne, R. Deric and Phan, NhatHai and Ku, Wei-Shinn", title="User Dynamics and Thematic Exploration in r/Depression During the COVID-19 Pandemic: Insights From Overlapping r/SuicideWatch Users", journal="J Med Internet Res", year="2024", month="May", day="20", volume="26", pages="e53968", keywords="reddit", keywords="natural language processing", keywords="NLP", keywords="suicidal ideation", keywords="SI", keywords="online communities", keywords="depression symptoms", keywords="COVID-19 pandemic", keywords="bidirectional encoder representations from transformers", keywords="BERT", keywords="r/SuicideWatch", keywords="r/Depression", abstract="Background: In 2023, the United States experienced its highest- recorded number of suicides, exceeding 50,000 deaths. In the realm of psychiatric disorders, major depressive disorder stands out as the most common issue, affecting 15\% to 17\% of the population and carrying a notable suicide risk of approximately 15\%. However, not everyone with depression has suicidal thoughts. While ``suicidal depression'' is not a clinical diagnosis, it may be observed in daily life, emphasizing the need for awareness. Objective: This study aims to examine the dynamics, emotional tones, and topics discussed in posts within the r/Depression subreddit, with a specific focus on users who had also engaged in the r/SuicideWatch community. The objective was to use natural language processing techniques and models to better understand the complexities of depression among users with potential suicide ideation, with the goal of improving intervention and prevention strategies for suicide. Methods: Archived posts were extracted from the r/Depression and r/SuicideWatch Reddit communities in English spanning from 2019 to 2022, resulting in a final data set of over 150,000 posts contributed by approximately 25,000 unique overlapping users. A broad and comprehensive mix of methods was conducted on these posts, including trend and survival analysis, to explore the dynamic of users in the 2 subreddits. The BERT family of models extracted features from data for sentiment and thematic analysis. Results: On August 16, 2020, the post count in r/SuicideWatch surpassed that of r/Depression. The transition from r/Depression to r/SuicideWatch in 2020 was the shortest, lasting only 26 days. Sadness emerged as the most prevalent emotion among overlapping users in the r/Depression community. In addition, physical activity changes, negative self-view, and suicidal thoughts were identified as the most common depression symptoms, all showing strong positive correlations with the emotion tone of disappointment. Furthermore, the topic ``struggles with depression and motivation in school and work'' (12\%) emerged as the most discussed topic aside from suicidal thoughts, categorizing users based on their inclination toward suicide ideation. Conclusions: Our study underscores the effectiveness of using natural language processing techniques to explore language markers and patterns associated with mental health challenges in online communities like r/Depression and r/SuicideWatch. These insights offer novel perspectives distinct from previous research. In the future, there will be potential for further refinement and optimization of machine classifications using these techniques, which could lead to more effective intervention and prevention strategies. ", doi="10.2196/53968", url="https://www.jmir.org/2024/1/e53968", url="http://www.ncbi.nlm.nih.gov/pubmed/38767953" } @Article{info:doi/10.2196/57234, author="Bauer, Brian and Norel, Raquel and Leow, Alex and Rached, Abi Zad and Wen, Bo and Cecchi, Guillermo", title="Using Large Language Models to Understand Suicidality in a Social Media--Based Taxonomy of Mental Health Disorders: Linguistic Analysis of Reddit Posts", journal="JMIR Ment Health", year="2024", month="May", day="16", volume="11", pages="e57234", keywords="natural language processing", keywords="explainable AI", keywords="suicide", keywords="mental health disorders", keywords="mental health disorder", keywords="mental health", keywords="social media", keywords="online discussions", keywords="online", keywords="large language model", keywords="LLM", keywords="downstream analyses", keywords="trauma", keywords="stress", keywords="depression", keywords="anxiety", keywords="AI", keywords="artificial intelligence", keywords="explainable artificial intelligence", keywords="web-based discussions", abstract="Background: Rates of suicide have increased by over 35\% since 1999. Despite concerted efforts, our ability to predict, explain, or treat suicide risk has not significantly improved over the past 50 years. Objective: The aim of this study was to use large language models to understand natural language use during public web-based discussions (on Reddit) around topics related to suicidality. Methods: We used large language model--based sentence embedding to extract the latent linguistic dimensions of user postings derived from several mental health--related subreddits, with a focus on suicidality. We then applied dimensionality reduction to these sentence embeddings, allowing them to be summarized and visualized in a lower-dimensional Euclidean space for further downstream analyses. We analyzed 2.9 million posts extracted from 30 subreddits, including r/SuicideWatch, between October 1 and December 31, 2022, and the same period in 2010. Results: Our results showed that, in line with existing theories of suicide, posters in the suicidality community (r/SuicideWatch) predominantly wrote about feelings of disconnection, burdensomeness, hopeless, desperation, resignation, and trauma. Further, we identified distinct latent linguistic dimensions (well-being, seeking support, and severity of distress) among all mental health subreddits, and many of the resulting subreddit clusters were in line with a statistically driven diagnostic classification system---namely, the Hierarchical Taxonomy of Psychopathology (HiTOP)---by mapping onto the proposed superspectra. Conclusions: Overall, our findings provide data-driven support for several language-based theories of suicide, as well as dimensional classification systems for mental health disorders. Ultimately, this novel combination of natural language processing techniques can assist researchers in gaining deeper insights about emotions and experiences shared on the web and may aid in the validation and refutation of different mental health theories. ", doi="10.2196/57234", url="https://mental.jmir.org/2024/1/e57234" } @Article{info:doi/10.2196/48564, author="Liu, Xiaoqi and Hu, Qingyuan and Wang, Jie and Wu, Xusheng and Hu, Dehua", title="Difference in Rumor Dissemination and Debunking Before and After the Relaxation of COVID-19 Prevention and Control Measures in China: Infodemiology Study", journal="J Med Internet Res", year="2024", month="May", day="15", volume="26", pages="e48564", keywords="new stage", keywords="public health emergency", keywords="information epidemic", keywords="propagation characteristic", keywords="debunking mechanism", keywords="China", abstract="Background: The information epidemic emerged along with the COVID-19 pandemic. While controlling the spread of COVID-19, the secondary harm of epidemic rumors to social order cannot be ignored. Objective: The objective of this paper was to understand the characteristics of rumor dissemination before and after the pandemic and the corresponding rumor management and debunking mechanisms. This study aimed to provide a theoretical basis and effective methods for relevant departments to establish a sound mechanism for managing network rumors related to public health emergencies such as COVID-19. Methods: This study collected data sets of epidemic rumors before and after the relaxation of the epidemic prevention and control measures, focusing on large-scale network rumors. Starting from 3 dimensions of rumor content construction, rumor propagation, and rumor-refuting response, the epidemic rumors were subdivided into 7 categories, namely, involved subjects, communication content, emotional expression, communication channels, communication forms, rumor-refuting subjects, and verification sources. Based on this framework, content coding and statistical analysis of epidemic rumors were carried out. Results: The study found that the rumor information was primarily directed at a clear target audience. The main themes of rumor dissemination were related to the public's immediate interests in the COVID-19 field, with significant differences in emotional expression and mostly negative emotions. Rumors mostly spread through social media interactions, community dissemination, and circle dissemination, with text content as the main form, but they lack factual evidence. The preferences of debunking subjects showed differences, and the frequent occurrence of rumors reflected the unsmooth channels of debunking. The $\chi$2 test of data before and after the pandemic showed that the P value was less than .05, indicating that the difference in rumor content before and after the pandemic had statistical significance. Conclusions: This study's results showed that the themes of rumors during the pandemic are closely related to the immediate interests of the public, and the emotions of the public accelerate the spread of these rumors, which are mostly disseminated through social networks. Therefore, to more effectively prevent and control the spread of rumors during the pandemic and to enhance the capability to respond to public health crises, relevant authorities should strengthen communication with the public, conduct emotional risk assessments, and establish a joint mechanism for debunking rumors. ", doi="10.2196/48564", url="https://www.jmir.org/2024/1/e48564", url="http://www.ncbi.nlm.nih.gov/pubmed/38748460" } @Article{info:doi/10.2196/51332, author="Zhang, Zhouqing and Liew, Kongmeng and Kuijer, Roeline and She, Jou Wan and Yada, Shuntaro and Wakamiya, Shoko and Aramaki, Eiji", title="Differing Content and Language Based on Poster-Patient Relationships on the Chinese Social Media Platform Weibo: Text Classification, Sentiment Analysis, and Topic Modeling of Posts on Breast Cancer", journal="JMIR Cancer", year="2024", month="May", day="9", volume="10", pages="e51332", keywords="cancer", keywords="social media", keywords="text classification", keywords="topic modeling", keywords="sentiment analysis", keywords="Weibo", abstract="Background: Breast cancer affects the lives of not only those diagnosed but also the people around them. Many of those affected share their experiences on social media. However, these narratives may differ according to who the poster is and what their relationship with the patient is; a patient posting about their experiences may post different content from someone whose friends or family has breast cancer. Weibo is 1 of the most popular social media platforms in China, and breast cancer--related posts are frequently found there. Objective: With the goal of understanding the different experiences of those affected by breast cancer in China, we aimed to explore how content and language used in relevant posts differ according to who the poster is and what their relationship with the patient is and whether there are differences in emotional expression and topic content if the patient is the poster themselves or a friend, family member, relative, or acquaintance. Methods: We used Weibo as a resource to examine how posts differ according to the different poster-patient relationships. We collected a total of 10,322 relevant Weibo posts. Using a 2-step analysis method, we fine-tuned 2 Chinese Robustly Optimized Bidirectional Encoder Representations from Transformers (BERT) Pretraining Approach models on this data set with annotated poster-patient relationships. These models were lined in sequence, first a binary classifier (no\_patient or patient) and then a multiclass classifier (post\_user, family\_members, friends\_relatives, acquaintances, heard\_relation), to classify poster-patient relationships. Next, we used the Linguistic Inquiry and Word Count lexicon to conduct sentiment analysis from 5 emotion categories (positive and negative emotions, anger, sadness, and anxiety), followed by topic modeling (BERTopic). Results: Our binary model (F1-score=0.92) and multiclass model (F1-score=0.83) were largely able to classify poster-patient relationships accurately. Subsequent sentiment analysis showed significant differences in emotion categories across all poster-patient relationships. Notably, negative emotions and anger were higher for the ``no\_patient'' class, but sadness and anxiety were higher for the ``family\_members'' class. Focusing on the top 30 topics, we also noted that topics on fears and anger toward cancer were higher in the ``no\_patient'' class, but topics on cancer treatment were higher in the ``family\_members'' class. Conclusions: Chinese users post different types of content, depending on the poster- poster-patient relationships. If the patient is family, posts are sadder and more anxious but also contain more content on treatments. However, if no patient is detected, posts show higher levels of anger. We think that these may stem from rants from posters, which may help with emotion regulation and gathering social support. ", doi="10.2196/51332", url="https://cancer.jmir.org/2024/1/e51332", url="http://www.ncbi.nlm.nih.gov/pubmed/38723250" } @Article{info:doi/10.2196/50551, author="Jessiman-Perreault, Genevi{\`e}ve and Boucher, Jean-Christophe and Kim, Youn So and Frenette, Nicole and Badami, Abbas and Smith, M. Henry and Allen Scott, K. Lisa", title="The Role of Scientific Research in Human Papillomavirus Vaccine Discussions on Twitter: Social Network Analysis", journal="JMIR Infodemiology", year="2024", month="May", day="9", volume="4", pages="e50551", keywords="human papillomavirus", keywords="HPV", keywords="vaccine", keywords="immunization", keywords="social media", keywords="misinformation", keywords="social network analysis", abstract="Background: Attitudes toward the human papillomavirus (HPV) vaccine and accuracy of information shared about this topic in web-based settings vary widely. As real-time, global exposure to web-based discourse about HPV immunization shapes the attitudes of people toward vaccination, the spread of misinformation and misrepresentation of scientific knowledge contribute to vaccine hesitancy. Objective: In this study, we aimed to better understand the type and quality of scientific research shared on Twitter (recently rebranded as X) by vaccine-hesitant and vaccine-confident communities. Methods: To analyze the use of scientific research on social media, we collected tweets and retweets using a list of keywords associated with HPV and HPV vaccines using the Academic Research Product Track application programming interface from January 2019 to May 2021. From this data set, we identified tweets referring to or sharing scientific literature through a Boolean search for any tweets with embedded links, hashtags, or keywords associated with scientific papers. First, we used social network analysis to build a retweet or reply network to identify the clusters of users belonging to either the vaccine-confident or vaccine-hesitant communities. Second, we thematically assessed all shared papers based on typology of evidence. Finally, we compared the quality of research evidence and bibliometrics between the shared papers in the vaccine-confident and vaccine-hesitant communities. Results: We extracted 250 unique scientific papers (including peer-reviewed papers, preprints, and gray literature) from approximately 1 million English-language tweets. Social network maps were generated for the vaccine-confident and vaccine-hesitant communities sharing scientific research on Twitter. Vaccine-hesitant communities share fewer scientific papers; yet, these are more broadly disseminated despite being published in less prestigious journals compared to those shared by the vaccine-confident community. Conclusions: Vaccine-hesitant communities have adopted communication tools traditionally wielded by health promotion communities. Vaccine-confident communities would benefit from a more cohesive communication strategy to communicate their messages more widely and effectively. ", doi="10.2196/50551", url="https://infodemiology.jmir.org/2024/1/e50551", url="http://www.ncbi.nlm.nih.gov/pubmed/38722678" } @Article{info:doi/10.2196/51698, author="Xue, Jia and Shier, L. Micheal and Chen, Junxiang and Wang, Yirun and Zheng, Chengda and Chen, Chen", title="A Typology of Social Media Use by Human Service Nonprofits: Mixed Methods Study", journal="J Med Internet Res", year="2024", month="May", day="8", volume="26", pages="e51698", keywords="human service nonprofits", keywords="sexual assault support centers", keywords="Canada", keywords="typology", keywords="theory", keywords="Twitter", keywords="machine learning", keywords="social media", keywords="tweet", keywords="tweets", keywords="nonprofit", keywords="nonprofits", keywords="crisis", keywords="sexual assault", keywords="sexual violence", keywords="sexual abuse", keywords="support center", keywords="support centers", keywords="communication", keywords="communications", keywords="organization", keywords="organizations", keywords="organizational", keywords="sentiment analysis", keywords="business", keywords="marketing", abstract="Background: Nonprofit organizations are increasingly using social media to improve their communication strategies with the broader population. However, within the domain of human service nonprofits, there is hesitancy to fully use social media tools, and there is limited scope among organizational personnel in applying their potential beyond self-promotion and service advertisement. There is a pressing need for greater conceptual clarity to support education and training on the varied reasons for using social media to increase organizational outcomes. Objective: This study leverages the potential of Twitter (subsequently rebranded as X [X Corp]) to examine the online communication content within a sample (n=133) of nonprofit sexual assault (SA) centers in Canada. To achieve this, we developed a typology using a qualitative and supervised machine learning model for the automatic classification of tweets posted by these centers. Methods: Using a mixed methods approach that combines machine learning and qualitative analysis, we manually coded 10,809 tweets from 133 SA centers in Canada, spanning the period from March 2009 to March 2023. These manually labeled tweets were used as the training data set for the supervised machine learning process, which allowed us to classify 286,551 organizational tweets. The classification model based on supervised machine learning yielded satisfactory results, prompting the use of unsupervised machine learning to classify the topics within each thematic category and identify latent topics. The qualitative thematic analysis, in combination with topic modeling, provided a contextual understanding of each theme. Sentiment analysis was conducted to reveal the emotions conveyed in the tweets. We conducted validation of the model with 2 independent data sets. Results: Manual annotation of 10,809 tweets identified seven thematic categories: (1) community engagement, (2) organization administration, (3) public awareness, (4) political advocacy, (5) support for others, (6) partnerships, and (7) appreciation. Organization administration was the most frequent segment, and political advocacy and partnerships were the smallest segments. The supervised machine learning model achieved an accuracy of 63.4\% in classifying tweets. The sentiment analysis revealed a prevalence of neutral sentiment across all categories. The emotion analysis indicated that fear was predominant, whereas joy was associated with the partnership and appreciation tweets. Topic modeling identified distinct themes within each category, providing valuable insights into the prevalent discussions surrounding SA and related issues. Conclusions: This research contributes an original theoretical model that sheds light on how human service nonprofits use social media to achieve their online organizational communication objectives across 7 thematic categories. The study advances our comprehension of social media use by nonprofits, presenting a comprehensive typology that captures the diverse communication objectives and contents of these organizations, which provide content to expand training and education for nonprofit leaders to connect and engage with the public, policy experts, other organizations, and potential service users. ", doi="10.2196/51698", url="https://www.jmir.org/2024/1/e51698", url="http://www.ncbi.nlm.nih.gov/pubmed/38718390" } @Article{info:doi/10.2196/49928, author="Romeiser, L. Jamie and Jusko, Nicole and Williams, A. Augusta", title="Emerging Trends in Information-Seeking Behavior for Alpha-Gal Syndrome: Infodemiology Study Using Time Series and Content Analysis", journal="J Med Internet Res", year="2024", month="May", day="8", volume="26", pages="e49928", keywords="alpha-gal", keywords="alpha gal", keywords="alpha-gal syndrome", keywords="lone star tick", keywords="infodemiology", keywords="time series", keywords="content analysis", keywords="Google Trends", keywords="allergy", keywords="allergic", keywords="immune", keywords="immunology", keywords="immunological", keywords="information behavior", keywords="information behaviour", keywords="information seeking", keywords="geographic", abstract="Background: Alpha-gal syndrome is an emerging allergy characterized by an immune reaction to the carbohydrate molecule alpha-gal found in red meat. This unique food allergy is likely triggered by a tick bite. Cases of the allergy are on the rise, but prevalence estimates do not currently exist. Furthermore, varying symptoms and limited awareness of the allergy among health care providers contribute to delayed diagnosis, leading individuals to seek out their own information and potentially self-diagnose. Objective: The study aimed to (1) describe the volume and patterns of information-seeking related to alpha-gal, (2) explore correlations between alpha-gal and lone star ticks, and (3) identify specific areas of interest that individuals are searching for in relation to alpha-gal. Methods: Google Trends Supercharged-Glimpse, a new extension of Google Trends, provides estimates of the absolute volume of searches and related search queries. This extension was used to assess trends in searches for alpha-gal and lone star ticks (lone star tick, alpha gal, and meat allergy, as well as food allergy for comparison) in the United States. Time series analyses were used to examine search volume trends over time, and Spearman correlation matrices and choropleth maps were used to explore geographic and temporal correlations between alpha-gal and lone star tick searches. Content analysis was performed on related search queries to identify themes and subcategories that are of interest to information seekers. Results: Time series analysis revealed a rapidly increasing trend in search volumes for alpha-gal beginning in 2015. After adjusting for long-term trends, seasonal trends, and media coverage, from 2015 to 2022, the predicted adjusted average annual percent change in search volume for alpha-gal was 33.78\%. The estimated overall change in average search volume was 627\%. In comparison, the average annual percent change was 9.23\% for lone star tick, 7.34\% for meat allergy, and 2.45\% for food allergy during this time. Geographic analysis showed strong significant correlations between alpha-gal and lone star tick searches especially in recent years ($\rho$=0.80; P<.001), with primary overlap and highest search rates found in the southeastern region of the United States. Content analysis identified 10 themes of primary interest: diet, diagnosis or testing, treatment, medications or contraindications of medications, symptoms, tick related, specific sources of information and locations, general education information, alternative words for alpha-gal, and unrelated or other. Conclusions: The study provides insights into the changing information-seeking patterns for alpha-gal, indicating growing awareness and interest. Alpha-gal search volume is increasing at a rapid rate. Understanding specific questions and concerns can help health care providers and public health educators to tailor communication strategies. The Google Trends Supercharged-Glimpse tool offers enhanced features for analyzing information-seeking behavior and can be valuable for infodemiology research. Further research is needed to explore the evolving prevalence and impact of alpha-gal syndrome. ", doi="10.2196/49928", url="https://www.jmir.org/2024/1/e49928", url="http://www.ncbi.nlm.nih.gov/pubmed/38717813" } @Article{info:doi/10.2196/54162, author="Stimpson, P. Jim and Park, Sungchul and Pruitt, L. Sandi and Ortega, N. Alexander", title="Variation in Trust in Cancer Information Sources by Perceptions of Social Media Health Mis- and Disinformation and by Race and Ethnicity Among Adults in the United States: Cross-Sectional Study", journal="JMIR Cancer", year="2024", month="May", day="8", volume="10", pages="e54162", keywords="cancer", keywords="United States", keywords="cross-sectional study", keywords="trust", keywords="consumer health information", keywords="misinformation", keywords="disinformation", keywords="race", keywords="ethnicity", keywords="cancer information", keywords="source", keywords="sources", keywords="perception", keywords="perceptions", keywords="social media", keywords="health information", keywords="cross-sectional data", keywords="misleading", abstract="Background: Mis- and disinformation on social media have become widespread, which can lead to a lack of trust in health information sources and, in turn, lead to negative health outcomes. Moreover, the effect of mis- and disinformation on trust in information sources may vary by racial and ethnic minoritized populations. Objective: We evaluated how trust in multiple sources of cancer information varied by perceptions of health mis- and disinformation on social media and by race and ethnicity. Methods: Cross-sectional, nationally representative survey data from noninstitutionalized adults in the United States from the 2022 Health Information National Trends Survey 6 (HINTS 6) were analyzed (N=4137). The dependent variable measured the level of trust in cancer information sources. The independent variables were perceptions about health mis- and disinformation on social media and race and ethnicity. Multivariable logistic regression models were adjusted for survey weight and design, age, birth gender, race and ethnicity, marital status, urban/rural designation, education, employment status, feelings about household income, frequency of social media visits, and personal and family history of cancer. We also tested the interaction effect between perceptions of social media health mis- and disinformation and participants' self-reported race and ethnicity. Results: Perception of ``a lot of'' health mis- and disinformation on social media, relative to perception of ``less than a lot,'' was associated with a lower likelihood of high levels of trusting cancer information from government health agencies (odds ratio [OR] 0.60, 95\% CI 0.47-0.77), family or friends (OR 0.56, 95\% CI 0.44-0.71), charitable organizations (OR 0.78, 95\% CI 0.63-0.96), and religious organizations and leaders (OR 0.64, 95\% CI 0.52-0.79). Among White participants, those who perceived a lot of health mis- and disinformation on social media were less likely to have high trust in cancer information from government health agencies (margin=61\%, 95\% CI 57\%-66\%) and family or friends (margin=49\%, 95\% CI 43\%-55\%) compared to those who perceived less than a lot of health mis- and disinformation on social media. Among Black participants, those who perceived a lot of health mis- and disinformation on social media were less likely to have high trust in cancer information from religious organizations and leaders (margin=20\%, 95\% CI 10\%-30\%) compared to participants who perceived no or a little health mis- and disinformation on social media. Conclusions: Certain sources of cancer information may need enhanced support against the threat of mis- and disinformation, such as government health agencies, charitable organizations, religious organizations and leaders, and family or friends. Moreover, interventions should partner with racial and ethnically minoritized populations that are more likely to have low trust in certain cancer information sources associated with mis- and disinformation on social media. ", doi="10.2196/54162", url="https://cancer.jmir.org/2024/1/e54162", url="http://www.ncbi.nlm.nih.gov/pubmed/38717800" } @Article{info:doi/10.2196/54433, author="Yuan, Yunhao and Kasson, Erin and Taylor, Jordan and Cavazos-Rehg, Patricia and De Choudhury, Munmun and Aledavood, Talayeh", title="Examining the Gateway Hypothesis and Mapping Substance Use Pathways on Social Media: Machine Learning Approach", journal="JMIR Form Res", year="2024", month="May", day="7", volume="8", pages="e54433", keywords="gateway hypothesis", keywords="substance use", keywords="social media", keywords="deep learning", keywords="natural language processing", abstract="Background: Substance misuse presents significant global public health challenges. Understanding transitions between substance types and the timing of shifts to polysubstance use is vital to developing effective prevention and recovery strategies. The gateway hypothesis suggests that high-risk substance use is preceded by lower-risk substance use. However, the source of this correlation is hotly contested. While some claim that low-risk substance use causes subsequent, riskier substance use, most people using low-risk substances also do not escalate to higher-risk substances. Social media data hold the potential to shed light on the factors contributing to substance use transitions. Objective: By leveraging social media data, our study aimed to gain a better understanding of substance use pathways. By identifying and analyzing the transitions of individuals between different risk levels of substance use, our goal was to find specific linguistic cues in individuals' social media posts that could indicate escalating or de-escalating patterns in substance use. Methods: We conducted a large-scale analysis using data from Reddit, collected between 2015 and 2019, consisting of over 2.29 million posts and approximately 29.37 million comments by around 1.4 million users from subreddits. These data, derived from substance use subreddits, facilitated the creation of a risk transition data set reflecting the substance use behaviors of over 1.4 million users. We deployed deep learning and machine learning techniques to predict the escalation or de-escalation transitions in risk levels, based on initial transition phases documented in posts and comments. We conducted a linguistic analysis to analyze the language patterns associated with transitions in substance use, emphasizing the role of n-gram features in predicting future risk trajectories. Results: Our results showed promise in predicting the escalation or de-escalation transition in risk levels, based on the historical data of Reddit users created on initial transition phases among drug-related subreddits, with an accuracy of 78.48\% and an F1-score of 79.20\%. We highlighted the vital predictive features, such as specific substance names and tools indicative of future risk escalations. Our linguistic analysis showed that terms linked with harm reduction strategies were instrumental in signaling de-escalation, whereas descriptors of frequent substance use were characteristic of escalating transitions. Conclusions: This study sheds light on the complexities surrounding the gateway hypothesis of substance use through an examination of web-based behavior on Reddit. While certain findings validate the hypothesis, indicating a progression from lower-risk substances such as marijuana to higher-risk ones, a significant number of individuals did not show this transition. The research underscores the potential of using machine learning with social media analysis to predict substance use transitions. Our results point toward future directions for leveraging social media data in substance use research, underlining the importance of continued exploration before suggesting direct implications for interventions. ", doi="10.2196/54433", url="https://formative.jmir.org/2024/1/e54433", url="http://www.ncbi.nlm.nih.gov/pubmed/38713904" } @Article{info:doi/10.2196/49335, author="Gouy, Giulia and Attali, Luisa and Voillot, Pam{\'e}la and Fournet, Patrick and Agostini, Aubert", title="Experiences of Women With Medical Abortion Care Reflected in Social Media (VEILLE Study): Noninterventional Retrospective Exploratory Infodemiology Study", journal="JMIR Infodemiology", year="2024", month="May", day="2", volume="4", pages="e49335", keywords="infodemiology", keywords="medical abortion", keywords="patient experience", keywords="real-world evidence", keywords="social media", keywords="abortion", keywords="women's health", keywords="reproduction", keywords="reproductive", keywords="obstetric", keywords="obstetrics", keywords="gynecology", keywords="gynecological", keywords="text mining", keywords="topic model", keywords="topic modeling", keywords="natural language processing", keywords="NLP", abstract="Background: Abortion (also known as termination of pregnancy) is an essential element of women's reproductive health care. Feedback from women who underwent medical termination of pregnancy about their experience is crucial to help practitioners identify women's needs and develop necessary tools to improve the abortion care process. However, the collection of this feedback is quite challenging. Social media offer anonymity for women who share their abortion experience. Objective: This exploratory infodemiology study aimed to analyze, through French social media posts, personal medical symptoms and the different experiences and information dynamics associated with the medical abortion process. Methods: A retrospective study was performed by analyzing posts geolocated in France and published from January 1, 2017, to November 30, 2021. Posts were extracted from all French-language general and specialized publicly available web forums using specific keywords. Extracted messages were cleaned and pseudonymized. Automatic natural language processing methods were used to identify posts from women having experienced medical abortion. Biterm topic modeling was used to identify the main discussion themes and the Medical Dictionary for Regulatory Activities was used to identify medical terms. Encountered difficulties were explored using qualitative research methods until the saturation of concepts was reached. Results: Analysis of 5398 identified posts (3409 users) led to the identification of 9 major topics: personal experience (n=2413 posts, 44.7\%), community support (n=1058, 19.6\%), pain and bleeding (n=797, 14.8\%), psychological experience (n=760, 14.1\%), questioned efficacy (n=410, 7.6\%), social pressure (n=373, 6.9\%), positive experiences (n=257, 4.8\%), menstrual cycle disorders (n=107, 2\%), and reported inefficacy (n=104, 1.9\%). Pain, which was mentioned in 1627 (30.1\%) of the 5398 posts by 1024 (30.0\%) of the 3409 users, was the most frequently reported medical term. Pain was considered severe to unbearable in 24.5\% of the cases (399 of the 1627 posts). Lack of information was the most frequently reported difficulty during and after the process. Conclusions: Our findings suggest that French women used social media to share their experiences, offer and find support, and provide and receive information regarding medical abortion. Infodemiology appears to be a useful tool to obtain women's feedback, therefore offering the opportunity to enhance care in women undergoing medical abortion. ", doi="10.2196/49335", url="https://infodemiology.jmir.org/2024/1/e49335", url="http://www.ncbi.nlm.nih.gov/pubmed/38696232" } @Article{info:doi/10.2196/51127, author="Gaysynsky, Anna and Senft Everson, Nicole and Heley, Kathryn and Chou, Sylvia Wen-Ying", title="Perceptions of Health Misinformation on Social Media: Cross-Sectional Survey Study", journal="JMIR Infodemiology", year="2024", month="Apr", day="30", volume="4", pages="e51127", keywords="social media", keywords="misinformation", keywords="health communication", keywords="health literacy", keywords="patient-provider communication", abstract="Background: Health misinformation on social media can negatively affect knowledge, attitudes, and behaviors, undermining clinical care and public health efforts. Therefore, it is vital to better understand the public's experience with health misinformation on social media. Objective: The goal of this analysis was to examine perceptions of the social media information environment and identify associations between health misinformation perceptions and health communication behaviors among US adults. Methods: Analyses used data from the 2022 Health Information National Trends Survey (N=6252). Weighted unadjusted proportions described respondents' perceptions of the amount of false or misleading health information on social media (``perceived misinformation amount'') and how difficult it is to discern true from false information on social media (``perceived discernment difficulty''). Weighted multivariable logistic regressions examined (1) associations of sociodemographic characteristics and subjective literacy measures with misinformation perceptions and (2) relationships between misinformation perceptions and health communication behaviors (ie, sharing personal or general health information on social media and using social media information in health decisions or in discussions with health care providers). Results: Over one-third of social media users (35.61\%) perceived high levels of health misinformation, and approximately two-thirds (66.56\%) reported high perceived discernment difficulty. Odds of perceiving high amounts of misinformation were lower among non-Hispanic Black/African American (adjusted odds ratio [aOR] 0.407, 95\% CI 0.282-0.587) and Hispanic (aOR 0.610, 95\% CI 0.449-0.831) individuals compared to White individuals. Those with lower subjective health literacy were less likely to report high perceived misinformation amount (aOR 0.602, 95\% CI 0.374-0.970), whereas those with lower subjective digital literacy were more likely to report high perceived misinformation amount (aOR 1.775, 95\% CI 1.400-2.251). Compared to White individuals, Hispanic individuals had lower odds of reporting high discernment difficulty (aOR 0.620, 95\% CI 0.462-0.831). Those with lower subjective digital literacy (aOR 1.873, 95\% CI 1.478-2.374) or numeracy (aOR 1.465, 95\% CI 1.047-2.049) were more likely to report high discernment difficulty. High perceived misinformation amount was associated with lower odds of sharing general health information on social media (aOR 0.742, 95\% CI 0.568-0.968), using social media information to make health decisions (aOR 0.273, 95\% CI 0.156-0.479), and using social media information in discussions with health care providers (aOR 0.460, 95\% CI 0.323-0.655). High perceived discernment difficulty was associated with higher odds of using social media information in health decisions (aOR 1.724, 95\% CI 1.208-2.460) and health care provider discussions (aOR 1.389, 95\% CI 1.035-1.864). Conclusions: Perceptions of high health misinformation prevalence and discernment difficulty are widespread among social media users, and each has unique associations with sociodemographic characteristics, literacy, and health communication behaviors. These insights can help inform future health communication interventions. ", doi="10.2196/51127", url="https://infodemiology.jmir.org/2024/1/e51127", url="http://www.ncbi.nlm.nih.gov/pubmed/38687591" } @Article{info:doi/10.2196/38761, author="Chadwick, L. Verity and Saich, Freya and Freeman, Joseph and Martiniuk, Alexandra", title="Media Discourse Regarding COVID-19 Vaccinations for Children Aged 5 to 11 Years in Australia, Canada, the United Kingdom, and the United States: Comparative Analysis Using the Narrative Policy Framework", journal="JMIR Form Res", year="2024", month="Apr", day="29", volume="8", pages="e38761", keywords="COVID-19", keywords="SARS-CoV-2", keywords="vaccine", keywords="mRNA", keywords="Pfizer-BioNTech", keywords="pediatric", keywords="children", keywords="media", keywords="news", keywords="web-based", keywords="infodemic", keywords="disinformation", abstract="Background: Media narratives can shape public opinion and actions, influencing the uptake of pediatric COVID-19 vaccines. The COVID-19 pandemic has occurred at a time where infodemics, misinformation, and disinformation are present, impacting the COVID-19 response. Objective: This study aims to investigate how narratives about pediatric COVID-19 vaccines in the media of 4 English-speaking countries: the United States, Australia, Canada, and the United Kingdom. Methods: The Narrative Policy Framework was used to guide the comparative analyses of the major print and web-based news agencies' media regarding COVID-19 vaccines for children aged 5 to 11 years. Data were sought using systematic searching on Factiva (Dow Jones) of 4 key phases of pediatric vaccine approval and rollout. Results: A total of 400 articles (n=287, 71.8\% in the United States, n=40, 10\% in Australia, n=60, 15\% in Canada, and n=13, 3\% in the United Kingdom) met the search criteria and were included. Using the Narrative Policy Framework, the following were identified in each article: hero, villain, survivor, and plot. The United States was the earliest country to vaccinate children, and other countries' media often lauded the United States for this. Australian and Canadian media narratives about vaccines for children aged 5 to 11 years were commonly about protecting susceptible people in society, whereas the US and the UK narratives focused more on the vaccine helping children return to school. All 4 countries focused on the vaccines for children aged 5 to 11 years as being key to ``ending'' the pandemic. Australian and Canadian narratives frequently compared vaccine rollouts across states or provinces and bemoaned local progress in vaccine delivery compared with other countries globally. Canadian and US narratives highlighted the ``infodemic'' about the COVID-19 pandemic and disinformation regarding child vaccines as impeding uptake. All 4 countries---the United States, Australia, the United Kingdom, and Canada---used war imagery in reporting about COVID-19 vaccines for children. The advent of the Omicron variant demonstrated that populations were fatigued by the COVID-19 pandemic, and the media reporting increasingly blamed the unvaccinated. The UK media narrative was unique in describing vaccinating children as a distraction from adult COVID-19 vaccination efforts. The United States and Canada had narratives expressing anger about potential vaccine passports for children. In Australia, general practitioners were labelled as heroes. Finally, the Canadian narrative suggested altruistic forgoing of COVID-19 vaccine ``boosters'' as well as pediatric COVID-19 vaccines to benefit those in poorer nations. Conclusions: Public health emergencies require clear; compelling and accurate communication. The stories told during this pandemic are compelling because they contain the classic elements of a narrative; however, they can be reductive and inaccurate. ", doi="10.2196/38761", url="https://formative.jmir.org/2024/1/e38761", url="http://www.ncbi.nlm.nih.gov/pubmed/36383344" } @Article{info:doi/10.2196/52189, author="Marsall, Matthias and Dinse, Hannah and Schr{\"o}der, Julia and Skoda, Eva-Maria and Teufel, Martin and B{\"a}uerle, Alexander", title="Assessing Electronic Health Literacy in Individuals With the Post--COVID-19 Condition Using the German Revised eHealth Literacy Scale: Validation Study", journal="JMIR Form Res", year="2024", month="Apr", day="25", volume="8", pages="e52189", keywords="eHealth literacy", keywords="eHEALS", keywords="factor analysis", keywords="measurement invariance", keywords="psychometric properties", keywords="infodemic", abstract="Background: The eHealth Literacy Scale (eHEALS) is a widely used instrument for measuring eHealth literacy (eHL). However, little is known so far about whether the instrument is valid for the assessment of eHL in persons who are affected by the post--COVID-19 condition. This is particularly important as people with the post--COVID-19 condition are frequently affected by false information from the internet. Objective: The objective of our study was to evaluate the validity and reliability of the German Revised eHealth Literacy Scale (GR-eHEALS) in individuals with the post--COVID-19 condition. Methods: A cross-sectional study was conducted from January to May 2022. The self-assessment survey consisted of the GR-eHEALS, health status-- and internet use--related variables, sociodemographic data, and (post)--COVID-19--related medical data. Confirmatory factor analysis (CFA), correlational analyses, and tests of measurement invariance were deployed. Results: In total, 330 participants were included in the statistical analyses. CFA revealed that the 2-factor model reached an excellent model fit (comparative fit index=1.00, Tucker--Lewis index=0.99, root mean square error of approximation=0.036, standardized root mean square residual=0.038). Convergent validity was confirmed by significant positive correlations between eHL and knowledge of internet-based health promotion programs, experience in using these programs, and the duration of private internet use. In addition, a significantly negative relationship of eHL with internet anxiety supported convergent validity. Further, significant relationships of eHL with mental health status and internal health locus of control confirmed the criterion validity of the instrument. However, relationships of eHL with physical health status and quality of life could not be confirmed. The 2-factor model was fully measurement invariant regarding gender. Regarding age and educational level, partial measurement invariance was confirmed. The subscales as well as the overall GR-eHEALS reached good-to-excellent reliability (Cronbach $\alpha$?.86). Conclusions: The GR-eHEALS is a reliable and largely valid instrument for assessing eHL in individuals with the post--COVID-19 condition. Measurement invariance regarding gender was fully confirmed and allows the interpretation of group differences. Regarding age and educational level, group differences should be interpreted with caution. Given the high likelihood that individuals with the post--COVID-19 condition will be confronted with misinformation on the Internet, eHL is a core competency that is highly relevant in this context, in both research and clinical practice. Therefore, future research should also explore alternative instruments to capture eHL to overcome shortcomings in the validity of the GR-eHEALS. ", doi="10.2196/52189", url="https://formative.jmir.org/2024/1/e52189", url="http://www.ncbi.nlm.nih.gov/pubmed/38662429" } @Article{info:doi/10.2196/51211, author="Wanberg, J. Lindsey and Pearson, R. David", title="Evaluating the Disease-Related Experiences of TikTok Users With Lupus Erythematosus: Qualitative and Content Analysis", journal="JMIR Infodemiology", year="2024", month="Apr", day="17", volume="4", pages="e51211", keywords="lupus", keywords="TikTok", keywords="autoimmune disease", keywords="qualitative research", keywords="quality of life", abstract="Background: Lupus erythematosus (LE) is an autoimmune condition that is associated with significant detriments to quality of life and daily functioning. TikTok, a popular social networking platform for sharing short videos, provides a unique opportunity to understand experiences with LE within a nonclinical sample, a population that is understudied in LE research. This is the first qualitative study that explores LE experiences using the TikTok platform. Objective: This study aims to evaluate the disease-related experiences of TikTok users with LE using qualitative and content analysis. Methods: TikTok videos were included if the hashtags included \#lupus, were downloadable, were in English, and involved the personal experience of an individual with LE. A codebook was developed using a standardized inductive approach of iterative coding until saturation was reached. NVivo (Lumivero), a qualitative analysis software platform, was used to code videos and perform content analysis. Inductive thematic analysis was used to derive themes from the data. Results: A total of 153 TikTok videos met the inclusion criteria. The most common codes were experiences with symptoms (106/153, 69.3\%), mucocutaneous symptoms (61/153, 39.9\%), and experiences with treatment (59/153, 38.6\%). Experiences with symptoms and mucocutaneous symptoms had the greatest cumulative views (25,381,074 and 14,879,109 views, respectively). Five thematic conclusions were derived from the data: (1) mucocutaneous symptoms had profound effects on the mental health and body image of TikTok users with LE; (2) TikTok users' negative experiences with health care workers were often derived from diagnostic delays and perceptions of ``medical gaslighting''; (3) TikTok users tended to portray pharmacologic and nonpharmacologic interventions, such as diet and naturopathic remedies, positively, whereas pharmacologic treatments were portrayed negatively or referred to as ``chemotherapy''; (4) LE symptoms, particularly musculoskeletal symptoms and fatigue, interfered with users' daily functioning; and (5) although TikTok users frequently had strong support systems, feelings of isolation were often attributed to battling an ``invisible illness.'' Conclusions: This study demonstrates that social media can provide important, clinically relevant information for health practitioners caring for patients with chronic conditions such as LE. As mucocutaneous symptoms were the predominant drivers of distress in our sample, the treatment of hair loss and rash is vital in this population. However, pharmacologic therapies were often depicted negatively, reinforcing the significance of discussions on the safety and effectiveness of these treatments. In addition, while TikTok users demonstrated robust support systems, feelings of having an ``invisible illness'' and ``medical gaslighting'' dominated negative interactions with others. This underscores the importance of providing validation in clinical interactions. ", doi="10.2196/51211", url="https://infodemiology.jmir.org/2024/1/e51211", url="http://www.ncbi.nlm.nih.gov/pubmed/38631030" } @Article{info:doi/10.2196/53608, author="Guan, Huixin and Wang, Wei", title="Factors Impacting Chinese Older Adults' Intention to Prevent COVID-19 in the Post--COVID-19 Pandemic Era: Survey Study", journal="JMIR Form Res", year="2024", month="Apr", day="17", volume="8", pages="e53608", keywords="COVID-19", keywords="SARS-CoV-2", keywords="health protection", keywords="social capital", keywords="media exposure", keywords="negative emotions", keywords="structural influence model of communication", keywords="SIM", keywords="protect", keywords="protection", keywords="protective", keywords="intent", keywords="intention", keywords="prevention", keywords="preventative", keywords="restriction", keywords="restrictions", keywords="public health measures", keywords="safety", keywords="news", keywords="newspaper", keywords="media", keywords="radio", keywords="health communication", keywords="influence", keywords="influencing", keywords="infectious", keywords="infection control", keywords="pandemic", keywords="gerontology", keywords="geriatric", keywords="geriatrics", keywords="older adult", keywords="older adults", keywords="older person", keywords="older people", keywords="aging", abstract="Background: Understanding the factors influencing individuals' health decisions is a dynamic research question. Particularly, after China announced the deregulation of the COVID-19 epidemic, health risks escalated rapidly. The convergence of ``no longer controlled'' viruses and the infodemic has created a distinctive social period during which multiple factors may have influenced people's decision-making. Among these factors, the precautionary intentions of older individuals, as a susceptible health group, deserve special attention. Objective: This study aims to examine the intention of older adults to engage in preventive behaviors and the influencing factors, including social, media, and individual factors, within the context of the postepidemic era. Drawing upon the structural influence model of communication, this study tests the potential mediating roles of 3 different types of media exposure between cognitive and structural social capital and protective behavior intention, as well as the moderating role of negative emotions between social capital and media exposure. Methods: In this study, a web survey was used to collect self-reported quantitative data on social capital, media exposure, negative emotions, and the intention to prevent COVID-19 among older adults aged ?60 years (N=399) in China. Results: The results indicate that cognitive social capital significantly influenced protective behavior intention (P<.001), with cell phone exposure playing an additional impactful role (P<.001). By contrast, newspaper and radio exposure and television exposure mediated the influence of structural social capital on protective behavior intention (P<.001). Furthermore, negative emotions played a moderating role in the relationship between cognitive social capital and cell phone exposure (P<.001). Conclusions: This study suggests that using tailored communication strategies across various media channels can effectively raise health awareness among older adults dealing with major pandemics in China, considering their diverse social capital characteristics and emotional states. ", doi="10.2196/53608", url="https://formative.jmir.org/2024/1/e53608", url="http://www.ncbi.nlm.nih.gov/pubmed/38630517" } @Article{info:doi/10.2196/55762, author="Bragazzi, Luigi Nicola and Garbarino, Sergio", title="Assessing the Accuracy of Generative Conversational Artificial Intelligence in Debunking Sleep Health Myths: Mixed Methods Comparative Study With Expert Analysis", journal="JMIR Form Res", year="2024", month="Apr", day="16", volume="8", pages="e55762", keywords="sleep", keywords="sleep health", keywords="sleep-related disbeliefs", keywords="generative conversational artificial intelligence", keywords="chatbot", keywords="ChatGPT", keywords="misinformation", keywords="artificial intelligence", keywords="comparative study", keywords="expert analysis", keywords="adequate sleep", keywords="well-being", keywords="sleep trackers", keywords="sleep health education", keywords="sleep-related", keywords="chronic disease", keywords="healthcare cost", keywords="sleep timing", keywords="sleep duration", keywords="presleep behaviors", keywords="sleep experts", keywords="healthy behavior", keywords="public health", keywords="conversational agents", abstract="Background: Adequate sleep is essential for maintaining individual and public health, positively affecting cognition and well-being, and reducing chronic disease risks. It plays a significant role in driving the economy, public safety, and managing health care costs. Digital tools, including websites, sleep trackers, and apps, are key in promoting sleep health education. Conversational artificial intelligence (AI) such as ChatGPT (OpenAI, Microsoft Corp) offers accessible, personalized advice on sleep health but raises concerns about potential misinformation. This underscores the importance of ensuring that AI-driven sleep health information is accurate, given its significant impact on individual and public health, and the spread of sleep-related myths. Objective: This study aims to examine ChatGPT's capability to debunk sleep-related disbeliefs. Methods: A mixed methods design was leveraged. ChatGPT categorized 20 sleep-related myths identified by 10 sleep experts and rated them in terms of falseness and public health significance, on a 5-point Likert scale. Sensitivity, positive predictive value, and interrater agreement were also calculated. A qualitative comparative analysis was also conducted. Results: ChatGPT labeled a significant portion (n=17, 85\%) of the statements as ``false'' (n=9, 45\%) or ``generally false'' (n=8, 40\%), with varying accuracy across different domains. For instance, it correctly identified most myths about ``sleep timing,'' ``sleep duration,'' and ``behaviors during sleep,'' while it had varying degrees of success with other categories such as ``pre-sleep behaviors'' and ``brain function and sleep.'' ChatGPT's assessment of the degree of falseness and public health significance, on the 5-point Likert scale, revealed an average score of 3.45 (SD 0.87) and 3.15 (SD 0.99), respectively, indicating a good level of accuracy in identifying the falseness of statements and a good understanding of their impact on public health. The AI-based tool showed a sensitivity of 85\% and a positive predictive value of 100\%. Overall, this indicates that when ChatGPT labels a statement as false, it is highly reliable, but it may miss identifying some false statements. When comparing with expert ratings, high intraclass correlation coefficients (ICCs) between ChatGPT's appraisals and expert opinions could be found, suggesting that the AI's ratings were generally aligned with expert views on falseness (ICC=.83, P<.001) and public health significance (ICC=.79, P=.001) of sleep-related myths. Qualitatively, both ChatGPT and sleep experts refuted sleep-related misconceptions. However, ChatGPT adopted a more accessible style and provided a more generalized view, focusing on broad concepts, while experts sometimes used technical jargon, providing evidence-based explanations. Conclusions: ChatGPT-4 can accurately address sleep-related queries and debunk sleep-related myths, with a performance comparable to sleep experts, even if, given its limitations, the AI cannot completely replace expert opinions, especially in nuanced and complex fields such as sleep health, but can be a valuable complement in the dissemination of updated information and promotion of healthy behaviors. ", doi="10.2196/55762", url="https://formative.jmir.org/2024/1/e55762", url="http://www.ncbi.nlm.nih.gov/pubmed/38501898" } @Article{info:doi/10.2196/53375, author="Zhang, M. Jueman and Wang, Yi and Mouton, Magali and Zhang, Jixuan and Shi, Molu", title="Public Discourse, User Reactions, and Conspiracy Theories on the X Platform About HIV Vaccines: Data Mining and Content Analysis", journal="J Med Internet Res", year="2024", month="Apr", day="3", volume="26", pages="e53375", keywords="HIV", keywords="vaccine", keywords="Twitter", keywords="X platform", keywords="infodemiology", keywords="machine learning", keywords="topic modeling", keywords="sentiment", keywords="conspiracy theory", keywords="COVID-19", abstract="Background: The initiation of clinical trials for messenger RNA (mRNA) HIV vaccines in early 2022 revived public discussion on HIV vaccines after 3 decades of unsuccessful research. These trials followed the success of mRNA technology in COVID-19 vaccines but unfolded amid intense vaccine debates during the COVID-19 pandemic. It is crucial to gain insights into public discourse and reactions about potential new vaccines, and social media platforms such as X (formerly known as Twitter) provide important channels. Objective: Drawing from infodemiology and infoveillance research, this study investigated the patterns of public discourse and message-level drivers of user reactions on X regarding HIV vaccines by analyzing posts using machine learning algorithms. We examined how users used different post types to contribute to topics and valence and how these topics and valence influenced like and repost counts. In addition, the study identified salient aspects of HIV vaccines related to COVID-19 and prominent anti--HIV vaccine conspiracy theories through manual coding. Methods: We collected 36,424 English-language original posts about HIV vaccines on the X platform from January 1, 2022, to December 31, 2022. We used topic modeling and sentiment analysis to uncover latent topics and valence, which were subsequently analyzed across post types in cross-tabulation analyses and integrated into linear regression models to predict user reactions, specifically likes and reposts. Furthermore, we manually coded the 1000 most engaged posts about HIV and COVID-19 to uncover salient aspects of HIV vaccines related to COVID-19 and the 1000 most engaged negative posts to identify prominent anti--HIV vaccine conspiracy theories. Results: Topic modeling revealed 3 topics: HIV and COVID-19, mRNA HIV vaccine trials, and HIV vaccine and immunity. HIV and COVID-19 underscored the connections between HIV vaccines and COVID-19 vaccines, as evidenced by subtopics about their reciprocal impact on development and various comparisons. The overall valence of the posts was marginally positive. Compared to self-composed posts initiating new conversations, there was a higher proportion of HIV and COVID-19--related and negative posts among quote posts and replies, which contribute to existing conversations. The topic of mRNA HIV vaccine trials, most evident in self-composed posts, increased repost counts. Positive valence increased like and repost counts. Prominent anti--HIV vaccine conspiracy theories often falsely linked HIV vaccines to concurrent COVID-19 and other HIV-related events. Conclusions: The results highlight COVID-19 as a significant context for public discourse and reactions regarding HIV vaccines from both positive and negative perspectives. The success of mRNA COVID-19 vaccines shed a positive light on HIV vaccines. However, COVID-19 also situated HIV vaccines in a negative context, as observed in some anti--HIV vaccine conspiracy theories misleadingly connecting HIV vaccines with COVID-19. These findings have implications for public health communication strategies concerning HIV vaccines. ", doi="10.2196/53375", url="https://www.jmir.org/2024/1/e53375", url="http://www.ncbi.nlm.nih.gov/pubmed/38568723" } @Article{info:doi/10.2196/50048, author="Singhal, Aditya and Neveditsin, Nikita and Tanveer, Hasnaat and Mago, Vijay", title="Toward Fairness, Accountability, Transparency, and Ethics in AI for Social Media and Health Care: Scoping Review", journal="JMIR Med Inform", year="2024", month="Apr", day="3", volume="12", pages="e50048", keywords="fairness, accountability, transparency, and ethics", keywords="artificial intelligence", keywords="social media", keywords="health care", abstract="Background: The use of social media for disseminating health care information has become increasingly prevalent, making the expanding role of artificial intelligence (AI) and machine learning in this process both significant and inevitable. This development raises numerous ethical concerns. This study explored the ethical use of AI and machine learning in the context of health care information on social media platforms (SMPs). It critically examined these technologies from the perspectives of fairness, accountability, transparency, and ethics (FATE), emphasizing computational and methodological approaches that ensure their responsible application. Objective: This study aims to identify, compare, and synthesize existing solutions that address the components of FATE in AI applications in health care on SMPs. Through an in-depth exploration of computational methods, approaches, and evaluation metrics used in various initiatives, we sought to elucidate the current state of the art and identify existing gaps. Furthermore, we assessed the strength of the evidence supporting each identified solution and discussed the implications of our findings for future research and practice. In doing so, we made a unique contribution to the field by highlighting areas that require further exploration and innovation. Methods: Our research methodology involved a comprehensive literature search across PubMed, Web of Science, and Google Scholar. We used strategic searches through specific filters to identify relevant research papers published since 2012 focusing on the intersection and union of different literature sets. The inclusion criteria were centered on studies that primarily addressed FATE in health care discussions on SMPs; those presenting empirical results; and those covering definitions, computational methods, approaches, and evaluation metrics. Results: Our findings present a nuanced breakdown of the FATE principles, aligning them where applicable with the American Medical Informatics Association ethical guidelines. By dividing these principles into dedicated sections, we detailed specific computational methods and conceptual approaches tailored to enforcing FATE in AI-driven health care on SMPs. This segmentation facilitated a deeper understanding of the intricate relationship among the FATE principles and highlighted the practical challenges encountered in their application. It underscored the pioneering contributions of our study to the discourse on ethical AI in health care on SMPs, emphasizing the complex interplay and the limitations faced in implementing these principles effectively. Conclusions: Despite the existence of diverse approaches and metrics to address FATE issues in AI for health care on SMPs, challenges persist. The application of these approaches often intersects with additional ethical considerations, occasionally leading to conflicts. Our review highlights the lack of a unified, comprehensive solution for fully and effectively integrating FATE principles in this domain. This gap necessitates careful consideration of the ethical trade-offs involved in deploying existing methods and underscores the need for ongoing research. ", doi="10.2196/50048", url="https://medinform.jmir.org/2024/1/e50048", url="http://www.ncbi.nlm.nih.gov/pubmed/38568737" } @Article{info:doi/10.2196/41559, author="Mishra, Vishala and Dexter, P. Joseph", title="Response of Unvaccinated US Adults to Official Information About the Pause in Use of the Johnson \& Johnson--Janssen COVID-19 Vaccine: Cross-Sectional Survey Study", journal="J Med Internet Res", year="2024", month="Apr", day="1", volume="26", pages="e41559", keywords="Centers for Disease Control and Prevention", keywords="CDC", keywords="COVID-19", keywords="health communication", keywords="health information", keywords="health literacy", keywords="public health", keywords="risk perception", keywords="SARS-CoV-2", keywords="vaccine hesitancy", keywords="web-based surveys", doi="10.2196/41559", url="https://www.jmir.org/2024/1/e41559", url="http://www.ncbi.nlm.nih.gov/pubmed/38557597" } @Article{info:doi/10.2196/49699, author="Kamba, Masaru and She, Jou Wan and Ferawati, Kiki and Wakamiya, Shoko and Aramaki, Eiji", title="Exploring the Impact of the COVID-19 Pandemic on Twitter in Japan: Qualitative Analysis of Disrupted Plans and Consequences", journal="JMIR Infodemiology", year="2024", month="Apr", day="1", volume="4", pages="e49699", keywords="COVID-19", keywords="natural language processing", keywords="NLP", keywords="Twitter", keywords="disrupted plans", keywords="concerns", abstract="Background: Despite being a pandemic, the impact of the spread of COVID-19 extends beyond public health, influencing areas such as the economy, education, work style, and social relationships. Research studies that document public opinions and estimate the long-term potential impact after the pandemic can be of value to the field. Objective: This study aims to uncover and track concerns in Japan throughout the COVID-19 pandemic by analyzing Japanese individuals' self-disclosure of disruptions to their life plans on social media. This approach offers alternative evidence for identifying concerns that may require further attention for individuals living in Japan. Methods: We extracted 300,778 tweets using the query phrase Corona-no-sei (``due to COVID-19,'' ``because of COVID-19,'' or ``considering COVID-19''), enabling us to identify the activities and life plans disrupted by the pandemic. The correlation between the number of tweets and COVID-19 cases was analyzed, along with an examination of frequently co-occurring words. Results: The top 20 nouns, verbs, and noun plus verb pairs co-occurring with Corona no-sei were extracted. The top 5 keywords were graduation ceremony, cancel, school, work, and event. The top 5 verbs were disappear, go, rest, can go, and end. Our findings indicate that education emerged as the top concern when the Japanese government announced the first state of emergency. We also observed a sudden surge in anxiety about material shortages such as toilet paper. As the pandemic persisted and more states of emergency were declared, we noticed a shift toward long-term concerns, including careers, social relationships, and education. Conclusions: Our study incorporated machine learning techniques for disease monitoring through the use of tweet data, allowing the identification of underlying concerns (eg, disrupted education and work conditions) throughout the 3 stages of Japanese government emergency announcements. The comparison with COVID-19 case numbers provides valuable insights into the short- and long-term societal impacts, emphasizing the importance of considering citizens' perspectives in policy-making and supporting those affected by the pandemic, particularly in the context of Japanese government decision-making. ", doi="10.2196/49699", url="https://infodemiology.jmir.org/2024/1/e49699", url="http://www.ncbi.nlm.nih.gov/pubmed/38557446" } @Article{info:doi/10.2196/53666, author="Zhou, Runtao and Xie, Zidian and Tang, Qihang and Li, Dongmei", title="Social Network Analysis of e-Cigarette--Related Social Media Influencers on Twitter/X: Observational Study", journal="JMIR Form Res", year="2024", month="Apr", day="1", volume="8", pages="e53666", keywords="social network", keywords="social media", keywords="influencer", keywords="electronic cigarettes", keywords="e-cigarette", keywords="vaping", keywords="vape", keywords="Twitter", keywords="observational study", keywords="aerosol", keywords="consumer", keywords="influencers", keywords="social network analysis", keywords="antivaping", keywords="campaigns", abstract="Background: An e-cigarette uses a battery to heat a liquid that generates an aerosol for consumers to inhale. e-Cigarette use (vaping) has been associated with respiratory disease, cardiovascular disease, and cognitive functions. Recently, vaping has become increasingly popular, especially among youth and young adults. Objective: The aim of this study was to understand the social networks of Twitter (now rebranded as X) influencers related to e-cigarettes through social network analysis. Methods: Through the Twitter streaming application programming interface, we identified 3,617,766 unique Twitter accounts posting e-cigarette--related tweets from May 3, 2021, to June 10, 2022. Among these, we identified 33 e-cigarette influencers. The followers of these influencers were grouped according to whether or not they post about e-cigarettes themselves; specifically, the former group was defined as having posted at least five e-cigarette--related tweets in the past year, whereas the latter group was defined as followers that had not posted any e-cigarette--related tweets in the past 3 years. We randomly sampled 100 user accounts among each group of e-cigarette influencer followers and created corresponding social networks for each e-cigarette influencer. We compared various network measures (eg, clustering coefficient) between the networks of the two follower groups. Results: Major topics from e-cigarette--related tweets posted by the 33 e-cigarette influencers included advocating against vaping policy (48.0\%), vaping as a method to quit smoking (28.0\%), and vaping product promotion (24.0\%). The follower networks of these 33 influencers showed more connections for those who also post about e-cigarettes than for followers who do not post about e-cigarettes, with significantly higher clustering coefficients for the former group (0.398 vs 0.098; P=.005). Further, networks of followers who post about e-cigarettes exhibited substantially more incoming and outgoing connections than those of followers who do not post about e-cigarettes, with significantly higher in-degree (0.273 vs 0.084; P=.02), closeness (0.452 vs 0.137; P=.04), betweenness (0.036 vs 0.008; P=.001), and out-of-degree (0.097 vs 0.014; P=.02) centrality values. The followers who post about e-cigarettes also had a significantly (P<.001) higher number of followers (n=322) than that of followers who do not post about e-cigarettes (n=201). The number of tweets in the networks of followers who post about e-cigarettes was significantly higher than that in the networks of followers who do not post about e-cigarettes (93 vs 43; P<.001). Two major topics discussed in the networks of followers who post about e-cigarettes included promoting e-cigarette products or vaping activity (55.7\%) and vaping being a help for smoking cessation and harm reduction (44.3\%). Conclusions: Followers of e-cigarette influencers who also post about e-cigarettes have more closely connected networks than those of followers who do not themselves post about e-cigarettes. These findings provide a potentially practical intervention approach for future antivaping campaigns. ", doi="10.2196/53666", url="https://formative.jmir.org/2024/1/e53666", url="http://www.ncbi.nlm.nih.gov/pubmed/38557555" } @Article{info:doi/10.2196/49921, author="Ahmed, Wasim and Aiyenitaju, Opeoluwa and Chadwick, Simon and Hardey, Mariann and Fenton, Alex", title="The Influence of Joe Wicks on Physical Activity During the COVID-19 Pandemic: Thematic, Location, and Social Network Analysis of X Data", journal="J Med Internet Res", year="2024", month="Mar", day="29", volume="26", pages="e49921", keywords="social media", keywords="social network analysis", keywords="COVID-19", keywords="influencers", keywords="public health", keywords="social network", keywords="physical activity", keywords="promotion", keywords="fitness", keywords="exercise", keywords="workout", keywords="Twitter", keywords="content creation", keywords="communication", abstract="Background: ?Social media (SM) was essential in promoting physical activity during the COVID-19 pandemic, especially among people confined to their homes. Joe Wicks, a fitness coach, became particularly popular on SM during this time, posting daily workouts that millions of people worldwide followed. Objective: ?This study aims to investigate the influence of Joe Wicks on SM and the impact of his content on physical activity levels among the public. Methods: ?We used NodeXL Pro (Social Media Research Foundation) to collect data from X (formerly Twitter) over 54 days (March 23, 2020, to May 15, 2020), corresponding to the strictest lockdowns in the United Kingdom. We collected 290,649 posts, which we analyzed using social network analysis, thematic analysis, time-series analysis, and location analysis. Results: ?We found that there was significant engagement with content generated by Wicks, including reposts, likes, and comments. The most common types of posts were those that contained images, videos, and text of young people (school-aged children) undertaking physical activity by watching content created by Joe Wicks and posts from schools encouraging pupils to engage with the content. Other shared posts included those that encouraged others to join the fitness classes run by Wicks and those that contained general commentary. We also found that Wicks' network of influence was extensive and complex. It contained numerous subcommunities and resembled a broadcast network shape. Other influencers added to engagement with Wicks via their networks. Our results show that influencers can create networks of influence that are exhibited in distinctive ways. Conclusions: Our study found that Joe Wicks was a highly influential figure on SM during the COVID-19 pandemic and that his content positively impacted physical activity levels among the public. Our findings suggest that influencers can play an important role in promoting public health and that government officials should consider working with influencers to communicate health messages and promote healthy behaviors. Our study has broader implications beyond the status of fitness influencers. Recognizing the critical role of individuals such as Joe Wicks in terms of health capital should be a critical area of inquiry for governments, public health authorities, and policy makers and mirrors the growing interest in health capital as part of embodied and digital experiences in everyday life. ", doi="10.2196/49921", url="https://www.jmir.org/2024/1/e49921", url="http://www.ncbi.nlm.nih.gov/pubmed/38551627" } @Article{info:doi/10.2196/45864, author="Ng, Reuben and Indran, Nicole", title="\#ProtectOurElders: Analysis of Tweets About Older Asian Americans and Anti-Asian Sentiments During the COVID-19 Pandemic", journal="J Med Internet Res", year="2024", month="Mar", day="29", volume="26", pages="e45864", keywords="AAPI", keywords="anti-Asian hate", keywords="anti-Asian", keywords="Asian Americans and Pacific Islanders", keywords="Asian-American", keywords="content analysis", keywords="coronavirus", keywords="COVID-19", keywords="discourse", keywords="discriminate", keywords="discrimination", keywords="discriminatory", keywords="Pacific Islander", keywords="racial", keywords="racism", keywords="racist", keywords="SARS-CoV-2", keywords="social media", keywords="tweet", keywords="Twitter", abstract="Background: A silver lining to the COVID-19 pandemic is that it cast a spotlight on a long-underserved group. The barrage of attacks against older Asian Americans during the crisis galvanized society into assisting them in various ways. On Twitter, now known as X, support for them coalesced around the hashtag \#ProtectOurElders. To date, discourse surrounding older Asian Americans has escaped the attention of gerontologists---a gap we seek to fill. Our study serves as a reflection of the level of support that has been extended to older Asian Americans, even as it provides timely insights that will ultimately advance equity for them. Objective: This study explores the kinds of discourse surrounding older Asian Americans during the COVID-19 crisis, specifically in relation to the surge in anti-Asian sentiments. The following questions guide this study: What types of discourse have emerged in relation to older adults in the Asian American community and the need to support them? How do age and race interact to shape these discourses? What are the implications of these discourses for older Asian Americans? Methods: We retrieved tweets (N=6099) through 2 search queries. For the first query, we collated tweets with the hashtag \#ProtectOurElders. For the second query, we collected tweets with an age-based term, for example, ``elderly'' or ``old(er) adults(s)'' and either the hashtag \#StopAAPIHate or \#StopAsianHate. Tweets were posted from January 1, 2020, to August 1, 2023. After applying the exclusion criteria, the final data set contained 994 tweets. Inductive and deductive approaches informed our qualitative content analysis. Results: A total of 4 themes emerged, with 50.1\% (498/994) of posts framing older Asian Americans as ``vulnerable and in need of protection'' (theme 1). Tweets in this theme either singled them out as a group in need of protection because of their vulnerable status or discussed initiatives aimed at safeguarding their well-being. Posts in theme 2 (309/994, 31\%) positioned them as ``heroic and resilient.'' Relevant tweets celebrated older Asian Americans for displaying tremendous strength in the face of attack or described them as individuals not to be trifled with. Tweets in theme 3 (102/994, 10.2\%) depicted them as ``immigrants who have made selfless contributions and sacrifices.'' Posts in this section referenced the immense sacrifices made by older Asian Americans as they migrated to the United States, as well as the systemic barriers they had to overcome. Posts in theme 4 (85/994, 8.5\%) venerated older Asian Americans as ``worthy of honor.'' Conclusions: The COVID-19 crisis had the unintended effect of garnering greater support for older Asian Americans. It is consequential that support be extended to this group not so much by virtue of their perceived vulnerability but more so in view of their boundless contributions and sacrifices. ", doi="10.2196/45864", url="https://www.jmir.org/2024/1/e45864", url="http://www.ncbi.nlm.nih.gov/pubmed/38551624" } @Article{info:doi/10.2196/48130, author="Chlabicz, Ma?gorzata and Nabo?ny, Aleksandra and Koszelew, Jolanta and ?aguna, Wojciech and Szpakowicz, Anna and Sowa, Pawe? and Budny, Wojciech and Guziejko, Katarzyna and R{\'o}g-Makal, Magdalena and Pancewicz, S?awomir and Kondrusik, Maciej and Czupryna, Piotr and Cudowska, Beata and Lebensztejn, Dariusz and Moniuszko-Malinowska, Anna and Wierzbicki, Adam and Kami?ski, A. Karol", title="Medical Misinformation in Polish on the World Wide Web During the COVID-19 Pandemic Period: Infodemiology Study", journal="J Med Internet Res", year="2024", month="Mar", day="29", volume="26", pages="e48130", keywords="infodemic", keywords="fake news", keywords="information credibility", keywords="online health information", keywords="evidence based medicine", keywords="EBM", keywords="false", keywords="credibility", keywords="credible", keywords="health information", keywords="online information", keywords="information quality", keywords="infoveillance", keywords="infodemiology", keywords="misinformation", keywords="disinformation", abstract="Background: Although researchers extensively study the rapid generation and spread of misinformation about the novel coronavirus during the pandemic, numerous other health-related topics are contaminating the internet with misinformation that have not received as much attention. Objective: This study aims to gauge the reach of the most popular medical content on the World Wide Web, extending beyond the confines of the pandemic. We conducted evaluations of subject matter and credibility for the years 2021 and 2022, following the principles of evidence-based medicine with assessments performed by experienced clinicians. Methods: We used 274 keywords to conduct web page searches through the BuzzSumo Enterprise Application. These keywords were chosen based on medical topics derived from surveys administered to medical practitioners. The search parameters were confined to 2 distinct date ranges: (1) January 1, 2021, to December 31, 2021; (2) January 1, 2022, to December 31, 2022. Our searches were specifically limited to web pages in the Polish language and filtered by the specified date ranges. The analysis encompassed 161 web pages retrieved in 2021 and 105 retrieved in 2022. Each web page underwent scrutiny by a seasoned doctor to assess its credibility, aligning with evidence-based medicine standards. Furthermore, we gathered data on social media engagements associated with the web pages, considering platforms such as Facebook, Pinterest, Reddit, and Twitter. Results: In 2022, the prevalence of unreliable information related to COVID-19 saw a noteworthy decline compared to 2021. Specifically, the percentage of noncredible web pages discussing COVID-19 and general vaccinations decreased from 57\% (43/76) to 24\% (6/25) and 42\% (10/25) to 30\% (3/10), respectively. However, during the same period, there was a considerable uptick in the dissemination of untrustworthy content on social media pertaining to other medical topics. The percentage of noncredible web pages covering cholesterol, statins, and cardiology rose from 11\% (3/28) to 26\% (9/35) and from 18\% (5/28) to 26\% (6/23), respectively. Conclusions: Efforts undertaken during the COVID-19 pandemic to curb the dissemination of misinformation seem to have yielded positive results. Nevertheless, our analysis suggests that these interventions need to be consistently implemented across both established and emerging medical subjects. It appears that as interest in the pandemic waned, other topics gained prominence, essentially ``filling the vacuum'' and necessitating ongoing measures to address misinformation across a broader spectrum of health-related subjects. ", doi="10.2196/48130", url="https://www.jmir.org/2024/1/e48130", url="http://www.ncbi.nlm.nih.gov/pubmed/38551638" } @Article{info:doi/10.2196/50552, author="Xue, Jia and Zhang, Qiaoru and Zhang, Yun and Shi, Hong and Zheng, Chengda and Fan, Jingchuan and Zhang, Linxiao and Chen, Chen and Li, Luye and Shier, L. Micheal", title="Bridging and Bonding Social Capital by Analyzing the Demographics, User Activities, and Social Network Dynamics of Sexual Assault Centers on Twitter: Mixed Methods Study", journal="J Med Internet Res", year="2024", month="Mar", day="27", volume="26", pages="e50552", keywords="social media", keywords="Twitter", keywords="sexual assault", keywords="nonprofits", keywords="Canada", keywords="violence", keywords="geolocation", keywords="communication", abstract="Background: Social media platforms have gained popularity as communication tools for organizations to engage with clients and the public, disseminate information, and raise awareness about social issues. From a social capital perspective, relationship building is seen as an investment, involving a complex interplay of tangible and intangible resources. Social media--based social capital signifies the diverse social networks that organizations can foster through their engagement on social media platforms. Literature underscores the great significance of further investigation into the scope and nature of social media use, particularly within sectors dedicated to service delivery, such as sexual assault organizations. Objective: This study aims to fill a research gap by investigating the use of Twitter by sexual assault support agencies in Canada. It seeks to understand the demographics, user activities, and social network structure within these organizations on Twitter, focusing on building social capital. The research questions explore the demographic profile, geographic distribution, and Twitter activity of these organizations as well as the social network dynamics of bridging and bonding social capital. Methods: This study used purposive sampling to investigate sexual assault centers in Canada with active Twitter accounts, resulting in the identification of 124 centers. The Twitter handles were collected, yielding 113 unique handles, and their corresponding Twitter IDs were obtained and validated. A total of 294,350 tweets were collected from these centers, covering >93.54\% of their Twitter activity. Preprocessing was conducted to prepare the data, and descriptive analysis was used to determine the center demographics and age. Furthermore, geolocation mapping was performed to visualize the center locations. Social network analysis was used to explore the intricate relationships within the network of sexual assault center Twitter accounts, using various metrics to assess the network structure and connectivity dynamics. Results: The results highlight the substantial presence of sexual assault organizations on Twitter, particularly in provinces such as Ontario, British Columbia, and Quebec, underscoring the importance of tailored engagement strategies considering regional disparities. The analysis of Twitter account creation years shows a peak in 2012, followed by a decline in new account creations in subsequent years. The monthly tweet activity shows November as the most active month, whereas July had the lowest activity. The study also reveals variations in Twitter activity, account creation patterns, and social network dynamics, identifying influential social queens and marginalized entities within the network. Conclusions: This study presents a comprehensive landscape of the demographics and activities of sexual assault centers in Canada on Twitter. This study suggests that future research should explore the long-term consequences of social media use and examine stakeholder perceptions, providing valuable insights to improve communication practices within the nonprofit human services sector and further the missions of these organizations. ", doi="10.2196/50552", url="https://www.jmir.org/2024/1/e50552", url="http://www.ncbi.nlm.nih.gov/pubmed/38536222" } @Article{info:doi/10.2196/47770, author="Mlambo, Christine Vongai and Keller, Eric and Mussatto, Caroline and Hwang, Gloria", title="Development of a Medical Social Media Ethics Scale and Assessment of \#IRad, \#CardioTwitter, and \#MedTwitter Posts: Mixed Methods Study", journal="JMIR Infodemiology", year="2024", month="Mar", day="27", volume="4", pages="e47770", keywords="ethics", keywords="social media", keywords="conflict of interest", keywords="interventional radiology", keywords="X", keywords="Twitter", keywords="cardiology", keywords="privacy", keywords="ethical issues", keywords="medical social media", keywords="prevalence", keywords="professional", keywords="professionalism", abstract="Background: Social media posts by clinicians are not bound by the same rules as peer-reviewed publications, raising ethical concerns that have not been extensively characterized or quantified. Objective: We aim to develop a scale to assess ethical issues on medical social media (SoMe) and use it to determine the prevalence of these issues among posts with 3 different hashtags: \#MedTwitter, \#IRad, and \#CardioTwitter. Methods: A scale was developed based on previous descriptions of professionalism and validated via semistructured cognitive interviewing with a sample of 11 clinicians and trainees, interrater agreement, and correlation of 100 posts. The final scale assessed social media posts in 6 domains. This was used to analyze 1500 Twitter posts, 500 each from the 3 hashtags. Analysis of posts was limited to original Twitter posts in English made by health care professionals in North America. The prevalence of potential issues was determined using descriptive statistics and compared across hashtags using the Fisher exact and $\chi$2 tests with Yates correction. Results: The final scale was considered reflective of potential ethical issues of SoMe by participants. There was good interrater agreement (Cohen $\kappa$=0.620, P<.01) and moderate to strong positive interrater correlation (=0.602, P<.001). The 6 scale domains showed minimal to no interrelation (Cronbach $\alpha$=0.206). Ethical concerns across all hashtags had a prevalence of 1.5\% or less except the conflict of interest concerns on \#IRad, which had a prevalence of 3.6\% (n=18). Compared to \#MedTwitter, posts with specialty-specific hashtags had more patient privacy and conflict of interest concerns. Conclusions: The SoMe professionalism scale we developed reliably reflects potential ethical issues. Ethical issues on SoMe are rare but important and vary in prevalence across medical communities. ", doi="10.2196/47770", url="https://infodemiology.jmir.org/2024/1/e47770", url="http://www.ncbi.nlm.nih.gov/pubmed/38536206" } @Article{info:doi/10.2196/51152, author="Ullah, Nazifa and Martin, Sam and Poduval, Shoba", title="A Snapshot of COVID-19 Vaccine Discourse Related to Ethnic Minority Communities in the United Kingdom Between January and April 2022: Mixed Methods Analysis", journal="JMIR Form Res", year="2024", month="Mar", day="26", volume="8", pages="e51152", keywords="COVID-19", keywords="ethnic minorities", keywords="vaccine", keywords="hesitancy", keywords="social media", keywords="discourse", keywords="minority groups", abstract="Background: Existing literature highlights the role of social media as a key source of information for the public during the COVID-19 pandemic and its influence on vaccination attempts. Yet there is little research exploring its role in the public discourse specifically among ethnic minority communities, who have the highest rates of vaccine hesitancy (delay or refusal of vaccination despite availability of services). Objective: This study aims to understand the discourse related to minority communities on social media platforms Twitter and YouTube. Methods: Social media data from the United Kingdom was extracted from Twitter and YouTube using the software Netlytics and YouTube Data Tools to provide a ``snapshot'' of the discourse between January and April 2022. A mixed method approach was used where qualitative data were contextualized into codes. Network analysis was applied to provide insight into the most frequent and weighted keywords and topics of conversations. Results: A total of 260 tweets and 156 comments from 4 YouTube videos were included in our analysis. Our data suggests that the most popular topics of conversation during the period sampled were related to communication strategies adopted during the booster vaccine rollout. These were noted to be divisive in nature and linked to wider conversations around racism and historical mistrust toward institutions. Conclusions: Our study suggests a shift in narrative from concerns about the COVID-19 vaccine itself, toward the strategies used in vaccination implementation, in particular the targeting of ethnic minority groups through vaccination campaigns. The implications for public health communication during crisis management in a pandemic context include acknowledging wider experiences of discrimination when addressing ethnic minority communities. ", doi="10.2196/51152", url="https://formative.jmir.org/2024/1/e51152", url="http://www.ncbi.nlm.nih.gov/pubmed/38530334" } @Article{info:doi/10.2196/50652, author="Klein, Z. Ari and Guti{\'e}rrez G{\'o}mez, Agust{\'i}n Jos{\'e} and Levine, D. Lisa and Gonzalez-Hernandez, Graciela", title="Using Longitudinal Twitter Data for Digital Epidemiology of Childhood Health Outcomes: An Annotated Data Set and Deep Neural Network Classifiers", journal="J Med Internet Res", year="2024", month="Mar", day="25", volume="26", pages="e50652", keywords="natural language processing", keywords="machine learning", keywords="data mining", keywords="social media", keywords="Twitter", keywords="pregnancy", keywords="epidemiology", keywords="developmental disabilities", keywords="asthma", doi="10.2196/50652", url="https://www.jmir.org/2024/1/e50652", url="http://www.ncbi.nlm.nih.gov/pubmed/38526542" } @Article{info:doi/10.2196/36441, author="Freeman, Eric and Patel, Darshilmukesh and Odeniyi, Folasade and Pasquinelli, Mary and Jain, Shikha", title="Where Do Oncology Patients Seek and Share Health Information? Survey Study", journal="J Med Internet Res", year="2024", month="Mar", day="25", volume="26", pages="e36441", keywords="oncology", keywords="social media", keywords="patient-physician relationship", keywords="patient-physician", keywords="patient-provider", keywords="cancer", keywords="information sharing", keywords="information seeking", keywords="information behavior", keywords="technology access", keywords="digital divide", doi="10.2196/36441", url="https://www.jmir.org/2024/1/e36441", url="http://www.ncbi.nlm.nih.gov/pubmed/38526546" } @Article{info:doi/10.2196/45563, author="Lee, Eugene and Schulz, J. Peter and Lee, Eun Hye", title="The Impact of COVID-19 and Exposure to Violent Media Content on Cyber Violence Victimization Among Adolescents in South Korea: National Population-Based Study", journal="J Med Internet Res", year="2024", month="Mar", day="22", volume="26", pages="e45563", keywords="cyber violence", keywords="adolescents", keywords="victimization", keywords="perpetration", keywords="COVID-19", abstract="Background: Because of the COVID-19 pandemic and consequent stay-at-home mandates, adolescents faced isolation and a decline in mental health. With increased online activity during this period, concerns arose regarding exposure to violent media content and cyber victimization among adolescents. Yet, the precise influence of pandemic-related measures on experiences of cyber violence remains unclear. Hence, it is pertinent to investigate whether the pandemic altered the dynamics of cyber violence victimization for individuals. Objective: This study aims to investigate the effects of COVID-19 and exposure to violent media content on cyber violence victimization among adolescents in South Korea. Methods: We used national survey data from 2019 (n=4779) and 2020 (n=4958) to investigate the potential impact of COVID-19 on the prevalence of cyber violence among young adolescents. The data encompassed responses from elementary fourth-grade students to senior high school students, probing their exposure to violent media content, average internet use, as well as experiences of victimization and perpetration. Results: The analysis revealed a noteworthy decline in cyber victimization during 2020 compared with 2019 (B=--0.12, t=--3.45, P<.001). Furthermore, being a perpetrator significantly contributed to cyber victimization (B=0.57, t=48.36, P<.001). Additionally, younger adolescents ($\beta$=--.06, t=--6.09, P<.001), those spending more time online ($\beta$=.18, t=13.83, P<.001), and those exposed to violent media ($\beta$=.14, t=13.89, P<.001) were found to be more susceptible to victimization. Conclusions: Despite the widespread belief that cyber violence among adolescents surged during COVID-19 due to increased online activity, the study findings counter this assumption. Surprisingly, COVID-19 did not exacerbate cyber victimization; rather, it decreased it. Given the strong correlation between cyber victimization and offline victimization, our attention should be directed toward implementing real-life interventions aimed at curbing violence originating from in-person violence at school. ", doi="10.2196/45563", url="https://www.jmir.org/2024/1/e45563", url="http://www.ncbi.nlm.nih.gov/pubmed/38517467" } @Article{info:doi/10.2196/47826, author="Molenaar, Annika and Lukose, Dickson and Brennan, Linda and Jenkins, L. Eva and McCaffrey, A. Tracy", title="Using Natural Language Processing to Explore Social Media Opinions on Food Security: Sentiment Analysis and Topic Modeling Study", journal="J Med Internet Res", year="2024", month="Mar", day="21", volume="26", pages="e47826", keywords="food security", keywords="food insecurity", keywords="public health", keywords="sentiment analysis", keywords="topic modeling", keywords="natural language processing", keywords="infodemiology", abstract="Background: Social media has the potential to be of great value in understanding patterns in public health using large-scale analysis approaches (eg, data science and natural language processing [NLP]), 2 of which have been used in public health: sentiment analysis and topic modeling; however, their use in the area of food security and public health nutrition is limited. Objective: This study aims to explore the potential use of NLP tools to gather insights from real-world social media data on the public health issue of food security. Methods: A search strategy for obtaining tweets was developed using food security terms. Tweets were collected using the Twitter application programming interface from January 1, 2019, to December 31, 2021, filtered for Australia-based users only. Sentiment analysis of the tweets was performed using the Valence Aware Dictionary and Sentiment Reasoner. Topic modeling exploring the content of tweets was conducted using latent Dirichlet allocation with BigML (BigML, Inc). Sentiment, topic, and engagement (the sum of likes, retweets, quotations, and replies) were compared across years. Results: In total, 38,070 tweets were collected from 14,880 Twitter users. Overall, the sentiment when discussing food security was positive, although this varied across the 3 years. Positive sentiment remained higher during the COVID-19 lockdown periods in Australia. The topic model contained 10 topics (in order from highest to lowest probability in the data set): ``Global production,'' ``Food insecurity and health,'' ``Use of food banks,'' ``Giving to food banks,'' ``Family poverty,'' ``Food relief provision,'' ``Global food insecurity,'' ``Climate change,'' ``Australian food insecurity,'' and ``Human rights.'' The topic ``Giving to food banks,'' which focused on support and donation, had the highest proportion of positive sentiment, and ``Global food insecurity,'' which covered food insecurity prevalence worldwide, had the highest proportion of negative sentiment. When compared with news, there were some events, such as COVID-19 support payment introduction and bushfires across Australia, that were associated with high periods of positive or negative sentiment. Topics related to food insecurity prevalence, poverty, and food relief in Australia were not consistently more prominent during the COVID-19 pandemic than before the pandemic. Negative tweets received substantially higher engagement across 2019 and 2020. There was no clear relationship between topics that were more likely to be positive or negative and have higher or lower engagement, indicating that the identified topics are discrete issues. Conclusions: In this study, we demonstrated the potential use of sentiment analysis and topic modeling to explore evolution in conversations on food security using social media data. Future use of NLP in food security requires the context of and interpretation by public health experts and the use of broader data sets, with the potential to track dimensions or events related to food security to inform evidence-based decision-making in this area. ", doi="10.2196/47826", url="https://www.jmir.org/2024/1/e47826", url="http://www.ncbi.nlm.nih.gov/pubmed/38512326" } @Article{info:doi/10.2196/53086, author="Ashraf, Reza Amir and Mackey, Ken Tim and Fittler, Andr{\'a}s", title="Search Engines and Generative Artificial Intelligence Integration: Public Health Risks and Recommendations to Safeguard Consumers Online", journal="JMIR Public Health Surveill", year="2024", month="Mar", day="21", volume="10", pages="e53086", keywords="generative artificial intelligence", keywords="artificial intelligence", keywords="comparative assessment", keywords="search engines", keywords="online pharmacies", keywords="patient safety", keywords="generative", keywords="safety", keywords="search engine", keywords="search", keywords="searches", keywords="searching", keywords="website", keywords="websites", keywords="Google", keywords="Bing", keywords="retrieval", keywords="information seeking", keywords="illegal", keywords="pharmacy", keywords="pharmacies", keywords="risk", keywords="risks", keywords="consumer", keywords="consumers", keywords="customer", keywords="customers", keywords="recommendation", keywords="recommendations", keywords="vendor", keywords="vendors", keywords="substance use", keywords="substance abuse", keywords="controlled substances", keywords="controlled substance", keywords="drug", keywords="drugs", keywords="pharmaceutic", keywords="pharmaceutics", keywords="pharmaceuticals", keywords="pharmaceutical", keywords="medication", keywords="medications", abstract="Background: The online pharmacy market is growing, with legitimate online pharmacies offering advantages such as convenience and accessibility. However, this increased demand has attracted malicious actors into this space, leading to the proliferation of illegal vendors that use deceptive techniques to rank higher in search results and pose serious public health risks by dispensing substandard or falsified medicines. Search engine providers have started integrating generative artificial intelligence (AI) into search engine interfaces, which could revolutionize search by delivering more personalized results through a user-friendly experience. However, improper integration of these new technologies carries potential risks and could further exacerbate the risks posed by illicit online pharmacies by inadvertently directing users to illegal vendors. Objective: The role of generative AI integration in reshaping search engine results, particularly related to online pharmacies, has not yet been studied. Our objective was to identify, determine the prevalence of, and characterize illegal online pharmacy recommendations within the AI-generated search results and recommendations. Methods: We conducted a comparative assessment of AI-generated recommendations from Google's Search Generative Experience (SGE) and Microsoft Bing's Chat, focusing on popular and well-known medicines representing multiple therapeutic categories including controlled substances. Websites were individually examined to determine legitimacy, and known illegal vendors were identified by cross-referencing with the National Association of Boards of Pharmacy and LegitScript databases. Results: Of the 262 websites recommended in the AI-generated search results, 47.33\% (124/262) belonged to active online pharmacies, with 31.29\% (82/262) leading to legitimate ones. However, 19.04\% (24/126) of Bing Chat's and 13.23\% (18/136) of Google SGE's recommendations directed users to illegal vendors, including for controlled substances. The proportion of illegal pharmacies varied by drug and search engine. A significant difference was observed in the distribution of illegal websites between search engines. The prevalence of links leading to illegal online pharmacies selling prescription medications was significantly higher (P=.001) in Bing Chat (21/86, 24\%) compared to Google SGE (6/92, 6\%). Regarding the suggestions for controlled substances, suggestions generated by Google led to a significantly higher number of rogue sellers (12/44, 27\%; P=.02) compared to Bing (3/40, 7\%). Conclusions: While the integration of generative AI into search engines offers promising potential, it also poses significant risks. This is the first study to shed light on the vulnerabilities within these platforms while highlighting the potential public health implications associated with their inadvertent promotion of illegal pharmacies. We found a concerning proportion of AI-generated recommendations that led to illegal online pharmacies, which could not only potentially increase their traffic but also further exacerbate existing public health risks. Rigorous oversight and proper safeguards are urgently needed in generative search to mitigate consumer risks, making sure to actively guide users to verified pharmacies and prioritize legitimate sources while excluding illegal vendors from recommendations. ", doi="10.2196/53086", url="https://publichealth.jmir.org/2024/1/e53086", url="http://www.ncbi.nlm.nih.gov/pubmed/38512343" } @Article{info:doi/10.2196/50518, author="Park, Daemin and Kim, Dasom and Park, Ah-hyun", title="Agendas on Nursing in South Korea Media: Natural Language Processing and Network Analysis of News From 2005 to 2022", journal="J Med Internet Res", year="2024", month="Mar", day="19", volume="26", pages="e50518", keywords="nurses", keywords="news", keywords="South Korea", keywords="natural language processing", keywords="NLP", keywords="network analysis", keywords="politicization", abstract="Background: In recent years, Korean society has increasingly recognized the importance of nurses in the context of population aging and infectious disease control. However, nurses still face difficulties with regard to policy activities that are aimed at improving the nursing workforce structure and working environment. Media coverage plays an important role in public awareness of a particular issue and can be an important strategy in policy activities. Objective: This study analyzed data from 18 years of news coverage on nursing-related issues. The focus of this study was to examine the drivers of the social, local, economic, and political agendas that were emphasized in the media by the analysis of main sources and their quotes. This analysis revealed which nursing media agendas were emphasized (eg, social aspects), neglected (eg, policy aspects), and negotiated. Methods: Descriptive analysis, natural language processing, and semantic network analysis were applied to analyze data collected from 2005 to 2022. BigKinds were used for the collection of data, automatic multi-categorization of news, named entity recognition of news sources, and extraction and topic modeling of quotes. The main news sources were identified by conducting a 1-mode network analysis with SNAnalyzer. The main agendas of nursing-related news coverage were examined through the qualitative analysis of major sources' quotes by section. The common and individual interests of the top-ranked sources were analyzed through a 2-mode network analysis using UCINET. Results: In total, 128,339 articles from 54 media outlets on nursing-related issues were analyzed. Descriptive analysis showed that nursing-related news was mainly covered in social (99,868/128,339, 77.82\%) and local (48,056/128,339, 48.56\%) sections, whereas it was rarely covered in economic (9439/128,339, 7.35\%) and political (7301/128,339, 5.69\%) sections. Furthermore, 445 sources that had made the top 20 list at least once by year and section were analyzed. Other than ``nurse,'' the main sources for each section were ``labor union,'' ``local resident,'' ``government,'' and ``Moon Jae-in.'' ``Nursing Bill'' emerged as a common interest among nurses and doctors, although the topic did not garner considerable attention from the Ministry of Health and Welfare. Analyzing quotes showed that nurses were portrayed as heroes, laborers, survivors of abuse, and perpetrators. The economic section focused on employment of youth and women in nursing. In the political section, conflicts between nurses and doctors, which may have caused policy confusion, were highlighted. Policy formulation processes were not adequately reported. Media coverage of the enactment of nursing laws tended to relate to confrontations between political parties. Conclusions: The media plays a crucial role in highlighting various aspects of nursing practice. However, policy formulation processes to solve nursing issues were not adequately reported in South Korea. This study suggests that nurses should secure policy compliance by persuading the public to understand their professional perspectives. ", doi="10.2196/50518", url="https://www.jmir.org/2024/1/e50518", url="http://www.ncbi.nlm.nih.gov/pubmed/38393293" } @Article{info:doi/10.2196/51113, author="Xian, Xuechang and Neuwirth, J. Rostam and Chang, Angela", title="Government-Nongovernmental Organization (NGO) Collaboration in Macao's COVID-19 Vaccine Promotion: Social Media Case Study", journal="JMIR Infodemiology", year="2024", month="Mar", day="19", volume="4", pages="e51113", keywords="COVID-19", keywords="government", keywords="vaccine", keywords="automated content analysis", keywords="Granger causality test", keywords="network agenda setting", keywords="QAP", keywords="social media", abstract="Background: The COVID-19 pandemic triggered unprecedented global vaccination efforts, with social media being a popular tool for vaccine promotion. Objective: This study probes into Macao's COVID-19 vaccine communication dynamics, with a focus on the multifaceted impacts of government agendas on social media. Methods: We scrutinized 22,986 vaccine-related Facebook posts from January 2020 to August 2022 in Macao. Using automated content analysis and advanced statistical methods, we unveiled intricate agenda dynamics between government and nongovernment entities. Results: ``Vaccine importance'' and ``COVID-19 risk'' were the most prominent topics co-occurring in the overall vaccine communication. The government tended to emphasize ``COVID-19 risk'' and ``vaccine effectiveness,'' while regular users prioritized vaccine safety and distribution, indicating a discrepancy in these agendas. Nonetheless, the government has limited impact on regular users in the aspects of vaccine importance, accessibility, affordability, and trust in experts. The agendas of government and nongovernment users intertwined, illustrating complex interactions. Conclusions: This study reveals the influence of government agendas on public discourse, impacting environmental awareness, public health education, and the social dynamics of inclusive communication during health crises. Inclusive strategies, accommodating public concerns, and involving diverse stakeholders are paramount for effective social media communication during health crises. ", doi="10.2196/51113", url="https://infodemiology.jmir.org/2024/1/e51113", url="http://www.ncbi.nlm.nih.gov/pubmed/38502184" } @Article{info:doi/10.2196/49198, author="AlMeshrafi, Azzam and AlHamad, F. Arwa and AlKuraidees, Hamoud and AlNasser, A. Lubna", title="Arabic Web-Based Information on Oral Lichen Planus: Content Analysis", journal="JMIR Form Res", year="2024", month="Mar", day="19", volume="8", pages="e49198", keywords="oral lichen planus", keywords="health information", keywords="Arabic", keywords="medical information", keywords="information seeking", keywords="quality", keywords="online information", keywords="Arab", keywords="oral", keywords="inflammatory", keywords="inflammation", keywords="chronic", keywords="mouth", keywords="mucous membrane", keywords="mucous membranes", keywords="reliable", keywords="reliability", keywords="credible", keywords="credibility", keywords="periodontology", keywords="dental", keywords="dentist", keywords="dentistry", abstract="Background: The use of web-based health information (WBHI) is on the rise, serving as a valuable tool for educating the public about health concerns and enhancing treatment adherence. Consequently, evaluating the availability and quality of context-specific WBHI is crucial to tackle disparities in health literacy and advance population health outcomes. Objective: This study aims to explore and assess the quality of the WBHI available and accessible to the public on oral lichen planus (OLP) in Arabic. Methods: The Arabic translation of the term OLP and its derivatives were searched in three general search platforms, and each platform's first few hundred results were reviewed for inclusion. We excluded content related to cutaneous LP, content not readily accessible to the public (eg, requiring subscription fees or directed to health care providers), and content not created by health care providers or organizations (ie, community forums, blogs, and social media). We assessed the quality of the Arabic WBHI with three standardized and validated tools: DISCERN, Journal of the American Medical Association (JAMA) benchmarks, and Health On the Net (HON). Results: Of the 911 resources of WBHI reviewed for eligibility, 49 were included in this study. Most WBHI resources were provided by commercial affiliations (n=28, 57.1\%), with the remainder from academic or not-for-profit affiliations. WBHI were often presented with visual aids (ie, images; n=33, 67.4\%). DISCERN scores were highest for WBHI resources that explicitly stated their aim, while the lowest scores were for providing the effect of OLP (or OLP treatment) on the quality of life. One-quarter of the resources (n=11, 22.4\%) met all 4 JAMA benchmarks, indicating the high quality of the WBHI, while the remainder of the WBHI failed to meet one or more of the JAMA benchmarks. HON scores showed that one-third of WBHI sources had scores above 75\%, indicating higher reliability and credibility of the WBHI source, while one-fifth of the sources scored below 50\%. Only 1 in 7 WBHI resources scored simultaneously high on all three quality instruments. Generally, WBHI from academic affiliations had higher quality scores than content provided by commercial affiliations. Conclusions: There are considerable variations in the quality of WBHI on OLP in Arabic. Most WBHI resources were deemed to be of moderate quality at best. Providers of WBHI could benefit from increasing collaboration between commercial and academic institutions in creating WBHI and integrating guidance from international quality assessment tools to improve the quality and, hopefully, the utility of these valuable WBHI resources. ", doi="10.2196/49198", url="https://formative.jmir.org/2024/1/e49198", url="http://www.ncbi.nlm.nih.gov/pubmed/38502161" } @Article{info:doi/10.2196/50431, author="Carboni, Alexa and Martini, Olnita and Kirk, Jessica and Marroquin, A. Nathaniel and Ricci, Corinne and Cheng, Melissa and Szeto, D. Mindy and Pulsipher, J. Kayd and Dellavalle, P. Robert", title="Does Male Skin Care Content on Instagram Neglect Skin Cancer Prevention?", journal="JMIR Dermatol", year="2024", month="Mar", day="13", volume="7", pages="e50431", keywords="men", keywords="male", keywords="male skin care", keywords="male skincare", keywords="sunscreen", keywords="sun protection", keywords="photoprotection", keywords="anti-aging", keywords="skin cancer prevention", keywords="Instagram", keywords="social media", keywords="marketing", keywords="advertising", keywords="dermatology", keywords="dermatologist", keywords="skin", keywords="man", keywords="oncology", keywords="oncologist", doi="10.2196/50431", url="https://derma.jmir.org/2024/1/e50431", url="http://www.ncbi.nlm.nih.gov/pubmed/38477962" } @Article{info:doi/10.2196/54107, author="Jia, Chenjin and Li, Pengcheng", title="Generation Z's Health Information Avoidance Behavior: Insights From Focus Group Discussions", journal="J Med Internet Res", year="2024", month="Mar", day="8", volume="26", pages="e54107", keywords="information avoidance", keywords="health information", keywords="Generation Z", keywords="information overload", keywords="planned risk information avoidance model", abstract="Background: Younger generations actively use social media to access health information. However, research shows that they also avoid obtaining health information online at times when confronted with uncertainty. Objective: This study aims to examine the phenomenon of health information avoidance among Generation Z, a representative cohort of active web users in this era. Methods: Drawing on the planned risk information avoidance model, we adopted a qualitative approach to explore the factors related to information avoidance within the context of health and risk communication. The researchers recruited 38 participants aged 16 to 25 years for the focus group discussion sessions. Results: In this study, we sought to perform a deductive qualitative analysis of the focus group interview content with open, focused, and theoretical coding. Our findings support several key components of the planned risk information avoidance model while highlighting the underlying influence of cognition on emotions. Specifically, socioculturally, group identity and social norms among peers lead some to avoid health information. Cognitively, mixed levels of risk perception, conflicting values, information overload, and low credibility of information sources elicited their information avoidance behaviors. Affectively, negative emotions such as anxiety, frustration, and the desire to stay positive contributed to avoidance. Conclusions: This study has implications for understanding young users' information avoidance behaviors in both academia and practice. ", doi="10.2196/54107", url="https://www.jmir.org/2024/1/e54107", url="http://www.ncbi.nlm.nih.gov/pubmed/38457223" } @Article{info:doi/10.2196/54000, author="Boatman, Dannell and Starkey, Abby and Acciavatti, Lori and Jarrett, Zachary and Allen, Amy and Kennedy-Rea, Stephenie", title="Using Social Listening for Digital Public Health Surveillance of Human Papillomavirus Vaccine Misinformation Online: Exploratory Study", journal="JMIR Infodemiology", year="2024", month="Mar", day="8", volume="4", pages="e54000", keywords="human papillomavirus", keywords="HPV", keywords="vaccine", keywords="vaccines", keywords="vaccination", keywords="vaccinations", keywords="sexually transmitted infection", keywords="STI", keywords="sexually transmitted disease", keywords="STD", keywords="sexual transmission", keywords="sexually transmitted", keywords="social media", keywords="social listening", keywords="cancer", keywords="surveillance", keywords="health communication", keywords="misinformation", keywords="artificial intelligence", keywords="AI", keywords="infodemiology", keywords="infoveillance", keywords="oncology", doi="10.2196/54000", url="https://infodemiology.jmir.org/2024/1/e54000", url="http://www.ncbi.nlm.nih.gov/pubmed/38457224" } @Article{info:doi/10.2196/47128, author="Kite, James and Grunseit, Anne and Mitchell, Glenn and Cooper, Pip and Chan, Lilian and Huang, Bo-Huei and Thomas, Margaret and O'Hara, Blythe and Smith, Abby", title="Impact of Traditional and New Media on Smoking Intentions and Behaviors: Secondary Analysis of Tasmania's Tobacco Control Mass Media Campaign Program, 2019-2021", journal="J Med Internet Res", year="2024", month="Mar", day="5", volume="26", pages="e47128", keywords="mass media campaign", keywords="tobacco control", keywords="evaluation", keywords="social media campaign", keywords="social media", keywords="digital platform", keywords="tobacco", keywords="smoking", keywords="survey", abstract="Background: Tasmania, the smallest state by population in Australia, has a comprehensive tobacco control mass media campaign program that includes traditional (eg, television) and ``new'' channels (eg, social media), run by Quit Tasmania. The campaign targets adult smokers, in particular men aged 18-44 years, and people from low socioeconomic areas. Objective: This study assesses the impact of the 2019-2021 campaign program on smokers' awareness of the campaign program, use of Quitline, and smoking-related intentions and behaviors. Methods: We used a tracking survey (conducted 8 times per year, immediately following a burst of campaign activity) to assess campaign recall and recognition, intentions to quit, and behavioral actions taken in response to the campaigns. The sample size was approximately 125 participants at each survey wave, giving a total sample size of 2000 participants over the 2 years. We merged these data with metrics including television target audience rating points, digital and Facebook (Meta) analytics, and Quitline activity data, and conducted regression and time-series modeling. Results: Over the evaluation period, unprompted recall of any Quit Tasmania campaign was 18\%, while prompted recognition of the most recent campaign was 50\%. Over half (52\%) of those who recognized a Quit Tasmania campaign reported that they had performed or considered a quitting-related behavioral action in response to the campaign. In the regression analyses, we found having different creatives within a single campaign burst was associated with higher campaign recall and recognition and an increase in the strength of behavioral actions taken. Higher target audience rating points were associated with higher campaign recall (but not recognition) and an increase in quit intentions, but not an increase in behavioral actions taken. Higher Facebook advertisement reach was associated with lower recall among survey participants, but recognition was higher when digital channels were used. The time-series analyses showed no systematic trends in Quitline activity over the evaluation period, but Quitline activity was higher when Facebook reach and advertisement spending were higher. Conclusions: Our evaluation suggests that a variety of creatives should be used simultaneously and supports the continued use of traditional broadcast channels, including television. However, the impact of television on awareness and behavior may be weakening. Future campaign evaluations should closely monitor the effectiveness of television as a result. We are also one of the first studies to explicitly examine the impact of digital and social media, finding some evidence that they influence quitting-related outcomes. While this evidence is promising for campaign implementation, future evaluations should consider adopting rigorous methods to further investigate this relationship. ", doi="10.2196/47128", url="https://www.jmir.org/2024/1/e47128", url="http://www.ncbi.nlm.nih.gov/pubmed/38441941" } @Article{info:doi/10.2196/54052, author="Groshon, Laurie and Waring, E. Molly and Blashill, J. Aaron and Dean, Kristen and Bankwalla, Sanaya and Palmer, Lindsay and Pagoto, Sherry", title="A Content Analysis of Indoor Tanning Twitter Chatter During COVID-19 Shutdowns: Cross-Sectional Qualitative Study", journal="JMIR Dermatol", year="2024", month="Mar", day="4", volume="7", pages="e54052", keywords="attitude", keywords="attitudes", keywords="content analysis", keywords="dermatology", keywords="opinion", keywords="perception", keywords="perceptions", keywords="perspective", keywords="perspectives", keywords="sentiment", keywords="skin", keywords="social media", keywords="sun", keywords="tan", keywords="tanner", keywords="tanners", keywords="tanning", keywords="tweet", keywords="tweets", keywords="Twitter", abstract="Background: Indoor tanning is a preventable risk factor for skin cancer. Statewide shutdowns during the COVID-19 pandemic resulted in temporary closures of tanning businesses. Little is known about how tanners reacted to losing access to tanning businesses. Objective: This study aimed to analyze Twitter (subsequently rebranded as X) chatter about indoor tanning during the statewide pandemic shutdowns. Methods: We collected tweets from March 15 to April 30, 2020, and performed a directed content analysis of a random sample of 20\% (1165/5811) of tweets from each week. The 2 coders independently rated themes ($\kappa$=0.67-1.0; 94\%-100\% agreement). Results: About half (589/1165, 50.6\%) of tweets were by people unlikely to indoor tan, and most of these mocked tanners or the act of tanning (562/589, 94.9\%). A total of 34\% (402/1165) of tweets were posted by users likely to indoor tan, and most of these (260/402, 64.7\%) mentioned missing tanning beds, often citing appearance- or mood-related reasons or withdrawal. Some tweets by tanners expressed a desire to purchase or use home tanning beds (90/402, 22\%), while only 3.9\% (16/402) mentioned tanning alternatives (eg, self-tanner). Very few tweets (29/1165, 2.5\%) were public health messages about the dangers of indoor tanning. Conclusions: Findings revealed that during statewide shutdowns, half of the tweets about indoor tanning were mocking tanning bed users and the tanned look, while about one-third were indoor tanners reacting to their inability to access tanning beds. Future work is needed to understand emerging trends in tanning post pandemic. ", doi="10.2196/54052", url="https://derma.jmir.org/2024/1/e54052", url="http://www.ncbi.nlm.nih.gov/pubmed/38437006" } @Article{info:doi/10.2196/49139, author="Deiner, S. Michael and Deiner, A. Natalie and Hristidis, Vagelis and McLeod, D. Stephen and Doan, Thuy and Lietman, M. Thomas and Porco, C. Travis", title="Use of Large Language Models to Assess the Likelihood of Epidemics From the Content of Tweets: Infodemiology Study", journal="J Med Internet Res", year="2024", month="Mar", day="1", volume="26", pages="e49139", keywords="conjunctivitis", keywords="microblog", keywords="social media", keywords="generative large language model", keywords="Generative Pre-trained Transformers", keywords="GPT-3.5", keywords="GPT-4", keywords="epidemic detection", keywords="Twitter", keywords="X formerly known as Twitter", keywords="infectious eye disease", abstract="Background: Previous work suggests that Google searches could be useful in identifying conjunctivitis epidemics. Content-based assessment of social media content may provide additional value in serving as early indicators of conjunctivitis and other systemic infectious diseases. Objective: We investigated whether large language models, specifically GPT-3.5 and GPT-4 (OpenAI), can provide probabilistic assessments of whether social media posts about conjunctivitis could indicate a regional outbreak. Methods: A total of 12,194 conjunctivitis-related tweets were obtained using a targeted Boolean search in multiple languages from India, Guam (United States), Martinique (France), the Philippines, American Samoa (United States), Fiji, Costa Rica, Haiti, and the Bahamas, covering the time frame from January 1, 2012, to March 13, 2023. By providing these tweets via prompts to GPT-3.5 and GPT-4, we obtained probabilistic assessments that were validated by 2 human raters. We then calculated Pearson correlations of these time series with tweet volume and the occurrence of known outbreaks in these 9 locations, with time series bootstrap used to compute CIs. Results: Probabilistic assessments derived from GPT-3.5 showed correlations of 0.60 (95\% CI 0.47-0.70) and 0.53 (95\% CI 0.40-0.65) with the 2 human raters, with higher results for GPT-4. The weekly averages of GPT-3.5 probabilities showed substantial correlations with weekly tweet volume for 44\% (4/9) of the countries, with correlations ranging from 0.10 (95\% CI 0.0-0.29) to 0.53 (95\% CI 0.39-0.89), with larger correlations for GPT-4. More modest correlations were found for correlation with known epidemics, with substantial correlation only in American Samoa (0.40, 95\% CI 0.16-0.81). Conclusions: These findings suggest that GPT prompting can efficiently assess the content of social media posts and indicate possible disease outbreaks to a degree of accuracy comparable to that of humans. Furthermore, we found that automated content analysis of tweets is related to tweet volume for conjunctivitis-related posts in some locations and to the occurrence of actual epidemics. Future work may improve the sensitivity and specificity of these methods for disease outbreak detection. ", doi="10.2196/49139", url="https://www.jmir.org/2024/1/e49139", url="http://www.ncbi.nlm.nih.gov/pubmed/38427404" } @Article{info:doi/10.2196/44726, author="ElSherief, Mai and Sumner, Steven and Krishnasamy, Vikram and Jones, Christopher and Law, Royal and Kacha-Ochana, Akadia and Schieber, Lyna and De Choudhury, Munmun", title="Identification of Myths and Misinformation About Treatment for Opioid Use Disorder on Social Media: Infodemiology Study", journal="JMIR Form Res", year="2024", month="Feb", day="23", volume="8", pages="e44726", keywords="addiction treatment", keywords="machine learning", keywords="misinformation", keywords="natural language processing", keywords="opioid use disorder", keywords="social media", keywords="substance use", abstract="Background: Health misinformation and myths about treatment for opioid use disorder (OUD) are present on social media and contribute to challenges in preventing drug overdose deaths. However, no systematic, quantitative methodology exists to identify what types of misinformation are being shared and discussed. Objective: We developed a multistage analytic pipeline to assess social media posts from Twitter (subsequently rebranded as X), YouTube, Reddit, and Drugs-Forum for the presence of health misinformation about treatment for OUD. Methods: Our approach first used document embeddings to identify potential new statements of misinformation from known myths. These statements were grouped into themes using hierarchical agglomerative clustering, and public health experts then reviewed the results for misinformation. Results: We collected a total of 19,953,599 posts discussing opioid-related content across the aforementioned platforms. Our multistage analytic pipeline identified 7 main clusters or discussion themes. Among a high-yield data set of posts (n=303) for further public health expert review, these included discussion about potential treatments for OUD (90/303, 29.8\%), the nature of addiction (68/303, 22.5\%), pharmacologic properties of substances (52/303, 16.9\%), injection drug use (36/303, 11.9\%), pain and opioids (28/303, 9.3\%), physical dependence of medications (22/303, 7.2\%), and tramadol use (7/303, 2.3\%). A public health expert review of the content within each cluster identified the presence of misinformation and myths beyond those used as seed myths to initialize the algorithm. Conclusions: Identifying and addressing misinformation through appropriate communication strategies could be an increasingly important component of preventing overdose deaths. To further this goal, we developed and tested an approach to aid in the identification of myths and misinformation about OUD from large-scale social media content. ", doi="10.2196/44726", url="https://formative.jmir.org/2024/1/e44726", url="http://www.ncbi.nlm.nih.gov/pubmed/38393772" } @Article{info:doi/10.2196/48324, author="Gu, Dongxiao and Wang, Qin and Chai, Yidong and Yang, Xuejie and Zhao, Wang and Li, Min and Zolotarev, Oleg and Xu, Zhengfei and Zhang, Gongrang", title="Identifying the Risk Factors of Allergic Rhinitis Based on Zhihu Comment Data Using a Topic-Enhanced Word-Embedding Model: Mixed Method Study and Cluster Analysis", journal="J Med Internet Res", year="2024", month="Feb", day="22", volume="26", pages="e48324", keywords="social media platforms", keywords="disease risk factor identification", keywords="chronic disease management", keywords="topic-enhanced word embedding", keywords="text mining", abstract="Background: Allergic rhinitis (AR) is a chronic disease, and several risk factors predispose individuals to the condition in their daily lives, including exposure to allergens and inhalation irritants. Analyzing the potential risk factors that can trigger AR can provide reference material for individuals to use to reduce its occurrence in their daily lives. Nowadays, social media is a part of daily life, with an increasing number of people using at least 1 platform regularly. Social media enables users to share experiences among large groups of people who share the same interests and experience the same afflictions. Notably, these channels promote the ability to share health information. Objective: This study aims to construct an intelligent method (TopicS-ClusterREV) for identifying the risk factors of AR based on these social media comments. The main questions were as follows: How many comments contained AR risk factor information? How many categories can these risk factors be summarized into? How do these risk factors trigger AR? Methods: This study crawled all the data from May 2012 to May 2022 under the topic of allergic rhinitis on Zhihu, obtaining a total of 9628 posts and 33,747 comments. We improved the Skip-gram model to train topic-enhanced word vector representations (TopicS) and then vectorized annotated text items for training the risk factor classifier. Furthermore, cluster analysis enabled a closer look into the opinions expressed in the category, namely gaining insight into how risk factors trigger AR. Results: Our classifier identified more comments containing risk factors than the other classification models, with an accuracy rate of 96.1\% and a recall rate of 96.3\%. In general, we clustered texts containing risk factors into 28 categories, with season, region, and mites being the most common risk factors. We gained insight into the risk factors expressed in each category; for example, seasonal changes and increased temperature differences between day and night can disrupt the body's immune system and lead to the development of allergies. Conclusions: Our approach can handle the amount of data and extract risk factors effectively. Moreover, the summary of risk factors can serve as a reference for individuals to reduce AR in their daily lives. The experimental data also provide a potential pathway that triggers AR. This finding can guide the development of management plans and interventions for AR. ", doi="10.2196/48324", url="https://www.jmir.org/2024/1/e48324", url="http://www.ncbi.nlm.nih.gov/pubmed/38386404" } @Article{info:doi/10.2196/50392, author="Buller, B. David and Sussman, L. Andrew and Thomson, A. Cynthia and Kepka, Deanna and Taren, Douglas and Henry, L. Kimberly and Warner, L. Echo and Walkosz, J. Barbara and Woodall, Gill W. and Nuss, Kayla and Blair, K. Cindy and Guest, D. Dolores and Borrayo, A. Evelinn and Gordon, S. Judith and Hatcher, Jennifer and Wetter, W. David and Kinsey, Alishia and Jones, F. Christopher and Yung, K. Angela and Christini, Kaila and Berteletti, Julia and Torres, A. John and Barraza Perez, Yessenya Emilia and Small, Annelise", title="\#4Corners4Health Social Media Cancer Prevention Campaign for Emerging Adults: Protocol for a Randomized Stepped-Wedge Trial", journal="JMIR Res Protoc", year="2024", month="Feb", day="22", volume="13", pages="e50392", keywords="cancer prevention", keywords="young adults", keywords="rural", keywords="social media", keywords="physical activity", keywords="diet", keywords="alcohol", keywords="tobacco control", keywords="sunburn", keywords="human papillomavirus", keywords="HPV vaccination", abstract="Background: Many emerging adults (EAs) are prone to making unhealthy choices, which increase their risk of premature cancer morbidity and mortality. In the era of social media, rigorous research on interventions to promote health behaviors for cancer risk reduction among EAs delivered over social media is limited. Cancer prevention information and recommendations may reach EAs more effectively over social media than in settings such as health care, schools, and workplaces, particularly for EAs residing in rural areas. Objective: This pragmatic randomized trial aims to evaluate a multirisk factor intervention using a social media campaign designed with community advisers aimed at decreasing cancer risk factors among EAs. The trial will target EAs from diverse backgrounds living in rural counties in the Four Corners states of Arizona, Colorado, New Mexico, and Utah. Methods: We will recruit a sample of EAs (n=1000) aged 18 to 26 years residing in rural counties (Rural-Urban Continuum Codes 4 to 9) in the Four Corners states from the Qualtrics' research panel and enroll them in a randomized stepped-wedge, quasi-experimental design. The inclusion criteria include English proficiency and regular social media engagement. A social media intervention will promote guideline-related goals for increased physical activity, healthy eating, and human papillomavirus vaccination and reduced nicotine product use, alcohol intake, and solar UV radiation exposure. Campaign posts will cover digital and media literacy skills, responses to misinformation, communication with family and friends, and referral to community resources. The intervention will be delivered over 12 months in Facebook private groups and will be guided by advisory groups of community stakeholders and EAs and focus groups with EAs. The EAs will complete assessments at baseline and at 12, 26, 39, 52, and 104 weeks after randomization. Assessments will measure 6 cancer risk behaviors, theoretical mediators, and participants' engagement with the social media campaign. Results: The trial is in its start-up phase. It is being led by a steering committee. Team members are working in 3 subcommittees to optimize community engagement, the social media intervention, and the measures to be used. The Stakeholder Organization Advisory Board and Emerging Adult Advisory Board were formed and provided initial input on the priority of cancer risk factors to target, social media use by EAs, and community resources available. A framework for the social media campaign with topics, format, and theoretical mediators has been created, along with protocols for campaign management. Conclusions: Social media can be used as a platform to counter misinformation and improve reliable health information to promote health behaviors that reduce cancer risks among EAs. Because of the popularity of web-based information sources among EAs, an innovative, multirisk factor intervention using a social media campaign has the potential to reduce their cancer risk behaviors. Trial Registration: ClinicalTrials.gov NCT05618158; https://classic.clinicaltrials.gov/ct2/show/NCT05618158 International Registered Report Identifier (IRRID): PRR1-10.2196/50392 ", doi="10.2196/50392", url="https://www.researchprotocols.org/2024/1/e50392", url="http://www.ncbi.nlm.nih.gov/pubmed/38386396" } @Article{info:doi/10.2196/54217, author="Olsson, Eva Sofia and Sreepad, Bhavana and Lee, Trevor and Fasih, Manal and Fijany, Arman", title="Public Interest in Acetyl Hexapeptide-8: Longitudinal Analysis", journal="JMIR Dermatol", year="2024", month="Feb", day="20", volume="7", pages="e54217", keywords="acetyl-hexapeptide-8", keywords="anti-aging", keywords="anti-wrinkle", keywords="Argireline", keywords="BoNT", keywords="botox", keywords="botulinum neurotoxin", keywords="cosmetic dermatology", keywords="cosmetic", keywords="dermatologist", keywords="dermatology", keywords="injectable neurotoxin", keywords="neurotoxin", keywords="skin specialist", keywords="topical agent", keywords="topical", abstract="Background: Acetyl hexapeptide-8, also known as Argireline, is a topical, short-acting, synthetic peptide that has recently gained popularity for its antiwrinkle effects. This agent has emerged as a more accessible alternative to botulinum neurotoxin. Objective: This study evaluates the public interest in acetyl hexapeptide-8 in the United States from 2013 to 2023, as described by search volume on Google, the most-used search engine. Methods: We analyzed the longitudinal relative monthly search volume from January 1, 2013, to January 1, 2023, for acetyl hexapeptide--related terms. We compared the internet search trends for ``Botox'' during this period to ``Argireline.'' Results: The terms ``Argireline'' and ``Botox in a Bottle'' both had substantial increases in search volume in 2022. Although its search volume is drastically increasing, ``Argireline'' was less searched than ``Botox,'' which had a stable, up-trending search volume over the past decade. Conclusions: The increasing interest in acetyl hexapeptide-8 may be due to its cost-effectiveness and use as a botulinum neurotoxin alternative. Affordability, over-the-counter availability, and ease of self-application of the agent suggest its potential to enhance accessibility to cosmetic dermatologic care. ", doi="10.2196/54217", url="https://derma.jmir.org/2024/1/e54217", url="http://www.ncbi.nlm.nih.gov/pubmed/38376906" } @Article{info:doi/10.2196/44395, author="Garrett, Camryn and Qiao, Shan and Li, Xiaoming", title="The Role of Social Media in Knowledge, Perceptions, and Self-Reported Adherence Toward COVID-19 Prevention Guidelines: Cross-Sectional Study", journal="JMIR Infodemiology", year="2024", month="Feb", day="16", volume="4", pages="e44395", keywords="COVID-19", keywords="digital media", keywords="social media", keywords="TikTok", keywords="Instagram", keywords="Twitter", keywords="Facebook", keywords="prevention guidelines", abstract="Background: Throughout the COVID-19 pandemic, social media has served as a channel of communication, a venue for entertainment, and a mechanism for information dissemination. Objective: This study aims to assess the associations between social media use patterns; demographics; and knowledge, perceptions, and self-reported adherence toward COVID-19 prevention guidelines, due to growing and evolving social media use. Methods: Quota-sampled data were collected through a web-based survey of US adults through the Qualtrics platform, from March 15, 2022, to March 23, 2022, to assess covariates (eg, demographics, vaccination, and political affiliation), frequency of social media use, social media sources of COVID-19 information, as well as knowledge, perceptions, and self-reported adherence toward COVID-19 prevention guidelines. Three linear regression models were used for data analysis. Results: A total of 1043 participants responded to the survey, with an average age of 45.3 years, among which 49.61\% (n=515) of participants were men, 66.79\% (n=696) were White, 11.61\% (n=121) were Black or African American, 13.15\% (n=137) were Hispanic or Latino, 37.71\% (n=382) were Democrat, 30.21\% (n=306) were Republican, and 25\% (n=260) were not vaccinated. After controlling for covariates, users of TikTok ($\beta$=--.29, 95\% CI --0.58 to --0.004; P=.047) were associated with lower knowledge of COVID-19 guidelines, users of Instagram ($\beta$=--.40, 95\% CI --0.68 to --0.12; P=.005) and Twitter ($\beta$=--.33, 95\% CI --0.58 to --0.08; P=.01) were associated with perceiving guidelines as strict, and users of Facebook ($\beta$=--.23, 95\% CI --0.42 to --0.043; P=.02) and TikTok ($\beta$=--.25, 95\% CI --0.5 to -0.009; P=.04) were associated with lower adherence to the guidelines (R2 0.06-0.23). Conclusions: These results allude to the complex interactions between online and physical environments. Future interventions should be tailored to subpopulations based on their demographics and social media site use. Efforts to mitigate misinformation and implement digital public health policy must account for the impact of the digital landscape on knowledge, perceptions, and level of adherence toward prevention guidelines for effective pandemic control. ", doi="10.2196/44395", url="https://infodemiology.jmir.org/2024/1/e44395", url="http://www.ncbi.nlm.nih.gov/pubmed/38194493" } @Article{info:doi/10.2196/47408, author="Valdez, Danny and Mena-Mel{\'e}ndez, Lucrecia and Crawford, L. Brandon and Jozkowski, N. Kristen", title="Analyzing Reddit Forums Specific to Abortion That Yield Diverse Dialogues Pertaining to Medical Information Seeking and Personal Worldviews: Data Mining and Natural Language Processing Comparative Study", journal="J Med Internet Res", year="2024", month="Feb", day="14", volume="26", pages="e47408", keywords="abortion", keywords="social media", keywords="Reddit", keywords="natural language processing", keywords="NLP", keywords="neural networks", abstract="Background: Attitudes toward abortion have historically been characterized via dichotomized labels, yet research suggests that these labels do not appropriately encapsulate beliefs on abortion. Rather, contexts, circumstances, and lived experiences often shape views on abortion into more nuanced and complex perspectives. Qualitative data have also been shown to underpin belief systems regarding abortion. Social media, as a form of qualitative data, could reveal how attitudes toward abortion are communicated publicly in web-based spaces. Furthermore, in some cases, social media can also be leveraged to seek health information. Objective: This study applies natural language processing and social media mining to analyze Reddit (Reddit, Inc) forums specific to abortion, including r/Abortion (the largest subreddit about abortion) and r/AbortionDebate (a subreddit designed to discuss and debate worldviews on abortion). Our analytical pipeline intends to identify potential themes within the data and the affect from each post. Methods: We applied a neural network--based topic modeling pipeline (BERTopic) to uncover themes in the r/Abortion (n=2151) and r/AbortionDebate (n=2815) subreddits. After deriving the optimal number of topics per subreddit using an iterative coherence score calculation, we performed a sentiment analysis using the Valence Aware Dictionary and Sentiment Reasoner to assess positive, neutral, and negative affect and an emotion analysis using the Text2Emotion lexicon to identify potential emotionality per post. Differences in affect and emotion by subreddit were compared. Results: The iterative coherence score calculation revealed 10 topics for both r/Abortion (coherence=0.42) and r/AbortionDebate (coherence=0.35). Topics in the r/Abortion subreddit primarily centered on information sharing or offering a source of social support; in contrast, topics in the r/AbortionDebate subreddit centered on contextualizing shifting or evolving views on abortion across various ethical, moral, and legal domains. The average compound Valence Aware Dictionary and Sentiment Reasoner scores for the r/Abortion and r/AbortionDebate subreddits were 0.01 (SD 0.44) and ?0.06 (SD 0.41), respectively. Emotionality scores were consistent across the r/Abortion and r/AbortionDebate subreddits; however, r/Abortion had a marginally higher average fear score of 0.36 (SD 0.39). Conclusions: Our findings suggest that people posting on abortion forums on Reddit are willing to share their beliefs, which manifested in diverse ways, such as sharing abortion stories including how their worldview changed, which critiques the value of dichotomized abortion identity labels, and information seeking. Notably, the style of discourse varied significantly by subreddit. r/Abortion was principally leveraged as an information and outreach source; r/AbortionDebate largely centered on debating across various legal, ethical, and moral abortion domains. Collectively, our findings suggest that abortion remains an opaque yet politically charged issue for people and that social media can be leveraged to understand views and circumstances surrounding abortion. ", doi="10.2196/47408", url="https://www.jmir.org/2024/1/e47408", url="http://www.ncbi.nlm.nih.gov/pubmed/38354044" } @Article{info:doi/10.2196/52660, author="Jaiswal, Aditi and Washington, Peter", title="Using \#ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study", journal="JMIR Form Res", year="2024", month="Feb", day="14", volume="8", pages="e52660", keywords="autism", keywords="autism spectrum disorder", keywords="machine learning", keywords="natural language processing", keywords="public health", keywords="sentiment analysis", keywords="social media analysis", keywords="Twitter", abstract="Background: The increasing use of social media platforms has given rise to an unprecedented surge in user-generated content, with millions of individuals publicly sharing their thoughts, experiences, and health-related information. Social media can serve as a useful means to study and understand public health. Twitter (subsequently rebranded as ``X'') is one such social media platform that has proven to be a valuable source of rich information for both the general public and health officials. We conducted the first study applying Twitter data mining to autism screening. Objective: We aimed to study the feasibility of autism screening from Twitter data and discuss the ethical implications of such models. Methods: We developed a machine learning model to attempt to distinguish individuals with autism from their neurotypical peers based on the textual patterns from their public communications on Twitter. We collected 6,515,470 tweets from users' self-identification with autism using ``\#ActuallyAutistic'' and a separate control group. To construct the data set, we targeted English-language tweets using the search query ``\#ActuallyAutistic'' posted from January 1, 2014 to December 31, 2022. We encrypted all user IDs and stripped the tweets of identifiable information such as the associated email address prior to analysis. From these tweets, we identified unique users who used keywords such as ``autism'' OR ``autistic'' OR ``neurodiverse'' in their profile description and collected all the tweets from their timelines. To build the control group data set, we formulated a search query excluding the hashtag ``\#ActuallyAutistic'' and collected 1000 tweets per day during the same time period. We trained a word2vec model and an attention-based, bidirectional long short-term memory model to validate the performance of per-tweet and per-profile classification models. We deleted the data set and the models after our analysis. Results: Our tweet classifier reached a 73\% accuracy, a 0.728 area under the receiver operating characteristic curve score, and an 0.71 F1-score using word2vec representations fed into a logistic regression model, while the user profile classifier achieved an 0.78 area under the receiver operating characteristic curve score and an F1-score of 0.805 using an attention-based, bidirectional long short-term memory model. Conclusions: We have shown that it is feasible to train machine learning models using social media data to predict use of the \#ActuallyAutistic hashtag, an imperfect proxy for self-reported autism. While analyzing textual differences in naturalistic text has the potential to help clinicians screen for autism, there remain ethical questions that must be addressed for such research to move forward and to translate into the real world. While machine learning has the potential to improve behavioral research, there are still a plethora of ethical issues in digital phenotyping studies using social media with respect to user consent of marginalized populations. Achieving this requires a more inclusive approach during the model development process that involves the autistic community directly in the ideation and consent processes. ", doi="10.2196/52660", url="https://formative.jmir.org/2024/1/e52660", url="http://www.ncbi.nlm.nih.gov/pubmed/38354045" } @Article{info:doi/10.2196/53025, author="Castillo, R. Louise I. and Tran, Vivian and Brachaniec, Mary and Chambers, T. Christine and Chessie, Kelly and Couros, Alec and LeRuyet, Andre and LeRuyet, Charmayne and Thorpe, Lilian and Williams, Jaime and Wheelwright, Sara and Hadjistavropoulos, Thomas", title="The \#SeePainMoreClearly Phase II Pain in Dementia Social Media Campaign: Implementation and Evaluation Study", journal="JMIR Aging", year="2024", month="Feb", day="8", volume="7", pages="e53025", keywords="knowledge translation", keywords="Twitter", keywords="older adults", keywords="Facebook", keywords="knowledge mobilization", abstract="Background: Social media platforms have been effective in raising awareness of the underassessment and undertreatment of pain in dementia. Objective: After a successful pilot campaign, we aimed to scale our pain-in-dementia knowledge mobilization pilot initiative (ie, \#SeePainMoreClearly) to several social media platforms with the aid of a digital media partner. The goal of the initiative was to increase awareness of the challenges in the assessment and management of pain among people with dementia. A variety of metrics were implemented to evaluate the effort. Through this work, we endeavored to highlight key differences between our pilot initiative (which was a grassroots initiative), focusing largely on Twitter and YouTube, and the current science-media partnership. We also aimed to generate recommendations suitable for other social media campaigns related to health or aging. Methods: Evidence-based information about pain in dementia was summarized into engaging content (eg, videos) tailored to the needs of various knowledge users (eg, health professionals, families, and policy makers). We disseminated information using Facebook (Meta Platforms), Twitter (X Corp), YouTube (Alphabet Inc), Instagram (Meta Platforms), and LinkedIn (LinkedIn Corp) and measured the success of the initiative over a 12-month period (2020 to 2021). The evaluation methods focused on web analytics and questionnaires related to social media content. Knowledge users' web responses about the initiative and semistructured interviews were analyzed using thematic analysis. Results: During the course of the campaign, >700 posts were shared across all platforms. Web analytics showed that we drew >60,000 users from 82 countries to our resource website. Of the social media platforms used, Facebook was the most effective in reaching knowledge users (ie, over 1,300,000 users). Questionnaire responses from users were favorable; interview responses indicated that the information shared throughout the initiative increased awareness of the problem of pain in dementia and influenced respondent behavior. Conclusions: In this investigation, we demonstrated success in directing knowledge users to a resource website with practical information that health professionals could use in patient care along with pain assessment and management information for caregivers and people living with dementia. The evaluation metrics suggested no considerable differences between our pilot campaign and broader initiative when accounting for the length of time of each initiative. The limitations of large-scale health campaigns were noted, and recommendations were outlined for other researchers aiming to leverage social media as a knowledge mobilization tool. ", doi="10.2196/53025", url="https://aging.jmir.org/2024/1/e53025", url="http://www.ncbi.nlm.nih.gov/pubmed/38329793" } @Article{info:doi/10.2196/50561, author="Ni, Chen-xu and Fei, Yi-bo and Wu, Ran and Cao, Wen-xiang and Liu, Wenhao and Huang, Fang and Shen, Fu-ming and Li, Dong-jie", title="Tumor Immunotherapy--Related Information on Internet-Based Videos Commonly Used by the Chinese Population: Content Quality Analysis", journal="JMIR Form Res", year="2024", month="Feb", day="7", volume="8", pages="e50561", keywords="immunotherapy", keywords="internet videos", keywords="quality", keywords="misinformation", keywords="health informatics", keywords="Chinese", abstract="Background: Tumor immunotherapy is an innovative treatment today, but there are limited data on the quality of immunotherapy information on social networks. Dissemination of misinformation through the internet is a major social issue. Objective: Our objective was to characterize the quality of information and presence of misinformation about tumor immunotherapy on internet-based videos commonly used by the Chinese population. Methods: Using the keyword ``tumor immunotherapy'' in Chinese, we searched TikTok, Tencent, iQIYI, and BiliBili on March 5, 2022. We reviewed the 118 screened videos using the Patient Education Materials Assessment Tool---a validated instrument to collect consumer health information. DISCERN quality criteria and the JAMA (Journal of the American Medical Association) Benchmark Criteria were used for assessing the quality and reliability of the health information. The videos' content was also evaluated. Results: The 118 videos about tumor immunotherapy were mostly uploaded by channels dedicated to lectures, health-related animations, and interviews; their median length was 5 minutes, and 79\% of them were published in and after 2018. The median understandability and actionability of the videos were 71\% and 71\%, respectively. However, the quality of information was moderate to poor on the validated DISCERN and JAMA assessments. Only 12 videos contained misinformation (score of >1 out of 5). Videos with a doctor (lectures and interviews) not only were significantly less likely to contain misinformation but also had better quality and a greater forwarding number. Moreover, the results showed that more than half of the videos contain little or no content on the risk factors and management of tumor immunotherapy. Overall, over half of the videos had some or more information on the definition, symptoms, evaluation, and outcomes of tumor immunotherapy. Conclusions: Although the quality of immunotherapy information on internet-based videos commonly used by Chinese people is moderate, these videos have less misinformation and better content. Caution must be exercised when using these videos as a source of tumor immunotherapy--related information. ", doi="10.2196/50561", url="https://formative.jmir.org/2024/1/e50561", url="http://www.ncbi.nlm.nih.gov/pubmed/38324352" } @Article{info:doi/10.2196/37881, author="Ueda, Ryuichiro and Han, Feng and Zhang, Hongjian and Aoki, Tomohiro and Ogasawara, Katsuhiko", title="Verification in the Early Stages of the COVID-19 Pandemic: Sentiment Analysis of Japanese Twitter Users", journal="JMIR Infodemiology", year="2024", month="Feb", day="6", volume="4", pages="e37881", keywords="COVID-19", keywords="sentiment analysis", keywords="Twitter", keywords="infodemiology", keywords="NLP", keywords="Natural Language Processing", abstract="Background: The COVID-19 pandemic prompted global behavioral restrictions, impacting public mental health. Sentiment analysis, a tool for assessing individual and public emotions from text data, gained importance amid the pandemic. This study focuses on Japan's early public health interventions during COVID-19, utilizing sentiment analysis in infodemiology to gauge public sentiment on social media regarding these interventions. Objective: This study aims to investigate shifts in public emotions and sentiments before and after the first state of emergency was declared in Japan. By analyzing both user-generated tweets and retweets, we aim to discern patterns in emotional responses during this critical period. Methods: We conducted a day-by-day analysis of Twitter (now known as X) data using 4,894,009 tweets containing the keywords ``corona,'' ``COVID-19,'' and ``new pneumonia'' from March 23 to April 21, 2020, approximately 2 weeks before and after the first declaration of a state of emergency in Japan. We also processed tweet data into vectors for each word, employing the Fuzzy-C-Means (FCM) method, a type of cluster analysis, for the words in the sentiment dictionary. We set up 7 sentiment clusters (negative: anger, sadness, surprise, disgust; neutral: anxiety; positive: trust and joy) and conducted sentiment analysis of the tweet groups and retweet groups. Results: The analysis revealed a mix of positive and negative sentiments, with ``joy'' significantly increasing in the retweet group after the state of emergency declaration. Negative emotions, such as ``worry'' and ``disgust,'' were prevalent in both tweet and retweet groups. Furthermore, the retweet group had a tendency to share more negative content compared to the tweet group. Conclusions: This study conducted sentiment analysis of Japanese tweets and retweets to explore public sentiments during the early stages of COVID-19 in Japan, spanning 2 weeks before and after the first state of emergency declaration. The analysis revealed a mix of positive (joy) and negative (anxiety, disgust) emotions. Notably, joy increased in the retweet group after the emergency declaration, but this group also tended to share more negative content than the tweet group. This study suggests that the state of emergency heightened positive sentiments due to expectations for infection prevention measures, yet negative information also gained traction. The findings propose the potential for further exploration through network analysis. ", doi="10.2196/37881", url="https://infodemiology.jmir.org/2024/1/e37881", url="http://www.ncbi.nlm.nih.gov/pubmed/38127840" } @Article{info:doi/10.2196/52768, author="Spies, Erica and Andreu, Thomas and Hartung, Matthias and Park, Josephine and Kamudoni, Paul", title="Exploring the Perspectives of Patients Living With Lupus: Retrospective Social Listening Study", journal="JMIR Form Res", year="2024", month="Feb", day="2", volume="8", pages="e52768", keywords="systemic lupus erythematosus", keywords="SLE", keywords="cutaneous lupus erythematosus", keywords="CLE", keywords="quality of life", keywords="health-related quality of life", keywords="HRQoL", keywords="social media listening", keywords="lupus", keywords="rare", keywords="cutaneous", keywords="social media", keywords="infodemiology", keywords="infoveillance", keywords="social listening", keywords="natural language processing", keywords="machine learning", keywords="experience", keywords="experiences", keywords="tagged", keywords="tagging", keywords="visualization", keywords="visualizations", keywords="knowledge graph", keywords="chronic", keywords="autoimmune", keywords="inflammation", keywords="inflammatory", keywords="skin", keywords="dermatology", keywords="dermatological", keywords="forum", keywords="forums", keywords="blog", keywords="blogs", abstract="Background: Systemic lupus erythematosus (SLE) is a chronic autoimmune inflammatory disease affecting various organs with a wide range of clinical manifestations. Cutaneous lupus erythematosus (CLE) can manifest as a feature of SLE or an independent skin ailment. Health-related quality of life (HRQoL) is frequently compromised in individuals living with lupus. Understanding patients' perspectives when living with a disease is crucial for effectively meeting their unmet needs. Social listening is a promising new method that can provide insights into the experiences of patients living with their disease (lupus) and leverage these insights to inform drug development strategies for addressing their unmet needs. Objective: The objective of this study is to explore the experience of patients living with SLE and CLE, including their disease and treatment experiences, HRQoL, and unmet needs, as discussed in web-based social media platforms such as blogs and forums. Methods: A retrospective exploratory social listening study was conducted across 13 publicly available English-language social media platforms from October 2019 to January 2022. Data were processed using natural language processing and knowledge graph tagging technology to clean, format, anonymize, and annotate them algorithmically before feeding them to Pharos, a Semalytix proprietary data visualization and analysis platform, for further analysis. Pharos was used to generate descriptive data statistics, providing insights into the magnitude of individual patient experience variables, their differences in the magnitude of variables, and the associations between algorithmically tagged variables. Results: A total of 45,554 posts from 3834 individuals who were algorithmically identified as patients with lupus were included in this study. Among them, 1925 (authoring 5636 posts) and 106 (authoring 243 posts) patients were identified as having SLE and CLE, respectively. Patients frequently mentioned various symptoms in relation to SLE and CLE including pain, fatigue, and rashes; pain and fatigue were identified as the main drivers of HRQoL impairment. The most affected aspects of HRQoL included ``mobility,'' ``cognitive capabilities,'' ``recreation and leisure,'' and ``sleep and rest.'' Existing pharmacological interventions poorly managed the most burdensome symptoms of lupus. Conversely, nonpharmacological treatments, such as exercise and meditation, were frequently associated with HRQoL improvement. Conclusions: Patients with lupus reported a complex interplay of symptoms and HRQoL aspects that negatively influenced one another. This study demonstrates that social listening is an effective method to gather insights into patients' experiences, preferences, and unmet needs, which can be considered during the drug development process to develop effective therapies and improve disease management. ", doi="10.2196/52768", url="https://formative.jmir.org/2024/1/e52768", url="http://www.ncbi.nlm.nih.gov/pubmed/38306157" } @Article{info:doi/10.2196/50388, author="Heaton, Dan and Nichele, Elena and Clos, J{\'e}r{\'e}mie and Fischer, E. Joel", title="Perceptions of the Agency and Responsibility of the NHS COVID-19 App on Twitter: Critical Discourse Analysis", journal="J Med Internet Res", year="2024", month="Feb", day="1", volume="26", pages="e50388", keywords="COVID-19", keywords="information system", keywords="automated decisions", keywords="agency metaphor", keywords="corpus linguistics", keywords="decision-making algorithm", keywords="transitivity", abstract="Background: Since September 2020, the National Health Service (NHS) COVID-19 contact-tracing app has been used to mitigate the spread of COVID-19 in the United Kingdom. Since its launch, this app has been a part of the discussion regarding the perceived social agency of decision-making algorithms. On the social media website Twitter, a plethora of views about the app have been found but only analyzed for sentiment and topic trajectories thus far, leaving the perceived social agency of the app underexplored. Objective: We aimed to examine the discussion of social agency in social media public discourse regarding algorithm-operated decisions, particularly when the artificial intelligence agency responsible for specific information systems is not openly disclosed in an example such as the COVID-19 contact-tracing app. To do this, we analyzed the presentation of the NHS COVID-19 App on Twitter, focusing on the portrayal of social agency and the impact of its deployment on society. We also aimed to discover what the presentation of social agents communicates about the perceived responsibility of the app. Methods: Using corpus linguistics and critical discourse analysis, underpinned by social actor representation, we used the link between grammatical and social agency and analyzed a corpus of 118,316 tweets from September 2020 to July 2021 to see whether the app was portrayed as a social actor. Results: We found that active presentations of the app---seen mainly through personalization and agency metaphor---dominated the discourse. The app was presented as a social actor in 96\% of the cases considered and grew in proportion to passive presentations over time. These active presentations showed the app to be a social actor in 5 main ways: informing, instructing, providing permission, disrupting, and functioning. We found a small number of occasions on which the app was presented passively through backgrounding and exclusion. Conclusions: Twitter users presented the NHS COVID-19 App as an active social actor with a clear sense of social agency. The study also revealed that Twitter users perceived the app as responsible for their welfare, particularly when it provided instructions or permission, and this perception remained consistent throughout the discourse, particularly during significant events. Overall, this study contributes to understanding how social agency is discussed in social media discourse related to algorithmic-operated decisions This research offers valuable insights into public perceptions of decision-making digital contact-tracing health care technologies and their perceptions on the web, which, even in a postpandemic world, may shed light on how the public might respond to forthcoming interventions. ", doi="10.2196/50388", url="https://www.jmir.org/2024/1/e50388", url="http://www.ncbi.nlm.nih.gov/pubmed/38300688" } @Article{info:doi/10.2196/47508, author="Guo, Feipeng and Liu, Zixiang and Lu, Qibei and Ji, Shaobo and Zhang, Chen", title="Public Opinion About COVID-19 on a Microblog Platform in China: Topic Modeling and Multidimensional Sentiment Analysis of Social Media", journal="J Med Internet Res", year="2024", month="Jan", day="31", volume="26", pages="e47508", keywords="COVID-19", keywords="social media public opinion", keywords="microblog", keywords="sentiment analysis", keywords="topic modeling", abstract="Background: The COVID-19 pandemic raised wide concern from all walks of life globally. Social media platforms became an important channel for information dissemination and an effective medium for public sentiment transmission during the COVID-19 pandemic. Objective: Mining and analyzing social media text information can not only reflect the changes in public sentiment characteristics during the COVID-19 pandemic but also help the government understand the trends in public opinion and reasonably control public opinion. Methods: First, this study collected microblog comments related to the COVID-19 pandemic as a data set. Second, sentiment analysis was carried out based on the topic modeling method combining latent Dirichlet allocation (LDA) and Bidirectional Encoder Representations from Transformers (BERT). Finally, a machine learning logistic regression (ML-LR) model combined with a sparse matrix was proposed to explore the evolutionary trend in public opinion on social media and verify the high accuracy of the model. Results: The experimental results show that, in different stages, the characteristics of public emotion are different, and the overall trend is from negative to positive. Conclusions: The proposed method can effectively reflect the characteristics of the different times and space of public opinion. The results provide theoretical support and practical reference in response to public health and safety events. ", doi="10.2196/47508", url="https://www.jmir.org/2024/1/e47508", url="http://www.ncbi.nlm.nih.gov/pubmed/38294856" } @Article{info:doi/10.2196/48599, author="Moens, Maarten and Van Doorslaer, Leen and Billot, Maxime and Eeckman, Edgard and Roulaud, Manuel and Rigoard, Philippe and Fobelets, Maaike and Goudman, Lisa", title="Examining the Type, Quality, and Content of Web-Based Information for People With Chronic Pain Interested in Spinal Cord Stimulation: Social Listening Study", journal="J Med Internet Res", year="2024", month="Jan", day="30", volume="26", pages="e48599", keywords="online information", keywords="social listening", keywords="neuromodulation", keywords="patient care", keywords="chronic pain", keywords="web-based data", abstract="Background: The increased availability of web-based medical information has encouraged patients with chronic pain to seek health care information from multiple sources, such as consultation with health care providers combined with web-based information. The type and quality of information that is available on the web is very heterogeneous, in terms of content, reliability, and trustworthiness. To date, no studies have evaluated what information is available about neuromodulation on the web for patients with chronic pain. Objective: This study aims to explore the type, quality, and content of web-based information regarding spinal cord stimulation (SCS) for chronic pain that is freely available and targeted at health care consumers. Methods: The social listening tool Awario was used to search Facebook (Meta Platforms, Inc), Twitter (Twitter, Inc), YouTube (Google LLC), Instagram (Meta Platforms, Inc), blogs, and the web for suitable hits with ``pain'' and ``neuromodulation'' as keywords. Quality appraisal of the extracted information was performed using the DISCERN instrument. A thematic analysis through inductive coding was conducted. Results: The initial search identified 2174 entries, of which 630 (28.98\%) entries were eventually withheld, which could be categorized as web pages, including news and blogs (114/630, 18.1\%); Reddit (Reddit, Inc) posts (32/630, 5.1\%); Vimeo (Vimeo, Inc) hits (38/630, 6\%); or YouTube (Google LLC) hits (446/630, 70.8\%). Most posts originated in the United States (519/630, 82.4\%). Regarding the content of information, 66.2\% (383/579) of the entries discussed (fully discussed or partially discussed) how SCS works. In total, 55.6\% (322/579) of the entries did not elaborate on the fact that there may be >1 potential treatment choice and 47.7\% (276/579) did not discuss the influence of SCS on the overall quality of life. The inductive coding revealed 4 main themes. The first theme of pain and the burden of pain (1274/8886, 14.34\% coding references) explained about pain, pain management, individual impact of pain, and patient experiences. The second theme included neuromodulation as a treatment approach (3258/8886, 36.66\% coding references), incorporating the background on neuromodulation, patient-centered care, SCS therapy, and risks. Third, several device-related aspects (1722/8886, 19.38\% coding references) were presented. As a final theme, patient benefits and testimonials of treatment with SCS (2632/8886, 29.62\% coding references) were revealed with subthemes regarding patient benefits, eligibility, and testimonials and expectations. Conclusions: Health care consumers have access to web-based information about SCS, where details about the surgical procedures, the type of material, working mechanisms, risks, patient expectations, testimonials, and the potential benefits of this therapy are discussed. The reliability, trustworthiness, and correctness of web-based sources should be carefully considered before automatically relying on the content. ", doi="10.2196/48599", url="https://www.jmir.org/2024/1/e48599", url="http://www.ncbi.nlm.nih.gov/pubmed/38289645" } @Article{info:doi/10.2196/54439, author="Ramjee, Serena and Hasan, Zeeshaan-ul", title="Strengthening TikTok Content Analysis in Academia Using Follower Count and Engagement", journal="JMIR Dermatol", year="2024", month="Jan", day="30", volume="7", pages="e54439", keywords="social media", keywords="skin of color", keywords="skin of colour", keywords="representation", keywords="TikTok", keywords="atopic dermatitis", keywords="dermatology", keywords="dermatologist", doi="10.2196/54439", url="https://derma.jmir.org/2024/1/e54439", url="http://www.ncbi.nlm.nih.gov/pubmed/38289654" } @Article{info:doi/10.2196/46087, author="Kaur, Mahakprit and Cargill, Taylor and Hui, Kevin and Vu, Minh and Bragazzi, Luigi Nicola and Kong, Dzevela Jude", title="A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets", journal="JMIR Form Res", year="2024", month="Jan", day="29", volume="8", pages="e46087", keywords="COVID-19", keywords="Twitter", keywords="social media", keywords="medical supply shortage", keywords="pandemic", keywords="global health", keywords="Granger", keywords="convergent cross-mapping", keywords="causal analysis", keywords="intensive care unit bed", keywords="ICU bed", abstract="Background: The COVID-19 pandemic has highlighted gaps in the current handling of medical resource demand surges and the need for prioritizing scarce medical resources to mitigate the risk of health care facilities becoming overwhelmed. Objective: During a health care emergency, such as the COVID-19 pandemic, the public often uses social media to express negative sentiment (eg, urgency, fear, and frustration) as a real-time response to the evolving crisis. The sentiment expressed in COVID-19 posts may provide valuable real-time information about the relative severity of medical resource demand in different regions of a country. In this study, Twitter (subsequently rebranded as X) sentiment analysis was used to investigate whether an increase in negative sentiment COVID-19 tweets corresponded to a greater demand for hospital intensive care unit (ICU) beds in specific regions of the United States, Brazil, and India. Methods: Tweets were collected from a publicly available data set containing COVID-19 tweets with sentiment labels and geolocation information posted between February 1, 2020, and March 31, 2021. Regional medical resource shortage data were gathered from publicly available data sets reporting a time series of ICU bed demand across each country. Negative sentiment tweets were analyzed using the Granger causality test and convergent cross-mapping (CCM) analysis to assess the utility of the time series of negative sentiment tweets in forecasting ICU bed shortages. Results: For the United States (30,742,934 negative sentiment tweets), the results of the Granger causality test (for whether negative sentiment COVID-19 tweets forecast ICU bed shortage, assuming a stochastic system) were significant (P<.05) for 14 (28\%) of the 50 states that passed the augmented Dickey-Fuller test at lag 2, and the results of the CCM analysis (for whether negative sentiment COVID-19 tweets forecast ICU bed shortage, assuming a dynamic system) were significant (P<.05) for 46 (92\%) of the 50 states. For Brazil (3,004,039 negative sentiment tweets), the results of the Granger causality test were significant (P<.05) for 6 (22\%) of the 27 federative units, and the results of the CCM analysis were significant (P<.05) for 26 (96\%) of the 27 federative units. For India (4,199,151 negative sentiment tweets), the results of the Granger causality test were significant (P<.05) for 6 (23\%) of the 26 included regions (25 states and the national capital region of Delhi), and the results of the CCM analysis were significant (P<.05) for 26 (100\%) of the 26 included regions. Conclusions: This study provides a novel approach for identifying the regions of high hospital bed demand during a health care emergency scenario by analyzing Twitter sentiment data. Leveraging analyses that take advantage of natural language processing--driven tweet extraction systems has the potential to be an effective method for the early detection of medical resource demand surges. ", doi="10.2196/46087", url="https://formative.jmir.org/2024/1/e46087", url="http://www.ncbi.nlm.nih.gov/pubmed/38285495" } @Article{info:doi/10.2196/49756, author="Yin, Shuhua and Chen, Shi and Ge, Yaorong", title="Dynamic Associations Between Centers for Disease Control and Prevention Social Media Contents and Epidemic Measures During COVID-19: Infoveillance Study", journal="JMIR Infodemiology", year="2024", month="Jan", day="23", volume="4", pages="e49756", keywords="infoveillance", keywords="social media", keywords="COVID-19", keywords="US Centers for Disease Control and Prevention", keywords="CDC", keywords="topic modeling", keywords="multivariate time series analysis", abstract="Background: Health agencies have been widely adopting social media to disseminate important information, educate the public on emerging health issues, and understand public opinions. The Centers for Disease Control and Prevention (CDC) widely used social media platforms during the COVID-19 pandemic to communicate with the public and mitigate the disease in the United States. It is crucial to understand the relationships between the CDC's social media communications and the actual epidemic metrics to improve public health agencies' communication strategies during health emergencies. Objective: This study aimed to identify key topics in tweets posted by the CDC during the pandemic, investigate the temporal dynamics between these key topics and the actual COVID-19 epidemic measures, and make recommendations for the CDC's digital health communication strategies for future health emergencies. Methods: Two types of data were collected: (1) a total of 17,524 COVID-19--related English tweets posted by the CDC between December 7, 2019, and January 15, 2022, and (2) COVID-19 epidemic measures in the United States from the public GitHub repository of Johns Hopkins University from January 2020 to July 2022. Latent Dirichlet allocation topic modeling was applied to identify key topics from all COVID-19--related tweets posted by the CDC, and the final topics were determined by domain experts. Various multivariate time series analysis techniques were applied between each of the identified key topics and actual COVID-19 epidemic measures to quantify the dynamic associations between these 2 types of time series data. Results: Four major topics from the CDC's COVID-19 tweets were identified: (1) information on the prevention of health outcomes of COVID-19; (2) pediatric intervention and family safety; (3) updates of the epidemic situation of COVID-19; and (4) research and community engagement to curb COVID-19. Multivariate analyses showed that there were significant variabilities of progression between the CDC's topics and the actual COVID-19 epidemic measures. Some CDC topics showed substantial associations with the COVID-19 measures over different time spans throughout the pandemic, expressing similar temporal dynamics between these 2 types of time series data. Conclusions: Our study is the first to comprehensively investigate the dynamic associations between topics discussed by the CDC on Twitter and the COVID-19 epidemic measures in the United States. We identified 4 major topic themes via topic modeling and explored how each of these topics was associated with each major epidemic measure by performing various multivariate time series analyses. We recommend that it is critical for public health agencies, such as the CDC, to update and disseminate timely and accurate information to the public and align major topics with key epidemic measures over time. We suggest that social media can help public health agencies to inform the public on health emergencies and to mitigate them effectively. ", doi="10.2196/49756", url="https://infodemiology.jmir.org/2024/1/e49756", url="http://www.ncbi.nlm.nih.gov/pubmed/38261367" } @Article{info:doi/10.2196/45168, author="Brassel, Sophie and Brunner, Melissa and Campbell, Andrew and Power, Emma and Togher, Leanne", title="Exploring Discussions About Virtual Reality on Twitter to Inform Brain Injury Rehabilitation: Content and Network Analysis", journal="J Med Internet Res", year="2024", month="Jan", day="19", volume="26", pages="e45168", keywords="virtual reality", keywords="Twitter", keywords="brain injury", keywords="rehabilitation", keywords="cognitive communication", keywords="social networks", keywords="social media", keywords="brain injury rehabilitation", keywords="engagement", keywords="development", keywords="clinical practice", keywords="injury", keywords="users", abstract="Background: Virtual reality (VR) use in brain injury rehabilitation is emerging. Recommendations for VR development in this field encourage end user engagement to determine the benefits and challenges of VR use; however, existing literature on this topic is limited. Data from social networking sites such as Twitter may further inform development and clinical practice related to the use of VR in brain injury rehabilitation. Objective: This study collected and analyzed VR-related tweets to (1) explore the VR tweeting community to determine topics of conversation and network connections, (2) understand user opinions and experiences of VR, and (3) identify tweets related to VR use in health care and brain injury rehabilitation. Methods: Publicly available tweets containing the hashtags \#virtualreality and \#VR were collected up to twice weekly during a 6-week period from July 2020 to August 2020 using NCapture (QSR International). The included tweets were analyzed using mixed methods. All tweets were coded using inductive content analysis. Relevant tweets (ie, coded as ``VR in health care'' or ``talking about VR'') were further analyzed using Dann's content coding. The biographies of users who sent relevant tweets were examined descriptively. Tweet data networks were visualized using Gephi computational analysis. Results: A total of 260,715 tweets were collected, and 70,051 (26.87\%) were analyzed following eligibility screening. The sample comprised 33.68\% (23,596/70,051) original tweets and 66.32\% (46,455/70,051) retweets. Content analysis generated 10 main categories of original tweets related to VR (ie, advertising and promotion, VR content, talking about VR, VR news, general technology, VR industry, VR live streams, VR in health care, VR events, and VR community). Approximately 4.48\% (1056/23,596) of original tweets were related to VR use in health care, whereas 0.19\% (45/23,596) referred to VR in brain injury rehabilitation. In total, 14.86\% (3506/23,596) of original tweets featured commentary on user opinions and experiences of VR applications, equipment, and software. The VR tweeting community comprised a large network of 26,001 unique Twitter users. Users that posted tweets related to ``VR in health care'' (2124/26,001, 8.17\%) did not form an interconnected VR network, whereas many users ``talking about VR'' (3752/26,001, 14.43\%) were connected within a central network. Conclusions: This study provides valuable data on community-based experiences and opinions related to VR. Tweets showcased various VR applications, including in health care, and identified important user-based considerations that can be used to inform VR use in brain injury rehabilitation (eg, technical design, accessibility, and VR sickness). Limited discussions and small user networks related to VR in brain injury rehabilitation reflect the paucity of literature on this topic and the potential underuse of this technology. These findings emphasize that further research is required to understand the specific needs and perspectives of people with brain injuries and clinicians regarding VR use in rehabilitation. ", doi="10.2196/45168", url="https://www.jmir.org/2024/1/e45168", url="http://www.ncbi.nlm.nih.gov/pubmed/38241072" } @Article{info:doi/10.2196/52306, author="Kanamori, Rie and Umemura, Futaba and Uemura, Kosuke and Miyagami, Taiju and Valenti, Simon and Fukui, Nobuyuki and Yuda, Mayumi and Saita, Mizue and Mori, Hirotake and Naito, Toshio", title="Web-Based Search Volume for HIV Tests and HIV-Testing Preferences During the COVID-19 Pandemic in Japan: Infodemiology Study", journal="JMIR Form Res", year="2024", month="Jan", day="18", volume="8", pages="e52306", keywords="HIV test", keywords="infodemiology", keywords="self-test", keywords="COVID-19", keywords="search engine", keywords="Japan", abstract="Background: Research has found a COVID-19 pandemic--related impact on HIV medical services, including clinic visits, testing, and antiviral therapy initiation in countries including Japan. However, the change in trend for HIV/AIDS testing during the COVID-19 pandemic has not been explored extensively in the Japanese population. Objective: This infodemiology study examines the web-based search interest for two types of HIV tests, self-test kits and facility-based tests, before and during the COVID-19 pandemic in Japan. Methods: The monthly search volume of queried search terms was obtained from Yahoo! JAPAN. Search volumes for the following terms were collected from November 2017 to October 2018: ``HIV test,'' ``HIV test kit,'' and ``HIV test health center.'' The search term ``Corona PCR'' and the number of new COVID-19 cases by month were used as a control for the search trends. The number of new HIV cases in the corresponding study period was obtained from the AIDS Trend Committee Quarterly Report from the AIDS Prevention Foundation. Results: Compared to the search volume of ``corona-PCR,'' which roughly fluctuated corresponding to the number of new COVID-19 cases in Japan, the search volume of ``HIV test'' was relatively stable from 2019 to 2022. When we further stratified by the type of HIV test, the respective web-based search interest in HIV self-testing and facility-based testing showed distinct patterns from 2018 to 2022. While the search volume of ``HIV test kit'' remained stable, that of ``HIV test health center'' displayed a decreasing trend starting in 2018 and has remained low since the beginning of the COVID-19 pandemic. Around 66\%-71\% of the search volume of ``HIV test kits'' was attributable to searches made by male internet users from 2018 to 2022, and the top three contributing age groups were those aged 30-39 (27\%-32\%), 20-29 (19\%-32\%), and 40-49 (19\%-25\%) years. On the other hand, the search volume of ``HIV test health centers'' by male users decreased from more than 500 from 2018 to 2019 to fewer than 300 from 2020 to 2022. Conclusions: Our study found a notable decrease in the search volume of ``HIV test health center'' during the pandemic, while the search volume for HIV self-testing kits remained stable before and during the COVID-19 crisis in Japan. This suggests that the previously reported COVID-19--related decrease in the number of HIV tests mostly likely referred to facility-based testing. This sheds light on the change in HIV-testing preferences in Japan, calling for a more comprehensive application and regulatory acceptance of HIV self-instructed tests. ", doi="10.2196/52306", url="https://formative.jmir.org/2024/1/e52306", url="http://www.ncbi.nlm.nih.gov/pubmed/38236622" } @Article{info:doi/10.2196/44923, author="Alwuqaysi, Bdour and Abdul-Rahman, Alfie and Borgo, Rita", title="The Impact of Social Media Use on Mental Health and Family Functioning Within Web-Based Communities in Saudi Arabia: Ethnographic Correlational Study", journal="JMIR Form Res", year="2024", month="Jan", day="16", volume="8", pages="e44923", keywords="social media use", keywords="mental health", keywords="family functioning", abstract="Background: In recent years, increasing numbers of parents, activists, and decision-makers have raised concerns about the potential adverse effects of social media use on both mental health and family functioning. Although some studies have indicated associations between social media use and negative mental health outcomes, others have found no evidence of mental health harm. Objective: This correlation study investigated the interplay between social media use, mental health, and family functioning. Analyzing data from 314 users, this study explores diverse mental health outcomes. The study places particular emphasis on the Saudi Arabian sample, providing valuable insights into the cultural context and shedding light on the specific dynamics of social media's impact on mental well-being and family dynamics in this demographic context. Methods: We collected data through a subsection of an anonymous web-based survey titled ``The Effect of COVID-19 on Social Media Usage, Mental Health, and Family Functioning.'' The survey was distributed through diverse web-based platforms in Saudi Arabia, emphasizing the Saudi sample. The participants indicated their social media accounts and estimated their daily use. Mental health was assessed using the General Health Questionnaire and family functioning was evaluated using the Family Assessment Device Questionnaire. In addition, 6 mental health conditions (anxiety, self-esteem, depression, body dysmorphia, social media addiction, and eating disorders) were self-reported by participants. Results: The study demonstrates a pattern of frequent social media use, with a significant portion dedicating 3-5 hours daily for web-based activities, and most of the sample accessed platforms multiple times a day. Despite concerns about social media addiction and perceived unhealthiness, participants cited staying connected with friends and family as their primary motivation for social media use. WhatsApp was perceived as the most positively impactful, whereas TikTok was considered the most negative for our Saudi sample. YouTube, Instagram, and Snapchat users reported poorer mental health compared with nonusers of these platforms. Mental health effects encompassed anxiety and addiction, with age and gender emerging as significant factors. Associations between social media use and family functioning were evident, with higher social media quartiles correlating with a greater likelihood of mental health and unhealthy family functioning. Logistic regression identified age and gender as factors linked to affected mental health, particularly noting that female participants aged 25-34 years were found to be more susceptible to affected mental health. In addition, multivariable analysis identified age and social media use quartiles as factors associated with poor family functioning. Conclusions: This study examined how social media affects mental health and family functioning in Saudi Arabia. These findings underscore the need for culturally tailored interventions to address these challenges, considering diverse demographic needs. Recognizing these nuances can guide the development of interventions to promote digital well-being, acknowledging the importance of familial connections in Saudi society. ", doi="10.2196/44923", url="https://formative.jmir.org/2024/1/e44923", url="http://www.ncbi.nlm.nih.gov/pubmed/38227352" } @Article{info:doi/10.2196/49749, author="Garg, Ashvita and Nyitray, G. Alan and Roberts, R. James and Shungu, Nicholas and Ruggiero, J. Kenneth and Chandler, Jessica and Damgacioglu, Haluk and Zhu, Yenan and Brownstein, C. Naomi and Sterba, R. Katherine and Deshmukh, A. Ashish and Sonawane, Kalyani", title="Consumption of Health-Related Videos and Human Papillomavirus Awareness: Cross-Sectional Analyses of a US National Survey and YouTube From the Urban-Rural Context", journal="J Med Internet Res", year="2024", month="Jan", day="15", volume="26", pages="e49749", keywords="awareness", keywords="health awareness", keywords="health information", keywords="health videos", keywords="HINTS", keywords="HPV vaccine", keywords="HPV", keywords="information behavior", keywords="information behaviors", keywords="information seeking", keywords="online information", keywords="reproductive health", keywords="rural", keywords="sexual health", keywords="sexually transmitted", keywords="social media", keywords="STD", keywords="STI", keywords="urban", keywords="video", keywords="videos", keywords="YouTube", abstract="Background: Nearly 70\% of Americans use the internet as their first source of information for health-related questions. Contemporary data on the consumption of web-based videos containing health information among American adults by urbanity or rurality is currently unavailable, and its link with health topic awareness, particularly for human papillomavirus (HPV), is not known. Objective: We aim to describe trends and patterns in the consumption of health-related videos on social media from an urban-rural context, examine the association between exposure to health-related videos on social media and awareness of health topics (ie, HPV and HPV vaccine), and understand public interest in HPV-related video content through search terms and engagement analytics. Methods: We conducted a cross-sectional analysis of the US Health Information National Trends Survey 6, a nationally representative survey that collects data from civilian, noninstitutionalized adults aged 18 years or older residing in the United States. Bivariable analyses were used to estimate the prevalence of consumption of health-related videos on social media among US adults overall and by urbanity or rurality. Multivariable logistic regression models were used to examine the association between the consumption of health-related videos and HPV awareness among urban and rural adults. To provide additional context on the public's interest in HPV-specific video content, we examined search volumes (quantitative) and related query searches (qualitative) for the terms ``HPV'' and ``HPV vaccine'' on YouTube. Results: In 2022, 59.6\% of US adults (152.3 million) consumed health-related videos on social media, an increase of nearly 100\% from 2017 to 2022. Prevalence increased among adults living in both urban (from 31.4\% in 2017 to 59.8\% in 2022; P<.001) and rural (from 22.4\% in 2017 to 58\% in 2022; P<.001) regions. Within the urban and rural groups, consumption of health-related videos on social media was most prevalent among adults aged between 18 and 40 years and college graduates or higher-educated adults. Among both urban and rural groups, adults who consumed health-related videos had a significantly higher probability of being aware of HPV and the HPV vaccine compared with those who did not watch health videos on the internet. The term ``HPV'' was more frequently searched on YouTube compared with ``HPV vaccine.'' Individuals were most commonly searching for videos that covered content about the HPV vaccine, HPV in males, and side effects of the HPV vaccine. Conclusions: The consumption of health-related videos on social media in the United States increased dramatically between 2017 and 2022. The rise was prominent among both urban and rural adults. Watching a health-related video on social media was associated with a greater probability of being aware of HPV and the HPV vaccine. Additional research on designing and developing social media strategies is needed to increase public awareness of health topics. ", doi="10.2196/49749", url="https://www.jmir.org/2024/1/e49749", url="http://www.ncbi.nlm.nih.gov/pubmed/38224476" } @Article{info:doi/10.2196/46693, author="Pearce, Emily and Raj, Hannah and Emezienna, Ngozika and Gilkey, B. Melissa and Lazard, J. Allison and Ribisl, M. Kurt and Savage, A. Sharon and Han, KJ Paul", title="The Use of Social Media to Express and Manage Medical Uncertainty in Dyskeratosis Congenita: Content Analysis", journal="JMIR Infodemiology", year="2024", month="Jan", day="15", volume="4", pages="e46693", keywords="social media", keywords="medical uncertainty", keywords="telomere biology disorder", keywords="dyskeratosis congenita", keywords="social support", abstract="Background: Social media has the potential to provide social support for rare disease communities; however, little is known about the use of social media for the expression of medical uncertainty, a common feature of rare diseases. Objective: This study aims to evaluate the expression of medical uncertainty on social media in the context of dyskeratosis congenita, a rare cancer-prone inherited bone marrow failure and telomere biology disorder (TBD). Methods: We performed a content analysis of uncertainty-related posts on Facebook and Twitter managed by Team Telomere, a patient advocacy group for this rare disease. We assessed the frequency of uncertainty-related posts, uncertainty sources, issues, and management and associations between uncertainty and social support. Results: Across all TBD social media platforms, 45.98\% (1269/2760) of posts were uncertainty related. Uncertainty-related posts authored by Team Telomere on Twitter focused on scientific (306/434, 70.5\%) or personal (230/434, 53\%) issues and reflected uncertainty arising from probability, ambiguity, or complexity. Uncertainty-related posts in conversations among patients and caregivers in the Facebook community group focused on scientific (429/511, 84\%), personal (157/511, 30.7\%), and practical (114/511, 22.3\%) issues, many of which were related to prognostic unknowns. Both platforms suggested uncertainty management strategies that focused on information sharing and community building. Posts reflecting response-focused uncertainty management strategies (eg, emotional regulation) were more frequent on Twitter compared with the Facebook community group ($\chi$21=3.9; P=.05), whereas posts reflecting uncertainty-focused management strategies (eg, ordering information) were more frequent in the Facebook community group compared with Twitter ($\chi$21=55.1; P<.001). In the Facebook community group, only 36\% (184/511) of members created posts during the study period, and those who created posts did so with a low frequency (median 3, IQR 1-7 posts). Analysis of post creator characteristics suggested that most users of TBD social media are White, female, and parents of patients with dyskeratosis congenita. Conclusions: Although uncertainty is a pervasive and multifactorial issue in TBDs, our findings suggest that the discussion of medical uncertainty on TBD social media is largely limited to brief exchanges about scientific, personal, or practical issues rather than ongoing supportive conversation. The nature of uncertainty-related conversations also varied by user group: patients and caregivers used social media primarily to discuss scientific uncertainties (eg, regarding prognosis), form social connections, or exchange advice on accessing and organizing medical care, whereas Team Telomere used social media to express scientific and personal issues of uncertainty and to address the emotional impact of uncertainty. The higher involvement of female parents on TBD social media suggests a potentially greater burden of uncertainty management among mothers compared with other groups. Further research is needed to understand the dynamics of social media engagement to manage medical uncertainty in the TBD community. ", doi="10.2196/46693", url="https://infodemiology.jmir.org/2024/1/e46693", url="http://www.ncbi.nlm.nih.gov/pubmed/38224480" } @Article{info:doi/10.2196/45573, author="Esmaeilzadeh, Pouyan", title="Privacy Concerns About Sharing General and Specific Health Information on Twitter: Quantitative Study", journal="JMIR Form Res", year="2024", month="Jan", day="12", volume="8", pages="e45573", keywords="concern for information privacy", keywords="CFIP", keywords="peer privacy concern", keywords="PrPC", keywords="health information disclosure", keywords="Twitter", keywords="empirical study", abstract="Background: Twitter is a common platform for people to share opinions, discuss health-related topics, and engage in conversations with a wide audience. Twitter users frequently share health information related to chronic diseases, mental health, and general wellness topics. However, sharing health information on Twitter raises privacy concerns as it involves sharing personal and sensitive data on a web-based platform. Objective: This study aims to adopt an interactive approach and develop a model consisting of privacy concerns related to web-based vendors and web-based peers. The research model integrates the 4 dimensions of concern for information privacy that express concerns related to the practices of companies and the 4 dimensions of peer privacy concern that reflect concerns related to web-based interactions with peers. This study examined how this interaction may affect individuals' information-sharing behavior on Twitter. Methods: Data were collected from 329 Twitter users in the United States using a web-based survey. Results: Results suggest that privacy concerns related to company practices might not significantly influence the sharing of general health information, such as details about hospitals and medications. However, privacy concerns related to companies and third parties can negatively shape the disclosure of specific health information, such as personal medical issues ($\beta$=?.43; P<.001). Findings show that peer-related privacy concerns significantly predict sharing patterns associated with general ($\beta$=?.38; P<.001) and specific health information ($\beta$=?.72; P<.001). In addition, results suggest that people may disclose more general health information than specific health information owing to peer-related privacy concerns (t165=4.72; P<.001). The model explains 41\% of the variance in general health information disclosure and 67\% in specific health information sharing on Twitter. Conclusions: The results can contribute to privacy research and propose some practical implications. The findings provide insights for developers, policy makers, and health communication professionals about mitigating privacy concerns in web-based health information sharing. It particularly underlines the importance of addressing peer-related privacy concerns. The study underscores the need to build a secure and trustworthy web-based environment, emphasizing the significance of peer interactions and highlighting the need for improved regulations, clear data handling policies, and users' control over their own data. ", doi="10.2196/45573", url="https://formative.jmir.org/2024/1/e45573", url="http://www.ncbi.nlm.nih.gov/pubmed/38214964" } @Article{info:doi/10.2196/46085, author="Subramanyam, Chaitra and Becker, Alyssa and Rizzo, Julianne and Afzal, Najiba and Nong, Yvonne and Sivamani, Raja", title="Visibility of Board-Certified Dermatologists on TikTok", journal="JMIR Dermatol", year="2024", month="Jan", day="5", volume="7", pages="e46085", keywords="board", keywords="certification", keywords="board certification", keywords="health", keywords="media", keywords="public", keywords="social", keywords="TikTok", keywords="social media", keywords="health information", keywords="misinformation", keywords="diagnosis", keywords="users", keywords="medical training", keywords="training", keywords="media content", keywords="skin", keywords="derma", keywords="derm", keywords="dermatologist", keywords="dermatology", keywords="epidermis", keywords="dermatitis", keywords="cellulitis", keywords="skin doctor", keywords="hair", keywords="nail", doi="10.2196/46085", url="https://derma.jmir.org/2024/1/e46085", url="http://www.ncbi.nlm.nih.gov/pubmed/38180786" } @Article{info:doi/10.2196/43850, author="Danias, George and Appel, Jacob", title="Public Interest in Psilocybin and Psychedelic Therapy in the Context of the COVID-19 Pandemic: Google Trends Analysis", journal="JMIR Form Res", year="2023", month="Dec", day="29", volume="7", pages="e43850", keywords="psilocybin", keywords="Google Trends", keywords="COVID-19", keywords="medical informatics", keywords="depression", keywords="anxiety", keywords="substance use", keywords="social media", keywords="trend analysis", keywords="antidepressant", abstract="Background: Psychedelic substances have demonstrated promise in the treatment of depression, anxiety, and substance use disorders. Significant media coverage has been dedicated to psychedelic medicine, but it is unclear whether the public associates psilocybin with its potential therapeutic benefits. The COVID-19 pandemic led to an increase in depression, anxiety, and substance abuse in the general population. Objective: This study attempts to link increases in interest in these disorders with increases in interest in psilocybin using Google Trends. Methods: Weekly interest-over-time Google Trends data for 4 years, from the week of March 11, 2018, to the week of March 6, 2022, were obtained for the following terms: ``psilocybin,'' ``psychedelic therapy,'' ``cannabis,'' ``cocaine,'' ``antidepressant,'' ``depression,'' ``anxiety,'' and ``addiction.'' Important psilocybin-related news and the declaration of the pandemic were noted. Trends data for each of the queried terms were plotted, and multiple regression analysis was performed to determine the slope of the prepandemic and postpandemic data with 95\% CIs. Nonparametric Tau-U analysis was performed correcting for baseline trends. Results from this test were used to make inferences about the pre- and postpandemic trends and inferences about the change in overall level of searches between the 2 groups. Results: Tau values for prepandemic data were significant for stable trends, all ranging --0.4 to 0.4. Tau values for postpandemic data showed positive trends for ``psilocybin,'' ``psychedelic therapy,'' and ``antidepressant.'' All other trends remained stable in the range of --0.4 to 0.4. When comparing Tau values for pre- and postpandemic data, overall increases in relative search volume (RSV) were seen for ``psilocybin,'' ``psychedelic therapy,'' and ``anxiety,'' and overall decreases in RSV were seen for ``depression,'' ``addiction,'' and ``cocaine.'' Overall RSVs for ``cannabis'' and ``antidepressant'' remained stable as Tau values ranged between --0.4 and 0.4. In the immediate aftermath of the declaration of the pandemic, drop-offs in interest were seen for all terms except for ``anxiety'' and ``cannabis.'' After the initial shock of a global pandemic, ``psilocybin'' and ``psychedelic therapy'' groups demonstrated increases in interest trends and overall RSV. Conclusions: These data suggest that overall interest in ``psilocybin'' and ``psychedelic therapy'' increased at higher rates and to higher levels after than before the declaration of the pandemic. This is consistent with our hypothesis that interest increased for these treatments after the pandemic as incidence of depression, anxiety, and addiction increased. However, there may be other drivers of interest for these topics, since interest in antidepressants---the typical pharmacologic treatments for depression and anxiety---followed the expected pattern of drop-off and accelerated interest back to prepandemic levels. Interest in ``psilocybin'' and ``psychedelic therapy'' may have also been partially driven by popular culture hype and novelty, explaining why interest increased at a higher rate post pandemic and continued to grow, surpassing prior interest. ", doi="10.2196/43850", url="https://formative.jmir.org/2023/1/e43850", url="http://www.ncbi.nlm.nih.gov/pubmed/38064635" } @Article{info:doi/10.2196/50276, author="Hansen, Rita-Kristin and Baiju, Nikita and Gabarron, Elia", title="Social Media as an Effective Provider of Quality-Assured and Accurate Information to Increase Vaccine Rates: Systematic Review", journal="J Med Internet Res", year="2023", month="Dec", day="26", volume="25", pages="e50276", keywords="social media", keywords="vaccines", keywords="vaccination", keywords="randomized controlled trials", keywords="information sources", abstract="Background: Vaccination programs are instrumental in prolonging and improving people's lives by preventing diseases such as measles, diphtheria, tetanus, pertussis, and influenza from escalating into fatal epidemics. Despite the significant impact of these programs, a substantial number of individuals, including 20 million infants annually, lack sufficient access to vaccines. Therefore, it is imperative to raise awareness about vaccination programs. Objective: This study aims to investigate the potential utilization of social media, assessing its scalability and robustness in delivering accurate and reliable information to individuals who are contemplating vaccination decisions for themselves or on behalf of their children. Methods: The protocol for this review is registered in PROSPERO (identifier CRD42022304229) and is being carried out in compliance with the Cochrane Handbook for Systematic Reviews of Interventions. Comprehensive searches have been conducted in databases including MEDLINE, Embase, PsycINFO, CINAHL (Cumulative Index to Nursing and Allied Health), CENTRAL (Cochrane Central Register of Controlled Trials), and Google Scholar. Only randomized controlled trials (RCTs) were deemed eligible for inclusion in this study. The target population encompasses the general public, including adults, children, and adolescents. The defined interventions comprise platforms facilitating 2-way communication for sharing information. These interventions were compared against traditional interventions and teaching methods, referred to as the control group. The outcomes assessed in the included studies encompassed days unvaccinated, vaccine acceptance, and the uptake of vaccines compared with baseline. The studies underwent a risk-of-bias assessment utilizing the Cochrane Risk-of-Bias tool for RCTs, and the certainty of evidence was evaluated using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) assessment. Results: This review included 10 studies, detailed in 12 articles published between 2012 and 2022, conducted in the United States, China, Jordan, Australia, and Israel. The studies involved platforms such as Facebook, Twitter, WhatsApp, and non--general-purpose social media. The outcomes examined in these studies focused on the uptake of vaccines compared with baseline, vaccine acceptance, and the number of days individuals remained unvaccinated. The overall sample size for this review was 26,286, with individual studies ranging from 58 to 21,592 participants. The effect direction plot derived from articles of good and fair quality indicated a nonsignificant outcome (P=.12). Conclusions: The findings suggest that, in a real-world scenario, an equal number of positive and negative results may be expected due to the interventions' impact on the acceptance and uptake of vaccines. Nevertheless, there is a rationale for accumulating experience to optimize the use of social media with the aim of enhancing vaccination rates. Social media can serve as a tool with the potential to disseminate information and boost vaccination rates within a population. However, relying solely on social media is not sufficient, given the complex structures at play in vaccine acceptance. Effectiveness hinges on various factors working in tandem. It is crucial that authorized personnel closely monitor and moderate discussions on social media to ensure responsible and accurate information dissemination. ", doi="10.2196/50276", url="https://www.jmir.org/2023/1/e50276", url="http://www.ncbi.nlm.nih.gov/pubmed/38147375" } @Article{info:doi/10.2196/49469, author="Smith, Patrice Brandi and Hoots, Brooke and DePadilla, Lara and Roehler, R. Douglas and Holland, M. Kristin and Bowen, A. Daniel and Sumner, A. Steven", title="Using Transformer-Based Topic Modeling to Examine Discussions of Delta-8 Tetrahydrocannabinol: Content Analysis", journal="J Med Internet Res", year="2023", month="Dec", day="21", volume="25", pages="e49469", keywords="social media", keywords="natural language processing", keywords="public health surveillance", keywords="machine learning", keywords="topic modeling", keywords="delta-8 tetrahydrocannabinol", keywords="cannabis", keywords="marijuana", abstract="Background: Delta-8 tetrahydrocannabinol (THC) is a psychoactive cannabinoid found in small amounts naturally in the cannabis plant; it can also be synthetically produced in larger quantities from hemp-derived cannabidiol. Most states permit the sale of hemp and hemp-derived cannabidiol products; thus, hemp-derived delta-8 THC products have become widely available in many state hemp marketplaces, even where delta-9 THC, the most prominently occurring THC isomer in cannabis, is not currently legal. Health concerns related to the processing of delta-8 THC products and their psychoactive effects remain understudied. Objective: The goal of this study is to implement a novel topic modeling approach based on transformers, a state-of-the-art natural language processing architecture, to identify and describe emerging trends and topics of discussion about delta-8 THC from social media discourse, including potential symptoms and adverse health outcomes experienced by people using delta-8 THC products. Methods: Posts from January 2008 to December 2021 discussing delta-8 THC were isolated from cannabis-related drug forums on Reddit (Reddit Inc), a social media platform that hosts the largest web-based drug forums worldwide. Unsupervised topic modeling with state-of-the-art transformer-based models was used to cluster posts into topics and assign labels describing the kinds of issues being discussed with respect to delta-8 THC. Results were then validated by human subject matter experts. Results: There were 41,191 delta-8 THC posts identified and 81 topics isolated, the most prevalent being (1) discussion of specific brands or products, (2) comparison of delta-8 THC to other hemp-derived cannabinoids, and (3) safety warnings. About 5\% (n=1220) of posts from the resulting topics included content discussing health-related symptoms such as anxiety, sleep disturbance, and breathing problems. Until 2020, Reddit posts contained fewer than 10 mentions of delta-8-THC for every 100,000 cannabis posts annually. However, in 2020, these rates increased by 13 times the 2019 rate (to 99.2 mentions per 100,000 cannabis posts) and continued to increase into 2021 (349.5 mentions per 100,000 cannabis posts). Conclusions: Our study provides insights into emerging public health concerns around delta-8 THC, a novel substance about which little is known. Furthermore, we demonstrate the use of transformer-based unsupervised learning approaches to derive intelligible topics from highly unstructured discussions of delta-8 THC, which may help improve the timeliness of identification of emerging health concerns related to new substances. ", doi="10.2196/49469", url="https://www.jmir.org/2023/1/e49469", url="http://www.ncbi.nlm.nih.gov/pubmed/38127427" } @Article{info:doi/10.2196/44912, author="Frennesson, Felicia Nessie and McQuire, Cheryl and Aijaz Khan, Saher and Barnett, Julie and Zuccolo, Luisa", title="Evaluating Messaging on Prenatal Health Behaviors Using Social Media Data: Systematic Review", journal="J Med Internet Res", year="2023", month="Dec", day="20", volume="25", pages="e44912", keywords="acceptability", keywords="design", keywords="development", keywords="effectiveness", keywords="health behavior", keywords="health messaging", keywords="messaging", keywords="prenatal health", keywords="prenatal", keywords="social media data", keywords="social media", keywords="tool", abstract="Background: Social media platforms are increasingly being used to disseminate messages about prenatal health. However, to date, we lack a systematic assessment of how to evaluate the impact of official prenatal health messaging and campaigns using social media data. Objective: This study aims to review both the published and gray literature on how official prenatal health messaging and campaigns have been evaluated to date in terms of impact, acceptability, effectiveness, and unintended consequences, using social media data. Methods: A total of 6 electronic databases were searched and supplemented with the hand-searching of reference lists. Both published and gray literature were eligible for review. Data were analyzed using content analysis for descriptive data and a thematic synthesis approach to summarize qualitative evidence. A quality appraisal tool, designed especially for use with social media data, was used to assess the quality of the included articles. Results: A total of 11 studies were eligible for the review. The results showed that the most common prenatal health behavior targeted was alcohol consumption, and Facebook was the most commonly used source of social media data. The majority (n=6) of articles used social media data for descriptive purposes only. The results also showed that there was a lack of evaluation of the effectiveness, acceptability, and unintended consequences of the prenatal health message or campaign. Conclusions: Social media is a widely used and potentially valuable resource for communicating and evaluating prenatal health messaging. However, this review suggests that there is a need to develop and adopt sound methodology on how to evaluate prenatal health messaging using social media data, for the benefit of future research and to inform public health practice. ", doi="10.2196/44912", url="https://www.jmir.org/2023/1/e44912", url="http://www.ncbi.nlm.nih.gov/pubmed/38117557" } @Article{info:doi/10.2196/44610, author="Song, Junxian and Cui, Yuxia and Song, Jing and Lee, Chongyou and Wu, Manyan and Chen, Hong", title="Evaluation of the Needs and Experiences of Patients with Hypertriglyceridemia: Social Media Listening Infosurveillance Study", journal="J Med Internet Res", year="2023", month="Dec", day="19", volume="25", pages="e44610", keywords="social media listening", keywords="hypertriglyceridemia", keywords="infosurveillance study", keywords="disease cognition", keywords="lifestyle intervention", keywords="lipid disorder", keywords="awareness", keywords="online search", keywords="telemedicine", keywords="self-medication", keywords="Chinese medicine", keywords="natural language processing", keywords="cardiovascular disease", keywords="stroke", keywords="online platform", keywords="self-management", keywords="Q\&A search platform", keywords="social media", abstract="Background: Hypertriglyceridemia is a risk factor for cardiovascular diseases. Internet usage in China is increasing, giving rise to large-scale data sources, especially to access, disseminate, and discuss medical information. Social media listening (SML) is a new approach to analyze and monitor online discussions related to various health-related topics in diverse diseases, which can generate insights into users' experiences and expectations. However, to date, no studies have evaluated the utility of SML to understand patients' cognizance and expectations pertaining to the management of hypertriglyceridemia. Objective: The aim of this study was to utilize SML to explore the disease cognition level of patients with hypertriglyceridemia, choice of intervention measures, and the status quo of online consultations and question-and-answer (Q\&A) search platforms. Methods: An infosurveillance study was conducted wherein a disease-specific comprehensive search was performed between 2004 and 2020 in Q\&A search and online consultation platforms. Predefined single and combined keywords related to hypertriglyceridemia were used in the search, including disease, symptoms, diagnosis, and treatment indicators; lifestyle interventions; and therapeutic agents. The search output was aggregated using an aggregator tool and evaluated. Results: Disease-specific consultation data (n=69,845) and corresponding response data (n=111,763) were analyzed from 20 data sources (6 Q\&A search platforms and 14 online consultation platforms). Doctors from inland areas had relatively high voice volumes and appear to exert a substantial influence on these platforms. Patients with hypertriglyceridemia engaging on the internet have an average level of cognition about the disease and its intervention measures. However, a strong demand for the concept of the disease and ``how to treat it'' was observed. More emphasis on the persistence of the disease and the safety of medications was observed. Young patients have a lower willingness for drug interventions, whereas patients with severe hypertriglyceridemia have a clearer intention to use drug intervention and few patients have a strong willingness for the use of traditional Chinese medicine. Conclusions: Findings from this disease-specific SML study revealed that patients with hypertriglyceridemia in China actively seek information from both online Q\&A search and consultation platforms. However, the integrity of internet doctors' suggestions on lifestyle interventions and the accuracy of drug intervention recommendations still need to be improved. Further, a combined prospective qualitative study with SML is required for added rigor and confirmation of the relevance of the findings. ", doi="10.2196/44610", url="https://www.jmir.org/2023/1/e44610", url="http://www.ncbi.nlm.nih.gov/pubmed/38113100" } @Article{info:doi/10.2196/49380, author="Li, Xiaoyu Jenny and Yacyshyn, Elaine", title="Thoughts and Experiences of Beh{\c{c}}et Disease From Participants on a Reddit Subforum: Qualitative Online Community Analysis", journal="JMIR Form Res", year="2023", month="Dec", day="12", volume="7", pages="e49380", keywords="Bechet disease", keywords="Beh{\c{c}}et", keywords="online community", keywords="Reddit", keywords="vasculitis", keywords="quality of life", keywords="QoL", keywords="qualitative", keywords="community", keywords="morbidity", keywords="support", keywords="diagnosis", keywords="symptoms", keywords="vascular", keywords="vascular system", keywords="vascular disease", abstract="Background: Beh{\c{c}}et disease (BD) is a type of vasculitis with relapsing episodes and multisystemic clinical features, associated with significant morbidity and impact on patients' lives. People affected by BD often participate in discussions of their illness experiences. In-person support groups have limited physical accessibility and a relative lack of anonymity; however, online communities have become increasingly popular. Objective: This study investigates the perspectives and experiences of people affected by BD by examining the content shared and discussed on a subforum of the website Reddit---a popular online space for anonymous discussions. Methods: All discussion threads posted between March 9, 2021, and March 12, 2022, including posts and comments, were examined from the subforum ``r/Behcets,'' an anonymous online community of 1100 members as of March 2022. A Grounded Theory analysis was completed to identify themes and subthemes, and notable quotes were extracted from the threads. Parameters extracted from each post included the number of comments, net upvotes, category, and subcategories. Two research team members read the posts separately to identify initial codes and themes to ensure data saturation was achieved. Results: Six recurring themes were identified: (1) finding connectedness and perspectives through shared experiences, (2) struggles of the diagnostic odyssey, (3) sharing or inquiring about symptoms, (4) expressing strong emotions relating to the experience of BD, (5) the impact of BD on quality of life and personal relationships, as well as (6) COVID-19 and the COVID-19 vaccination in relation to BD. Subthemes within each theme were also identified and explored. Conclusions: This novel study provides a qualitative exploration of the perspectives and experiences of people affected by BD, shared in the anonymous and accessible online community of Reddit. The study found that people impacted by an illness seek to connect and receive validation through shared conditions and experiences. By examining the content shared in r/Behcets, this study highlights the needs of people affected by BD, identifying gaps and areas for improvement in the in-person support they receive. ", doi="10.2196/49380", url="https://formative.jmir.org/2023/1/e49380", url="http://www.ncbi.nlm.nih.gov/pubmed/38085563" } @Article{info:doi/10.2196/48975, author="Rowson, C. Antonia and Rowson, J. Saskia", title="Derm-ographics: The Australian Dermatologist and Social Media", journal="JMIR Dermatol", year="2023", month="Dec", day="5", volume="6", pages="e48975", keywords="dermatology", keywords="social media", keywords="patient education", keywords="LinkedIn", keywords="Facebook", keywords="online presence", keywords="dermatologist", keywords="dermatologists", keywords="demographic", keywords="Twitter", keywords="X", keywords="YouTube", keywords="TikTok", keywords="ResearchGate", keywords="Instagram", keywords="provider", keywords="physician", keywords="technology use", doi="10.2196/48975", url="https://derma.jmir.org/2023/1/e48975", url="http://www.ncbi.nlm.nih.gov/pubmed/38051576" } @Article{info:doi/10.2196/49074, author="Kim, Seoyun and Cha, Junyeop and Kim, Dongjae and Park, Eunil", title="Understanding Mental Health Issues in Different Subdomains of Social Networking Services: Computational Analysis of Text-Based Reddit Posts", journal="J Med Internet Res", year="2023", month="Nov", day="30", volume="25", pages="e49074", keywords="mental health", keywords="sentiment analysis", keywords="mental disorder", keywords="text analysis", keywords="NLP", keywords="natural language processing", keywords="clustering", abstract="Background: Users increasingly use social networking services (SNSs) to share their feelings and emotions. For those with mental disorders, SNSs can also be used to seek advice on mental health issues. One available SNS is Reddit, in which users can freely discuss such matters on relevant health diagnostic subreddits. Objective: In this study, we analyzed the distinctive linguistic characteristics in users' posts on specific mental disorder subreddits (depression, anxiety, bipolar disorder, borderline personality disorder, schizophrenia, autism, and mental health) and further validated their distinctiveness externally by comparing them with posts of subreddits not related to mental illness. We also confirmed that these differences in linguistic formulations can be learned through a machine learning process. Methods: Reddit posts uploaded by users were collected for our research. We used various statistical analysis methods in Linguistic Inquiry and Word Count (LIWC) software, including 1-way ANOVA and subsequent post hoc tests, to see sentiment differences in various lexical features within mental health--related subreddits and against unrelated ones. We also applied 3 supervised and unsupervised clustering methods for both cases after extracting textual features from posts on each subreddit using bidirectional encoder representations from transformers (BERT) to ensure that our data set is suitable for further machine learning or deep learning tasks. Results: We collected 3,133,509 posts of 919,722 Reddit users. The results using the data indicated that there are notable linguistic differences among the subreddits, consistent with the findings of prior research. The findings from LIWC analyses revealed that patients with each mental health issue show significantly different lexical and semantic patterns, such as word count or emotion, throughout their online social networking activities, with P<.001 for all cases. Furthermore, distinctive features of each subreddit group were successfully identified through supervised and unsupervised clustering methods, using the BERT embeddings extracted from textual posts. This distinctiveness was reflected in the Davies-Bouldin scores ranging from 0.222 to 0.397 and the silhouette scores ranging from 0.639 to 0.803 in the former case, with scores of 1.638 and 0.729, respectively, in the latter case. Conclusions: By taking a multifaceted approach, analyzing textual posts related to mental health issues using statistical, natural language processing, and machine learning techniques, our approach provides insights into aspects of recent lexical usage and information about the linguistic characteristics of patients with specific mental health issues, which can inform clinicians about patients' mental health in diagnostic terms to aid online intervention. Our findings can further promote research areas involving linguistic analysis and machine learning approaches for patients with mental health issues by identifying and detecting mentally vulnerable groups of people online. ", doi="10.2196/49074", url="https://www.jmir.org/2023/1/e49074", url="http://www.ncbi.nlm.nih.gov/pubmed/38032730" } @Article{info:doi/10.2196/43700, author="Sigalo, Nekabari and Frias-Martinez, Vanessa", title="Using COVID-19 Vaccine Attitudes Found in Tweets to Predict Vaccine Perceptions in Traditional Surveys: Infodemiology Study", journal="JMIR Infodemiology", year="2023", month="Nov", day="30", volume="3", pages="e43700", keywords="social media", keywords="Twitter", keywords="COVID-19", keywords="vaccine", keywords="surveys", keywords="SARS-CoV-2", keywords="vaccinations", keywords="hesitancy", abstract="Background: Traditionally, surveys are conducted to answer questions related to public health but can be costly to execute. However, the information that researchers aim to extract from surveys could potentially be retrieved from social media, which possesses data that are highly accessible and lower in cost to collect. Objective: This study aims to evaluate whether attitudes toward COVID-19 vaccines collected from the Household Pulse Survey (HPS) could be predicted using attitudes extracted from Twitter (subsequently rebranded X). Ultimately, this study aimed to determine whether Twitter can provide us with similar information to that observed in traditional surveys or whether saving money comes at the cost of losing rich data. Methods: COVID-19 vaccine attitudes were extracted from the HPS conducted between January 6 and May 25, 2021. Twitter's streaming application programming interface was used to collect COVID-19 vaccine tweets during the same period. A sentiment and emotion analysis of tweets was conducted to examine attitudes toward the COVID-19 vaccine on Twitter. Generalized linear models and generalized linear mixed models were used to evaluate the ability of COVID-19 vaccine attitudes on Twitter to predict vaccine attitudes in the HPS. Results: The results revealed that vaccine perceptions expressed on Twitter performed well in predicting vaccine perceptions in the survey. Conclusions: These findings suggest that the information researchers aim to extract from surveys could potentially also be retrieved from a more accessible data source, such as Twitter. Leveraging Twitter data alongside traditional surveys can provide a more comprehensive and nuanced understanding of COVID-19 vaccine perceptions, facilitating evidence-based decision-making and tailored public health strategies. ", doi="10.2196/43700", url="https://infodemiology.jmir.org/2023/1/e43700", url="http://www.ncbi.nlm.nih.gov/pubmed/37903294" } @Article{info:doi/10.2196/50152, author="Noh, Youran and Kim, Maryanne and Hong, Hee Song", title="Identification of Emotional Spectrums of Patients Taking an Erectile Dysfunction Medication: Ontology-Based Emotion Analysis of Patient Medication Reviews on Social Media", journal="J Med Internet Res", year="2023", month="Nov", day="29", volume="25", pages="e50152", keywords="erectile dysfunction", keywords="PDE5 inhibitor", keywords="social media", keywords="emotion analysis", keywords="sentiment analysis", keywords="emotions", keywords="patient medication experience", keywords="tailored patient medication", keywords="patient-centered care", keywords="men's health", keywords="medications", keywords="drugs", abstract="Background: Patient medication reviews on social networking sites provide valuable insights into the experiences and sentiments of individuals taking specific medications. Understanding the emotional spectrum expressed by patients can shed light on their overall satisfaction with medication treatment. This study aims to explore the emotions expressed by patients taking phosphodiesterase type 5 (PDE5) inhibitors and their impact on sentiment. Objective: This study aimed to (1) identify the distribution of 6 Parrot emotions in patient medication reviews across different patient characteristics and PDE5 inhibitors, (2) determine the relative impact of each emotion on the overall sentiment derived from the language expressed in each patient medication review while controlling for different patient characteristics and PDE5 inhibitors, and (3) assess the predictive power of the overall sentiment in explaining patient satisfaction with medication treatment. Methods: A data set of patient medication reviews for sildenafil, vardenafil, and tadalafil was collected from 3 popular social networking sites such as WebMD, Ask-a-Patient, and Drugs.com. The Parrot emotion model, which categorizes emotions into 6 primary classes (surprise, anger, love, joy, sadness, and fear), was used to analyze the emotional content of the reviews. Logistic regression and sentiment analysis techniques were used to examine the distribution of emotions across different patient characteristics and PDE5 inhibitors and to quantify their contribution to sentiment. Results: The analysis included 3070 patient medication reviews. The most prevalent emotions expressed were joy and sadness, with joy being the most prevalent among positive emotions and sadness being the most prevalent among negative emotions. Emotion distributions varied across patient characteristics and PDE5 inhibitors. Regression analysis revealed that joy had the strongest positive impact on sentiment, while sadness had the most negative impact. The sentiment score derived from patient reviews significantly predicted patient satisfaction with medication treatment, explaining 19\% of the variance (increase in R2) when controlling for patient characteristics and PDE5 inhibitors. Conclusions: This study provides valuable insights into the emotional experiences of patients taking PDE5 inhibitors. The findings highlight the importance of emotions in shaping patient sentiment and satisfaction with medication treatment. Understanding these emotional dynamics can aid health care providers in better addressing patient needs and improving overall patient care. ", doi="10.2196/50152", url="https://www.jmir.org/2023/1/e50152", url="http://www.ncbi.nlm.nih.gov/pubmed/38019570" } @Article{info:doi/10.2196/50367, author="Tin, Jason and Stevens, Hannah and Rasul, Ehab Muhammad and Taylor, D. Laramie", title="Incivility in COVID-19 Vaccine Mandate Discourse and Moral Foundations: Natural Language Processing Approach", journal="JMIR Form Res", year="2023", month="Nov", day="29", volume="7", pages="e50367", keywords="incivility", keywords="vaccine hesitancy", keywords="moral foundations", keywords="COVID-19", keywords="vaccines", keywords="morality", keywords="social media", keywords="natural language processing", keywords="machine learning", abstract="Background: Vaccine hesitancy poses a substantial threat to efforts to mitigate the harmful effects of the COVID-19 pandemic. To combat vaccine hesitancy, officials in the United States issued vaccine mandates, which were met with strong antivaccine discourse on social media platforms such as Reddit. The politicized and polarized nature of COVID-19 on social media has fueled uncivil discourse related to vaccine mandates, which is known to decrease confidence in COVID-19 vaccines. Objective: This study examines the moral foundations underlying uncivil COVID-19 vaccine discourse. Moral foundations theory poses that individuals make decisions to express approval or disapproval (ie, uncivil discourse) based on innate moral values. We examine whether moral foundations are associated with dimensions of incivility. Further, we explore whether there are any differences in the presence of incivility between the r/coronaviruscirclejerk and r/lockdownskepticism subreddits. Methods: Natural language processing methodologies were leveraged to analyze the moral foundations underlying uncivil discourse in 2 prominent antivaccine subreddits, r/coronaviruscirclejerk and r/lockdownskepticism. All posts and comments from both of the subreddits were collected since their inception in March 2022. This was followed by filtering the data set for key terms associated with the COVID-19 vaccine (eg, ``vaccinate'' and ``Pfizer'') and mandates (eg, ``forced'' and ``mandating''). These key terms were selected based on a review of existing literature and because of their salience in both of the subreddits. A 10\% sample of the filtered key terms was used for the final analysis. Results: Findings suggested that moral foundations play a role in the psychological processes underlying uncivil vaccine mandate discourse. Specifically, we found substantial associations between all moral foundations (ie, care and harm, fairness and cheating, loyalty and betrayal, authority and subversion, and sanctity and degradation) and dimensions of incivility (ie, toxicity, insults, profanity, threat, and identity attack) except for the authority foundation. We also found statistically significant differences between r/coronaviruscirclejerk and r/lockdownskepticism for the presence of the dimensions of incivility. Specifically, the mean of identity attack, insult, toxicity, profanity, and threat in the r/lockdownskepticism subreddit was significantly lower than that in the r/coronaviruscirclejerk subreddit (P<.001). Conclusions: This study shows that moral foundations may play a substantial role in the presence of incivility in vaccine discourse. On the basis of the findings of the study, public health practitioners should tailor messaging by addressing the moral values underlying the concerns people may have about vaccines, which could manifest as uncivil discourse. Another way to tailor public health messaging could be to direct it to parts of social media platforms with increased uncivil discourse. By integrating moral foundations, public health messaging may increase compliance and promote civil discourse surrounding COVID-19. ", doi="10.2196/50367", url="https://formative.jmir.org/2023/1/e50367", url="http://www.ncbi.nlm.nih.gov/pubmed/38019581" } @Article{info:doi/10.2196/47849, author="Thornton, Christopher and Lanyi, Kate and Wilkins, Georgina and Potter, Rhiannon and Hunter, Emily and Kolehmainen, Niina and Pearson, Fiona", title="Scoping the Priorities and Concerns of Parents: Infodemiology Study of Posts on Mumsnet and Reddit", journal="J Med Internet Res", year="2023", month="Nov", day="28", volume="25", pages="e47849", keywords="childhood", keywords="child", keywords="toddler", keywords="infant", keywords="behavior", keywords="parent", keywords="parenting", keywords="topic modeling", keywords="data mining", keywords="social media", keywords="infodemiology", keywords="Reddit", keywords="web-based forum", keywords="well-being", keywords="children", keywords="data", keywords="family health", abstract="Background: Health technology innovation is increasingly supported by a bottom-up approach to priority setting, aiming to better reflect the concerns of its intended beneficiaries. Web-based forums provide parents with an outlet to share concerns, advice, and information related to parenting and the health and well-being of their children. They provide a rich source of data on parenting concerns and priorities that could inform future child health research and innovation. Objective: The aim of the study is to identify common concerns expressed on 2 major web-based forums and cluster these to identify potential family health concern topics as indicative priority areas for future research and innovation. Methods: We text-mined the r/Parenting subreddit (69,846 posts) and the parenting section of Mumsnet (99,848 posts) to create a large corpus of posts. A generative statistical model (latent Dirichlet allocation) was used to identify the most discussed topics in the corpus, and content analysis was applied to identify the parenting concerns found in a subset of posts. Results: A model with 25 topics produced the highest coherence and a wide range of meaningful parenting concern topics. The most frequently expressed parenting concerns are related to their child's sleep, self-care, eating (and food), behavior, childcare context, and the parental context including parental conflict. Topics directly associated with infants, such as potty training and bottle feeding, were more common on Mumsnet, while parental context and screen time were more common on r/Parenting. Conclusions: Latent Dirichlet allocation topic modeling can be applied to gain a rapid, yet meaningful overview of parent concerns expressed on a large and diverse set of social media posts and used to complement traditional insight gathering methods. Parents framed their concerns in terms of children's everyday health concerns, generating topics that overlap significantly with established family health concern topics. We provide evidence of the range of family health concerns found at these sources and hope this can be used to generate material for use alongside traditional insight gathering methods. ", doi="10.2196/47849", url="https://www.jmir.org/2023/1/e47849", url="http://www.ncbi.nlm.nih.gov/pubmed/38015600" } @Article{info:doi/10.2196/49435, author="Gable, M. Jessica S. and Sauvayre, Romy and Chauvi{\`e}re, C{\'e}dric", title="Fight Against the Mandatory COVID-19 Immunity Passport on Twitter: Natural Language Processing Study", journal="J Med Internet Res", year="2023", month="Nov", day="23", volume="25", pages="e49435", keywords="mandatory vaccination", keywords="public policy", keywords="public health measures", keywords="COVID-19", keywords="vaccine", keywords="social media analysis", keywords="Twitter", keywords="natural language processing", keywords="deep learning", keywords="social media", keywords="public health", keywords="vaccination", keywords="immunity", keywords="social distancing", keywords="neural network", keywords="effectiveness", abstract="Background: To contain and curb the spread of COVID-19, the governments of countries around the world have used different strategies (lockdown, mandatory vaccination, immunity passports, voluntary social distancing, etc). Objective: This study aims to examine the reactions produced by the public announcement of a binding political decision presented by the president of the French Republic, Emmanuel Macron, on July 12, 2021, which imposed vaccination on caregivers and an immunity passport on all French people to access restaurants, cinemas, bars, and so forth. Methods: To measure these announcement reactions, 901,908 unique tweets posted on Twitter (Twitter Inc) between July 12 and August 11, 2021, were extracted. A neural network was constructed to examine the arguments of the tweets and to identify the types of arguments used by Twitter users. Results: This study shows that in the debate about mandatory vaccination and immunity passports, mostly ``con'' arguments (399,803/847,725, 47\%; $\chi$26=952.8; P<.001) and ``scientific'' arguments (317,156/803,583, 39\%; $\chi$26=5006.8; P<.001) were used. Conclusions: This study shows that during July and August 2021, social events permeating the public sphere and discussions about mandatory vaccination and immunity passports collided on Twitter. Moreover, a political decision based on scientific arguments led citizens to challenge it using pseudoscientific arguments contesting the effectiveness of vaccination and the validity of these political decisions. ", doi="10.2196/49435", url="https://www.jmir.org/2023/1/e49435", url="http://www.ncbi.nlm.nih.gov/pubmed/37850906" } @Article{info:doi/10.2196/43891, author="Cuff, P. Jordan and Dighe, Nivrutti Shrinivas and Watson, E. Sophie and Badell-Grau, A. Rafael and Weightman, J. Andrew and Jones, L. Davey and Kille, Peter", title="Monitoring SARS-CoV-2 Using Infoveillance, National Reporting Data, and Wastewater in Wales, United Kingdom: Mixed Methods Study", journal="JMIR Infodemiology", year="2023", month="Nov", day="23", volume="3", pages="e43891", keywords="COVID-19", keywords="Google Trends", keywords="infodemiology", keywords="quantitative reverse transcription polymerase chain reaction", keywords="RT-qPCR", keywords="wastewater", keywords="infoveillance", keywords="public health care", keywords="health care statistics", keywords="correlation analysis", keywords="analysis", keywords="public health", keywords="online health", keywords="eHealth", keywords="public interest", abstract="Background: The COVID-19 pandemic necessitated rapid real-time surveillance of epidemiological data to advise governments and the public, but the accuracy of these data depends on myriad auxiliary assumptions, not least accurate reporting of cases by the public. Wastewater monitoring has emerged internationally as an accurate and objective means for assessing disease prevalence with reduced latency and less dependence on public vigilance, reliability, and engagement. How public interest aligns with COVID-19 personal testing data and wastewater monitoring is, however, very poorly characterized. Objective: This study aims to assess the associations between internet search volume data relevant to COVID-19, public health care statistics, and national-scale wastewater monitoring of SARS-CoV-2 across South Wales, United Kingdom, over time to investigate how interest in the pandemic may reflect the prevalence of SARS-CoV-2, as detected by national testing and wastewater monitoring, and how these data could be used to predict case numbers. Methods: Relative search volume data from Google Trends for search terms linked to the COVID-19 pandemic were extracted and compared against government-reported COVID-19 statistics and quantitative reverse transcription polymerase chain reaction (RT-qPCR) SARS-CoV-2 data generated from wastewater in South Wales, United Kingdom, using multivariate linear models, correlation analysis, and predictions from linear models. Results: Wastewater monitoring, most infoveillance terms, and nationally reported cases significantly correlated, but these relationships changed over time. Wastewater surveillance data and some infoveillance search terms generated predictions of case numbers that correlated with reported case numbers, but the accuracy of these predictions was inconsistent and many of the relationships changed over time. Conclusions: Wastewater monitoring presents a valuable means for assessing population-level prevalence of SARS-CoV-2 and could be integrated with other data types such as infoveillance for increasingly accurate inference of virus prevalence. The importance of such monitoring is increasingly clear as a means of objectively assessing the prevalence of SARS-CoV-2 to circumvent the dynamic interest and participation of the public. Increased accessibility of wastewater monitoring data to the public, as is the case for other national data, may enhance public engagement with these forms of monitoring. ", doi="10.2196/43891", url="https://infodemiology.jmir.org/2023/1/e43891", url="http://www.ncbi.nlm.nih.gov/pubmed/37903300" } @Article{info:doi/10.2196/45021, author="Kariya, Azusa and Okada, Hiroshi and Suzuki, Shota and Dote, Satoshi and Nishikawa, Yoshitaka and Araki, Kazuo and Takahashi, Yoshimitsu and Nakayama, Takeo", title="Internet-Based Inquiries From Users With the Intention to Overdose With Over-the-Counter Drugs: Qualitative Analysis of Yahoo! Chiebukuro", journal="JMIR Form Res", year="2023", month="Nov", day="22", volume="7", pages="e45021", keywords="abuse", keywords="consumer-generated media", keywords="CGM", keywords="overdose", keywords="over-the-counter drug", keywords="OTC drug", keywords="question and answer site", keywords="Q and A site", abstract="Background: Public concern with regard to over-the-counter (OTC) drug abuse is growing rapidly across countries. OTC drug abuse has serious effects on the mind and body, such as poisoning symptoms, and often requires specialized treatments. In contrast, there is concern about people who potentially abuse OTC drugs whose symptoms are not serious enough to consult medical institutions or drug addiction rehabilitation centers yet are at high risk of becoming drug dependent in the future. Objective: Consumer-generated media (CGM), which allows users to disseminate information, is being used by people who abuse (and those who are trying to abuse) OTC drugs to obtain information about OTC drug abuse. This study aims to analyze the content of CGM to explore the questions of people who potentially abuse OTC drugs. Methods: The subject of this research was Yahoo! Chiebukuro, the largest question and answer website in Japan. A search was performed using the names of drugs commonly used in OTC drug abuse and the keywords overdose and OD, and the number of questions posted on the content of OTC drug abuse was counted. Furthermore, a thematic analysis was conducted by extracting text data on the most abused antitussive and expectorant drug, BRON. Results: The number of questions about the content of overdose medications containing the keyword BRON has increased sharply as compared with other product names. Furthermore, 467 items of question data that met the eligibility criteria were obtained from 528 items of text data on BRON; 26 codes, 6 categories, and 3 themes were generated from the 578 questions contained in these items. Questions were asked about the effects they would gain from abusing OTC drugs and the information they needed to obtain the effects they sought, as well as about the effects of abuse on their bodies. Moreover, there were questions on how to stop abusing and what is needed when seeking help from a health care provider if they become dependent. It has become clear that people who abuse OTC drugs have difficulty in consulting face-to-face with others, and CGM is used as a means to obtain the necessary information anonymously. Conclusions: On CGM, people who abused or tried to abuse OTC drugs were asking questions about their abuse expectations and anxieties. In addition, when they became dependent, they sought advice to quit their abuse. CGM was used to exchange information about OTC drug abuse, and many questions on anxieties and hesitations were posted. This study suggests that it is necessary to produce and disseminate information on OTC drug abuse, considering the situation of those who abuse or are willing to abuse OTC drugs. Support from pharmacies and drugstores would also be essential to reduce opportunities for OTC drug abuse. ", doi="10.2196/45021", url="https://formative.jmir.org/2023/1/e45021", url="http://www.ncbi.nlm.nih.gov/pubmed/37991829" } @Article{info:doi/10.2196/48858, author="Yang, Jinqing and Liu, Zhifeng and Wang, Qicong and Lu, Na", title="Factors Influencing the Answerability and Popularity of a Health-Related Post in the Question-and-Answer Community: Infodemiology Study of Metafilter", journal="J Med Internet Res", year="2023", month="Nov", day="17", volume="25", pages="e48858", keywords="user behavior", keywords="dynamic network analysis", keywords="health consultation", keywords="health question and answers community", keywords="question-and-answer", keywords="Q\&A", keywords="negative binomial regression", abstract="Background: The web-based health question-and-answer (Q\&A) community has become the primary and handy way for people to access health information and knowledge directly. Objective: The objective of our study is to investigate how content-related, context-related, and user-related variables influence the answerability and popularity of health-related posts based on a user-dynamic, social network, and topic-dynamic semantic network, respectively. Methods: Full-scale data on health consultations were acquired from the Metafilter Q\&A community. These variables were designed in terms of context, content, and contributors. Negative binomial regression models were used to examine the influence of these variables on the favorite and comment counts of a health-related post. Results: A total of 18,099 post records were collected from a well-known Q\&A community. The findings of this study include the following. Content-related variables have a strong impact on both the answerability and popularity of posts. Notably, sentiment values were positively related to favorite counts and negatively associated with comment counts. User-related variables significantly affected the answerability and popularity of posts. Specifically, participation intensity was positively related to comment count and negatively associated with favorite count. Sociability breadth only had a significant impact on comment count. Context-related variables have a more substantial influence on the popularity of posts than on their answerability. The topic diversity variable exhibits an inverse correlation with the comment count while manifesting a positive correlation with the favorite count. Nevertheless, topic intensity has a significant effect only on favorite count. Conclusions: The research results not only reveal the factors influencing the answerability and popularity of health-related posts, which can help them obtain high-quality answers more efficiently, but also provide a theoretical basis for platform operators to enhance user engagement within health Q\&A communities. ", doi="10.2196/48858", url="https://www.jmir.org/2023/1/e48858", url="http://www.ncbi.nlm.nih.gov/pubmed/37976090" } @Article{info:doi/10.2196/43812, author="Ge, Ying and Yao, Dongning and Ung, Lam Carolina Oi and Xue, Yan and Li, Meng and Lin, Jiabao and Hu, Hao and Lai, Yunfeng", title="Digital Medical Information Services Delivered by Pharmaceutical Companies via WeChat: Qualitative Analytical Study", journal="J Med Internet Res", year="2023", month="Nov", day="17", volume="25", pages="e43812", keywords="digital medical information service", keywords="pharmaceutical company", keywords="WeChat", keywords="social media", keywords="digital health", abstract="Background: Social media has become one of the primary information sources for medical professionals and patients. Pharmaceutical companies are committed to using various social media platforms to provide stakeholders with digital medical information services (DMISs), which remain experimental and immature. In China, WeChat tops the list of popular social media platforms. To date, little is known about the service model of DMISs delivered by pharmaceutical companies via WeChat. Objective: This study aims to explore the emerging service model of DMISs delivered by pharmaceutical companies via WeChat in China. Methods: This study applied a qualitative research design combining case study and documentary analysis to explore the DMISs of 6 leading pharmaceutical companies in China. Materials were collected from their official WeChat platforms. Thematic analysis was conducted on the data. Results: The DMISs of 6 pharmaceutical companies were investigated. Themes emerged regarding 2 essential information services delivered by pharmaceutical companies via WeChat: business operation services and DMISs (ie, public information services, professional services, science and education services, and e-commerce services). Business operation services mainly function to assist or facilitate the company's operations and development trends for general visitors. Public-oriented information services are realized through health science popularization, academic frontiers, product information, and road maps to hospitals and pharmacies. Internet hospital and pharmacy services are the main patient-oriented professional services. Medical staff--oriented science and education services commonly include continuing education, clinical assistance, academic research, and journal searching. Public-oriented e-commerce services include health products and health insurance. Conclusions: Pharmaceutical companies in China use WeChat to provide stakeholders with diversified DMISs, which remain in the exploratory stage. The service model of DMISs requires more distinct innovations to provide personalized digital health and patient-centric services. Moreover, specific regulations on the DMISs of pharmaceutical companies need to be established to guard public health interests. ", doi="10.2196/43812", url="https://www.jmir.org/2023/1/e43812", url="http://www.ncbi.nlm.nih.gov/pubmed/37976079" } @Article{info:doi/10.2196/46589, author="Chen, Chen and Zhu, Junli", title="Quantifying Health Policy Uncertainty in China Using Newspapers: Text Mining Study", journal="J Med Internet Res", year="2023", month="Nov", day="14", volume="25", pages="e46589", keywords="China", keywords="health policy", keywords="newspaper", keywords="uncertainty", keywords="severe acute respiratory syndrome", keywords="SARS", keywords="COVID-19", abstract="Background: From the severe acute respiratory syndrome (SARS) outbreak in 2003 to the COVID-19 pandemic in 2019, a series of health measures and policies have been introduced from the central to the local level in China. However, no study has constructed an uncertainty index that can reflect the volatility, risk, and policy characteristics of the health environment. Objective: We used text mining analysis on mainstream newspapers to quantify the volume of reports about health policy and the total number of news articles and to construct a series of indexes that could reflect the uncertainty of health policy in China. Methods: Using the Wisenews database, 11 of the most influential newspapers in mainland China were selected to obtain the sample articles. The health policy uncertainty (HPU) index for each month from 2003 to 2022 was constructed by searching articles containing the specified keywords and calculating their frequency. Robustness tests were conducted through correlation analysis. The HPU index was plotted using STATA (version 16.0), and a comparative analysis of the China and US HPU indexes was then performed. Results: We retrieved 6482 sample articles from 7.49 million news articles in 11 newspapers. The China HPU index was constructed, and the robustness test showed a correlation coefficient greater than 0.74, which indicates good robustness. Key health events can cause index fluctuations. At the beginning of COVID-19 (May 2020), the HPU index climbed to 502.0. In December 2022, China's HPU index reached its highest value of 613.8 after the release of the ``New Ten Rules'' pandemic prevention and control policy. There were significant differences in HPU index fluctuations between China and the United States during SARS and COVID-19, as well as during the Affordable Care Act period. Conclusions: National health policy is a guide for health development, and uncertainty in health policy can affect not only the implementation of policy by managers but also the health-seeking behavior of the people. Here, we conclude that changes in critical health policies, major national or international events, and infectious diseases with widespread impact can create significant uncertainty in China's health policies. The uncertainty of health policies in China and the United States is quite different due to different political systems and news environments. What is the same is that COVID-19 has brought great policy volatility to both countries. To the best of our knowledge, our work is the first systematic text mining study of HPU in China. ", doi="10.2196/46589", url="https://www.jmir.org/2023/1/e46589", url="http://www.ncbi.nlm.nih.gov/pubmed/37962937" } @Article{info:doi/10.2196/45660, author="Carabot, Federico and Donat-Vargas, Carolina and Santoma-Vilaclara, Javier and Ortega, A. Miguel and Garc{\'i}a-Montero, Cielo and Fraile-Mart{\'i}nez, Oscar and Zaragoza, Cristina and Monserrat, Jorge and Alvarez-Mon, Melchor and Alvarez-Mon, Angel Miguel", title="Exploring Perceptions About Paracetamol, Tramadol, and Codeine on Twitter Using Machine Learning: Quantitative and Qualitative Observational Study", journal="J Med Internet Res", year="2023", month="Nov", day="14", volume="25", pages="e45660", keywords="awareness", keywords="codeine", keywords="machine learning", keywords="pain", keywords="painkiller", keywords="perception", keywords="recreational use", keywords="social media", keywords="twitter", abstract="Background: Paracetamol, codeine, and tramadol are commonly used to manage mild pain, and their availability without prescription or medical consultation raises concerns about potential opioid addiction. Objective: This study aims to explore the perceptions and experiences of Twitter users concerning these drugs. Methods: We analyzed the tweets in English or Spanish mentioning paracetamol, tramadol, or codeine posted between January 2019 and December 2020. Out of 152,056 tweets collected, 49,462 were excluded. The content was categorized using a codebook, distinguishing user types (patients, health care professionals, and institutions), and classifying medical content based on efficacy and adverse effects. Scientific accuracy and nonmedical content themes (commercial, economic, solidarity, and trivialization) were also assessed. A total of 1000 tweets for each drug were manually classified to train, test, and validate machine learning classifiers. Results: Of classifiable tweets, 42,840 mentioned paracetamol and 42,131 mentioned weak opioids (tramadol or codeine). Patients accounted for 73.10\% (60,771/83,129) of the tweets, while health care professionals and institutions received the highest like-tweet and tweet-retweet ratios. Medical content distribution significantly differed for each drug (P<.001). Nonmedical content dominated opioid tweets (23,871/32,307, 73.9\%), while paracetamol tweets had a higher prevalence of medical content (33,943/50,822, 66.8\%). Among medical content tweets, 80.8\% (41,080/50,822) mentioned drug efficacy, with only 6.9\% (3501/50,822) describing good or sufficient efficacy. Nonmedical content distribution also varied significantly among the different drugs (P<.001). Conclusions: Patients seeking relief from pain are highly interested in the effectiveness of drugs rather than potential side effects. Alarming trends include a significant number of tweets trivializing drug use and recreational purposes, along with a lack of awareness regarding side effects. Monitoring conversations related to analgesics on social media is essential due to common illegal web-based sales and purchases without prescriptions. ", doi="10.2196/45660", url="https://www.jmir.org/2023/1/e45660", url="http://www.ncbi.nlm.nih.gov/pubmed/37962927" } @Article{info:doi/10.2196/50138, author="Scales, David and Hurth, Lindsay and Xi, Wenna and Gorman, Sara and Radhakrishnan, Malavika and Windham, Savannah and Akunne, Azubuike and Florman, Julia and Leininger, Lindsey and Gorman, Jack", title="Addressing Antivaccine Sentiment on Public Social Media Forums Through Web-Based Conversations Based on Motivational Interviewing Techniques: Observational Study", journal="JMIR Infodemiology", year="2023", month="Nov", day="14", volume="3", pages="e50138", keywords="anti-vaccine", keywords="digital environment", keywords="engagement", keywords="health misinformation", keywords="infodemic", keywords="infodemiology", keywords="information environment", keywords="medical misinformation", keywords="misinformation", keywords="observational study", keywords="social media engagement metrics", keywords="social media", abstract="Background: Health misinformation shared on social media can have negative health consequences; yet, there is a dearth of field research testing interventions to address health misinformation in real time, digitally, and in situ on social media. Objective: We describe a field study of a pilot program of ``infodemiologists'' trained with evidence-informed intervention techniques heavily influenced by principles of motivational interviewing. Here we provide a detailed description of the nature of infodemiologists' interventions on posts sharing misinformation about COVID-19 vaccines, present an initial evaluation framework for such field research, and use available engagement metrics to quantify the impact of these in-group messengers on the web-based threads on which they are intervening. Methods: We monitored Facebook (Meta Platforms, Inc) profiles of news organizations marketing to 3 geographic regions (Newark, New Jersey; Chicago, Illinois; and central Texas). Between December 2020 and April 2021, infodemiologists intervened in 145 Facebook news posts that generated comments containing either false or misleading information about vaccines or overt antivaccine sentiment. Engagement (emojis plus replies) data were collected on Facebook news posts, the initial comment containing misinformation (level 1 comment), and the infodemiologist's reply (level 2 reply comment). A comparison-group evaluation design was used, with numbers of replies, emoji reactions, and engagements for level 1 comments compared with the median metrics of matched comments using the Wilcoxon signed rank test. Level 2 reply comments (intervention) were also benchmarked against the corresponding metric of matched reply comments (control) using the Wilcoxon signed rank test (paired at the level 1 comment level). Infodemiologists' level 2 reply comments (intervention) and matched reply comments (control) were further compared using 3 Poisson regression models. Results: In total, 145 interventions were conducted on 132 Facebook news posts. The level 1 comments received a median of 3 replies, 3 reactions, and 7 engagements. The matched comments received a median of 1.5 (median of IQRs 3.75) engagements. Infodemiologists made 322 level 2 reply comments, precipitating 189 emoji reactions and a median of 0.5 (median of IQRs IQR 0) engagements. The matched reply comments received a median of 1 (median of IQRs 2.5) engagement. Compared to matched comments, level 1 comments received more replies, emoji reactions, and engagements. Compared to matched reply comments, level 2 reply comments received fewer and narrower ranges of replies, reactions, and engagements, except for the median comparison for replies. Conclusions: Overall, empathy-first communication strategies based on motivational interviewing garnered less engagement relative to matched controls. One possible explanation is that our interventions quieted contentious, misinformation-laden threads about vaccines on social media. This work reinforces research on accuracy nudges and cyberbullying interventions that also reduce engagement. More research leveraging field studies of real-time interventions is needed, yet data transparency by technology platforms will be essential to facilitate such experiments. ", doi="10.2196/50138", url="https://infodemiology.jmir.org/2023/1/e50138", url="http://www.ncbi.nlm.nih.gov/pubmed/37962940" } @Article{info:doi/10.2196/42810, author="Nair, Isha and Patel, P. Sophia and Bolen, Ashley and Roger, Samantha and Bucci, Kayla and Schwab-Reese, Laura and DeMaria, L. Andrea", title="Reproductive Health Experiences Shared on TikTok by Young People: Content Analysis", journal="JMIR Infodemiology", year="2023", month="Nov", day="13", volume="3", pages="e42810", keywords="TikTok", keywords="social media", keywords="reproductive health", keywords="women's health", keywords="health outcome", keywords="content analysis", keywords="health information", keywords="sexual health", keywords="web-based information", keywords="COVID-19", keywords="health message", abstract="Background: TikTok is a popular social media platform that allows users to create and share content through short videos. It has become a place for everyday users, especially Generation Z users, to share experiences about their reproductive health. Owing to its growing popularity and easy accessibility, TikTok can help raise awareness for reproductive health issues as well as help destigmatize these conversations. Objective: We aimed to identify and understand the visual, audio, and written components of content that TikTok users create about their reproductive health experiences. Methods: A sampling framework was implemented to narrow down the analytic data set. The top 6 videos from each targeted hashtag (eg, \#BirthControl, \#MyBodyMyChoice, and \#LoveYourself) were extracted biweekly for 16 weeks (July-November 2020). During data collection, we noted video characteristics such as captioning, music, likes, and cited sources. Qualitative content analysis was performed on the extracted videos. Results: The top videos in each hashtag were consistent over time; for example, only 11 videos appeared in the top 6 category for \#BirthControl throughout the data collection. Most videos fell into 2 primary categories: personal experiences and informational content. Among the personal experiences, people shared stories (eg, intrauterine device removal experiences), crafts (eg, painting their pill case), or humor (eg, celebrations of the arrival of their period). Dancing and demonstrations were commonly used in informational content. Conclusions: TikTok is used to share messages on myriad reproductive health topics. Understanding users' exposure provides important insights into their beliefs and knowledge of sexual and reproductive health. The study findings can be used to generate valuable information for teenagers and young adults, their health care providers, and their communities. Producing health messages that are both meaningful and accessible will contribute to the cocreation of critical health information for professional and personal use. ", doi="10.2196/42810", url="https://infodemiology.jmir.org/2023/1/e42810", url="http://www.ncbi.nlm.nih.gov/pubmed/37831780" } @Article{info:doi/10.2196/49416, author="Al-Rawi, Ahmed and Blackwell, Breanna and Zemenchik, Kiana and Lee, Kelley", title="Twitter Misinformation Discourses About Vaping: Systematic Content Analysis", journal="J Med Internet Res", year="2023", month="Nov", day="10", volume="25", pages="e49416", keywords="vaping", keywords="e-cigarette", keywords="smoking", keywords="misinformation", keywords="fact checking", keywords="social media", keywords="Twitter", keywords="nicotine", keywords="content analysis", keywords="fact-checking", keywords="disinformation", keywords="weaponized", keywords="health risk", keywords="risk", keywords="health education", keywords="education", keywords="communication", keywords="electronic nicotine delivery systems", keywords="ENDS", abstract="Background: While there has been substantial analysis of social media content deemed to spread misinformation about electronic nicotine delivery systems use, the strategic use of misinformation accusations to undermine opposing views has received limited attention. Objective: This study aims to fill this gap by analyzing how social media users discuss the topic of misinformation related to electronic nicotine delivery systems, notably vaping products. Additionally, this study identifies and analyzes the actors commonly blamed for spreading such misinformation and how these claims support both the provaping and antivaping narratives. Methods: Using Twitter's (subsequently rebranded as X) academic application programming interface, we collected tweets referencing \#vape and \#vaping and keywords associated with fake news and misinformation. This study uses systematic content analysis to analyze the tweets and identify common themes and actors who discuss or possibly spread misinformation. Results: This study found that provape users dominate the platform regarding discussions about misinformation about vaping, with provaping tweets being more frequent and having higher overall user engagement. The most common narrative for provape tweets surrounds the conversation of vaping being perceived as safe. On the other hand, the most common topic from the antivape narrative is that vaping is indeed harmful. This study also points to a general distrust in authority figures, with news outlets, public health authorities, and political actors regularly accused of spreading misinformation, with both placing blame. However, specific actors differ depending on their positionalities. The vast number of accusations from provaping advocates is found to shape what is considered misinformation and works to silence other narratives. Additionally, allegations against reliable and proven sources, such as public health authorities, work to discredit assessments about the health impacts, which is detrimental to public health overall for both provaping and antivaping advocates. Conclusions: We conclude that the spread of misinformation and the accusations of misinformation dissemination using terms such as ``fact check,'' ``misinformation,'' ``fake news,'' and ``disinformation'' have become weaponized and co-opted by provaping actors to delegitimize criticisms about vaping and to increase confusion about the potential health risks. The study discusses the mixed types of impact of vaping on public health for both smokers and nonsmokers. Additionally, we discuss the implications for effective health education and communication about vaping and how misinformation claims can affect evidence-based discourse on Twitter as well as informed vaping decisions. ", doi="10.2196/49416", url="https://www.jmir.org/2023/1/e49416", url="http://www.ncbi.nlm.nih.gov/pubmed/37948118" } @Article{info:doi/10.2196/42905, author="Peri{\'c}, Zinaida and Basak, Grzegorz and Koenecke, Christian and Moiseev, Ivan and Chauhan, Jyoti and Asaithambi, Sathyaraj and Sagkriotis, Alexandros and Gunes, Sibel and Penack, Olaf", title="Understanding the Needs and Lived Experiences of Patients With Graft-Versus-Host Disease: Real-World European Public Social Media Listening Study", journal="JMIR Cancer", year="2023", month="Nov", day="10", volume="9", pages="e42905", keywords="graft-versus-host disease", keywords="GVHD", keywords="infoveillance", keywords="patient journey", keywords="quality of life", keywords="real-world evidence", keywords="social media listening", keywords="social media", abstract="Background: Graft-versus-host disease (GVHD) is the major cause of short- and long-term morbidity and mortality after allogeneic hematopoietic stem cell transplantation. Treatment options beyond corticosteroid therapy remain limited, and prolonged treatment often leads to impaired quality of life (QoL). A better understanding of the needs and experiences of patients with GVHD is required to improve patient care. Objective: The aim of this study is to explore different social media (SM) channels for gathering and analyzing the needs and experiences of patients and other stakeholders across 14 European countries. Methods: We conducted a retrospective analysis of SM data from the public domain. The Talkwalker social analytics tool collected data from open-access forums, blogs, and various social networking sites using predefined search strings. The raw data set derived from the aggregator tool was automatically screened for the relevancy of posts, generating the curated data set that was manually reviewed to identify posts that fell within the predefined inclusion and exclusion criteria. This final data set was then used for the deep-dive analysis. Results: A total of 9016 posts relating to GVHD were identified between April 2019 and April 2021. Deduplication and relevancy checks resulted in 325 insightful posts, with Twitter contributing 250 (77\%) posts; blogs, 49 (15\%) posts; forums, 13 (4\%) posts; Facebook, 7 (2\%) posts; and Instagram and YouTube, 4 (1\%) posts. Patients with GVHD were the primary stakeholders, contributing 63\% of all SM posts. In 234 posts, treatment was the most discussed stage of the patient journey (68\%), followed by symptoms (33\%), and diagnosis and tests (21\%). Among treatment-related posts (n=159), steroid therapy was most frequently reported (54/159, 34\%). Posts relating to treatment features (n=110) identified efficacy (45/110, 41\%), side effects (38/110, 35\%), and frequency and dosage (32/110, 29\%), as the most frequently discussed features. Symptoms associated with GVHD were described in 24\% (77/325) of posts, including skin-related conditions (49/77, 64\%), dry eyes or vision change (13/77, 17\%), pain and cramps (16/77, 21\%), and fatigue or muscle weakness (12/77, 16\%). The impacts of GVHD on QoL were discussed in 51\% (165/325) of all posts, with the emotional, physical and functional, social, and financial impacts mentioned in 69\% (114/165), 50\% (82/165), 5\% (8/165), and 2\% (3/165) of these posts, respectively. Unmet needs were reported by patients or caregivers in 24\% (77/325) of analyzed conversations, with treatment-related side effects being the most common (35/77, 45\%) among these posts. Conclusions: SM listening is a useful tool to identify medical needs. Treatment of GVHD, including treatment-related side effects, as well as its emotional and physical impact on QoL, are the major topics that GVHD stakeholders mention on SM. We encourage a structured discussion of these topics in interactions between health care providers and patients with GVHD. Trial Registration: Not applicable ", doi="10.2196/42905", url="https://cancer.jmir.org/2023/1/e42905", url="http://www.ncbi.nlm.nih.gov/pubmed/37948101" } @Article{info:doi/10.2196/49753, author="Zhou, Xinyu and Song, Suhang and Zhang, Ying and Hou, Zhiyuan", title="Deep Learning Analysis of COVID-19 Vaccine Hesitancy and Confidence Expressed on Twitter in 6 High-Income Countries: Longitudinal Observational Study", journal="J Med Internet Res", year="2023", month="Nov", day="6", volume="25", pages="e49753", keywords="COVID-19 vaccine", keywords="hesitancy", keywords="confidence", keywords="social media", keywords="machine learning", abstract="Background: An ongoing monitoring of national and subnational trajectory of COVID-19 vaccine hesitancy could offer support in designing tailored policies on improving vaccine uptake. Objective: We aim to track the temporal and spatial distribution of COVID-19 vaccine hesitancy and confidence expressed on Twitter during the entire pandemic period in major English-speaking countries. Methods: We collected 5,257,385 English-language tweets regarding COVID-19 vaccination between January 1, 2020, and June 30, 2022, in 6 countries---the United States, the United Kingdom, Australia, New Zealand, Canada, and Ireland. Transformer-based deep learning models were developed to classify each tweet as intent to accept or reject COVID-19 vaccination and the belief that COVID-19 vaccine is effective or unsafe. Sociodemographic factors associated with COVID-19 vaccine hesitancy and confidence in the United States were analyzed using bivariate and multivariable linear regressions. Results: The 6 countries experienced similar evolving trends of COVID-19 vaccine hesitancy and confidence. On average, the prevalence of intent to accept COVID-19 vaccination decreased from 71.38\% of 44,944 tweets in March 2020 to 34.85\% of 48,167 tweets in June 2022 with fluctuations. The prevalence of believing COVID-19 vaccines to be unsafe continuously rose by 7.49 times from March 2020 (2.84\% of 44,944 tweets) to June 2022 (21.27\% of 48,167 tweets). COVID-19 vaccine hesitancy and confidence varied by country, vaccine manufacturer, and states within a country. The democrat party and higher vaccine confidence were significantly associated with lower vaccine hesitancy across US states. Conclusions: COVID-19 vaccine hesitancy and confidence evolved and were influenced by the development of vaccines and viruses during the pandemic. Large-scale self-generated discourses on social media and deep learning models provide a cost-efficient approach to monitoring routine vaccine hesitancy. ", doi="10.2196/49753", url="https://www.jmir.org/2023/1/e49753", url="http://www.ncbi.nlm.nih.gov/pubmed/37930788" } @Article{info:doi/10.2196/48710, author="Leslie, Abimbola and Okunromade, Omolola and Sarker, Abeed", title="Public Perceptions About Monkeypox on Twitter: Thematic Analysis", journal="JMIR Form Res", year="2023", month="Nov", day="3", volume="7", pages="e48710", keywords="monkeypox", keywords="social media", keywords="public health", keywords="Twitter", keywords="perception", keywords="digital platform", keywords="infectious disease", keywords="outbreak", keywords="awareness", keywords="analyses", keywords="misinformation", abstract="Background: Social media has emerged as an important source of information generated by large segments of the population, which can be particularly valuable during infectious disease outbreaks. The recent outbreak of monkeypox led to an increase in discussions about the topic on social media, thus presenting the opportunity to conduct studies based on the generated data. Objective: By analyzing posts from Twitter (subsequently rebranded X), we aimed to identify the topics of public discourse as well as knowledge and opinions about the monkeypox virus during the 2022 outbreak. Methods: We collected data from Twitter focusing on English-language posts containing key phrases like ``monkeypox,'' ``mpoxvirus,'' and ``monkey pox,'' as well as their hashtag equivalents from August to October 2022. We preprocessed the data using natural language processing to remove duplicates and filter out noise. We then selected a random sample from the collected posts. Three annotators reviewed a sample of the posts and created a guideline for coding based on discussion. Finally, the annotators analyzed, coded, and manually categorized them first into topics and then into coarse-grained themes. Disagreements were resolved via discussion among all authors. Results: A total of 128,615 posts were collected over a 3-month period, and 200 tweets were selected and included for manual analyses. The following 8 themes were generated from the Twitter posts: monkeypox doubts, media, monkeypox transmission, effect of monkeypox, knowledge of monkeypox, politics, monkeypox vaccine, and general comments. The most common themes from our study were monkeypox doubts and media, each accounting for 22\% (44/200) of the posts. The posts represented a mix of useful information reflecting emerging knowledge on the topic as well as misinformation. Conclusions: Social networks, such as Twitter, are useful sources of information in the early stages of outbreaks. Close to real-time identification and analyses of misinformation may help authorities take the necessary steps in a timely manner. ", doi="10.2196/48710", url="https://formative.jmir.org/2023/1/e48710", url="http://www.ncbi.nlm.nih.gov/pubmed/37921866" } @Article{info:doi/10.2196/49300, author="Dai, Jing and Lyu, Fang and Yu, Lin and He, Yunyu", title="Temporal and Emotional Variations in People's Perceptions of Mass Epidemic Infectious Disease After the COVID-19 Pandemic Using Influenza A as an Example: Topic Modeling and Sentiment Analysis Based on Weibo Data", journal="J Med Internet Res", year="2023", month="Nov", day="2", volume="25", pages="e49300", keywords="mass epidemic infections", keywords="sentiment analysis", keywords="text mining", keywords="spatial differences", keywords="temporal differences", keywords="influenza A", keywords="COVID-19", abstract="Background: The COVID-19 pandemic has had profound impacts on society, including public health, the economy, daily life, and social interactions. Social distancing measures, travel restrictions, and the influx of pandemic-related information on social media have all led to a significant shift in how individuals perceive and respond to health crises. In this context, there is a growing awareness of the role that social media platforms such as Weibo, among the largest and most influential social media sites in China, play in shaping public sentiment and influencing people's behavior during public health emergencies. Objective: This study aims to gain a comprehensive understanding of the sociospatial impact of mass epidemic infectious disease by analyzing the spatiotemporal variations and emotional orientations of the public after the COVID-19 pandemic. We use the outbreak of influenza A after the COVID-19 pandemic as a case study. Through temporal and spatial analyses, we aim to uncover specific variations in the attention and emotional orientations of people living in different provinces in China regarding influenza A. We sought to understand the societal impact of large-scale infectious diseases and the public's stance after the COVID-19 pandemic to improve public health policies and communication strategies. Methods: We selected Weibo as the data source and collected all influenza A--related Weibo posts from November 1, 2022, to March 31, 2023. These data included user names, geographic locations, posting times, content, repost counts, comments, likes, user types, and more. Subsequently, we used latent Dirichlet allocation topic modeling to analyze the public's focus as well as the bidirectional long short-term memory model to conduct emotional analysis. We further classified the focus areas and emotional orientations of different regions. Results: The research findings indicate that, compared with China's western provinces, the eastern provinces exhibited a higher volume of Weibo posts, demonstrating a greater interest in influenza A. Moreover, inland provinces displayed elevated levels of concern compared with coastal regions. In addition, female users of Weibo exhibited a higher level of engagement than male users, with regular users comprising the majority of user types. The public's focus was categorized into 23 main themes, with the overall emotional sentiment predominantly leaning toward negativity (making up 7562 out of 9111 [83\%] sentiments). Conclusions: The results of this study underscore the profound societal impact of the COVID-19 pandemic. People tend to be pessimistic toward new large-scale infectious diseases, and disparities exist in the levels of concern and emotional sentiments across different regions. This reflects diverse societal responses to health crises. By gaining an in-depth understanding of the public's attitudes and focal points regarding these infectious diseases, governments and decision makers can better formulate policies and action plans to cater to the specific needs of different regions and enhance public health awareness. ", doi="10.2196/49300", url="https://www.jmir.org/2023/1/e49300", url="http://www.ncbi.nlm.nih.gov/pubmed/37917144" } @Article{info:doi/10.2196/46874, author="Christodoulakis, Nicolette and Abdelkader, Wael and Lokker, Cynthia and Cotterchio, Michelle and Griffith, E. Lauren and Vanderloo, M. Leigh and Anderson, N. Laura", title="Public Health Surveillance of Behavioral Cancer Risk Factors During the COVID-19 Pandemic: Sentiment and Emotion Analysis of Twitter Data", journal="JMIR Form Res", year="2023", month="Nov", day="2", volume="7", pages="e46874", keywords="cancer risk factors", keywords="Twitter", keywords="sentiment analysis", keywords="emotion analysis", keywords="social media", keywords="physical inactivity", keywords="poor nutrition", keywords="alcohol", keywords="smoking", abstract="Background: The COVID-19 pandemic and its associated public health mitigation strategies have dramatically changed patterns of daily life activities worldwide, resulting in unintentional consequences on behavioral risk factors, including smoking, alcohol consumption, poor nutrition, and physical inactivity. The infodemic of social media data may provide novel opportunities for evaluating changes related to behavioral risk factors during the pandemic. Objective: We explored the feasibility of conducting a sentiment and emotion analysis using Twitter data to evaluate behavioral cancer risk factors (physical inactivity, poor nutrition, alcohol consumption, and smoking) over time during the first year of the COVID-19 pandemic. Methods: Tweets during 2020 relating to the COVID-19 pandemic and the 4 cancer risk factors were extracted from the George Washington University Libraries Dataverse. Tweets were defined and filtered using keywords to create 4 data sets. We trained and tested a machine learning classifier using a prelabeled Twitter data set. This was applied to determine the sentiment (positive, negative, or neutral) of each tweet. A natural language processing package was used to identify the emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, and trust) based on the words contained in the tweets. Sentiments and emotions for each of the risk factors were evaluated over time and analyzed to identify keywords that emerged. Results: The sentiment analysis revealed that 56.69\% (51,479/90,813) of the tweets about physical activity were positive, 16.4\% (14,893/90,813) were negative, and 26.91\% (24,441/90,813) were neutral. Similar patterns were observed for nutrition, where 55.44\% (27,939/50,396), 15.78\% (7950/50,396), and 28.79\% (14,507/50,396) of the tweets were positive, negative, and neutral, respectively. For alcohol, the proportions of positive, negative, and neutral tweets were 46.85\% (34,897/74,484), 22.9\% (17,056/74,484), and 30.25\% (22,531/74,484), respectively, and for smoking, they were 41.2\% (11,628/28,220), 24.23\% (6839/28,220), and 34.56\% (9753/28,220), respectively. The sentiments were relatively stable over time. The emotion analysis suggests that the most common emotion expressed across physical activity and nutrition tweets was trust (69,495/320,741, 21.67\% and 42,324/176,564, 23.97\%, respectively); for alcohol, it was joy (49,147/273,128, 17.99\%); and for smoking, it was fear (23,066/110,256, 20.92\%). The emotions expressed remained relatively constant over the observed period. An analysis of the most frequent words tweeted revealed further insights into common themes expressed in relation to some of the risk factors and possible sources of bias. Conclusions: This analysis provided insight into behavioral cancer risk factors as expressed on Twitter during the first year of the COVID-19 pandemic. It was feasible to extract tweets relating to all 4 risk factors, and most tweets had a positive sentiment with varied emotions across the different data sets. Although these results can play a role in promoting public health, a deeper dive via qualitative analysis can be conducted to provide a contextual examination of each tweet. ", doi="10.2196/46874", url="https://formative.jmir.org/2023/1/e46874", url="http://www.ncbi.nlm.nih.gov/pubmed/37917123" } @Article{info:doi/10.2196/44420, author="Dou, Xuelin and Liu, Yang and Liao, Aijun and Zhong, Yuping and Fu, Rong and Liu, Lihong and Cui, Canchan and Wang, Xiaohong and Lu, Jin", title="Patient Journey Toward a Diagnosis of Light Chain Amyloidosis in a National Sample: Cross-Sectional Web-Based Study", journal="JMIR Form Res", year="2023", month="Nov", day="2", volume="7", pages="e44420", keywords="systemic light chain amyloidosis", keywords="AL amyloidosis", keywords="rare disease", keywords="big data", keywords="network analysis", keywords="machine model", keywords="natural language processing", keywords="web-based", abstract="Background: Systemic light chain (AL) amyloidosis is a rare and multisystem disease associated with increased morbidity and a poor prognosis. Delayed diagnoses are common due to the heterogeneity of the symptoms. However, real-world insights from Chinese patients with AL amyloidosis have not been investigated. Objective: This study aimed to describe the journey to an AL amyloidosis diagnosis and to build an in-depth understanding of the diagnostic process from the perspective of both clinicians and patients to obtain a correct and timely diagnosis. Methods: Publicly available disease-related content from social media platforms between January 2008 and April 2021 was searched. After performing data collection steps with a machine model, a series of disease-related posts were extracted. Natural language processing was used to identify the relevance of variables, followed by further manual evaluation and analysis. Results: A total of 2204 valid posts related to AL amyloidosis were included in this study, of which 1968 were posted on haodf.com. Of these posts, 1284 were posted by men (median age 57, IQR 46-67 years); 1459 posts mentioned renal-related symptoms, followed by heart (n=833), liver (n=491), and stomach (n=368) symptoms. Furthermore, 1502 posts mentioned symptoms related to 2 or more organs. Symptoms for AL amyloidosis most frequently mentioned by suspected patients were nonspecific weakness (n=252), edema (n=196), hypertrophy (n=168), and swelling (n=140). Multiple physician visits were common, and nephrologists (n=265) and hematologists (n=214) were the most frequently visited specialists by suspected patients for initial consultation. Additionally, interhospital referrals were also commonly seen, centralizing in tertiary hospitals. Conclusions: Chinese patients with AL amyloidosis experienced referrals during their journey toward accurate diagnosis. Increasing awareness of the disease and early referral to a specialized center with expertise may reduce delayed diagnosis and improve patient management. ", doi="10.2196/44420", url="https://formative.jmir.org/2023/1/e44420", url="http://www.ncbi.nlm.nih.gov/pubmed/37917132" } @Article{info:doi/10.2196/46296, author="Nicmanis, Mitchell and Chur-Hansen, Anna and Linehan, Karen", title="The Information Needs and Experiences of People Living With Cardiac Implantable Electronic Devices: Qualitative Content Analysis of Reddit Posts", journal="JMIR Cardio", year="2023", month="Nov", day="1", volume="7", pages="e46296", keywords="implantable cardioverter defibrillator", keywords="pacemaker", keywords="cardiac resynchronization therapy", keywords="social media", keywords="patients", keywords="peer support", keywords="content analysis", keywords="experiences", abstract="Background: Cardiac implantable electronic devices (CIEDs) are used to treat a range of cardiovascular diseases and can lead to substantial clinical improvements. However, studies evaluating patients' experiences of living with these devices are sparse and have focused mainly on implantable cardioverter defibrillators. In addition, there has been limited evaluation of how people living with a CIED use social media to gain insight into their condition. Objective: This study aims to analyze posts from web-based communities called subreddits on the website Reddit, intended for people living with a CIED, to characterize the informational needs and experiences of patients. Methods: Reddit was systematically searched for appropriate subreddits, and we found 1 subreddit that could be included in the analysis. A Python-based web scraping script using the Reddit application programming interface was used to extract posts from this subreddit. Each post was individually screened for relevancy, and a register of participants' demographic information was created. Conventional qualitative content analysis was used to inductively classify the qualitative data collected into codes, subcategories, and overarching categories. Results: Of the 484 posts collected using the script, 186 were excluded, resulting in 298 posts from 196 participants being included in the analysis. The median age of the participants who reported this was 33 (IQR 22.0-39.5; range 17-72) years, and the majority had a permanent pacemaker. The content analysis yielded 5 overarching categories: use of the subreddit by participants, questions and experiences related to the daily challenges of living with a CIED, physical sequelae of CIED implantation, psychological experiences of living with a CIED, and questions and experiences related to health care while living with a CIED. These categories provided insight into the diverse experiences and informational needs of participants living with a CIED. The data predominantly represented the experiences of younger and more physically active participants. Conclusions: Social media provides a platform through which people living with a CIED can share information and provide support to their peers. Participants generally sought information about the experiences of others living with a CIED. This was often done to help overcome a range of challenges faced by participants, including the need to adapt to living with a CIED, difficulties with navigating health care, psychological difficulties, and various aversive physical sequelae. These challenges may be particularly difficult for younger and physically active people. Health care professionals may leverage peer support and other aid to help people overcome the challenges they face while living with a CIED. ", doi="10.2196/46296", url="https://cardio.jmir.org/2023/1/e46296", url="http://www.ncbi.nlm.nih.gov/pubmed/37766632" } @Article{info:doi/10.2196/50013, author="Carabot, Federico and Fraile-Mart{\'i}nez, Oscar and Donat-Vargas, Carolina and Santoma, Javier and Garcia-Montero, Cielo and Pinto da Costa, Mariana and Molina-Ruiz, M. Rosa and Ortega, A. Miguel and Alvarez-Mon, Melchor and Alvarez-Mon, Angel Miguel", title="Understanding Public Perceptions and Discussions on Opioids Through Twitter: Cross-Sectional Infodemiology Study", journal="J Med Internet Res", year="2023", month="Oct", day="31", volume="25", pages="e50013", keywords="awareness", keywords="epidemic", keywords="fentanyl", keywords="health communication", keywords="infodemiology", keywords="machine learning", keywords="opioids", keywords="recreational use", keywords="social media listening", keywords="Twitter", keywords="user", abstract="Background: Opioids are used for the treatment of refractory pain, but their inappropriate use has detrimental consequences for health. Understanding the current experiences and perceptions of patients in a spontaneous and colloquial environment regarding the key drugs involved in the opioid crisis is of utmost significance. Objective: The study aims to analyze Twitter content related to opioids, with objectives including characterizing users participating in these conversations, identifying prevalent topics and gauging public perception, assessing opinions on drug efficacy and tolerability, and detecting discussions related to drug dispensing, prescription, or acquisition. Methods: In this cross-sectional study, we gathered public tweets concerning major opioids posted in English or Spanish between January 1, 2019, and December 31, 2020. A total of 256,218 tweets were collected. Approximately 27\% (69,222/256,218) were excluded. Subsequently, 7000 tweets were subjected to manual analysis based on a codebook developed by the researchers. The remaining databases underwent analysis using machine learning classifiers. In the codebook, the type of user was the initial classification domain. We differentiated between patients, family members and friends, health care professionals, and institutions. Next, a distinction was made between medical and nonmedical content. If it was medical in nature, we classified it according to whether it referred to the drug's efficacy or adverse effects. In nonmedical content tweets, we analyzed whether the content referred to management issues (eg, pharmacy dispensation, medical appointment prescriptions, commercial advertisements, or legal aspects) or the trivialization of the drug. Results: Among the entire array of scrutinized pharmaceuticals, fentanyl emerged as the predominant subject, featuring in 27\% (39,997/148,335 posts) of the tweets. Concerning user categorization, roughly 70\% (101,259/148,335) were classified as patients. Nevertheless, tweets posted by health care professionals obtained the highest number of retweets (37/16,956, 0.2\% of their posts received over 100 retweets). We found statistically significant differences in the distribution concerning efficacy and side effects among distinct drug categories (P<.001). Nearly 60\% (84,401/148,335) of the posts were devoted to nonmedical subjects. Within this category, legal facets and recreational use surfaced as the most prevalent themes, while in the medical discourse, efficacy constituted the most frequent topic, with over 90\% (45,621/48,777) of instances characterizing it as poor or null. The opioid with the greatest proportion of tweets concerning legal considerations was fentanyl. Furthermore, fentanyl was the drug most frequently offered for sale on Twitter, while methadone generated the most tweets about pharmacy delivery. Conclusions: The opioid crisis is present on social media, where tweets discuss legal and recreational use. Opioid users are the most active participants, prioritizing medication efficacy over side effects. Surprisingly, health care professionals generate the most engagement, indicating their positive reception. Authorities must monitor web-based opioid discussions to detect illicit acquisitions and recreational use. ", doi="10.2196/50013", url="https://www.jmir.org/2023/1/e50013", url="http://www.ncbi.nlm.nih.gov/pubmed/37906234" } @Article{info:doi/10.2196/49400, author="Luo, Tingyan and Zhou, Jie and Yang, Jing and Xie, Yulan and Wei, Yiru and Mai, Huanzhuo and Lu, Dongjia and Yang, Yuecong and Cui, Ping and Ye, Li and Liang, Hao and Huang, Jiegang", title="Early Warning and Prediction of Scarlet Fever in China Using the Baidu Search Index and Autoregressive Integrated Moving Average With Explanatory Variable (ARIMAX) Model: Time Series Analysis", journal="J Med Internet Res", year="2023", month="Oct", day="30", volume="25", pages="e49400", keywords="scarlet fever", keywords="Baidu search index", keywords="autoregressive integrated moving average", keywords="ARIMA", keywords="warning", keywords="prediction", abstract="Background: Internet-derived data and the autoregressive integrated moving average (ARIMA) and ARIMA with explanatory variable (ARIMAX) models are extensively used for infectious disease surveillance. However, the effectiveness of the Baidu search index (BSI) in predicting the incidence of scarlet fever remains uncertain. Objective: Our objective was to investigate whether a low-cost BSI monitoring system could potentially function as a valuable complement to traditional scarlet fever surveillance in China. Methods: ARIMA and ARIMAX models were developed to predict the incidence of scarlet fever in China using data from the National Health Commission of the People's Republic of China between January 2011 and August 2022. The procedures included establishing a keyword database, keyword selection and filtering through Spearman rank correlation and cross-correlation analyses, construction of the scarlet fever comprehensive search index (CSI), modeling with the training sets, predicting with the testing sets, and comparing the prediction performances. Results: The average monthly incidence of scarlet fever was 4462.17 (SD 3011.75) cases, and annual incidence exhibited an upward trend until 2019. The keyword database contained 52 keywords, but only 6 highly relevant ones were selected for modeling. A high Spearman rank correlation was observed between the scarlet fever reported cases and the scarlet fever CSI (rs=0.881). We developed the ARIMA(4,0,0)(0,1,2)(12) model, and the ARIMA(4,0,0)(0,1,2)(12) + CSI (Lag=0) and ARIMAX(1,0,2)(2,0,0)(12) models were combined with the BSI. The 3 models had a good fit and passed the residuals Ljung-Box test. The ARIMA(4,0,0)(0,1,2)(12), ARIMA(4,0,0)(0,1,2)(12) + CSI (Lag=0), and ARIMAX(1,0,2)(2,0,0)(12) models demonstrated favorable predictive capabilities, with mean absolute errors of 1692.16 (95\% CI 584.88-2799.44), 1067.89 (95\% CI 402.02-1733.76), and 639.75 (95\% CI 188.12-1091.38), respectively; root mean squared errors of 2036.92 (95\% CI 929.64-3144.20), 1224.92 (95\% CI 559.04-1890.79), and 830.80 (95\% CI 379.17-1282.43), respectively; and mean absolute percentage errors of 4.33\% (95\% CI 0.54\%-8.13\%), 3.36\% (95\% CI --0.24\% to 6.96\%), and 2.16\% (95\% CI --0.69\% to 5.00\%), respectively. The ARIMAX models outperformed the ARIMA models and had better prediction performances with smaller values. Conclusions: This study demonstrated that the BSI can be used for the early warning and prediction of scarlet fever, serving as a valuable supplement to traditional surveillance systems. ", doi="10.2196/49400", url="https://www.jmir.org/2023/1/e49400", url="http://www.ncbi.nlm.nih.gov/pubmed/37902815" } @Article{info:doi/10.2196/48789, author="Lan, Duo and Ren, Wujiong and Ni, Ke and Zhu, Yicheng", title="Topic and Trend Analysis of Weibo Discussions About COVID-19 Medications Before and After China's Exit from the Zero-COVID Policy: Retrospective Infoveillance Study", journal="J Med Internet Res", year="2023", month="Oct", day="27", volume="25", pages="e48789", keywords="zero-COVID policy", keywords="topic modeling", keywords="Weibo", keywords="COVID-19 medications", keywords="social risk", keywords="personal risk", keywords="social media", keywords="COVID-19", keywords="China", keywords="pandemic", keywords="self-medication", abstract="Background: After 3 years of its zero-COVID policy, China lifted its stringent pandemic control measures with the announcement of the 10 new measures on December 7, 2022. Existing estimates suggest 90\%-97\% of the total population was infected during December. This change created a massive demand for COVID-19 medications and treatments, either modern medicines or traditional Chinese medicine (TCM). Objective: This study aimed to explore (1) how China's exit from the zero-COVID policy impacted media and the public's attention to COVID-19 medications; (2) how social COVID-19 medication discussions were related to existing model estimates of daily cases during that period; (3) what the diversified themes and topics were and how they changed and developed from November 1 to December 31, 2022; and (4) which topics about COVID-19 medications were focused on by mainstream and self-media accounts during the exit. The answers to these questions could help us better understand the consequences of exit strategies and explore the utilities of Sina Weibo data for future infoveillance studies. Methods: Using a scrapper for data retrieval and the structural topic modeling (STM) algorithm for analysis, this study built 3 topic models (all data, before a policy change, and after a policy change) of relevant discussions on the Chinese social media platform Weibo. We compared topic distributions against existing estimates of daily cases and between models before and after the change. We also compared proportions of weibos published by mainstream versus self-media accounts over time on different topics. Results: We found that Weibo discussions shifted sharply from concerns of social risks (case tracking, governmental regulations, etc) to those of personal risks (symptoms, purchases, etc) surrounding COVID-19 infection after the exit from the zero-COVID policy. Weibo topics of ``symptom sharing'' and ``purchase and shortage'' of modern medicines correlated more strongly with existing susceptible-exposed-infected-recovered (SEIR) model estimates compared to ``TCM formulae'' and other topics. During the exit, mainstream accounts showed efforts to specifically engage in topics related to worldwide pandemic control policy comparison and regulations about import and reimbursement of medications. Conclusions: The exit from the zero-COVID policy in China was accompanied by a sudden increase in social media discussions about COVID-19 medications, the demand for which substantially increased after the exit. A large proportion of Weibo discussions were emotional and expressed increased risk concerns over medication shortage, unavailability, and delay in delivery. Topic keywords showed that self-medication was sometimes practiced alone or with unprofessional help from others, while mainstream accounts also tried to provide certain medication instructions. Of the 16 topics identified in all 3 STM models, only ``symptom sharing'' and ``purchase and shortage'' showed a considerable correlation with SEIR model estimates of daily cases. Future studies could consider topic exploration before conducting predictive infoveillance analysis, even with narrowly defined search criteria with Weibo data. ", doi="10.2196/48789", url="https://www.jmir.org/2023/1/e48789", url="http://www.ncbi.nlm.nih.gov/pubmed/37889532" } @Article{info:doi/10.2196/48905, author="Chi, Yu and Chen, Huai-yu", title="Investigating Substance Use via Reddit: Systematic Scoping Review", journal="J Med Internet Res", year="2023", month="Oct", day="25", volume="25", pages="e48905", keywords="substance use", keywords="systematic scoping review", keywords="Reddit", keywords="social media", keywords="drug use", keywords="tobacco use", keywords="alcohol use", abstract="Background: Reddit's (Reddit Inc) large user base, diverse communities, and anonymity make it a useful platform for substance use research. Despite a growing body of literature on substance use on Reddit, challenges and limitations must be carefully considered. However, no systematic scoping review has been conducted on the use of Reddit as a data source for substance use research. Objective: This review aims to investigate the use of Reddit for studying substance use by examining previous studies' objectives, reasons, limitations, and methods for using Reddit. In addition, we discuss the implications and contributions of previous studies and identify gaps in the literature that require further attention. Methods: A total of 7 databases were searched using keyword combinations including Reddit and substance-related keywords in April 2022. The initial search resulted in 456 articles, and 227 articles remained after removing duplicates. All included studies were peer reviewed, empirical, available in full text, and pertinent to Reddit and substance use, and they were all written in English. After screening, 60 articles met the eligibility criteria for the review, with 57 articles identified from the initial database search and 3 from the ancestry search. A codebook was developed, and qualitative content analysis was performed to extract relevant evidence related to the research questions. Results: The use of Reddit for studying substance use has grown steadily since 2015, with a sharp increase in 2021. The primary objective was to identify tendencies and patterns in various types of substance use discussions (52/60, 87\%). Reddit was also used to explore unique user experiences, propose methodologies, investigate user interactions, and develop interventions. A total of 9 reasons for using Reddit to study substance use were identified, such as the platform's anonymity, its widespread popularity, and the explicit topics of subreddits. However, 7 limitations were noted, including the platform's low representativeness of the general population with substance use and the lack of demographic information. Most studies use application programming interfaces for data collection and quantitative approaches for analysis, with few using qualitative approaches. Machine learning algorithms are commonly used for natural language processing tasks. The theoretical, methodological, and practical implications and contributions of the included articles are summarized and discussed. The most prevalent practical implications are investigating prevailing topics in Reddit discussions, providing recommendations for clinical practices and policies, and comparing Reddit discussions on substance use across various sources. Conclusions: This systematic scoping review provides an overview of Reddit's use as a data source for substance use research. Although the limitations of Reddit data must be considered, analyzing them can be useful for understanding patterns and user experiences related to substance use. Our review also highlights gaps in the literature and suggests avenues for future research. ", doi="10.2196/48905", url="https://www.jmir.org/2023/1/e48905", url="http://www.ncbi.nlm.nih.gov/pubmed/37878361" } @Article{info:doi/10.2196/48669, author="Ball, J. Katelin and Muse, W. Brandon and Cook, Bailey and Quinn, P. Alyssa and Brooks, D. Benjamin", title="Hell's Itch: A Unique Reaction to UV Exposure", journal="JMIR Dermatol", year="2023", month="Oct", day="24", volume="6", pages="e48669", keywords="Hell's Itch", keywords="social media", keywords="sunburn", keywords="sun", keywords="survey", keywords="skin", keywords="dermatology", keywords="dermatological", keywords="itch", keywords="itchiness", keywords="itchy", keywords="symptoms", keywords="experience", keywords="ultraviolet", keywords="UV", keywords="dermatologist", keywords="teledermatology", keywords="hair", keywords="nails", keywords="scratch", doi="10.2196/48669", url="https://derma.jmir.org/2023/1/e48669", url="http://www.ncbi.nlm.nih.gov/pubmed/37874633" } @Article{info:doi/10.2196/50199, author="Unlu, Ali and Truong, Sophie and Tammi, Tuukka and Lohiniva, Anna-Leena", title="Exploring Political Mistrust in Pandemic Risk Communication: Mixed-Method Study Using Social Media Data Analysis", journal="J Med Internet Res", year="2023", month="Oct", day="20", volume="25", pages="e50199", keywords="political trust", keywords="social media", keywords="text classification", keywords="topic modeling", keywords="COVID-19", keywords="Finland", keywords="trust", keywords="authority", keywords="public health outcome", keywords="pandemic", keywords="perception", keywords="mistrust", keywords="interaction", keywords="Twitter", keywords="Facebook", keywords="analysis", keywords="computational method", keywords="natural language processing", keywords="misinformation", keywords="communication", keywords="crisis", abstract="Background: This research extends prior studies by the Finnish Institute for Health and Welfare on pandemic-related risk perception, concentrating on the role of trust in health authorities and its impact on public health outcomes. Objective: The paper aims to investigate variations in trust levels over time and across social media platforms, as well as to further explore 12 subcategories of political mistrust. It seeks to understand the dynamics of political trust, including mistrust accumulation, fluctuations over time, and changes in topic relevance. Additionally, the study aims to compare qualitative research findings with those obtained through computational methods. Methods: Data were gathered from a large-scale data set consisting of 13,629 Twitter and Facebook posts from 2020 to 2023 related to COVID-19. For analysis, a fine-tuned FinBERT model with an 80\% accuracy rate was used for predicting political mistrust. The BERTopic model was also used for superior topic modeling performance. Results: Our preliminary analysis identifies 43 mistrust-related topics categorized into 9 major themes. The most salient topics include COVID-19 mortality, coping strategies, polymerase chain reaction testing, and vaccine efficacy. Discourse related to mistrust in authority is associated with perceptions of disease severity, willingness to adopt health measures, and information-seeking behavior. Our findings highlight that the distinct user engagement mechanisms and platform features of Facebook and Twitter contributed to varying patterns of mistrust and susceptibility to misinformation during the pandemic. Conclusions: The study highlights the effectiveness of computational methods like natural language processing in managing large-scale engagement and misinformation. It underscores the critical role of trust in health authorities for effective risk communication and public compliance. The findings also emphasize the necessity for transparent communication from authorities, concluding that a holistic approach to public health communication is integral for managing health crises effectively. ", doi="10.2196/50199", url="https://www.jmir.org/2023/1/e50199", url="http://www.ncbi.nlm.nih.gov/pubmed/37862088" } @Article{info:doi/10.2196/47677, author="Koskan, M. Alexis and Sivanandam, Shalini and Roschke, Kristy and Irby, Jonathan and Helitzer, L. Deborah and Doebbeling, Bradley", title="Sharing Reliable COVID-19 Information and Countering Misinformation: In-Depth Interviews With Information Advocates", journal="JMIR Infodemiology", year="2023", month="Oct", day="20", volume="3", pages="e47677", keywords="COVID-19", keywords="coronavirus", keywords="pandemic", keywords="infodemic", keywords="misinformation", keywords="social media", keywords="qualitative research", keywords="public health", keywords="health communication", abstract="Background: The rampant spread of misinformation about COVID-19 has been linked to a lower uptake of preventive behaviors such as vaccination. Some individuals, however, have been able to resist believing in COVID-19 misinformation. Further, some have acted as information advocates, spreading accurate information and combating misinformation about the pandemic. Objective: This work explores highly knowledgeable information advocates' perspectives, behaviors, and information-related practices. Methods: To identify participants for this study, we used outcomes of survey research of a national sample of 1498 adults to find individuals who scored a perfect or near-perfect score on COVID-19 knowledge questions and who also self-reported actively sharing or responding to news information within the past week. Among this subsample, we selected a diverse sample of 25 individuals to participate in a 1-time, phone-based, semistructured interview. Interviews were recorded and transcribed, and the team conducted an inductive thematic analysis. Results: Participants reported trusting in science, data-driven sources, public health, medical experts, and organizations. They had mixed levels of trust in various social media sites to find reliable health information, noting distrust in particular sites such as Facebook (Meta Platforms) and more trust in specific accounts on Twitter (X Corp) and Reddit (Advance Publications). They reported relying on multiple sources of information to find facts instead of depending on their intuition and emotions to inform their perspectives about COVID-19. Participants determined the credibility of information by cross-referencing it, identifying information sources and their potential biases, clarifying information they were unclear about with health care providers, and using fact-checking sites to verify information. Most participants reported ignoring misinformation. Others, however, responded to misinformation by flagging, reporting, and responding to it on social media sites. Some described feeling more comfortable responding to misinformation in person than online. Participants' responses to misinformation posted on the internet depended on various factors, including their relationship to the individual posting the misinformation, their level of outrage in response to it, and how dangerous they perceived it could be if others acted on such information. Conclusions: This research illustrates how well-informed US adults assess the credibility of COVID-19 information, how they share it, and how they respond to misinformation. It illustrates web-based and offline information practices and describes how the role of interpersonal relationships contributes to their preferences for acting on such information. Implications of our findings could help inform future training in health information literacy, interpersonal information advocacy, and organizational information advocacy. It is critical to continue working to share reliable health information and debunk misinformation, particularly since this information informs health behaviors. ", doi="10.2196/47677", url="https://infodemiology.jmir.org/2023/1/e47677", url="http://www.ncbi.nlm.nih.gov/pubmed/37862066" } @Article{info:doi/10.2196/50011, author="Pathak, Nitin Gaurav and Chandy, John Rithi and Naini, Vidisha and Razi, Shazli and Feldman, R. Steven", title="A Social Media Analysis of Pemphigus", journal="JMIR Dermatol", year="2023", month="Oct", day="19", volume="6", pages="e50011", keywords="pemphigus", keywords="social media", keywords="pemphigus vulgaris", keywords="Facebook", keywords="YouTube", keywords="Twitter", keywords="Instagram", keywords="dissemination", keywords="medical information", keywords="autoimmune disease", keywords="diagnosis", keywords="engagement", keywords="educational", keywords="content", keywords="awareness", doi="10.2196/50011", url="https://derma.jmir.org/2023/1/e50011", url="http://www.ncbi.nlm.nih.gov/pubmed/37856177" } @Article{info:doi/10.2196/49901, author="Lane, Hakan and Walker, Mark", title="The Impact of Temperature, Humidity, and Sunshine on Internet Search Volumes Related to Psoriasis", journal="JMIR Dermatol", year="2023", month="Oct", day="19", volume="6", pages="e49901", keywords="psoriasis", keywords="infodemiology", keywords="internet search", keywords="internet searching", keywords="web search", keywords="information seeking", keywords="information search behavior", keywords="information search behaviour", keywords="dermatology", keywords="skin", keywords="weather", keywords="temperature", keywords="humidity", keywords="sunshine", doi="10.2196/49901", url="https://derma.jmir.org/2023/1/e49901", url="http://www.ncbi.nlm.nih.gov/pubmed/37856189" } @Article{info:doi/10.2196/48641, author="Chau, Brian and Taba, Melody and Dodd, Rachael and McCaffery, Kirsten and Bonner, Carissa", title="Twitch Data in Health Promotion Research: Protocol for a Case Study Exploring COVID-19 Vaccination Views Among Young People", journal="JMIR Res Protoc", year="2023", month="Oct", day="18", volume="12", pages="e48641", keywords="twitch", keywords="social media", keywords="COVID-19", keywords="vaccination communication", keywords="video gaming", keywords="gaming", keywords="health promotion", keywords="streaming", abstract="Background: Social media platforms have emerged as a useful channel for health promotion communication, offering different channels to reach targeted populations. For example, social media has recently been used to disseminate information about COVID-19 vaccination across various demographics. Traditional modes of health communication such as television, health events, and newsletters may not reach all groups within a community. Health communications for younger generations are increasingly disseminated through social media to reflect key information sources. This paper explores a social media gaming platform as an alternative way to reach young people in health promotion research. Objective: This protocol study aimed to pilot-test the potential of Twitch, a live streaming platform initially designed for video gaming, to conduct health promotion research with young people. We used COVID-19 vaccination as a topical case study that was recommended by Australian health authorities at the time of the research. Methods: The research team worked with a Twitch Account Manager to design and test a case study within the guidelines and ethics protocols required by Twitch, identify suitable streamers to approach and establish a protocol for conducting research on the platform. This involved conducting a poll to initiate discussion about COVID-19 vaccination, monitoring the chat in 3 live Twitch sessions with 2 streamers to pilot the protocol, and briefly analyze Twitch chat logs to observe the range of response types that may be acquired from this methodology. Results: The Twitch streams provided logs and videos on demand that were derived from the live session. These included demographics of viewers, chat logs, and polling results. The results of the poll showed a range of engagement in health promotion for the case study topic: the majority of participants had received their vaccination by the time of the poll; however, there was still a proportion that had not received their vaccination yet or had decided to not be vaccinated. Analysis of the Twitch chat logs demonstrated a range of both positive and negative themes regarding health promotion for the case study topic. This included irrelevant comments, misinformation (compared to health authority information at the time of this study), comedic and conspiracy responses, as well as vaccine status, provaccine comments, and vaccine-hesitant comments. Conclusions: This study developed and tested a protocol for using Twitch data for health promotion research with young people. With live polling, open text discussion between participants and immediate responses to questions, Twitch can be used to collect both quantitative and qualitative research data from demographics that use social media. The platform also presents some challenges when engaging with independent streamers and sensitive health topics. This study provides an initial protocol for future researchers to use and build on. International Registered Report Identifier (IRRID): RR1-10.2196/48641 ", doi="10.2196/48641", url="https://www.researchprotocols.org/2023/1/e48641", url="http://www.ncbi.nlm.nih.gov/pubmed/37851494" } @Article{info:doi/10.2196/45085, author="Yang, Liuyang and Zhang, Ting and Han, Xuan and Yang, Jiao and Sun, Yanxia and Ma, Libing and Chen, Jialong and Li, Yanming and Lai, Shengjie and Li, Wei and Feng, Luzhao and Yang, Weizhong", title="Influenza Epidemic Trend Surveillance and Prediction Based on Search Engine Data: Deep Learning Model Study", journal="J Med Internet Res", year="2023", month="Oct", day="17", volume="25", pages="e45085", keywords="early warning", keywords="epidemic intelligence", keywords="infectious disease", keywords="influenza-like illness", keywords="surveillance", abstract="Background: Influenza outbreaks pose a significant threat to global public health. Traditional surveillance systems and simple algorithms often struggle to predict influenza outbreaks in an accurate and timely manner. Big data and modern technology have offered new modalities for disease surveillance and prediction. Influenza-like illness can serve as a valuable surveillance tool for emerging respiratory infectious diseases like influenza and COVID-19, especially when reported case data may not fully reflect the actual epidemic curve. Objective: This study aimed to develop a predictive model for influenza outbreaks by combining Baidu search query data with traditional virological surveillance data. The goal was to improve early detection and preparedness for influenza outbreaks in both northern and southern China, providing evidence for supplementing modern intelligence epidemic surveillance methods. Methods: We collected virological data from the National Influenza Surveillance Network and Baidu search query data from January 2011 to July 2018, totaling 3,691,865 and 1,563,361 respective samples. Relevant search terms related to influenza were identified and analyzed for their correlation with influenza-positive rates using Pearson correlation analysis. A distributed lag nonlinear model was used to assess the lag correlation of the search terms with influenza activity. Subsequently, a predictive model based on the gated recurrent unit and multiple attention mechanisms was developed to forecast the influenza-positive trend. Results: This study revealed a high correlation between specific Baidu search terms and influenza-positive rates in both northern and southern China, except for 1 term. The search terms were categorized into 4 groups: essential facts on influenza, influenza symptoms, influenza treatment and medicine, and influenza prevention, all of which showed correlation with the influenza-positive rate. The influenza prevention and influenza symptom groups had a lag correlation of 1.4-3.2 and 5.0-8.0 days, respectively. The Baidu search terms could help predict the influenza-positive rate 14-22 days in advance in southern China but interfered with influenza surveillance in northern China. Conclusions: Complementing traditional disease surveillance systems with information from web-based data sources can aid in detecting warning signs of influenza outbreaks earlier. However, supplementation of modern surveillance with search engine information should be approached cautiously. This approach provides valuable insights for digital epidemiology and has the potential for broader application in respiratory infectious disease surveillance. Further research should explore the optimization and customization of search terms for different regions and languages to improve the accuracy of influenza prediction models. ", doi="10.2196/45085", url="https://www.jmir.org/2023/1/e45085", url="http://www.ncbi.nlm.nih.gov/pubmed/37847532" } @Article{info:doi/10.2196/47014, author="Laison, Elolo Elda Kokoe and Hamza Ibrahim, Mohamed and Boligarla, Srikanth and Li, Jiaxin and Mahadevan, Raja and Ng, Austen and Muthuramalingam, Venkataraman and Lee, Yi Wee and Yin, Yijun and Nasri, R. Bouchra", title="Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis", journal="J Med Internet Res", year="2023", month="Oct", day="16", volume="25", pages="e47014", keywords="Lyme disease", keywords="Twitter", keywords="BERT", keywords="Bidirectional Encoder Representations from Transformers", keywords="emojis", keywords="machine learning", keywords="natural language processing", abstract="Background: Lyme disease is among the most reported tick-borne diseases worldwide, making it a major ongoing public health concern. An effective Lyme disease case reporting system depends on timely diagnosis and reporting by health care professionals, and accurate laboratory testing and interpretation for clinical diagnosis validation. A lack of these can lead to delayed diagnosis and treatment, which can exacerbate the severity of Lyme disease symptoms. Therefore, there is a need to improve the monitoring of Lyme disease by using other data sources, such as web-based data. Objective: We analyzed global Twitter data to understand its potential and limitations as a tool for Lyme disease surveillance. We propose a transformer-based classification system to identify potential Lyme disease cases using self-reported tweets. Methods: Our initial sample included 20,000 tweets collected worldwide from a database of over 1.3 million Lyme disease tweets. After preprocessing and geolocating tweets, tweets in a subset of the initial sample were manually labeled as potential Lyme disease cases or non-Lyme disease cases using carefully selected keywords. Emojis were converted to sentiment words, which were then replaced in the tweets. This labeled tweet set was used for the training, validation, and performance testing of DistilBERT (distilled version of BERT [Bidirectional Encoder Representations from Transformers]), ALBERT (A Lite BERT), and BERTweet (BERT for English Tweets) classifiers. Results: The empirical results showed that BERTweet was the best classifier among all evaluated models (average F1-score of 89.3\%, classification accuracy of 90.0\%, and precision of 97.1\%). However, for recall, term frequency-inverse document frequency and k-nearest neighbors performed better (93.2\% and 82.6\%, respectively). On using emojis to enrich the tweet embeddings, BERTweet had an increased recall (8\% increase), DistilBERT had an increased F1-score of 93.8\% (4\% increase) and classification accuracy of 94.1\% (4\% increase), and ALBERT had an increased F1-score of 93.1\% (5\% increase) and classification accuracy of 93.9\% (5\% increase). The general awareness of Lyme disease was high in the United States, the United Kingdom, Australia, and Canada, with self-reported potential cases of Lyme disease from these countries accounting for around 50\% (9939/20,000) of the collected English-language tweets, whereas Lyme disease--related tweets were rare in countries from Africa and Asia. The most reported Lyme disease--related symptoms in the data were rash, fatigue, fever, and arthritis, while symptoms, such as lymphadenopathy, palpitations, swollen lymph nodes, neck stiffness, and arrythmia, were uncommon, in accordance with Lyme disease symptom frequency. Conclusions: The study highlights the robustness of BERTweet and DistilBERT as classifiers for potential cases of Lyme disease from self-reported data. The results demonstrated that emojis are effective for enrichment, thereby improving the accuracy of tweet embeddings and the performance of classifiers. Specifically, emojis reflecting sadness, empathy, and encouragement can reduce false negatives. ", doi="10.2196/47014", url="https://www.jmir.org/2023/1/e47014", url="http://www.ncbi.nlm.nih.gov/pubmed/37843893" } @Article{info:doi/10.2196/43701, author="Batheja, Sapna and Schopp, M. Emma and Pappas, Samantha and Ravuri, Siri and Persky, Susan", title="Characterizing Precision Nutrition Discourse on Twitter: Quantitative Content Analysis", journal="J Med Internet Res", year="2023", month="Oct", day="12", volume="25", pages="e43701", keywords="nutrigenetics", keywords="nutrigenomics", keywords="precision nutrition", keywords="Twitter", keywords="credibility", keywords="misinformation", keywords="content analysis", abstract="Background: It is possible that tailoring dietary approaches to an individual's genomic profile could provide optimal dietary inputs for biological functioning and support adherence to dietary management protocols. The science required for such nutrigenetic and nutrigenomic profiling is not yet considered ready for broad application by the scientific and medical communities; however, many personalized nutrition products are available in the marketplace, creating the potential for hype and misleading information on social media. Twitter provides a unique big data source that provides real-time information. Therefore, it has the potential to disseminate evidence-based health information, as well as misinformation. Objective: We sought to characterize the landscape of precision nutrition content on Twitter, with a specific focus on nutrigenetics and nutrigenomics. We focused on tweet authors, types of content, and presence of misinformation. Methods: Twitter Archiver was used to capture tweets from September 1, 2020, to December 1, 2020, using keywords related to nutrition and genetics. A random sample of tweets was coded using quantitative content analysis by 4 trained coders. Codebook-driven, quantified information about tweet authors, content details, information quality, and engagement metrics were compiled and analyzed. Results: The most common categories of tweets were precision nutrition products and nutrigenomic concepts. About a quarter (132/504, 26.2\%) of tweet authors presented themselves as science experts, medicine experts, or both. Nutrigenetics concepts most frequently came from authors with science and medicine expertise, and tweets about the influence of genes on weight were more likely to come from authors with neither type of expertise. A total of 14.9\% (75/504) of the tweets were noted to contain untrue information; these were most likely to occur in the nutrigenomics concepts topic category. Conclusions: By evaluating social media discourse on precision nutrition on Twitter, we made several observations about the content available in the information environment through which individuals can learn about related concepts and products. Tweet content was consistent with the indicators of medical hype, and the inclusion of potentially misleading and untrue information was common. We identified a contingent of users with scientific and medical expertise who were active in discussing nutrigenomics concepts and products and who may be encouraged to share credible expert advice on precision nutrition and tackle false information as this technology develops. ", doi="10.2196/43701", url="https://www.jmir.org/2023/1/e43701", url="http://www.ncbi.nlm.nih.gov/pubmed/37824190" } @Article{info:doi/10.2196/48607, author="Hui, Vivian and Eby, Malavika and Constantino, Eva Rose and Lee, Heeyoung and Zelazny, Jamie and Chang, C. Judy and He, Daqing and Lee, Ji Young", title="Examining the Supports and Advice That Women With Intimate Partner Violence Experience Received in Online Health Communities: Text Mining Approach", journal="J Med Internet Res", year="2023", month="Oct", day="9", volume="25", pages="e48607", keywords="intimate partner violence", keywords="text mining", keywords="social media", keywords="online health communities", keywords="linguistic features", abstract="Background: Intimate partner violence (IPV) is an underreported public health crisis primarily affecting women associated with severe health conditions and can lead to a high rate of homicide. Owing to the COVID-19 pandemic, more women with IPV experiences visited online health communities (OHCs) to seek help because of anonymity. However, little is known regarding whether their help requests were answered and whether the information provided was delivered in an appropriate manner. To understand the help-seeking information sought and given in OHCs, extraction of postings and linguistic features could be helpful to develop automated models to improve future help-seeking experiences. Objective: The objective of this study was to examine the types and patterns (ie, communication styles) of the advice offered by OHC members and whether the information received from women matched their expressed needs in their initial postings. Methods: We examined data from Reddit using data from subreddit community r/domesticviolence posts from November 14, 2020, through November 14, 2021, during the COVID-19 pandemic. We included posts from women aged ?18 years who self-identified or described experiencing IPV and requested advice or help in this subreddit community. Posts from nonabused women and women aged <18 years, non-English posts, good news announcements, gratitude posts without any advice seeking, and posts related to advertisements were excluded. We developed a codebook and annotated the postings in an iterative manner. Initial posts were also quantified using Linguistic Inquiry and Word Count to categorize linguistic and posting features. Postings were then classified into 2 categories (ie, matched needs and unmatched needs) according to the types of help sought and received in OHCs to capture the help-seeking result. Nonparametric statistical analysis (ie, 2-tailed t test or Mann-Whitney U test) was used to compare the linguistic and posting features between matched and unmatched needs. Results: Overall, 250 postings were included, and 200 (80\%) posting response comments matched with the type of help requested in initial postings, with legal advice and IPV knowledge achieving the highest matching rate. Overall, 17 linguistic or posting features were found to be significantly different between the 2 groups (ie, matched help and unmatched help). Positive title sentiment and linguistic features in postings containing health and wellness wordings were associated with unmatched needs postings, whereas the other 14 features were associated with postings with matched needs. Conclusions: OHCs can extract the linguistic and posting features to understand the help-seeking result among women with IPV experiences. Features identified in this corpus reflected the differences found between the 2 groups. This is the first study that leveraged Linguistic Inquiry and Word Count to shed light on generating predictive features from unstructured text in OHCs, which could guide future algorithm development to detect help-seeking results within OHCs effectively. ", doi="10.2196/48607", url="https://www.jmir.org/2023/1/e48607", url="http://www.ncbi.nlm.nih.gov/pubmed/37812467" } @Article{info:doi/10.2196/43060, author="Tanner, P. Joshua and Takats, Courtney and Lathan, Stuart Hannah and Kwan, Amy and Wormer, Rachel and Romero, Diana and Jones, E. Heidi", title="Approaches to Research Ethics in Health Research on YouTube: Systematic Review", journal="J Med Internet Res", year="2023", month="Oct", day="4", volume="25", pages="e43060", keywords="data anonymization", keywords="research ethics", keywords="ethics", keywords="informed consent", keywords="public health", keywords="research", keywords="social media", keywords="YouTube", abstract="Background: YouTube has become a popular source of health care information, reaching an estimated 81\% of adults in 2021; approximately 35\% of adults in the United States have used the internet to self-diagnose a condition. Public health researchers are therefore incorporating YouTube data into their research, but guidelines for best practices around research ethics using social media data, such as YouTube, are unclear. Objective: This study aims to describe approaches to research ethics for public health research implemented using YouTube data. Methods: We implemented a systematic review of articles found in PubMed, SocINDEX, Web of Science, and PsycINFO following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. To be eligible to be included, studies needed to be published in peer-reviewed journals in English between January 1, 2006, and October 31, 2019, and include analyses on publicly available YouTube data on health or public health topics; studies using primary data collection, such as using YouTube for study recruitment, interventions, or dissemination evaluations, were not included. We extracted data on the presence of user identifying information, institutional review board (IRB) review, and informed consent processes, as well as research topic and methodology. Results: This review includes 119 articles from 88 journals. The most common health and public health topics studied were in the categories of chronic diseases (44/119, 37\%), mental health and substance use (26/119, 21.8\%), and infectious diseases (20/119, 16.8\%). The majority (82/119, 68.9\%) of articles made no mention of ethical considerations or stated that the study did not meet the definition of human participant research (16/119, 13.4\%). Of those that sought IRB review (15/119, 12.6\%), 12 out of 15 (80\%) were determined to not meet the definition of human participant research and were therefore exempt from IRB review, and 3 out of 15 (20\%) received IRB approval. None of the 3 IRB-approved studies contained identifying information; one was explicitly told not to include identifying information by their ethics committee. Only 1 study sought informed consent from YouTube users. Of 119 articles, 33 (27.7\%) contained identifying information about content creators or video commenters, one of which attempted to anonymize direct quotes by not including user information. Conclusions: Given the variation in practice, concrete guidelines on research ethics for social media research are needed, especially around anonymizing and seeking consent when using identifying information. Trial Registration: PROSPERO CRD42020148170; https://www.crd.york.ac.uk/prospero/display\_record.php?RecordID=148170 ", doi="10.2196/43060", url="https://www.jmir.org/2023/1/e43060", url="http://www.ncbi.nlm.nih.gov/pubmed/37792443" } @Article{info:doi/10.2196/48189, author="Lustig, Andrew and Brookes, Gavin", title="Corpus-Based Discourse Analysis of a Reddit Community of Users of Crystal Methamphetamine: Mixed Methods Study", journal="JMIR Infodemiology", year="2023", month="Sep", day="29", volume="3", pages="e48189", keywords="methamphetamine", keywords="social media", keywords="substance-related disorders", keywords="discourse analysis", keywords="mental health", keywords="mixed methods", keywords="corpus analysis", keywords="web-based health", abstract="Background: Methamphetamine is a highly addictive stimulant that affects the central nervous system. Crystal methamphetamine is a form of the drug resembling glass fragments or shiny bluish-white rocks that can be taken through smoking, swallowing, snorting, or injecting the powder once it has been dissolved in water or alcohol. Objective: The objective of this study is to examine how identities are socially (discursively) constructed by people who use methamphetamine within a subreddit for people who regularly use crystal meth. Methods: Using a mixed methods approach, we analyzed 1000 threads (318,422 words) from a subreddit for regular crystal meth users. The qualitative component of the analysis used concordancing and corpus-based discourse analysis to identify discursive themes informed by assemblage theory. The quantitative portion of the analysis used corpus linguistic techniques including keyword analysis to identify words occurring with statistically marked frequency in the corpus and collocation analysis to analyze their discursive context. Results: Our findings reveal that the subreddit contributors use a rich and varied lexicon to describe crystal meth and other substances, ranging from a neuroscientific register (eg, methamphetamine and dopamine) to informal vernacular (eg, meth, dope, and fent) and commercial appellations (eg, Adderall and Seroquel). They also use linguistic resources to construct symbolic boundaries between different types of methamphetamine users, differentiating between the esteemed category of ``functional addicts'' and relegating others to the stigmatized category of ``tweakers.'' In addition, contributors contest the dominant view that methamphetamine use inevitably leads to psychosis, arguing instead for a more nuanced understanding that considers the interplay of factors such as sleep deprivation, poor nutrition, and neglected hygiene. Conclusions: The subreddit contributors' discourse offers a ``set and setting'' perspective, which provides a fresh viewpoint on drug-induced psychosis and can guide future harm reduction strategies and research. In contrast to this view, many previous studies overlook the real-world complexities of methamphetamine use, perhaps due to the use of controlled experimental settings. Actual drug use, intoxication, and addiction are complex, multifaceted, and elusive phenomena that defy straightforward characterization. ", doi="10.2196/48189", url="https://infodemiology.jmir.org/2023/1/e48189", url="http://www.ncbi.nlm.nih.gov/pubmed/37773617" } @Article{info:doi/10.2196/46488, author="Uhawenimana, Claudien Thierry and Musabwasoni, Sandra Marie Grace and Nsengiyumva, Richard and Mukamana, Donatilla", title="Sexuality and Sexual and Reproductive Health Depiction in Social Media: Content Analysis of Kinyarwanda YouTube Channels", journal="J Med Internet Res", year="2023", month="Sep", day="27", volume="25", pages="e46488", keywords="sexuality", keywords="sexual and reproductive health", keywords="Kinyarwanda YouTube channels", keywords="content analysis", keywords="social media", keywords="media platform", keywords="COVID-19", abstract="Background: Social media platforms such as YouTube can be used to educate people of reproductive age about healthy and nonrisky sexual and reproductive health (SRH) practices and behaviors. However, there is a paucity of evidence to ascertain the authenticity of sexuality and SRH content on Kinyarwanda YouTube, making it difficult to determine the extent to which these topics are covered, the characteristics of available videos, and the themes covered by these videos. Objective: The aims of this study were (1) to determine the extent to which YouTube channels in Kinyarwanda-language videos address sexuality and SRH issues, identify the general characteristics of the available videos (type of video, when published, intention for the audience, and content focus), and the aspects of sexuality and SRH covered; and (2) to identify the themes covered by retrieved Kinyarwanda videos, and the extent to which the channels have been used to communicate issues of sexuality and SRH during the COVID-19 pandemic. Methods: Using a content analysis approach, we searched Kinyarwanda YouTube channels to analyze videos about sexuality and SRH. The adopted framework for data collection from social media platforms builds on three key steps: (1) development, (2) application, and (3) assessment of search filters. To be included, an audio and/or visual video had to be in Kinyarwanda and the video had to be directed to the general public. Descriptive statistics (frequency and percentages) were computed to characterize the basic characteristics of retrieved channels, portrayal of the videos, and presentation of sexuality and SRH themes that emerged from retrieved videos. Further analysis involved cross-tabulations to explore associations between the focus of the channel and the date when the channel was opened and the focus of the channel and who was involved in the video. Results: The YouTube search retrieved 21,506 videos that tackled sexuality and SRH topics. During the COVID-19 pandemic, there was a 4-fold increase (from 7.2\% to 30.6\%) in channels that solely focused on sexually explicit content. The majority of the 1369 retrieved channels (n=1150, 84.0\%) tackled the topic of sexuality, with sexually explicit content predominantly found in the majority of these videos (n=1082, 79\%), and only 16\% (n=287) of the videos covered SRH topics. Conclusions: This is the first study to analyze the use of YouTube in communicating about sexuality and SRH in the Kinyarwanda language. This study relied on videos that appeared online. Further research should gather information about who accesses the videos, and how channel owners and individuals involved in the videos perceive the impact of their videos on the Rwandan community's sexuality and SRH. ", doi="10.2196/46488", url="https://www.jmir.org/2023/1/e46488", url="http://www.ncbi.nlm.nih.gov/pubmed/37756040" } @Article{info:doi/10.2196/47202, author="Cornell, Samuel and Brander, Robert and Peden, Amy", title="Selfie-Related Incidents: Narrative Review and Media Content Analysis", journal="J Med Internet Res", year="2023", month="Sep", day="27", volume="25", pages="e47202", keywords="selfie", keywords="aquatic locations", keywords="death", keywords="injury", keywords="risk", keywords="communication", keywords="social media", keywords="drowning", keywords="mobile phone", abstract="Background: Selfie-related injury has become a public health concern amid the near ubiquitous use of smartphones and social media apps. Of particular concern are selfie-related deaths at aquatic locations; areas often frequented because of their photogenic allure. Unfortunately, such places exhibit hazards inherent with their environment. Objective: This study aimed to ascertain current evidence regarding selfie-related injuries and recommended risk treatment measures in the academic literature as well as how selfie-related injuries and deaths were being reported by the media, allowing us to identify key challenges facing land managers and public health practitioners in mitigating selfie-related injuries and deaths. Methods: Between October and December 2022, we performed a narrative review of peer-reviewed literature published since January 2011. Literature was screened to identify causal factors implicated in selfie-related deaths and injuries, as well as risk treatments recommended. Furthermore, we used an environmental scan methodology to search for media reports of selfie-related injuries and deaths at aquatic locations in Australia and the United States. Individual cases of selfie-related aquatic injuries and deaths sourced from news reports were analyzed to assess epidemiological characteristics, and a thematic content analysis was conducted to identify key themes of news reporting on selfie-related deaths and injuries. Results: In total, 5 peer-reviewed studies were included. Four studies identified falls from height as the most common injury mechanism in selfie incidents. Drowning was the second most common cause of death. Recommended risk treatments were limited but included the adoption of ``no selfie zones,'' physical barriers, signage, and provision of information on dangerous locations to social media users. In total, 12 cases were identified from media reports (4 injuries and 8 fatalities; 7 in Australia and 5 in the United States). The mean age of the reported victims was 22.1 (SD 6.93) years with victims more likely to be female tourists. Content analysis revealed 3 key themes from media reports: ``blame,'' ``warning,'' and ``prevention and education.'' Few media reports (n=8) provided safety recommendations. Conclusions: The selfie-related incident phenomenon should be viewed as a public health problem that requires a public health risk communication response. To date, little attention has been paid to averting selfie-related incidents through behavior change methodologies or direct messaging to users, including through social media apps. Although previous research has recommended ``no selfie zones,'' barriers, and signage as ways to prevent selfie incidents, our results suggest this may not be enough, and it may be prudent to also engage in direct safety messaging to social media users. Media reporting of selfie incidents should focus on preventive messaging rather than blame or warning. ", doi="10.2196/47202", url="https://www.jmir.org/2023/1/e47202", url="http://www.ncbi.nlm.nih.gov/pubmed/37756044" } @Article{info:doi/10.2196/41863, author="Faviez, Carole and Talmatkadi, Manissa and Foulqui{\'e}, Pierre and Mebarki, Adel and Sch{\"u}ck, St{\'e}phane and Burgun, Anita and Chen, Xiaoyi", title="Assessment of the Early Detection of Anosmia and Ageusia Symptoms in COVID-19 on Twitter: Retrospective Study", journal="JMIR Infodemiology", year="2023", month="Sep", day="25", volume="3", pages="e41863", keywords="social media", keywords="COVID-19", keywords="anosmia", keywords="ageusia", keywords="infodemiology", keywords="symptom", keywords="Twitter", keywords="psychological", keywords="tweets", keywords="pandemic", keywords="rapid stage", keywords="epidemic", keywords="information", keywords="knowledge", keywords="online health", keywords="misinformation", keywords="education", keywords="online education", keywords="ehealth", keywords="qualitative", abstract="Background: During the unprecedented COVID-19 pandemic, social media has been extensively used to amplify the spread of information and to express personal health-related experiences regarding symptoms, including anosmia and ageusia, 2 symptoms that have been reported later than other symptoms. Objective: Our objective is to investigate to what extent Twitter users reported anosmia and ageusia symptoms in their tweets and if they connected them to COVID-19, to evaluate whether these symptoms could have been identified as COVID-19 symptoms earlier using Twitter rather than the official notice. Methods: We collected French tweets posted between January 1, 2020, and March 31, 2020, containing anosmia- or ageusia-related keywords. Symptoms were detected using fuzzy matching. The analysis consisted of 3 parts. First, we compared the coverage of anosmia and ageusia symptoms in Twitter and in traditional media to determine if the association between COVID-19 and anosmia or ageusia could have been identified earlier through Twitter. Second, we conducted a manual analysis of anosmia- and ageusia-related tweets to obtain quantitative and qualitative insights regarding their nature and to assess when the first associations between COVID-19 and these symptoms were established. We randomly annotated tweets from 2 periods: the early stage and the rapid spread stage of the epidemic. For each tweet, each symptom was annotated regarding 3 modalities: symptom (yes or no), associated with COVID-19 (yes, no, or unknown), and whether it was experienced by someone (yes, no, or unknown). Third, to evaluate if there was a global increase of tweets mentioning anosmia or ageusia in early 2020, corresponding to the beginning of the COVID-19 epidemic, we compared the tweets reporting experienced anosmia or ageusia between the first periods of 2019 and 2020. Results: In total, 832 (respectively 12,544) tweets containing anosmia (respectively ageusia) related keywords were extracted over the analysis period in 2020. The comparison to traditional media showed a strong correlation without any lag, which suggests an important reactivity of Twitter but no earlier detection on Twitter. The annotation of tweets from 2020 showed that tweets correlating anosmia or ageusia with COVID-19 could be found a few days before the official announcement. However, no association could be found during the first stage of the pandemic. Information about the temporality of symptoms and the psychological impact of these symptoms could be found in the tweets. The comparison between early 2020 and early 2019 showed no difference regarding the volumes of tweets. Conclusions: Based on our analysis of French tweets, associations between COVID-19 and anosmia or ageusia by web users could have been found on Twitter just a few days before the official announcement but not during the early stage of the pandemic. Patients share qualitative information on Twitter regarding anosmia or ageusia symptoms that could be of interest for future analyses. ", doi="10.2196/41863", url="https://infodemiology.jmir.org/2023/1/e41863", url="http://www.ncbi.nlm.nih.gov/pubmed/37643302" } @Article{info:doi/10.2196/44129, author="Schick, Sofie Teresa and H{\"o}llerl, Lea and Biedermann, Tilo and Zink, Alexander and Ziehfreund, Stefanie", title="Impact of Digital Media on the Patient Journey and Patient-Physician Relationship Among Dermatologists and Adult Patients With Skin Diseases: Qualitative Interview Study", journal="J Med Internet Res", year="2023", month="Sep", day="22", volume="25", pages="e44129", keywords="digital media", keywords="dermatology", keywords="patient journey", keywords="patient-physician relationship", keywords="semistructured interview", keywords="qualitative content analysis", abstract="Background: Digital media are easily accessible without time restrictions and are widely used for health- or disease-related purposes. However, their influence on the patient journey and the patient-physician relationship has not yet been sufficiently investigated. Objective: This qualitative interview study was designed to explore dermatologists' and patients' experiences with digital media for medical purposes in the context of patient journeys and patient-physician relationships. Methods: Twenty-eight semistructured video conference--based interviews were conducted and audiorecorded by experienced interviewers between November 2021 and June 2022 in Germany. Eligible patients were those who were aged ?18 years, were affected by at least one physician-confirmed skin disease, and were fluent in the German language. The eligibility criterion for dermatologists was that they were currently practicing dermatology in an outpatient setting or in a hospital. Randomly selected dermatologists from the listing of the German National Association of Statutory Health Insurance Physicians and dermatologists from personal academic and professional networks were invited for participation via postal mail and asked to identify potential patient volunteers from their patient bases. All recorded data were pseudonymized, fully transcribed verbatim, and subsequently analyzed according to Mayring's qualitative content analysis by 2 researchers, allowing for both a qualitative interview text analysis and a quantitative assessment of category assignments. Results: In total, 28 participants were interviewed: 16 adult patients and 12 dermatologists. Eight main categories emerged as key areas of interest: (1) the search for diagnosis and symptom triggers, (2) preconsultation digital media use, (3) in-depth information and exchange with other patients, (4) self-treatment, (5) patient-physician interaction, (6) roles of dermatologists and patients, (7) patient eHealth literacy, and (8) opportunities and risks. Categories 1 and 2 were only coded for patients; the other categories were coded for both patients and dermatologists. Patients reported searches for diagnosis or treatment options were most frequently (8/16) caused by a mismatch of symptoms and diagnosis or dissatisfaction with current therapies. Concerns regarding a potentially severe diagnosis prompted searches for initial or in-depth information before or after dermatological consultations. However, the large volume of information of varying quality often confused patients, leading dermatologists to assume the role of evaluating information from preinformed patients. Dermatologists generally encouraged the use of digital media, considered teledermatology advantageous, and viewed big data and artificial intelligence as being potentially beneficial, particularly when searching for rare diagnoses. A single, easily accessible, and free-of-charge platform with high quality information in lay language was recommended by the dermatologists and desired by patients. Conclusions: Digital media are widely accepted by both patients and dermatologists and can positively influence both the dermatological patient journey and patient-physician relationship. Digital media may therefore have great potential to improve specialized health care if patients and dermatologists embrace their new roles. ", doi="10.2196/44129", url="https://www.jmir.org/2023/1/e44129", url="http://www.ncbi.nlm.nih.gov/pubmed/37738078" } @Article{info:doi/10.2196/45019, author="Li, Ziyu and Wu, Xiaoqian and Xu, Lin and Liu, Ming and Huang, Cheng", title="Hot Topic Recognition of Health Rumors Based on Anti-Rumor Articles on the WeChat Official Account Platform: Topic Modeling", journal="J Med Internet Res", year="2023", month="Sep", day="21", volume="25", pages="e45019", keywords="topic model", keywords="health rumors", keywords="social media", keywords="WeChat official account", keywords="content analysis", keywords="public health", keywords="machine learning", keywords="Twitter", keywords="social network", keywords="misinformation", keywords="users", keywords="disease", keywords="diet", abstract="Background: Social networks have become one of the main channels for obtaining health information. However, they have also become a source of health-related misinformation, which seriously threatens the public's physical and mental health. Governance of health-related misinformation can be implemented through topic identification of rumors on social networks. However, little attention has been paid to studying the types and routes of dissemination of health rumors on the internet, especially rumors regarding health-related information in Chinese social media. Objective: This study aims to explore the types of health-related misinformation favored by WeChat public platform users and their prevalence trends and to analyze the modeling results of the text by using the Latent Dirichlet Allocation model. Methods: We used a web crawler tool to capture health rumor--dispelling articles on WeChat rumor-dispelling public accounts. We collected information from health-debunking articles posted between January 1, 2016, and August 31, 2022. Following word segmentation of the collected text, a document topic generation model called Latent Dirichlet Allocation was used to identify and generalize the most common topics. The proportion distribution of the themes was calculated, and the negative impact of various health rumors in different periods was analyzed. Additionally, the prevalence of health rumors was analyzed by the number of health rumors generated at each time point. Results: We collected 9366 rumor-refuting articles from January 1, 2016, to August 31, 2022, from WeChat official accounts. Through topic modeling, we divided the health rumors into 8 topics, that is, rumors on prevention and treatment of infectious diseases (1284/9366, 13.71\%), disease therapy and its effects (1037/9366, 11.07\%), food safety (1243/9366, 13.27\%), cancer and its causes (946/9366, 10.10\%), regimen and disease (1540/9366, 16.44\%), transmission (914/9366, 9.76\%), healthy diet (1068/9366, 11.40\%), and nutrition and health (1334/9366, 14.24\%). Furthermore, we summarized the 8 topics under 4 themes, that is, public health, disease, diet and health, and spread of rumors. Conclusions: Our study shows that topic modeling can provide analysis and insights into health rumor governance. The rumor development trends showed that most rumors were on public health, disease, and diet and health problems. Governments still need to implement relevant and comprehensive rumor management strategies based on the rumors prevalent in their countries and formulate appropriate policies. Apart from regulating the content disseminated on social media platforms, the national quality of health education should also be improved. Governance of social networks should be clearly implemented, as these rapidly developed platforms come with privacy issues. Both disseminators and receivers of information should ensure a realistic attitude and disseminate health information correctly. In addition, we recommend that sentiment analysis--related studies be conducted to verify the impact of health rumor--related topics. ", doi="10.2196/45019", url="https://www.jmir.org/2023/1/e45019", url="http://www.ncbi.nlm.nih.gov/pubmed/37733396" } @Article{info:doi/10.2196/51760, author="Taguchi, Kazuho and Matsoso, Precious and Driece, Roland and da Silva Nunes, Tovar and Soliman, Ahmed and Tangcharoensathien, Viroj", title="Effective Infodemic Management: A Substantive Article of the Pandemic Accord", journal="JMIR Infodemiology", year="2023", month="Sep", day="20", volume="3", pages="e51760", keywords="Pandemic Accord", keywords="infodemic", keywords="infodemic management", keywords="COVID-19", keywords="social media", keywords="Intergovernmental Negotiating Body", keywords="INB", keywords="INB Bureau", keywords="World Health Organization", keywords="WHO", keywords="misinformation", keywords="disinformation", keywords="public health", doi="10.2196/51760", url="https://infodemiology.jmir.org/2023/1/e51760", url="http://www.ncbi.nlm.nih.gov/pubmed/37728969" } @Article{info:doi/10.2196/45846, author="Zhang, Wang and Zhu, Zhu and Zhao, Yonggen and Li, Zheming and Chen, Lingdong and Huang, Jian and Li, Jing and Yu, Gang", title="Analyzing and Forecasting Pediatric Fever Clinic Visits in High Frequency Using Ensemble Time-Series Methods After the COVID-19 Pandemic in Hangzhou, China: Retrospective Study", journal="JMIR Med Inform", year="2023", month="Sep", day="20", volume="11", pages="e45846", keywords="time-series forecasting", keywords="outpatient visits", keywords="hospital management", keywords="pediatric fever clinic", keywords="long sequence", keywords="visits in high frequency", keywords="COVID-19", abstract="Background: The COVID-19 pandemic has significantly altered the global health and medical landscape. In response to the outbreak, Chinese hospitals have established 24-hour fever clinics to serve patients with COVID-19. The emergence of these clinics and the impact of successive epidemics have led to a surge in visits, placing pressure on hospital resource allocation and scheduling. Therefore, accurate prediction of outpatient visits is essential for informed decision-making in hospital management. Objective: Hourly visits to fever clinics can be characterized as a long-sequence time series in high frequency, which also exhibits distinct patterns due to the particularity of pediatric treatment behavior in an epidemic context. This study aimed to build models to forecast fever clinic visit with outstanding prediction accuracy and robust generalization in forecast horizons. In addition, this study hopes to provide a research paradigm for time-series forecasting problems, which involves an exploratory analysis revealing data patterns before model development. Methods: An exploratory analysis, including graphical analysis, autocorrelation analysis, and seasonal-trend decomposition, was conducted to reveal the seasonality and structural patterns of the retrospective fever clinic visit data. The data were found to exhibit multiseasonality and nonlinearity. On the basis of these results, an ensemble of time-series analysis methods, including individual models and their combinations, was validated on the data set. Root mean square error and mean absolute error were used as accuracy metrics, with the cross-validation of rolling forecasting origin conducted across different forecast horizons. Results: Hybrid models generally outperformed individual models across most forecast horizons. A novel model combination, the hybrid neural network autoregressive (NNAR)-seasonal and trend decomposition using Loess forecasting (STLF), was identified as the optimal model for our forecasting task, with the best performance in all accuracy metrics (root mean square error=20.1, mean absolute error=14.3) for the 15-days-ahead forecasts and an overall advantage for forecast horizons that were 1 to 30 days ahead. Conclusions: Although forecast accuracy tends to decline with an increasing forecast horizon, the hybrid NNAR-STLF model is applicable for short-, medium-, and long-term forecasts owing to its ability to fit multiseasonality (captured by the STLF component) and nonlinearity (captured by the NNAR component). The model identified in this study is also applicable to hospitals in other regions with similar epidemic outpatient configurations or forecasting tasks whose data conform to long-sequence time series in high frequency exhibiting multiseasonal and nonlinear patterns. However, as external variables and disruptive events were not accounted for, the model performance declined slightly following changes in the COVID-19 containment policy in China. Future work may seek to improve accuracy by incorporating external variables that characterize moving events or other factors as well as by adding data from different organizations to enhance algorithm generalization. ", doi="10.2196/45846", url="https://medinform.jmir.org/2023/1/e45846", url="http://www.ncbi.nlm.nih.gov/pubmed/37728972" } @Article{info:doi/10.2196/48620, author="Adebesin, Funmi and Smuts, Hanlie and Mawela, Tendani and Maramba, George and Hattingh, Marie", title="The Role of Social Media in Health Misinformation and Disinformation During the COVID-19 Pandemic: Bibliometric Analysis", journal="JMIR Infodemiology", year="2023", month="Sep", day="20", volume="3", pages="e48620", keywords="bibliometric analysis", keywords="COVID-19", keywords="fake news", keywords="health disinformation", keywords="health misinformation", keywords="social media", abstract="Background: The use of social media platforms to seek information continues to increase. Social media platforms can be used to disseminate important information to people worldwide instantaneously. However, their viral nature also makes it easy to share misinformation, disinformation, unverified information, and fake news. The unprecedented reliance on social media platforms to seek information during the COVID-19 pandemic was accompanied by increased incidents of misinformation and disinformation. Consequently, there was an increase in the number of scientific publications related to the role of social media in disseminating health misinformation and disinformation at the height of the COVID-19 pandemic. Health misinformation and disinformation, especially in periods of global public health disasters, can lead to the erosion of trust in policy makers at best and fatal consequences at worst. Objective: This paper reports a bibliometric analysis aimed at investigating the evolution of research publications related to the role of social media as a driver of health misinformation and disinformation since the start of the COVID-19 pandemic. Additionally, this study aimed to identify the top trending keywords, niche topics, authors, and publishers for publishing papers related to the current research, as well as the global collaboration between authors on topics related to the role of social media in health misinformation and disinformation since the start of the COVID-19 pandemic. Methods: The Scopus database was accessed on June 8, 2023, using a combination of Medical Subject Heading and author-defined terms to create the following search phrases that targeted the title, abstract, and keyword fields: (``Health*'' OR ``Medical'') AND (``Misinformation'' OR ``Disinformation'' OR ``Fake News'') AND (``Social media'' OR ``Twitter'' OR ``Facebook'' OR ``YouTube'' OR ``WhatsApp'' OR ``Instagram'' OR ``TikTok'') AND (``Pandemic*'' OR ``Corona*'' OR ``Covid*''). A total of 943 research papers published between 2020 and June 2023 were analyzed using Microsoft Excel (Microsoft Corporation), VOSviewer (Centre for Science and Technology Studies, Leiden University), and the Biblioshiny package in Bibliometrix (K-Synth Srl) for RStudio (Posit, PBC). Results: The highest number of publications was from 2022 (387/943, 41\%). Most publications (725/943, 76.9\%) were articles. JMIR published the most research papers (54/943, 5.7\%). Authors from the United States collaborated the most, with 311 coauthored research papers. The keywords ``Covid-19,'' ``social media,'' and ``misinformation'' were the top 3 trending keywords, whereas ``learning systems,'' ``learning models,'' and ``learning algorithms'' were revealed as the niche topics on the role of social media in health misinformation and disinformation during the COVID-19 outbreak. Conclusions: Collaborations between authors can increase their productivity and citation counts. Niche topics such as ``learning systems,'' ``learning models,'' and ``learning algorithms'' could be exploited by researchers in future studies to analyze the influence of social media on health misinformation and disinformation during periods of global public health emergencies. ", doi="10.2196/48620", url="https://infodemiology.jmir.org/2023/1/e48620", url="http://www.ncbi.nlm.nih.gov/pubmed/37728981" } @Article{info:doi/10.2196/45767, author="Dolatabadi, Elham and Moyano, Diana and Bales, Michael and Spasojevic, Sofija and Bhambhoria, Rohan and Bhatti, Junaid and Debnath, Shyamolima and Hoell, Nicholas and Li, Xin and Leng, Celine and Nanda, Sasha and Saab, Jad and Sahak, Esmat and Sie, Fanny and Uppal, Sara and Vadlamudi, Khatri Nirma and Vladimirova, Antoaneta and Yakimovich, Artur and Yang, Xiaoxue and Kocak, Akinli Sedef and Cheung, M. Angela", title="Using Social Media to Help Understand Patient-Reported Health Outcomes of Post--COVID-19 Condition: Natural Language Processing Approach", journal="J Med Internet Res", year="2023", month="Sep", day="19", volume="25", pages="e45767", keywords="long COVID", keywords="post--COVID-19 condition", keywords="PCC", keywords="social media", keywords="natural language processing", keywords="transformer models", keywords="bidirectional encoder representations from transformers", keywords="machine learning", keywords="Twitter", keywords="Reddit", keywords="PRO", keywords="patient-reported outcome", keywords="patient-reported symptom", keywords="health outcome", keywords="symptom", keywords="entity extraction", keywords="entity normalization", abstract="Background: While scientific knowledge of post--COVID-19 condition (PCC) is growing, there remains significant uncertainty in the definition of the disease, its expected clinical course, and its impact on daily functioning. Social media platforms can generate valuable insights into patient-reported health outcomes as the content is produced at high resolution by patients and caregivers, representing experiences that may be unavailable to most clinicians. Objective: In this study, we aimed to determine the validity and effectiveness of advanced natural language processing approaches built to derive insight into PCC-related patient-reported health outcomes from social media platforms Twitter and Reddit. We extracted PCC-related terms, including symptoms and conditions, and measured their occurrence frequency. We compared the outputs with human annotations and clinical outcomes and tracked symptom and condition term occurrences over time and locations to explore the pipeline's potential as a surveillance tool. Methods: We used bidirectional encoder representations from transformers (BERT) models to extract and normalize PCC symptom and condition terms from English posts on Twitter and Reddit. We compared 2 named entity recognition models and implemented a 2-step normalization task to map extracted terms to unique concepts in standardized terminology. The normalization steps were done using a semantic search approach with BERT biencoders. We evaluated the effectiveness of BERT models in extracting the terms using a human-annotated corpus and a proximity-based score. We also compared the validity and reliability of the extracted and normalized terms to a web-based survey with more than 3000 participants from several countries. Results: UmlsBERT-Clinical had the highest accuracy in predicting entities closest to those extracted by human annotators. Based on our findings, the top 3 most commonly occurring groups of PCC symptom and condition terms were systemic (such as fatigue), neuropsychiatric (such as anxiety and brain fog), and respiratory (such as shortness of breath). In addition, we also found novel symptom and condition terms that had not been categorized in previous studies, such as infection and pain. Regarding the co-occurring symptoms, the pair of fatigue and headaches was among the most co-occurring term pairs across both platforms. Based on the temporal analysis, the neuropsychiatric terms were the most prevalent, followed by the systemic category, on both social media platforms. Our spatial analysis concluded that 42\% (10,938/26,247) of the analyzed terms included location information, with the majority coming from the United States, United Kingdom, and Canada. Conclusions: The outcome of our social media--derived pipeline is comparable with the results of peer-reviewed articles relevant to PCC symptoms. Overall, this study provides unique insights into patient-reported health outcomes of PCC and valuable information about the patient's journey that can help health care providers anticipate future needs. International Registered Report Identifier (IRRID): RR2-10.1101/2022.12.14.22283419 ", doi="10.2196/45767", url="https://www.jmir.org/2023/1/e45767", url="http://www.ncbi.nlm.nih.gov/pubmed/37725432" } @Article{info:doi/10.2196/43630, author="Fisher, Andrew and Young, Maclaren Matthew and Payer, Doris and Pacheco, Karen and Dubeau, Chad and Mago, Vijay", title="Automating Detection of Drug-Related Harms on Social Media: Machine Learning Framework", journal="J Med Internet Res", year="2023", month="Sep", day="19", volume="25", pages="e43630", keywords="early warning system", keywords="social media", keywords="law enforcement", keywords="public health", keywords="new psychoactive substances", keywords="development", keywords="drug", keywords="dosage", keywords="Canada", keywords="Twitter", keywords="poisoning", keywords="monitoring", keywords="community", keywords="public safety", keywords="machine learning", keywords="Fleiss", keywords="tweet", keywords="tweet annotations", keywords="pharmacology", keywords="addiction", abstract="Background: A hallmark of unregulated drug markets is their unpredictability and constant evolution with newly introduced substances. People who use drugs and the public health workforce are often unaware of the appearance of new drugs on the unregulated market and their type, safe dosage, and potential adverse effects. This increases risks to people who use drugs, including the risk of unknown consumption and unintentional drug poisoning. Early warning systems (EWSs) can help monitor the landscape of emerging drugs in a given community by collecting and tracking up-to-date information and determining trends. However, there are currently few ways to systematically monitor the appearance and harms of new drugs on the unregulated market in Canada. Objective: The goal of this work is to examine how artificial intelligence can assist in identifying patterns of drug-related risks and harms, by monitoring the social media activity of public health and law enforcement groups. This information is beneficial in the form of an EWS as it can be used to identify new and emerging drug trends in various communities. Methods: To collect data for this study, 145 relevant Twitter accounts throughout Quebec (n=33), Ontario (n=78), and British Columbia (n=34) were manually identified. Tweets posted between August 23 and December 21, 2021, were collected via the application programming interface developed by Twitter for a total of 40,393 tweets. Next, subject matter experts (1) developed keyword filters that reduced the data set to 3746 tweets and (2) manually identified relevant tweets for monitoring and early warning efforts for a total of 464 tweets. Using this information, a zero-shot classifier was applied to tweets from step 1 with a set of keep (drug arrest, drug discovery, and drug report) and not-keep (drug addiction support, public safety report, and others) labels to see how accurately it could extract the tweets identified in step 2. Results: When looking at the accuracy in identifying relevant posts, the system extracted a total of 584 tweets and had an overlap of 392 out of 477 (specificity of {\textasciitilde}84.5\%) with the subject matter experts. Conversely, the system identified a total of 3162 irrelevant tweets and had an overlap of 3090 (sensitivity of {\textasciitilde}94.1\%) with the subject matter experts. Conclusions: This study demonstrates the benefits of using artificial intelligence to assist in finding relevant tweets for an EWS. The results showed that it can be quite accurate in filtering out irrelevant information, which greatly reduces the amount of manual work required. Although the accuracy in retaining relevant information was observed to be lower, an analysis showed that the label definitions can impact the results significantly and would therefore be suitable for future work to refine. Nonetheless, the performance is promising and demonstrates the usefulness of artificial intelligence in this domain. ", doi="10.2196/43630", url="https://www.jmir.org/2023/1/e43630", url="http://www.ncbi.nlm.nih.gov/pubmed/37725410" } @Article{info:doi/10.2196/44656, author="Bizzotto, Nicole and Schulz, Johannes Peter and de Bruijn, Gert-Jan", title="The ``Loci'' of Misinformation and Its Correction in Peer- and Expert-Led Online Communities for Mental Health: Content Analysis", journal="J Med Internet Res", year="2023", month="Sep", day="18", volume="25", pages="e44656", keywords="online communities", keywords="social media", keywords="mental health", keywords="misinformation", keywords="empowerment", keywords="content analysis", keywords="online community", keywords="infodemiology", keywords="information seeking", keywords="help seeking", keywords="information behavior", keywords="online search", keywords="search query", keywords="information quality", keywords="information accuracy", abstract="Background: Mental health problems are recognized as a pressing public health issue, and an increasing number of individuals are turning to online communities for mental health to search for information and support. Although these virtual platforms have the potential to provide emotional support and access to anecdotal experiences, they can also present users with large amounts of potentially inaccurate information. Despite the importance of this issue, limited research has been conducted, especially on the differences that might emerge due to the type of content moderation of online communities: peer-led or expert-led. Objective: We aim to fill this gap by examining the prevalence, the communicative context, and the persistence of mental health misinformation on Facebook online communities for mental health, with a focus on understanding the mechanisms that enable effective correction of inaccurate information and differences between expert-led and peer-led groups. Methods: We conducted a content analysis of 1534 statements (from 144 threads) in 2 Italian-speaking Facebook groups. Results: The study found that an alarming number of comments (26.1\%) contained medically inaccurate information. Furthermore, nearly 60\% of the threads presented at least one misinformation statement without any correction attempt. Moderators were more likely to correct misinformation than members; however, they were not immune to posting content containing misinformation, which was an unexpected finding. Discussions about aspects of treatment (including side effects or treatment interruption) significantly increased the probability of encountering misinformation. Additionally, the study found that misinformation produced in the comments of a thread, rather than as the first post, had a lower probability of being corrected, particularly in peer-led communities. Conclusions: The high prevalence of misinformation in online communities, particularly when left uncorrected, underscores the importance of conducting additional research to identify effective mechanisms to prevent its spread. This is especially important given the study's finding that misinformation tends to be more prevalent around specific ``loci'' of discussion that, once identified, can serve as a starting point to develop strategies for preventing and correcting misinformation within them. ", doi="10.2196/44656", url="https://www.jmir.org/2023/1/e44656", url="http://www.ncbi.nlm.nih.gov/pubmed/37721800" } @Article{info:doi/10.2196/46814, author="Silva, Martha and Anaba, Udochisom and Jani Tulsani, Nrupa and Sripad, Pooja and Walker, Jonathan and Aisiri, Adolor", title="Gender-Based Violence Narratives in Internet-Based Conversations in Nigeria: Social Listening Study", journal="J Med Internet Res", year="2023", month="Sep", day="15", volume="25", pages="e46814", keywords="gender-based violence", keywords="social listening", keywords="sexual health", keywords="consent", keywords="social media", keywords="Twitter", keywords="Nigeria", keywords="gender inequalities", keywords="discrimination", keywords="natural language processing", keywords="sexual consent", abstract="Background: Overcoming gender inequities is a global priority recognized as essential for improved health and human development. Gender-based violence (GBV) is an extreme manifestation of gender inequities enacted in real-world and internet-based environments. In Nigeria, GBV has come to the forefront of attention since 2020, when a state of emergency was declared due to increased reporting of sexual violence. Understanding GBV-related social narratives is important to design public health interventions. Objective: We explore how gender-related internet-based conversations in Nigeria specifically related to sexual consent (actively agreeing to sexual behavior), lack of consent, and slut-shaming (stigmatization in the form of insults based on actual or perceived sexuality and behaviors) manifest themselves and whether they changed between 2017 and 2022. Additionally, we explore what role events or social movements have in shaping gender-related narratives in Nigeria. Methods: Social listening was carried out on 12,031 social media posts (Twitter, Facebook, forums, and blogs) and almost 2 million public searches (Google and Yahoo search engines) between April 2017 and May 2022. The data were analyzed using natural language processing to determine the most salient conversation thematic clusters, qualitatively analyze time trends in discourse, and compare data against selected key events. Results: Between 2017 and 2022, internet-based conversation about sexual consent increased 72,633\%, from an average 3 to 2182 posts per month, while slut-shaming conversation (perpetrating or condemning) shrunk by 9\%, from an average 3560 to 3253 posts per month. Thematic analysis shows conversation revolves around the objectification of women, poor comprehension of elements of sexual consent, and advocacy for public education about sexual consent. Additionally, posters created space for sexual empowerment and expressions of sex positivity, pushing back against others who weaponize posts in support of slut-shaming narrative. Time trend analysis shows a greater sense of empowerment in advocating for education around the legal age of consent for sexual activity, calling out double standards, and rejecting slut-shaming. However, analysis of emotions in social media posts shows anger was most prominent in sexual consent (n=1213, 73\%) and slut-shaming (n=226, 64\%) posts. Organic social movements and key events (\#ArewaMeToo and \#ChurchToo, the \#SexforGrades scandal, and the \#BBNaija television program) played a notable role in sparking discourse related to sexual consent and slut-shaming. Conclusions: Social media narratives are significantly impacted by popular culture events, mass media programs, social movements, and micro influencers speaking out against GBV. Hashtags, media clips, and other content can be leveraged effectively to spread awareness and spark conversation around evolving gender norms. Public health practitioners and other stakeholders including policymakers, researchers, and social advocates should be prepared to capitalize on social media events and discourse to help shape the conversation in support of a normative environment that rejects GBV in all its forms. ", doi="10.2196/46814", url="https://www.jmir.org/2023/1/e46814", url="http://www.ncbi.nlm.nih.gov/pubmed/37713260" } @Article{info:doi/10.2196/49061, author="Ng, Margaret Yee Man and Hoffmann Pham, Katherine and Luengo-Oroz, Miguel", title="Exploring YouTube's Recommendation System in the Context of COVID-19 Vaccines: Computational and Comparative Analysis of Video Trajectories", journal="J Med Internet Res", year="2023", month="Sep", day="15", volume="25", pages="e49061", keywords="algorithmic auditing", keywords="antivaccine sentiment", keywords="crowdsourcing", keywords="recommendation systems", keywords="watch history", keywords="YouTube", abstract="Background: Throughout the COVID-19 pandemic, there has been a concern that social media may contribute to vaccine hesitancy due to the wide availability of antivaccine content on social media platforms. YouTube has stated its commitment to removing content that contains misinformation on vaccination. Nevertheless, such claims are difficult to audit. There is a need for more empirical research to evaluate the actual prevalence of antivaccine sentiment on the internet. Objective: This study examines recommendations made by YouTube's algorithms in order to investigate whether the platform may facilitate the spread of antivaccine sentiment on the internet. We assess the prevalence of antivaccine sentiment in recommended videos and evaluate how real-world users' experiences are different from the personalized recommendations obtained by using synthetic data collection methods, which are often used to study YouTube's recommendation systems. Methods: We trace trajectories from a credible seed video posted by the World Health Organization to antivaccine videos, following only video links suggested by YouTube's recommendation system. First, we gamify the process by asking real-world participants to intentionally find an antivaccine video with as few clicks as possible. Having collected crowdsourced trajectory data from respondents from (1) the World Health Organization and United Nations system (nWHO/UN=33) and (2) Amazon Mechanical Turk (nAMT=80), we next compare the recommendations seen by these users to recommended videos that are obtained from (3) the YouTube application programming interface's RelatedToVideoID parameter (nRTV=40) and (4) from clean browsers without any identifying cookies (nCB=40), which serve as reference points. We develop machine learning methods to classify antivaccine content at scale, enabling us to automatically evaluate 27,074 video recommendations made by YouTube. Results: We found no evidence that YouTube promotes antivaccine content; the average share of antivaccine videos remained well below 6\% at all steps in users' recommendation trajectories. However, the watch histories of users significantly affect video recommendations, suggesting that data from the application programming interface or from a clean browser do not offer an accurate picture of the recommendations that real users are seeing. Real users saw slightly more provaccine content as they advanced through their recommendation trajectories, whereas synthetic users were drawn toward irrelevant recommendations as they advanced. Rather than antivaccine content, videos recommended by YouTube are likely to contain health-related content that is not specifically related to vaccination. These videos are usually longer and contain more popular content. Conclusions: Our findings suggest that the common perception that YouTube's recommendation system acts as a ``rabbit hole'' may be inaccurate and that YouTube may instead be following a ``blockbuster'' strategy that attempts to engage users by promoting other content that has been reliably successful across the platform. ", doi="10.2196/49061", url="https://www.jmir.org/2023/1/e49061", url="http://www.ncbi.nlm.nih.gov/pubmed/37713243" } @Article{info:doi/10.2196/46153, author="Llanes, D. Karla and Ling, M. Pamela and Guillory, Jamie and Vogel, A. Erin", title="Young Adults' Perceptions of and Intentions to Use Nicotine and Cannabis Vaporizers in Response to e-Cigarette or Vaping-Associated Lung Injury Instagram Posts: Experimental Study", journal="J Med Internet Res", year="2023", month="Sep", day="14", volume="25", pages="e46153", keywords="EVALI", keywords="risk perception, nicotine", keywords="cannabis", keywords="e-cigarettes", keywords="young adult", keywords="vaping", keywords="social media", keywords="Instagram", keywords="harmful effect", abstract="Background: Inhaling aerosolized nicotine and cannabis (colloquially called ``vaping'') is prevalent among young adults. Instagram influencers often promote both nicotine and cannabis vaporizer products. However, Instagram posts discouraging the use of both products received national media attention during the 2019 outbreak of e-cigarette or vaping-associated lung injury (EVALI). Objective: This experiment tested the impact of viewing Instagram posts about EVALI, varying in image and text valence, on young adults' perceived harmfulness of nicotine and cannabis products, perceived risk of nicotine and cannabis vaporizer use, and intentions to use nicotine and cannabis vaporizers in the future. Methods: Participants (N=1229) aged 18-25 (mean 21.40, SD 2.22) years were recruited through Qualtrics Research Services, oversampling for ever-use of nicotine or cannabis vaporizers (618/1229, 50.3\%). Participants were randomly assigned to view Instagram posts from young people portraying their experiences of EVALI in a 2 (image valence: positive or negative) {\texttimes} 2 (text valence: positive or negative) between-subjects experiment. Positive images were attractive and aesthetically pleasing selfies. The positive text was supportive and uplifting regarding quitting the use of vaporized products. Negative images and text were graphic and fear inducing. After viewing 3 posts, participants reported the perceived harmfulness of nicotine and cannabis products, the perceived risk of nicotine and cannabis vaporizer use, and intentions to use nicotine and cannabis vaporizers in the future. Ordinal logistic regression models assessed the main effects and interactions of image and text valence on perceived harmfulness and risk. Binary logistic regression models assessed the main effects and interactions of image and text valence on intentions to use nicotine and cannabis vaporizers. Analyses were adjusted for product use history. Results: Compared to viewing positive images, viewing negative images resulted in significantly greater perceived harm of nicotine (P=.02 for disposable pod-based vaporizers and P=.04 for other e-cigarette ``mods'' devices) and cannabis vaporized products (P=.01), greater perceived risk of nicotine vaporizers (P<.01), and lower odds of intentions to use nicotine (P=.02) but not cannabis (P=.43) vaporizers in the future. There were no significant main effects of text valence on perceived harm, perceived risk, and intentions to use nicotine and cannabis vaporized products. No significant interaction effects of image and text valence were found. Conclusions: Negative imagery in Instagram posts about EVALI may convey the risks of vaporized product use and discourage young adults from this behavior, regardless of the valence of the post's text. Public health messaging regarding EVALI on Instagram should emphasize the risk of cannabis vaporizer use, as young adults may otherwise believe that only nicotine vaporizer use increases their risk for EVALI. ", doi="10.2196/46153", url="https://www.jmir.org/2023/1/e46153", url="http://www.ncbi.nlm.nih.gov/pubmed/37552552" } @Article{info:doi/10.2196/41364, author="Lin, Li-Yin and Lin, Chun-Ji and Kuan, Chen-I and Chiou, Hung-Yi", title="Potential Determinants Contributing to COVID-19 Vaccine Acceptance and Hesitancy in Taiwan: Rapid Qualitative Mixed Methods Study", journal="JMIR Form Res", year="2023", month="Sep", day="12", volume="7", pages="e41364", keywords="COVID-19", keywords="vaccine acceptance", keywords="vaccine hesitancy", keywords="google trends", keywords="public health", keywords="vaccination", keywords="health promotion", keywords="thematic analysis", keywords="infoveillance", abstract="Background: Although vaccination has been shown to be one of the most important interventions, COVID-19 vaccine hesitancy remains one of the top 10 global public health challenges worldwide. Objective: The objective of this study is to investigate (1) major determinants of vaccine hesitancy, (2) changes in the determinants of vaccine hesitancy at different time periods, and (3) the potential factors affecting vaccine acceptance. Methods: This study applied a mixed methods approach to explore the potential determinants contributing to vaccine hesitancy among the Taiwanese population. The quantitative design of this study involved using Google Trends search query data. We chose the search term ``??`` (vaccine), selected ''??'' (Taiwan) as the location, and selected the period between December 18, 2020, and July 31, 2021. The rising keywords related to vaccine acceptance and hesitancy were collected. Based on the responses obtained from the qualitative study and the rising keywords obtained in Google Trends, the 3 most popular keywords related to vaccine hesitancy were identified and used as search queries in Google Trends between December 18, 2020, and July 31, 2021, to generate relative search volumes (RSVs). Lastly, autoregressive integrated moving average modeling was used to forecast the RSVs for the 3 keywords between May 29 and July 31, 2021. The estimated RSVs were compared to the observed RSVs in Google Trends within the same time frame. Results: The 4 prevailing factors responsible for COVID-19 vaccine acceptance and hesitancy were doubts about the government and manufacturers, side effects, deaths associated with vaccination, and efficacy of vaccination. During the vaccine observation period, ``political role'' was the overarching consideration leading to vaccine hesitancy. During the peak of the pandemic, side effects, death, and vaccine protection were the main factors contributing to vaccine hesitancy. The popularity of the 3 frequently searched keywords ``side effects,'' ``vaccine associated deaths,'' and ``vaccine protection'' continued to rise throughout the pandemic outbreak. Lastly, the highest Google search queries related to COVID-19 vaccines emerged as ``side effects'' prior to vaccination, deaths associated with vaccines during the period when single vaccines were available, and ``side effects'' and ``vaccine protection'' during the period when multiple vaccines were available. Conclusions: Investigating the key factors influencing COVID-19 vaccine hesitancy appears to be a fundamental task that needs to be undertaken to ensure effective implementation of COVID-19 vaccination. Google Trends may be used as a complementary infoveillance tool by government agencies for future vaccine policy implementation and communication. ", doi="10.2196/41364", url="https://formative.jmir.org/2023/1/e41364", url="http://www.ncbi.nlm.nih.gov/pubmed/37698904" } @Article{info:doi/10.2196/49220, author="Malhotra, Kashish and Kempegowda, Punith", title="Appraising Unmet Needs and Misinformation Spread About Polycystic Ovary Syndrome in 85,872 YouTube Comments Over 12 Years: Big Data Infodemiology Study", journal="J Med Internet Res", year="2023", month="Sep", day="11", volume="25", pages="e49220", keywords="polycystic ovary syndrome", keywords="PCOS", keywords="public", keywords="YouTube", keywords="global health", keywords="online trends", keywords="global equity", keywords="infodemiology", keywords="big data", keywords="comments", keywords="sentiment", keywords="network analysis", keywords="contextualization", keywords="word association", keywords="misinformation", keywords="endocrinopathy", keywords="women", keywords="gender", keywords="users", keywords="treatment", keywords="fatigue", keywords="pain", keywords="motherhood", abstract="Background: Polycystic ovary syndrome (PCOS) is the most common endocrinopathy in women, resulting in substantial burden related to metabolic, reproductive, and psychological complications. While attempts have been made to understand the themes and sentiments of the public regarding PCOS at the local and regional levels, no study has explored worldwide views, mainly due to financial and logistical limitations. YouTube is one of the largest sources of health-related information, where many visitors share their views as questions or comments. These can be used as a surrogate to understand the public's perceptions. Objective: We analyzed the comments of all videos related to PCOS published on YouTube from May 2011 to April 2023 and identified trends over time in the comments, their context, associated themes, gender-based differences, and underlying sentiments. Methods: After extracting all the comments using the YouTube application programming interface, we contextually studied the keywords and analyzed gender differences using the Benjamini-Hochberg procedure. We applied a multidimensional approach to analyzing the content via association mining using Mozdeh. We performed network analysis to study associated themes using the Fruchterman-Reingold algorithm and then manually screened the comments for content analysis. The sentiments associated with YouTube comments were analyzed using SentiStrength. Results: A total of 85,872 comments from 940 PCOS videos on YouTube were extracted. We identified a specific gender for 13,106 comments. Of these, 1506 were matched to male users (11.5\%), and 11,601 comments to female users (88.5\%). Keywords including diagnosing PCOS, symptoms of PCOS, pills for PCOS (medication), and pregnancy were significantly associated with female users. Keywords such as herbal treatment, natural treatment, curing PCOS, and online searches were significantly associated with male users. The key themes associated with female users were symptoms of PCOS, positive personal experiences (themes such as helpful and love), negative personal experiences (fatigue and pain), motherhood (infertility and trying to conceive), self-diagnosis, and use of professional terminology detailing their journey. The key themes associated with male users were misinformation regarding the ``cure'' for PCOS, using natural and herbal remedies to cure PCOS, fake testimonies from spammers selling their courses and consultations, finding treatment for PCOS, and sharing perspectives of female family members. The overall average positive sentiment was 1.6651 (95\% CI 1.6593-1.6709), and the average negative sentiment was 1.4742 (95\% CI 1.4683-1.4802) with a net positive difference of 0.1909. Conclusions: There may be a disparity in views on PCOS between women and men, with the latter associated with non--evidence-based approaches and misinformation. The improving sentiment noticed with YouTube comments may reflect better health care services. Prioritizing and promoting evidence-based care and disseminating pragmatic online coverage is warranted to improve public sentiment and limit misinformation spread. ", doi="10.2196/49220", url="https://www.jmir.org/2023/1/e49220", url="http://www.ncbi.nlm.nih.gov/pubmed/37695666" } @Article{info:doi/10.2196/42446, author="Chu, MY Amanda and Chong, Y. Andy C. and Lai, T. Nick H. and Tiwari, Agnes and So, P. Mike K.", title="Enhancing the Predictive Power of Google Trends Data Through Network Analysis: Infodemiology Study of COVID-19", journal="JMIR Public Health Surveill", year="2023", month="Sep", day="7", volume="9", pages="e42446", keywords="internet search volumes", keywords="network analysis", keywords="pandemic risk", keywords="health care analytics", keywords="network connectedness", keywords="infodemiology", keywords="infoveillance", keywords="mobile phone", keywords="COVID-19", abstract="Background: The COVID-19 outbreak has revealed a high demand for timely surveillance of pandemic developments. Google Trends (GT), which provides freely available search volume data, has been proven to be a reliable forecast and nowcast measure for public health issues. Previous studies have tended to use relative search volumes from GT directly to analyze associations and predict the progression of pandemic. However, GT's normalization of the search volumes data and data retrieval restrictions affect the data resolution in reflecting the actual search behaviors, thus limiting the potential for using GT data to predict disease outbreaks. Objective: This study aimed to introduce a merged algorithm that helps recover the resolution and accuracy of the search volume data extracted from GT over long observation periods. In addition, this study also aimed to demonstrate the extended application of merged search volumes (MSVs) in combination of network analysis, via tracking the COVID-19 pandemic risk. Methods: We collected relative search volumes from GT and transformed them into MSVs using our proposed merged algorithm. The MSVs of the selected coronavirus-related keywords were compiled using the rolling window method. The correlations between the MSVs were calculated to form a dynamic network. The network statistics, including network density and the global clustering coefficients between the MSVs, were also calculated. Results: Our research findings suggested that although GT restricts the search data retrieval into weekly data points over a long period, our proposed approach could recover the daily search volume over the same investigation period to facilitate subsequent research analyses. In addition, the dynamic time warping diagrams show that the dynamic networks were capable of predicting the COVID-19 pandemic trends, in terms of the number of COVID-19 confirmed cases and severity risk scores. Conclusions: The innovative method for handling GT search data and the application of MSVs and network analysis to broaden the potential for GT data are useful for predicting the pandemic risk. Further investigation of the GT dynamic network can focus on noncommunicable diseases, health-related behaviors, and misinformation on the internet. ", doi="10.2196/42446", url="https://publichealth.jmir.org/2023/1/e42446", url="http://www.ncbi.nlm.nih.gov/pubmed/37676701" } @Article{info:doi/10.2196/49325, author="Scotti Requena, Simone and Pirkis, Jane and Currier, Dianne and Conway, Mike and Lee, Simon and Turnure, Jackie and Cummins, Jennifer and Nicholas, Angela", title="An Evaluation of the Boys Do Cry Suicide Prevention Media Campaign on Twitter: Mixed Methods Approach", journal="JMIR Form Res", year="2023", month="Sep", day="7", volume="7", pages="e49325", keywords="help-seeking", keywords="masculinity", keywords="media campaign", keywords="men", keywords="men's health", keywords="mental health", keywords="self-reliance", keywords="social media", keywords="suicide prevention", keywords="suicide", abstract="Background: In most countries, men are more likely to die by suicide than women. Adherence to dominant masculine norms, such as being self-reliant, is linked to suicide in men in Western cultures. We created a suicide prevention media campaign, ``Boys Do Cry,'' designed to challenge the ``self-reliance'' norm and encourage help-seeking in men. A music video was at the core of the campaign, which was an adapted version of the ``Boys Don't Cry'' song from ``The Cure.'' There is evidence that suicide prevention media campaigns can encourage help-seeking for mental health difficulties. Objective: We aimed to explore the reach, engagement, and themes of discussion prompted by the Boys Do Cry campaign on Twitter. Methods: We used Twitter analytics data to investigate the reach and engagement of the Boys Do Cry campaign, including analyzing the characteristics of tweets posted by the campaign's hosts. Throughout the campaign and immediately after, we also used Twitter data derived from the Twitter Application Programming Interface to analyze the tweeting patterns of users related to the campaign. In addition, we qualitatively analyzed the content of Boys Do Cry--related tweets during the campaign period. Results: During the campaign, Twitter users saw the tweets posted by the hosts of the campaign a total of 140,650 times and engaged with its content a total of 4477 times. The 10 highest-performing tweets by the campaign hosts involved either a video or an image. Among the 10 highest-performing tweets, the first was one that included the campaign's core video; the second was a screenshot of the tweet posted by Robert Smith, the lead singer of The Cure, sharing the Boys Do Cry campaign's video and tagging the campaign's hosts. In addition, the pattern of Twitter activity for the campaign-related tweets was considerably higher during the campaign than in the immediate postcampaign period, with half of the activity occurring during the first week of the campaign when Robert Smith promoted the campaign. Some of the key topics of discussions prompted by the Boys Do Cry campaign on Twitter involved users supporting the campaign; referencing the original song, band, or lead singer; reiterating the campaign's messages; and having emotional responses to the campaign. Conclusions: This study demonstrates that a brief media campaign such as Boys Do Cry can achieve good reach and engagement and can prompt discussions on Twitter about masculinity and suicide. Such discussions may lead to greater awareness about the importance of seeking help and providing support to those with mental health difficulties. However, this study suggests that longer, more intensive campaigns may be needed in order to amplify and sustain these results. ", doi="10.2196/49325", url="https://formative.jmir.org/2023/1/e49325", url="http://www.ncbi.nlm.nih.gov/pubmed/37676723" } @Article{info:doi/10.2196/49452, author="Kureyama, Nari and Terada, Mitsuo and Kusudo, Maho and Nozawa, Kazuki and Wanifuchi-Endo, Yumi and Fujita, Takashi and Asano, Tomoko and Kato, Akiko and Mori, Makiko and Horisawa, Nanae and Toyama, Tatsuya", title="Fact-Checking Cancer Information on Social Media in Japan: Retrospective Study Using Twitter", journal="JMIR Form Res", year="2023", month="Sep", day="6", volume="7", pages="e49452", keywords="cancer", keywords="fact-check", keywords="misinformation", keywords="social media", keywords="twitter", abstract="Background: The widespread use of social media has made it easier for patients to access cancer information. However, a large amount of misinformation and harmful information that could negatively impact patients' decision-making is also disseminated on social media platforms. Objective: We aimed to determine the actual amount of misinformation and harmful information as well as trends in the dissemination of cancer-related information on Twitter, a representative social media platform. Our findings can support decision-making among Japanese patients with cancer. Methods: Using the Twitter app programming interface, we extracted tweets containing the term ``cancer'' in Japanese that were posted between August and September of 2022. The eligibility criteria were the cancer-related tweets with the following information: (1) reference to the occurrence or prognosis of cancer, (2) recommendation or nonrecommendation of actions, (3) reference to the course of cancer treatment or adverse events, (4) results of cancer research, and (5) other cancer-related knowledge and information. Finally, we selected the top 100 tweets with the highest number of ``likes.'' For each tweet, 2 independent reviewers evaluated whether the information was factual or misinformation, and whether it was harmful or safe with the reasons for the decisions on the misinformation and harmful tweets. Additionally, we examined the frequency of information dissemination using the number of retweets for the top 100 tweets and investigated trends in the dissemination of information. Results: The extracted tweets totaled 69,875. Of the top 100 cancer-related tweets with the most ``likes'' that met the eligibility criteria, 44 (44\%) contained misinformation, 31 (31\%) contained harmful information, and 30 (30\%) contained both misinformation and harmful information. Misinformation was described as Unproven (29/94, 40.4\%), Disproven (19/94, 20.2\%), Inappropriate application (4/94, 4.3\%), Strength of evidence mischaracterized (14/94, 14.9\%), Misleading (18/94, 18\%), and Other misinformation (1/94, 1.1\%). Harmful action was described as Harmful action (9/59, 15.2\%), Harmful inaction (43/59, 72.9\%), Harmful interactions (3/59, 5.1\%), Economic harm (3/59, 5.1\%), and Other harmful information (1/59, 1.7\%). Harmful information was liked more often than safe information (median 95, IQR 43-1919 vs 75.0 IQR 43-10,747; P=.04). The median number of retweets for the leading 100 tweets was 13.5 (IQR 0-2197). Misinformation was retweeted significantly more often than factual information (median 29.0, IQR 0-502 vs 7.5, IQR 0-2197; P=.01); harmful information was also retweeted significantly more often than safe information (median 35.0, IQR 0-502 vs 8.0, IQR 0-2197; P=.002). Conclusions: It is evident that there is a prevalence of misinformation and harmful information related to cancer on Twitter in Japan and it is crucial to increase health literacy and awareness regarding this issue. Furthermore, we believe that it is important for government agencies and health care professionals to continue providing accurate medical information to support patients and their families in making informed decisions. ", doi="10.2196/49452", url="https://formative.jmir.org/2023/1/e49452", url="http://www.ncbi.nlm.nih.gov/pubmed/37672310" } @Article{info:doi/10.2196/45867, author="Hirabayashi, Mai and Shibata, Daisaku and Shinohara, Emiko and Kawazoe, Yoshimasa", title="Influence of Tweets Indicating False Rumors on COVID-19 Vaccination: Case Study", journal="JMIR Form Res", year="2023", month="Sep", day="5", volume="7", pages="e45867", keywords="coronavirus", keywords="correlation", keywords="COVID-19", keywords="disinformation", keywords="false information", keywords="infodemiology", keywords="misinformation", keywords="rumor", keywords="rumor-indication", keywords="SARS-CoV-2", keywords="social media", keywords="tweet", keywords="Twitter", keywords="vaccination", keywords="vaccine", abstract="Background: As of December 2022, the outbreak of COVID-19 showed no sign of abating, continuing to impact people's lives, livelihoods, economies, and more. Vaccination is an effective way to achieve mass immunity. However, in places such as Japan, where vaccination is voluntary, there are people who choose not to receive the vaccine, even if an effective vaccine is offered. To promote vaccination, it is necessary to clarify what kind of information on social media can influence attitudes toward vaccines. Objective: False rumors and counterrumors are often posted and spread in large numbers on social media, especially during emergencies. In this paper, we regard tweets that contain questions or point out errors in information as counterrumors. We analyze counterrumors tweets related to the COVID-19 vaccine on Twitter. We aimed to answer the following questions: (1) what kinds of COVID-19 vaccine--related counterrumors were posted on Twitter, and (2) are the posted counterrumors related to social conditions such as vaccination status? Methods: We use the following data sets: (1) counterrumors automatically collected by the ``rumor cloud'' (18,593 tweets); and (2) the number of COVID-19 vaccine inoculators from September 27, 2021, to August 15, 2022, published on the Prime Minister's Office's website. First, we classified the contents contained in counterrumors. Second, we counted the number of COVID-19 vaccine--related counterrumors from data set 1. Then, we examined the cross-correlation coefficients between the numbers of data sets 1 and 2. Through this verification, we examined the correlation coefficients for the following three periods: (1) the same period of data; (2) the case where the occurrence of the suggestion of counterrumors precedes the vaccination (negative time lag); and (3) the case where the vaccination precedes the occurrence of counterrumors (positive time lag). The data period used for the validation was from October 4, 2021, to April 18, 2022. Results: Our classification results showed that most counterrumors about the COVID-19 vaccine were negative. Moreover, the correlation coefficients between the number of counterrumors and vaccine inoculators showed significant and strong positive correlations. The correlation coefficient was over 0.7 at ?8, ?7, and ?1 weeks of lag. Results suggest that the number of vaccine inoculators tended to increase with an increase in the number of counterrumors. Significant correlation coefficients of 0.5 to 0.6 were observed for lags of 1 week or more and 2 weeks or more. This implies that an increase in vaccine inoculators increases the number of counterrumors. These results suggest that the increase in the number of counterrumors may have been a factor in inducing vaccination behavior. Conclusions: Using quantitative data, we were able to reveal how counterrumors influence the vaccination status of the COVID-19 vaccine. We think that our findings would be a foundation for considering countermeasures of vaccination. ", doi="10.2196/45867", url="https://formative.jmir.org/2023/1/e45867", url="http://www.ncbi.nlm.nih.gov/pubmed/37669092" } @Article{info:doi/10.2196/49399, author="Malhotra, Kashish and Aggarwal, Pranshul and Malhotra, Sakshi and Goyal, Kashish", title="Evaluating the Global Digital Impact of Psoriasis Action Month and World Psoriasis Day: Serial Cross-Sectional Study", journal="JMIR Dermatol", year="2023", month="Sep", day="4", volume="6", pages="e49399", keywords="psoriasis", keywords="psoriasis awareness", keywords="social media", keywords="digital health", keywords="Psoriasis Action Day", keywords="World Psoriasis Day", keywords="skin", keywords="dermatology", keywords="awareness", keywords="health promotion", keywords="trends", keywords="Twitter", keywords="tweets", keywords="cross-sectional", doi="10.2196/49399", url="https://derma.jmir.org/2023/1/e49399", url="http://www.ncbi.nlm.nih.gov/pubmed/37665631" } @Article{info:doi/10.2196/43995, author="Zhang, Jiayuan and Xu, Wei and Lei, Cheng and Pu, Yang and Zhang, Yubo and Zhang, Jingyu and Yu, Hongfan and Su, Xueyao and Huang, Yanyan and Gong, Ruoyan and Zhang, Lijun and Shi, Qiuling", title="Using Clinician-Patient WeChat Group Communication Data to Identify Symptom Burdens in Patients With Uterine Fibroids Under Focused Ultrasound Ablation Surgery Treatment: Qualitative Study", journal="JMIR Form Res", year="2023", month="Sep", day="1", volume="7", pages="e43995", keywords="social media", keywords="group chats", keywords="text mining", keywords="free texts", keywords="symptom burdens", keywords="WeChat", keywords="natural language processing", keywords="NLP", abstract="Background: Unlike research project--based health data collection (questionnaires and interviews), social media platforms allow patients to freely discuss their health status and obtain peer support. Previous literature has pointed out that both public and private social platforms can serve as data sources for analysis. Objective: This study aimed to use natural language processing (NLP) techniques to identify concerns regarding the postoperative quality of life and symptom burdens in patients with uterine fibroids after focused ultrasound ablation surgery. Methods: Screenshots taken from clinician-patient WeChat groups were converted into free texts using image text recognition technology and used as the research object of this study. From 408 patients diagnosed with uterine fibroids in Chongqing Haifu Hospital between 2010 and 2020, we searched for symptom burdens in over 900,000 words of WeChat group chats. We first built a corpus of symptoms by manually coding 30\% of the WeChat texts and then used regular expressions in Python to crawl symptom information from the remaining texts based on this corpus. We compared the results with a manual review (gold standard) of the same records. Finally, we analyzed the relationship between the population baseline data and conceptual symptoms; quantitative and qualitative results were examined. Results: A total of 408 patients with uterine fibroids were included in the study; 190,000 words of free text were obtained after data cleaning. The mean age of the patients was 39.94 (SD 6.81) years, and their mean BMI was 22.18 (SD 2.78) kg/m2. The median reporting times of the 7 major symptoms were 21, 26, 57, 2, 18, 30, and 49 days. Logistic regression models identified preoperative menstrual duration (odds ratio [OR] 1.14, 95\% CI 5.86-6.37; P=.009), age of menophania (OR --1.02 , 95\% CI 11.96-13.47; P=.03), and the number (OR 2.34, 95\% CI 1.45-1.83; P=.04) and size of fibroids (OR 0.12, 95\% CI 2.43-3.51; P=.04) as significant risk factors for postoperative symptoms. Conclusions: Unstructured free texts from social media platforms extracted by NLP technology can be used for analysis. By extracting the conceptual information about patients' health-related quality of life, we can adopt personalized treatment for patients at different stages of recovery to improve their quality of life. Python-based text mining of free-text data can accurately extract symptom burden and save considerable time compared to manual review, maximizing the utility of the extant information in population-based electronic health records for comparative effectiveness research. ", doi="10.2196/43995", url="https://formative.jmir.org/2023/1/e43995", url="http://www.ncbi.nlm.nih.gov/pubmed/37656501" } @Article{info:doi/10.2196/46343, author="Esener, Yildiz and McCall, Terika and Lakdawala, Adnan and Kim, Heejun", title="Seeking and Providing Social Support on Twitter for Trauma and Distress During the COVID-19 Pandemic: Content and Sentiment Analysis", journal="J Med Internet Res", year="2023", month="Aug", day="31", volume="25", pages="e46343", keywords="COVID-19", keywords="social support", keywords="trauma", keywords="distress", keywords="posttraumatic stress disorder", keywords="PTSD", keywords="Twitter", keywords="social media", keywords="mental health", abstract="Background: The COVID-19 pandemic can be recognized as a traumatic event that led to stressors, resulting in trauma or distress among the general population. Social support is vital in the management of these stressors, especially during a traumatic event, such as the COVID-19 pandemic. Because of the limited face-to-face interactions enforced by physical distancing regulations during the pandemic, people sought solace on social media platforms to connect with, and receive support from, one another. Hence, it is crucial to investigate the ways in which people seek and offer support on social media for mental health management. Objective: The research aimed to examine the types of social support (eg, emotional, informational, instrumental, and appraisal) sought and provided for trauma or distress on Twitter during the COVID-19 pandemic. In addition, this study aimed to gain insight into the difficulties and concerns of people during the pandemic by identifying the associations between terms representing the topics of interest related to trauma or distress and their corresponding sentiments. Methods: The study methods included content analysis to investigate the type of social support people sought for trauma or distress during the pandemic. Sentiment analysis was also performed to track the negative and positive sentiment tweets posted between January 1, 2020, and March 15, 2021. Association rule mining was used to uncover associations between terms and sentiments in tweets. In addition, the research used Kruskal-Wallis and Mann-Whitney U tests to determine whether the retweet count and like count varied based on the social support type. Results: Most Twitter users who indicated trauma or distress sought emotional support. Regarding sentiment, Twitter users mostly posted negative sentiment tweets, particularly in January 2021. An intriguing observation was that wearing masks could trigger and exacerbate trauma or distress. The results revealed that people mostly sought and provided emotional support on Twitter regarding difficulties with wearing masks, mental health status, financial hardships, and treatment methods for trauma or distress. In addition, tweets regarding emotional support received the most endorsements from other users, highlighting the critical role of social support in fostering a sense of community and reducing the feelings of isolation during the pandemic. Conclusions: This study demonstrates the potential of social media as a platform to exchange social support during challenging times and to identify the specific concerns (eg, wearing masks and exacerbated symptoms) of individuals with self-reported trauma or distress. The findings provide insights into the types of support that were most beneficial for those struggling with trauma or distress during the pandemic and may inform policy makers and health organizations regarding better practices for pandemic response and special considerations for groups with a history of trauma or distress. ", doi="10.2196/46343", url="https://www.jmir.org/2023/1/e46343", url="http://www.ncbi.nlm.nih.gov/pubmed/37651178" } @Article{info:doi/10.2196/50346, author="Dobbs, D. Page and Boykin, Ames Allison and Ezike, Nnamdi and Myers, J. Aaron and Colditz, B. Jason and Primack, A. Brian", title="Twitter Sentiment About the US Federal Tobacco 21 Law: Mixed Methods Analysis", journal="JMIR Form Res", year="2023", month="Aug", day="31", volume="7", pages="e50346", keywords="social media", keywords="Twitter", keywords="Tobacco 21", keywords="mixed methods", keywords="tobacco policy", keywords="sentiment", keywords="tweet", keywords="tweets", keywords="tobacco", keywords="smoke", keywords="smoking", keywords="smoker", keywords="policy", keywords="policies", keywords="law", keywords="regulation", keywords="regulations", keywords="laws", keywords="attitude", keywords="attitudes", keywords="opinion", keywords="opinions", abstract="Background: On December 20, 2019, the US ``Tobacco 21'' law raised the minimum legal sales age of tobacco products to 21 years. Initial research suggests that misinformation about Tobacco 21 circulated via news sources on Twitter and that sentiment about the law was associated with particular types of tobacco products and included discussions about other age-related behaviors. However, underlying themes about this sentiment as well as temporal trends leading up to enactment of the law have not been explored. Objective: This study sought to examine (1) sentiment (pro-, anti-, and neutral policy) about Tobacco 21 on Twitter and (2) volume patterns (number of tweets) of Twitter discussions leading up to the enactment of the federal law. Methods: We collected tweets related to Tobacco 21 posted between September 4, 2019, and December 31, 2019. A 2\% subsample of tweets (4628/231,447) was annotated by 2 experienced, trained coders for policy-related information and sentiment. To do this, a codebook was developed using an inductive procedure that outlined the operational definitions and examples for the human coders to annotate sentiment (pro-, anti-, and neutral policy). Following the annotation of the data, the researchers used a thematic analysis to determine emergent themes per sentiment category. The data were then annotated again to capture frequencies of emergent themes. Concurrently, we examined trends in the volume of Tobacco 21--related tweets (weekly rhythms and total number of tweets over the time data were collected) and analyzed the qualitative discussions occurring at those peak times. Results: The most prevalent category of tweets related to Tobacco 21 was neutral policy (514/1113, 46.2\%), followed by antipolicy (432/1113, 38.8\%); 167 of 1113 (15\%) were propolicy or supportive of the law. Key themes identified among neutral tweets were news reports and discussion of political figures, parties, or government involvement in general. Most discussions were generated from news sources and surfaced in the final days before enactment. Tweets opposing Tobacco 21 mentioned that the law was unfair to young audiences who were addicted to nicotine and were skeptical of the law's efficacy and importance. Methods used to evade the law were found to be represented in both neutral and antipolicy tweets. Propolicy tweets focused on the protection of youth and described the law as a sensible regulatory approach rather than a complete ban of all products or flavored products. Four spikes in daily volume were noted, 2 of which corresponded with political speeches and 2 with the preparation and passage of the legislation. Conclusions: Understanding themes of public sentiment---as well as when Twitter activity is most active---will help public health professionals to optimize health promotion activities to increase community readiness and respond to enforcement needs including education for retailers and the general public. ", doi="10.2196/50346", url="https://formative.jmir.org/2023/1/e50346", url="http://www.ncbi.nlm.nih.gov/pubmed/37651169" } @Article{info:doi/10.2196/49255, author="Lu, Chang and Hu, Bo and Li, Qiang and Bi, Chao and Ju, Xing-Da", title="Psychological Inoculation for Credibility Assessment, Sharing Intention, and Discernment of Misinformation: Systematic Review and Meta-Analysis", journal="J Med Internet Res", year="2023", month="Aug", day="29", volume="25", pages="e49255", keywords="psychological inoculation", keywords="misinformation", keywords="discernment", keywords="sharing", keywords="meta-analysis", abstract="Background: The prevalence of misinformation poses a substantial threat to individuals' daily lives, necessitating the deployment of effective remedial approaches. One promising strategy is psychological inoculation, which pre-emptively immunizes individuals against misinformation attacks. However, uncertainties remain regarding the extent to which psychological inoculation effectively enhances the capacity to differentiate between misinformation and real information. Objective: To reduce the potential risk of misinformation about digital health, this study aims to examine the effectiveness of psychological inoculation in countering misinformation with a focus on several factors, including misinformation credibility assessment, real information credibility assessment, credibility discernment, misinformation sharing intention, real information sharing intention, and sharing discernment. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we conducted a meta-analysis by searching 4 databases (Web of Science, APA PsycINFO, Proquest, and PubMed) for empirical studies based on inoculation theory and outcome measure--related misinformation published in the English language. Moderator analyses were used to examine the differences in intervention strategy, intervention type, theme, measurement time, team, and intervention design. Results: Based on 42 independent studies with 42,530 subjects, we found that psychological inoculation effectively reduces misinformation credibility assessment (d=--0.36, 95\% CI --0.50 to --0.23; P<.001) and improves real information credibility assessment (d=0.20, 95\% CI 0.06-0.33; P=.005) and real information sharing intention (d=0.09, 95\% CI 0.03-0.16; P=.003). However, psychological inoculation does not significantly influence misinformation sharing intention (d=--0.35, 95\% CI --0.79 to 0.09; P=.12). Additionally, we find that psychological inoculation effectively enhances credibility discernment (d=0.20, 95\% CI 0.13-0.28; P<.001) and sharing discernment (d=0.18, 95\% CI 0.12-0.24; P<.001). Regarding health misinformation, psychological inoculation effectively decreases misinformation credibility assessment and misinformation sharing intention. The results of the moderator analyses showed that content-based, passive inoculation was more effective in increasing credibility and sharing intention. The theme of climate change demonstrates a stronger effect on real information credibility. Comparing intervention types showed that pre-post interventions are more effective for misinformation credibility assessment, while post-only interventions are better for credibility discernment. Conclusions: This study indicated that psychological inoculation enhanced individuals' ability to discern real information from misinformation and share real information. Incorporating psychological inoculation to cultivate an informed public is crucial for societal resilience against misinformation threats in an age of information proliferation. As a scalable and cost-effective intervention strategy, institutions can apply psychological inoculation to mitigate potential misinformation crises. ", doi="10.2196/49255", url="https://www.jmir.org/2023/1/e49255", url="http://www.ncbi.nlm.nih.gov/pubmed/37560816" } @Article{info:doi/10.2196/44810, author="Emanuel, K. Rebecca H. and Docherty, D. Paul and Lunt, Helen and Campbell, E. Rebecca", title="Comparing Literature- and Subreddit-Derived Laboratory Values in Polycystic Ovary Syndrome (PCOS): Validation of Clinical Data Posted on PCOS Reddit Forums", journal="JMIR Form Res", year="2023", month="Aug", day="25", volume="7", pages="e44810", keywords="androgens", keywords="clinical treatment", keywords="cohort", keywords="laboratory tests", keywords="medical intervention", keywords="metabolic markers", keywords="online forum", keywords="ovary", keywords="PCOS", keywords="polycystic ovary syndrome", keywords="reddit", keywords="reproductive hormones", keywords="reproductive", keywords="social media", keywords="validation study", abstract="Background: Polycystic ovary syndrome (PCOS) is a heterogeneous condition that affects 4\% to 21\% of people with ovaries. Inaccessibility or dissatisfaction with clinical treatment for PCOS has led to some individuals with the condition discussing their experiences in specialized web-based forums. Objective: This study explores the feasibility of using such web-based forums for clinical research purposes by gathering and analyzing laboratory test results posted in an active PCOS forum, specifically the PCOS subreddit hosted on Reddit. Methods: We gathered around 45,000 posts from the PCOS subreddit. A random subset of 5000 posts was manually read, and the presence of laboratory test results was labeled. These labeled posts were used to train a machine learning model to identify which of the remaining posts contained laboratory results. The laboratory results were extracted manually from the identified posts. These self-reported laboratory test results were compared with values in the published literature to assess whether the results were concordant with researcher-published values for PCOS cohorts. A total of 10 papers were chosen to represent published PCOS literature, with selection criteria including the Rotterdam diagnostic criteria for PCOS, a publication date within the last 20 years, and at least 50 participants with PCOS. Results: Overall, the general trends observed in the laboratory test results from the PCOS web-based forum were consistent with clinically reported PCOS. A number of results, such as follicle stimulating hormone, fasting insulin, and anti-Mullerian hormone, were concordant with published values for patients with PCOS. The high consistency of these results among the literature and when compared to the subreddit suggests that follicle stimulating hormone, fasting insulin, and anti-Mullerian hormone are more consistent across PCOS phenotypes than other test results. Some results, such as testosterone, sex hormone--binding globulin, and homeostasis model assessment--estimated insulin resistance index, were between those of PCOS literature values and normal values, as defined by clinical testing limits. Interestingly, other results, including dehydroepiandrosterone sulfate, luteinizing hormone, and fasting glucose, appeared to be slightly more dysregulated than those reported in the literature. Conclusions: The differences between the forum-posted results and those published in the literature may be due to the selection process in clinical studies and the possibility that the forum disproportionally describes PCOS phenotypes that are less likely to be alleviated with medical intervention. However, the degree of concordance in most laboratory test values implied that the PCOS web-based forum participants were representative of research-identified PCOS cohorts. This validation of the PCOS subreddit grants the possibility for more research into the contents of the subreddit and the idea of undertaking similar research using the contents of other medical internet forums. ", doi="10.2196/44810", url="https://formative.jmir.org/2023/1/e44810", url="http://www.ncbi.nlm.nih.gov/pubmed/37624626" } @Article{info:doi/10.2196/45583, author="Jones, M. Christopher and Diethei, Daniel and Sch{\"o}ning, Johannes and Shrestha, Rehana and Jahnel, Tina and Sch{\"u}z, Benjamin", title="Impact of Social Reference Cues on Misinformation Sharing on Social Media: Series of Experimental Studies", journal="J Med Internet Res", year="2023", month="Aug", day="24", volume="25", pages="e45583", keywords="misinformation", keywords="social media", keywords="health literacy", keywords="COVID-19", keywords="fake news", keywords="Twitter", keywords="tweet", keywords="infodemiology", keywords="information behavior", keywords="information sharing", keywords="sharing behavior", keywords="behavior change", keywords="social cue", keywords="social reference", keywords="flag", abstract="Background: Health-related misinformation on social media is a key challenge to effective and timely public health responses. Existing mitigation measures include flagging misinformation or providing links to correct information, but they have not yet targeted social processes. Current approaches focus on increasing scrutiny, providing corrections to misinformation (debunking), or alerting users prospectively about future misinformation (prebunking and inoculation). Here, we provide a test of a complementary strategy that focuses on the social processes inherent in social media use, in particular, social reinforcement, social identity, and injunctive norms. Objective: This study aimed to examine whether providing balanced social reference cues (ie, cues that provide information on users sharing and, more importantly, not sharing specific content) in addition to flagging COVID-19--related misinformation leads to reductions in sharing behavior and improvement in overall sharing quality. Methods: A total of 3 field experiments were conducted on Twitter's native social media feed (via a newly developed browser extension). Participants' feed was augmented to include misleading and control information, resulting in 4 groups: no-information control, Twitter's own misinformation warning (misinformation flag), social cue only, and combined misinformation flag and social cue. We tracked the content shared or liked by participants. Participants were provided with social information by referencing either their personal network on Twitter or all Twitter users. Results: A total of 1424 Twitter users participated in 3 studies (n=824, n=322, and n=278). Across all 3 studies, we found that social cues that reference users' personal network combined with a misinformation flag reduced the sharing of misleading but not control information and improved overall sharing quality. We show that this improvement could be driven by a change in injunctive social norms (study 2) but not social identity (study 3). Conclusions: Social reference cues combined with misinformation flags can significantly and meaningfully reduce the amount of COVID-19--related misinformation shared and improve overall sharing quality. They are a feasible and scalable way to effectively curb the sharing of COVID-19--related misinformation on social media. ", doi="10.2196/45583", url="https://www.jmir.org/2023/1/e45583", url="http://www.ncbi.nlm.nih.gov/pubmed/37616030" } @Article{info:doi/10.2196/42669, author="Vargas Meza, Xanat and Park, Woo Han", title="Information Circulation Among Spanish-Speaking and Caribbean Communities Related to COVID-19: Social Media--Based Multidimensional Analysis", journal="J Med Internet Res", year="2023", month="Aug", day="23", volume="25", pages="e42669", keywords="COVID-19", keywords="social media", keywords="Spanish", keywords="multidimensional analysis", keywords="Caribbean", keywords="accessibility", abstract="Background: Scienti?c studies from North America and Europe tend to predominate the internet and bene?t English-speaking users. Meanwhile, the COVID-19 death rate was high at the onset of the pandemic in Spanish-speaking countries, and information about nearby Caribbean countries was rarely highlighted. Given the rise in social media use in these regions, the web-based dissemination of scientific information related to COVID-19 must be thoroughly examined. Objective: This study aimed to provide a multidimensional analysis of peer-reviewed information circulation related to COVID-19 in Spanish-speaking and Caribbean regions. Methods: COVID-19--related, peer-reviewed resources shared by web-based accounts located in Spanish-speaking and Caribbean regions were identified through the Altmetric website, and their information was collected. A multidimensional model was used to examine these resources, considering time, individuality, place, activity, and relations. Time was operationalized as the 6 dates of data collection, individuality as the knowledge area and accessibility level, place as the publication venue and affiliation countries, activity as the Altmetric score and number of mentions in the selected regions, and relations as coauthorship between countries and types of social media users who disseminated COVID-19--related information. Results: The highest information circulation peaks in Spanish-speaking countries were from April 2020 to August 2020 and from December 2020 to April 2021, whereas the highest peaks in Caribbean regions were from December 2019 to April 2020. Regarding Spanish-speaking regions, at the onset of the pandemic, scientific expertise was concentrated on a few peer-reviewed sources written in English. The top scienti?c journals mentioned were from English-speaking, westernized regions, whereas the top scienti?c authorships were from China. The most mentioned scientific resources were about breakthrough findings in the medical and health sciences area, written in highly technical language. The top relationships were self-loops in China, whereas international collaborations were between China and the United States. Argentina had high closeness and betweenness, and Spain had high closeness. On the basis of social media data, a combination of media outlets; educational institutions; and expert associations, particularly from Panama, influenced the diffusion of peer-reviewed information. Conclusions: We determined the diffusion patterns of peer-reviewed resources in Spanish-speaking countries and Caribbean territories. This study aimed to advance the management and analysis of web-based public data from non-white people to improve public health communication in their regions. ", doi="10.2196/42669", url="https://www.jmir.org/2023/1/e42669", url="http://www.ncbi.nlm.nih.gov/pubmed/37402284" } @Article{info:doi/10.2196/44031, author="Du, Min and Yan, Wenxin and Zhu, Lin and Liang, Wannian and Liu, Min and Liu, Jue", title="Trends in the Baidu Index in Search Activity Related to Mpox at Geographical and Economic Levels and Associated Factors in China: National Longitudinal Analysis", journal="JMIR Form Res", year="2023", month="Aug", day="23", volume="7", pages="e44031", keywords="mpox", keywords="internet attention", keywords="emergency", keywords="disparities", keywords="China", abstract="Background: Research assessing trends in online search activity related to mpox in China is scarce. Objective: We aimed to provide evidence for an overview of online information searching during an infectious disease outbreak by analyzing trends in online search activity related to mpox at geographical and economic levels in China and explore influencing factors. Methods: We used the Baidu index to present online search activity related to mpox from May 19 to September 19, 2022. Segmented interrupted time-series analysis was used to estimate trends in online search activity. Factors influencing these trends were analyzed using a general linear regression (GLM) model. We calculated the concentration index to measure economic-related inequality in online search activity and related trends. Results: Online search activity was highest on the day the first imported case of mpox appeared in Chongqing compared to 3 other cutoff time points. After the day of the first imported mpox case in Taiwan, the declaration of a public health emergency of international concern, the first imported mpox case in Hong Kong, and the first imported mpox case in Chongqing, national online search activity increased by 0.642\%, 1.035\%, 1.199\%, and 2.023\%, respectively. The eastern regions had higher increases than the central and western regions. Across 31 provinces, municipalities, and autonomous regions, the top 3 areas with higher increases were Beijing, Shanghai, and Tianjin at 3 time points, with the exception of the day of the first imported mpox case in Chongqing (Chongqing replaced Tianjin on that day). When AIDS incidence increased by 1 per 100,000 people, there was an increase after the day of the first imported mpox case in Chongqing of 36.22\% (95\% CI 3.29\%-69.15\%; P=.04) after controlling for other covariates. Online search activity (concentration index=0.18; P<.001) was more concentrated among populations with a higher economic status. Unlike the central area, the eastern (concentration index=0.234; P<.001) and western areas (concentration index=0.047; P=.04) had significant economic-related disparities (P for difference <.001) in online search activity. The overall concentration index of changes in online search activity became lower over time. Conclusions: Regions with a higher economic level showed more interest in mpox, especially Beijing and Shanghai. After the day of the first imported mpox case in Chongqing, changes in online search activity were affected by AIDS incidence rate. Economic-related disparities in changes in online search activity became lower over time. It would be desirable to construct a reliable information source in regions with a higher economic level and higher AIDS incidence rate and promote public knowledge in regions with a lower economic level in China, especially after important public events. ", doi="10.2196/44031", url="https://formative.jmir.org/2023/1/e44031", url="http://www.ncbi.nlm.nih.gov/pubmed/37610816" } @Article{info:doi/10.2196/43685, author="Alvarez-Mon, Angel Miguel and Pereira-Sanchez, Victor and Hooker, R. Elizabeth and Sanchez, Facundo and Alvarez-Mon, Melchor and Teo, R. Alan", title="Content and User Engagement of Health-Related Behavior Tweets Posted by Mass Media Outlets From Spain and the United States Early in the COVID-19 Pandemic: Observational Infodemiology Study", journal="JMIR Infodemiology", year="2023", month="Aug", day="22", volume="3", pages="e43685", keywords="COVID-19", keywords="health communication", keywords="social media", keywords="Twitter", keywords="health promotion", keywords="public health", keywords="mass media", abstract="Background: During the early pandemic, there was substantial variation in public and government responses to COVID-19 in Europe and the United States. Mass media are a vital source of health information and news, frequently disseminating this information through social media, and may influence public and policy responses to the pandemic. Objective: This study aims to describe the extent to which major media outlets in the United States and Spain tweeted about health-related behaviors (HRBs) relevant to COVID-19, compare the tweeting patterns between media outlets of both countries, and determine user engagement in response to these tweets. Methods: We investigated tweets posted by 30 major media outlets (n=17, 57\% from Spain and n=13, 43\% from the United States) between December 1, 2019 and May 31, 2020, which included keywords related to HRBs relevant to COVID-19. We classified tweets into 6 categories: mask-wearing, physical distancing, handwashing, quarantine or confinement, disinfecting objects, or multiple HRBs (any combination of the prior HRB categories). Additionally, we assessed the likes and retweets generated by each tweet. Poisson regression analyses compared the average predicted number of likes and retweets between the different HRB categories and between countries. Results: Of 50,415 tweets initially collected, 8552 contained content associated with an HRB relevant to COVID-19. Of these, 600 were randomly chosen for training, and 2351 tweets were randomly selected for manual content analysis. Of the 2351 COVID-19--related tweets included in the content analysis, 62.91\% (1479/2351) mentioned at least one HRB. The proportion of COVID-19 tweets mentioning at least one HRB differed significantly between countries (P=.006). Quarantine or confinement was mentioned in nearly half of all the HRB tweets in both countries. In contrast, the least frequently mentioned HRBs were disinfecting objects in Spain 6.9\% (56/809) and handwashing in the United States 9.1\% (61/670). For tweets from the United States mentioning at least one HRB, disinfecting objects had the highest median likes and retweets, whereas mask-wearing-- and handwashing-related tweets achieved the highest median number of likes in Spain. Tweets from Spain that mentioned social distancing or disinfecting objects had a significantly lower predicted count of likes compared with tweets mentioning a different HRB (P=.02 and P=.01, respectively). Tweets from the United States that mentioned quarantine or confinement or disinfecting objects had a significantly lower predicted number of likes compared with tweets mentioning a different HRB (P<.001), whereas mask- and handwashing-related tweets had a significantly greater predicted number of likes (P=.04 and P=.02, respectively). Conclusions: The type of HRB content and engagement with media outlet tweets varied between Spain and the United States early in the pandemic. However, content related to quarantine or confinement and engagement with handwashing was relatively high in both countries. ", doi="10.2196/43685", url="https://infodemiology.jmir.org/2023/1/e43685", url="http://www.ncbi.nlm.nih.gov/pubmed/37347948" } @Article{info:doi/10.2196/47317, author="White, K. Becky and Gombert, Arnault and Nguyen, Tim and Yau, Brian and Ishizumi, Atsuyoshi and Kirchner, Laura and Le{\'o}n, Alicia and Wilson, Harry and Jaramillo-Gutierrez, Giovanna and Cerquides, Jesus and D'Agostino, Marcelo and Salvi, Cristiana and Sreenath, Shankar Ravi and Rambaud, Kimberly and Samhouri, Dalia and Briand, Sylvie and Purnat, D. Tina", title="Using Machine Learning Technology (Early Artificial Intelligence--Supported Response With Social Listening Platform) to Enhance Digital Social Understanding for the COVID-19 Infodemic: Development and Implementation Study", journal="JMIR Infodemiology", year="2023", month="Aug", day="21", volume="3", pages="e47317", keywords="infodemic", keywords="sentiment", keywords="narrative analysis", keywords="social listening", keywords="natural language processing", keywords="social media", keywords="public health", keywords="pandemic preparedness", keywords="pandemic response", keywords="artificial intelligence", keywords="AI text analytics", keywords="COVID-19", keywords="information voids", keywords="machine learning", abstract="Background: Amid the COVID-19 pandemic, there has been a need for rapid social understanding to inform infodemic management and response. Although social media analysis platforms have traditionally been designed for commercial brands for marketing and sales purposes, they have been underused and adapted for a comprehensive understanding of social dynamics in areas such as public health. Traditional systems have challenges for public health use, and new tools and innovative methods are required. The World Health Organization Early Artificial Intelligence--Supported Response with Social Listening (EARS) platform was developed to overcome some of these challenges. Objective: This paper describes the development of the EARS platform, including data sourcing, development, and validation of a machine learning categorization approach, as well as the results from the pilot study. Methods: Data for EARS are collected daily from web-based conversations in publicly available sources in 9 languages. Public health and social media experts developed a taxonomy to categorize COVID-19 narratives into 5 relevant main categories and 41 subcategories. We developed a semisupervised machine learning algorithm to categorize social media posts into categories and various filters. To validate the results obtained by the machine learning--based approach, we compared it to a search-filter approach, applying Boolean queries with the same amount of information and measured the recall and precision. Hotelling T2 was used to determine the effect of the classification method on the combined variables. Results: The EARS platform was developed, validated, and applied to characterize conversations regarding COVID-19 since December 2020. A total of 215,469,045 social posts were collected for processing from December 2020 to February 2022. The machine learning algorithm outperformed the Boolean search filters method for precision and recall in both English and Spanish languages (P<.001). Demographic and other filters provided useful insights on data, and the gender split of users in the platform was largely consistent with population-level data on social media use. Conclusions: The EARS platform was developed to address the changing needs of public health analysts during the COVID-19 pandemic. The application of public health taxonomy and artificial intelligence technology to a user-friendly social listening platform, accessible directly by analysts, is a significant step in better enabling understanding of global narratives. The platform was designed for scalability; iterations and new countries and languages have been added. This research has shown that a machine learning approach is more accurate than using only keywords and has the benefit of categorizing and understanding large amounts of digital social data during an infodemic. Further technical developments are needed and planned for continuous improvements, to meet the challenges in the generation of infodemic insights from social media for infodemic managers and public health professionals. ", doi="10.2196/47317", url="https://infodemiology.jmir.org/2023/1/e47317", url="http://www.ncbi.nlm.nih.gov/pubmed/37422854" } @Article{info:doi/10.2196/43703, author="Sigalo, Nekabari and Awasthi, Naman and Abrar, Mohammad Saad and Frias-Martinez, Vanessa", title="Using COVID-19 Vaccine Attitudes on Twitter to Improve Vaccine Uptake Forecast Models in the United States: Infodemiology Study of Tweets", journal="JMIR Infodemiology", year="2023", month="Aug", day="21", volume="3", pages="e43703", keywords="social media", keywords="Twitter", keywords="COVID-19", keywords="vaccine", keywords="surveys", keywords="SARS-CoV-2", keywords="vaccinations", keywords="hesitancy", keywords="vaccine hesitancy", keywords="forecast model", keywords="vaccine uptake", keywords="health promotion", keywords="infodemiology", keywords="health information", keywords="misinformation", abstract="Background: Since the onset of the COVID-19 pandemic, there has been a global effort to develop vaccines that protect against COVID-19. Individuals who are fully vaccinated are far less likely to contract and therefore transmit the virus to others. Researchers have found that the internet and social media both play a role in shaping personal choices about vaccinations. Objective: This study aims to determine whether supplementing COVID-19 vaccine uptake forecast models with the attitudes found in tweets improves over baseline models that only use historical vaccination data. Methods: Daily COVID-19 vaccination data at the county level was collected for the January 2021 to May 2021 study period. Twitter's streaming application programming interface was used to collect COVID-19 vaccine tweets during this same period. Several autoregressive integrated moving average models were executed to predict the vaccine uptake rate using only historical data (baseline autoregressive integrated moving average) and individual Twitter-derived features (autoregressive integrated moving average exogenous variable model). Results: In this study, we found that supplementing baseline forecast models with both historical vaccination data and COVID-19 vaccine attitudes found in tweets reduced root mean square error by as much as 83\%. Conclusions: Developing a predictive tool for vaccination uptake in the United States will empower public health researchers and decisionmakers to design targeted vaccination campaigns in hopes of achieving the vaccination threshold required for the United States to reach widespread population protection. ", doi="10.2196/43703", url="https://infodemiology.jmir.org/2023/1/e43703", url="http://www.ncbi.nlm.nih.gov/pubmed/37390402" } @Article{info:doi/10.2196/47530, author="Quijote, Llew Kirk and Casta{\~n}eda, Therese Arielle Marie and Guevara, Edgar Bryan and Tangtatco, Aileen Jennifer", title="A Descriptive Analysis of Dermatology Content and Creators on Social Media in the Philippines", journal="JMIR Dermatol", year="2023", month="Aug", day="21", volume="6", pages="e47530", keywords="social media", keywords="dermatology", keywords="dermatologist", keywords="creator", keywords="content", keywords="impact", keywords="Philippines", keywords="Facebook", keywords="Instagram", keywords="Twitter", keywords="TikTok", keywords="YouTube", doi="10.2196/47530", url="https://derma.jmir.org/2023/1/e47530", url="http://www.ncbi.nlm.nih.gov/pubmed/37603392" } @Article{info:doi/10.2196/45146, author="Shin, Hocheol and Yuniar, Tri Cindra and Oh, SuA and Purja, Sujata and Park, Sera and Lee, Haeun and Kim, Eunyoung", title="The Adverse Effects and Nonmedical Use of Methylphenidate Before and After the Outbreak of COVID-19: Machine Learning Analysis", journal="J Med Internet Res", year="2023", month="Aug", day="16", volume="25", pages="e45146", keywords="methylphenidate", keywords="attention-deficit/hyperactivity disorder (ADHD)", keywords="social network services", keywords="adverse effect", keywords="nonmedical use", keywords="machine learning", keywords="deep learning", keywords="child", keywords="adolescent", keywords="psychiatric disorder", abstract="Background: Methylphenidate is an effective first-line treatment for attention-deficit/hyperactivity disorder (ADHD). However, many adverse effects of methylphenidate have been recorded from randomized clinical trials and patient-reported outcomes, but it is difficult to determine abuse from them. In the context of COVID-19, it is important to determine how drug use evaluation, as well as misuse of drugs, have been affected by the pandemic. As people share their reasons for using medication, patient sentiments, and the effects of medicine on social networking services (SNSs), the application of machine learning and SNS data can be a method to overcome the limitations. Proper machine learning models could be evaluated to validate the effects of the COVID-19 pandemic on drug use. Objective: To analyze the effect of the COVID-19 pandemic on the use of methylphenidate, this study analyzed the adverse effects and nonmedical use of methylphenidate and evaluated the change in frequency of nonmedical use based on SNS data before and after the outbreak of COVID-19. Moreover, the performance of 4 machine learning models for classifying methylphenidate use based on SNS data was compared. Methods: In this cross-sectional study, SNS data on methylphenidate from Twitter, Facebook, and Instagram from January 2019 to December 2020 were collected. The frequency of adverse effects, nonmedical use, and drug use before and after the COVID-19 pandemic were compared and analyzed. Interrupted time series analysis about the frequency and trends of nonmedical use of methylphenidate was conducted for 24 months from January 2019 to December 2020. Using the labeled training data set and features, the following 4 machine learning models were built using the data, and their performance was evaluated using F-1 scores: na{\"i}ve Bayes classifier, random forest, support vector machine, and long short-term memory. Results: This study collected 146,352 data points and detected that 4.3\% (6340/146,352) were firsthand experience data. Psychiatric problems (521/1683, 31\%) had the highest frequency among the adverse effects. The highest frequency of nonmedical use was for studies or work (741/2016, 36.8\%). While the frequency of nonmedical use before and after the outbreak of COVID-19 has been similar (odds ratio [OR] 1.02 95\% CI 0.91-1.15), its trend has changed significantly due to the pandemic (95\% CI 2.36-22.20). Among the machine learning models, RF had the highest performance of 0.75. Conclusions: The trend of nonmedical use of methylphenidate has changed significantly due to the COVID-19 pandemic. Among the machine learning models using SNS data to analyze the adverse effects and nonmedical use of methylphenidate, the random forest model had the highest performance. ", doi="10.2196/45146", url="https://www.jmir.org/2023/1/e45146", url="http://www.ncbi.nlm.nih.gov/pubmed/37585250" } @Article{info:doi/10.2196/43011, author="Dasgupta, Pritam and Amin, Janaki and Paris, Cecile and MacIntyre, Raina C.", title="News Coverage of Face Masks in Australia During the Early COVID-19 Pandemic: Topic Modeling Study", journal="JMIR Infodemiology", year="2023", month="Aug", day="16", volume="3", pages="e43011", keywords="face masks", keywords="mask", keywords="COVID-19", keywords="web-based news", keywords="community sentiment", keywords="topic modeling", keywords="latent Dirichlet allocation", abstract="Background: During the COVID-19 pandemic, web-based media coverage of preventative strategies proliferated substantially. News media was constantly informing people about changes in public health policy and practices such as mask-wearing. Hence, exploring news media content on face mask use is useful to analyze dominant topics and their trends. Objective: The aim of the study was to examine news related to face masks as well as to identify related topics and temporal trends in Australian web-based news media during the early COVID-19 pandemic period. Methods: Following data collection from the Google News platform, a trend analysis on the mask-related news titles from Australian news publishers was conducted. Then, a latent Dirichlet allocation topic modeling algorithm was applied along with evaluation matrices (quantitative and qualitative measures). Afterward, topic trends were developed and analyzed in the context of mask use during the pandemic. Results: A total of 2345 face mask--related eligible news titles were collected from January 25, 2020, to January 25, 2021. Mask-related news showed an increasing trend corresponding to increasing COVID-19 cases in Australia. The best-fitted latent Dirichlet allocation model discovered 8 different topics with a coherence score of 0.66 and a perplexity measure of --11.29. The major topics were T1 (mask-related international affairs), T2 (introducing mask mandate in places such as Melbourne and Sydney), and T4 (antimask sentiment). Topic trends revealed that T2 was the most frequent topic in January 2021 (77 news titles), corresponding to the mandatory mask-wearing policy in Sydney. Conclusions: This study demonstrated that Australian news media reflected a wide range of community concerns about face masks, peaking as COVID-19 incidence increased. Harnessing the news media platforms for understanding the media agenda and community concerns may assist in effective health communication during a pandemic response. ", doi="10.2196/43011", url="https://infodemiology.jmir.org/2023/1/e43011", url="http://www.ncbi.nlm.nih.gov/pubmed/37379362" } @Article{info:doi/10.2196/45381, author="Goel, Rahul and Modhukur, Vijayachitra and T{\"a}{\"a}r, Katrin and Salumets, Andres and Sharma, Rajesh and Peters, Maire", title="Users' Concerns About Endometriosis on Social Media: Sentiment Analysis and Topic Modeling Study", journal="J Med Internet Res", year="2023", month="Aug", day="15", volume="25", pages="e45381", keywords="endometriosis", keywords="latent Dirichlet allocation", keywords="pain", keywords="Reddit", keywords="sentiment analysis", keywords="social media", keywords="surgery", keywords="topic modeling", keywords="user engagement", abstract="Background: Endometriosis is a debilitating and difficult-to-diagnose gynecological disease. Owing to limited information and awareness, women often rely on social media platforms as a support system to engage in discussions regarding their disease-related concerns. Objective: This study aimed to apply computational techniques to social media posts to identify discussion topics about endometriosis and to identify themes that require more attention from health care professionals and researchers. We also aimed to explore whether, amid the challenging nature of the disease, there are themes within the endometriosis community that gather posts with positive sentiments. Methods: We retrospectively extracted posts from the subreddits r/Endo and r/endometriosis from January 2011 to April 2022. We analyzed 45,693 Reddit posts using sentiment analysis and topic modeling--based methods in machine learning. Results: Since 2011, the number of posts and comments has increased steadily. The posts were categorized into 11 categories, and the highest number of posts were related to either asking for information (Question); sharing the experiences (Rant/Vent); or diagnosing and treating endometriosis, especially surgery (Surgery related). Sentiment analysis revealed that 92.09\% (42,077/45,693) of posts were associated with negative sentiments, only 2.3\% (1053/45,693) expressed positive feelings, and there were no categories with more positive than negative posts. Topic modeling revealed 27 major topics, and the most popular topics were Surgery, Questions/Advice, Diagnosis, and Pain. The Survey/Research topic, which brought together most research-related posts, was the last in terms of posts. Conclusions: Our study shows that posts on social media platforms can provide insights into the concerns of women with endometriosis symptoms. The analysis of the posts confirmed that women with endometriosis have to face negative emotions and pain daily. The large number of posts related to asking questions shows that women do not receive sufficient information from physicians and need community support to cope with the disease. Health care professionals should pay more attention to the symptoms and diagnosis of endometriosis, discuss these topics with patients to reduce their dissatisfaction with doctors, and contribute more to the overall well-being of women with endometriosis. Researchers should also become more involved in social media and share new science-based knowledge regarding endometriosis. ", doi="10.2196/45381", url="https://www.jmir.org/2023/1/e45381", url="http://www.ncbi.nlm.nih.gov/pubmed/37581905" } @Article{info:doi/10.2196/40003, author="Long, Memphis and Forbes, E. Laura and Papagerakis, Petros and Lieffers, L. Jessica R.", title="YouTube Videos on Nutrition and Dental Caries: Content Analysis", journal="JMIR Infodemiology", year="2023", month="Aug", day="10", volume="3", pages="e40003", keywords="dental caries", keywords="diet", keywords="nutrition", keywords="YouTube", keywords="internet", keywords="consumer health information", abstract="Background: Dental caries is the most common health condition worldwide, and nutrition and dental caries have a strong interconnected relationship. Foods and eating behaviors can be both harmful (eg, sugar) and healthful (eg, meal spacing) for dental caries. YouTube is a popular source for the public to access information. To date, there is no information available on the nutrition and dental caries content of easily accessible YouTube videos. Objective: This study aimed to analyze the content of YouTube videos on nutrition and dental caries. Methods: In total, 6 YouTube searches were conducted using keywords related to nutrition and dental caries. The first 20 videos were selected from each search. Video content was scored (17 possible points; higher scores were associated with more topics covered) by 2 individuals based on the inclusion of information regarding various foods and eating behaviors that impact dental caries risk. For each video, information on video characteristics (ie, view count, length, number of likes, number of dislikes, and video age) was captured. Videos were divided into 2 groups by view rate (views/day); differences in scores and types of nutrition messages between groups were determined using nonparametric statistics. Results: In total, 42 videos were included. Most videos were posted by or featured oral health professionals (24/42, 57\%). The mean score was 4.9 (SD 3.4) out of 17 points. Videos with >30 views/day (high view rate; 20/42, 48\% videos) had a trend toward a lower score (mean 4.0, SD 3.7) than videos with ?30 views/day (low view rate; 22/42, 52\%; mean 5.8, SD 3.0; P=.06), but this result was not statistically significant. Sugar was the most consistently mentioned topic in the videos (31/42, 74\%). No other topics were mentioned in more than 50\% of videos. Low--view rate videos were more likely to mention messaging on acidic foods and beverages (P=.04), water (P=.09), and frequency of sugar intake (P=.047) than high--view rate videos. Conclusions: Overall, the analyzed videos had low scores for nutritional and dental caries content. This study provides insights into the messaging available on nutrition and dental caries for the public and guidance on how to make improvements in this area. ", doi="10.2196/40003", url="https://infodemiology.jmir.org/2023/1/e40003", url="http://www.ncbi.nlm.nih.gov/pubmed/37561564" } @Article{info:doi/10.2196/45731, author="El Mikati, K. Ibrahim and Hoteit, Reem and Harb, Tarek and El Zein, Ola and Piggott, Thomas and Melki, Jad and Mustafa, A. Reem and Akl, A. Elie", title="Defining Misinformation and Related Terms in Health-Related Literature: Scoping Review", journal="J Med Internet Res", year="2023", month="Aug", day="9", volume="25", pages="e45731", keywords="misinformation", keywords="disinformation", keywords="infodemic", keywords="fake news", keywords="malinformation", keywords="health", keywords="COVID-19", keywords="scoping review", keywords="health-related literature", keywords="electronic database", keywords="misleading", keywords="related term", keywords="systematic review", abstract="Background: Misinformation poses a serious challenge to clinical and policy decision-making in the health field. The COVID-19 pandemic amplified interest in misinformation and related terms and witnessed a proliferation of definitions. Objective: We aim to assess the definitions of misinformation and related terms used in health-related literature. Methods: We conducted a scoping review of systematic reviews by searching Ovid MEDLINE, Embase, Cochrane, and Epistemonikos databases for articles published within the last 5 years up till March 2023. Eligible studies were systematic reviews that stated misinformation or related terms as part of their objectives, conducted a systematic search of at least one database, and reported at least 1 definition for misinformation or related terms. We extracted definitions for the terms misinformation, disinformation, fake news, infodemic, and malinformation. Within each definition, we identified concepts and mapped them across misinformation-related terms. Results: We included 41 eligible systematic reviews, out of which 32 (78\%) reviews addressed the topic of public health emergencies (including the COVID-19 pandemic) and contained 75 definitions for misinformation and related terms. The definitions consisted of 20 for misinformation, 19 for disinformation, 10 for fake news, 24 for infodemic, and 2 for malinformation. ``False/inaccurate/incorrect'' was mentioned in 15 of 20 definitions of misinformation, 13 of 19 definitions of disinformation, 5 of 10 definitions of fake news, 6 of 24 definitions of infodemic, and 0 of 2 definitions of malinformation. Infodemic had 19 of 24 definitions addressing ``information overload'' and malinformation had 2 of 2 definitions with ``accurate'' and 1 definition ``used in the wrong context.'' Out of all the definitions, 56 (75\%) were referenced from other sources. Conclusions: While the definitions of misinformation and related terms in the health field had inconstancies and variability, they were largely consistent. Inconstancies related to the intentionality in misinformation definitions (7 definitions mention ``unintentional,'' while 5 definitions have ``intentional''). They also related to the content of infodemic (9 definitions mention ``valid and invalid info,'' while 6 definitions have ``false/inaccurate/incorrect''). The inclusion of concepts such as ``intentional'' may be difficult to operationalize as it is difficult to ascertain one's intentions. This scoping review has the strength of using a systematic method for retrieving articles but does not cover all definitions in the extant literature outside the field of health. This scoping review of the health literature identified several definitions for misinformation and related terms, which showed variability and included concepts that are difficult to operationalize. Health practitioners need to exert caution before labeling a piece of information as misinformation or any other related term and only do so after ascertaining accurateness and sometimes intentionality. Additional efforts are needed to allow future consensus around clear and operational definitions. ", doi="10.2196/45731", url="https://www.jmir.org/2023/1/e45731", url="http://www.ncbi.nlm.nih.gov/pubmed/37556184" } @Article{info:doi/10.2196/48140, author="Thang, J. Christopher and Garate, David and Thang, Joseph and Lipoff, B. Jules and Barbieri, S. John", title="Short-Form Medical Media: A Multi-Platform Analysis of Acne Treatment Information in TikTok Videos, Instagram Reels, and YouTube Shorts", journal="JMIR Dermatol", year="2023", month="Aug", day="9", volume="6", pages="e48140", keywords="general dermatology", keywords="medical dermatology", keywords="acne", keywords="acne treatment", keywords="social media", keywords="TikTok", keywords="Instagram Reels", keywords="YouTube Shorts", keywords="YouTube", keywords="Instagram", keywords="video", keywords="dermatology", keywords="skin", keywords="patient education", keywords="health information", keywords="online information", keywords="dermatologist", doi="10.2196/48140", url="https://derma.jmir.org/2023/1/e48140", url="http://www.ncbi.nlm.nih.gov/pubmed/37624704" } @Article{info:doi/10.2196/45069, author="Zaidi, Zainab and Ye, Mengbin and Samon, Fergus and Jama, Abdisalan and Gopalakrishnan, Binduja and Gu, Chenhao and Karunasekera, Shanika and Evans, Jamie and Kashima, Yoshihisa", title="Topics in Antivax and Provax Discourse: Yearlong Synoptic Study of COVID-19 Vaccine Tweets", journal="J Med Internet Res", year="2023", month="Aug", day="8", volume="25", pages="e45069", keywords="COVID-19 vaccine", keywords="vaccine hesitancy", keywords="antivax", keywords="stance detection", keywords="topic modeling", keywords="misinformation", keywords="disinformation", abstract="Background: Developing an understanding of the public discourse on COVID-19 vaccination on social media is important not only for addressing the ongoing COVID-19 pandemic but also for future pathogen outbreaks. There are various research efforts in this domain, although, a need still exists for a comprehensive topic-wise analysis of tweets in favor of and against COVID-19 vaccines. Objective: This study characterizes the discussion points in favor of and against COVID-19 vaccines posted on Twitter during the first year of the pandemic. The aim of this study was primarily to contrast the views expressed by both camps, their respective activity patterns, and their correlation with vaccine-related events. A further aim was to gauge the genuineness of the concerns expressed in antivax tweets. Methods: We examined a Twitter data set containing 75 million English tweets discussing the COVID-19 vaccination from March 2020 to March 2021. We trained a stance detection algorithm using natural language processing techniques to classify tweets as antivax or provax and examined the main topics of discourse using topic modeling techniques. Results: Provax tweets (37 million) far outnumbered antivax tweets (10 million) and focused mostly on vaccine development, whereas antivax tweets covered a wide range of topics, including opposition to vaccine mandate and concerns about safety. Although some antivax tweets included genuine concerns, there was a large amount of falsehood. Both stances discussed many of the same topics from opposite viewpoints. Memes and jokes were among the most retweeted messages. Most tweets from both stances (9,007,481/10,566,679, 85.24\% antivax and 24,463,708/37,044,507, 66.03\% provax tweets) came from dual-stance users who posted both provax and antivax tweets during the observation period. Conclusions: This study is a comprehensive account of COVID-19 vaccine discourse in the English language on Twitter from March 2020 to March 2021. The broad range of discussion points covered almost the entire conversation, and their temporal dynamics revealed a significant correlation with COVID-19 vaccine--related events. We did not find any evidence of polarization and prevalence of antivax discourse over Twitter. However, targeted countering of falsehoods is important because only a small fraction of antivax discourse touched on a genuine issue. Future research should examine the role of memes and humor in driving web-based social media activity. ", doi="10.2196/45069", url="https://www.jmir.org/2023/1/e45069", url="http://www.ncbi.nlm.nih.gov/pubmed/37552535" } @Article{info:doi/10.2196/44774, author="Wang, Yijun and Chukwusa, Emeka and Koffman, Jonathan and Curcin, Vasa", title="Public Opinions About Palliative and End-of-Life Care During the COVID-19 Pandemic: Twitter-Based Content Analysis", journal="JMIR Form Res", year="2023", month="Aug", day="7", volume="7", pages="e44774", keywords="palliative care", keywords="end-of-life care", keywords="COVID-19", keywords="Twitter", keywords="public opinions", abstract="Background: Palliative and end-of-life care (PEoLC) played a critical role in relieving distress and providing grief support in response to the heavy toll caused by the COVID-19 pandemic. However, little is known about public opinions concerning PEoLC during the pandemic. Given that social media have the potential to collect real-time public opinions, an analysis of this evidence is vital to guide future policy-making. Objective: This study aimed to use social media data to investigate real-time public opinions regarding PEoLC during the COVID-19 crisis and explore the impact of vaccination programs on public opinions about PEoLC. Methods: This Twitter-based study explored tweets across 3 English-speaking countries: the United States, the United Kingdom, and Canada. From October 2020 to March 2021, a total of 7951 PEoLC-related tweets with geographic tags were retrieved and identified from a large-scale COVID-19 Twitter data set through the Twitter application programming interface. Topic modeling realized through a pointwise mutual information--based co-occurrence network and Louvain modularity was used to examine latent topics across the 3 countries and across 2 time periods (pre- and postvaccination program periods). Results: Commonalities and regional differences among PEoLC topics in the United States, the United Kingdom, and Canada were identified specifically: cancer care and care facilities were of common interest to the public across the 3 countries during the pandemic; the public expressed positive attitudes toward the COVID-19 vaccine and highlighted the protection it affords to PEoLC professionals; and although Twitter users shared their personal experiences about PEoLC in the web-based community during the pandemic, this was more prominent in the United States and Canada. The implementation of the vaccination programs raised the profile of the vaccine discussion; however, this did not influence public opinions about PEoLC. Conclusions: Public opinions on Twitter reflected a need for enhanced PEoLC services during the COVID-19 pandemic. The insignificant impact of the vaccination program on public discussion on social media indicated that public concerns regarding PEoLC continued to persist even after the vaccination efforts. Insights gleaned from public opinions regarding PEoLC could provide some clues for policy makers on how to ensure high-quality PEoLC during public health emergencies. In this post--COVID-19 era, PEoLC professionals may wish to continue to examine social media and learn from web-based public discussion how to ease the long-lasting trauma caused by this crisis and prepare for public health emergencies in the future. Besides, our results showed social media's potential in acting as an effective tool to reflect public opinions in the context of PEoLC. ", doi="10.2196/44774", url="https://formative.jmir.org/2023/1/e44774", url="http://www.ncbi.nlm.nih.gov/pubmed/37368840" } @Article{info:doi/10.2196/47582, author="Kami?ski, Miko?aj and Czarny, Jakub and Skrzypczak, Piotr and Sienicki, Krzysztof and Roszak, Magdalena", title="The Characteristics, Uses, and Biases of Studies Related to Malignancies Using Google Trends: Systematic Review", journal="J Med Internet Res", year="2023", month="Aug", day="4", volume="25", pages="e47582", keywords="Google Trends", keywords="oncology", keywords="malignancies", keywords="prophylaxis", keywords="celebrity", keywords="infodemiology", keywords="infoveillance", keywords="cancer", keywords="carcinoma", keywords="lymphoma", keywords="leukemia", keywords="multiple myeloma", keywords="sarcoma", keywords="internet", keywords="tumor", keywords="bias", keywords="quality", abstract="Background: The internet is a primary source of health information for patients, supplementing physician care. Google Trends (GT), a popular tool, allows the exploration of public interest in health-related phenomena. Despite the growing volume of GT studies, none have focused explicitly on oncology, creating a need for a systematic review to bridge this gap. Objective: We aimed to systematically characterize studies related to oncology using GT to describe its utilities and biases. Methods: We included all studies that used GT to analyze Google searches related to malignancies. We excluded studies written in languages other than English. The search was performed using the PubMed engine on August 1, 2022. We used the following search input: ``Google trends'' AND (``oncology'' OR ``cancer'' or ``malignancy'' OR ``tumor'' OR ``lymphoma'' OR ``multiple myeloma'' OR ``leukemia''). We analyzed sources of bias that included using search terms instead of topics, lack of confrontation of GT statistics with real-world data, and absence of sensitivity analysis. We performed descriptive statistics. Results: A total of 85 articles were included. The first study using GT for oncology research was published in 2013, and since then, the number of publications has increased annually. The studies were categorized as follows: 22\% (19/85) were related to prophylaxis, 20\% (17/85) pertained to awareness events, 11\% (9/85) were celebrity-related, 13\% (11/85) were related to COVID-19, and 47\% (40/85) fell into other categories. The most frequently analyzed cancers were breast (n=28), prostate (n=26), lung (n=18), and colorectal cancers (n=18). We discovered that of the 85 studies, 17 (20\%) acknowledged using GT topics instead of search terms, 79 (93\%) disclosed all search input details necessary for replicating their results, and 34 (40\%) compared GT statistics with real-world data. The most prevalent methods for analyzing the GT data were correlation analysis (55/85, 65\%) and peak analysis (43/85, 51\%). The authors of only 11\% (9/85) of the studies performed a sensitivity analysis. Conclusions: The number of studies related to oncology using GT data has increased annually. The studies included in this systematic review demonstrate a variety of concerning topics, search strategies, and statistical methodologies. The most frequently analyzed cancers were breast, prostate, lung, colorectal, skin, and cervical cancers, potentially reflecting their prevalence in the population or public interest. Although most researchers provided reproducible search inputs, only one-fifth used GT topics instead of search terms, and many studies lacked a sensitivity analysis. Scientists using GT for medical research should ensure the quality of studies by providing a transparent search strategy to reproduce results, preferring to use topics over search terms, and performing robust statistical calculations coupled with sensitivity analysis. ", doi="10.2196/47582", url="https://www.jmir.org/2023/1/e47582", url="http://www.ncbi.nlm.nih.gov/pubmed/37540544" } @Article{info:doi/10.2196/43020, author="Furth, Garrett and Marroquin, A. Nathaniel and Kirk, Jessica and Ajmal, Hamza and Szeto, D. Mindy and Zueger, Morgan and Quinn, P. Alyssa and Carboni, Alexa and Dellavalle, P. Robert", title="Cutaneous Manifestations of Anabolic-Androgenic Steroid Use in Bodybuilders and the Dermatologist's Role in Patient Care", journal="JMIR Dermatol", year="2023", month="Aug", day="3", volume="6", pages="e43020", keywords="anabolic steroids", keywords="androgenic steroids", keywords="anabolic-androgenic steroids", keywords="acne", keywords="acne fulminans", keywords="isotretinoin", keywords="bodybuilding", keywords="bodybuilder", keywords="social media", keywords="skin", keywords="dermatology", keywords="dermatologist", keywords="athlete", keywords="sport", keywords="steroid", keywords="cutaneous", doi="10.2196/43020", url="https://derma.jmir.org/2023/1/e43020", url="http://www.ncbi.nlm.nih.gov/pubmed/37632935" } @Article{info:doi/10.2196/32592, author="van Gastel, Dani{\"e}lle and Antheunis, L. Marjolijn and Tenfelde, Kim and van de Graaf, L. Dani{\"e}lle and Geerts, Marieke and Nieboer, E. Theodoor and Bongers, Y. Marlies", title="Social Support Among Women With Potential Essure-Related Complaints: Analysis of Facebook Group Content", journal="JMIR Form Res", year="2023", month="Aug", day="3", volume="7", pages="e32592", keywords="Essure", keywords="social support", keywords="Facebook", keywords="sterilization", keywords="patient online communities", keywords="social media", keywords="social networks", abstract="Background: Social support groups are an important resource for people to cope with problems. Previous studies have reported the different types of support in these groups, but little is known about the type of reactions that sharing of personal experiences induce among members. It is important to know how and to what extent members of support groups influence each other regarding the consumption of medical care. We researched this in a web-based Facebook group of women sterilized with Essure. Essure was a device intended for permanent contraception. From 2015 onward, women treated with Essure for tubal occlusion raised safety concerns and numerous complaints. Objective: This study aimed to evaluate the use of social support in a Facebook community named ``Essure problemen Nederland'' (EPN; in English, ``Essure problems in the Netherlands''). Methods: All posts in the closed Facebook group EPN between March 8 and May 8, 2018, were included. In total, 3491 Facebook posts were analyzed using a modified version of the Social Support Behavior Codes framework created by Cutrona and Suhr in 1992. Posts were abstracted and aggregated into a database. Two investigators evaluated the posts, developed a modified version of the Social Support Behavior Codes framework, and applied the codes to the collected data. Results: We found that 92\% of messages contained a form of social support. In 68.8\% of posts, social support was provided, and in 31.2\% of posts, social support was received. Informational and emotional support was the most frequently used form of provided social support (40.6\% and 55.5\%, respectively). The same distribution was seen with received social support: informational support in 81.5\% and emotional support in 17.4\% of cases. Our analysis showed a strong correlation between providing or receiving social support and the main form of social support (P<.001). In a total of only 74 (2.2\%) cases, women advised each other to seek medical care. Conclusions: The main purpose of women in the EPN Facebook group was to provide and receive informational or emotional support or both. ", doi="10.2196/32592", url="https://formative.jmir.org/2023/1/e32592", url="http://www.ncbi.nlm.nih.gov/pubmed/37535412" } @Article{info:doi/10.2196/48786, author="Meksawasdichai, Sununtha and Lerksuthirat, Tassanee and Ongphiphadhanakul, Boonsong and Sriphrapradang, Chutintorn", title="Perspectives and Experiences of Patients With Thyroid Cancer at a Global Level: Retrospective Descriptive Study of Twitter Data", journal="JMIR Cancer", year="2023", month="Aug", day="2", volume="9", pages="e48786", keywords="data mining", keywords="internet", keywords="natural language processing", keywords="sentiment analysis", keywords="social media", keywords="thyroid neoplasms", keywords="twitter", keywords="tweet", keywords="tweets", keywords="neoplasm", keywords="neoplasms", keywords="cancer", keywords="oncology", keywords="thyroid", keywords="NLP", keywords="perspective", keywords="perspectives", keywords="sentiment", keywords="sentiments", keywords="experience", keywords="experiences", abstract="Background: Twitter has become a popular platform for individuals to broadcast their daily experiences and opinions on a wide range of topics and emotions. Tweets from patients with cancer could offer insights into their needs. However, limited research has been conducted using Twitter data to understand the needs of patients with cancer despite the substantial amount of health-related data posted on the platform daily. Objective: This study aimed to uncover the potential of using Twitter data to understand the perspectives and experiences of patients with thyroid cancer at a global level. Methods: ?This retrospective descriptive study collected tweets relevant to thyroid cancer in 2020 using the Twitter scraping tool. Only English-language tweets were included, and data preprocessing was performed to remove irrelevant tweets, duplicates, and retweets. Both tweets and Twitter users were manually classified into various groups based on the content. Each tweet underwent sentiment analysis and was classified as either positive, neutral, or negative. Results: A total of 13,135 tweets related to thyroid cancer were analyzed. The authors of the tweets included patients with thyroid cancer (3225 tweets, 24.6\%), patient's families and friends (2449 tweets, 18.6\%), medical journals and media (1733 tweets, 13.2\%), health care professionals (1093 tweets, 8.3\%), and medical health organizations (940 tweets, 7.2\%), respectively. The most discussed topics related to living with cancer (3650 tweets, 27.8\%), treatment (2891 tweets, 22\%), diagnosis (1613 tweets, 12.3\%), risk factors and prevention (1137 tweets, 8.7\%), and research (953 tweets, 7.3\%). An average of 36 tweets pertaining to thyroid cancer were posted daily. Notably, the release of a film addressing thyroid cancer and the public disclosure of a news reporter's personal diagnosis of thyroid cancer resulted in a significant escalation in the volume of tweets. From the sentiment analysis, 53.5\% (7025/13,135) of tweets were classified as neutral statements and 32.7\% (4299/13,135) of tweets expressed negative emotions. Tweets from patients with thyroid cancer had the highest proportion of negative emotion (1385/3225 tweets, 42.9\%), particularly when discussing symptoms. Conclusions: ?This study provides new insights on using?Twitter data as a valuable data source to understand the experiences of patients with thyroid cancer. Twitter may provide an opportunity to improve patient and physician engagement or apply as a potential research data source. ", doi="10.2196/48786", url="https://cancer.jmir.org/2023/1/e48786", url="http://www.ncbi.nlm.nih.gov/pubmed/37531163" } @Article{info:doi/10.2196/47068, author="Golder, Su and O'Connor, Karen and Wang, Yunwen and Gonzalez Hernandez, Graciela", title="The Role of Social Media for Identifying Adverse Drug Events Data in Pharmacovigilance: Protocol for a Scoping Review", journal="JMIR Res Protoc", year="2023", month="Aug", day="2", volume="12", pages="e47068", keywords="adverse event", keywords="pharmacovigilance", keywords="social media", keywords="real-world data", keywords="scoping review", keywords="protocol", keywords="review method", keywords="pharmacology", keywords="pharmaceutics", keywords="pharmacy", keywords="adverse drug event", keywords="adverse drug reaction", abstract="Background: Adverse drug events (ADEs) are a considerable public health burden resulting in disability, hospitalization, and death. Even those ADEs deemed nonserious can severely impact a patient's quality of life and adherence to intervention. Monitoring medication safety, however, is challenging. Social media may be a useful adjunct for obtaining real-world data on ADEs. While many studies have been undertaken to detect adverse events on social media, a consensus has not yet been reached as to the value of social media in pharmacovigilance or its role in pharmacovigilance in relation to more traditional data sources. Objective: The aim of the study is to evaluate and characterize the use of social media in ADE detection and pharmacovigilance as compared to other data sources. Methods: A scoping review will be undertaken. We will search 11 bibliographical databases as well as Google Scholar, hand-searching, and forward and backward citation searching. Records will be screened in Covidence by 2 independent reviewers at both title and abstract stage as well as full text. Studies will be included if they used any type of social media (such as Twitter or patient forums) to detect any type of adverse event associated with any type of medication and then compared the results from social media to any other data source (such as spontaneous reporting systems or clinical literature). Data will be extracted using a data extraction sheet piloted by the authors. Important data on the types of methods used (such as machine learning), any limitations of the methods used, types of adverse events and drugs searched for and included, availability of data and code, details of the comparison data source, and the results and conclusions will be extracted. Results: We will present descriptive summary statistics as well as identify any patterns in the types and timing of ADEs detected, including but not limited to the similarities and differences in what is reported, gaps in the evidence, and the methods used to extract ADEs from social media data. We will also summarize how the data from social media compares to conventional data sources. The literature will be organized by the data source for comparison. Where possible, we will analyze the impact of the types of adverse events, the social media platform used, and the methods used. Conclusions: This scoping review will provide a valuable summary of a large body of research and important information for pharmacovigilance as well as suggest future directions of further research in this area. Through the comparisons with other data sources, we will be able to conclude the added value of social media in monitoring adverse events of medications, in terms of type of adverse events and timing. International Registered Report Identifier (IRRID): PRR1-10.2196/47068 ", doi="10.2196/47068", url="https://www.researchprotocols.org/2023/1/e47068", url="http://www.ncbi.nlm.nih.gov/pubmed/37531158" } @Article{info:doi/10.2196/43720, author="Arshanapally, Suraj and Starr, Tiearra and Elsberry, Blackmun Lauren and Rinker, Robin", title="The Use of Travel as an Appeal to Motivate Millennial Parents on Facebook to Get Vaccinated Against COVID-19: Message Framing Evaluation", journal="JMIR Form Res", year="2023", month="Aug", day="1", volume="7", pages="e43720", keywords="COVID-19", keywords="coronavirus", keywords="vaccination", keywords="travel", keywords="parents", keywords="millennial", keywords="appeal", keywords="health communication", keywords="social media", keywords="Facebook", keywords="infectious disease", keywords="message testing", keywords="public health", keywords="messaging", keywords="parenting", keywords="program", abstract="Background: In summer 2021, the Centers for Disease Control and Prevention recommended that people get fully vaccinated against COVID-19 before fall travel to protect themselves and others from getting and spreading COVID-19 and new variants. Only 61\% of parents had reported receiving at least 1 dose of the COVID-19 vaccine, according to a Kaiser Family Foundation study. Millennial parents, ages 25 to 40 years, were a particularly important parent population because they were likely to have children aged 12 years or younger (the age cutoff for COVID-19 vaccine eligibility during this time period) and were still planning to travel. Since Facebook has been identified as a popular platform for millennials and parents, the Centers for Disease Control and Prevention's Travelers' Health Branch determined an evaluation of public health messages was needed to identify which message appeals would resonate best with this population on Facebook. Objective: The objective was to evaluate which travel-based public health message appeals aimed at addressing parental concerns and sentiments about COVID-19 vaccination would resonate most with Millennial parents (25 to 40 years old) using Facebook Ads Manager and social media metrics. Methods: Six travel-based public health message appeals on parental concerns and sentiments around COVID-19 were developed and disseminated to millennial parents using Facebook Ads Manager. The messages ran from October 23, 2021, to November 8, 2021. Primary outcomes included the number of people reached and the number of impressions delivered. Secondary outcomes included engagements, clicks, click-through rate, and audience sentiments. A thematic analysis was conducted to analyze comments. The advertisement budget was evaluated by cost-per-mille and cost-per-click metrics. Results: All messages reached a total of 6,619,882 people and garnered 7,748,375 impressions. The Family (n=3,572,140 people reached, 53.96\%; 4,515,836 impressions, 58.28\%) and Return to normalcy (n=1,639,476 people reached, 24.77\%; 1,754,227 impressions, 22.64\%) message appeals reached the greatest number of people and garnered the most impressions out of all 6 message appeals. The Family message appeal received 3255 engagements (60.46\%), and the Return to normalcy message appeal received 1148 engagements (21.28\%). The Family appeal also received the highest number of positive post reactions (n=82, 28.37\%). Most of the comments portrayed negative opinions about COVID-19 vaccination (n=46, 68.66\%). All 6 message appeals were either on par with or outperformed cost-per-mille benchmarks set by other similar public health campaigns. Conclusions: Health communicators can use travel, specifically the Family and Return to normalcy message appeals, to successfully reach parents in their future COVID-19 vaccination campaigns and potentially inform health communication messaging efforts for other vaccine-preventable infectious disease campaigns. Public health programs can also utilize the lessons learned from this evaluation to communicate important COVID-19 information to their parent populations through travel messaging. ", doi="10.2196/43720", url="https://formative.jmir.org/2023/1/e43720", url="http://www.ncbi.nlm.nih.gov/pubmed/37437085" } @Article{info:doi/10.2196/48405, author="Parker, A. Maria and Valdez, Danny and Rao, K. Varun and Eddens, S. Katherine and Agley, Jon", title="Results and Methodological Implications of the Digital Epidemiology of Prescription Drug References Among Twitter Users: Latent Dirichlet Allocation (LDA) Analyses", journal="J Med Internet Res", year="2023", month="Jul", day="28", volume="25", pages="e48405", keywords="Twitter", keywords="LDA", keywords="drug use", keywords="digital epidemiology", keywords="unsupervised analysis", keywords="tweet", keywords="tweets", keywords="social media", keywords="epidemiology", keywords="epidemiological", keywords="machine learning", keywords="text mining", keywords="data mining", keywords="pharmacy", keywords="pharmaceutic", keywords="pharmaceutical", keywords="pharmaceuticals", keywords="drug", keywords="prescription", keywords="NLP", keywords="natural language processing", abstract="Background: Social media is an important information source for a growing subset of the population and can likely be leveraged to provide insight into the evolving drug overdose epidemic. Twitter can provide valuable insight into trends, colloquial information available to potential users, and how networks and interactivity might influence what people are exposed to and how they engage in communication around drug use. Objective: This exploratory study was designed to investigate the ways in which unsupervised machine learning analyses using natural language processing could identify coherent themes for tweets containing substance names. Methods: This study involved harnessing data from Twitter, including large-scale collection of brand name (N=262,607) and street name (N=204,068) prescription drug--related tweets and use of unsupervised machine learning analyses (ie, natural language processing) of collected data with data visualization to identify pertinent tweet themes. Latent Dirichlet allocation (LDA) with coherence score calculations was performed to compare brand (eg, OxyContin) and street (eg, oxys) name tweets. Results: We found people discussed drug use differently depending on whether a brand name or street name was used. Brand name categories often contained political talking points (eg, border, crime, and political handling of ongoing drug mitigation strategies). In contrast, categories containing street names occasionally referenced drug misuse, though multiple social uses for a term (eg, Sonata) muddled topic clarity. Conclusions: Content in the brand name corpus reflected discussion about the drug itself and less often reflected personal use. However, content in the street name corpus was notably more diverse and resisted simple LDA categorization. We speculate this may reflect effective use of slang terminology to clandestinely discuss drug-related activity. If so, straightforward analyses of digital drug-related communication may be more difficult than previously assumed. This work has the potential to be used for surveillance and detection of harmful drug use information. It also might be used for appropriate education and dissemination of information to persons engaged in drug use content on Twitter. ", doi="10.2196/48405", url="https://www.jmir.org/2023/1/e48405", url="http://www.ncbi.nlm.nih.gov/pubmed/37505795" } @Article{info:doi/10.2196/43749, author="Lazard, J. Allison and Nicolla, Sydney and Vereen, N. Rhyan and Pendleton, Shanetta and Charlot, Marjory and Tan, Hung-Jui and DiFranzo, Dominic and Pulido, Marlyn and Dasgupta, Nabarun", title="Exposure and Reactions to Cancer Treatment Misinformation and Advice: Survey Study", journal="JMIR Cancer", year="2023", month="Jul", day="28", volume="9", pages="e43749", keywords="cancer", keywords="misinformation", keywords="social media", keywords="prosocial intervening", keywords="treatment", keywords="false information", keywords="alternative medicine", keywords="information spread", keywords="dissemination", keywords="infodemiology", keywords="mobile phone", abstract="Background: Cancer treatment misinformation, or false claims about alternative cures, often spreads faster and farther than true information on social media. Cancer treatment misinformation can harm the psychosocial and physical health of individuals with cancer and their cancer care networks by causing distress and encouraging people to abandon support, potentially leading to deviations from evidence-based care. There is a pressing need to understand how cancer treatment misinformation is shared and uncover ways to reduce misinformation. Objective: We aimed to better understand exposure and reactions to cancer treatment misinformation, including the willingness of study participants to prosocially intervene and their intentions to share Instagram posts with cancer treatment misinformation. Methods: We conducted a survey on cancer treatment misinformation among US adults in December 2021. Participants reported their exposure and reactions to cancer treatment misinformation generally (saw or heard, source, type of advice, and curiosity) and specifically on social media (platform, believability). Participants were then randomly assigned to view 1 of 3 cancer treatment misinformation posts or an information post and asked to report their willingness to prosocially intervene and their intentions to share. Results: Among US adult participants (N=603; mean age 46, SD 18.83 years), including those with cancer and cancer caregivers, almost 1 in 4 (142/603, 23.5\%) received advice about alternative ways to treat or cure cancer. Advice was primarily shared through family (39.4\%) and friends (37.3\%) for digestive (30.3\%) and natural (14.1\%) alternative cancer treatments, which generated curiosity among most recipients (106/142, 74.6\%). More than half of participants (337/603, 55.9\%) saw any cancer treatment misinformation on social media, with significantly higher exposure for those with cancer (53/109, 70.6\%) than for those without cancer (89/494, 52.6\%; P<.001). Participants saw cancer misinformation on Facebook (39.8\%), YouTube (27\%), Instagram (22.1\%), and TikTok (14.1\%), among other platforms. Participants (429/603, 71.1\%) thought cancer treatment misinformation was true, at least sometimes, on social media. More than half (357/603, 59.2\%) were likely to share any cancer misinformation posts shown. Many participants (412/603, 68.3\%) were willing to prosocially intervene for any cancer misinformation posts, including flagging the cancer treatment misinformation posts as false (49.7\%-51.4\%) or reporting them to the platform (48.1\%-51.4\%). Among the participants, individuals with cancer and those who identified as Black or Hispanic reported greater willingness to intervene to reduce cancer misinformation but also higher intentions to share misinformation. Conclusions: Cancer treatment misinformation reaches US adults through social media, including on widely used platforms for support. Many believe that social media posts about alternative cancer treatment are true at least some of the time. The willingness of US adults, including those with cancer and members of susceptible populations, to prosocially intervene could initiate the necessary community action to reduce cancer treatment misinformation if coupled with strategies to help individuals discern false claims. ", doi="10.2196/43749", url="https://cancer.jmir.org/2023/1/e43749", url="http://www.ncbi.nlm.nih.gov/pubmed/37505790" } @Article{info:doi/10.2196/43210, author="Delnoij, J. Diana M. and Derks, Meggie and Koolen, Laura and Shekary, Shuka and Suitela, Jozua", title="Using Patient Blogs on Social Media to Assess the Content Validity of Patient-Reported Outcome Measures: Qualitative Analysis of Patient-Written Blogs", journal="JMIR Form Res", year="2023", month="Jul", day="28", volume="7", pages="e43210", keywords="patient stories", keywords="patient-reported outcome measure", keywords="PROM", keywords="social media", keywords="narrative", keywords="patient story", keywords="storytelling", keywords="blogger", keywords="experiential", keywords="experience", keywords="content validity", keywords="content analysis", keywords="qualitative", keywords="cross sectional", keywords="cross-sectional", keywords="chronic disease", keywords="noncommunicable diseases", keywords="NCD", keywords="rheumatoid arthritis", keywords="Parkinson disease, diabetes mellitus", keywords="diabetes", keywords="type II diabetes", keywords="cancer", keywords="breast cancer", keywords="oncology", keywords="International Consortium for Health Outcome Measurement", keywords="ICHOM", keywords="data dictionary", keywords="Health Assessment Questionnaire", keywords="HAQ", keywords="Parkinson Disease Quality of Life Questionnaire", keywords="PDQ", keywords="inductive", keywords="inductive code", abstract="Background: Patient-reported outcome measures (PROMs) are questionnaires that measure patient outcomes related to quality of life, health, and functioning, and are increasingly used to assess important outcomes from the patient's perspective. For PROMs to contribute to better health and better care, it is vital that their content validity be adequate. This requires patient involvement in various steps of PROM development. PROM developers not only recognize the benefits of patient involvement but also report difficulties in recruiting patients and experience patient involvement as time-consuming, logistically challenging, and expensive. Objective: This study seeks to explore different strategies for disclosing the experiential knowledge of patients, namely through analyzing patient stories on the web and social media. The research questions are as follows: (1) how do bloggers living with a disease experience their health-related quality of life? (2) How are these experiences reflected in the domains and items of PROMs related to their disease? Methods: First, a qualitative analysis of blogs written by patients was performed. Second, subthemes and underlying codes resulting from this qualitative analysis were systematically compared with the domains and items in PROMs for the respective diseases that the bloggers write about. Blogs were identified via the Google search engine between December 2019 and May 2021. Results: Bloggers describe a wide range of experiences regarding their physical functioning and health; mental well-being; social network and support; daily life, education, work, and leisure; coping; and self-management. Bloggers also write about their positive and negative experiences with health care delivery, the organization of health care, and health care professionals. In general, patients' experiences as described in blogs were reflected in the domains and items of the PROMs related to their disease. However, except for diabetes mellitus, in all the sets of PROMs, potentially missing topics could be identified. Similarly, with the exception of Parkinson disease, all PROMs address issues that patients did not write about in their blogs and that might therefore be redundant. Conclusions: Web-based patient stories in the form of blogs reveal how people living with a certain disease experience their health-related quality of life. These stories enable analyses of patients' experiences that can be used to assess the content validity of PROMs. This can be a useful step for researchers who are looking for sets of measuring instruments that match their purposes. ", doi="10.2196/43210", url="https://formative.jmir.org/2023/1/e43210", url="http://www.ncbi.nlm.nih.gov/pubmed/37505797" } @Article{info:doi/10.2196/43628, author="Jin, Qiang and Raza, Hassan Syed and Yousaf, Muhammad and Zaman, Umer and Ogadimma, C. Emenyeonu and Shah, Ali Amjad and Core, Rachel and Malik, Aqdas", title="Assessing How Risk Communication Surveillance Prompts COVID-19 Vaccine Acceptance Among Internet Users by Applying the Situational Theory of Problem Solving: Cross-Sectional Study", journal="JMIR Form Res", year="2023", month="Jul", day="26", volume="7", pages="e43628", keywords="COVID-19", keywords="vaccine safety", keywords="risk communication", keywords="digital interventions", keywords="health communication", keywords="Situational Theory of Problem Solving", abstract="Background: The World Health Organization has recently raised concerns regarding the low number of people fully vaccinated against COVID-19. The low ratio of fully vaccinated people and the emergence of renewed infectious variants correspond to worsening public health. Global health managers have highlighted COVID-19 vaccine--related infodemics as a significant risk perception factor hindering mass vaccination campaigns. Objective: Given the ambiguous digital communication environment that has fostered infodemics, resource-limited nations struggle to boost public willingness to encourage people to fully vaccinate. Authorities have launched some risk communication--laden digital interventions in response to infodemics. However, the value of the risk communication strategies used to tackle infodemics needs to be evaluated. The current research using the tenets of the Situational Theory of Problem Solving is novel, as it explores the impending effects of risk communication strategies. The relationship between infodemic-induced risk perception of COVID-19 vaccine safety and risk communication actions to intensify willingness to be fully vaccinated was examined. Methods: This study used a cross-sectional research design vis-{\`a}-vis a nationally representative web-based survey. We collected data from 1946 internet users across Pakistan. Participants voluntarily participated in this research after completing the consent form and reading ethical permissions. Responses were received over 3 months, from May 2022 to July 2022. Results: The results delineated that infodemics positively affected risk perception. This realization pushed the public to engage in risky communicative actions through reliance on and searches for accurate information. Therefore, the prospect of managing infodemics through risk information exposure (eg, digital interventions) using the situational context could predict robust willingness to be fully vaccinated against COVID-19. Conclusions: These pioneering results offer strategic considerations for health authorities to effectively manage the descending spiral of optimal protection against COVID-19. This research concludes that the likelihood of managing infodemics using situational context through exposure to relevant information could improve one's knowledge of forfending and selection, which can lead to robust protection against COVID-19. Hence, more situation-specific information about the underlying problem (ie, the selection of an appropriate vaccine) can be made accessible through several official digital sources to achieve a more active public health response. ", doi="10.2196/43628", url="https://formative.jmir.org/2023/1/e43628", url="http://www.ncbi.nlm.nih.gov/pubmed/37315198" } @Article{info:doi/10.2196/43516, author="Kopsco, L. Heather and Krell, K. Rayda and Mather, N. Thomas and Connally, P. Neeta", title="Identifying Trusted Sources of Lyme Disease Prevention Information Among Internet Users Connected to Academic Public Health Resources: Internet-Based Survey Study", journal="JMIR Form Res", year="2023", month="Jul", day="26", volume="7", pages="e43516", keywords="communication", keywords="consumer health information", keywords="disease", keywords="internet", keywords="Lyme disease", keywords="online", keywords="pathogen", keywords="prevention", keywords="public health", keywords="resources", keywords="social media", keywords="survey", keywords="tickborne disease", keywords="ticks", abstract="Background: Misinformation about Lyme disease and other tick-transmitted pathogens circulates frequently on the internet and can compete with, or even overshadow, science-based guidance on tick-borne disease (TBD) prevention. Objective: We surveyed internet users connected to academic tick-related resources to identify trusted sources of Lyme disease prevention information, explore confidence in tick bite prevention information, and examine associations of these responses with answers to commonly disputed issues. Methods: The survey was conducted through social media and website pages for Western Connecticut State University Tickborne Disease Prevention Laboratory and the University of Rhode Island TickEncounter Resource Center. Results: Respondents (N=1190) were predominantly female (903/1190, 76.3\%), middle-aged (574/1182, 48.6\%), and resided in New England states (663/1190, 55.7\%). In total 984 of 1186 (83\%) respondents identified conventional experts (eg, the Centers for Disease Control [CDC] or other government health agencies, physicians who follow Infectious Diseases Society of America guidelines for Lyme disease treatment guidelines, and academics) as trustworthy TBD prevention resources. However, nearly one-fourth of respondents would first consult personal contacts and web-based communities regarding prevention information before consulting conventional expert sources. The opinions of public health experts and physicians were rated among the top motivators underlying personal prevention decisions; yet, more than 50\% of participants revealed distrustful attitudes toward, or were uncertain about, CDC-supported statements related to time to transmission of Lyme disease (708/1190, 59.5\%), the safety of diethyltoluamide-based repellents for children (604/1183, 51.1\%), and recommended use of antibiotic prophylaxis (773/1181, 65.4\%). Multimodal regression models revealed that participants from high-Lyme-disease-incidence states were more likely to first seek TBD prevention information from personal networks and nontraditional sources before approaching conventional sources of TBD prevention information. We found that those reporting high rates of social media usage were more than twice as likely to first seek traditional expert sources of prevention information but were overall more likely to reject CDC-promoted Lyme disease information, in particular the established time to transmission of Lyme disease bacteria. Models also predicted that those participants who disagreed with the conventional scientific view on the antibiotic prophylaxis prevention statement were less likely to be confident in their ability to protect themselves from a tick bite. Overall, uncertainty in one's ability to protect oneself against tick bites was strongly associated with uncertainty about beliefs in CDC-promoted TBD prevention information. Self-reported trust in experts and frequency of social media use suggest that these platforms may provide opportunities to engage directly with the public about TBD prevention practices. Conclusions: Using strategies to improve public trust and provide information where the public engages on social media may improve prevention communication and adoption of best practices. ", doi="10.2196/43516", url="https://formative.jmir.org/2023/1/e43516", url="http://www.ncbi.nlm.nih.gov/pubmed/37494089" } @Article{info:doi/10.2196/45757, author="Xia, Xinming and Zhang, Yi and Jiang, Wenting and Wu, Yuhao Connor", title="Staying Home, Tweeting Hope: Mixed Methods Study of Twitter Sentiment Geographical Index During US Stay-At-Home Orders", journal="J Med Internet Res", year="2023", month="Jul", day="24", volume="25", pages="e45757", keywords="COVID-19", keywords="Twitter", keywords="stay-at-home orders", keywords="dynamics of public opinion", keywords="multiperiod difference-in-differences model", abstract="Background: Stay-at-home orders were one of the controversial interventions to curb the spread of COVID-19 in the United States. The stay-at-home orders, implemented in 51 states and territories between March 7 and June 30, 2020, impacted the lives of individuals and communities and accelerated the heavy usage of web-based social networking sites. Twitter sentiment analysis can provide valuable insight into public health emergency response measures and allow for better formulation and timing of future public health measures to be released in response to future public health emergencies. Objective: This study evaluated how stay-at-home orders affect Twitter sentiment in the United States. Furthermore, this study aimed to understand the feedback on stay-at-home orders from groups with different circumstances and backgrounds. In addition, we particularly focused on vulnerable groups, including older people groups with underlying medical conditions, small and medium enterprises, and low-income groups. Methods: We constructed a multiperiod difference-in-differences regression model based on the Twitter sentiment geographical index quantified from 7.4 billion geo-tagged tweets data to analyze the dynamics of sentiment feedback on stay-at-home orders across the United States. In addition, we used moderated effects analysis to assess differential feedback from vulnerable groups. Results: We combed through the implementation of stay-at-home orders, Twitter sentiment geographical index, and the number of confirmed cases and deaths in 51 US states and territories. We identified trend changes in public sentiment before and after the stay-at-home orders. Regression results showed that stay-at-home orders generated a positive response, contributing to a recovery in Twitter sentiment. However, vulnerable groups faced greater shocks and hardships during the COVID-19 pandemic. In addition, economic and demographic characteristics had a significant moderating effect. Conclusions: This study showed a clear positive shift in public opinion about COVID-19, with this positive impact occurring primarily after stay-at-home orders. However, this positive sentiment is time-limited, with 14 days later allowing people to be more influenced by the status quo and trends, so feedback on the stay-at-home orders is no longer positively significant. In particular, negative sentiment is more likely to be generated in states with a large proportion of vulnerable groups, and the policy plays a limited role. The pandemic hit older people, those with underlying diseases, and small and medium enterprises directly but hurt states with cross-cutting economic situations and more complex demographics over time. Based on large-scale Twitter data, this sociological perspective allows us to monitor the evolution of public opinion more directly, assess the impact of social events on public opinion, and understand the heterogeneity in the face of pandemic shocks. ", doi="10.2196/45757", url="https://www.jmir.org/2023/1/e45757", url="http://www.ncbi.nlm.nih.gov/pubmed/37486758" } @Article{info:doi/10.2196/41582, author="Marani, Husayn and Song, Yunju Melodie and Jamieson, Margaret and Roerig, Monika and Allin, Sara", title="Public Officials' Engagement on Social Media During the Rollout of the COVID-19 Vaccine: Content Analysis of Tweets", journal="JMIR Infodemiology", year="2023", month="Jul", day="20", volume="3", pages="e41582", keywords="Twitter", keywords="COVID-19", keywords="vaccines", keywords="sentiment analysis", keywords="public officials", abstract="Background: Social media is an important way for governments to communicate with the public. This is particularly true in times of crisis, such as the COVID-19 pandemic, during which government officials played a strong role in promoting public health measures such as vaccines. Objective: In Canada, provincial COVID-19 vaccine rollout was delivered in 3 phases aligned with federal government COVID-19 vaccine guidance for priority populations. In this study, we examined how Canadian public officials used Twitter to engage with the public about vaccine rollout and how this engagement has shaped public response to vaccines across jurisdictions. Methods: We conducted a content analysis of tweets posted between December 28, 2020, and August 31, 2021. Leveraging the social media artificial intelligence tool Brandwatch Analytics, we constructed a list of public officials in 3 jurisdictions (Ontario, Alberta, and British Columbia) organized across 6 public official types and then conducted an English and French keyword search for tweets about vaccine rollout and delivery that mentioned, retweeted, or replied to the public officials. We identified the top 30 tweets with the highest impressions in each jurisdiction in each of the 3 phases (approximately a 26-day window) of the vaccine rollout. The metrics of engagement (impressions, retweets, likes, and replies) from the top 30 tweets per phase in each jurisdiction were extracted for additional annotation. We specifically annotated sentiment toward public officials' vaccine responses (ie, positive, negative, and neutral) in each tweet and annotated the type of social media engagement. A thematic analysis of tweets was then conducted to add nuance to extracted data characterizing sentiment and interaction type. Results: Among the 6 categories of public officials, 142 prominent accounts were included from Ontario, Alberta, and British Columbia. In total, 270 tweets were included in the content analysis and 212 tweets were direct tweets by public officials. Public officials mostly used Twitter for information provision (139/212, 65.6\%), followed by horizontal engagement (37/212, 17.5\%), citizen engagement (24/212, 11.3\%), and public service announcements (12/212, 5.7\%). Information provision by government bodies (eg, provincial government and public health authorities) or municipal leaders is more prominent than tweets by other public official groups. Neutral sentiment accounted for 51.5\% (139/270) of all the tweets, whereas positive sentiment was the second most common sentiment (117/270, 43.3\%). In Ontario, 60\% (54/90) of the tweets were positive. Negative sentiment (eg, public officials criticizing vaccine rollout) accounted for 12\% (11/90) of all the tweets. Conclusions: As governments continue to promote the uptake of the COVID-19 booster doses, findings from this study are useful in informing how governments can best use social media to engage with the public to achieve democratic goals. ", doi="10.2196/41582", url="https://infodemiology.jmir.org/2023/1/e41582", url="http://www.ncbi.nlm.nih.gov/pubmed/37315194" } @Article{info:doi/10.2196/45267, author="Yang, Genevieve and King, G. Sarah and Lin, Hung-Mo and Goldstein, Z. Rita", title="Emotional Expression on Social Media Support Forums for Substance Cessation: Observational Study of Text-Based Reddit Posts", journal="J Med Internet Res", year="2023", month="Jul", day="19", volume="25", pages="e45267", keywords="sentiment analysis", keywords="text mining", keywords="addiction phenotype", keywords="subjective experience phenotype", keywords="naturalistic big data", keywords="natural language processing", keywords="phenomenology", keywords="experience sampling", abstract="Background: Substance use disorder is characterized by distinct cognitive processes involved in emotion regulation as well as unique emotional experiences related to the relapsing cycle of drug use and recovery. Web-based communities and the posts they generate represent an unprecedented resource for studying subjective emotional experiences, capturing population types and sizes not typically available in the laboratory. Here, we mined text data from Reddit, a social media website that hosts discussions from pseudonymous users on specific topic forums, including forums for individuals who are trying to abstain from using drugs, to explore the putative specificity of the emotional experience of substance cessation. Objective: An important motivation for this study was to investigate transdiagnostic clues that could ultimately be used for mental health outreach. Specifically, we aimed to characterize the emotions associated with cessation of 3 major substances and compare them to emotional experiences reported in nonsubstance cessation posts, including on forums related to psychiatric conditions of high comorbidity with addiction. Methods: Raw text from 2 million posts made, respectively, in the fall of 2020 (discovery data set) and fall of 2019 (replication data set) were obtained from 394 forums hosted by Reddit through the application programming interface. We quantified emotion word frequencies in 3 substance cessation forums for alcohol, nicotine, and cannabis topic categories and performed comparisons with general forums. Emotion word frequencies were classified into distinct categories and represented as a multidimensional emotion vector for each forum. We further quantified the degree of emotional resemblance between different forums by computing cosine similarity on these vectorized representations. For substance cessation posts with self-reported time since last use, we explored changes in the use of emotion words as a function of abstinence duration. Results: Compared to posts from general forums, substance cessation posts showed more expressions of anxiety, disgust, pride, and gratitude words. ``Anxiety'' emotion words were attenuated for abstinence durations >100 days compared to shorter durations (t12=3.08, 2-tailed; P=.001). The cosine similarity analysis identified an emotion profile preferentially expressed in the cessation posts across substances, with lesser but still prominent similarities to posts about social anxiety and attention-deficit/hyperactivity disorder. These results were replicated in the 2019 (pre--COVID-19) data and were distinct from control analyses using nonemotion words. Conclusions: We identified a unique subjective experience phenotype of emotions associated with the cessation of 3 major substances, replicable across 2 time periods, with changes as a function of abstinence duration. Although to a lesser extent, this phenotype also quantifiably resembled the emotion phenomenology of other relevant subjective experiences (social anxiety and attention-deficit/hyperactivity disorder). Taken together, these transdiagnostic results suggest a novel approach for the future identification of at-risk populations, allowing for the development and deployment of specific and timely interventions. ", doi="10.2196/45267", url="https://www.jmir.org/2023/1/e45267", url="http://www.ncbi.nlm.nih.gov/pubmed/37467010" } @Article{info:doi/10.2196/41806, author="Darien, Kaja and Lee, Susan and Knowles, Kayla and Wood, Sarah and Langer, D. Miriam and Lazar, Nellie and Dowshen, Nadia", title="Health Information From Web Search Engines and Virtual Assistants About Pre-Exposure Prophylaxis for HIV Prevention in Adolescents and Young Adults: Content Analysis", journal="JMIR Pediatr Parent", year="2023", month="Jul", day="18", volume="6", pages="e41806", keywords="pre-exposure prophylaxis", keywords="PrEP", keywords="prophylaxis", keywords="internet use", keywords="search engine", keywords="adolescent", keywords="youth", keywords="pediatric", keywords="adolescence", keywords="young adult", keywords="readability", keywords="human immunodeficiency virus", keywords="HIV", keywords="virtual assistant", keywords="health information", keywords="information quality", keywords="accuracy", keywords="credibility", keywords="patient education", keywords="comprehension", keywords="comprehensible", keywords="web-based", keywords="online information", keywords="sexual health", keywords="reading level", abstract="Background: Adolescents and young adults are disproportionately affected by HIV, suggesting that HIV prevention methods such as pre-exposure prophylaxis (PrEP) should focus on this group as a priority. As digital natives, youth likely turn to internet resources regarding health topics they may not feel comfortable discussing with their medical providers. To optimize informed decision-making by adolescents and young adults most impacted by HIV, the information from internet searches should be educational, accurate, and readable. Objective: The aims of this study were to compare the accuracy of web-based PrEP information found using web search engines and virtual assistants, and to assess the readability of the resulting information. Methods: Adolescent HIV prevention clinical experts developed a list of 23 prevention-related questions that were posed to search engines (Ask.com, Bing, Google, and Yahoo) and virtual assistants (Amazon Alexa, Microsoft Cortana, Google Assistant, and Apple Siri). The first three results from search engines and virtual assistant web references, as well as virtual assistant verbal responses, were recorded and coded using a six-tier scale to assess the quality of information produced. The results were also entered in a web-based tool determining readability using the Flesch-Kincaid Grade Level scale. Results: Google web search engine and Google Assistant more frequently produced PrEP information of higher quality than the other search engines and virtual assistants with scores ranging from 3.4 to 3.7 and 2.8 to 3.3, respectively. Additionally, the resulting information generally was presented in language at a seventh and 10th grade reading level according to the Flesch-Kincaid Grade Level scale. Conclusions: Adolescents and young adults are large consumers of technology and may experience discomfort discussing their sexual health with providers. It is important that efforts are made to ensure the information they receive about HIV prevention methods, and PrEP in particular, is comprehensive, comprehensible, and widely available. ", doi="10.2196/41806", url="https://pediatrics.jmir.org/2023/1/e41806", url="http://www.ncbi.nlm.nih.gov/pubmed/37463044" } @Article{info:doi/10.2196/43901, author="Wang, Yanyan and Zhang, Jin", title="A Study on User-Oriented Subjects of Child Abuse on Wikipedia: Temporal Analysis of Wikipedia History Versions and Traffic Data", journal="J Med Internet Res", year="2023", month="Jul", day="17", volume="25", pages="e43901", keywords="child abuse", keywords="user-oriented subject", keywords="subject schema", keywords="subject change", keywords="popularity trend", keywords="temporal analysis", keywords="Wikipedia", abstract="Background: Many people turn to online open encyclopedias such as Wikipedia to seek knowledge about child abuse. However, the information available on this website is often disorganized and incomplete. Objective: The aim of this study is to analyze Wikipedia's coverage of child abuse and provide a more accessible way for users to browse child abuse--related content. The study explored the main themes and subjects related to child abuse on Wikipedia and proposed a multilayer user-oriented subject schema from the general users' perspective. Methods: The knowledge of child abuse on Wikipedia is presented in the child abuse--related articles on it. The study analyzed child abuse--related articles on Wikipedia, examining their history versions and yearly page views data to reveal the evolution of content and popularity. The themes and subjects were identified from the articles' text using the open coding, self-organizing map, and n-gram approaches. The subjects in different periods were compared to reveal changes in content. Results: This study collected and investigated 241 associated Wikipedia articles and their history versions and traffic data. Four facets were identified: (1) maltreatment behavior (n=118, 48.9\%); (2) people and environment (n=28, 11.6\%); (3) problems and risks (n=33, 13.7\%); and (4) protection and support (n=62, 25.7\%). A total of 8 themes and 51 subjects were generated from the text, and a user-oriented subject schema linking the facets, themes, subjects, and articles was created. Maltreatment behavior (number of total views = 1.15 {\texttimes} 108) was the most popular facet viewed by users, while people and environment (number of total views = 2.42 {\texttimes} 107) was the least popular. The popularity of child abuse increased from 2010 to 2014 but decreased after that. Conclusions: The user-oriented subject schema provides an easier way for users to seek information and learn about child abuse. The knowledge of child abuse on Wikipedia covers the harms done to children, the problems caused by child abuse, the protection of children, and the people involved in child abuse. However, there was an inconsistency between the interests of general users and Wikipedia editors, and the child abuse knowledge on Wikipedia was found to be deficient, lacking content about typical child abuse types. To meet users' needs, health information creators need to generate more information to fill the knowledge gap. ", doi="10.2196/43901", url="https://www.jmir.org/2023/1/e43901", url="http://www.ncbi.nlm.nih.gov/pubmed/37459149" } @Article{info:doi/10.2196/47328, author="Shankar, Kavitha and Chandrasekaran, Ranganathan and Jeripity Venkata, Pruthvinath and Miketinas, Derek", title="Investigating the Role of Nutrition in Enhancing Immunity During the COVID-19 Pandemic: Twitter Text-Mining Analysis", journal="J Med Internet Res", year="2023", month="Jul", day="10", volume="25", pages="e47328", keywords="social media", keywords="nutrition discourse", keywords="text mining", keywords="immunity building", keywords="food groups", keywords="Twitter", keywords="nutrition", keywords="food", keywords="immunity", keywords="COVID-19", keywords="diet", keywords="immune system", keywords="assessment", keywords="tweets", keywords="dieticians", keywords="nutritionists", abstract="Background: The COVID-19 pandemic has brought to the spotlight the critical role played by a balanced and healthy diet in bolstering the human immune system. There is burgeoning interest in nutrition-related information on social media platforms like Twitter. There is a critical need to assess and understand public opinion, attitudes, and sentiments toward nutrition-related information shared on Twitter. Objective: This study uses text mining to analyze nutrition-related messages on Twitter to identify and analyze how the general public perceives various food groups and diets for improving immunity to the SARS-CoV-2 virus. Methods: We gathered 71,178 nutrition-related tweets that were posted between January 01, 2020, and September 30, 2020. The Correlated Explanation text mining algorithm was used to identify frequently discussed topics that users mentioned as contributing to immunity building against SARS-CoV-2. We assessed the relative importance of these topics and performed a sentiment analysis. We also qualitatively examined the tweets to gain a closer understanding of nutrition-related topics and food groups. Results: Text-mining yielded 10 topics that users discussed frequently on Twitter, viz proteins, whole grains, fruits, vegetables, dairy-related, spices and herbs, fluids, supplements, avoidable foods, and specialty diets. Supplements were the most frequently discussed topic (23,913/71,178, 33.6\%) with a higher proportion (20,935/23,913, 87.75\%) exhibiting a positive sentiment with a score of 0.41. Consuming fluids (17,685/71,178, 24.85\%) and fruits (14,807/71,178, 20.80\%) were the second and third most frequent topics with favorable, positive sentiments. Spices and herbs (8719/71,178, 12.25\%) and avoidable foods (8619/71,178, 12.11\%) were also frequently discussed. Negative sentiments were observed for a higher proportion of avoidable foods (7627/8619, 84.31\%) with a sentiment score of --0.39. Conclusions: This study identified 10 important food groups and associated sentiments that users discussed as a means to improve immunity. Our findings can help dieticians and nutritionists to frame appropriate interventions and diet programs. ", doi="10.2196/47328", url="https://www.jmir.org/2023/1/e47328", url="http://www.ncbi.nlm.nih.gov/pubmed/37428522" } @Article{info:doi/10.2196/44603, author="Cummins, A. Jack and Lipworth, D. Adam", title="Reddit and Google Activity Related to Non-COVID Epidemic Diseases Surged at Start of COVID-19 Pandemic: Retrospective Study", journal="JMIR Form Res", year="2023", month="Jul", day="6", volume="7", pages="e44603", keywords="COVID-19", keywords="Reddit", keywords="Google Trends", keywords="chikungunya", keywords="Ebola", keywords="H1N1", keywords="Middle Eastern respiratory syndrome", keywords="MERS", keywords="severe acute respiratory syndrome", keywords="SARS", keywords="Zika", keywords="infectious disease", keywords="social media", keywords="search data", keywords="search query", keywords="web-based search", keywords="information behavior", keywords="information seeking", keywords="public interest", abstract="Background: Resources such as Google Trends and Reddit provide opportunities to gauge real-time popular interest in public health issues. Despite the potential for these publicly available and free resources to help optimize public health campaigns, use for this purpose has been limited. Objective: The purpose of this study is to determine whether early public awareness of COVID-19 correlated with elevated public interest in other infectious diseases of public health importance. Methods: Google Trends search data and Reddit comment data were analyzed from 2018 through 2020 for the frequency of keywords ``chikungunya,'' ``Ebola,'' ``H1N1,'' ``MERS,'' ``SARS,'' and ``Zika,'' 6 highly publicized epidemic diseases in recent decades. After collecting Google Trends relative popularity scores for each of these 6 terms, unpaired 2-tailed t tests were used to compare the 2020 weekly scores for each term to their average level over the 3-year study period. The number of Reddit comments per month with each of these 6 terms was collected and then adjusted for the total estimated Reddit monthly comment volume to derive a measure of relative use, analogous to the Google Trends popularity score. The relative monthly incidence of comments with each search term was then compared to the corresponding search term's pre-COVID monthly comment data, again using unpaired 2-tailed t tests. P value cutoffs for statistical significance were determined a priori with a Bonferroni correction. Results: Google Trends and Reddit data both demonstrate large and statistically significant increases in the usage of each evaluated disease term through at least the initial months of the pandemic. Google searches and Reddit comments that included any of the evaluated infectious disease search terms rose significantly in the first months of 2020 above their baseline usage, peaking in March 2020. Google searches for ``SARS'' and ``MERS'' remained elevated for the entirety of the 2020 calendar year, as did Reddit comments with the words ``Ebola,'' ``H1N1,'' ``MERS,'' and ``SARS'' (P<.001, for each weekly or monthly comparison, respectively). Conclusions: Google Trends and Reddit can readily be used to evaluate real-time general interest levels in public health--related topics, providing a tool to better time and direct public health initiatives that require a receptive target audience. The start of the COVID-19 pandemic correlated with increased public interest in other epidemic infectious diseases. We have demonstrated that for 6 distinct infectious causes of epidemics over the last 2 decades, public interest rose substantially and rapidly with the outbreak of COVID-19. Our data suggests that for at least several months after the initial outbreak, the public may have been particularly receptive to dialogue on these topics. Public health officials should consider using Google Trends and social media data to identify patterns of engagement with public health topics in real time and to optimize the timing of public health campaigns. ", doi="10.2196/44603", url="https://formative.jmir.org/2023/1/e44603", url="http://www.ncbi.nlm.nih.gov/pubmed/37256832" } @Article{info:doi/10.2196/47210, author="Zheng, Shusen and Tong, Xinyu and Wan, Dalong and Hu, Chen and Hu, Qing and Ke, Qinghong", title="Quality and Reliability of Liver Cancer--Related Short Chinese Videos on TikTok and Bilibili: Cross-Sectional Content Analysis Study", journal="J Med Internet Res", year="2023", month="Jul", day="5", volume="25", pages="e47210", keywords="liver cancer", keywords="short videos", keywords="information quality", keywords="social media", keywords="TikTok", keywords="Bilibili", keywords="GQS", keywords="global quality score", keywords="DISCERN", keywords="reliability", abstract="Background: Liver cancer incidence has been increasing in China in the recent years, leading to increased public concern regarding the burden of this disease. Short videos on liver cancer are disseminated through TikTok and Bilibili apps, which have gained popularity in recent years as an easily accessible source of health information. However, the credibility, quality, and usefulness of the information in these short videos and the professional knowledge of the individuals uploading health information--based videos in these platforms have not yet been evaluated. Objective: Our study aims to assess the quality of the information in Chinese short videos on liver cancer shared on the TikTok and Bilibili short video--sharing platforms. Methods: In March 2023, we assessed the top 100 Chinese short videos on liver cancer in TikTok and Bilibili (200 videos in total) for their information quality and reliability by using 2 rating tools, namely, global quality score (GQS) and the DISCERN instrument. Correlation and Poisson regression analyses were applied to discuss the factors that could impact video quality. Results: Compared to Bilibili, TikTok is more popular, although the length of the videos on TikTok is shorter than that of the videos on Bilibili (P<.001). The quality of the short videos on liver cancer in TikTok and Bilibili was not satisfactory, with median GQS of 3 (IQR 2-4) and 2 (IQR 1-5) and median DISCERN scores of 5 (IQR 4-6) and 4 (IQR 2-7), respectively. In general, the quality of videos sourced from professional institutions and individuals was better than that of those sourced from nonprofessionals, and videos involving disease-related knowledge were of better quality than those covering news and reports. No significant differences were found in the quality of videos uploaded by individuals from different professions, with the exception of those uploaded by traditional Chinese medicine professionals, which demonstrated poorer quality. Only video shares were positively correlated with the GQS (r=0.17, P=.01), and no video variables could predict the video quality. Conclusions: Our study shows that the quality of short videos on health information related to liver cancer is poor on Bilibili and TikTok, but videos uploaded by health care professionals can be considered reliable in terms of comprehensiveness and content quality. Thus, short videos providing medical information on TikTok and Bilibili must be carefully considered for scientific soundness by active information seekers before they make decisions on their health care management. ", doi="10.2196/47210", url="https://www.jmir.org/2023/1/e47210", url="http://www.ncbi.nlm.nih.gov/pubmed/37405825" } @Article{info:doi/10.2196/41594, author="Filipponi, Chiara and Chichua, Mariam and Masiero, Marianna and Mazzoni, Davide and Pravettoni, Gabriella", title="Cancer Pain Experience Through the Lens of Patients and Caregivers: Mixed Methods Social Media Study", journal="JMIR Cancer", year="2023", month="Jul", day="3", volume="9", pages="e41594", keywords="pain", keywords="cancer", keywords="quality of life", keywords="social support", keywords="emotion", keywords="personality", keywords="decision-making", abstract="Background: Cancer pain represents a challenge for cancer patients and their family members. Despite progression in pain management, pain is still underreported and undertreated, and there is limited information on the related needs that patients and caregivers may have. Online platforms represent a fundamental tool for research to reveal the unmet needs of these users and their emotions outside the medical setting. Objective: This study aimed to (1) reveal the unmet needs of both patients and caregivers and (2) detect the emotional activation associated with cancer pain by analyzing the textual patterns of both users. Methods: A descriptive and quantitative analysis of qualitative data was performed in RStudio v.2022.02.3 (RStudio Team). We analyzed 679 posts (161 from caregivers and 518 from patients) published over 10 years on the ``cancer'' subreddit of Reddit to identify unmet needs and emotions related to cancer pain. Hierarchical clustering, and emotion and sentiment analysis were conducted. Results: The language used for describing experiences related to cancer pain and expressed needs differed between patients and caregivers. For patients (agglomerative coefficient=0.72), the large cluster labeled unmet needs included the following clusters: (1A) reported experiences, with the subclusters (a) relationship with doctors/spouse and (b) reflections on physical features; and (1B) changes observed over time, with the subclusters (a) regret and (b) progress. For caregivers (agglomerative coefficient=0.80), the main clusters were as follows: (1A) social support and (1B) reported experiences, with the subclusters (a) psychosocial challenges and (b) grief. Moreover, comparison between the 2 groups (entanglement coefficient=0.28) showed that they shared a common cluster labeled uncertainty. Regarding emotion and sentiment analysis, patients expressed a significantly higher negative sentiment than caregivers (z=?2.14; P<.001). On the contrary, caregivers expressed a higher positive sentiment compared with patients (z=?2.26; P<.001), with trust (z=?4.12; P<.001) and joy (z=?2.03; P<.001) being the most prevalent positive emotions. Conclusions: Our study emphasized different perceptions of cancer pain in patients and caregivers. We revealed different needs and emotional activations in the 2 groups. Moreover, our study findings highlight the importance of considering caregivers in medical care. Overall, this study increases knowledge about the unmet needs and emotions of patients and caregivers, which may have important clinical implications in pain management. ", doi="10.2196/41594", url="https://cancer.jmir.org/2023/1/e41594", url="http://www.ncbi.nlm.nih.gov/pubmed/37399067" } @Article{info:doi/10.2196/47343, author="Qin, Lang and Zheng, Ming and Schwebel, C. David and Li, Li and Cheng, Peixia and Rao, Zhenzhen and Peng, Ruisha and Ning, Peishan and Hu, Guoqing", title="Content Quality of Web-Based Short-Form Videos for Fire and Burn Prevention in China: Content Analysis", journal="J Med Internet Res", year="2023", month="Jun", day="30", volume="25", pages="e47343", keywords="fire", keywords="burn", keywords="prevention", keywords="first aid", keywords="short video", keywords="content quality", keywords="public impact", keywords="China", abstract="Background: Web-based short-form videos are increasingly popular for disseminating fire and burn prevention information, but their content quality is unknown. Objective: We aimed to systematically assess the characteristics, content quality, and public impact of web-based short-form videos offering primary and secondary (first aid) prevention recommendations for fires and burns in China between 2018 and 2021. Methods: We retrieved short-form videos offering both primary and secondary (first aid) information to prevent fire and burn injuries published on the 3 most popular web-based short-form video platforms in China: TikTok, Kwai, and Bilibili. To assess video content quality, we calculated the proportion of short-form videos that included information on each of the 15 recommendations for burn prevention education from the World Health Organization (WHO; P1) and that correctly disseminated each recommendation (P2). High P1 and P2 indicated better content quality. To assess their public impact, we calculated the median (IQR) of 3 indicators: the number of comments, likes, and saves as a favorite by viewers. Chi-square test, trend chi-square test, and Kruskal-Wallis H test examined differences in indicators across the 3 platforms, years, content, and time duration of videos and between videos disseminating correct versus incorrect information. Results: Overall, 1459 eligible short-form videos were included. The number of short-form videos increased by 16 times between 2018 and 2021. Of them, 93.97\% (n=1371) were about secondary prevention (first aid) and 86.02\% (n=1255) lasted <2 minutes. The proportion of short-form videos including each of the 15 WHO recommendations ranged from 0\% to 77.86\% (n=1136). Recommendations 8, 13, and 11 had the highest proportions (n=1136, 77.86\%; n=827, 56.68\%; and n=801, 54.9\%, respectively), whereas recommendations 3 and 5 were never mentioned. Among the short-form videos that included the WHO recommendations, recommendations 1, 2, 4, 6, 9, and 12 were always disseminated correctly, but the other 9 recommendations were correctly disseminated in 59.11\% (120/203) to 98.68\% (1121/1136) of videos. The proportion of short-form videos including and correctly disseminating the WHO recommendations varied across platforms and years. The public impact of short videos varied greatly across videos, with a median (IQR) of 5 (0-34) comments, 62 (7-841) likes, and 4 (0-27) saves as a favorite. Short-form videos disseminating correct recommendations had larger public impact than those disseminating either partially correct or incorrect knowledge (median 5 vs 4 comments, 68 vs 51 likes, and 5 vs 3 saves as a favorite, respectively; all P<.05). Conclusions: Despite the rapid increase in the number of web-based short-form videos about fire and burn prevention available in China, their content quality and public impact were generally low. Systematic efforts are recommended to improve the content quality and public impact of short-form videos on injury prevention topics such as fire and burn prevention. ", doi="10.2196/47343", url="https://www.jmir.org/2023/1/e47343", url="http://www.ncbi.nlm.nih.gov/pubmed/37389906" } @Article{info:doi/10.2196/46342, author="Pleasants, Elizabeth and Ryan, Holmes Julia and Ren, Cheng and Prata, Ndola and Gomez, Manchikanti Anu and Marshall, Cassondra", title="Exploring Language Used in Posts on r/birthcontrol: Case Study Using Data From Reddit Posts and Natural Language Processing to Advance Contraception Research", journal="J Med Internet Res", year="2023", month="Jun", day="30", volume="25", pages="e46342", keywords="contraception", keywords="big data", keywords="Reddit", keywords="social networking site", keywords="contraceptive side effects", keywords="natural language processing", keywords="reproductive autonomy", abstract="Background: Contraceptive choice is central to reproductive autonomy. The internet, including social networking sites like Reddit, is an important resource for people seeking contraceptive information and support. A subreddit dedicated to contraception, r/birthcontrol, provides a platform for people to post about contraception. Objective: This study explored the use of r/birthcontrol, from the inception of the subreddit through the end of 2020. We describe the web-based community, identify distinctive interests and themes based upon the textual content of posts, and explore the content of posts with the most user engagement (ie, ``popular'' posts). Methods: Data were obtained from the PushShift Reddit application programming interface from the establishment of r/birthcontrol to the start date of analysis (July 21, 2011, to December 31, 2020). User interactions within the subreddit were analyzed to describe community use over time, specifically the commonality of use based on the volume of posts, the length of posts (character count), and the proportion of posts with any and each flair applied. ``Popular'' posts on r/birthcontrol were determined based on the number of comments and ``scores,'' or upvotes minus downvotes; popular posts had 9 comments and a score of ?3. Term Frequency-Inverse Document Frequency (TF-IDF) analyses were run on all posts with flairs applied, posts within each flair group, and popular posts within each flair group to characterize and compare the distinctive language used in each group. Results: There were 105,485 posts to r/birthcontrol during the study period, with the volume of posts increasing over time. Within the time frame for which flairs were available on r/birthcontrol (after February 4, 2016), users applied flairs to 78\% (n=73,426) of posts. Most posts contained exclusively textual content (n=66,071, 96\%), had comments (n=59,189, 86\%), and had a score (n=66,071, 96\%). Posts averaged 731 characters in length (median 555). ``SideEffects!?'' was the most frequently used flair overall (n=27,530, 40\%), while ``Experience'' (n=719, 31\%) and ``SideEffects!?'' (n=672, 29\%) were most common among popular posts. TF-IDF analyses of all posts showed interest in contraceptive methods, menstrual experiences, timing, feelings, and unprotected sex. While TF-IDF results for posts with each flair varied, the contraceptive pill, menstrual experiences, and timing were discussed across flair groups. Among popular posts, intrauterine devices and contraceptive use experiences were often discussed. Conclusions: People commonly wrote about contraceptive side effects and experiences using methods, highlighting the value of r/birthcontrol as a space to post about aspects of contraceptive use that are not well addressed by clinical contraceptive counseling. The value of real-time, open-access data on contraceptive users' interests is especially high given the shifting landscape of and increasing constraints on reproductive health care in the United States. ", doi="10.2196/46342", url="https://www.jmir.org/2023/1/e46342", url="http://www.ncbi.nlm.nih.gov/pubmed/37389907" } @Article{info:doi/10.2196/45024, author="Yang, Kunhao and Tanaka, Mikihito", title="Crowdsourcing Knowledge Production of COVID-19 Information on Japanese Wikipedia in the Face of Uncertainty: Empirical Analysis", journal="J Med Internet Res", year="2023", month="Jun", day="29", volume="25", pages="e45024", keywords="scientific uncertainty", keywords="COVID-19", keywords="Wikipedia", keywords="crowdsourcing information production", abstract="Background: A worldwide overabundance of information comprising misinformation, rumors, and propaganda concerning COVID-19 has been observed in addition to the pandemic. By addressing this data confusion, Wikipedia has become an important source of information. Objective: This study aimed to investigate how the editors of Wikipedia have handled COVID-19--related information. Specifically, it focused on 2 questions: What were the knowledge preferences of the editors who participated in producing COVID-19--related information? and How did editors with different knowledge preferences collaborate? Methods: This study used a large-scale data set, including >2 million edits in the histories of 1857 editors who edited 133 articles related to COVID-19 on Japanese Wikipedia. Machine learning methods, including graph neural network methods, Bayesian inference, and Granger causality analysis, were used to establish the editors' topic proclivity and collaboration patterns. Results: Overall, 3 trends were observed. Two groups of editors were involved in the production of information on COVID-19. One group had a strong preference for sociopolitical topics (social-political group), and the other group strongly preferred scientific and medical topics (scientific-medical group). The social-political group played a central role (contributing 16,544,495/23,485,683, 70.04\% of bits of content and 57,969/76,673, 75.61\% of the references) in the information production part of the COVID-19 articles on Wikipedia, whereas the scientific-medical group played only a secondary role. The severity of the pandemic in Japan activated the editing behaviors of the social-political group, leading them to contribute more to COVID-19 information production on Wikipedia while simultaneously deactivating the editing behaviors of the scientific-medical group, resulting in their less contribution to COVID-19 information production on Wikipedia (Pearson correlation coefficient=0.231; P<.001). Conclusions: The results of this study showed that lay experts (ie, Wikipedia editors) in the fields of science and medicine tended to remain silent when facing high scientific uncertainty related to the pandemic. Considering the high quality of the COVID-19--related articles on Japanese Wikipedia, this research also suggested that the sidelining of the science and medicine editors in discussions is not necessarily a problem. Instead, the social and political context of the issues with high scientific uncertainty is more important than the scientific discussions that support accuracy. ", doi="10.2196/45024", url="https://www.jmir.org/2023/1/e45024", url="http://www.ncbi.nlm.nih.gov/pubmed/37384371" } @Article{info:doi/10.2196/39895, author="Stone, Haley and Heslop, David and Lim, Samsung and Sarmiento, Ines and Kunasekaran, Mohana and MacIntyre, Raina C.", title="Open-Source Intelligence for Detection of Radiological Events and Syndromes Following the Invasion of Ukraine in 2022: Observational Study", journal="JMIR Infodemiology", year="2023", month="Jun", day="28", volume="3", pages="e39895", keywords="artificial intelligence", keywords="contamination", keywords="data source", keywords="early warning", keywords="emergency response", keywords="environmental health", keywords="open source", keywords="open-source intelligence", keywords="OSINT", keywords="power plant", keywords="public health", keywords="radiation", keywords="radiobiological events", keywords="radiological", keywords="sensor", keywords="Ukraine", abstract="Background: On February 25, 2022, Russian forces took control of the Chernobyl power plant after continuous fighting within the Chernobyl exclusion zone. Continual events occurred in the month of March, which raised the risk of potential contamination of previously uncontaminated areas and the potential for impacts on human and environmental health. The disruption of war has caused interruptions to normal preventive activities, and radiation monitoring sensors have been nonfunctional. Open-source intelligence can be informative when formal reporting and data are unavailable. Objective: This paper aimed to demonstrate the value of open-source intelligence in Ukraine to identify signals of potential radiological events of health significance during the Ukrainian conflict. Methods: Data were collected from search terminology for radiobiological events and acute radiation syndrome detection between February 1 and March 20, 2022, using 2 open-source intelligence (OSINT) systems, EPIWATCH and Epitweetr. Results: Both EPIWATCH and Epitweetr identified signals of potential radiobiological events throughout Ukraine, particularly on March 4 in Kyiv, Bucha, and Chernobyl. Conclusions: Open-source data can provide valuable intelligence and early warning about potential radiation hazards in conditions of war, where formal reporting and mitigation may be lacking, to enable timely emergency and public health responses. ", doi="10.2196/39895", url="https://infodemiology.jmir.org/2023/1/e39895", url="http://www.ncbi.nlm.nih.gov/pubmed/37379069" } @Article{info:doi/10.2196/41134, author="Isip Tan, Thiele Iris and Cleofas, Jerome and Solano, Geoffrey and Pillejera, Genevive Jeanne and Catapang, Kyle Jasper", title="Interdisciplinary Approach to Identify and Characterize COVID-19 Misinformation on Twitter: Mixed Methods Study", journal="JMIR Form Res", year="2023", month="Jun", day="28", volume="7", pages="e41134", keywords="COVID-19", keywords="misinformation", keywords="natural language processing", keywords="Twitter", keywords="biterm topic modeling", abstract="Background: Studying COVID-19 misinformation on Twitter presents methodological challenges. A computational approach can analyze large data sets, but it is limited when interpreting context. A qualitative approach allows for a deeper analysis of content, but it is labor-intensive and feasible only for smaller data sets. Objective: We aimed to identify and characterize tweets containing COVID-19 misinformation. Methods: Tweets geolocated to the Philippines (January 1 to March 21, 2020) containing the words coronavirus, covid, and ncov were mined using the GetOldTweets3 Python library. This primary corpus (N=12,631) was subjected to biterm topic modeling. Key informant interviews were conducted to elicit examples of COVID-19 misinformation and determine keywords. Using NVivo (QSR International) and a combination of word frequency and text search using key informant interview keywords, subcorpus A (n=5881) was constituted and manually coded to identify misinformation. Constant comparative, iterative, and consensual analyses were used to further characterize these tweets. Tweets containing key informant interview keywords were extracted from the primary corpus and processed to constitute subcorpus B (n=4634), of which 506 tweets were manually labeled as misinformation. This training set was subjected to natural language processing to identify tweets with misinformation in the primary corpus. These tweets were further manually coded to confirm labeling. Results: Biterm topic modeling of the primary corpus revealed the following topics: uncertainty, lawmaker's response, safety measures, testing, loved ones, health standards, panic buying, tragedies other than COVID-19, economy, COVID-19 statistics, precautions, health measures, international issues, adherence to guidelines, and frontliners. These were categorized into 4 major topics: nature of COVID-19, contexts and consequences, people and agents of COVID-19, and COVID-19 prevention and management. Manual coding of subcorpus A identified 398 tweets with misinformation in the following formats: misleading content (n=179), satire and/or parody (n=77), false connection (n=53), conspiracy (n=47), and false context (n=42). The discursive strategies identified were humor (n=109), fear mongering (n=67), anger and disgust (n=59), political commentary (n=59), performing credibility (n=45), overpositivity (n=32), and marketing (n=27). Natural language processing identified 165 tweets with misinformation. However, a manual review showed that 69.7\% (115/165) of tweets did not contain misinformation. Conclusions: An interdisciplinary approach was used to identify tweets with COVID-19 misinformation. Natural language processing mislabeled tweets, likely due to tweets written in Filipino or a combination of the Filipino and English languages. Identifying the formats and discursive strategies of tweets with misinformation required iterative, manual, and emergent coding by human coders with experiential and cultural knowledge of Twitter. An interdisciplinary team composed of experts in health, health informatics, social science, and computer science combined computational and qualitative methods to gain a better understanding of COVID-19 misinformation on Twitter. ", doi="10.2196/41134", url="https://formative.jmir.org/2023/1/e41134", url="http://www.ncbi.nlm.nih.gov/pubmed/37220196" } @Article{info:doi/10.2196/45392, author="Yim, Dobin and Khuntia, Jiban and King, Elliot and Treskon, Matthew and Galiatsatos, Panagis", title="Expert Credibility and Sentiment in Infodemiology of Hydroxychloroquine's Efficacy on Cable News Programs: Empirical Analysis", journal="JMIR Infodemiology", year="2023", month="Jun", day="27", volume="3", pages="e45392", keywords="source credibility", keywords="infodemic", keywords="infoveillance", keywords="broadcasting", keywords="cable television", keywords="COVID-19", abstract="Background: Infodemic exacerbates public health concerns by disseminating unreliable and false scientific facts to a population. During the COVID-19 pandemic, the efficacy of hydroxychloroquine as a therapeutic solution emerged as a challenge to public health communication. Internet and social media spread information about hydroxychloroquine, whereas cable television was a vital source. To exemplify, experts discussed in cable television broadcasts about hydroxychloroquine for treating COVID-19. However, how the experts' comments influenced airtime allocation on cable television to help in public health communication, either during COVID-10 or at other times, is not understood. Objective: This study aimed to examine how 3 factors, that is, the credibility of experts as doctors (DOCTOREXPERT), the credibility of government representatives (GOVTEXPERT), and the sentiments (SENTIMENT) expressed in discussions and comments, influence the allocation of airtime (AIRTIME) in cable television broadcasts. SENTIMENT pertains to the information credibility conveyed through the tone and language of experts' comments during cable television broadcasts, in contrast to the individual credibility of the doctor or government representatives because of the degree or affiliations. Methods: We collected transcriptions of relevant hydroxychloroquine-related broadcasts on cable television between March 2020 and October 2020. We coded the experts as DOCTOREXPERT or GOVTEXPERT using publicly available data. To determine the sentiments expressed in the broadcasts, we used a machine learning algorithm to code them as POSITIVE, NEGATIVE, NEUTRAL, or MIXED sentiments. Results: The analysis revealed a counterintuitive association between the expertise of doctors (DOCTOREXPERT) and the allocation of airtime, with doctor experts receiving less airtime (P<.001) than the nonexperts in a base model. A more nuanced interaction model suggested that government experts with a doctorate degree received even less airtime (P=.03) compared with nonexperts. Sentiments expressed during the broadcasts played a significant role in airtime allocation, particularly for their direct effects on airtime allocation, more so for NEGATIVE (P<.001), NEUTRAL (P<.001), and MIXED (P=.03) sentiments. Only government experts expressing POSITIVE sentiments during the broadcast received a more extended airtime (P<.001) than nonexperts. Furthermore, NEGATIVE sentiments in the broadcasts were associated with less airtime both for DOCTOREXPERT (P<.001) and GOVTEXPERT (P<.001). Conclusions: Source credibility plays a crucial role in infodemics by ensuring the accuracy and trustworthiness of the information communicated to audiences. However, cable television media may prioritize likeability over credibility, potentially hindering this goal. Surprisingly, the findings of our study suggest that doctors did not get good airtime on hydroxychloroquine-related discussions on cable television. In contrast, government experts as sources received more airtime on hydroxychloroquine-related discussions. Doctors presenting facts with negative sentiments may not help them gain airtime. Conversely, government experts expressing positive sentiments during broadcasts may have better airtime than nonexperts. These findings have implications on the role of source credibility in public health communications. ", doi="10.2196/45392", url="https://infodemiology.jmir.org/2023/1/e45392", url="http://www.ncbi.nlm.nih.gov/pubmed/37204334" } @Article{info:doi/10.2196/43349, author="Fu, Jiaqi and Li, Chaixiu and Zhou, Chunlan and Li, Wenji and Lai, Jie and Deng, Shisi and Zhang, Yujie and Guo, Zihan and Wu, Yanni", title="Methods for Analyzing the Contents of Social Media for Health Care: Scoping Review", journal="J Med Internet Res", year="2023", month="Jun", day="26", volume="25", pages="e43349", keywords="social media", keywords="health care", keywords="internet information", keywords="content analysis", keywords="big data mining", keywords="review method", keywords="scoping", keywords="online information", keywords="methodology", abstract="Background: Given the rapid development of social media, effective extraction and analysis of the contents of social media for health care have attracted widespread attention from health care providers. As far as we know, most of the reviews focus on the application of social media, and there is a lack of reviews that integrate the methods for analyzing social media information for health care. Objective: This scoping review aims to answer the following 4 questions: (1) What types of research have been used to investigate social media for health care, (2) what methods have been used to analyze the existing health information on social media, (3) what indicators should be applied to collect and evaluate the characteristics of methods for analyzing the contents of social media for health care, and (4) what are the current problems and development directions of methods used to analyze the contents of social media for health care? Methods: A scoping review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted. We searched PubMed, the Web of Science, EMBASE, the Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Library for the period from 2010 to May 2023 for primary studies focusing on social media and health care. Two independent reviewers screened eligible studies against inclusion criteria. A narrative synthesis of the included studies was conducted. Results: Of 16,161 identified citations, 134 (0.8\%) studies were included in this review. These included 67 (50.0\%) qualitative designs, 43 (32.1\%) quantitative designs, and 24 (17.9\%) mixed methods designs. The applied research methods were classified based on the following aspects: (1) manual analysis methods (content analysis methodology, grounded theory, ethnography, classification analysis, thematic analysis, and scoring tables) and computer-aided analysis methods (latent Dirichlet allocation, support vector machine, probabilistic clustering, image analysis, topic modeling, sentiment analysis, and other natural language processing technologies), (2) categories of research contents, and (3) health care areas (health practice, health services, and health education). Conclusions: Based on an extensive literature review, we investigated the methods for analyzing the contents of social media for health care to determine the main applications, differences, trends, and existing problems. We also discussed the implications for the future. Traditional content analysis is still the mainstream method for analyzing social media content, and future research may be combined with big data research. With the progress of computers, mobile phones, smartwatches, and other smart devices, social media information sources will become more diversified. Future research can combine new sources, such as pictures, videos, and physiological signals, with online social networking to adapt to the development trend of the internet. More medical information talents need to be trained in the future to better solve the problem of network information analysis. Overall, this scoping review can be useful for a large audience that includes researchers entering the field. ", doi="10.2196/43349", url="https://www.jmir.org/2023/1/e43349", url="http://www.ncbi.nlm.nih.gov/pubmed/37358900" } @Article{info:doi/10.2196/44586, author="Lotto, Matheus and Zakir Hussain, Irfhana and Kaur, Jasleen and Butt, Ahmad Zahid and Cruvinel, Thiago and Morita, P. Plinio", title="Analysis of Fluoride-Free Content on Twitter: Topic Modeling Study", journal="J Med Internet Res", year="2023", month="Jun", day="20", volume="25", pages="e44586", keywords="fluoride", keywords="health information", keywords="infodemiology", keywords="infoveillance", keywords="misinformation", keywords="social media", keywords="Twitter", keywords="oral care", keywords="healthy lifestyle", keywords="hygiene", abstract="Background: Although social media has the potential to spread misinformation, it can also be a valuable tool for elucidating the social factors that contribute to the onset of negative beliefs. As a result, data mining has become a widely used technique in infodemiology and infoveillance research to combat misinformation effects. On the other hand, there is a lack of studies that specifically aim to investigate misinformation about fluoride on Twitter. Web-based individual concerns on the side effects of fluoridated oral care products and tap water stimulate the emergence and propagation of convictions that boost antifluoridation activism. In this sense, a previous content analysis--driven study demonstrated that the term fluoride-free was frequently associated with antifluoridation interests. Objective: This study aimed to analyze ``fluoride-free'' tweets regarding their topics and frequency of publication over time. Methods: A total of 21,169 tweets published in English between May 2016 and May 2022 that included the keyword ``fluoride-free'' were retrieved by the Twitter application programming interface. Latent Dirichlet allocation (LDA) topic modeling was applied to identify the salient terms and topics. The similarity between topics was calculated through an intertopic distance map. Moreover, an investigator manually assessed a sample of tweets depicting each of the most representative word groups that determined specific issues. Lastly, additional data visualization was performed regarding the total count of each topic of fluoride-free record and its relevance over time, using Elastic Stack software. Results: We identified 3 issues by applying the LDA topic modeling: ``healthy lifestyle'' (topic 1), ``consumption of natural/organic oral care products'' (topic 2), and ``recommendations for using fluoride-free products/measures'' (topic 3). Topic 1 was related to users' concerns about leading a healthier lifestyle and the potential impacts of fluoride consumption, including its hypothetical toxicity. Complementarily, topic 2 was associated with users' personal interests and perceptions of consuming natural and organic fluoride-free oral care products, whereas topic 3 was linked to users' recommendations for using fluoride-free products (eg, switching from fluoridated toothpaste to fluoride-free alternatives) and measures (eg, consuming unfluoridated bottled water instead of fluoridated tap water), comprising the propaganda of dental products. Additionally, the count of tweets on fluoride-free content decreased between 2016 and 2019 but increased again from 2020 onward. Conclusions: Public concerns toward a healthy lifestyle, including the adoption of natural and organic cosmetics, seem to be the main motivation of the recent increase of ``fluoride-free'' tweets, which can be boosted by the propagation of fluoride falsehoods on the web. Therefore, public health authorities, health professionals, and legislators should be aware of the spread of fluoride-free content on social media to create and implement strategies against their potential health damage for the population. ", doi="10.2196/44586", url="https://www.jmir.org/2023/1/e44586", url="http://www.ncbi.nlm.nih.gov/pubmed/37338975" } @Article{info:doi/10.2196/45787, author="Kato, Mio and Yoshimatsu, Fumi and Saito, Tomoya", title="Trends in Media Coverage During the Monkeypox Outbreak: Content Analysis", journal="J Med Internet Res", year="2023", month="Jun", day="19", volume="25", pages="e45787", keywords="risk perception", keywords="protection motivation theory", keywords="agenda setting", keywords="news media", keywords="media", keywords="infectious disease", keywords="monkeypox", doi="10.2196/45787", url="https://www.jmir.org/2023/1/e45787", url="http://www.ncbi.nlm.nih.gov/pubmed/37335596" } @Article{info:doi/10.2196/45897, author="Mao, Lingchao and Chu, Emily and Gu, Jinghong and Hu, Tao and Weiner, J. Bryan and Su, Yanfang", title="A 4D Theoretical Framework for Measuring Topic-Specific Influence on Twitter: Development and Usability Study on Dietary Sodium Tweets", journal="J Med Internet Res", year="2023", month="Jun", day="13", volume="25", pages="e45897", keywords="social media", keywords="health education", keywords="health promotion", keywords="dissemination strategy", keywords="influence", keywords="Twitter", keywords="activity", keywords="priority", keywords="originality", keywords="popularity", abstract="Background: Social media has emerged as a prominent approach for health education and promotion. However, it is challenging to understand how to best promote health-related information on social media platforms such as Twitter. Despite commercial tools and prior studies attempting to analyze influence, there is a gap to fill in developing a publicly accessible and consolidated framework to measure influence and analyze dissemination strategies. Objective: We aimed to develop a theoretical framework to measure topic-specific user influence on Twitter and to examine its usability by analyzing dietary sodium tweets to support public health agencies in improving their dissemination strategies. Methods: We designed a consolidated framework for measuring influence that can capture topic-specific tweeting behaviors. The core of the framework is a summary indicator of influence decomposable into 4 dimensions: activity, priority, originality, and popularity. These measures can be easily visualized and efficiently computed for any Twitter account without the need for private access. We demonstrated the proposed methods by using a case study on dietary sodium tweets with sampled stakeholders and then compared the framework with a traditional measure of influence. Results: More than half a million dietary sodium tweets from 2006 to 2022 were retrieved for 16 US domestic and international stakeholders in 4 categories, that is, public agencies, academic institutions, professional associations, and experts. We discovered that World Health Organization, American Heart Association, Food and Agriculture Organization of the United Nations (UN-FAO), and World Action on Salt (WASH) were the top 4 sodium influencers in the sample. Each had different strengths and weaknesses in their dissemination strategies, and 2 stakeholders with similar overall influence, that is, UN-FAO and WASH, could have significantly different tweeting patterns. In addition, we identified exemplars in each dimension of influence. Regarding tweeting activity, a dedicated expert published more sodium tweets than any organization in the sample in the past 16 years. In terms of priority, WASH had more than half of its tweets dedicated to sodium. UN-FAO had both the highest proportion of original sodium tweets and posted the most popular sodium tweets among all sampled stakeholders. Regardless of excellence in 1 dimension, the 4 most influential stakeholders excelled in at least 2 out of 4 dimensions of influence. Conclusions: Our findings demonstrate that our method not only aligned with a traditional measure of influence but also advanced influence analysis by analyzing the 4 dimensions that contribute to topic-specific influence. This consolidated framework provides quantifiable measures for public health entities to understand their bottleneck of influence and refine their social media campaign strategies. Our framework can be applied to improve the dissemination of other health topics as well as assist policy makers and public campaign experts to maximize population impact. ", doi="10.2196/45897", url="https://www.jmir.org/2023/1/e45897", url="http://www.ncbi.nlm.nih.gov/pubmed/37310774" } @Article{info:doi/10.2196/45187, author="Gresenz, Roan Carole and Singh, Lisa and Wang, Yanchen and Haber, Jaren and Liu, Yaguang", title="Development and Assessment of a Social Media--Based Construct of Firearm Ownership: Computational Derivation and Benchmark Comparison", journal="J Med Internet Res", year="2023", month="Jun", day="13", volume="25", pages="e45187", keywords="criterion validity", keywords="firearms ownership", keywords="gun violence", keywords="machine learning", keywords="social media data", abstract="Background: Gun violence research is characterized by a dearth of data available for measuring key constructs. Social media data may offer a potential opportunity to significantly reduce that gap, but developing methods for deriving firearms-related constructs from social media data and understanding the measurement properties of such constructs are critical precursors to their broader use. Objective: This study aimed to develop a machine learning model of individual-level firearm ownership from social media data and assess the criterion validity of a state-level construct of ownership. Methods: We used survey responses to questions on firearm ownership linked with Twitter data to construct different machine learning models of firearm ownership. We externally validated these models using a set of firearm-related tweets hand-curated from the Twitter Streaming application programming interface and created state-level ownership estimates using a sample of users collected from the Twitter Decahose application programming interface. We assessed the criterion validity of state-level estimates by comparing their geographic variance to benchmark measures from the RAND State-Level Firearm Ownership Database. Results: We found that the logistic regression classifier for gun ownership performs the best with an accuracy of 0.7 and an F1-score of 0.69. We also found a strong positive correlation between Twitter-based estimates of gun ownership and benchmark ownership estimates. For states meeting a threshold requirement of a minimum of 100 labeled Twitter users, the Pearson and Spearman correlation coefficients are 0.63 (P<.001) and 0.64 (P<.001), respectively. Conclusions: Our success in developing a machine learning model of firearm ownership at the individual level with limited training data as well as a state-level construct that achieves a high level of criterion validity underscores the potential of social media data for advancing gun violence research. The ownership construct is an important precursor for understanding the representativeness of and variability in outcomes that have been the focus of social media analyses in gun violence research to date, such as attitudes, opinions, policy stances, sentiments, and perspectives on gun violence and gun policy. The high criterion validity we achieved for state-level gun ownership suggests that social media data may be a useful complement to traditional sources of information on gun ownership such as survey and administrative data, especially for identifying early signals of changes in geographic patterns of gun ownership, given the immediacy of the availability of social media data, their continuous generation, and their responsiveness. These results also lend support to the possibility that other computationally derived, social media--based constructs may be derivable, which could lend additional insight into firearm behaviors that are currently not well understood. More work is needed to develop other firearms-related constructs and to assess their measurement properties. ", doi="10.2196/45187", url="https://www.jmir.org/2023/1/e45187", url="http://www.ncbi.nlm.nih.gov/pubmed/37310779" } @Article{info:doi/10.2196/42363, author="Kim, Hyunuk and Proctor, R. Chris and Walker, Dylan and McCarthy, R. Ronan", title="Understanding the Consumption of Antimicrobial Resistance--Related Content on Social Media: Twitter Analysis", journal="J Med Internet Res", year="2023", month="Jun", day="12", volume="25", pages="e42363", keywords="antimicrobial resistance", keywords="AMR", keywords="social media", keywords="Twitter", keywords="engagement", keywords="antimicrobial", keywords="effective", keywords="public health", keywords="awareness", keywords="disease", keywords="microbiology", keywords="pathogen", keywords="development", abstract="Background: Antimicrobial resistance (AMR) is one of the most pressing concerns in our society. Today, social media can function as an important channel to disseminate information about AMR. The way in which this information is engaged with depends on a number of factors, including the target audience and the content of the social media post. Objective: The aim of this study is to better understand how AMR-related content is consumed on the social media platform Twitter and to understand some of the drivers of engagement. This is essential to designing effective public health strategies, raising awareness about antimicrobial stewardship, and enabling academics to effectively promote their research on social media. Methods: We took advantage of unrestricted access to the metrics associated with the Twitter bot @AntibioticResis, which has over 13,900 followers. This bot posts the latest AMR research in the format of a title and a URL link to the PubMed page for an article. The tweets do not contain other attributes such as author, affiliation, or journal. Therefore, engagement with the tweets is only affected by the words used in the titles. Using negative binomial regression models, we measured the impact of pathogen names in paper titles, academic attention inferred from publication counts, and general attention estimated from Twitter on URL clicks to AMR research papers. Results: Followers of @AntibioticResis consisted primarily of health care professionals and academic researchers whose interests comprised mainly AMR, infectious diseases, microbiology, and public health. Three World Health Organization (WHO) critical priority pathogens---Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacteriaceae---were positively associated with URL clicks. Papers with shorter titles tended to have more engagements. We also described some key linguistic characteristics that should be considered when a researcher is trying to maximize engagement with their publication. Conclusions: Our finding suggests that specific pathogens gain more attention on Twitter than others and that the levels of attention do not necessarily correspond to their status on the WHO priority pathogen list. This suggests that more targeted public health strategies may be needed to raise awareness about AMR among specific pathogens. Analysis of follower data suggests that in the busy schedules of health care professionals, social media offers a fast and accessible gateway to staying abreast of the latest developments in this field. ", doi="10.2196/42363", url="https://www.jmir.org/2023/1/e42363", url="http://www.ncbi.nlm.nih.gov/pubmed/37307042" } @Article{info:doi/10.2196/39484, author="Lane, M. Jamil and Habib, Daniel and Curtis, Brenda", title="Linguistic Methodologies to Surveil the Leading Causes of Mortality: Scoping Review of Twitter for Public Health Data", journal="J Med Internet Res", year="2023", month="Jun", day="12", volume="25", pages="e39484", keywords="Twitter", keywords="public health interventions", keywords="surveillance data", keywords="health communication", keywords="natural language processing", abstract="Background: Twitter has become a dominant source of public health data and a widely used method to investigate and understand public health--related issues internationally. By leveraging big data methodologies to mine Twitter for health-related data at the individual and community levels, scientists can use the data as a rapid and less expensive source for both epidemiological surveillance and studies on human behavior. However, limited reviews have focused on novel applications of language analyses that examine human health and behavior and the surveillance of several emerging diseases, chronic conditions, and risky behaviors. Objective: The primary focus of this scoping review was to provide a comprehensive overview of relevant studies that have used Twitter as a data source in public health research to analyze users' tweets to identify and understand physical and mental health conditions and remotely monitor the leading causes of mortality related to emerging disease epidemics, chronic diseases, and risk behaviors. Methods: A literature search strategy following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extended guidelines for scoping reviews was used to search specific keywords on Twitter and public health on 5 databases: Web of Science, PubMed, CINAHL, PsycINFO, and Google Scholar. We reviewed the literature comprising peer-reviewed empirical research articles that included original research published in English-language journals between 2008 and 2021. Key information on Twitter data being leveraged for analyzing user language to study physical and mental health and public health surveillance was extracted. Results: A total of 38 articles that focused primarily on Twitter as a data source met the inclusion criteria for review. In total, two themes emerged from the literature: (1) language analysis to identify health threats and physical and mental health understandings about people and societies and (2) public health surveillance related to leading causes of mortality, primarily representing 3 categories (ie, respiratory infections, cardiovascular disease, and COVID-19). The findings suggest that Twitter language data can be mined to detect mental health conditions, disease surveillance, and death rates; identify heart-related content; show how health-related information is shared and discussed; and provide access to users' opinions and feelings. Conclusions: Twitter analysis shows promise in the field of public health communication and surveillance. It may be essential to use Twitter to supplement more conventional public health surveillance approaches. Twitter can potentially fortify researchers' ability to collect data in a timely way and improve the early identification of potential health threats. Twitter can also help identify subtle signals in language for understanding physical and mental health conditions. ", doi="10.2196/39484", url="https://www.jmir.org/2023/1/e39484", url="http://www.ncbi.nlm.nih.gov/pubmed/37307062" } @Article{info:doi/10.2196/44356, author="Morita, Pelegrini Plinio and Zakir Hussain, Irfhana and Kaur, Jasleen and Lotto, Matheus and Butt, Ahmad Zahid", title="Tweeting for Health Using Real-time Mining and Artificial Intelligence--Based Analytics: Design and Development of a Big Data Ecosystem for Detecting and Analyzing Misinformation on Twitter", journal="J Med Internet Res", year="2023", month="Jun", day="9", volume="25", pages="e44356", keywords="big data", keywords="deep learning", keywords="infodemics", keywords="misinformation", keywords="social media", keywords="infoveillance", abstract="Background: Digital misinformation, primarily on social media, has led to harmful and costly beliefs in the general population. Notably, these beliefs have resulted in public health crises to the detriment of governments worldwide and their citizens. However, public health officials need access to a comprehensive system capable of mining and analyzing large volumes of social media data in real time. Objective: This study aimed to design and develop a big data pipeline and ecosystem (UbiLab Misinformation Analysis System [U-MAS]) to identify and analyze false or misleading information disseminated via social media on a certain topic or set of related topics. Methods: U-MAS is a platform-independent ecosystem developed in Python that leverages the Twitter V2 application programming interface and the Elastic Stack. The U-MAS expert system has 5 major components: data extraction framework, latent Dirichlet allocation (LDA) topic model, sentiment analyzer, misinformation classification model, and Elastic Cloud deployment (indexing of data and visualizations). The data extraction framework queries the data through the Twitter V2 application programming interface, with queries identified by public health experts. The LDA topic model, sentiment analyzer, and misinformation classification model are independently trained using a small, expert-validated subset of the extracted data. These models are then incorporated into U-MAS to analyze and classify the remaining data. Finally, the analyzed data are loaded into an index in the Elastic Cloud deployment and can then be presented on dashboards with advanced visualizations and analytics pertinent to infodemiology and infoveillance analysis. Results: U-MAS performed efficiently and accurately. Independent investigators have successfully used the system to extract significant insights into a fluoride-related health misinformation use case (2016 to 2021). The system is currently used for a vaccine hesitancy use case (2007 to 2022) and a heat wave--related illnesses use case (2011 to 2022). Each component in the system for the fluoride misinformation use case performed as expected. The data extraction framework handles large amounts of data within short periods. The LDA topic models achieved relatively high coherence values (0.54), and the predicted topics were accurate and befitting to the data. The sentiment analyzer performed at a correlation coefficient of 0.72 but could be improved in further iterations. The misinformation classifier attained a satisfactory correlation coefficient of 0.82 against expert-validated data. Moreover, the output dashboard and analytics hosted on the Elastic Cloud deployment are intuitive for researchers without a technical background and comprehensive in their visualization and analytics capabilities. In fact, the investigators of the fluoride misinformation use case have successfully used the system to extract interesting and important insights into public health, which have been published separately. Conclusions: The novel U-MAS pipeline has the potential to detect and analyze misleading information related to a particular topic or set of related topics. ", doi="10.2196/44356", url="https://www.jmir.org/2023/1/e44356", url="http://www.ncbi.nlm.nih.gov/pubmed/37294603" } @Article{info:doi/10.2196/45184, author="Solans Noguero, David and Ram{\'i}rez-Cifuentes, Diana and R{\'i}ssola, Andr{\'e}s Esteban and Freire, Ana", title="Gender Bias When Using Artificial Intelligence to Assess Anorexia Nervosa on Social Media: Data-Driven Study", journal="J Med Internet Res", year="2023", month="Jun", day="8", volume="25", pages="e45184", keywords="anorexia nervosa", keywords="gender bias", keywords="artificial intelligence", keywords="social media", abstract="Background: Social media sites are becoming an increasingly important source of information about mental health disorders. Among them, eating disorders are complex psychological problems that involve unhealthy eating habits. In particular, there is evidence showing that signs and symptoms of anorexia nervosa can be traced in social media platforms. Knowing that input data biases tend to be amplified by artificial intelligence algorithms and, in particular, machine learning, these methods should be revised to mitigate biased discrimination in such important domains. Objective: The main goal of this study was to detect and analyze the performance disparities across genders in algorithms trained for the detection of anorexia nervosa on social media posts. We used a collection of automated predictors trained on a data set in Spanish containing cases of 177 users that showed signs of anorexia (471,262 tweets) and 326 control cases (910,967 tweets). Methods: We first inspected the predictive performance differences between the algorithms for male and female users. Once biases were detected, we applied a feature-level bias characterization to evaluate the source of such biases and performed a comparative analysis of such features and those that are relevant for clinicians. Finally, we showcased different bias mitigation strategies to develop fairer automated classifiers, particularly for risk assessment in sensitive domains. Results: Our results revealed concerning predictive performance differences, with substantially higher false negative rates (FNRs) for female samples (FNR=0.082) compared with male samples (FNR=0.005). The findings show that biological processes and suicide risk factors were relevant for classifying positive male cases, whereas age, emotions, and personal concerns were more relevant for female cases. We also proposed techniques for bias mitigation, and we could see that, even though disparities can be mitigated, they cannot be eliminated. Conclusions: We concluded that more attention should be paid to the assessment of biases in automated methods dedicated to the detection of mental health issues. This is particularly relevant before the deployment of systems that are thought to assist clinicians, especially considering that the outputs of such systems can have an impact on the diagnosis of people at risk. ", doi="10.2196/45184", url="https://www.jmir.org/2023/1/e45184", url="http://www.ncbi.nlm.nih.gov/pubmed/37289496" } @Article{info:doi/10.2196/43841, author="Edinger, Andy and Valdez, Danny and Walsh-Buhi, Eric and Trueblood, S. Jennifer and Lorenzo-Luaces, Lorenzo and Rutter, A. Lauren and Bollen, Johan", title="Misinformation and Public Health Messaging in the Early Stages of the Mpox Outbreak: Mapping the Twitter Narrative With Deep Learning", journal="J Med Internet Res", year="2023", month="Jun", day="6", volume="25", pages="e43841", keywords="COVID-19", keywords="deep learning", keywords="misinformation", keywords="monkeypox", keywords="mpox", keywords="outbreak", keywords="public health", keywords="social media", keywords="Twitter", abstract="Background: Shortly after the worst of the COVID-19 pandemic, an outbreak of mpox introduced another critical public health emergency. Like the COVID-19 pandemic, the mpox outbreak was characterized by a rising prevalence of public health misinformation on social media, through which many US adults receive and engage with news. Digital misinformation continues to challenge the efforts of public health officials in providing accurate and timely information to the public. We examine the evolving topic distributions of social media narratives during the mpox outbreak to map the tension between rapidly diffusing misinformation and public health communication. Objective: This study aims to observe topical themes occurring in a large-scale collection of tweets about mpox using deep learning. Methods: We leveraged a data set comprised of all mpox-related tweets that were posted between May 7, 2022, and July 23, 2022. We then applied Sentence Bidirectional Encoder Representations From Transformers (S-BERT) to the content of each tweet to generate a representation of its content in high-dimensional vector space, where semantically similar tweets will be located closely together. We projected the set of tweet embeddings to a 2D map by applying principal component analysis and Uniform Manifold Approximation Projection (UMAP). Finally, we group these data points into 7 topical clusters using k-means clustering and analyze each cluster to determine its dominant topics. We analyze the prevalence of each cluster over time to evaluate longitudinal thematic changes. Results: Our deep-learning pipeline revealed 7 distinct clusters of content: (1) cynicism, (2) exasperation, (3) COVID-19, (4) men who have sex with men, (5) case reports, (6) vaccination, and (7) World Health Organization (WHO). Clusters that largely communicated erroneous or irrelevant information began earlier and grew faster, reaching a wider audience than later communications by official instances and health officials. Conclusions: Within a few weeks of the first reported mpox cases, an avalanche of mostly false, misleading, irrelevant, or damaging information started to circulate on social media. Official institutions, including the WHO, acted promptly, providing case reports and accurate information within weeks, but were overshadowed by rapidly spreading social media chatter. Our results point to the need for real-time monitoring of social media content to optimize responses to public health emergencies. ", doi="10.2196/43841", url="https://www.jmir.org/2023/1/e43841", url="http://www.ncbi.nlm.nih.gov/pubmed/37163694" } @Article{info:doi/10.2196/39105, author="Tan, Ying Si and Tang, Sun Matilda Swee and Ong, Johnny Chin-Ann and Tan, Mien Veronique Kiak and Shannon, Brian Nicholas", title="Impact of COVID-19 on Public Interest in Breast Cancer Screening and Related Symptoms: Google Trends Analysis", journal="JMIR Cancer", year="2023", month="Jun", day="6", volume="9", pages="e39105", keywords="breast cancer screening", keywords="breast cancer symptoms", keywords="COVID-19", keywords="public interest", keywords="Google Trends", keywords="screening", keywords="breast cancer", keywords="symptoms", keywords="cancer", keywords="trend", keywords="mammography", keywords="monitoring", abstract="Background: The COVID-19 pandemic has led to a decrease in cancer screening due to the redeployment of health care resources and public avoidance of health care facilities. Breast cancer is the most common cancer diagnosed in female individuals, with improved survival rates from early detection. An avoidance of screening, resulting in late detection, greatly affects survival and increases health care resource burden and costs. Objective: This study aimed to evaluate if a sustained decrease in public interest in screening occurred and to evaluate other search terms, and hence interest, associated with that. Methods: This study used Google Trends to analyze public interest in breast cancer screening and symptoms. We queried search data for 4 keyword terms (``mammogram,'' ``breast pain,'' ``breast lump,'' and ``nipple discharge'') from January 1, 2019, to January 1, 2022. The relative search frequency metric was used to assess interest in these terms, and related queries were retrieved for each keyword to evaluate trends in search patterns. Results: Despite an initial drastic drop in interest in mammography from March to April 2020, this quickly recovered by July 2020. After this period, alongside the recovery of interest in screening, there was a rapid increase in interest for arranging for mammography. Relative search frequencies of perceived breast cancer--related symptoms such as breast lump, nipple discharge, and breast pain remained stable. There was increase public interest in natural and alternative therapy of breast lumps despite the recovery of interest in mammography and breast biopsy. There was a significant correlation between search activity and Breast Cancer Awareness Month in October. Conclusions: Online search interest in breast cancer screening experienced a sharp decline at the beginning of the COVID-19 pandemic, with a subsequent return to baseline interest in arranging for mammography followed this short period of decreased interest. ", doi="10.2196/39105", url="https://cancer.jmir.org/2023/1/e39105", url="http://www.ncbi.nlm.nih.gov/pubmed/37163461" } @Article{info:doi/10.2196/47225, author="Wang, Siqin and Ning, Huan and Huang, Xiao and Xiao, Yunyu and Zhang, Mengxi and Yang, Fan Ellie and Sadahiro, Yukio and Liu, Yan and Li, Zhenlong and Hu, Tao and Fu, Xiaokang and Li, Zi and Zeng, Ye", title="Public Surveillance of Social Media for Suicide Using Advanced Deep Learning Models in Japan: Time Series Study From 2012 to 2022", journal="J Med Internet Res", year="2023", month="Jun", day="2", volume="25", pages="e47225", keywords="suicide", keywords="suicidal ideation", keywords="suicide-risk identification", keywords="natural language processing", keywords="social media", keywords="Japan", abstract="Background: Social media platforms have been increasingly used to express suicidal thoughts, feelings, and acts, raising public concerns over time. A large body of literature has explored the suicide risks identified by people's expressions on social media. However, there is not enough evidence to conclude that social media provides public surveillance for suicide without aligning suicide risks detected on social media with actual suicidal behaviors. Corroborating this alignment is a crucial foundation for suicide prevention and intervention through social media and for estimating and predicting suicide in countries with no reliable suicide statistics. Objective: This study aimed to corroborate whether the suicide risks identified on social media align with actual suicidal behaviors. This aim was achieved by tracking suicide risks detected by 62 million tweets posted in Japan over a 10-year period and assessing the locational and temporal alignment of such suicide risks with actual suicide behaviors recorded in national suicide statistics. Methods: This study used a human-in-the-loop approach to identify suicide-risk tweets posted in Japan from January 2013 to December 2022. This approach involved keyword-filtered data mining, data scanning by human efforts, and data refinement via an advanced natural language processing model termed Bidirectional Encoder Representations from Transformers. The tweet-identified suicide risks were then compared with actual suicide records in both temporal and spatial dimensions to validate if they were statistically correlated. Results: Twitter-identified suicide risks and actual suicide records were temporally correlated by month in the 10 years from 2013 to 2022 (correlation coefficient=0.533; P<.001); this correlation coefficient is higher at 0.652 when we advanced the Twitter-identified suicide risks 1 month earlier to compare with the actual suicide records. These 2 indicators were also spatially correlated by city with a correlation coefficient of 0.699 (P<.001) for the 10-year period. Among the 267 cities with the top quintile of suicide risks identified from both tweets and actual suicide records, 73.5\% (n=196) of cities overlapped. In addition, Twitter-identified suicide risks were at a relatively lower level after midnight compared to a higher level in the afternoon, as well as a higher level on Sundays and Saturdays compared to weekdays. Conclusions: Social media platforms provide an anonymous space where people express their suicidal thoughts, ideation, and acts. Such expressions can serve as an alternative source to estimating and predicting suicide in countries without reliable suicide statistics. It can also provide real-time tracking of suicide risks, serving as an early warning for suicide. The identification of areas where suicide risks are highly concentrated is crucial for location-based mental health planning, enabling suicide prevention and intervention through social media in a spatially and temporally explicit manner. ", doi="10.2196/47225", url="https://www.jmir.org/2023/1/e47225", url="http://www.ncbi.nlm.nih.gov/pubmed/37267022" } @Article{info:doi/10.2196/38323, author="Fahim, Christine and Cooper, Jeanette and Theivendrampillai, Suvabna and Pham, Ba' and Straus, Sharon", title="Ontarians' Perceptions of Public Health Communications and Misinformation During the COVID-19 Pandemic: Survey Study", journal="JMIR Form Res", year="2023", month="Jun", day="2", volume="7", pages="e38323", keywords="misinformation", keywords="information seeking", keywords="COVID-19", keywords="trust", keywords="dissemination", keywords="health communication", keywords="risk", keywords="communication", keywords="policy maker", keywords="transmission", keywords="health emergency", keywords="age", keywords="gender", keywords="survey", abstract="Background: Clear, accurate, and transparent risk communication is critical to providing policy makers and the public with directions to effectively implement public health strategies during a health emergency. Objective: We aimed to explore the public's preferred sources of obtaining COVID-19 information, perceptions on the prevalence and drivers of misinformation during the pandemic, and suggestions to optimize health communications during future public health emergencies. Methods: We administered a web-based survey that included Likert scale, multiple choice and open-ended response questions to residents of Ontario, Canada. We aimed to recruit a sample that reflected population diversity with respect to age and gender. Data were collected between June 10, 2020, and December 31, 2020, and were analyzed using descriptive statistics; open-ended data were analyzed using content analysis. Subgroup analyses to explore perceptions by age and gender were conducted using ordinal regression. Results: A total of 1823 individuals participated in the survey (n=990, 54\% women; n=703, 39\% men; n=982, 54\% aged 18-40 years; n=518, 28\% aged 41-60 years; and n=215, 12\% aged ?61 years). Participants most commonly obtained COVID-19 information from local television news (n=1118, 61\%) followed by social media (n=938, 51\%), national or international television news (n=888, 49\%), and friends and family (n=835, 46\%). Approximately 55\% (n=1010) of the participants believed they had encountered COVID-19--related misinformation; 70\% (n=1284) of the participants reported high levels of trust in health authority websites and health care providers; 66\% (n=1211) reported high levels of trust in health ministers or public health organizations. Sources perceived to be less trustworthy included friends and family, talk radio, social media, as well as blogs and opinion websites. Men were more likely to report encountering misinformation and to trust friends or family (odds ratio [OR] 1.49, 95\% CI 1.24-1.79) and blogs or opinion websites (OR 1.24, 95\% CI 1.03-1.50), compared to women. Compared to those aged 18-40 years, participants aged ?41years were more likely to trust all assessed information sources, with the exception of web-based media sources, and less likely to report encountering misinformation. Of those surveyed, 58\% (n=1053) had challenges identifying or appraising COVID-19 information. Conclusions: Over half of our participants perceived that they had encountered COVID-19 misinformation, and 58\% had challenges identifying or appraising COVID-19 information. Gender and age differences in perceptions of misinformation and trust in information sources were observed. Future research to confirm the validity of these perceptions and to explore information-seeking patterns by population subgroups may provide useful insights on how to optimize health communication during public health emergencies. ", doi="10.2196/38323", url="https://formative.jmir.org/2023/1/e38323", url="http://www.ncbi.nlm.nih.gov/pubmed/37159394" } @Article{info:doi/10.2196/41672, author="Harter, Claire and Ness, Marina and Goldin, Aleah and Lee, Christine and Merenda, Christine and Riberdy, Anne and Saha, Anindita and Araojo, Richardae and Tarver, Michelle", title="Exploring Chronic Pain and Pain Management Perspectives: Qualitative Pilot Analysis of Web-Based Health Community Posts", journal="JMIR Infodemiology", year="2023", month="May", day="30", volume="3", pages="e41672", keywords="chronic pain", keywords="pain management", keywords="online health community", abstract="Background: Patient?perspectives are central to the?US Food and Drug Administration's benefit-risk decision-making process in the evaluation of medical products. Traditional channels of communication may not be feasible for all patients and consumers. Social media websites have increasingly been recognized by researchers as a means to gain insights into patients' views about treatment and diagnostic options, the health care system, and their experiences living with their conditions. Consideration of multiple patient perspective data sources offers the Food and Drug Administration the opportunity to capture diverse patient voices and experiences with chronic pain. Objective: This pilot study explores posts from a web-based patient platform to gain insights into the key challenges and barriers to treatment faced by patients with chronic pain and their caregivers. Methods: This research compiles and analyzes unstructured patient data to draw out the key themes. To extract relevant posts for this study, predefined keywords were identified. Harvested posts were published between January 1, 2017, and October 22, 2019, and had to include \#ChronicPain and at least one other relevant disease tag, a relevant chronic pain management tag, or a chronic pain management tag for a treatment or activity specific to chronic pain. Results: The most common topics discussed among persons living with chronic pain were related to disease burden, the need for support, advocacy, and proper diagnosis. Patients' discussions focused on the negative impact chronic pain had on their emotions, playing sports, or exercising, work and school, sleep, social life, and other activities of daily life. The 2 most frequently discussed treatments were opioids or narcotics and devices such as transcutaneous electrical nerve stimulation machines and spinal cord stimulators. Conclusions: Social listening data may provide valuable insights into patients' and caregivers' perspectives, preferences, and unmet needs, especially when conditions may be highly stigmatized. ", doi="10.2196/41672", url="https://infodemiology.jmir.org/2023/1/e41672", url="http://www.ncbi.nlm.nih.gov/pubmed/37252767" } @Article{info:doi/10.2196/44714, author="Lenti, Jacopo and Mejova, Yelena and Kalimeri, Kyriaki and Panisson, Andr{\'e} and Paolotti, Daniela and Tizzani, Michele and Starnini, Michele", title="Global Misinformation Spillovers in the Vaccination Debate Before and During the COVID-19 Pandemic: Multilingual Twitter Study", journal="JMIR Infodemiology", year="2023", month="May", day="24", volume="3", pages="e44714", keywords="vaccination hesitancy", keywords="vaccine", keywords="misinformation", keywords="Twitter", keywords="social media", keywords="COVID-19", abstract="Background: Antivaccination views pervade online social media, fueling distrust in scientific expertise and increasing the number of vaccine-hesitant individuals. Although previous studies focused on specific countries, the COVID-19 pandemic has brought the vaccination discourse worldwide, underpinning the need to tackle low-credible information flows on a global scale to design effective countermeasures. Objective: This study aimed to quantify cross-border misinformation flows among users exposed to antivaccination (no-vax) content and the effects of content moderation on vaccine-related misinformation. Methods: We collected 316 million vaccine-related Twitter (Twitter, Inc) messages in 18 languages from October 2019 to March 2021. We geolocated users in 28 different countries and reconstructed a retweet network and cosharing network for each country. We identified communities of users exposed to no-vax content by detecting communities in the retweet network via hierarchical clustering and manual annotation. We collected a list of low-credibility domains and quantified the interactions and misinformation flows among no-vax communities of different countries. Results: The findings showed that during the pandemic, no-vax communities became more central in the country-specific debates and their cross-border connections strengthened, revealing a global Twitter antivaccination network. US users are central in this network, whereas Russian users also became net exporters of misinformation during vaccination rollout. Interestingly, we found that Twitter's content moderation efforts, in particular the suspension of users following the January 6 US Capitol attack, had a worldwide impact in reducing the spread of misinformation about vaccines. Conclusions: These findings may help public health institutions and social media platforms mitigate the spread of health-related, low-credibility information by revealing vulnerable web-based communities. ", doi="10.2196/44714", url="https://infodemiology.jmir.org/2023/1/e44714", url="http://www.ncbi.nlm.nih.gov/pubmed/37223965" } @Article{info:doi/10.2196/42097, author="Elkaim, M. Lior and Levett, J. Jordan and Niazi, Farbod and Alvi, A. Mohammed and Shlobin, A. Nathan and Linzey, R. Joseph and Robertson, Faith and Bokhari, Rakan and Alotaibi, M. Naif and Lasry, Oliver", title="Cervical Myelopathy and Social Media: Mixed Methods Analysis", journal="J Med Internet Res", year="2023", month="May", day="22", volume="25", pages="e42097", keywords="social media", keywords="twitter", keywords="cervical", keywords="myelopathy", keywords="spine", keywords="neurological", keywords="condition", keywords="degenerative", keywords="patient", keywords="caretaker", keywords="clinician", keywords="researcher", keywords="user", keywords="tweets", keywords="engagement", keywords="online", keywords="education", keywords="support", abstract="Background: Degenerative cervical myelopathy (DCM) is a progressive neurologic condition caused by age-related degeneration of the cervical spine. Social media has become a crucial part of many patients' lives; however, little is known about social media use pertaining to DCM. Objective: This manuscript describes the landscape of social media use and DCM in patients, caretakers, clinicians, and researchers. Methods: A comprehensive search of the entire Twitter application programing interface database from inception to March 2022 was performed to identify all tweets about cervical myelopathy. Data on Twitter users included geographic location, number of followers, and number of tweets. The number of tweet likes, retweets, quotes, and total engagement were collected. Tweets were also categorized based on their underlying themes. Mentions pertaining to past or upcoming surgical procedures were recorded. A natural language processing algorithm was used to assign a polarity score, subjectivity score, and analysis label to each tweet for sentiment analysis. Results: Overall, 1859 unique tweets from 1769 accounts met the inclusion criteria. The highest frequency of tweets was seen in 2018 and 2019, and tweets decreased significantly in 2020 and 2021. Most (888/1769, 50.2\%) of the tweets' authors were from the United States, United Kingdom, or Canada. Account categorization showed that 668 of 1769 (37.8\%) users discussing DCM on Twitter were medical doctors or researchers, 415 of 1769 (23.5\%) were patients or caregivers, and 201 of 1769 (11.4\%) were news media outlets. The 1859 tweets most often discussed research (n=761, 40.9\%), followed by spreading awareness or informing the public on DCM (n=559, 30.1\%). Tweets describing personal patient perspectives on living with DCM were seen in 296 (15.9\%) posts, with 65 (24\%) of these discussing upcoming or past surgical experiences. Few tweets were related to advertising (n=31, 1.7\%) or fundraising (n=7, 0.4\%). A total of 930 (50\%) tweets included a link, 260 (14\%) included media (ie, photos or videos), and 595 (32\%) included a hashtag. Overall, 847 of the 1859 tweets (45.6\%) were classified as neutral, 717 (38.6\%) as positive, and 295 (15.9\%) as negative. Conclusions: When categorized thematically, most tweets were related to research, followed by spreading awareness or informing the public on DCM. Almost 25\% (65/296) of tweets describing patients' personal experiences with DCM discussed past or upcoming surgical interventions. Few posts pertained to advertising or fundraising. These data can help identify areas for improvement of public awareness online, particularly regarding education, support, and fundraising. ", doi="10.2196/42097", url="https://www.jmir.org/2023/1/e42097", url="http://www.ncbi.nlm.nih.gov/pubmed/37213188" } @Article{info:doi/10.2196/43439, author="Chen, Liuliu and Jeong, Jiwon and Simpkins, Bridgette and Ferrara, Emilio", title="Exploring the Behavior of Users With Attention-Deficit/Hyperactivity Disorder on Twitter: Comparative Analysis of Tweet Content and User Interactions", journal="J Med Internet Res", year="2023", month="May", day="17", volume="25", pages="e43439", keywords="social media", keywords="mental health", keywords="attention-deficit/hyperactivity disorder", keywords="ADHD", keywords="Twitter", keywords="behaviors", keywords="interactions", abstract="Background: With the widespread use of social media, people share their real-time thoughts and feelings via interactions on these platforms, including those revolving around mental health problems. This can provide a new opportunity for researchers to collect health-related data to study and analyze mental disorders. However, as one of the most common mental disorders, there are few studies regarding the manifestations of attention-deficit/hyperactivity disorder (ADHD) on social media. Objective: This study aims to examine and identify the different behavioral patterns and interactions of users with ADHD on Twitter through the text content and metadata of their posted tweets. Methods: First, we built 2 data sets: an ADHD user data set containing 3135 users who explicitly reported having ADHD on Twitter and a control data set made up of 3223 randomly selected Twitter users without ADHD. All historical tweets of users in both data sets were collected. We applied mixed methods in this study. We performed Top2Vec topic modeling to extract topics frequently mentioned by users with ADHD and those without ADHD and used thematic analysis to further compare the differences in contents that were discussed by the 2 groups under these topics. We used a distillBERT sentiment analysis model to calculate the sentiment scores for the emotion categories and compared the sentiment intensity and frequency. Finally, we extracted users' posting time, tweet categories, and the number of followers and followings from the metadata of tweets and compared the statistical distribution of these features between ADHD and non-ADHD groups. Results: In contrast to the control group of the non-ADHD data set, users with ADHD tweeted about the inability to concentrate and manage time, sleep disturbance, and drug abuse. Users with ADHD felt confusion and annoyance more frequently, while they felt less excitement, caring, and curiosity (all P<.001). Users with ADHD were more sensitive to emotions and felt more intense feelings of nervousness, sadness, confusion, anger, and amusement (all P<.001). As for the posting characteristics, compared with controls, users with ADHD were more active in posting tweets (P=.04), especially at night between midnight and 6 AM (P<.001); posting more tweets with original content (P<.001); and following fewer people on Twitter (P<.001). Conclusions: This study revealed how users with ADHD behave and interact differently on Twitter compared with those without ADHD. On the basis of these differences, researchers, psychiatrists, and clinicians can use Twitter as a potentially powerful platform to monitor and study people with ADHD, provide additional health care support to them, improve the diagnostic criteria of ADHD, and design complementary tools for automatic ADHD detection. ", doi="10.2196/43439", url="https://www.jmir.org/2023/1/e43439", url="http://www.ncbi.nlm.nih.gov/pubmed/37195757" } @Article{info:doi/10.2196/42707, author="Braz, Rodrigues Patricia and Moreira, Ricardo Tiago and Ribeiro, Queiroz Andr{\'e}ia and de Faria, Ribeiro Luciane and Carbogim, Costa Fabio da and P{\"u}schel, Ara{\'u}jo Vilanice Alves de and Fhon, Silva Jack Roberto and Freitas, Rezende Eduarda and Pinto, Carvalho Ione and Zacharias, Machado Fabiana Costa and Cruz, Panitz Gylce Eloisa Cabreira and Machado, Miranda Richardson and Santana, Ferreira Rosimere and de Souza, Alfradique Priscilla and Bitencourt, Ribeiro Graziele and Bulgarelli, Favero Alexandre and Cavalcante, Bezerra Ricardo", title="COVID-19 Infodemic and Impacts on the Mental Health of Older People: Cross-sectional Multicenter Survey Study", journal="JMIR Aging", year="2023", month="May", day="17", volume="6", pages="e42707", keywords="information dissemination", keywords="health communication", keywords="COVID-19", keywords="COVID-19 pandemic", keywords="public health", keywords="health of older people", keywords="mental health", abstract="Background: The COVID-19 pandemic received widespread media coverage due to its novelty, an early lack of data, and the rapid rise in deaths and cases. This excessive coverage created a secondary ``infodemic'' that was considered to be a serious public and mental health problem by the World Health Organization and the international scientific community. The infodemic particularly affected older individuals, specifically those who are vulnerable to misinformation due to political positions, low interpretive and critical analysis capacity, and limited technical-scientific knowledge. Thus, it is important to understand older people's reaction to COVID-19 information disseminated by the media and the effect on their lives and mental health. Objective: We aimed to describe the profile of exposure to COVID-19 information among older Brazilian individuals and the impact on their mental health, perceived stress, and the presence of generalized anxiety disorder (GAD). Methods: This cross-sectional, exploratory study surveyed 3307 older Brazilians via the web, social networks, and email between July 2020 and March 2021. Descriptive analysis and bivariate analysis were performed to estimate associations of interest. Results: Major proportions of the 3307 participants were aged 60 to 64 years (n=1285, 38.9\%), female (n=2250, 68.4\%), and married (n=1835, 55.5\%) and self-identified as White (n=2364, 71.5\%). Only 295 (8.9\%) had never started or completed a basic education. COVID-19 information was mainly accessed on television (n=2680, 81.1\%) and social networks (n=1943, 58.8\%). Television exposure was ?3 hours in 1301 (39.3\%) participants, social network use was 2 to 5 hours in 1084 (32.8\%) participants, and radio exposure was ?1 hour in 1223 (37\%) participants. Frequency of exposure to social networks was significantly associated with perceived stress (P=.04) and GAD (P=.01). A Bonferroni post hoc test revealed significantly different perceived stress in participants who were exposed to social networks for 1 hour (P=.04) and those who had no exposure (P=.04). A crude linear regression showed that ``some'' social media use (P=.02) and 1 hour of exposure to social media (P<.001) were associated with perceived stress. Adjusting for sociodemographic variables revealed no associations with this outcome variable. In a crude logistic regression, some social media use (P<.001) and 2 to 5 hours of exposure to social media (P=.03) were associated with GAD. Adjusting for the indicated variables showed that some social network use (P<.001) and 1 hour (P=.04) and 2 to 5 hours (P=.03) of exposure to social media were associated with GAD. Conclusions: Older people, especially women, were often exposed to COVID-19--related information through television and social networks; this affected their mental health, specifically GAD and stress. Thus, the impact of the infodemic should be considered during anamnesis for older people, so that they can share their feelings about it and receive appropriate psychosocial care. ", doi="10.2196/42707", url="https://aging.jmir.org/2023/1/e42707", url="http://www.ncbi.nlm.nih.gov/pubmed/37195762" } @Article{info:doi/10.2196/44186, author="Wang, Zhaohan and He, Jun and Jin, Bolin and Zhang, Lizhi and Han, Chenyu and Wang, Meiqi and Wang, Hao and An, Shuqi and Zhao, Meifang and Zhen, Qing and Tiejun, Shui and Zhang, Xinyao", title="Using Baidu Index Data to Improve Chickenpox Surveillance in Yunnan, China: Infodemiology Study", journal="J Med Internet Res", year="2023", month="May", day="16", volume="25", pages="e44186", keywords="Baidu index", keywords="chickenpox", keywords="support vector machine regression model", keywords="disease surveillance", keywords="disease", keywords="infectious", keywords="vaccine", keywords="surveillance system", keywords="model", keywords="prevention", keywords="control", keywords="monitoring", keywords="epidemic", abstract="Background: Chickenpox is an old but easily neglected infectious disease. Although chickenpox is preventable by vaccines, vaccine breakthroughs often occur, and the chickenpox epidemic is on the rise. Chickenpox is not included in the list of regulated communicable diseases that must be reported and controlled by public and health departments; therefore, it is crucial to rapidly identify and report varicella outbreaks during the early stages. The Baidu index (BDI) can supplement the traditional surveillance system for infectious diseases, such as brucellosis and dengue, in China. The number of reported chickenpox cases and internet search data also showed a similar trend. BDI can be a useful tool to display the outbreak of infectious diseases. Objective: This study aimed to develop an efficient disease surveillance method that uses BDI to assist in traditional surveillance. Methods: Chickenpox incidence data (weekly from January 2017 to June 2021) reported by the Yunnan Province Center for Disease Control and Prevention were obtained to evaluate the relationship between the incidence of chickenpox and BDI. We applied a support vector machine regression (SVR) model and a multiple regression prediction model with BDI to predict the incidence of chickenpox. In addition, we used the SVR model to predict the number of chickenpox cases from June 2021 to the first week of April 2022. Results: The analysis showed that there was a close correlation between the weekly number of newly diagnosed cases and the BDI. In the search terms we collected, the highest Spearman correlation coefficient was 0.747. Most BDI search terms, such as ``chickenpox,'' ``chickenpox treatment,'' ``treatment of chickenpox,'' ``chickenpox symptoms,'' and ``chickenpox virus,'' trend consistently. Some BDI search terms, such as ``chickenpox pictures,'' ``symptoms of chickenpox,'' ``chickenpox vaccine,'' and ``is chickenpox vaccine necessary,'' appeared earlier than the trend of ``chickenpox virus.'' The 2 models were compared, the SVR model performed better in all the applied measurements: fitting effect, R2=0.9108, root mean square error (RMSE)=96.2995, and mean absolute error (MAE)=73.3988; and prediction effect, R2=0.548, RMSE=189.1807, and MAE=147.5412. In addition, we applied the SVR model to predict the number of reported cases weekly in Yunnan from June 2021 to April 2022 using the same period of the BDI. The results showed that the fluctuation of the time series from July 2021 to April 2022 was similar to that of the last year and a half with no change in the level of prevention and control. Conclusions: These findings indicated that the BDI in Yunnan Province can predict the incidence of chickenpox in the same period. Thus, the BDI is a useful tool for monitoring the chickenpox epidemic and for complementing traditional monitoring systems. ", doi="10.2196/44186", url="https://www.jmir.org/2023/1/e44186", url="http://www.ncbi.nlm.nih.gov/pubmed/37191983" } @Article{info:doi/10.2196/40005, author="Pollack, Catherine and Gilbert-Diamond, Diane and Onega, Tracy and Vosoughi, Soroush and O'Malley, James A. and Emond, A. Jennifer", title="Obesity-Related Discourse on Facebook and Instagram Throughout the COVID-19 Pandemic: Comparative Longitudinal Evaluation", journal="JMIR Infodemiology", year="2023", month="May", day="16", volume="3", pages="e40005", keywords="obesity", keywords="Facebook", keywords="Instagram", keywords="COVID-19", keywords="social media", keywords="news", keywords="infodemiology", keywords="public health", keywords="online health information", abstract="Background: COVID-19 severity is amplified among individuals with obesity, which may have influenced mainstream media coverage of the disease by both improving understanding of the condition and increasing weight-related stigma. Objective: We aimed to measure obesity-related conversations on Facebook and Instagram around key dates during the first year of the COVID-19 pandemic. Methods: Public Facebook and Instagram posts were extracted for 29-day windows in 2020 around January 28 (the first US COVID-19 case), March 11 (when COVID-19 was declared a global pandemic), May 19 (when obesity and COVID-19 were linked in mainstream media), and October 2 (when former US president Trump contracted COVID-19 and obesity was mentioned most frequently in the mainstream media). Trends in daily posts and corresponding interactions were evaluated using interrupted time series. The 10 most frequent obesity-related topics on each platform were also examined. Results: On Facebook, there was a temporary increase in 2020 in obesity-related posts and interactions on May 19 (posts +405, 95\% CI 166 to 645; interactions +294,930, 95\% CI 125,986 to 463,874) and October 2 (posts +639, 95\% CI 359 to 883; interactions +182,814, 95\% CI 160,524 to 205,105). On Instagram, there were temporary increases in 2020 only in interactions on May 19 (+226,017, 95\% CI 107,323 to 344,708) and October 2 (+156,974, 95\% CI 89,757 to 224,192). Similar trends were not observed in controls. Five of the most frequent topics overlapped (COVID-19, bariatric surgery, weight loss stories, pediatric obesity, and sleep); additional topics specific to each platform included diet fads, food groups, and clickbait. Conclusions: Social media conversations surged in response to obesity-related public health news. Conversations contained both clinical and commercial content of possibly dubious accuracy. Our findings support the idea that major public health announcements may coincide with the spread of health-related content (truthful or otherwise) on social media. ", doi="10.2196/40005", url="https://infodemiology.jmir.org/2023/1/e40005", url="http://www.ncbi.nlm.nih.gov/pubmed/37191990" } @Article{info:doi/10.2196/46084, author="Xue, Jia and Zhang, Bolun and Zhang, Qiaoru and Hu, Ran and Jiang, Jielin and Liu, Nian and Peng, Yingdong and Li, Ziqian and Logan, Judith", title="Using Twitter-Based Data for Sexual Violence Research: Scoping Review", journal="J Med Internet Res", year="2023", month="May", day="15", volume="25", pages="e46084", keywords="Twitter data", keywords="sexual violence", keywords="sexual assault", keywords="scoping review", keywords="review method", keywords="data analysis", keywords="data collection", keywords="Twitter", keywords="social media", keywords="women's health", keywords="violence", keywords="abuse", keywords="public health", keywords="domestic violence", abstract="Background: Scholars have used data from in-person interviews, administrative systems, and surveys for sexual violence research. Using Twitter as a data source for examining the nature of sexual violence is a relatively new and underexplored area of study. Objective: We aimed to perform a scoping review of the current literature on using Twitter data for researching sexual violence, elaborate on the validity of the methods, and discuss the implications and limitations of existing studies. Methods: We performed a literature search in the following 6 databases: APA PsycInfo (Ovid), Scopus, PubMed, International Bibliography of Social Sciences (ProQuest), Criminal Justice Abstracts (EBSCO), and Communications Abstracts (EBSCO), in April 2022. The initial search identified 3759 articles that were imported into Covidence. Seven independent reviewers screened these articles following 2 steps: (1) title and abstract screening, and (2) full-text screening. The inclusion criteria were as follows: (1) empirical research, (2) focus on sexual violence, (3) analysis of Twitter data (ie, tweets or Twitter metadata), and (4) text in English. Finally, we selected 121 articles that met the inclusion criteria and coded these articles. Results: We coded and presented the 121 articles using Twitter-based data for sexual violence research. About 70\% (89/121, 73.6\%) of the articles were published in peer-reviewed journals after 2018. The reviewed articles collectively analyzed about 79.6 million tweets. The primary approaches to using Twitter as a data source were content text analysis (112/121, 92.5\%) and sentiment analysis (31/121, 25.6\%). Hashtags (103/121, 85.1\%) were the most prominent metadata feature, followed by tweet time and date, retweets, replies, URLs, and geotags. More than a third of the articles (51/121, 42.1\%) used the application programming interface to collect Twitter data. Data analyses included qualitative thematic analysis, machine learning (eg, sentiment analysis, supervised machine learning, unsupervised machine learning, and social network analysis), and quantitative analysis. Only 10.7\% (13/121) of the studies discussed ethical considerations. Conclusions: We described the current state of using Twitter data for sexual violence research, developed a new taxonomy describing Twitter as a data source, and evaluated the methodologies. Research recommendations include the following: development of methods for data collection and analysis, in-depth discussions about ethical norms, exploration of specific aspects of sexual violence on Twitter, examination of tweets in multiple languages, and decontextualization of Twitter data. This review demonstrates the potential of using Twitter data in sexual violence research. ", doi="10.2196/46084", url="https://www.jmir.org/2023/1/e46084", url="http://www.ncbi.nlm.nih.gov/pubmed/37184899" } @Article{info:doi/10.2196/46655, author="Swanson, Karl and Ravi, Akshay and Saleh, Sameh and Weia, Benjamin and Pleasants, Elizabeth and Arvisais-Anhalt, Simone", title="Effect of Recent Abortion Legislation on Twitter User Engagement, Sentiment, and Expressions of Trust in Clinicians and Privacy of Health Information: Content Analysis", journal="J Med Internet Res", year="2023", month="May", day="12", volume="25", pages="e46655", keywords="Roe v Wade", keywords="Dobbs v Jackson's Women's Health Organization", keywords="abortion", keywords="family planning", keywords="sentiment analysis", keywords="women's rights", keywords="twitter", keywords="trust", keywords="natural language processing", keywords="legislation", keywords="social media", keywords="reproductive health care", keywords="health information", keywords="users", abstract="Background: The Supreme Court ruling in Dobbs v Jackson Women's Health Organization (Dobbs) overrules precedents established by Roe v Wade and Planned Parenthood v Casey and allows states to individually regulate access to abortion care services. While many states have passed laws to protect access to abortion services since the ruling, the ruling has also triggered the enforcement of existing laws and the creation of new ones that ban or restrict abortion. In addition to denying patients the full spectrum of reproductive health care, one major concern in the medical community is how the ruling will undermine trust in the patient-clinician relationship by influencing perceptions of the privacy of patient health information. Objective: This study aimed to study the effect of recent abortion legislation on Twitter user engagement, sentiment, expressions of trust in clinicians, and privacy of health information. Methods: We scraped tweets containing keywords of interest between January 1, 2020, and October 17, 2022, to capture tweets posted before and after the leak of the Supreme Court decision. We then trained a Latent Dirichlet Allocation model to select tweets pertinent to the topic of interest and performed a sentiment analysis using Robustly Optimized Bidirectional Encoder Representations from Transformers Pre-training Approach model and a causal impact time series analysis to examine engagement and sentiment. In addition, we used a Word2Vec model to study the terms of interest against a latent trust dimension to capture how expressions of trust for our terms of interest changed over time and used term frequency, inverse-document frequency to measure the volume of tweets before and after the decision with respect to the negative and positive sentiments that map to our terms of interest. Results: Our study revealed (1) a transient increase in the number of daily users by 576.86\% (95\% CI 545.34\%-607.92\%; P<.001), tweeting about abortion, health care, and privacy of health information postdecision leak; (2) a sustained and statistically significant decrease in the average daily sentiment on these topics by 19.81\% (95\% CI ?22.98\% to ?16.59\%; P=.001) postdecision leak; (3) a decrease in the association of the latent dimension of trust across most clinician-related and health information--related terms of interest; (4) an increased frequency of tweets with these clinician-related and health information--related terms and concomitant negative sentiment in the postdecision leak period. Conclusions: The study suggests that the Dobbs ruling has consequences for health systems and reproductive health care that extend beyond denying patients access to the full spectrum of reproductive health services. The finding of a decrease in the expression of trust in clinicians and health information--related terms provides evidence to support advocacy and initiatives that proactively address concerns of trust in health systems and services. ", doi="10.2196/46655", url="https://www.jmir.org/2023/1/e46655", url="http://www.ncbi.nlm.nih.gov/pubmed/37171873" } @Article{info:doi/10.2196/43046, author="Li, Xuan and Tang, Kun", title="The Effects of Online Health Information--Seeking Behavior on Sexually Transmitted Disease in China: Infodemiology Study of the Internet Search Queries", journal="J Med Internet Res", year="2023", month="May", day="12", volume="25", pages="e43046", keywords="sexually transmitted infections", keywords="Baidu search index", keywords="Baidu search rate", keywords="online health information-seeking behavior", keywords="long-term effect", keywords="effect", keywords="disease", keywords="internet", keywords="prevention", keywords="data", keywords="treatment", keywords="surveillance", abstract="Background: Sexually transmitted diseases (STDs) are a serious issue worldwide. With the popularity of the internet, online health information-seeking behavior (OHISB) has been widely adopted to improve health and prevent disease. Objective: This study aimed to investigate the short-term and long-term effects of different types of OHISBs on STDs, including syphilis, gonorrhea, and AIDS due to HIV, based on the Baidu index. Methods: Multisource big data were collected, including case numbers of STDs, search queries based on the Baidu index, provincial total population, male-female ratio, the proportion of the population older than 65 years, gross regional domestic product (GRDP), and health institution number data in 2011-2018 in mainland China. We categorized OHISBs into 4 types: concept, symptoms, treatment, and prevention. Before and after controlling for socioeconomic and medical conditions, we applied multiple linear regression to analyze associations between the Baidu search index (BSI) and Baidu search rate (BSR) and STD case numbers. In addition, we compared the effects of 4 types of OHISBs and performed time lag cross-correlation analyses to investigate the long-term effect of OHISB. Results: The distributions of both STD case numbers and OHISBs presented variability. For case number, syphilis, and gonorrhea, cases were mainly distributed in southeastern and northwestern areas of China, while HIV/AIDS cases were mostly distributed in southwestern areas. For the search query, the eastern region had the highest BSI and BSR, while the western region had the lowest ones. For 4 types of OHISB for 3 diseases, the BSI was positively related to the case number, while the BSR was significantly negatively related to the case number (P<.05). Different categories of OHISB have different effects on STD case numbers. Searches for prevention tended to have a larger impact, while searches for treatment tended to have a smaller impact. Besides, due to the time lag effect, those impacts would increase over time. Conclusions: Our study validated the significant associations between 4 types of OHISBs and STD case numbers, and the impact of OHISBs on STDs became stronger over time. It may provide insights into how to use internet big data to better achieve disease surveillance and prevention goals. ", doi="10.2196/43046", url="https://www.jmir.org/2023/1/e43046", url="http://www.ncbi.nlm.nih.gov/pubmed/37171864" } @Article{info:doi/10.2196/45684, author="Roberts-Lewis, F. Sarah and Baxter, A. Helen and Mein, Gill and Quirke-McFarlane, Sophia and Leggat, J. Fiona and Garner, M. Hannah and Powell, Martha and White, Sarah and Bearne, Lindsay", title="The Use of Social Media for Dissemination of Research Evidence to Health and Social Care Practitioners: Protocol for a Systematic Review", journal="JMIR Res Protoc", year="2023", month="May", day="12", volume="12", pages="e45684", keywords="dissemination", keywords="health care", keywords="podcast", keywords="practitioners", keywords="research evidence", keywords="social care", keywords="social media", keywords="social networking", keywords="Twitter", keywords="videos", abstract="Background: Effective dissemination of research to health and social care practitioners enhances clinical practice and evidence-based care. Social media use has potential to facilitate dissemination to busy practitioners. Objective: This is a protocol for a systematic review that will quantitatively synthesize evidence of the effectiveness of social media, compared with no social media, for dissemination of research evidence to health and social care practitioners. Social media platforms, formats, and sharing mechanisms used for effective dissemination of research evidence will also be identified and compared. Methods: Electronic database searches (MEDLINE, PsycINFO, CINAHL, ERIC, LISTA, and OpenGrey) will be conducted from January 1, 2010, to January 10, 2023, for studies published in English. Randomized, nonrandomized, pre-post study designs or case studies evaluating the effect of social media on dissemination of research evidence to postregistration health and social care practitioners will be included. Studies that do not involve social media or dissemination or those that evaluate dissemination of nonresearch information (eg, multisource educational materials) to students or members of the public only, or without quantitative data on outcomes of interest, will be excluded. Screening will be carried out by 2 independent reviewers. Data extraction and quality assessment, using either the Cochrane tool for assessing risk of bias or the Newcastle-Ottawa Scale, will be completed by 2 independent reviewers. Outcomes of interest will be reported in 4 domains (reach, engagement, dissemination, and impact). Data synthesis will include quantitative comparisons using narrative text, tables, and figures. A meta-analysis of standardized pooled effects will be undertaken, and subgroup analyses will be applied, if appropriate. Results: Searches and screening will be completed by the end of May 2023. Data extraction and analyses will be completed by the end of July 2023, after which findings will be synthesized and reported by the end of October 2023. Conclusions: This systematic review will summarize the evidence for the effectiveness of social media for the dissemination of research evidence to health and social care practitioners. The limitations of the evidence may include multiple outcomes or methodological heterogeneity that limit meta-analyses, potential risk of bias in included studies, and potential publication bias. The limitations of the study design may include potential insensitivity of the electronic database search strategy. The findings from this review will inform the dissemination practice of health and care research. Trial Registration: PROSPERO CRD42022378793; https://www.crd.york.ac.uk/prospero/display\_record.php?RecordID=378793 International Registered Report Identifier (IRRID): DERR1-10.2196/45684 ", doi="10.2196/45684", url="https://www.researchprotocols.org/2023/1/e45684", url="http://www.ncbi.nlm.nih.gov/pubmed/37171840" } @Article{info:doi/10.2196/44307, author="Maleki, Negar and Padmanabhan, Balaji and Dutta, Kaushik", title="The Effect of Monetary Incentives on Health Care Social Media Content: Study Based on Topic Modeling and Sentiment Analysis", journal="J Med Internet Res", year="2023", month="May", day="11", volume="25", pages="e44307", keywords="health care analytics", keywords="social media", keywords="incentive mechanisms", keywords="content analysis", keywords="contrastive topic modeling", abstract="Background: While there is high-quality online health information, a lot of recent work has unfortunately highlighted significant issues with the health content on social media platforms (eg, fake news and misinformation), the consequences of which are severe in health care. One solution is to investigate methods that encourage users to post high-quality content. Objective: Incentives have been shown to work in many domains, but until recently, there was no method to provide financial incentives easily on social media for users to generate high-quality content. This study investigates the following question: What effect does the provision of incentives have on the creation of social media health care content? Methods: We analyzed 8328 health-related posts from an incentive-based platform (Steemit) and 1682 health-related posts from a traditional platform (Reddit). Using topic modeling and sentiment analysis--based methods in machine learning, we analyzed these posts across the following 3 dimensions: (1) emotion and language style using the IBM Watson Tone Analyzer service, (2) topic similarity and difference from contrastive topic modeling, and (3) the extent to which posts resemble clickbait. We also conducted a survey using 276 Amazon Mechanical Turk (MTurk) users and asked them to score the quality of Steemit and Reddit posts. Results: Using the Watson Tone Analyzer in a sample of 2000 posts from Steemit and Reddit, we found that more than double the number of Steemit posts had a confident language style compared with Reddit posts (77 vs 30). Moreover, 50\% more Steemit posts had analytical content and 33\% less Steemit posts had a tentative language style compared with Reddit posts (619 vs 430 and 416 vs 627, respectively). Furthermore, more than double the number of Steemit posts were considered joyful compared with Reddit posts (435 vs 200), whereas negative posts (eg, sadness, fear, and anger) were 33\% less on Steemit than on Reddit (384 vs 569). Contrastive topic discovery showed that only 20\% (2/10) of topics were common, and Steemit had more unique topics than Reddit (5 vs 3). Qualitatively, Steemit topics were more informational, while Reddit topics involved discussions, which may explain some of the quantitative differences. Manual labeling marked more Steemit headlines as clickbait than Reddit headlines (66 vs 26), and machine learning model labeling consistently identified a higher percentage of Steemit headlines as clickbait than Reddit headlines. In the survey, MTurk users said that at least 57\% of Steemit posts had better quality than Reddit posts, and they were at least 52\% more likely to like and comment on Steemit posts than Reddit posts. Conclusions: It is becoming increasingly important to ensure high-quality health content on social media; therefore, incentive-based social media could be important in the design of next-generation social platforms for health information. ", doi="10.2196/44307", url="https://www.jmir.org/2023/1/e44307", url="http://www.ncbi.nlm.nih.gov/pubmed/37166952" } @Article{info:doi/10.2196/43596, author="Keddem, Shimrit and Agha, Aneeza and Morawej, Sabrina and Buck, Amy and Cronholm, Peter and Sonalkar, Sarita and Kearney, Matthew", title="Characterizing Twitter Content About HIV Pre-exposure Prophylaxis (PrEP) for Women: Qualitative Content Analysis", journal="J Med Internet Res", year="2023", month="May", day="11", volume="25", pages="e43596", keywords="HIV pre-exposure prophylaxis", keywords="women", keywords="Twitter", keywords="social media", keywords="health communication", keywords="communication", keywords="HIV", keywords="barrier", keywords="awareness", keywords="tweets", keywords="application", keywords="prevention", abstract="Background: HIV remains a persistent health problem in the United States, especially among women. Approved in 2012, HIV pre-exposure prophylaxis (PrEP) is a daily pill or bimonthly injection that can be taken by individuals at increased risk of contracting HIV to reduce their risk of new infection. Women who are at risk of HIV face numerous barriers to HIV services and information, underscoring the critical need for strategies to increase awareness of evidence-based HIV prevention methods, such as HIV PrEP, among women. Objective: We aimed to identify historical trends in the use of Twitter hashtags specific to women and HIV PrEP and explore content about women and PrEP shared through Twitter. Methods: This was a qualitative descriptive study using a purposive sample of tweets containing hashtags related to women and HIV PrEP from 2009 to 2022. Tweets were collected via Twitter's API. Each Twitter user profile, tweet, and related links were coded using content analysis, guided by the framework of the Health Belief Model (HBM) to generate results. We used a factor analysis to identify salient clusters of tweets. Results: A total of 1256 tweets from 396 unique users were relevant to our study focus of content about PrEP specifically for women (1256/2908, 43.2\% of eligible tweets). We found that this sample of tweets was posted mostly by organizations. The 2 largest groups of individual users were activists and advocates (61/396, 15.4\%) and personal users (54/396, 13.6\%). Among individual users, most were female (100/166, 60\%) and American (256/396, 64.6\%). The earliest relevant tweet in our sample was posted in mid-2014 and the number of tweets significantly decreased after 2018. We found that 61\% (496/820) of relevant tweets contained links to informational websites intended to provide guidance and resources or promote access to PrEP. Most tweets specifically targeted people of color, including through the use of imagery and symbolism. In addition to inclusive imagery, our factor analysis indicated that more than a third of tweets were intended to share information and promote PrEP to people of color. Less than half of tweets contained any HBM concepts, and only a few contained cues to action. Lastly, while our sample included only tweets relevant to women, we found that the tweets directed to lesbian, gay, bisexual, transgender, queer (LGBTQ) audiences received the highest levels of audience engagement. Conclusions: These findings point to several areas for improvement in future social media campaigns directed at women about PrEP. First, future posts would benefit from including more theoretical constructs, such as self-efficacy and cues to action. Second, organizations posting on Twitter should continue to broaden their audience and followers to reach more people. Lastly, tweets should leverage the momentum and strategies used by the LGBTQ community to reach broader audiences and destigmatize PrEP use across all communities. ", doi="10.2196/43596", url="https://www.jmir.org/2023/1/e43596", url="http://www.ncbi.nlm.nih.gov/pubmed/37166954" } @Article{info:doi/10.2196/37540, author="Ondrikova, Nikola and Harris, P. John and Douglas, Amy and Hughes, E. Helen and Iturriza-Gomara, Miren and Vivancos, Roberto and Elliot, J. Alex and Cunliffe, A. Nigel and Clough, E. Helen", title="Predicting Norovirus in England Using Existing and Emerging Syndromic Data: Infodemiology Study", journal="J Med Internet Res", year="2023", month="May", day="8", volume="25", pages="e37540", keywords="syndromic data", keywords="syndromic surveillance", keywords="surveillance", keywords="infodemiology", keywords="norovirus", keywords="Google Trends", keywords="Wikipedia", keywords="prediction", keywords="variable importance", keywords="mental model", keywords="infoveillance", keywords="trend", keywords="gastroenteritis", keywords="gastroenterology", keywords="gastroenterologist", keywords="internal medicine", keywords="viral disease", keywords="viral", keywords="virus", keywords="communicable disease", keywords="infection prevention", keywords="infection control", keywords="infectious disease", keywords="viral infection", keywords="disease spread", keywords="big data", keywords="Granger causality framework", keywords="predict", keywords="model", keywords="web-based data", keywords="internet data", keywords="transmission", abstract="Background: Norovirus is associated with approximately 18\% of the global burden of gastroenteritis and affects all age groups. There is currently no licensed vaccine or available antiviral treatment. However, well-designed early warning systems and forecasting can guide nonpharmaceutical approaches to norovirus infection prevention and control. Objective: This study evaluates the predictive power of existing syndromic surveillance data and emerging data sources, such as internet searches and Wikipedia page views, to predict norovirus activity across a range of age groups across England. Methods: We used existing syndromic surveillance and emerging syndromic data to predict laboratory data indicating norovirus activity. Two methods are used to evaluate the predictive potential of syndromic variables. First, the Granger causality framework was used to assess whether individual variables precede changes in norovirus laboratory reports in a given region or an age group. Then, we used random forest modeling to estimate the importance of each variable in the context of others with two methods: (1) change in the mean square error and (2) node purity. Finally, these results were combined into a visualization indicating the most influential predictors for norovirus laboratory reports in a specific age group and region. Results: Our results suggest that syndromic surveillance data include valuable predictors for norovirus laboratory reports in England. However, Wikipedia page views are less likely to provide prediction improvements on top of Google Trends and Existing Syndromic Data. Predictors displayed varying relevance across age groups and regions. For example, the random forest modeling based on selected existing and emerging syndromic variables explained 60\% variance in the ?65 years age group, 42\% in the East of England, but only 13\% in the South West region. Emerging data sets highlighted relative search volumes, including ``flu symptoms,'' ``norovirus in pregnancy,'' and norovirus activity in specific years, such as ``norovirus 2016.'' Symptoms of vomiting and gastroenteritis in multiple age groups were identified as important predictors within existing data sources. Conclusions: Existing and emerging data sources can help predict norovirus activity in England in some age groups and geographic regions, particularly, predictors concerning vomiting, gastroenteritis, and norovirus in the vulnerable populations and historical terms such as stomach flu. However, syndromic predictors were less relevant in some age groups and regions likely due to contrasting public health practices between regions and health information--seeking behavior between age groups. Additionally, predictors relevant to one norovirus season may not contribute to other seasons. Data biases, such as low spatial granularity in Google Trends and especially in Wikipedia data, also play a role in the results. Moreover, internet searches can provide insight into mental models, that is, an individual's conceptual understanding of norovirus infection and transmission, which could be used in public health communication strategies. ", doi="10.2196/37540", url="https://www.jmir.org/2023/1/e37540", url="http://www.ncbi.nlm.nih.gov/pubmed/37155231" } @Article{info:doi/10.2196/45281, author="Simhadri, Suguna and Yalamanchi, Sriha and Stone, Sean and Srinivasan, Mythily", title="Perceptions on Oral Ulcers From Facebook Page Categories: Observational Study", journal="JMIR Form Res", year="2023", month="May", day="8", volume="7", pages="e45281", keywords="oral ulcer", keywords="internet", keywords="Facebook", keywords="information", keywords="apthous stomatitis", keywords="cold sore", abstract="Background: Oral ulcers are a common condition affecting a considerable proportion of the population, and they are often associated with trauma and stress. They are very painful, and interfere with eating. As they are usually considered an annoyance, people may turn to social media for potential management options. Facebook is one of the most commonly accessed social media platforms and is the primary source of news information, including health information, for a significant percentage of American adults. Given the increasing importance of social media as a source of health information, potential remedies, and prevention strategies, it is essential to understand the type and quality of information available on Facebook regarding oral ulcers. Objective: The goal of our study was to evaluate information on recurrent oral ulcers that can be accessed via the most popular social media network---Facebook. Methods: We performed a keyword search of Facebook pages on 2 consecutive days in March 2022, using duplicate, newly created accounts, and then anonymized all posts. The collected pages were filtered, using predefined criteria to include only English-language pages wherein oral ulcer information was posted by the general public and to exclude pages created by professional dentists, associated professionals, organizations, and academic researchers. The selected pages were then screened for page origin and Facebook categories. Results: Our initial keyword search yielded 517 pages; interestingly however, only 112 (22\%) of pages had information relevant to oral ulcers, and 405 (78\%) had irrelevant information, with ulcers being mentioned in relation to other parts of the human body. Excluding professional pages and pages without relevant posts resulted in 30 pages, of which 9 (30\%) were categorized as ``health/beauty'' pages or as ``product/service'' pages, 3 (10\%) were categorized as ``medical \& health'' pages, and 5 (17\%) were categorized as ``community'' pages. Majority of the pages (22/30, 73\%) originated from 6 countries; most originated from the United States (7 pages), followed by India (6 pages). There was little information on oral ulcer prevention, long-term treatment, and complications. Conclusions: Facebook, in oral ulcer information dissemination, appears to be primarily used as an adjunct to business enterprises for marketing or for enhancing access to a product. Consequently, it was unsurprising that there was little information on oral ulcer prevention, long-term treatment, and complications. Although we made efforts to identify and select Facebook pages related to oral ulcers, we did not manually verify the authenticity or accuracy of the pages included in our analysis, potentially limiting the reliability of our findings or resulting in bias toward specific products or services. Although this work forms something of a pilot project, we plan to expand the project to encompass text mining for content analysis and include multiple social media platforms in the future. ", doi="10.2196/45281", url="https://formative.jmir.org/2023/1/e45281", url="http://www.ncbi.nlm.nih.gov/pubmed/37155234" } @Article{info:doi/10.2196/44754, author="Malhotra, Kashish and Dagli, Marcel Mert and Santangelo, Gabrielle and Wathen, Connor and Ghenbot, Yohannes and Goyal, Kashish and Bawa, Ashvind and Ozturk, K. Ali and Welch, C. William", title="The Digital Impact of Neurosurgery Awareness Month: Retrospective Infodemiology Study", journal="JMIR Form Res", year="2023", month="May", day="8", volume="7", pages="e44754", keywords="\#NeurosurgeryAwarenessMonth", keywords="\#Neurosurgery", keywords="Neurosurgery Awareness Month", keywords="neurosurgery", keywords="neural", keywords="neuro", keywords="health care awareness event", keywords="health care", keywords="awareness", keywords="infodemiology", keywords="social media", keywords="campaign", keywords="neuroscience", keywords="neurological", keywords="sentiment", keywords="public opinion", keywords="Google Trends", keywords="tweet", keywords="Twitter", keywords="brain", keywords="cognition", keywords="cognitive", keywords="machine learning algorithm", keywords="network analysis", keywords="digital media", keywords="sentiment analysis", keywords="node", keywords="Sentiment Viz", keywords="scatterplot", keywords="circumplex model", abstract="Background: Neurosurgery Awareness Month (August) was initiated by the American Association of Neurological Surgeons with the aim of bringing neurological conditions to the forefront and educating the public about these conditions. Digital media is an important tool for disseminating information and connecting with influencers, general public, and other stakeholders. Hence, it is crucial to understand the impact of awareness campaigns such as Neurosurgery Awareness Month to optimize resource allocation, quantify the efficiency and reach of these initiatives, and identify areas for improvement. Objective: The purpose of our study was to examine the digital impact of Neurosurgery Awareness Month globally and identify areas for further improvement. Methods: We used 4 social media (Twitter) assessment tools (Sprout Social, SocioViz, Sentiment Viz, and Symplur) and Google Trends to extract data using various search queries. Using regression analysis, trends were studied in the total number of tweets posted in August between 2014 and 2022. Two search queries were used in this analysis: one specifically targeting tweets related to Neurosurgery Awareness Month and the other isolating all neurosurgery-related posts. Total impressions and top influencers for \#neurosurgery were calculated using Symplur's machine learning algorithm. To study the context of the tweets, we used SocioViz to isolate the top 100 popular hashtags, keywords, and collaborations between influencers. Network analysis was performed to illustrate the interactions and connections within the digital media environment using ForceAtlas2 model. Sentiment analysis was done to study the underlying emotion of the tweets. Google Trends was used to study the global search interest by studying relative search volume data. Results: A total of 10,007 users were identified as tweeting about neurosurgery during Neurosurgery Awareness Month using the ``\#neurosurgery'' hashtag. These tweets generated over 29.14 million impressions globally. Of the top 10 most influential users, 5 were faculty neurosurgeons at US university hospitals. Other influential users included notable organizations and journals in the field of neurosurgery. The network analysis of the top 100 influencers showed a collaboration rate of 81\%. However, only 1.6\% of the total neurosurgery tweets were advocating about neurosurgery awareness during Neurosurgery Awareness Month, and only 13 tweets were posted by verified users using the \#neurosurgeryawarenessmonth hashtag. The sentiment analysis revealed that the majority of the tweets about Neurosurgery Awareness Month were pleasant with subdued emotion. Conclusions: The global digital impact of Neurosurgery Awareness Month is nascent, and support from other international organizations and neurosurgical influencers is needed to yield a significant digital reach. Increasing collaboration and involvement from underrepresented communities may help to increase the global reach. By better understanding the digital impact of Neurosurgery Awareness Month, future health care awareness campaigns can be optimized to increase global awareness of neurosurgery and the challenges facing the field. ", doi="10.2196/44754", url="https://formative.jmir.org/2023/1/e44754", url="http://www.ncbi.nlm.nih.gov/pubmed/37155226" } @Article{info:doi/10.2196/40461, author="Goadsby, Peter and Ruiz de la Torre, Elena and Constantin, Luminita and Amand, Caroline", title="Social Media Listening and Digital Profiling Study of People With Headache and Migraine: Retrospective Infodemiology Study", journal="J Med Internet Res", year="2023", month="May", day="5", volume="25", pages="e40461", keywords="brand, headache", keywords="internet", keywords="migraine", keywords="social media", keywords="social support", keywords="self-management", keywords="management", keywords="digital", keywords="technology", keywords="symptoms", keywords="medicinal treatment", keywords="treatment", keywords="Twitter", keywords="blog", keywords="Youtube", keywords="drugs", keywords="ibuprofen", keywords="hydration", keywords="relaxation", abstract="Background: There is an unmet need for a better understanding and management of headache, particularly migraine, beyond specialist centers, which may be facilitated using digital technology. Objective: The objective of this study was to identify where, when, and how people with headache and migraine describe their symptoms and the nonpharmaceutical and medicinal treatments used as indicated on social media. Methods: Social media sources, including Twitter, web-based forums, blogs, YouTube, and review sites, were searched using a predefined search string related to headache and migraine. The real-time data from social media posts were collected retrospectively for a 1-year period from January 1, 2018, to December 31, 2018 (Japan), or a 2-year period from January 1, 2017, to December 31, 2018 (Germany and France). The data were analyzed after collection, using content analysis and audience profiling. Results: A total of 3,509,828 social media posts related to headache and migraine were obtained from Japan in 1 year and 146,257 and 306,787 posts from Germany and France, respectively, in 2 years. Among social media sites, Twitter was the most used platform across these countries. Japanese sufferers used specific terminology, such as ``tension headaches'' or ``cluster headaches'' (36\%), whereas French sufferers even mentioned specific migraine types, such as ocular (7\%) and aura (2\%). The most detailed posts on headache or migraine were from Germany. The French sufferers explicitly mentioned ``headache or migraine attacks'' in the ``evening (41\%) or morning (38\%),'' whereas Japanese mentioned ``morning (48\%) or night (27\%)'' and German sufferers mentioned ``evening (22\%) or night (41\%).'' The use of ``generic terms'' such as medicine, tablet, and pill were prevalent. The most discussed drugs were ibuprofen and naproxen combination (43\%) in Japan; ibuprofen (29\%) in Germany; and acetylsalicylic acid, paracetamol, and caffeine combination (75\%) in France. The top 3 nonpharmaceutical treatments are hydration, caffeinated beverages, and relaxation methods. Of the sufferers, 44\% were between 18 and 24 years of age. Conclusions: In this digital era, social media listening studies present an opportunity to provide unguided, self-reported, sufferers' perceptions in the real world. The generation of social media evidence requires appropriate methodology to translate data into scientific information and relevant medical insights. This social media listening study showed country-specific differences in headache and migraine symptoms experienced and in the times of the day and treatments used. Furthermore, this study highlighted the prevalence of social media usage by younger sufferers compared to that by older sufferers. ", doi="10.2196/40461", url="https://www.jmir.org/2023/1/e40461", url="http://www.ncbi.nlm.nih.gov/pubmed/37145844" } @Article{info:doi/10.2196/38245, author="Eaton, C. Melissa and Probst, C. Yasmine and Smith, A. Marc", title="Characterizing the Discourse of Popular Diets to Describe Information Dispersal and Identify Leading Voices, Interaction, and Themes of Mental Health: Social Network Analysis", journal="JMIR Infodemiology", year="2023", month="May", day="5", volume="3", pages="e38245", keywords="social media", keywords="popular diets", keywords="nutrition", keywords="public health", keywords="social network analysis", abstract="Background: Social media has transformed the way health messages are communicated. This has created new challenges and ethical considerations while providing a platform to share nutrition information for communities to connect and for information to spread. However, research exploring the web-based diet communities of popular diets is limited. Objective: This study aims to characterize the web-based discourse of popular diets, describe information dissemination, identify influential voices, and explore interactions between community networks and themes of mental health. Methods: This exploratory study used Twitter social media posts for an online social network analysis. Popular diet keywords were systematically developed, and data were collected and analyzed using the NodeXL metrics tool (Social Media Research Foundation) to determine the key network metrics (vertices, edges, cluster algorithms, graph visualization, centrality measures, text analysis, and time-series analytics). Results: The vegan and ketogenic diets had the largest networks, whereas the zone diet had the smallest network. In total, 31.2\% (54/173) of the top users endorsed the corresponding diet, and 11\% (19/173) claimed a health or science education, which included 1.2\% (2/173) of dietitians. Complete fragmentation and hub and spoke messaging were the dominant network structures. In total, 69\% (11/16) of the networks interacted, where the ketogenic diet was mentioned most, with depression and anxiety and eating disorder words most prominent in the ``zone diet'' network and the least prominent in the ``soy-free,'' ``vegan,'' ``dairy-free,'' and ``gluten-free'' diet networks. Conclusions: Social media activity reflects diet trends and provides a platform for nutrition information to spread through resharing. A longitudinal exploration of popular diet networks is needed to further understand the impact social media can have on dietary choices. Social media training is vital, and nutrition professionals must work together as a community to actively reshare evidence-based posts on the web. ", doi="10.2196/38245", url="https://infodemiology.jmir.org/2023/1/e38245", url="http://www.ncbi.nlm.nih.gov/pubmed/37159259" } @Article{info:doi/10.2196/44870, author="Nishiyama, Tomohiro and Yada, Shuntaro and Wakamiya, Shoko and Hori, Satoko and Aramaki, Eiji", title="Transferability Based on Drug Structure Similarity in the Automatic Classification of Noncompliant Drug Use on Social Media: Natural Language Processing Approach", journal="J Med Internet Res", year="2023", month="May", day="3", volume="25", pages="e44870", keywords="data mining", keywords="machine learning", keywords="medication noncompliance", keywords="natural language processing", keywords="pharmacovigilance", keywords="transfer learning", keywords="text classification", abstract="Background: Medication noncompliance is a critical issue because of the increased number of drugs sold on the web. Web-based drug distribution is difficult to control, causing problems such as drug noncompliance and abuse. The existing medication compliance surveys lack completeness because it is impossible to cover patients who do not go to the hospital or provide accurate information to their doctors, so a social media--based approach is being explored to collect information about drug use. Social media data, which includes information on drug usage by users, can be used to detect drug abuse and medication compliance in patients. Objective: This study aimed to assess how the structural similarity of drugs affects the efficiency of machine learning models for text classification of drug noncompliance. Methods: This study analyzed 22,022 tweets about 20 different drugs. The tweets were labeled as either noncompliant use or mention, noncompliant sales, general use, or general mention. The study compares 2 methods for training machine learning models for text classification: single-sub-corpus transfer learning, in which a model is trained on tweets about a single drug and then tested on tweets about other drugs, and multi-sub-corpus incremental learning, in which models are trained on tweets about drugs in order of their structural similarity. The performance of a machine learning model trained on a single subcorpus (a data set of tweets about a specific category of drugs) was compared to the performance of a model trained on multiple subcorpora (data sets of tweets about multiple categories of drugs). Results: The results showed that the performance of the model trained on a single subcorpus varied depending on the specific drug used for training. The Tanimoto similarity (a measure of the structural similarity between compounds) was weakly correlated with the classification results. The model trained by transfer learning a corpus of drugs with close structural similarity performed better than the model trained by randomly adding a subcorpus when the number of subcorpora was small. Conclusions: The results suggest that structural similarity improves the classification performance of messages about unknown drugs if the drugs in the training corpus are few. On the other hand, this indicates that there is little need to consider the influence of the Tanimoto structural similarity if a sufficient variety of drugs are ensured. ", doi="10.2196/44870", url="https://www.jmir.org/2023/1/e44870", url="http://www.ncbi.nlm.nih.gov/pubmed/37133915" } @Article{info:doi/10.2196/34315, author="Chopra, Harshita and Vashishtha, Aniket and Pal, Ridam and and Tyagi, Ananya and Sethi, Tavpritesh", title="Mining Trends of COVID-19 Vaccine Beliefs on Twitter With Lexical Embeddings: Longitudinal Observational Study", journal="JMIR Infodemiology", year="2023", month="May", day="2", volume="3", pages="e34315", keywords="COVID-19", keywords="COVID-19 vaccination", keywords="vaccine hesitancy", keywords="public health", keywords="unsupervised word embeddings", keywords="natural language preprocessing", keywords="social media", keywords="Twitter", abstract="Background: Social media plays a pivotal role in disseminating news globally and acts as a platform for people to express their opinions on various topics. A wide variety of views accompany COVID-19 vaccination drives across the globe, often colored by emotions that change along with rising cases, approval of vaccines, and multiple factors discussed online. Objective: This study aims to analyze the temporal evolution of different emotions and the related influencing factors in tweets belonging to 5 countries with vital vaccine rollout programs, namely India, the United States, Brazil, the United Kingdom, and Australia. Methods: We extracted a corpus of nearly 1.8 million Twitter posts related to COVID-19 vaccination and created 2 classes of lexical categories---emotions and influencing factors. Using cosine distance from selected seed words' embeddings, we expanded the vocabulary of each category and tracked the longitudinal change in their strength from June 2020 to April 2021 in each country. Community detection algorithms were used to find modules in positive correlation networks. Results: Our findings indicated the varying relationship among emotions and influencing factors across countries. Tweets expressing hesitancy toward vaccines represented the highest mentions of health-related effects in all countries, which reduced from 41\% to 39\% in India. We also observed a significant change (P<.001) in the linear trends of categories like hesitation and contentment before and after approval of vaccines. After the vaccine approval, 42\% of tweets coming from India and 45\% of tweets from the United States represented the ``vaccine\_rollout'' category. Negative emotions like rage and sorrow gained the highest importance in the alluvial diagram and formed a significant module with all the influencing factors in April 2021, when India observed the second wave of COVID-19 cases. Conclusions: By extracting and visualizing these tweets, we propose that such a framework may help guide the design of effective vaccine campaigns and be used by policy makers to model vaccine uptake and targeted interventions. ", doi="10.2196/34315", url="https://infodemiology.jmir.org/2023/1/e34315", url="http://www.ncbi.nlm.nih.gov/pubmed/37192952" } @Article{info:doi/10.2196/43001, author="Josey, Maria and Gaid, Dina and Bishop, D. Lisa and Blackwood, Michael and Najafizada, Maisam and Donnan, R. Jennifer", title="The Quality, Readability, and Accuracy of the Information on Google About Cannabis and Driving: Quantitative Content Analysis", journal="JMIR Infodemiology", year="2023", month="May", day="2", volume="3", pages="e43001", keywords="cannabis", keywords="driving", keywords="quality", keywords="readability", keywords="accuracy", keywords="public education", keywords="internet", keywords="Google search", keywords="analysis", keywords="accessibility", keywords="information", keywords="evaluation", keywords="tool", keywords="data", keywords="misinterpretation", abstract="Background: The public perception of driving under the influence of cannabis (DUIC) is not consistent with current evidence. The internet is an influential source of information available for people to find information about cannabis. Objective: The purpose of this study was to assess the quality, readability, and accuracy of the information about DUIC found on the internet using the Google Canada search engine. Methods: A quantitative content analysis of the top Google search web pages was conducted to analyze the information available to the public about DUIC. Google searches were performed using keywords, and the first 20 pages were selected. Web pages or web-based resources were eligible if they had text on cannabis and driving in English. We assessed (1) the quality of information using the Quality Evaluation Scoring Tool (QUEST) and the presence of the Health on the Net (HON) code; (2) the readability of information using the Gunning Fox Index (GFI), Flesch Reading Ease Scale (FRES), Flesch-Kincaid Grade Level (FKGL), and Simple Measure of Gobbledygook (SMOG) scores; and (3) the accuracy of information pertaining to the effects of cannabis consumption, prevalence of DUIC, DUIC effects on driving ability, risk of collision, and detection by law enforcement using an adapted version of the 5Cs website evaluation tool. Results: A total of 82 web pages were included in the data analysis. The average QUEST score was 17.4 (SD 5.6) out of 28. The average readability scores were 9.7 (SD 2.3) for FKGL, 11.4 (SD 2.9) for GFI, 12.2 (SD 1.9) for SMOG index, and 49.9 (SD 12.3) for FRES. The readability scores demonstrated that 8 (9.8\%) to 16 (19.5\%) web pages were considered readable by the public. The accuracy results showed that of the web pages that presented information on each key topic, 96\% (22/23) of them were accurate about the effects of cannabis consumption; 97\% (30/31) were accurate about the prevalence of DUIC; 92\% (49/53) were accurate about the DUIC effects on driving ability; 80\% (41/51) were accurate about the risk of collision; and 71\% (35/49) were accurate about detection by law enforcement. Conclusions: Health organizations should consider health literacy of the public when creating content to help prevent misinterpretation and perpetuate prevailing misperceptions surrounding DUIC. Delivering high quality, readable, and accurate information in a way that is comprehensible to the public is needed to support informed decision-making. ", doi="10.2196/43001", url="https://infodemiology.jmir.org/2023/1/e43001" } @Article{info:doi/10.2196/45108, author="Movahedi Nia, Zahra and Bragazzi, Nicola and Asgary, Ali and Orbinski, James and Wu, Jianhong and Kong, Jude", title="Mpox Panic, Infodemic, and Stigmatization of the Two-Spirit, Lesbian, Gay, Bisexual, Transgender, Queer or Questioning, Intersex, Asexual Community: Geospatial Analysis, Topic Modeling, and Sentiment Analysis of a Large, Multilingual Social Media Database", journal="J Med Internet Res", year="2023", month="May", day="1", volume="25", pages="e45108", keywords="monkeypox", keywords="infectious outbreak", keywords="infodemic", keywords="stigma", keywords="natural language processing", keywords="sentiment analysis", keywords="Twitter", keywords="community", keywords="discrimination", keywords="social media", keywords="virus", abstract="Background: The global Mpox (formerly, Monkeypox) outbreak is disproportionately affecting the gay and bisexual men having sex with men community. Objective: The aim of this study is to use social media to study country-level variations in topics and sentiments toward Mpox and Two-Spirit, Lesbian, Gay, Bisexual, Transgender, Queer or Questioning, Intersex, Asexual (2SLGBTQIAP+)--related topics. Previous infectious outbreaks have shown that stigma intensifies an outbreak. This work helps health officials control fear and stop discrimination. Methods: In total, 125,424 Twitter and Facebook posts related to Mpox and the 2SLGBTQIAP+ community were extracted from May 1 to December 25, 2022, using Twitter application programming interface academic accounts and Facebook-scraper tools. The tweets' main topics were discovered using Latent Dirichlet Allocation in the sklearn library. The pysentimiento package was used to find the sentiments of English and Spanish posts, and the CamemBERT package was used to recognize the sentiments of French posts. The tweets' and Facebook posts' languages were understood using the Twitter application programming interface platform and pycld3 library, respectively. Using ArcGis Online, the hot spots of the geotagged tweets were identified. Mann-Whitney U, ANOVA, and Dunn tests were used to compare the sentiment polarity of different topics and countries. Results: The number of Mpox posts and the number of posts with Mpox and 2SLGBTQIAP+ keywords were 85\% correlated (P<.001). Interestingly, the number of posts with Mpox and 2SLGBTQIAP+ keywords had a higher correlation with the number of Mpox cases (correlation=0.36, P<.001) than the number of posts on Mpox (correlation=0.24, P<.001). Of the 10 topics, 8 were aimed at stigmatizing the 2SLGBTQIAP+ community, 3 of which had a significantly lower sentiment score than other topics (ANOVA P<.001). The Mann-Whitney U test shows that negative sentiments have a lower intensity than neutral and positive sentiments (P<.001) and neutral sentiments have a lower intensity than positive sentiments (P<.001). In addition, English sentiments have a higher negative and lower neutral and positive intensities than Spanish and French sentiments (P<.001), and Spanish sentiments have a higher negative and lower positive intensities than French sentiments (P<.001). The hot spots of the tweets with Mpox and 2SLGBTQIAP+ keywords were recognized as the United States, the United Kingdom, Canada, Spain, Portugal, India, Ireland, and Italy. Canada was identified as having more tweets with negative polarity and a lower sentiment score (P<.04). Conclusions: The 2SLGBTQIAP+ community is being widely stigmatized for spreading the Mpox virus on social media. This turns the community into a highly vulnerable population, widens the disparities, increases discrimination, and accelerates the spread of the virus. By identifying the hot spots and key topics of the related tweets, this work helps decision makers and health officials inform more targeted policies. ", doi="10.2196/45108", url="https://www.jmir.org/2023/1/e45108", url="http://www.ncbi.nlm.nih.gov/pubmed/37126377" } @Article{info:doi/10.2196/44990, author="Nguyen, T. Thu and Merchant, S. Junaid and Criss, Shaniece and Makres, Katrina and Gowda, N. Krishik and Mane, Heran and Yue, Xiaohe and Hswen, Yulin and Glymour, Maria M. and Nguyen, C. Quynh and Allen, M. Amani", title="Examining Twitter-Derived Negative Racial Sentiment as Indicators of Cultural Racism: Observational Associations With Preterm Birth and Low Birth Weight Among a Multiracial Sample of Mothers, 2011-2021", journal="J Med Internet Res", year="2023", month="Apr", day="28", volume="25", pages="e44990", keywords="birth outcomes", keywords="health disparities", keywords="machine learning, racial sentiment", keywords="social media", abstract="Background: Large racial and ethnic disparities in adverse birth outcomes persist. Increasing evidence points to the potential role of racism in creating and perpetuating these disparities. Valid measures of area-level racial attitudes and bias remain elusive, but capture an important and underexplored form of racism that may help explain these disparities. Cultural values and attitudes expressed through social media reflect and shape public norms and subsequent behaviors. Few studies have quantified attitudes toward different racial groups using social media with the aim of examining associations with birth outcomes. Objective: We used Twitter data to measure state-level racial sentiments and investigate associations with preterm birth (PTB) and low birth weight (LBW) in a multiracial or ethnic sample of mothers in the United States. Methods: A random 1\% sample of publicly available tweets from January 1, 2011, to December 31, 2021, was collected using Twitter's Academic Application Programming Interface (N=56,400,097). Analyses were on English-language tweets from the United States that used one or more race-related keywords. We assessed the sentiment of each tweet using support vector machine, a supervised machine learning model. We used 5-fold cross-validation to assess model performance and achieved high accuracy for negative sentiment classification (91\%) and a high F1 score (84\%). For each year, the state-level racial sentiment was merged with birth data during that year ({\textasciitilde}3 million births per year). We estimated incidence ratios for LBW and PTB using log binomial regression models, among all mothers, Black mothers, racially minoritized mothers (Asian, Black, or Latina mothers), and White mothers. Models were controlled for individual-level maternal characteristics and state-level demographics. Results: Mothers living in states in the highest tertile of negative racial sentiment for tweets referencing racial and ethnic minoritized groups had an 8\% higher (95\% CI 3\%-13\%) incidence of LBW and 5\% higher (95\% CI 0\%-11\%) incidence of PTB compared to mothers living in the lowest tertile. Negative racial sentiment referencing racially minoritized groups was associated with adverse birth outcomes in the total population, among minoritized mothers, and White mothers. Black mothers living in states in the highest tertile of negative Black sentiment had 6\% (95\% CI 1\%-11\%) and 7\% (95\% CI 2\%-13\%) higher incidence of LBW and PTB, respectively, compared to mothers living in the lowest tertile. Negative Latinx sentiment was associated with a 6\% (95\% CI 1\%-11\%) and 3\% (95\% CI 0\%-6\%) higher incidence of LBW and PTB among Latina mothers, respectively. Conclusions: Twitter-derived negative state-level racial sentiment toward racially minoritized groups was associated with a higher risk of adverse birth outcomes among the total population and racially minoritized groups. Policies and supports establishing an inclusive environment accepting of all races and cultures may decrease the overall risk of adverse birth outcomes and reduce racial birth outcome disparities. ", doi="10.2196/44990", url="https://www.jmir.org/2023/1/e44990", url="http://www.ncbi.nlm.nih.gov/pubmed/37115602" } @Article{info:doi/10.2196/42721, author="Li, Jiayu and He, Zhiyu and Zhang, Min and Ma, Weizhi and Jin, Ye and Zhang, Lei and Zhang, Shuyang and Liu, Yiqun and Ma, Shaoping", title="Estimating Rare Disease Incidences With Large-scale Internet Search Data: Development and Evaluation of a Two-step Machine Learning Method", journal="JMIR Infodemiology", year="2023", month="Apr", day="28", volume="3", pages="e42721", keywords="disease incidence estimation", keywords="rare disease", keywords="internet search engine", keywords="infoveillance", keywords="deep learning", keywords="public health", abstract="Background: As rare diseases (RDs) receive increasing attention, obtaining accurate RD incidence estimates has become an essential concern in public health. Since RDs are difficult to diagnose, include diverse types, and have scarce cases, traditional epidemiological methods are costly in RD registries. With the development of the internet, users have become accustomed to searching for disease-related information through search engines before seeking medical treatment. Therefore, online search data provide a new source for estimating RD incidences. Objective: The aim of this study was to estimate the incidences of multiple RDs in distinct regions of China with online search data. Methods: Our research scale included 15 RDs in China from 2016 to 2019. The online search data were obtained from Sogou, one of the top 3 commercial search engines in China. By matching to multilevel keywords related to 15 RDs during the 4 years, we retrieved keyword-matched RD-related queries. The queries used before and after the keyword-matched queries formed the basis of the RD-related search sessions. A two-step method was developed to estimate RD incidences with users' intents conveyed by the sessions. In the first step, a combination of long short-term memory and multilayer perceptron algorithms was used to predict whether the intents of search sessions were RD-concerned, news-concerned, or others. The second step utilized a linear regression (LR) model to estimate the incidences of multiple RDs in distinct regions based on the RD- and news-concerned session numbers. For evaluation, the estimated incidences were compared with RD incidences collected from China's national multicenter clinical database of RDs. The root mean square error (RMSE) and relative error rate (RER) were used as the evaluation metrics. Results: The RD-related online data included 2,749,257 queries and 1,769,986 sessions from 1,380,186 users from 2016 to 2019. The best LR model with sessions as the input estimated the RD incidences with an RMSE of 0.017 (95\% CI 0.016-0.017) and an RER of 0.365 (95\% CI 0.341-0.388). The best LR model with queries as input had an RMSE of 0.023 (95\% CI 0.017-0.029) and an RER of 0.511 (95\% CI 0.377-0.645). Compared with queries, using session intents achieved an error decrease of 28.57\% in terms of the RER (P=.01). Analysis of different RDs and regions showed that session input was more suitable for estimating the incidences of most diseases (14 of 15 RDs). Moreover, examples focusing on two RDs showed that news-concerned session intents reflected news of an outbreak and helped correct the overestimation of incidences. Experiments on RD types further indicated that type had no significant influence on the RD estimation task. Conclusions: This work sheds light on a novel method for rapid estimation of RD incidences in the internet era, and demonstrates that search session intents were especially helpful for the estimation. The proposed two-step estimation method could be a valuable supplement to the traditional registry for understanding RDs, planning policies, and allocating medical resources. The utilization of search sessions in disease detection and estimation could be transferred to infoveillance of large-scale epidemics or chronic diseases. ", doi="10.2196/42721", url="https://infodemiology.jmir.org/2023/1/e42721" } @Article{info:doi/10.2196/37237, author="Dupuy-Zini, Alexandre and Audeh, Bissan and G{\'e}rardin, Christel and Duclos, Catherine and Gagneux-Brunon, Amandine and Bousquet, Cedric", title="Users' Reactions to Announced Vaccines Against COVID-19 Before Marketing in France: Analysis of Twitter Posts", journal="J Med Internet Res", year="2023", month="Apr", day="24", volume="25", pages="e37237", keywords="COVID-19 Vaccines", keywords="Social Media", keywords="Deep Learning", keywords="France", keywords="Sentiment Analysis", abstract="Background: Within a few months, the COVID-19 pandemic had spread to many countries and had been a real challenge for health systems all around the world. This unprecedented crisis has led to a surge of online discussions about potential cures for the disease. Among them, vaccines have been at the heart of the debates and have faced lack of confidence before marketing in France. Objective: This study aims to identify and investigate the opinions of French Twitter users on the announced vaccines against COVID-19 through sentiment analysis. Methods: This study was conducted in 2 phases. First, we filtered a collection of tweets related to COVID-19 available on Twitter from February 2020 to August 2020 with a set of keywords associated with vaccine mistrust using word embeddings. Second, we performed sentiment analysis using deep learning to identify the characteristics of vaccine mistrust. The model was trained on a hand-labeled subset of 4548 tweets. Results: A set of 69 relevant keywords were identified as the semantic concept of the word ``vaccin'' (vaccine in French) and focused mainly on conspiracies, pharmaceutical companies, and alternative treatments. Those keywords enabled us to extract nearly 350,000 tweets in French. The sentiment analysis model achieved 0.75 accuracy. The model then predicted 16\% of positive tweets, 41\% of negative tweets, and 43\% of neutral tweets. This allowed us to explore the semantic concepts of positive and negative tweets and to plot the trends of each sentiment. The main negative rhetoric identified from users' tweets was that vaccines are perceived as having a political purpose and that COVID-19 is a commercial argument for the pharmaceutical companies. Conclusions: Twitter might be a useful tool to investigate the arguments for vaccine mistrust because it unveils political criticism contrasting with the usual concerns on adverse drug reactions. As the opposition rhetoric is more consistent and more widely spread than the positive rhetoric, we believe that this research provides effective tools to help health authorities better characterize the risk of vaccine mistrust. ", doi="10.2196/37237", url="https://www.jmir.org/2023/1/e37237", url="http://www.ncbi.nlm.nih.gov/pubmed/36596215" } @Article{info:doi/10.2196/45408, author="Lam, Sing Chun and Zhou, Keary and Loong, Ho-Fung Herbert and Chung, Chi-Ho Vincent and Ngan, Chun-Kit and Cheung, Ting Yin", title="The Use of Traditional, Complementary, and Integrative Medicine in Cancer: Data-Mining Study of 1 Million Web-Based Posts From Health Forums and Social Media Platforms", journal="J Med Internet Res", year="2023", month="Apr", day="21", volume="25", pages="e45408", keywords="traditional", keywords="complementary", keywords="integrative", keywords="social media", keywords="cancer", keywords="forums, digital health", keywords="traditional, complementary, and integrative medicine", keywords="TCIM", keywords="perceptions", keywords="machine learning", keywords="cancer care", abstract="Background: Patients with cancer are increasingly using forums and social media platforms to access health information and share their experiences, particularly in the use of traditional, complementary, and integrative medicine (TCIM). Despite the popularity of TCIM among patients with cancer, few related studies have used data from these web-based sources to explore the use of TCIM among patients with cancer. Objective: This study leveraged multiple forums and social media platforms to explore patients' use, interest, and perception of TCIM for cancer care. Methods: Posts (in English) related to TCIM were collected from Facebook, Twitter, Reddit, and 16 health forums from inception until February 2022. Both manual assessments and natural language processing were performed. Descriptive analyses were performed to explore the most commonly discussed TCIM modalities for each symptom and cancer type. Sentiment analyses were performed to measure the polarity of each post or comment, and themes were identified from posts with positive and negative sentiments. TCIM modalities that are emerging or recommended in the guidelines were identified a priori. Exploratory topic-modeling analyses with latent Dirichlet allocation were conducted to investigate the patients' perceptions of these modalities. Results: Among the 1,620,755 posts available, cancer-related symptoms, such as pain (10/10, 100\% cancer types), anxiety and depression (9/10, 90\%), and poor sleep (9/10, 90\%), were commonly discussed. Cannabis was among the most frequently discussed TCIM modalities for pain in 7 (70\%) out of 10 cancer types, as well as nausea and vomiting, loss of appetite, anxiety and depression, and poor sleep. A total of 7 positive and 7 negative themes were also identified. The positive themes included TCIM, making symptoms manageable, and reducing the need for medication and their side effects. The belief that TCIM and conventional treatments were not mutually exclusive and intolerance to conventional treatment may facilitate TCIM use. Conversely, TCIM was viewed as leading to patients' refusal of conventional treatment or delays in diagnosis and treatment. Doctors' ignorance regarding TCIM and the lack of information provided about TCIM may be barriers to its use. Exploratory analyses showed that TCIM recommendations were well discussed among patients; however, these modalities were also used for many other indications. Other notable topics included concerns about the legalization of cannabis, acupressure techniques, and positive experiences of meditation. Conclusions: Using machine learning techniques, social media and health forums provide a valuable resource for patient-generated data regarding the pattern of use and patients' perceptions of TCIM. Such information will help clarify patients' needs and concerns and provide directions for research on integrating TCIM into cancer care. Our results also suggest that effective communication about TCIM should be achieved and that doctors should be more open-minded to actively discuss TCIM use with their patients. ", doi="10.2196/45408", url="https://www.jmir.org/2023/1/e45408", url="http://www.ncbi.nlm.nih.gov/pubmed/37083752" } @Article{info:doi/10.2196/44413, author="Porras Fimbres, Cristina Denisse and Quinn, P. Alyssa and Cooper, R. Benjamin and Presley, L. Colby and Jacobs, Jennifer and Rundle, W. Chandler and Dellavalle, P. Robert", title="Cross-sectional Analysis of Dermatologists and Sponsored Content on TikTok", journal="JMIR Dermatol", year="2023", month="Apr", day="21", volume="6", pages="e44413", keywords="social media", keywords="TikTok", keywords="sponsorship", keywords="stewardship", keywords="ethics", keywords="dermatology", keywords="dermatologist", keywords="content analysis", doi="10.2196/44413", url="https://derma.jmir.org/2023/1/e44413", url="http://www.ncbi.nlm.nih.gov/pubmed/37632930" } @Article{info:doi/10.2196/46254, author="Choi, Won-Seok and Han, Junhee and Hong, Ju Hyun", title="Association Between Internet Searches Related to Suicide/Self-harm and Adolescent Suicide Death in South Korea in 2016-2020: Secondary Data Analysis", journal="J Med Internet Res", year="2023", month="Apr", day="20", volume="25", pages="e46254", keywords="adolescent", keywords="suicide", keywords="self-mutilation", keywords="internet", keywords="search engine", keywords="Korea", keywords="suicide death", keywords="surveillance", keywords="monitoring", keywords="internet search", abstract="Background: Previous studies have investigated the association between suicide and internet search volumes of terms related to suicide or self-harm. However, the results varied by people's age, period, and country, and no study has exclusively investigated suicide or self-harm rates among adolescents. Objective: This study aims to determine the association between the internet search volumes of terms related to suicide/self-harm and the number of suicides among South Korean adolescents. We investigated gender differences in this association and the time lag between the internet search volumes of the terms and the connected suicide deaths. Methods: We selected 26 search terms related to suicide and self-harm among South Korean adolescents, and the search volumes of these terms for adolescents aged 13-18 years were obtained from the leading internet search engine in South Korea (Naver Datalab). A data set was constructed by combining data from Naver Datalab and the number of suicide deaths of adolescents on a daily basis from January 1, 2016, to December 31, 2020. Spearman rank correlation and multivariate Poisson regression analyses were performed to identify the association between the search volumes of the terms and the suicide deaths during that period. The time lag between suicide death and the increasing trend in the search volumes of the related terms was estimated from the cross-correlation coefficients. Results: Significant correlations were observed within the search volumes of the 26 terms related to suicide/self-harm. The internet search volumes of several terms were associated with the number of suicide deaths among South Korean adolescents, and this association differed by gender. The search volume for ``dropout'' showed a statistically significant correlation with the number of suicides in all adolescent population groups. The correlation between the internet search volume for ``dropout'' and the connected suicide deaths was the strongest for a time lag of 0 days. In females, self-harm and academic score showed significant associations with suicide deaths, but academic score showed a negative correlation, and the time lags with the strongest correlations were 0 and --11 days, respectively. In the total population, self-harm and suicide method were associated with the number of suicides, and the time lags with the strongest correlations were +7 and 0 days, respectively. Conclusions: This study identifies a correlation between suicides and internet search volumes related to suicide/self-harm among South Korean adolescents, but the relatively weak correlation (incidence rate ratio 0.990-1.068) should be interpreted with caution. ", doi="10.2196/46254", url="https://www.jmir.org/2023/1/e46254", url="http://www.ncbi.nlm.nih.gov/pubmed/37079349" } @Article{info:doi/10.2196/45249, author="Yao, Franzl Lean and Ferawati, Kiki and Liew, Kongmeng and Wakamiya, Shoko and Aramaki, Eiji", title="Disruptions in the Cystic Fibrosis Community's Experiences and Concerns During the COVID-19 Pandemic: Topic Modeling and Time Series Analysis of Reddit Comments", journal="J Med Internet Res", year="2023", month="Apr", day="20", volume="25", pages="e45249", keywords="COVID-19", keywords="Reddit", keywords="time series analysis", keywords="BERTopic", keywords="topic modeling", keywords="cystic fibrosis", abstract="Background: The COVID-19 pandemic disrupted the needs and concerns of the cystic fibrosis community. Patients with cystic fibrosis were particularly vulnerable during the pandemic due to overlapping symptoms in addition to the challenges patients with rare diseases face, such as the need for constant medical aid and limited information regarding their disease or treatments. Even before the pandemic, patients vocalized these concerns on social media platforms like Reddit and formed communities and networks to share insight and information. This data can be used as a quick and efficient source of information about the experiences and concerns of patients with cystic fibrosis in contrast to traditional survey- or clinical-based methods. Objective: This study applies topic modeling and time series analysis to identify the disruption caused by the COVID-19 pandemic and its impact on the cystic fibrosis community's experiences and concerns. This study illustrates the utility of social media data in gaining insight into the experiences and concerns of patients with rare diseases. Methods: We collected comments from the subreddit r/CysticFibrosis to represent the experiences and concerns of the cystic fibrosis community. The comments were preprocessed before being used to train the BERTopic model to assign each comment to a topic. The number of comments and active users for each data set was aggregated monthly per topic and then fitted with an autoregressive integrated moving average (ARIMA) model to study the trends in activity. To verify the disruption in trends during the COVID-19 pandemic, we assigned a dummy variable in the model where a value of ``1'' was assigned to months in 2020 and ``0'' otherwise and tested for its statistical significance. Results: A total of 120,738 comments from 5827 users were collected from March 24, 2011, until August 31, 2022. We found 22 topics representing the cystic fibrosis community's experiences and concerns. Our time series analysis showed that for 9 topics, the COVID-19 pandemic was a statistically significant event that disrupted the trends in user activity. Of the 9 topics, only 1 showed significantly increased activity during this period, while the other 8 showed decreased activity. This mixture of increased and decreased activity for these topics indicates a shift in attention or focus on discussion topics during this period. Conclusions: There was a disruption in the experiences and concerns the cystic fibrosis community faced during the COVID-19 pandemic. By studying social media data, we were able to quickly and efficiently study the impact on the lived experiences and daily struggles of patients with cystic fibrosis. This study shows how social media data can be used as an alternative source of information to gain insight into the needs of patients with rare diseases and how external factors disrupt them. ", doi="10.2196/45249", url="https://www.jmir.org/2023/1/e45249", url="http://www.ncbi.nlm.nih.gov/pubmed/37079359" } @Article{info:doi/10.2196/40913, author="Zheng, Zihe and Xie, Zidian and Goniewicz, Maciej and Rahman, Irfan and Li, Dongmei", title="Potential Impact of the COVID-19 Pandemic on Public Perception of Water Pipes on Reddit: Observational Study", journal="JMIR Infodemiology", year="2023", month="Apr", day="20", volume="3", pages="e40913", keywords="water pipes", keywords="Reddit", keywords="COVID-19", keywords="COVID-19 pandemic", keywords="public perception", abstract="Background: Socializing is one of the main motivations for water pipe smoking. Restrictions on social gatherings during the COVID-19 pandemic might have influenced water pipe smokers' behaviors. As one of the most popular social media platforms, Reddit has been used to study public opinions and user experiences. Objective: In this study, we aimed to examine the influence of the COVID-19 pandemic on public perception and discussion of water pipe tobacco smoking using Reddit data. Methods: We collected Reddit posts between December 1, 2018, and June 30, 2021, from a Reddit archive (PushShift) using keywords such as ``waterpipe,'' ``hookah,'' and ``shisha.'' We examined the temporal trend in Reddit posts mentioning water pipes and different locations (such as homes and lounges or bars). The temporal trend was further tested using interrupted time series analysis. Sentiment analysis was performed to study the change in sentiment of water pipe--related posts before and during the pandemic. Topic modeling using latent Dirichlet allocation (LDA) was used to examine major topics discussed in water pipe--related posts before and during the pandemic. Results: A total of 45,765 nonpromotion water pipe--related Reddit posts were collected and used for data analysis. We found that the weekly number of Reddit posts mentioning water pipes significantly increased at the beginning of the COVID-19 pandemic (P<.001), and gradually decreased afterward (P<.001). In contrast, Reddit posts mentioning water pipes and lounges or bars showed an opposite trend. Compared to the period before the COVID-19 pandemic, the average number of Reddit posts mentioning lounges or bars was lower at the beginning of the pandemic but gradually increased afterward, while the average number of Reddit posts mentioning the word ``home'' remained similar during the COVID-19 pandemic (P=.29). While water pipe--related posts with a positive sentiment were dominant (12,526/21,182, 59.14\% before the pandemic; 14,686/24,583, 59.74\% after the pandemic), there was no change in the proportion of water pipe--related posts with different sentiments before and during the pandemic (P=.19, P=.26, and P=.65 for positive, negative, and neutral posts, respectively). Most topics related to water pipes on Reddit were similar before and during the pandemic. There were more discussions about the opening and closing of hookah lounges or bars during the pandemic. Conclusions: This study provides a first evaluation of the possible impact of the COVID-19 pandemic on public perceptions of and discussions about water pipes on Reddit. ", doi="10.2196/40913", url="https://infodemiology.jmir.org/2023/1/e40913", url="http://www.ncbi.nlm.nih.gov/pubmed/37124245" } @Article{info:doi/10.2196/43609, author="Bui, Tam Kim and Li, Zoe and Dhillon, M. Haryana and Kiely, E. Belinda and Blinman, Prunella", title="Scanxiety Conversations on Twitter: Observational Study", journal="JMIR Cancer", year="2023", month="Apr", day="19", volume="9", pages="e43609", keywords="anxiety", keywords="cancer", keywords="medical imaging", keywords="oncology", keywords="psycho-oncology", keywords="social media", keywords="twitter", keywords="tweet", keywords="scanxiety", keywords="mental health", keywords="sentiment analysis", keywords="thematic analysis", keywords="screen time", keywords="scan", keywords="hyperawareness", keywords="radiology", abstract="Background: Scan-associated anxiety (or ``scanxiety'') is commonly experienced by people having cancer-related scans. Social media platforms such as Twitter provide a novel source of data for observational research. Objective: We aimed to identify posts on Twitter (or ``tweets'') related to scanxiety, describe the volume and content of these tweets, and describe the demographics of users posting about scanxiety. Methods: We manually searched for ``scanxiety'' and associated keywords in cancer-related, publicly available, English-language tweets posted between January 2018 and December 2020. We defined ``conversations'' as a primary tweet (the first tweet about scanxiety) and subsequent tweets (interactions stemming from the primary tweet). User demographics and the volume of primary tweets were assessed. Conversations underwent inductive thematic and content analysis. Results: A total of 2031 unique Twitter users initiated a conversation about scanxiety from cancer-related scans. Most were patients (n=1306, 64\%), female (n=1343, 66\%), from North America (n=1130, 56\%), and had breast cancer (449/1306, 34\%). There were 3623 Twitter conversations, with a mean of 101 per month (range 40-180). Five themes were identified. The first theme was experiences of scanxiety, identified in 60\% (2184/3623) of primary tweets, which captured the personal account of scanxiety by patients or their support person. Scanxiety was often described with negative adjectives or similes, despite being experienced differently by users. Scanxiety had psychological, physical, and functional impacts. Contributing factors to scanxiety included the presence and duration of uncertainty, which was exacerbated during the COVID-19 pandemic. The second theme (643/3623, 18\%) was the acknowledgment of scanxiety, where users summarized or labeled an experience as scanxiety without providing emotive clarification, and advocacy of scanxiety, where users raised awareness of scanxiety without describing personal experiences. The third theme was messages of support (427/3623, 12\%), where users expressed well wishes and encouraged positivity for people experiencing scanxiety. The fourth theme was strategies to reduce scanxiety (319/3623, 9\%), which included general and specific strategies for patients and strategies that required improvements in clinical practice by clinicians or health care systems. The final theme was research about scanxiety (50/3623, 1\%), which included tweets about the epidemiology, impact, and contributing factors of scanxiety as well as novel strategies to reduce scanxiety. Conclusions: Scanxiety was often a negative experience described by patients having cancer-related scans. Social media platforms like Twitter enable individuals to share their experiences and offer support while providing researchers with unique data to improve their understanding of a problem. Acknowledging scanxiety as a term and increasing awareness of scanxiety is an important first step in reducing scanxiety. Research is needed to guide evidence-based approaches to reduce scanxiety, though some low-cost, low-resource practical strategies identified in this study could be rapidly introduced into clinical care. ", doi="10.2196/43609", url="https://cancer.jmir.org/2023/1/e43609", url="http://www.ncbi.nlm.nih.gov/pubmed/37074770" } @Article{info:doi/10.2196/41156, author="Kj{\ae}rulff, M{\o}lholm Emilie and Andersen, Helms Tue and Kingod, Natasja and Nex{\o}, Andersen Mette", title="When People With Chronic Conditions Turn to Peers on Social Media to Obtain and Share Information: Systematic Review of the Implications for Relationships With Health Care Professionals", journal="J Med Internet Res", year="2023", month="Apr", day="17", volume="25", pages="e41156", keywords="patient-physician relationship", keywords="social media", keywords="internet", keywords="health information", keywords="diabetes", keywords="chronic diseases", keywords="systematic review", keywords="information-seeking behavior", keywords="retrieval", keywords="sharing", abstract="Background: People living with chronic conditions such as diabetes turn to peers on social media to obtain and share information. Although social media use has grown dramatically in the past decade, little is known about its implications for the relationships between people with chronic conditions and health care professionals (HCPs). Objective: We aimed to systematically review the content and quality of studies examining what the retrieval and sharing of information by people with chronic conditions on social media implies for their relationships with HCPs. Methods: We conducted a search of studies in MEDLINE (Ovid), Embase (Ovid), PsycINFO (Ovid), and CINAHL (EBSCO). Eligible studies were primary studies; examined social media use; included adults with any type of diabetes, cardiovascular diseases that are closely linked with diabetes, obesity, hypertension, or dyslipidemia; and reported on the implications for people with chronic conditions--HCP relationships when people with chronic conditions access and share information on social media. We used the Mixed Methods Appraisal Tool version 2018 to assess the quality of the studies, and the included studies were narratively synthesized. Results: Of the 3111 screened studies, 17 (0.55\%) were included. Most studies (13/17, 76\%) were of low quality. The narrative synthesis identified implications for people with chronic conditions--HCP relationships when people with chronic conditions access and share information on social media, divided into 3 main categories with 7 subcategories. These categories of implications address how the peer interactions of people with chronic conditions on social media can influence their communication with HCPs, how people with chronic conditions discuss advice and medical information from HCPs on social media, and how relationships with HCPs are discussed by people with chronic conditions on social media. The implications are illustrated collectively in a conceptual model. Conclusions: More evidence is needed to draw conclusions, but the findings indicate that the peer interactions of people with chronic conditions on social media are implicated in the ways in which people with chronic conditions equip themselves for clinical consultations, evaluate the information and advice provided by HCPs, and manage their relationships with HCPs. Future populations with chronic conditions will be raised in a digital world, and social media will likely remain a strategy for obtaining support and information. However, the generally low quality of the studies included in this review points to the relatively immature state of research exploring social media and its implications for people with chronic conditions--HCP relationships. Better study designs and methods for conducting research on social media are needed to generate robust evidence. ", doi="10.2196/41156", url="https://www.jmir.org/2023/1/e41156", url="http://www.ncbi.nlm.nih.gov/pubmed/37067874" } @Article{info:doi/10.2196/45051, author="Wu, Xiaoqian and Li, Ziyu and Xu, Lin and Li, Pengfei and Liu, Ming and Huang, Cheng", title="COVID-19 Vaccine--Related Information on the WeChat Public Platform: Topic Modeling and Content Analysis", journal="J Med Internet Res", year="2023", month="Apr", day="14", volume="25", pages="e45051", keywords="health belief model", keywords="COVID-19 vaccines", keywords="WeChat", keywords="content analysis", keywords="topic modeling", keywords="public health", keywords="COVID-19", abstract="Background: The COVID-19 vaccine is an effective tool in the fight against the COVID-19 outbreak. As the main channel of information dissemination in the context of the epidemic, social media influences public trust and acceptance of the vaccine. The rational application of health behavior theory is a guarantee of effective public health information dissemination. However, little is known about the application of health behavior theory in web-based COVID-19 vaccine messages, especially from Chinese social media posts. Objective: This study aimed to understand the main topics and communication characteristics of hot papers related to COVID-19 vaccine on the WeChat platform and assess the health behavior theory application with the aid of health belief model (HBM). Methods: A systematic search was conducted on the Chinese social media platform WeChat to identify COVID-19 vaccine--related papers. A coding scheme was established based on the HBM, and the sample was managed and coded using NVivo 12 (QSR International) to assess the application of health behavior theory. The main topics of the papers were extracted through the Latent Dirichlet Allocation algorithm. Finally, temporal analysis was used to explore trends in the evolution of themes and health belief structures in the papers. Results: A total of 757 papers were analyzed. Almost all (671/757, 89\%) of the papers did not have an original logo. By topic modeling, 5 topics were identified, which were vaccine development and effectiveness (267/757, 35\%), disease infection and protection (197/757, 26\%), vaccine safety and adverse reactions (52/757, 7\%), vaccine access (136/757, 18\%), and vaccination science popularization (105/757, 14\%). All papers identified at least one structure in the extended HBM, but only 29 papers included all of the structures. Descriptions of solutions to obstacles (585/757, 77\%) and benefit (468/757, 62\%) were the most emphasized components in all samples. Relatively few elements of susceptibility (208/757, 27\%) and the least were descriptions of severity (135/757, 18\%). Heat map visualization revealed the change in health belief structure before and after vaccine entry into the market. Conclusions: To the best of our knowledge, this is the first study to assess the structural expression of health beliefs in information related to the COVID-19 vaccine on the WeChat public platform based on an HBM. The study also identified topics and communication characteristics before and after the market entry of vaccines. Our findings can inform customized education and communication strategies to promote vaccination not only in this pandemic but also in future pandemics. ", doi="10.2196/45051", url="https://www.jmir.org/2023/1/e45051", url="http://www.ncbi.nlm.nih.gov/pubmed/37058349" } @Article{info:doi/10.2196/46661, author="Handayani, Wuri Putu and Zagatti, Augusto Guilherme and Kefi, Hajer and Bressan, St{\'e}phane", title="Impact of Social Media Usage on Users' COVID-19 Protective Behavior: Survey Study in Indonesia", journal="JMIR Form Res", year="2023", month="Apr", day="13", volume="7", pages="e46661", keywords="COVID-19", keywords="pandemic", keywords="infectious diseases", keywords="social media", keywords="trust", keywords="behavior", keywords="Indonesia", abstract="Background: Social media have become the source of choice for many users to search for health information on COVID-19 despite possible detrimental consequences. Several studies have analyzed the association between health information--searching behavior and mental health. Some of these studies examined users' intentions in searching health information on social media and the impact of social media use on mental health in Indonesia. Objective: This study investigates both active and passive participation in social media, shedding light on cofounding effects from these different forms of engagement. In addition, this study analyses the role of trust in social media platforms and its effect on public health outcomes. Thus, the purpose of this study is to analyze the impact of social media usage on COVID-19 protective behavior in Indonesia. The most commonly used social media platforms are Instagram, Facebook, YouTube, TikTok, and Twitter. Methods: We used primary data from an online survey. We processed 414 answers to a structured questionnaire to evaluate the relationship between these users' active and passive participation in social media, trust in social media, anxiety, self-efficacy, and protective behavior to COVID-19. We modeled the data using partial least square structural equation modeling. Results: This study reveals that social media trust is a crucial antecedent, where trust in social media is positively associated with active contribution and passive consumption of COVID-19 content in social media, users' anxiety, self-efficacy, and protective behavior. This study found that active contribution of content related to COVID-19 on social media is positively correlated with anxiety, while passive participation increases self-efficacy and, in turn, protective behavior. This study also found that active participation is associated with negative health outcomes, while passive participation has the opposite effects. The results of this study can potentially be used for other infectious diseases, for example, dengue fever and diseases that can be transmitted through the air and have handling protocols similar to that of COVID-19. Conclusions: Public health campaigns can use social media for health promotion. Public health campaigns should post positive messages and distil the received information parsimoniously to avoid unnecessary and possibly counterproductive increased anxiety of the users. ", doi="10.2196/46661", url="https://formative.jmir.org/2023/1/e46661", url="http://www.ncbi.nlm.nih.gov/pubmed/37052987" } @Article{info:doi/10.2196/42710, author="Shepherd, Thomas and Robinson, Michelle and Mallen, Christian", title="Online Health Information Seeking for Mpox in Endemic and Nonendemic Countries: Google Trends Study", journal="JMIR Form Res", year="2023", month="Apr", day="13", volume="7", pages="e42710", keywords="monkeypox", keywords="mpox", keywords="infodemiology: surveillance", keywords="public health", keywords="health information seeking", keywords="Google Trends", keywords="joinpoint regression", keywords="epidemic", keywords="outbreak", keywords="infectious disease", keywords="disease", keywords="online", abstract="Background: The recent global outbreak of mpox (monkeypox) has already been declared a public health emergency of international concern by the World Health Organization. Given the health, social, and economic impacts of the COVID-19 pandemic, there is understandable concern and anxiety around the emergence of another infectious disease---especially one about which little is known. Objective: We used Google Trends to explore online health information seeking patterns for mpox in endemic and nonendemic countries and investigated the impact of the publication of the first in-country case on internet search volume. Methods: Google Trends is a publicly accessible and free data source that aggregates worldwide Google search data. Google search data were used as a surrogate measure of online health information seeking for 178 days between February 18 and August 18, 2022. Searching data were downloaded across this time period for nonendemic countries with the highest case count (United States, Spain, Germany, United Kingdom, and France) and 5 endemic countries (Democratic Republic of Congo, Nigeria, Ghana, Central African Republic, and Cameroon). Joinpoint regression analysis was used to measure changes in searching trends for mpox preceding and following the announcement of the first human case. Results: Online health information seeking significantly increased after the publication of the first case in all the nonendemic countries---United States, Spain, Germany, United Kingdom, and France, as illustrated by significant joinpoint regression models. Joinpoint analysis revealed that models with 3 significant joinpoints were the most appropriate fit for these data, where the first joinpoint represents the initial rise in mpox searching trend, the second joinpoint reflects the start of the decrease in the mpox searching trend, and the third joinpoint represents searching trends' return to searching levels prior to the first case announcement. Although this model was also found in 2 endemic countries (ie, Ghana and Nigeria), it was not found in Central African Republic, Democratic Republic of Congo, or Cameroon. Conclusions: Findings demonstrate a surge in online heath information seeking relating to mpox after the first in-country case was publicized in all the nonendemic countries and in Ghana and Nigeria among the endemic counties. The observed increases in mpox searching levels are characterized by sharp but short-lived periods of searching before steep declines back to levels observed prior to the publication of the first case. These findings emphasize the importance of the provision of accurate, relevant online public health information during disease outbreaks. However, online health information seeking behaviors only occur for a short time period, and the provision of accurate information needs to be timely in relation to the publication of new case-related information. ", doi="10.2196/42710", url="https://formative.jmir.org/2023/1/e42710", url="http://www.ncbi.nlm.nih.gov/pubmed/37052999" } @Article{info:doi/10.2196/41319, author="Lindel{\"o}f, Gabriel and Aledavood, Talayeh and Keller, Barbara", title="Dynamics of the Negative Discourse Toward COVID-19 Vaccines: Topic Modeling Study and an Annotated Data Set of Twitter Posts", journal="J Med Internet Res", year="2023", month="Apr", day="12", volume="25", pages="e41319", keywords="COVID-19 vaccines", keywords="SARS-CoV-2", keywords="vaccine hesitancy", keywords="social media", keywords="Twitter", keywords="natural language processing", keywords="machine learning", keywords="stance detection", keywords="topic modeling", abstract="Background: Since the onset of the COVID-19 pandemic, vaccines have been an important topic in public discourse. The discussions around vaccines are polarized, as some see them as an important measure to end the pandemic, and others are hesitant or find them harmful. A substantial portion of these discussions occurs openly on social media platforms. This allows us to closely monitor the opinions of different groups and their changes over time. Objective: This study investigated posts related to COVID-19 vaccines on Twitter (Twitter Inc) and focused on those that had a negative stance toward vaccines. It examined the evolution of the percentage of negative tweets over time. It also examined the different topics discussed in these tweets to understand the concerns and discussion points of those holding a negative stance toward the vaccines. Methods: A data set of 16,713,238 English tweets related to COVID-19 vaccines was collected, covering the period from March 1, 2020, to July 31, 2021. We used the scikit-learn Python library to apply a support vector machine classifier to identify the tweets with a negative stance toward COVID-19 vaccines. A total of 5163 tweets were used to train the classifier, of which a subset of 2484 tweets was manually annotated by us and made publicly available along with this paper. We used the BERTopic model to extract the topics discussed within the negative tweets and investigate them, including how they changed over time. Results: We showed that the negativity with respect to COVID-19 vaccines has decreased over time along with the vaccine rollouts. We identified 37 topics of discussion and presented their respective importance over time. We showed that popular topics not only consisted of conspiratorial discussions, such as 5G towers and microchips, but also contained legitimate concerns around vaccination safety and side effects as well as concerns about policies. The most prevalent topic among vaccine-hesitant tweets was related to the use of messenger RNA and fears about its speculated negative effects on our DNA. Conclusions: Hesitancy toward vaccines existed before the COVID-19 pandemic. However, given the dimension of and circumstances surrounding the COVID-19 pandemic, some new areas of hesitancy and negativity toward COVID-19 vaccines have arisen, for example, whether there has been enough time for them to be properly tested. There is also an unprecedented number of conspiracy theories associated with them. Our study shows that even unpopular opinions or conspiracy theories can become widespread when paired with a widely popular discussion topic such as COVID-19 vaccines. Understanding the concerns, the discussed topics, and how they change over time is essential for policy makers and public health authorities to provide better in-time information and policies to facilitate the vaccination of the population in future similar crises. ", doi="10.2196/41319", url="https://www.jmir.org/2023/1/e41319", url="http://www.ncbi.nlm.nih.gov/pubmed/36877804" } @Article{info:doi/10.2196/42218, author="Murthy, Dhiraj and Lee, Juhan and Dashtian, Hassan and Kong, Grace", title="Influence of User Profile Attributes on e-Cigarette--Related Searches on YouTube: Machine Learning Clustering and Classification", journal="JMIR Infodemiology", year="2023", month="Apr", day="12", volume="3", pages="e42218", keywords="electronic cigarettes", keywords="electronic nicotine delivery systems", keywords="ENDS", keywords="tobacco products", keywords="YouTube", keywords="social media", keywords="minority groups", keywords="exposure", keywords="youth", keywords="behavior", keywords="user", keywords="machine learning", keywords="policy", abstract="Background: The proliferation of e-cigarette content on YouTube is concerning because of its possible effect on youth use behaviors. YouTube has a personalized search and recommendation algorithm that derives attributes from a user's profile, such as age and sex. However, little is known about whether e-cigarette content is shown differently based on user characteristics. Objective: The aim of this study was to understand the influence of age and sex attributes of user profiles on e-cigarette--related YouTube search results. Methods: We created 16 fictitious YouTube profiles with ages of 16 and 24 years, sex (female and male), and ethnicity/race to search for 18 e-cigarette--related search terms. We used unsupervised (k-means clustering and classification) and supervised (graph convolutional network) machine learning and network analysis to characterize the variation in the search results of each profile. We further examined whether user attributes may play a role in e-cigarette--related content exposure by using networks and degree centrality. Results: We analyzed 4201 nonduplicate videos. Our k-means clustering suggested that the videos could be clustered into 3 categories. The graph convolutional network achieved high accuracy (0.72). Videos were classified based on content into 4 categories: product review (49.3\%), health information (15.1\%), instruction (26.9\%), and other (8.5\%). Underage users were exposed mostly to instructional videos (37.5\%), with some indication that more female 16-year-old profiles were exposed to this content, while young adult age groups (24 years) were exposed mostly to product review videos (39.2\%). Conclusions: Our results indicate that demographic attributes factor into YouTube's algorithmic systems in the context of e-cigarette--related queries on YouTube. Specifically, differences in the age and sex attributes of user profiles do result in variance in both the videos presented in YouTube search results as well as in the types of these videos. We find that underage profiles were exposed to e-cigarette content despite YouTube's age-restriction policy that ostensibly prohibits certain e-cigarette content. Greater enforcement of policies to restrict youth access to e-cigarette content is needed. ", doi="10.2196/42218", url="https://infodemiology.jmir.org/2023/1/e42218", url="http://www.ncbi.nlm.nih.gov/pubmed/37124246" } @Article{info:doi/10.2196/42346, author="Xie, Zidian and Xue, Siyu and Gao, Yankun and Li, Dongmei", title="Characterizing e-Cigarette--Related Videos on TikTok: Observational Study", journal="JMIR Form Res", year="2023", month="Apr", day="5", volume="7", pages="e42346", keywords="e-cigarette", keywords="TikTok", keywords="video", keywords="provaping", keywords="antivaping", abstract="Background: As a popular social networking platform for sharing short videos, TikTok has been widely used for sharing e-cigarettes or vaping-related videos, especially among the youth. Objective: This study aims to characterize e-cigarette or vaping-related videos and their user engagement on TikTok through descriptive analysis. Methods: From TikTok, a total of 417 short videos, posted between October 4, 2018, and February 27, 2021, were collected using e-cigarette or vaping-related hashtags. Two human coders independently hand-coded the video category and the attitude toward vaping (provaping or antivaping) for each vaping-related video. The social media user engagement measures (eg, the comment count, like count, and share count) for each video category were compared within provaping and antivaping groups. The user accounts posting these videos were also characterized. Results: Among 417 vaping-related TikTok videos, 387 (92.8\%) were provaping, and 30 (7.2\%) were antivaping videos. Among provaping TikTok videos, the most popular category is vaping tricks (n=107, 27.65\%), followed by advertisement (n=85, 21.95\%), customization (n=75, 19.38\%), TikTok trend (n=70, 18.09\%), others (n=44, 11.37\%), and education (n=6, 1.55\%). By comparison, videos showing the TikTok trend had significantly higher user engagement (like count per video) than other provaping videos. Antivaping videos included 15 (50\%) videos with the TikTok trend, 10 (33.33\%) videos on education, and 5 (16.67\%) videos about others. Videos with education have a significantly lower number of likes than other antivaping videos. Most TikTok users posting vaping-related videos are personal accounts (119/203, 58.62\%). Conclusions: Vaping-related TikTok videos are dominated by provaping videos focusing on vaping tricks, advertisement, customization, and TikTok trend. Videos with the TikTok trend have higher user engagement than other video categories. Our findings provide important information on vaping-related videos shared on TikTok and their user engagement levels, which might provide valuable guidance on future policy making, such as possible restrictions on provaping videos posted on TikTok, as well as how to effectively communicate with the public about the potential health risks of vaping. ", doi="10.2196/42346", url="https://formative.jmir.org/2023/1/e42346", url="http://www.ncbi.nlm.nih.gov/pubmed/37018026" } @Article{info:doi/10.2196/45777, author="Zhu, Jianghong and Li, Zepeng and Zhang, Xiu and Zhang, Zhenwen and Hu, Bin", title="Public Attitudes Toward Anxiety Disorder on Sina Weibo: Content Analysis", journal="J Med Internet Res", year="2023", month="Apr", day="4", volume="25", pages="e45777", keywords="anxiety disorder", keywords="linguistic feature", keywords="topic model", keywords="public attitude", keywords="social media", abstract="Background: Anxiety disorder has become a major clinical and public health problem, causing a significant economic burden worldwide. Public attitudes toward anxiety can impact the psychological state, help-seeking behavior, and social activities of people with anxiety disorder. Objective: The purpose of this study was to explore public attitudes toward anxiety disorders and the changing trends of these attitudes by analyzing the posts related to anxiety disorders on Sina Weibo, a Chinese social media platform that has about 582 million users, as well as the psycholinguistic and topical features in the text content of the posts. Methods: From April 2018 to March 2022, 325,807 Sina Weibo posts with the keyword ``anxiety disorder'' were collected and analyzed. First, we analyzed the changing trends in the number and total length of posts every month. Second, a Chinese Linguistic Psychological Text Analysis System (TextMind) was used to analyze the changing trends in the language features of the posts, in which 20 linguistic features were selected and presented. Third, a topic model (biterm topic model) was used for semantic content analysis to identify specific themes in Weibo users' attitudes toward anxiety. Results: The changing trends in the number and the total length of posts indicated that anxiety-related posts significantly increased from April 2018 to March 2022 (R2=0.6512; P<.001 to R2=0.8133; P<.001, respectively) and were greatly impacted by the beginning of a new semester (spring/fall). The analysis of linguistic features showed that the frequency of the cognitive process (R2=0.1782; P=.003), perceptual process (R2=0.1435; P=.008), biological process (R2=0.3225; P<.001), and assent words (R2=0.4412; P<.001) increased significantly over time, while the frequency of the social process words (R2=0.2889; P<.001) decreased significantly, and public anxiety was greatly impacted by the COVID-19 pandemic. Feature correlation analysis showed that the frequencies of words related to work and family are almost negatively correlated with those of other psychological words. Semantic content analysis identified 5 common topical areas: discrimination and stigma, symptoms and physical health, treatment and support, work and social, and family and life. Our results showed that the occurrence probability of the topical area ``discrimination and stigma'' reached the highest value and averagely accounted for 26.66\% in the 4-year period. The occurrence probability of the topical area ``family and life'' (R2=0.1888; P=.09) decreased over time, while that of the other 4 topical areas increased. Conclusions: The findings of our study indicate that public discrimination and stigma against anxiety disorder remain high, particularly in the aspects of self-denial and negative emotions. People with anxiety disorders should receive more social support to reduce the impact of discrimination and stigma. ", doi="10.2196/45777", url="https://www.jmir.org/2023/1/e45777", url="http://www.ncbi.nlm.nih.gov/pubmed/37014691" } @Article{info:doi/10.2196/43497, author="Ahmed, Wasim and Das, Ronnie and Vidal-Alaball, Josep and Hardey, Mariann and Fuster-Casanovas, A{\"i}na", title="Twitter's Role in Combating the Magnetic Vaccine Conspiracy Theory: Social Network Analysis of Tweets", journal="J Med Internet Res", year="2023", month="Mar", day="31", volume="25", pages="e43497", keywords="COVID-19", keywords="coronavirus", keywords="Twitter", keywords="social network analysis", keywords="misinformation", keywords="online social capital", abstract="Background: The popularity of the magnetic vaccine conspiracy theory and other conspiracy theories of a similar nature creates challenges to promoting vaccines and disseminating accurate health information. Objective: Health conspiracy theories are gaining in popularity. This study's objective was to evaluate the Twitter social media network related to the magnetic vaccine conspiracy theory and apply social capital theory to analyze the unique social structures of influential users. As a strategy for web-based public health surveillance, we conducted a social network analysis to identify the important opinion leaders sharing the conspiracy, the key websites, and the narratives. Methods: A total of 18,706 tweets were retrieved and analyzed by using social network analysis. Data were retrieved from June 1 to June 13, 2021, using the keyword vaccine magnetic. Tweets were retrieved via a dedicated Twitter application programming interface. More specifically, the Academic Track application programming interface was used, and the data were analyzed by using NodeXL Pro (Social Media Research Foundation) and Gephi. Results: There were a total of 22,762 connections between Twitter users within the data set. This study found that the most influential user within the network consisted of a news account that was reporting on the magnetic vaccine conspiracy. There were also several other users that became influential, such as an epidemiologist, a health economist, and a retired sports athlete who exerted their social capital within the network. Conclusions: Our study found that influential users were effective broadcasters against the conspiracy, and their reach extended beyond their own networks of Twitter followers. We emphasize the need for trust in influential users with regard to health information, particularly in the context of the widespread social uncertainty resulting from the COVID-19 pandemic, when public sentiment on social media may be unpredictable. This study highlights the potential of influential users to disrupt information flows of conspiracy theories via their unique social capital. ", doi="10.2196/43497", url="https://www.jmir.org/2023/1/e43497", url="http://www.ncbi.nlm.nih.gov/pubmed/36927550" } @Article{info:doi/10.2196/45482, author="Agley, Jon and Xiao, Yunyu and Thompson, E. Esi and Golzarri-Arroyo, Lilian", title="Using Normative Language When Describing Scientific Findings: Randomized Controlled Trial of Effects on Trust and Credibility", journal="J Med Internet Res", year="2023", month="Mar", day="30", volume="25", pages="e45482", keywords="trust", keywords="trust in science", keywords="scientific communication", keywords="meta-science", keywords="RCT", abstract="Background: Scientists often make cognitive claims (eg, the results of their work) and normative claims (eg, what should be done based on those results). Yet, these types of statements contain very different information and implications. This randomized controlled trial sought to characterize the granular effects of using normative language in science communication. Objective: Our study examined whether viewing a social media post containing scientific claims about face masks for COVID-19 using both normative and cognitive language (intervention arm) would reduce perceptions of trust and credibility in science and scientists compared with an identical post using only cognitive language (control arm). We also examined whether effects were mediated by political orientation. Methods: This was a 2-arm, parallel group, randomized controlled trial. We aimed to recruit 1500 US adults (age 18+) from the Prolific platform who were representative of the US population census by cross sections of age, race/ethnicity, and gender. Participants were randomly assigned to view 1 of 2 images of a social media post about face masks to prevent COVID-19. The control image described the results of a real study (cognitive language), and the intervention image was identical, but also included recommendations from the same study about what people should do based on the results (normative language). Primary outcomes were trust in science and scientists (21-item scale) and 4 individual items related to trust and credibility; 9 additional covariates (eg, sociodemographics, political orientation) were measured and included in analyses. Results: From September 4, 2022, to September 6, 2022, 1526 individuals completed the study. For the sample as a whole (eg, without interaction terms), there was no evidence that a single exposure to normative language affected perceptions of trust or credibility in science or scientists. When including the interaction term (study arm {\texttimes} political orientation), there was some evidence of differential effects, such that individuals with liberal political orientation were more likely to trust scientific information from the social media post's author if the post included normative language, and political conservatives were more likely to trust scientific information from the post's author if the post included only cognitive language ($\beta$=0.05, 95\% CI 0.00 to 0.10; P=.04). Conclusions: This study does not support the authors' original hypotheses that single exposures to normative language can reduce perceptions of trust or credibility in science or scientists for all people. However, the secondary preregistered analyses indicate the possibility that political orientation may differentially mediate the effect of normative and cognitive language from scientists on people's perceptions. We do not submit this paper as definitive evidence thereof but do believe that there is sufficient evidence to support additional research into this topic, which may have implications for effective scientific communication. Trial Registration: OSF Registries osf.io/kb3yh; https://osf.io/kb3yh International Registered Report Identifier (IRRID): RR2-10.2196/41747 ", doi="10.2196/45482", url="https://www.jmir.org/2023/1/e45482", url="http://www.ncbi.nlm.nih.gov/pubmed/36995753" } @Article{info:doi/10.2196/38404, author="Jones, Ffion Leah and Bonfield, Stefanie and Farrell, Jade and Weston, Dale", title="Understanding the Public's Attitudes Toward COVID-19 Vaccines in Nottinghamshire, United Kingdom: Qualitative Social Media Analysis", journal="J Med Internet Res", year="2023", month="Mar", day="29", volume="25", pages="e38404", keywords="COVID-19", keywords="vaccine", keywords="social media", keywords="qualitative", keywords="vaccine hesitancy", keywords="infodemic", keywords="misinformation", keywords="infodemiology", keywords="online health information", keywords="content analysis", keywords="Facebook", keywords="Twitter", keywords="transmission", abstract="Background: COVID-19 vaccines remain central to the UK government's plan for tackling the COVID-19 pandemic. Average uptake of 3 doses in the United Kingdom stood at 66.7\% as of March 2022; however, this rate varies across localities. Understanding the views of groups who have low vaccine uptake is crucial to guide efforts to improve vaccine uptake. Objective: This study aims to understand the public's attitudes toward COVID-19 vaccines in Nottinghamshire, United Kingdom. Methods: A qualitative thematic analysis of social media posts from Nottinghamshire-based profiles and data sources was conducted. A manual search strategy was used to search the Nottingham Post website and local Facebook and Twitter accounts from September 2021 to October 2021. Only comments in the public domain and in English were included in the analysis. Results: A total of 3508 comments from 1238 users on COVID-19 vaccine posts by 10 different local organizations were analyzed, and 6 overarching themes were identified: trust in the vaccines, often characterized by a lack of trust in vaccine information, information sources including the media, and the government; beliefs about safety including doubts about the speed of development and approval process, the severity of side effects, and belief that the ingredients are harmful; belief that the vaccines are not effective as people can still become infected and spread the virus and that the vaccines may increase transmission through shedding; belief that the vaccines are not necessary due to low perceived risk of death and severe outcomes and use of other protective measures such as natural immunity, ventilation, testing, face coverings, and self-isolation; individual rights and freedoms to be able to choose to be vaccinated or not without judgement or discrimination; and barriers to physical access. Conclusions: The findings revealed a wide range of beliefs and attitudes toward COVID-19 vaccination. Implications for the vaccine program in Nottinghamshire include communication strategies delivered by trusted sources to address the gaps in knowledge identified while acknowledging some negatives such as side effects alongside emphasizing the benefits. These strategies should avoid perpetuating myths and avoid using scare tactics when addressing risk perceptions. Accessibility should also be considered with a review of current vaccination site locations, opening hours, and transport links. Additional research may benefit from using qualitative interviews or focus groups to further probe on the themes identified and explore the acceptability of the recommended interventions. ", doi="10.2196/38404", url="https://www.jmir.org/2023/1/e38404", url="http://www.ncbi.nlm.nih.gov/pubmed/36812390" } @Article{info:doi/10.2196/43623, author="Beauchamp, M. Alaina and Lehmann, U. Christoph and Medford, J. Richard and Hughes, E. Amy", title="The Association of a Geographically Wide Social Media Network on Depression: County-Level Ecological Analysis", journal="J Med Internet Res", year="2023", month="Mar", day="27", volume="25", pages="e43623", keywords="Facebook", keywords="social connectedness", keywords="depression", keywords="county-level analysis", keywords="social media", keywords="mental health", keywords="research", keywords="ecological", keywords="geography", keywords="GIS", abstract="Background: Social connectedness decreases human mortality, improves cancer survival, cardiovascular health, and body mass, results in better-controlled glucose levels, and strengthens mental health. However, few public health studies have leveraged large social media data sets to classify user network structure and geographic reach rather than the sole use of social media platforms. Objective: The objective of this study was to determine the association between population-level digital social connectedness and reach and depression in the population across geographies of the United States. Methods: Our study used an ecological assessment of aggregated, cross-sectional population measures of social connectedness, and self-reported depression across all counties in the United States. This study included all 3142 counties in the contiguous United States. We used measures obtained between 2018 and 2020 for adult residents in the study area. The study's main exposure of interest is the Social Connectedness Index (SCI), a pair-wise composite index describing the ``strength of connectedness between 2 geographic areas as represented by Facebook friendship ties.'' This measure describes the density and geographical reach of average county residents' social network using Facebook friendships and can differentiate between local and long-distance Facebook connections. The study's outcome of interest is self-reported depressive disorder as published by the Centers for Disease Control and Prevention. Results: On average, 21\% (21/100) of all adult residents in the United States reported a depressive disorder. Depression frequency was the lowest for counties in the Northeast (18.6\%) and was highest for southern counties (22.4\%). Social networks in northeastern counties involved moderately local connections (SCI 5-10 the 20th percentile for n=70, 36\% of counties), whereas social networks in Midwest, southern, and western counties contained mostly local connections (SCI 1-2 the 20th percentile for n=598, 56.7\%, n=401, 28.2\%, and n=159, 38.4\%, respectively). As the quantity and distance that social connections span (ie, SCI) increased, the prevalence of depressive disorders decreased by 0.3\% (SE 0.1\%) per rank. Conclusions: Social connectedness and depression showed, after adjusting for confounding factors such as income, education, cohabitation, natural resources, employment categories, accessibility, and urbanicity, that a greater social connectedness score is associated with a decreased prevalence of depression. ", doi="10.2196/43623", url="https://www.jmir.org/2023/1/e43623", url="http://www.ncbi.nlm.nih.gov/pubmed/36972109" } @Article{info:doi/10.2196/45011, author="Renner, Simon and Loussikian, Paul and Foulqui{\'e}, Pierre and Marrel, Alexia and Barbier, Valentin and Mebarki, Adel and Sch{\"u}ck, St{\'e}phane and Bharmal, Murtuza", title="Patient and Caregiver Perceptions of Advanced Bladder Cancer Systemic Treatments: Infodemiology Study Based on Social Media Data", journal="JMIR Cancer", year="2023", month="Mar", day="27", volume="9", pages="e45011", keywords="bladder cancer", keywords="social media", keywords="patient", keywords="caregiver", keywords="chemotherapy", keywords="immunotherapy", keywords="qualitative research", keywords="cancer treatment", keywords="first-line therapy", keywords="patient support", keywords="adverse event", keywords="peer support", keywords="cancer", keywords="oncology", keywords="perception", keywords="pharmacotherapy", keywords="opinion", keywords="attitude", abstract="Background: In 2022, it was estimated that more than 80,000 new cases of bladder cancer (BC) were diagnosed in the United States, 12\% of which were locally advanced or metastatic BC (advanced BC). These forms of cancer are aggressive and have a poor prognosis, with a 5-year survival rate of 7.7\% for metastatic BC. Despite recent therapeutic advances for advanced BC, little is known about patient and caregiver perceptions of different systemic treatments. To further explore this topic, social media can be used to collect the perceptions of patients and caregivers when they discuss their experiences on forums and online communities. Objective: The aim of this study was to assess patient and caregiver perceptions of chemotherapy and immunotherapy for treating advanced BC from social media--posted data. Methods: Public posts on social media in the United States between January 2015 and April 2021 from patients with advanced BC and their caregivers were collected. The posts included in this analysis were geolocalized to the United States; collected from publicly available domains and sites, including social media sites such as Twitter and forums such as patient association forums; and were written in English. Posts mentioning any line of chemotherapy or immunotherapy were qualitatively analyzed by two researchers to classify perceptions of treatments (positive, negative, mixed, or without perception). Results: A total of 80 posts by 69 patients and 142 posts by 127 caregivers mentioning chemotherapy, and 42 posts by 31 patients and 35 posts by 32 caregivers mentioning immunotherapy were included for analysis. These posts were retrieved from 39 public social media sites. Among patients with advanced BC and their caregivers, treatment perceptions of chemotherapy were more negative (36\%) than positive (7\%). Most of the patients' posts (71\%) mentioned chemotherapy factually without expressing a perception of the treatment. The caregivers' perceptions of treatment were negative in 44\%, mixed in 8\%, and positive in 7\% of posts. In combined patient and caregiver posts, immunotherapy was perceived positively in 47\% of posts and negatively in 22\% of posts. Caregivers also posted more negative perceptions (37\%) of immunotherapy than patients (9\%). Negative perceptions of both chemotherapy and immunotherapy were mainly due to side effects and perceived lack of effectiveness. Conclusions: Despite chemotherapy being standard first-line therapy for advanced BC, negative perceptions were identified on social media, particularly among caregivers. Addressing these negative perceptions of treatment may improve treatment adoption. Strengthening support for patients receiving chemotherapy and their caregivers to help them manage side effects and understand the role of chemotherapy in the treatment of advanced BC would potentially enable a more positive experience. ", doi="10.2196/45011", url="https://cancer.jmir.org/2023/1/e45011", url="http://www.ncbi.nlm.nih.gov/pubmed/36972135" } @Article{info:doi/10.2196/41882, author="Li, Yue and Gee, William and Jin, Kun and Bond, Robert", title="Examining Homophily, Language Coordination, and Analytical Thinking in Web-Based Conversations About Vaccines on Reddit: Study Using Deep Neural Network Language Models and Computer-Assisted Conversational Analyses", journal="J Med Internet Res", year="2023", month="Mar", day="23", volume="25", pages="e41882", keywords="vaccine hesitancy", keywords="social media", keywords="web-based conversations", keywords="neural network language models", keywords="computer-assisted conversational analyses", abstract="Background: Vaccine hesitancy has been deemed one of the top 10 threats to global health. Antivaccine information on social media is a major barrier to addressing vaccine hesitancy. Understanding how vaccine proponents and opponents interact with each other on social media may help address vaccine hesitancy. Objective: We aimed to examine conversations between vaccine proponents and opponents on Reddit to understand whether homophily in web-based conversations impedes opinion exchange, whether people are able to accommodate their languages to each other in web-based conversations, and whether engaging with opposing viewpoints stimulates higher levels of analytical thinking. Methods: We analyzed large-scale conversational text data about human vaccines on Reddit from 2016 to 2018. Using deep neural network language models and computer-assisted conversational analyses, we obtained each Redditor's stance on vaccines, each post's stance on vaccines, each Redditor's language coordination score, and each post or comment's analytical thinking score. We then performed chi-square tests, 2-tailed t tests, and multilevel modeling to test 3 questions of interest. Results: The results show that both provaccine and antivaccine Redditors are more likely to selectively respond to Redditors who indicate similar views on vaccines (P<.001). When Redditors interact with others who hold opposing views on vaccines, both provaccine and antivaccine Redditors accommodate their language to out-group members (provaccine Redditors: P=.044; antivaccine Redditors: P=.047) and show no difference in analytical thinking compared with interacting with congruent views (P=.63), suggesting that Redditors do not engage in motivated reasoning. Antivaccine Redditors, on average, showed higher analytical thinking in their posts and comments than provaccine Redditors (P<.001). Conclusions: This study shows that although vaccine proponents and opponents selectively communicate with their in-group members on Reddit, they accommodate their language and do not engage in motivated reasoning when communicating with out-group members. These findings may have implications for the design of provaccine campaigns on social media. ", doi="10.2196/41882", url="https://www.jmir.org/2023/1/e41882", url="http://www.ncbi.nlm.nih.gov/pubmed/36951921" } @Article{info:doi/10.2196/44055, author="Gilbert, James Barnabas and Lu, Chunling and Yom-Tov, Elad", title="Tracking Population-Level Anxiety Using Search Engine Data: Ecological Study", journal="JMIR Form Res", year="2023", month="Mar", day="22", volume="7", pages="e44055", keywords="anxiety disorders", keywords="anxiety themes", keywords="Bing search", keywords="country-level", keywords="epidemiology", keywords="Google trends", keywords="internet search data", keywords="mental disorder", keywords="search engine", keywords="socioeconomic", abstract="Background: Anxiety disorders are the most prevalent mental disorders globally, with a substantial impact on quality of life. The prevalence of anxiety disorders has increased substantially following the COVID-19 pandemic, and it is likely to be further affected by a global economic recession. Understanding anxiety themes and how they change over time and across countries is crucial for preventive and treatment strategies. Objective: The aim of this study was to track the trends in anxiety themes between 2004 and 2020 in the 50 most populous countries with high volumes of internet search data. This study extends previous research by using a novel search-based methodology and including a longer time span and more countries at different income levels. Methods: We used a crowdsourced questionnaire, alongside Bing search query data and Google Trends search volume data, to identify themes associated with anxiety disorders across 50 countries from 2004 to 2020. We analyzed themes and their mutual interactions and investigated the associations between countries' socioeconomic attributes and anxiety themes using time-series linear models. This study was approved by the Microsoft Research Institutional Review Board. Results: Query volume for anxiety themes was highly stable in countries from 2004 to 2019 (Spearman r=0.89) and moderately correlated with geography (r=0.49 in 2019). Anxiety themes were predominantly long-term and personal, with ``having kids,'' ``pregnancy,'' and ``job'' the most voluminous themes in most countries and years. In 2020, ``COVID-19'' became a dominant theme in 27 countries. Countries with a constant volume of anxiety themes over time had lower fragile state indexes (P=.007) and higher individualism (P=.003). An increase in the volume of the most searched anxiety themes was associated with a reduction in the volume of the remaining themes in 13 countries and an increase in 17 countries, and these 30 countries had a lower prevalence of mental disorders (P<.001) than the countries where no correlations were found. Conclusions: Internet search data could be a potential source for predicting the country-level prevalence of anxiety disorders, especially in understudied populations or when an in-person survey is not viable. ", doi="10.2196/44055", url="https://formative.jmir.org/2023/1/e44055", url="http://www.ncbi.nlm.nih.gov/pubmed/36947130" } @Article{info:doi/10.2196/45147, author="Almomani, Hamzeh and Patel, Nilesh and Donyai, Parastou", title="News Media Coverage of the Problem of Purchasing Fake Prescription Medicines on the Internet: Thematic Analysis", journal="JMIR Form Res", year="2023", month="Mar", day="21", volume="7", pages="e45147", keywords="prescription medicine", keywords="internet", keywords="online pharmacy", keywords="fake medicine", keywords="media", keywords="newspaper article", keywords="Theory of Planned Behavior", keywords="thematic analysis", abstract="Background: More people are turning to internet pharmacies to purchase their prescription medicines. This kind of purchase is associated with serious risks, including the risk of buying fake medicines, which are widely available on the internet. This underresearched issue has been highlighted by many newspaper articles in the past few years. Newspapers can play an important role in shaping public perceptions of the risks associated with purchasing prescription medicines on the internet. Thus, it is important to understand how the news media present this issue. Objective: This study aimed to explore newspaper coverage of the problem of purchasing fake prescription medicines on the internet. Methods: Newspaper articles were retrieved from the ProQuest electronic database using search terms related to the topic of buying fake prescription medicines on the internet. The search was limited to articles published between April 2019 and March 2022 to retrieve relevant articles in this fast-developing field. Articles were included if they were published in English and focused on prescription medicines. Thematic analysis was employed to analyze the articles, and the Theory of Planned Behavior framework was used as a conceptual lens to develop the coding of themes. Results: A total of 106 articles were included and analyzed using thematic analysis. We identified 4 superordinate themes that represent newspaper coverage of the topic of buying prescription medicines on the internet. These themes are (1) the risks of purchasing medicines on the internet (eg, health risks and product quality concerns, financial risks, lack of accountability, risk of purchasing stolen medicines), (2) benefits that entice consumers to make the purchase (eg, convenience and quick purchase, lower cost, privacy of the purchase), (3) social influencing factors of the purchase (influencers, health care providers), and (4) facilitators of the purchase (eg, medicines shortages, pandemic disease such as COVID-19, social media, search engines, accessibility, low risk perception). Conclusions: This theory-based study explored the news media coverage of the problem of fake prescription medicines being purchased on the internet by highlighting the complexity of personal beliefs and the range of external circumstances that could influence people to make these purchases. Further research is needed in this area to identify the factors that lead people to buy prescription medicines on the internet. Identifying these factors could enable the development of interventions to dissuade people from purchasing medicines from unsafe sources on the internet, thus protecting consumers from unsafe or illegal medicines. ", doi="10.2196/45147", url="https://formative.jmir.org/2023/1/e45147", url="http://www.ncbi.nlm.nih.gov/pubmed/36943354" } @Article{info:doi/10.2196/39061, author="Roe, L. Kyle and Giordano, R. Katherine and Ezzell, A. Gary and Lifshitz, Jonathan", title="Public Awareness of the Fencing Response as an Indicator of Traumatic Brain Injury: Quantitative Study of Twitter and Wikipedia Data", journal="JMIR Form Res", year="2023", month="Mar", day="17", volume="7", pages="e39061", keywords="athlete", keywords="brain", keywords="concussion", keywords="fencing response", keywords="health communication", keywords="health information", keywords="injury pattern", keywords="posture", keywords="public education", keywords="science communication", keywords="social media", keywords="sport", keywords="trauma", keywords="traumatic brain injury", abstract="Background: Traumatic brain injury (TBI) is a disruption in normal brain function caused by an impact of external forces on the head. TBI affects millions of individuals per year, many potentially experiencing chronic symptoms and long-term disability, creating a public health crisis and an economic burden on society. The public discourse around sport-related TBIs has increased in recent decades; however, recognition of a possible TBI remains a challenge. The fencing response is an immediate posturing of the limbs, which can occur in individuals who sustain a TBI and can be used as an overt indicator of TBI. Typically, an individual demonstrating the fencing response exhibits extension in 1 arm and flexion in the contralateral arm immediately upon impact to the head; variations of forearm posturing among each limb have been observed. The tonic posturing is retained for several seconds, sufficient for observation and recognition of a TBI. Since the publication of the original peer-reviewed article on the fencing response, there have been efforts to raise awareness of the fencing response as a visible sign of TBI through publicly available web-based platforms, such as Twitter and Wikipedia. Objective: We aimed to quantify trends that demonstrate levels of public discussion and awareness of the fencing response over time using data from Twitter and Wikipedia. Methods: Raw Twitter data from January 1, 2010, to December 31, 2019, were accessed using the RStudio package academictwitteR and queried for the text ``fencing response.'' Data for page views of the Fencing Response Wikipedia article from January 1, 2010, to December 31, 2019, were accessed using the RStudio packages wikipediatrend and pageviews. Data were clustered by weekday, month, half-year (to represent the American football season vs off-season), and year to identify trends over time. Seasonal regression analysis was used to analyze the relationship between the number of fencing response tweets and page views and month of the year. Results: Twitter mentions of the fencing response and Wikipedia page views increased overall from 2010 to 2019, with hundreds of tweets and hundreds of thousands of Wikipedia page views per year. Twitter mentions peaked during the American football season, especially on and following game days. Wikipedia page views did not demonstrate a clear weekday or seasonal pattern, but instead had multiple peaks across various months and years, with January having more page views than May. Conclusions: Here, we demonstrated increased awareness of the fencing response over time using public data from Twitter and Wikipedia. Effective scientific communication through free public platforms can help spread awareness of clinical indicators of TBI, such as the fencing response. Greater awareness of the fencing response as a ``red-flag'' sign of TBI among coaches, athletic trainers, and sports organizations can help with medical care and return-to-play decisions. ", doi="10.2196/39061", url="https://formative.jmir.org/2023/1/e39061", url="http://www.ncbi.nlm.nih.gov/pubmed/36930198" } @Article{info:doi/10.2196/44965, author="Ueda, Michiko and Watanabe, Kohei and Sueki, Hajime", title="Emotional Distress During COVID-19 by Mental Health Conditions and Economic Vulnerability: Retrospective Analysis of Survey-Linked Twitter Data With a Semisupervised Machine Learning Algorithm", journal="J Med Internet Res", year="2023", month="Mar", day="16", volume="25", pages="e44965", keywords="mental health", keywords="COVID-19", keywords="Twitter", keywords="social media", keywords="depression", keywords="suicidal ideation", keywords="loneliness", keywords="public health crisis", keywords="psychological well-being", keywords="infodemiology", keywords="machine learning framework", keywords="digital surveillance", keywords="emotional distress", keywords="online survey", abstract="Background: Monitoring the psychological conditions of social media users during rapidly developing public health crises, such as the COVID-19 pandemic, using their posts on social media has rapidly gained popularity as a relatively easy and cost-effective method. However, the characteristics of individuals who created these posts are largely unknown, making it difficult to identify groups of individuals most affected by such crises. In addition, large annotated data sets for mental health conditions are not easily available, and thus, supervised machine learning algorithms can be infeasible or too costly. Objective: This study proposes a machine learning framework for the real-time surveillance of mental health conditions that does not require extensive training data. Using survey-linked tweets, we tracked the level of emotional distress during the COVID-19 pandemic by the attributes and psychological conditions of social media users in Japan. Methods: We conducted online surveys of adults residing in Japan in May 2022 and collected their basic demographic information, socioeconomic status, and mental health conditions, along with their Twitter handles (N=2432). We computed emotional distress scores for all the tweets posted by the study participants between January 1, 2019, and May 30, 2022 (N=2,493,682) using a semisupervised algorithm called latent semantic scaling (LSS), with higher values indicating higher levels of emotional distress. After excluding users by age and other criteria, we examined 495,021 (19.85\%) tweets generated by 560 (23.03\%) individuals (age 18-49 years) in 2019 and 2020. We estimated fixed-effect regression models to examine their emotional distress levels in 2020 relative to the corresponding weeks in 2019 by the mental health conditions and characteristics of social media users. Results: The estimated level of emotional distress of our study participants increased in the week when school closure started (March 2020), and it peaked at the beginning of the state of emergency (estimated coefficient=0.219, 95\% CI 0.162-0.276) in early April 2020. Their level of emotional distress was unrelated to the number of COVID-19 cases. We found that the government-induced restrictions disproportionately affected the psychological conditions of vulnerable individuals, including those with low income, precarious employment, depressive symptoms, and suicidal ideation. Conclusions: This study establishes a framework to implement near-real-time monitoring of the emotional distress level of social media users, highlighting a great potential to continuously monitor their well-being using survey-linked social media posts as a complement to administrative and large-scale survey data. Given its flexibility and adaptability, the proposed framework is easily extendable for other purposes, such as detecting suicidality among social media users, and can be used on streaming data for continuous measurement of the conditions and sentiment of any group of interest. ", doi="10.2196/44965", url="https://www.jmir.org/2023/1/e44965", url="http://www.ncbi.nlm.nih.gov/pubmed/36809798" } @Article{info:doi/10.2196/38429, author="Shiyab, Wa'ed and Halcomb, Elizabeth and Rolls, Kaye and Ferguson, Caleb", title="The Impact of Social Media Interventions on Weight Reduction and Physical Activity Improvement Among Healthy Adults: Systematic Review", journal="J Med Internet Res", year="2023", month="Mar", day="16", volume="25", pages="e38429", keywords="social media", keywords="physical activity", keywords="overweight", keywords="lifestyle risk factors", abstract="Background: A sedentary lifestyle and being overweight or obese are well-established cardiovascular risk factors and contribute substantially to the global burden of disease. Changing such behavior is complex and requires support. Social media interventions show promise in supporting health behavior change, but their impact is unclear. Moreover, previous reviews have reported contradictory evidence regarding the relationship between engagement with social media interventions and the efficacy of these interventions. Objective: This review aimed to critically synthesize available evidence regarding the impact of social media interventions on physical activity and weight among healthy adults. In addition, this review examined the effect of engagement with social media interventions on their efficacy. Methods: CINAHL and MEDLINE were searched for relevant randomized trials that were conducted to investigate the impact of social media interventions on weight and physical activity and were published between 2011 and 2021 in the English language. Studies were included if the intervention used social media tools that provided explicit interactions between the participants. Studies were excluded if the intervention was passively delivered through an app website or if the participants had a known chronic disease. Eligible studies were appraised for quality and synthesized using narrative synthesis. Results: A total of 17 papers reporting 16 studies from 4 countries, with 7372 participants, were identified. Overall, 56\% (9/16) of studies explored the effect of social media interventions on physical activity; 38\% (6/16) of studies investigated weight reduction; and 6\% (1/16) of studies assessed the effect on both physical activity and weight reduction. Evidence of the effects of social media interventions on physical activity and weight loss was mixed across the included studies. There were no standard metrics for measuring engagement with social media, and the relationship between participant engagement with the intervention and subsequent behavior change was also mixed. Although 35\% (6/16) of studies reported that engagement was not a predictor of behavior change, engagement with social media interventions was found to be related to behavior change in 29\% (5/16) of studies. Conclusions: Despite the promise of social media interventions, evidence regarding their effectiveness is mixed. Further robust studies are needed to elucidate the components of social media interventions that lead to successful behavior change. Furthermore, the effect of engagement with social media interventions on behavior change needs to be clearly understood. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42022311430; https://www.crd.york.ac.uk/prospero/display\_record.php?RecordID=311430 ", doi="10.2196/38429", url="https://www.jmir.org/2023/1/e38429", url="http://www.ncbi.nlm.nih.gov/pubmed/36927627" } @Article{info:doi/10.2196/45419, author="Wu, Jiageng and Wang, Lumin and Hua, Yining and Li, Minghui and Zhou, Li and Bates, W. David and Yang, Jie", title="Trend and Co-occurrence Network of COVID-19 Symptoms From Large-Scale Social Media Data: Infoveillance Study", journal="J Med Internet Res", year="2023", month="Mar", day="14", volume="25", pages="e45419", keywords="social media", keywords="network analysis", keywords="public health", keywords="data mining", keywords="COVID-19", abstract="Background: For an emergent pandemic, such as COVID-19, the statistics of symptoms based on hospital data may be biased or delayed due to the high proportion of asymptomatic or mild-symptom infections that are not recorded in hospitals. Meanwhile, the difficulty in accessing large-scale clinical data also limits many researchers from conducting timely research. Objective: Given the wide coverage and promptness of social media, this study aimed to present an efficient workflow to track and visualize the dynamic characteristics and co-occurrence of symptoms for the COVID-19 pandemic from large-scale and long-term social media data. Methods: This retrospective study included 471,553,966 COVID-19--related tweets from February 1, 2020, to April 30, 2022. We curated a hierarchical symptom lexicon for social media containing 10 affected organs/systems, 257 symptoms, and 1808 synonyms. The dynamic characteristics of COVID-19 symptoms over time were analyzed from the perspectives of weekly new cases, overall distribution, and temporal prevalence of reported symptoms. The symptom evolutions between virus strains (Delta and Omicron) were investigated by comparing the symptom prevalence during their dominant periods. A co-occurrence symptom network was developed and visualized to investigate inner relationships among symptoms and affected body systems. Results: This study identified 201 COVID-19 symptoms and grouped them into 10 affected body systems. There was a significant correlation between the weekly quantity of self-reported symptoms and new COVID-19 infections (Pearson correlation coefficient=0.8528; P<.001). We also observed a 1-week leading trend (Pearson correlation coefficient=0.8802; P<.001) between them. The frequency of symptoms showed dynamic changes as the pandemic progressed, from typical respiratory symptoms in the early stage to more musculoskeletal and nervous symptoms in the later stages. We identified the difference in symptoms between the Delta and Omicron periods. There were fewer severe symptoms (coma and dyspnea), more flu-like symptoms (throat pain and nasal congestion), and fewer typical COVID symptoms (anosmia and taste altered) in the Omicron period than in the Delta period (all P<.001). Network analysis revealed co-occurrences among symptoms and systems corresponding to specific disease progressions, including palpitations (cardiovascular) and dyspnea (respiratory), and alopecia (musculoskeletal) and impotence (reproductive). Conclusions: This study identified more and milder COVID-19 symptoms than clinical research and characterized the dynamic symptom evolution based on 400 million tweets over 27 months. The symptom network revealed potential comorbidity risk and prognostic disease progression. These findings demonstrate that the cooperation of social media and a well-designed workflow can depict a holistic picture of pandemic symptoms to complement clinical studies. ", doi="10.2196/45419", url="https://www.jmir.org/2023/1/e45419", url="http://www.ncbi.nlm.nih.gov/pubmed/36812402" } @Article{info:doi/10.2196/43694, author="Sarker, Abeed and Lakamana, Sahithi and Liao, Ruqi and Abbas, Aamir and Yang, Yuan-Chi and Al-Garadi, Mohammed", title="The Early Detection of Fraudulent COVID-19 Products From Twitter Chatter: Data Set and Baseline Approach Using Anomaly Detection", journal="JMIR Infodemiology", year="2023", month="Mar", day="14", volume="3", pages="e43694", keywords="coronavirus", keywords="COVID-19 drug treatment", keywords="social media", keywords="infodemiology", keywords="public health surveillance", keywords="COVID-19", keywords="misinformation", keywords="natural language processing", keywords="neural network", keywords="data mining", abstract="Background: Social media has served as a lucrative platform for spreading misinformation and for promoting fraudulent products for the treatment, testing, and prevention of COVID-19. This has resulted in the issuance of many warning letters by the US Food and Drug Administration (FDA). While social media continues to serve as the primary platform for the promotion of such fraudulent products, it also presents the opportunity to identify these products early by using effective social media mining methods. Objective: Our objectives were to (1) create a data set of fraudulent COVID-19 products that can be used for future research and (2) propose a method using data from Twitter for automatically detecting heavily promoted COVID-19 products early. Methods: We created a data set from FDA-issued warnings during the early months of the COVID-19 pandemic. We used natural language processing and time-series anomaly detection methods for automatically detecting fraudulent COVID-19 products early from Twitter. Our approach is based on the intuition that increases in the popularity of fraudulent products lead to corresponding anomalous increases in the volume of chatter regarding them. We compared the anomaly signal generation date for each product with the corresponding FDA letter issuance date. We also performed a brief manual analysis of chatter associated with 2 products to characterize their contents. Results: FDA warning issue dates ranged from March 6, 2020, to June 22, 2021, and 44 key phrases representing fraudulent products were included. From 577,872,350 posts made between February 19 and December 31, 2020, which are all publicly available, our unsupervised approach detected 34 out of 44 (77.3\%) signals about fraudulent products earlier than the FDA letter issuance dates, and an additional 6 (13.6\%) within a week following the corresponding FDA letters. Content analysis revealed misinformation, information, political, and conspiracy theories to be prominent topics. Conclusions: Our proposed method is simple, effective, easy to deploy, and does not require high-performance computing machinery unlike deep neural network--based methods. The method can be easily extended to other types of signal detection from social media data. The data set may be used for future research and the development of more advanced methods. ", doi="10.2196/43694", url="https://infodemiology.jmir.org/2023/1/e43694", url="http://www.ncbi.nlm.nih.gov/pubmed/37113382" } @Article{info:doi/10.2196/41969, author="Wu, Jiaxi and Origgi, Manuel Juan and Ranker, R. Lynsie and Bhatnagar, Aruni and Robertson, Marie Rose and Xuan, Ziming and Wijaya, Derry and Hong, Traci and Fetterman, L. Jessica", title="Compliance With the US Food and Drug Administration's Guidelines for Health Warning Labels and Engagement in Little Cigar and Cigarillo Content: Computer Vision Analysis of Instagram Posts", journal="JMIR Infodemiology", year="2023", month="Mar", day="14", volume="3", pages="e41969", keywords="tobacco", keywords="cigar", keywords="little cigar", keywords="cigarillo", keywords="Instagram", keywords="social media", keywords="influencer promotion", keywords="tobacco advertising", keywords="health warning", keywords="machine learning", keywords="computer vision", keywords="warning label", keywords="health label", keywords="health promotion", keywords="advertising", keywords="advertise", keywords="smoking", keywords="smoker", keywords="algorithm", keywords="visualization", abstract="Background: Health warnings in tobacco advertisements provide health information while also increasing the perceived risks of tobacco use. However, existing federal laws requiring warnings on advertisements for tobacco products do not specify whether the rules apply to social media promotions. Objective: This study aims to examine the current state of influencer promotions of little cigars and cigarillos (LCCs) on Instagram and the use of health warnings in influencer promotions. Methods: Instagram influencers were identified as those who were tagged by any of the 3 leading LCC brand Instagram pages between 2018 and 2021. Posts from identified influencers, which mentioned one of the three brands were considered LCC influencer promotions. A novel Warning Label Multi-Layer Image Identification computer vision algorithm was developed to measure the presence and properties of health warnings in a sample of 889 influencer posts. Negative binomial regressions were performed to examine the associations of health warning properties with post engagement (number of likes and comments). Results: The Warning Label Multi-Layer Image Identification algorithm was 99.3\% accurate in detecting the presence of health warnings. Only 8.2\% (n=73) of LCC influencer posts included a health warning. Influencer posts that contained health warnings received fewer likes (incidence rate ratio 0.59, P<.001, 95\% CI 0.48-0.71) and fewer comments (incidence rate ratio 0.46, P<.001, 95\% CI 0.31-0.67). Conclusions: Health warnings are rarely used by influencers tagged by LCC brands' Instagram accounts. Very few influencer posts met the US Food and Drug Administration's health warning requirement of size and placement for tobacco advertising. The presence of a health warning was associated with lower social media engagement. Our study provides support for the implementation of comparable health warning requirements to social media tobacco promotions. Using an innovative computer vision approach to detect health warning labels in influencer promotions on social media is a novel strategy for monitoring health warning compliance in social media tobacco promotions. ", doi="10.2196/41969", url="https://infodemiology.jmir.org/2023/1/e41969", url="http://www.ncbi.nlm.nih.gov/pubmed/37113379" } @Article{info:doi/10.2196/45571, author="Lorenzo-Luaces, Lorenzo and Dierckman, Clare and Adams, Sydney", title="Attitudes and (Mis)information About Cognitive Behavioral Therapy on TikTok: An Analysis of Video Content", journal="J Med Internet Res", year="2023", month="Mar", day="13", volume="25", pages="e45571", keywords="social media", keywords="cognitive behavioral therapy", keywords="misinformation", keywords="public health", keywords="mental health", keywords="TikTok", keywords="psychotherapy", keywords="content analysis", keywords="therapist", keywords="online health information", doi="10.2196/45571", url="https://www.jmir.org/2023/1/e45571", url="http://www.ncbi.nlm.nih.gov/pubmed/36912883" } @Article{info:doi/10.2196/41867, author="Willis, Erin and Friedel, Kate and Heisten, Mark and Pickett, Melissa and Bhowmick, Amrita", title="Communicating Health Literacy on Prescription Medications on Social Media: In-depth Interviews With ``Patient Influencers''", journal="J Med Internet Res", year="2023", month="Mar", day="13", volume="25", pages="e41867", keywords="social media", keywords="social media influencer", keywords="pharmaceutical advertising", keywords="health literacy", abstract="Background: Historically, pharmaceutical companies have struggled with trust and brand reputation among key stakeholders and have adopted innovative marketing strategies to reach patients directly and rebuild those relationships. Social media influencers are a popular strategy to influence younger demographics, including Generation Z and millennials. It is common for social media influencers to work in paid partnerships with brands; this is a multibillion-dollar industry. Long have patients been active in online health communities and social media platforms such as Twitter and Instagram, but in recent years, pharmaceutical marketers have noticed the power of patient persuasion and begun to leverage ``patient influencers'' in brand campaigns. Objective: This study aimed to explore how patient influencers communicate health literacy on pharmaceutical medications on social media to their communities of followers. Methods: A total of 26 in-depth interviews were conducted with patient influencers using a snowball sampling technique. This study is part of a larger project using an interview guide that included a range of topics such as social media practices, logistics of being an influencer, considerations for brand partnerships, and views on the ethical nature of patient influencers. The constructs of the Health Belief Model were used in this study's data analysis: perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, and self-efficacy. This study was approved by the institutional review board of the University of Colorado and adhered to ethical standards in interview practice. Results: As patient influencers are a new phenomenon, it was our goal to identify how health literacy on prescription medications and pharmaceuticals is being communicated on social media. Using the constructs of the Health Belief Model to guide the analysis, 3 themes were identified: understanding disease through experience, staying informed on the science or field, and suggesting that physicians know best. Conclusions: Patients are actively exchanging health information on social media channels and connecting with other patients who share similar diagnoses. Patient influencers share their knowledge and experience in efforts to help other patients learn about disease self-management and improve their quality of life. Similar to traditional direct-to-consumer advertising, the phenomenon of patient influencers raises ethical questions that need more investigation. In a way, patient influencers are health education agents who may also share prescription medication or pharmaceutical information. They can break down complex health information based on expertise and experience and mitigate the loneliness and isolation that other patients may feel without the support of a community. ", doi="10.2196/41867", url="https://www.jmir.org/2023/1/e41867", url="http://www.ncbi.nlm.nih.gov/pubmed/36912881" } @Article{info:doi/10.2196/40575, author="Honcharov, Vlad and Li, Jiawei and Sierra, Maribel and Rivadeneira, A. Natalie and Olazo, Kristan and Nguyen, T. Thu and Mackey, K. Tim and Sarkar, Urmimala", title="Public Figure Vaccination Rhetoric and Vaccine Hesitancy: Retrospective Twitter Analysis", journal="JMIR Infodemiology", year="2023", month="Mar", day="10", volume="3", pages="e40575", keywords="Twitter", keywords="anti-vaccination", keywords="Biterm Topic modeling", keywords="inductive content analysis", keywords="COVID-19", keywords="social media", keywords="health information", keywords="vaccination", keywords="vaccine hesitancy", keywords="infodemiology", keywords="misinformation", abstract="Background: Social media has emerged as a critical mass communication tool, with both health information and misinformation now spread widely on the web. Prior to the COVID-19 pandemic, some public figures promulgated anti-vaccine attitudes, which spread widely on social media platforms. Although anti-vaccine sentiment has pervaded social media throughout the COVID-19 pandemic, it is unclear to what extent interest in public figures is generating anti-vaccine discourse. Objective: We examined Twitter messages that included anti-vaccination hashtags and mentions of public figures to assess the connection between interest in these individuals and the possible spread of anti-vaccine messages. Methods: We used a data set of COVID-19--related Twitter posts collected from the public streaming application programming interface from March to October 2020 and filtered it for anti-vaccination hashtags ``antivaxxing,'' ``antivaxx,'' ``antivaxxers,'' ``antivax,'' ``anti-vaxxer,'' ``discredit,'' ``undermine,'' ``confidence,'' and ``immune.'' Next, we applied the Biterm Topic model (BTM) to output topic clusters associated with the entire corpus. Topic clusters were manually screened by examining the top 10 posts most highly correlated in each of the 20 clusters, from which we identified 5 clusters most relevant to public figures and vaccination attitudes. We extracted all messages from these clusters and conducted inductive content analysis to characterize the discourse. Results: Our keyword search yielded 118,971 Twitter posts after duplicates were removed, and subsequently, we applied BTM to parse these data into 20 clusters. After removing retweets, we manually screened the top 10 tweets associated with each cluster (200 messages) to identify clusters associated with public figures. Extraction of these clusters yielded 768 posts for inductive analysis. Most messages were either pro-vaccination (n=329, 43\%) or neutral about vaccination (n=425, 55\%), with only 2\% (14/768) including anti-vaccination messages. Three main themes emerged: (1) anti-vaccination accusation, in which the message accused the public figure of holding anti-vaccination beliefs; (2) using ``anti-vax'' as an epithet; and (3) stating or implying the negative public health impact of anti-vaccination discourse. Conclusions: Most discussions surrounding public figures in common hashtags labelled as ``anti-vax'' did not reflect anti-vaccination beliefs. We observed that public figures with known anti-vaccination beliefs face scorn and ridicule on Twitter. Accusing public figures of anti-vaccination attitudes is a means of insulting and discrediting the public figure rather than discrediting vaccines. The majority of posts in our sample condemned public figures expressing anti-vax beliefs by undermining their influence, insulting them, or expressing concerns over public health ramifications. This points to a complex information ecosystem, where anti-vax sentiment may not reside in common anti-vax--related keywords or hashtags, necessitating further assessment of the influence that public figures have on this discourse. ", doi="10.2196/40575", url="https://infodemiology.jmir.org/2023/1/e40575", url="http://www.ncbi.nlm.nih.gov/pubmed/37113377" } @Article{info:doi/10.2196/39209, author="Ahmed, Wasim and Vidal-Alaball, Josep and Vilaseca Llobet, Maria Josep", title="Analyzing Discussions Around Rural Health on Twitter During the COVID-19 Pandemic: Social Network Analysis of Twitter Data", journal="JMIR Infodemiology", year="2023", month="Mar", day="8", volume="3", pages="e39209", keywords="rural health", keywords="Twitter messaging", keywords="social media", keywords="COVID-19", keywords="SARS-CoV-2", keywords="coronavirus", keywords="social network analysis", abstract="Background: Individuals from rural areas are increasingly using social media as a means of communication, receiving information, or actively complaining of inequalities and injustices. Objective: The aim of our study is to analyze conversations about rural health taking place on Twitter during a particular phase of the COVID-19 pandemic. Methods: This study captured 57 days' worth of Twitter data related to rural health from June to August 2021, using English-language keywords. The study used social network analysis and natural language processing to analyze the data. Results: It was found that Twitter served as a fruitful platform to raise awareness of problems faced by users living in rural areas. Overall, Twitter was used in rural areas to express complaints, debate, and share information. Conclusions: Twitter could be leveraged as a powerful social listening tool for individuals and organizations that want to gain insight into popular narratives around rural health. ", doi="10.2196/39209", url="https://infodemiology.jmir.org/2023/1/e39209", url="http://www.ncbi.nlm.nih.gov/pubmed/36936067" } @Article{info:doi/10.2196/40308, author="Gyorda, A. Joseph and Lekkas, Damien and Price, George and Jacobson, C. Nicholas", title="Evaluating the Impact of Mask Mandates and Political Party Affiliation on Mental Health Internet Search Behavior in the United States During the COVID-19 Pandemic: Generalized Additive Mixed Model Framework", journal="J Med Internet Res", year="2023", month="Mar", day="3", volume="25", pages="e40308", keywords="mental health", keywords="Google Trends", keywords="mask mandates", keywords="political party", keywords="generalized additive mixed modeling", keywords="COVID-19", abstract="Background: The impacts of the COVID-19 pandemic on mental health worldwide and in the United States have been well documented. However, there is limited research examining the long-term effects of the pandemic on mental health, particularly in relation to pervasive policies such as statewide mask mandates and political party affiliation. Objective: The goal of this study was to examine whether statewide mask mandates and political party affiliations yielded differential changes in mental health symptoms across the United States by leveraging state-specific internet search query data. Methods: This study leveraged Google search queries from March 24, 2020, to March 29, 2021, in each of the 50 states in the United States. Of the 50 states, 39 implemented statewide mask mandates---with 16 of these states being Republican---to combat the spread of COVID-19. This study investigated whether mask mandates were associated differentially with mental health in states with and without mandates by exploring variations in mental health search queries across the United States. In addition, political party affiliation was examined as a potential covariate to determine whether mask mandates had differential associations with mental health in Republican and Democratic states. Generalized additive mixed models were implemented to model associations among mask mandates, political party affiliation, and mental health search volume for up to 7 months following the implementation of a mask mandate. Results: The results of generalized additive mixed models revealed that search volume for ``restless'' significantly increased following a mask mandate across all states, whereas the search volume for ``irritable'' and ``anxiety'' increased and decreased, respectively, following a mandate for Republican states in comparison with Democratic states. Most mental health search terms did not exhibit significant changes in search volume in relation to mask mandate implementation. Conclusions: These findings suggest that mask mandates were associated nonlinearly with significant changes in mental health search behavior, with the most notable associations occurring in anxiety-related search terms. Therefore, policy makers should consider monitoring and providing additional support for these mental health symptoms following the implementation of public health--related mandates such as mask mandates. Nevertheless, these results do not provide evidence for an overwhelming impact of mask mandates on population-level mental health in the United States. ", doi="10.2196/40308", url="https://www.jmir.org/2023/1/e40308", url="http://www.ncbi.nlm.nih.gov/pubmed/36735836" } @Article{info:doi/10.2196/40518, author="Robinson, Eric and Jones, Andrew", title="Hangover-Related Internet Searches Before and During the COVID-19 Pandemic in England: Observational Study", journal="JMIR Form Res", year="2023", month="Mar", day="3", volume="7", pages="e40518", keywords="alcohol", keywords="COVID-19", keywords="hangover", keywords="Google Trends", keywords="social media", keywords="public health", keywords="online information", keywords="alcohol use", keywords="internet search", abstract="Background: It is unclear whether heavy alcohol use and associated hangover symptoms changed as a result of the COVID-19 pandemic. Due to a lack of available accurate and nonretrospective self-reported data, it is difficult to directly assess hangover symptoms during the COVID-19 pandemic. Objective: This study aimed to examine whether alcohol-induced hangover-related internet searches (eg, ``how to cure a hangover?'') increased, decreased, or remained the same in England before versus during the COVID-19 pandemic (2020-2021) and during periods of national lockdown. Secondary aims were to examine if hangover-related internet searches in England differed compared to a country that did not impose similar COVID-19 lockdown restrictions. Methods: Using historical data from Google Trends for England, we compared the relative search volume (RSV) of hangover-related searches in the years before (2016-2019) versus during the COVID-19 pandemic (2020-2021), as well as in periods of national lockdown versus the same periods in 2016-2019. We also compared the RSV of hangover-related searches during the same time frames in a European country that did not introduce national COVID-19 lockdowns at the beginning of the pandemic (Sweden). Hangover-related search terms were identified through consultation with a panel of alcohol researchers and a sample from the general public. Statistical analyses were preregistered prior to data collection. Results: There was no overall significant difference in the RSV of hangover-related terms in England during 2016-2019 versus 2020-2021 (P=.10; robust d=0.02, 95\% CI 0.00-0.03). However, during national lockdowns, searches for hangover-related terms were lower, particularly during the first national lockdown in England (P<.001; d=.19, 95\% CI 0.16-0.24; a 44\% relative decrease). In a comparison country that did not introduce a national lockdown in the early stages of the pandemic (Sweden), there was no significant decrease in hangover-related searches during the same time period (P=.06). However, across both England and Sweden, during later periods of COVID-19 restrictions in 2020 and 2021, the RSV of hangover-related terms was lower than that in the same periods during 2016-2019. Exploratory analyses revealed that national monthly variation in alcohol sales both before and during the COVID-19 pandemic were positively correlated with the frequency of hangover-related searches, suggesting that changes in hangover-related searches may act as a proxy for changes in alcohol consumption. Conclusions: Hangover-related internet searches did not differ before versus during the COVID-19 pandemic in England but did reduce during periods of national lockdown. Further research is required to confirm how changes in hangover-related search volume relate to heavy episodic alcohol use. Trial Registration: Open Science Framework 2Y86E; https://osf.io/2Y86E ", doi="10.2196/40518", url="https://formative.jmir.org/2023/1/e40518", url="http://www.ncbi.nlm.nih.gov/pubmed/36827489" } @Article{info:doi/10.2196/40403, author="Mokhberi, Maryam and Biswas, Ahana and Masud, Zarif and Kteily-Hawa, Roula and Goldstein, Abby and Gillis, Roy Joseph and Rayana, Shebuti and Ahmed, Ishtiaque Syed", title="Development of a COVID-19--Related Anti-Asian Tweet Data Set: Quantitative Study", journal="JMIR Form Res", year="2023", month="Feb", day="28", volume="7", pages="e40403", keywords="COVID-19", keywords="stigma", keywords="hate speech", keywords="classification", keywords="annotation", keywords="data set", keywords="Sinophobia", keywords="Twitter", keywords="BERT", keywords="pandemic", keywords="data", keywords="online", keywords="community", keywords="Asian", keywords="research", keywords="discrimination", abstract="Background: Since the advent of the COVID-19 pandemic, individuals of Asian descent (colloquial usage prevalent in North America, where ``Asian'' is used to refer to people from East Asia, particularly China) have been the subject of stigma and hate speech in both offline and online communities. One of the major venues for encountering such unfair attacks is social networks, such as Twitter. As the research community seeks to understand, analyze, and implement detection techniques, high-quality data sets are becoming immensely important. Objective: In this study, we introduce a manually labeled data set of tweets containing anti-Asian stigmatizing content. Methods: We sampled over 668 million tweets posted on Twitter from January to July 2020 and used an iterative data construction approach that included 3 different stages of algorithm-driven data selection. Finally, we found volunteers who manually annotated the tweets by hand to arrive at a high-quality data set of tweets and a second, more sampled data set with higher-quality labels from multiple annotators. We presented this final high-quality Twitter data set on stigma toward Chinese people during the COVID-19 pandemic. The data set and instructions for labeling can be viewed in the Github repository. Furthermore, we implemented some state-of-the-art models to detect stigmatizing tweets to set initial benchmarks for our data set. Results: Our primary contributions are labeled data sets. Data Set v3.0 contained 11,263 tweets with primary labels (unknown/irrelevant, not-stigmatizing, stigmatizing-low, stigmatizing-medium, stigmatizing-high) and tweet subtopics (eg, wet market and eating habits, COVID-19 cases, bioweapon). Data Set v3.1 contained 4998 (44.4\%) tweets randomly sampled from Data Set v3.0, where a second annotator labeled them only on the primary labels and then a third annotator resolved conflicts between the first and second annotators. To demonstrate the usefulness of our data set, preliminary experiments on the data set showed that the Bidirectional Encoder Representations from Transformers (BERT) model achieved the highest accuracy of 79\% when detecting stigma on unseen data with traditional models, such as a support vector machine (SVM) performing at 73\% accuracy. Conclusions: Our data set can be used as a benchmark for further qualitative and quantitative research and analysis around the issue. It first reaffirms the existence and significance of widespread discrimination and stigma toward the Asian population worldwide. Moreover, our data set and subsequent arguments should assist other researchers from various domains, including psychologists, public policy authorities, and sociologists, to analyze the complex economic, political, historical, and cultural underlying roots of anti-Asian stigmatization and hateful behaviors. A manually annotated data set is of paramount importance for developing algorithms that can be used to detect stigma or problematic text, particularly on social media. We believe this contribution will help predict and subsequently design interventions that will significantly help reduce stigma, hate, and discrimination against marginalized populations during future crises like COVID-19. ", doi="10.2196/40403", url="https://formative.jmir.org/2023/1/e40403", url="http://www.ncbi.nlm.nih.gov/pubmed/36693148" } @Article{info:doi/10.2196/40706, author="Ramjee, Divya and Pollack, C. Catherine and Charpignon, Marie-Laure and Gupta, Shagun and Rivera, Malaty Jessica and El Hayek, Ghinwa and Dunn, G. Adam and Desai, N. Angel and Majumder, S. Maimuna", title="Evolving Face Mask Guidance During a Pandemic and Potential Harm to Public Perception: Infodemiology Study of Sentiment and Emotion on Twitter", journal="J Med Internet Res", year="2023", month="Feb", day="27", volume="25", pages="e40706", keywords="face masks", keywords="COVID-19", keywords="Twitter", keywords="science communication", keywords="political communication", keywords="public policy", keywords="public health", keywords="sentiment analysis", keywords="emotion analysis", keywords="infodemiology", keywords="infoveillance", abstract="Background: Throughout the COVID-19 pandemic, US Centers for Disease Control and Prevention policies on face mask use fluctuated. Understanding how public health communications evolve around key policy decisions may inform future decisions on preventative measures by aiding the design of communication strategies (eg, wording, timing, and channel) that ensure rapid dissemination and maximize both widespread adoption and sustained adherence. Objective: We aimed to assess how sentiment on masks evolved surrounding 2 changes to mask guidelines: (1) the recommendation for mask use on April 3, 2020, and (2) the relaxation of mask use on May 13, 2021. Methods: We applied an interrupted time series method to US Twitter data surrounding each guideline change. Outcomes were changes in the (1) proportion of positive, negative, and neutral tweets and (2) number of words within a tweet tagged with a given emotion (eg, trust). Results were compared to COVID-19 Twitter data without mask keywords for the same period. Results: There were fewer neutral mask-related tweets in 2020 ($\beta$=--3.94 percentage points, 95\% CI --4.68 to --3.21; P<.001) and 2021 ($\beta$=--8.74, 95\% CI --9.31 to --8.17; P<.001). Following the April 3 recommendation ($\beta$=.51, 95\% CI .43-.59; P<.001) and May 13 relaxation ($\beta$=3.43, 95\% CI 1.61-5.26; P<.001), the percent of negative mask-related tweets increased. The quantity of trust-related terms decreased following the policy change on April 3 ($\beta$=--.004, 95\% CI --.004 to --.003; P<.001) and May 13 ($\beta$=--.001, 95\% CI --.002 to 0; P=.008). Conclusions: The US Twitter population responded negatively and with less trust following guideline shifts related to masking, regardless of whether the guidelines recommended or relaxed mask usage. Federal agencies should ensure that changes in public health recommendations are communicated concisely and rapidly. ", doi="10.2196/40706", url="https://www.jmir.org/2023/1/e40706", url="http://www.ncbi.nlm.nih.gov/pubmed/36763687" } @Article{info:doi/10.2196/36667, author="Khademi, Sedigh and Hallinan, Mary Christine and Conway, Mike and Bonomo, Yvonne", title="Using Social Media Data to Investigate Public Perceptions of Cannabis as a Medicine: Narrative Review", journal="J Med Internet Res", year="2023", month="Feb", day="27", volume="25", pages="e36667", keywords="social media", keywords="medicinal cannabis", keywords="public health surveillance", keywords="internet", keywords="medical marijuana", abstract="Background: The use and acceptance of medicinal cannabis is on the rise across the globe. To support the interests of public health, evidence relating to its use, effects, and safety is required to match this community demand. Web-based user-generated data are often used by researchers and public health organizations for the investigation of consumer perceptions, market forces, population behaviors, and for pharmacoepidemiology. Objective: In this review, we aimed to summarize the findings of studies that have used user-generated text as a data source to study medicinal cannabis or the use of cannabis as medicine. Our objectives were to categorize the insights provided by social media research on cannabis as medicine and describe the role of social media for consumers using medicinal cannabis. Methods: The inclusion criteria for this review were primary research studies and reviews that reported on the analysis of web-based user-generated content on cannabis as medicine. The MEDLINE, Scopus, Web of Science, and Embase databases were searched from January 1974 to April 2022. Results: We examined 42 studies published in English and found that consumers value their ability to exchange experiences on the web and tend to rely on web-based information sources. Cannabis discussions have portrayed the substance as a safe and natural medicine to help with many health conditions including cancer, sleep disorders, chronic pain, opioid use disorders, headaches, asthma, bowel disease, anxiety, depression, and posttraumatic stress disorder. These discussions provide a rich resource for researchers to investigate medicinal cannabis--related consumer sentiment and experiences, including the opportunity to monitor cannabis effects and adverse events, given the anecdotal and often biased nature of the information is properly accounted for. Conclusions: The extensive web-based presence of the cannabis industry coupled with the conversational nature of social media discourse results in rich but potentially biased information that is often not well-supported by scientific evidence. This review summarizes what social media is saying about the medicinal use of cannabis and discusses the challenges faced by health governance agencies and professionals to make use of web-based resources to both learn from medicinal cannabis users and provide factual, timely, and reliable evidence-based health information to consumers. ", doi="10.2196/36667", url="https://www.jmir.org/2023/1/e36667", url="http://www.ncbi.nlm.nih.gov/pubmed/36848191" } @Article{info:doi/10.2196/42227, author="Pierri, Francesco and DeVerna, R. Matthew and Yang, Kai-Cheng and Axelrod, David and Bryden, John and Menczer, Filippo", title="One Year of COVID-19 Vaccine Misinformation on Twitter: Longitudinal Study", journal="J Med Internet Res", year="2023", month="Feb", day="24", volume="25", pages="e42227", keywords="content analysis", keywords="COVID-19", keywords="infodemiology", keywords="misinformation", keywords="online health information", keywords="social media", keywords="trend analysis", keywords="Twitter", keywords="vaccines", keywords="vaccine hesitancy", abstract="Background: Vaccinations play a critical role in mitigating the impact of COVID-19 and other diseases. Past research has linked misinformation to increased hesitancy and lower vaccination rates. Gaps remain in our knowledge about the main drivers of vaccine misinformation on social media and effective ways to intervene. Objective: Our longitudinal study had two primary objectives: (1) to investigate the patterns of prevalence and contagion of COVID-19 vaccine misinformation on Twitter in 2021, and (2) to identify the main spreaders of vaccine misinformation. Given our initial results, we further considered the likely drivers of misinformation and its spread, providing insights for potential interventions. Methods: We collected almost 300 million English-language tweets related to COVID-19 vaccines using a list of over 80 relevant keywords over a period of 12 months. We then extracted and labeled news articles at the source level based on third-party lists of low-credibility and mainstream news sources, and measured the prevalence of different kinds of information. We also considered suspicious YouTube videos shared on Twitter. We focused our analysis of vaccine misinformation spreaders on verified and automated Twitter accounts. Results: Our findings showed a relatively low prevalence of low-credibility information compared to the entirety of mainstream news. However, the most popular low-credibility sources had reshare volumes comparable to those of many mainstream sources, and had larger volumes than those of authoritative sources such as the US Centers for Disease Control and Prevention and the World Health Organization. Throughout the year, we observed an increasing trend in the prevalence of low-credibility news about vaccines. We also observed a considerable amount of suspicious YouTube videos shared on Twitter. Tweets by a small group of approximately 800 ``superspreaders'' verified by Twitter accounted for approximately 35\% of all reshares of misinformation on an average day, with the top superspreader (@RobertKennedyJr) responsible for over 13\% of retweets. Finally, low-credibility news and suspicious YouTube videos were more likely to be shared by automated accounts. Conclusions: The wide spread of misinformation around COVID-19 vaccines on Twitter during 2021 shows that there was an audience for this type of content. Our findings are also consistent with the hypothesis that superspreaders are driven by financial incentives that allow them to profit from health misinformation. Despite high-profile cases of deplatformed misinformation superspreaders, our results show that in 2021, a few individuals still played an outsized role in the spread of low-credibility vaccine content. As a result, social media moderation efforts would be better served by focusing on reducing the online visibility of repeat spreaders of harmful content, especially during public health crises. ", doi="10.2196/42227", url="https://www.jmir.org/2023/1/e42227", url="http://www.ncbi.nlm.nih.gov/pubmed/36735835" } @Article{info:doi/10.2196/38676, author="Abrams, P. Matthew and Pelullo, P. Arthur and Meisel, F. Zachary and Merchant, M. Raina and Purtle, Jonathan and Agarwal, K. Anish", title="State and Federal Legislators' Responses on Social Media to the Mental Health and Burnout of Health Care Workers Throughout the COVID-19 Pandemic: Natural Language Processing and Sentiment Analysis", journal="JMIR Infodemiology", year="2023", month="Feb", day="24", volume="3", pages="e38676", keywords="burnout", keywords="wellness", keywords="mental health", keywords="social media", keywords="policy", keywords="health care workforce", keywords="COVID-19", keywords="infodemiology", keywords="healthcare worker", keywords="mental well-being", keywords="psychological distress", keywords="Twitter", keywords="content analysis", keywords="thematic analysis", keywords="policy maker", keywords="healthcare workforce", keywords="legislator", abstract="Background: Burnout and the mental health burden of the COVID-19 pandemic have disproportionately impacted health care workers. The links between state policies, federal regulations, COVID-19 case counts, strains on health care systems, and the mental health of health care workers continue to evolve. The language used by state and federal legislators in public-facing venues such as social media is important, as it impacts public opinion and behavior, and it also reflects current policy-leader opinions and planned legislation. Objective: The objective of this study was to examine legislators' social media content on Twitter and Facebook throughout the COVID-19 pandemic to thematically characterize policy makers' attitudes and perspectives related to mental health and burnout in the health care workforce. Methods: Legislators' social media posts about mental health and burnout in the health care workforce were collected from January 2020 to November 2021 using Quorum, a digital database of policy-related documents. The total number of relevant social media posts per state legislator per calendar month was calculated and compared with COVID-19 case volume. Differences between themes expressed in Democratic and Republican posts were estimated using the Pearson chi-square test. Words within social media posts most associated with each political party were determined. Machine-learning was used to evaluate naturally occurring themes in the burnout- and mental health--related social media posts. Results: A total of 4165 social media posts (1400 tweets and 2765 Facebook posts) were generated by 2047 unique state and federal legislators and 38 government entities. The majority of posts (n=2319, 55.68\%) were generated by Democrats, followed by Republicans (n=1600, 40.34\%). Among both parties, the volume of burnout-related posts was greatest during the initial COVID-19 surge. However, there was significant variation in the themes expressed by the 2 major political parties. Themes most correlated with Democratic posts were (1) frontline care and burnout, (2) vaccines, (3) COVID-19 outbreaks, and (4) mental health services. Themes most correlated with Republican social media posts were (1) legislation, (2) call for local action, (3) government support, and (4) health care worker testing and mental health. Conclusions: State and federal legislators use social media to share opinions and thoughts on key topics, including burnout and mental health strain among health care workers. Variations in the volume of posts indicated that a focus on burnout and the mental health of the health care workforce existed early in the pandemic but has waned. Significant differences emerged in the content posted by the 2 major US political parties, underscoring how each prioritized different aspects of the crisis. ", doi="10.2196/38676", url="https://infodemiology.jmir.org/2023/1/e38676", url="http://www.ncbi.nlm.nih.gov/pubmed/37013000" } @Article{info:doi/10.2196/42357, author="Beirakdar, Safwat and Klingborg, Leon and Herzig van Wees, Sibylle", title="Attitudes of Swedish Language Twitter Users Toward COVID-19 Vaccination: Exploratory Qualitative Study", journal="JMIR Infodemiology", year="2023", month="Feb", day="22", volume="3", pages="e42357", keywords="COVID-19", keywords="vaccine hesitancy", keywords="COVID-19 vaccines", keywords="social media", keywords="Twitter", keywords="qualitative analysis", keywords="World Health Organization", keywords="WHO's 3C model", abstract="Background: Social media have played an important role in shaping COVID-19 vaccine choices during the pandemic. Understanding people's attitudes toward the vaccine as expressed on social media can help address the concerns of vaccine-hesitant individuals. Objective: The aim of this study was to understand the attitudes of Swedish-speaking Twitter users toward COVID-19 vaccines. Methods: This was an exploratory qualitative study that used a social media--listening approach. Between January and March 2022, a total of 2877 publicly available tweets in Swedish were systematically extracted from Twitter. A deductive thematic analysis was conducted using the World Health Organization's 3C model (confidence, complacency, and convenience). Results: Confidence in the safety and effectiveness of the COVID-19 vaccine appeared to be a major concern expressed on Twitter. Unclear governmental strategies in managing the pandemic in Sweden and the belief in conspiracy theories have further influenced negative attitudes toward vaccines. Complacency---the perceived risk of COVID-19 was low and booster vaccination was unnecessary; many expressed trust in natural immunity. Convenience---in terms of accessing the right information and the vaccine---highlighted a knowledge gap about the benefits and necessity of the vaccine, as well as complaints about the quality of vaccination services. Conclusions: Swedish-speaking Twitter users in this study had negative attitudes toward COVID-19 vaccines, particularly booster vaccines. We identified attitudes toward vaccines and misinformation, indicating that social media monitoring can help policy makers respond by developing proactive health communication interventions. ", doi="10.2196/42357", url="https://infodemiology.jmir.org/2023/1/e42357", url="http://www.ncbi.nlm.nih.gov/pubmed/37012999" } @Article{info:doi/10.2196/42671, author="Hong, Yimin and Xie, Fang and An, Xinyu and Lan, Xue and Liu, Chunhe and Yan, Lei and Zhang, Han", title="Evolution of Public Attitudes and Opinions Regarding COVID-19 Vaccination During the Vaccine Campaign in China: Year-Long Infodemiology Study of Weibo Posts", journal="J Med Internet Res", year="2023", month="Feb", day="16", volume="25", pages="e42671", keywords="COVID-19 vaccines", keywords="social media", keywords="infodemiology", keywords="sentiment analysis", keywords="opinion analysis", keywords="monitoring public attitude", keywords="gender differences", keywords="LDA", keywords="COVID-19", abstract="Background: Monitoring people's perspectives on the COVID-19 vaccine is crucial for understanding public vaccination hesitancy and developing effective, targeted vaccine promotion strategies. Although this is widely recognized, studies on the evolution of public opinion over the course of an actual vaccination campaign are rare. Objective: We aimed to track the evolution of public opinion and sentiment toward COVID-19 vaccines in online discussions over an entire vaccination campaign. Moreover, we aimed to reveal the pattern of gender differences in attitudes and perceptions toward vaccination. Methods: We collected COVID-19 vaccine--related posts by the general public that appeared on Sina Weibo from January 1, 2021, to December 31, 2021; this period covered the entire vaccination process in China. We identified popular discussion topics using latent Dirichlet allocation. We further examined changes in public sentiment and topics during the 3 stages of the vaccination timeline. Gender differences in perceptions toward vaccination were also investigated. Results: Of 495,229 crawled posts, 96,145 original posts from individual accounts were included. Most posts presented positive sentiments (positive: 65,981/96,145, 68.63\%; negative: 23,184/96,145, 24.11\%; neutral: 6980/96,145, 7.26\%). The average sentiment scores were 0.75 (SD 0.35) for men and 0.67 (SD 0.37) for women. The overall trends in sentiment scores showed a mixed response to the number of new cases and significant events related to vaccine development and important holidays. The sentiment scores showed a weak correlation with new case numbers (R=0.296; P=.03). Significant sentiment score differences were observed between men and women (P<.001). Common and distinguishing characteristics were found among frequently discussed topics during the different stages, with significant differences in topic distribution between men and women (January 1, 2021, to March 31, 2021: $\chi$23=3030.9; April 1, 2021, to September 30, 2021: $\chi$24=8893.8; October 1, 2021, to December 31, 2021: $\chi$25=3019.5; P<.001). Women were more concerned with side effects and vaccine effectiveness. In contrast, men reported broader concerns around the global pandemic, the progress of vaccine development, and economics affected by the pandemic. Conclusions: Understanding public concerns regarding vaccination is essential for reaching vaccine-induced herd immunity. This study tracked the year-long evolution of attitudes and opinions on COVID-19 vaccines according to the different stages of vaccination in China. These findings provide timely information that will enable the government to understand the reasons for low vaccine uptake and promote COVID-19 vaccination nationwide. ", doi="10.2196/42671", url="https://www.jmir.org/2023/1/e42671", url="http://www.ncbi.nlm.nih.gov/pubmed/36795467" } @Article{info:doi/10.2196/41716, author="Diamond, Carrie and Quinn, P. Alyssa and Presley, L. Colby and Jacobs, Jennifer and Laughter, R. Melissa and Anderson, Jaclyn and Rundle, Chandler", title="Telangiectasia-Related Social Media Posts: Cross-sectional Analysis of TikTok and Instagram", journal="JMIR Dermatol", year="2023", month="Feb", day="16", volume="6", pages="e41716", keywords="social media", keywords="telangiectasias", keywords="varicose veins", keywords="health information", keywords="misinformation", keywords="dermatology", keywords="health education", keywords="dermatologic information", keywords="health content", keywords="accuracy", keywords="educational content", doi="10.2196/41716", url="https://derma.jmir.org/2023/1/e41716", url="http://www.ncbi.nlm.nih.gov/pubmed/37632919" } @Article{info:doi/10.2196/42985, author="Liu, Yongtai and Yin, Zhijun and Ni, Congning and Yan, Chao and Wan, Zhiyu and Malin, Bradley", title="Examining Rural and Urban Sentiment Difference in COVID-19--Related Topics on Twitter: Word Embedding--Based Retrospective Study", journal="J Med Internet Res", year="2023", month="Feb", day="15", volume="25", pages="e42985", keywords="COVID-19", keywords="social media", keywords="word embedding", keywords="topic analysis", keywords="sentiment analysis", keywords="Twitter", keywords="data", keywords="vaccination", keywords="prevention", keywords="urban", keywords="rural", keywords="epidemic", keywords="management", keywords="model", keywords="training", keywords="machine learning", abstract="Background: By the end of 2022, more than 100 million people were infected with COVID-19 in the United States, and the cumulative death rate in rural areas (383.5/100,000) was much higher than in urban areas (280.1/100,000). As the pandemic spread, people used social media platforms to express their opinions and concerns about COVID-19--related topics. Objective: This study aimed to (1) identify the primary COVID-19--related topics in the contiguous United States communicated over Twitter and (2) compare the sentiments urban and rural users expressed about these topics. Methods: We collected tweets containing geolocation data from May 2020 to January 2022 in the contiguous United States. We relied on the tweets' geolocations to determine if their authors were in an urban or rural setting. We trained multiple word2vec models with several corpora of tweets based on geospatial and timing information. Using a word2vec model built on all tweets, we identified hashtags relevant to COVID-19 and performed hashtag clustering to obtain related topics. We then ran an inference analysis for urban and rural sentiments with respect to the topics based on the similarity between topic hashtags and opinion adjectives in the corresponding urban and rural word2vec models. Finally, we analyzed the temporal trend in sentiments using monthly word2vec models. Results: We created a corpus of 407 million tweets, 350 million (86\%) of which were posted by users in urban areas, while 18 million (4.4\%) were posted by users in rural areas. There were 2666 hashtags related to COVID-19, which clustered into 20 topics. Rural users expressed stronger negative sentiments than urban users about COVID-19 prevention strategies and vaccination (P<.001). Moreover, there was a clear political divide in the perception of politicians by urban and rural users; these users communicated stronger negative sentiments about Republican and Democratic politicians, respectively (P<.001). Regarding misinformation and conspiracy theories, urban users exhibited stronger negative sentiments about the ``covidiots'' and ``China virus'' topics, while rural users exhibited stronger negative sentiments about the ``Dr. Fauci'' and ``plandemic'' topics. Finally, we observed that urban users' sentiments about the economy appeared to transition from negative to positive in late 2021, which was in line with the US economic recovery. Conclusions: This study demonstrates there is a statistically significant difference in the sentiments of urban and rural Twitter users regarding a wide range of COVID-19--related topics. This suggests that social media can be relied upon to monitor public sentiment during pandemics in disparate types of regions. This may assist in the geographically targeted deployment of epidemic prevention and management efforts. ", doi="10.2196/42985", url="https://www.jmir.org/2023/1/e42985", url="http://www.ncbi.nlm.nih.gov/pubmed/36790847" } @Article{info:doi/10.2196/40371, author="Bouchacourt, Lindsay and Henson-Garci?a, Mike and Sussman, Leah Kristen and Mandell, Dorothy and Wilcox, Gary and Mackert, Michael", title="Web-Based Conversations Regarding Fathers Before and During the COVID-19 Pandemic: Qualitative Content Analysis", journal="JMIR Pediatr Parent", year="2023", month="Feb", day="15", volume="6", pages="e40371", keywords="social media", keywords="expecting fathers", keywords="new fathers", keywords="Twitter", keywords="Reddit", keywords="content analysis", keywords="topic model", keywords="topic analysis", keywords="parent", keywords="father", abstract="Background: Studies of new and expecting parents largely focus on the mother, leaving a gap in knowledge about fathers. Objective: This study aimed to understand web-based conversations regarding new and expecting fathers on social media and to explore whether the COVID-19 pandemic has changed the web-based conversation. Methods: A social media analysis was conducted. Brandwatch (Cision) captured social posts related to new and expecting fathers between February 1, 2019, and February 12, 2021. Overall, 2 periods were studied: 1 year before and 1 year during the pandemic. SAS Text Miner analyzed the data and produced 47\% (9/19) of the topics in the first period and 53\% (10/19) of the topics in the second period. The 19 topics were organized into 6 broad themes. Results: Overall, 26\% (5/19) of the topics obtained during each period were the same, showing consistency in conversation. In total, 6 broad themes were created: fatherhood thoughts, fatherhood celebrations, advice seeking, fatherhood announcements, external parties targeting fathers, and miscellaneous. Conclusions: Fathers use social media to make announcements, celebrate fatherhood, seek advice, and interact with other fathers. Others used social media to advertise baby products and promote baby-related resources for fathers. Overall, the arrival of the COVID-19 pandemic appeared to have little impact on the excitement and resiliency of new fathers as they transition to parenthood. Altogether, these findings provide insight and guidance on the ways in which public health professionals can rapidly gather information about special populations---such as new and expecting fathers via the web---to monitor their beliefs, attitudes, emotional reactions, and unique lived experiences in context (ie, throughout a global pandemic). ", doi="10.2196/40371", url="https://pediatrics.jmir.org/2023/1/e40371", url="http://www.ncbi.nlm.nih.gov/pubmed/36790850" } @Article{info:doi/10.2196/42863, author="Lin, Shuo-Yu and Cheng, Xiaolu and Zhang, Jun and Yannam, Sindhu Jaya and Barnes, J. Andrew and Koch, Randy J. and Hayes, Rashelle and Gimm, Gilbert and Zhao, Xiaoquan and Purohit, Hemant and Xue, Hong", title="Social Media Data Mining of Antitobacco Campaign Messages: Machine Learning Analysis of Facebook Posts", journal="J Med Internet Res", year="2023", month="Feb", day="13", volume="25", pages="e42863", keywords="tobacco control", keywords="social media campaign", keywords="content analysis", keywords="natural language processing", keywords="topic modeling", keywords="social media", keywords="public health", keywords="tobacco", keywords="youth", keywords="Facebook", keywords="engagement", keywords="use", keywords="smoking", abstract="Background: Social media platforms provide a valuable source of public health information, as one-third of US adults seek specific health information online. Many antitobacco campaigns have recognized such trends among youth and have shifted their advertising time and effort toward digital platforms. Timely evidence is needed to inform the adaptation of antitobacco campaigns to changing social media platforms. Objective: In this study, we conducted a content analysis of major antitobacco campaigns on Facebook using machine learning and natural language processing (NLP) methods, as well as a traditional approach, to investigate the factors that may influence effective antismoking information dissemination and user engagement. Methods: We collected 3515 posts and 28,125 associated comments from 7 large national and local antitobacco campaigns on Facebook between 2018 and 2021, including the Real Cost, Truth, CDC Tobacco Free (formally known as Tips from Former Smokers, where ``CDC'' refers to the Centers for Disease Control and Prevention), the Tobacco Prevention Toolkit, Behind the Haze VA, the Campaign for Tobacco-Free Kids, and Smoke Free US campaigns. NLP methods were used for content analysis, including parsimonious rule--based models for sentiment analysis and topic modeling. Logistic regression models were fitted to examine the relationship of antismoking message-framing strategies and viewer responses and engagement. Results: We found that large campaigns from government and nonprofit organizations had more user engagements compared to local and smaller campaigns. Facebook users were more likely to engage in negatively framed campaign posts. Negative posts tended to receive more negative comments (odds ratio [OR] 1.40, 95\% CI 1.20-1.65). Positively framed posts generated more negative comments (OR 1.41, 95\% CI 1.19-1.66) as well as positive comments (OR 1.29, 95\% CI 1.13-1.48). Our content analysis and topic modeling uncovered that the most popular campaign posts tended to be informational (ie, providing new information), where the key phrases included talking about harmful chemicals (n=43, 43\%) as well as the risk to pets (n=17, 17\%). Conclusions: Facebook users tend to engage more in antitobacco educational campaigns that are framed negatively. The most popular campaign posts are those providing new information, with key phrases and topics discussing harmful chemicals and risks of secondhand smoke for pets. Educational campaign designers can use such insights to increase the reach of antismoking campaigns and promote behavioral changes. ", doi="10.2196/42863", url="https://www.jmir.org/2023/1/e42863", url="http://www.ncbi.nlm.nih.gov/pubmed/36780224" } @Article{info:doi/10.2196/42706, author="Zhou, Runtao and Tang, Qihang and Xie, Zidian and Li, Dongmei", title="Public Perceptions of the Food and Drug Administration's Proposed Rules Prohibiting Menthol Cigarettes on Twitter: Observational Study", journal="JMIR Form Res", year="2023", month="Feb", day="10", volume="7", pages="e42706", keywords="menthol cigarettes", keywords="Food and Drug Administration", keywords="FDA", keywords="FDA's proposed rules", keywords="Twitter", keywords="perception", abstract="Background: On April 28, 2022, the Food and Drug Administration (FDA) proposed rules that prohibited all menthol-flavored cigarettes and other flavored cigars to prevent the initiation of tobacco use in youth and reduce tobacco-related diseases and death. Objective: The objective of this study was to investigate public perceptions of the FDA's proposed menthol cigarette rules on Twitter. Methods: Through the Twitter streaming application programming interface, tobacco-related tweets were collected between April 28, 2022, and May 27, 2022, using a set of keywords, such as smoking, cigarette, and nicotine. Furthermore, 1941 tweets related to the FDA's proposed menthol cigarette rules were extracted. Based on 300 randomly selected example tweets, the codebook for the attitudes toward the FDA's proposed rules and related topics was developed by 2 researchers and was used to label the rest of the tweets. Results: Among tweets related to the FDA's proposed menthol cigarette rules, 536 (27.61\%) showed a positive attitude, 443 (22.82\%) had a negative attitude, and 962 (49.56\%) had a neutral attitude toward the proposed rules. Social justice (210/536, 39\%) and health issues (117/536, 22\%) were two major topics in tweets with a positive attitude. For tweets with a negative attitude, alternative tobacco or nicotine products (127/443, 29\%) and racial discrimination (84/536, 16\%) were two of the most popular topics. Conclusions: In general, the public had a positive attitude toward the FDA's proposed menthol cigarette rules. Our study provides important information to the FDA on the public perceptions of the proposed menthol cigarette rules, which will be helpful for future FDA regulations on menthol cigarettes. ", doi="10.2196/42706", url="https://formative.jmir.org/2023/1/e42706", url="http://www.ncbi.nlm.nih.gov/pubmed/36763414" } @Article{info:doi/10.2196/40569, author="Klein, Z. Ari and Kunatharaju, Shriya and O'Connor, Karen and Gonzalez-Hernandez, Graciela", title="Pregex: Rule-Based Detection and Extraction of Twitter Data in Pregnancy", journal="J Med Internet Res", year="2023", month="Feb", day="9", volume="25", pages="e40569", keywords="natural language processing", keywords="data mining", keywords="social media", keywords="pregnancy", doi="10.2196/40569", url="https://www.jmir.org/2023/1/e40569", url="http://www.ncbi.nlm.nih.gov/pubmed/36757756" } @Article{info:doi/10.2196/41518, author="He, Zixuan and Wang, Zhijie and Song, Yihang and Liu, Yilong and Kang, Le and Fang, Xue and Wang, Tongchang and Fan, Xuanming and Li, Zhaoshen and Wang, Shuling and Bai, Yu", title="The Reliability and Quality of Short Videos as a Source of Dietary Guidance for Inflammatory Bowel Disease: Cross-sectional Study", journal="J Med Internet Res", year="2023", month="Feb", day="9", volume="25", pages="e41518", keywords="inflammatory bowel disease", keywords="diet", keywords="information quality", keywords="social media", keywords="gastroenterology", keywords="nutrition", keywords="videos", keywords="health communication", abstract="Background: Dietary management is considered a potential adjunctive treatment for inflammatory bowel disease (IBD). Short-video sharing platforms have enabled patients to obtain dietary advice more conveniently. However, accessing useful resources while avoiding misinformation is not an easy task for most patients. Objective: This study aimed to evaluate the quality of the information in IBD diet--related videos on Chinese short-video sharing platforms. Methods: We collected and extracted information from a total of 125 video samples related to the IBD diet on the 3 Chinese short-video sharing platforms with the most users: TikTok, Bilibili, and Kwai. Two independent physicians evaluated each video in terms of content comprehensiveness, quality (rated by Global Quality Score), and reliability (rated by a modified DISCERN tool). Finally, comparative analyses of the videos from different sources were conducted. Results: The videos were classified into 6 groups based on the identity of the uploaders, which included 3 kinds of medical professionals (ie, gastroenterologists, nongastroenterologists, and clinical nutritionists) and 3 types of non--medical professionals (ie, nonprofit organizations, individual science communicators, and IBD patients). The overall quality of the videos was poor. Further group comparisons demonstrated that videos from medical professionals were more instructive in terms of content comprehensiveness, quality, and reliability than those from non--medical professionals. Moreover, IBD diet--related recommendations from clinical nutritionists and gastroenterologists were of better quality than those from nongastroenterologists, while recommendations from nonprofit organizations did not seem to be superior to other groups of uploaders. Conclusions: The overall quality of the information in IBD diet-related videos is unsatisfactory and varies significantly depending on the source. Videos from medical professionals, especially clinical nutritionists and gastroenterologists, may provide dietary guidance with higher quality for IBD patients. ", doi="10.2196/41518", url="https://www.jmir.org/2023/1/e41518", url="http://www.ncbi.nlm.nih.gov/pubmed/36757757" } @Article{info:doi/10.2196/39162, author="Sun, Fei and Zheng, Shusen and Wu, Jian", title="Quality of Information in Gallstone Disease Videos on TikTok: Cross-sectional Study", journal="J Med Internet Res", year="2023", month="Feb", day="8", volume="25", pages="e39162", keywords="hepatobiliary", keywords="gallstone", keywords="gallbladder", keywords="TikTok", keywords="social media", keywords="video quality", keywords="DISCERN", keywords="Journal of American Medical Association", keywords="JAMA", keywords="Global Quality Score", keywords="GQS", keywords="content analysis", keywords="health information", keywords="online health information", keywords="digital health", keywords="disease knowledge", keywords="medical information", keywords="misinformation", keywords="infodemiology", keywords="patient education", keywords="dissemination", keywords="accuracy", keywords="credibility", keywords="credible", keywords="reliability", keywords="reliable", keywords="information quality", abstract="Background: TikTok was an important channel for consumers to access and adopt health information. But the quality of health content in TikTok remains underinvestigated. Objective: Our study aimed to identify upload sources, contents, and feature information of gallstone disease videos on TikTok and further evaluated the factors related to video quality. Methods: We investigated the first 100 gallstone-related videos on TikTok and analyzed these videos' upload sources, content, and characteristics. The quality of videos was evaluated using quantitative scoring tools such as DISCERN instrument, the Journal of American Medical Association (JAMA) benchmark criteria, and Global Quality Scores (GQS). Moreover, the correlation between video quality and video characteristics, including duration, likes, comments, and shares, was further investigated. Results: According to video sources, 81\% of the videos were posted by doctors. Furthermore, disease knowledge was the most dominant video content, accounting for 56\% of all the videos. The mean DISCERN, JAMA, and GQS scores of all 100 videos are 39.61 (SD 11.36), 2.00 (SD 0.40), and 2.76 (SD 0.95), respectively. According to DISCERN and GQS, gallstone-related videos' quality score on TikTok is not high, mainly at fair (43/100, 43\%,) and moderate (46/100, 46\%). The total DISCERN scores of doctors were significantly higher than that of individuals and news agencies, surgery techniques were significantly higher than lifestyle and news, and disease knowledge was significantly higher than news, respectively. DISCERN scores and video duration were positively correlated. Negative correlations were found between DISCERN scores and likes and shares of videos. In GQS analysis, no significant differences were found between groups based on different sources or different contents. JAMA was excluded in the video quality and correlation analysis due to a lack of discrimination and inability to evaluate the video quality accurately. Conclusions: Although the videos of gallstones on TikTok are mainly provided by doctors and contain disease knowledge, they are of low quality. We found a positive correlation between video duration and video quality. High-quality videos received low attention, and popular videos were of low quality. Medical information on TikTok is currently not rigorous enough to guide patients to make accurate judgments. TikTok was not an appropriate source of knowledge to educate patients due to the low quality and reliability of the information. ", doi="10.2196/39162", url="https://www.jmir.org/2023/1/e39162", url="http://www.ncbi.nlm.nih.gov/pubmed/36753307" } @Article{info:doi/10.2196/42519, author="Athanasiou, Maria and Fragkozidis, Georgios and Zarkogianni, Konstantia and Nikita, S. Konstantina", title="Long Short-term Memory--Based Prediction of the Spread of Influenza-Like Illness Leveraging Surveillance, Weather, and Twitter Data: Model Development and Validation", journal="J Med Internet Res", year="2023", month="Feb", day="6", volume="25", pages="e42519", keywords="influenza-like illness", keywords="epidemiological surveillance", keywords="machine learning", keywords="deep learning", keywords="social media", keywords="Twitter", keywords="meteorological parameters", abstract="Background: The potential to harness the plurality of available data in real time along with advanced data analytics for the accurate prediction of influenza-like illness (ILI) outbreaks has gained significant scientific interest. Different methodologies based on the use of machine learning techniques and traditional and alternative data sources, such as ILI surveillance reports, weather reports, search engine queries, and social media, have been explored with the ultimate goal of being used in the development of electronic surveillance systems that could complement existing monitoring resources. Objective: The scope of this study was to investigate for the first time the combined use of ILI surveillance data, weather data, and Twitter data along with deep learning techniques toward the development of prediction models able to nowcast and forecast weekly ILI cases. By assessing the predictive power of both traditional and alternative data sources on the use case of ILI, this study aimed to provide a novel approach for corroborating evidence and enhancing accuracy and reliability in the surveillance of infectious diseases. Methods: The model's input space consisted of information related to weekly ILI surveillance, web-based social (eg, Twitter) behavior, and weather conditions. For the design and development of the model, relevant data corresponding to the period of 2010 to 2019 and focusing on the Greek population and weather were collected. Long short-term memory (LSTM) neural networks were leveraged to efficiently handle the sequential and nonlinear nature of the multitude of collected data. The 3 data categories were first used separately for training 3 LSTM-based primary models. Subsequently, different transfer learning (TL) approaches were explored with the aim of creating various feature spaces combining the features extracted from the corresponding primary models' LSTM layers for the latter to feed a dense layer. Results: The primary model that learned from weather data yielded better forecast accuracy (root mean square error [RMSE]=0.144; Pearson correlation coefficient [PCC]=0.801) than the model trained with ILI historical data (RMSE=0.159; PCC=0.794). The best performance was achieved by the TL-based model leveraging the combination of the 3 data categories (RMSE=0.128; PCC=0.822). Conclusions: The superiority of the TL-based model, which considers Twitter data, weather data, and ILI surveillance data, reflects the potential of alternative public sources to enhance accurate and reliable prediction of ILI spread. Despite its focus on the use case of Greece, the proposed approach can be generalized to other locations, populations, and social media platforms to support the surveillance of infectious diseases with the ultimate goal of reinforcing preparedness for future epidemics. ", doi="10.2196/42519", url="https://www.jmir.org/2023/1/e42519", url="http://www.ncbi.nlm.nih.gov/pubmed/36745490" } @Article{info:doi/10.2196/40156, author="Myneni, Sahiti and Cuccaro, Paula and Montgomery, Sarah and Pakanati, Vivek and Tang, Jinni and Singh, Tavleen and Dominguez, Olivia and Cohen, Trevor and Reininger, Belinda and Savas, S. Lara and Fernandez, E. Maria", title="Lessons Learned From Interdisciplinary Efforts to Combat COVID-19 Misinformation: Development of Agile Integrative Methods From Behavioral Science, Data Science, and Implementation Science", journal="JMIR Infodemiology", year="2023", month="Feb", day="3", volume="3", pages="e40156", keywords="COVID-19", keywords="misinformation", keywords="social media", keywords="health belief model", keywords="deep learning", keywords="community engagement", abstract="Background: Despite increasing awareness about and advances in addressing social media misinformation, the free flow of false COVID-19 information has continued, affecting individuals' preventive behaviors, including masking, testing, and vaccine uptake. Objective: In this paper, we describe our multidisciplinary efforts with a specific focus on methods to (1) gather community needs, (2) develop interventions, and (3) conduct large-scale agile and rapid community assessments to examine and combat COVID-19 misinformation. Methods: We used the Intervention Mapping framework to perform community needs assessment and develop theory-informed interventions. To supplement these rapid and responsive efforts through large-scale online social listening, we developed a novel methodological framework, comprising qualitative inquiry, computational methods, and quantitative network models to analyze publicly available social media data sets to model content-specific misinformation dynamics and guide content tailoring efforts. As part of community needs assessment, we conducted 11 semistructured interviews, 4 listening sessions, and 3 focus groups with community scientists. Further, we used our data repository with 416,927 COVID-19 social media posts to gather information diffusion patterns through digital channels. Results: Our results from community needs assessment revealed the complex intertwining of personal, cultural, and social influences of misinformation on individual behaviors and engagement. Our social media interventions resulted in limited community engagement and indicated the need for consumer advocacy and influencer recruitment. The linking of theoretical constructs underlying health behaviors to COVID-19--related social media interactions through semantic and syntactic features using our computational models has revealed frequent interaction typologies in factual and misleading COVID-19 posts and indicated significant differences in network metrics such as degree. The performance of our deep learning classifiers was reasonable, with an F-measure of 0.80 for speech acts and 0.81 for behavior constructs. Conclusions: Our study highlights the strengths of community-based field studies and emphasizes the utility of large-scale social media data sets in enabling rapid intervention tailoring to adapt grassroots community interventions to thwart misinformation seeding and spread among minority communities. Implications for consumer advocacy, data governance, and industry incentives are discussed for the sustainable role of social media solutions in public health. ", doi="10.2196/40156", url="https://infodemiology.jmir.org/2023/1/e40156", url="http://www.ncbi.nlm.nih.gov/pubmed/37113378" } @Article{info:doi/10.2196/42623, author="Park, Susan and Suh, Young-Kyoon", title="A Comprehensive Analysis of COVID-19 Vaccine Discourse by Vaccine Brand on Twitter in Korea: Topic and Sentiment Analysis", journal="J Med Internet Res", year="2023", month="Jan", day="31", volume="25", pages="e42623", keywords="COVID-19", keywords="vaccine", keywords="vaccination", keywords="Pfizer", keywords="Moderna", keywords="AstraZeneca", keywords="Janssen", keywords="Novavax", abstract="Background: ?The unprecedented speed of COVID-19 vaccine development and approval has raised public concern about its safety. However, studies on public discourses and opinions on social media focusing on adverse events (AEs) related to COVID-19 vaccine are rare. Objective: ?This study aimed to analyze Korean tweets about COVID-19 vaccines (Pfizer, Moderna, AstraZeneca, Janssen, and Novavax) after the vaccine rollout, explore the topics and sentiments of tweets regarding COVID-19 vaccines, and examine their changes over time. We also analyzed topics and sentiments focused on AEs related to vaccination using only tweets with terms about AEs. Methods: ?We devised a sophisticated methodology consisting of 5 steps: keyword search on Twitter, data collection, data preprocessing, data analysis, and result visualization. We used the Twitter Representational State Transfer application programming interface for data collection. A total of 1,659,158 tweets were collected from February 1, 2021, to March 31, 2022. Finally, 165,984 data points were analyzed after excluding retweets, news, official announcements, advertisements, duplicates, and tweets with <2 words. We applied a variety of preprocessing techniques that are suitable for the Korean language. We ran a suite of analyses using various Python packages, such as latent Dirichlet allocation, hierarchical latent Dirichlet allocation, and sentiment analysis. Results: ?The topics related to COVID-19 vaccines have a very large spectrum, including vaccine-related AEs, emotional reactions to vaccination, vaccine development and supply, and government vaccination policies. Among them, the top major topic was AEs related to COVID-19 vaccination. The AEs ranged from the adverse reactions listed in the safety profile (eg, myalgia, fever, fatigue, injection site pain, myocarditis or pericarditis, and thrombosis) to unlisted reactions (eg, irregular menstruation, changes in appetite and sleep, leukemia, and deaths). Our results showed a notable difference in the topics for each vaccine brand. The topics pertaining to the Pfizer vaccine mainly mentioned AEs. Negative public opinion has prevailed since the early stages of vaccination. In the sentiment analysis based on vaccine brand, the topics related to the Pfizer vaccine expressed the strongest negative sentiment. Conclusions: ?Considering the discrepancy between academic evidence and public opinions related to COVID-19 vaccination, the government should provide accurate information and education. Furthermore, our study suggests the need for management to correct the misinformation related to vaccine-related AEs, especially those affecting negative sentiments. This study provides valuable insights into the public discourses and opinions regarding COVID-19 vaccination. ", doi="10.2196/42623", url="https://www.jmir.org/2023/1/e42623", url="http://www.ncbi.nlm.nih.gov/pubmed/36603153" } @Article{info:doi/10.2196/41823, author="Han, Nuo and Li, Sijia and Huang, Feng and Wen, Yeye and Wang, Xiaoyang and Liu, Xiaoqian and Li, Linyan and Zhu, Tingshao", title="Sensing Psychological Well-being Using Social Media Language: Prediction Model Development Study", journal="J Med Internet Res", year="2023", month="Jan", day="31", volume="25", pages="e41823", keywords="mental health", keywords="psychological well-being", keywords="social media", keywords="machine learning", keywords="domain knowledge", keywords="mental well being", keywords="mental wellbeing", keywords="linguistic", keywords="predict", keywords="model", keywords="ground truth", keywords="lexicon", abstract="Background: Positive mental health is arguably increasingly important and can be revealed, to some extent, in terms of psychological well-being (PWB). However, PWB is difficult to assess in real time on a large scale. The popularity and proliferation of social media make it possible to sense and monitor online users' PWB in a nonintrusive way, and the objective of this study is to test the effectiveness of using social media language expression as a predictor of PWB. Objective: This study aims to investigate the predictive power of social media corresponding to ground truth well-being data in a psychological way. Methods: We recruited 1427 participants. Their well-being was evaluated using 6 dimensions of PWB. Their posts on social media were collected, and 6 psychological lexicons were used to extract linguistic features. A multiobjective prediction model was then built with the extracted linguistic features as input and PWB as the output. Further, the validity of the prediction model was confirmed by evaluating the model's discriminant validity, convergent validity, and criterion validity. The reliability of the model was also confirmed by evaluating the split-half reliability. Results: The correlation coefficients between the predicted PWB scores of social media users and the actual scores obtained using the linguistic prediction model of this study were between 0.49 and 0.54 (P<.001), which means that the model had good criterion validity. In terms of the model's structural validity, it exhibited excellent convergent validity but less than satisfactory discriminant validity. The results also suggested that our model had good split-half reliability levels for every dimension (ranging from 0.65 to 0.85; P<.001). Conclusions: By confirming the availability and stability of the linguistic prediction model, this study verified the predictability of social media corresponding to ground truth well-being data from the perspective of PWB. Our study has positive implications for the use of social media to predict mental health in nonprofessional settings such as self-testing or a large-scale user study. ", doi="10.2196/41823", url="https://www.jmir.org/2023/1/e41823", url="http://www.ncbi.nlm.nih.gov/pubmed/36719723" } @Article{info:doi/10.2196/34982, author="Poirier, Canelle and Bouzill{\'e}, Guillaume and Bertaud, Val{\'e}rie and Cuggia, Marc and Santillana, Mauricio and Lavenu, Audrey", title="Gastroenteritis Forecasting Assessing the Use of Web and Electronic Health Record Data With a Linear and a Nonlinear Approach: Comparison Study", journal="JMIR Public Health Surveill", year="2023", month="Jan", day="31", volume="9", pages="e34982", keywords="infectious disease", keywords="acute gastroenteritis", keywords="modeling", keywords="modeling disease outbreaks", keywords="machine learning", keywords="public health", keywords="machine learning in public health", keywords="forecasting", keywords="digital data", abstract="Background: Disease surveillance systems capable of producing accurate real-time and short-term forecasts can help public health officials design timely public health interventions to mitigate the effects of disease outbreaks in affected populations. In France, existing clinic-based disease surveillance systems produce gastroenteritis activity information that lags real time by 1 to 3 weeks. This temporal data gap prevents public health officials from having a timely epidemiological characterization of this disease at any point in time and thus leads to the design of interventions that do not take into consideration the most recent changes in dynamics. Objective: The goal of this study was to evaluate the feasibility of using internet search query trends and electronic health records to predict acute gastroenteritis (AG) incidence rates in near real time, at the national and regional scales, and for long-term forecasts (up to 10 weeks). Methods: We present 2 different approaches (linear and nonlinear) that produce real-time estimates, short-term forecasts, and long-term forecasts of AG activity at 2 different spatial scales in France (national and regional). Both approaches leverage disparate data sources that include disease-related internet search activity, electronic health record data, and historical disease activity. Results: Our results suggest that all data sources contribute to improving gastroenteritis surveillance for long-term forecasts with the prominent predictive power of historical data owing to the strong seasonal dynamics of this disease. Conclusions: The methods we developed could help reduce the impact of the AG peak by making it possible to anticipate increased activity by up to 10 weeks. ", doi="10.2196/34982", url="https://publichealth.jmir.org/2023/1/e34982", url="http://www.ncbi.nlm.nih.gov/pubmed/36719726" } @Article{info:doi/10.2196/43253, author="Rochford, Ben and Pendse, Sachin and Kumar, Neha and De Choudhury, Munmun", title="Leveraging Symptom Search Data to Understand Disparities in US Mental Health Care: Demographic Analysis of Search Engine Trace Data", journal="JMIR Ment Health", year="2023", month="Jan", day="30", volume="10", pages="e43253", keywords="mental health", keywords="search engine algorithms", keywords="digital mental health", keywords="health equity", abstract="Background: In the United States, 1 out of every 3 people lives in a mental health professional shortage area. Shortage areas tend to be rural, have higher levels of poverty, and have poor mental health outcomes. Previous work has demonstrated that these poor outcomes may arise from interactions between a lack of resources and lack of recognition of mental illness by medical professionals. Objective: We aimed to understand the differences in how people in shortage and nonshortage areas search for information about mental health on the web. Methods: We analyzed search engine log data related to health from 2017-2021 and examined the differences in mental health search behavior between shortage and nonshortage areas. We analyzed several axes of difference, including shortage versus nonshortage comparisons, urban versus rural comparisons, and temporal comparisons. Results: We found specific differences in search behavior between shortage and nonshortage areas. In shortage areas, broader and more general mental health symptom categories, namely anxiety (mean 2.03\%, SD 0.44\%), depression (mean 1.15\%, SD 0.27\%), fatigue (mean 1.21\%, SD 0.28\%), and headache (mean 1.03\%, SD 0.23\%), were searched significantly more often (Q<.0003). In contrast, specific symptom categories and mental health disorders such as binge eating (mean 0.02\%, SD 0.02\%), psychosis (mean 0.37\%, SD 0.06\%), and attention-deficit/hyperactivity disorder (mean 0.77\%, SD 0.10\%) were searched significantly more often (Q<.0009) in nonshortage areas. Although suicide rates are consistently known to be higher in shortage and rural areas, we see that the rates of suicide-related searching are lower in shortage areas (mean 0.05\%, SD 0.04\%) than in nonshortage areas (mean 0.10\%, SD 0.03\%; Q<.0003), more so when a shortage area is rural (mean 0.024\%, SD 0.029\%; Q<2 {\texttimes} 10--12). Conclusions: This study demonstrates differences in how people from geographically marginalized groups search on the web for mental health. One main implication of this work is the influence that search engine ranking algorithms and interface design might have on the kinds of resources that individuals use when in distress. Our results support the idea that search engine algorithm designers should be conscientious of the role that structural factors play in expressions of distress and they should attempt to design search engine algorithms and interfaces to close gaps in care. ", doi="10.2196/43253", url="https://mental.jmir.org/2023/1/e43253", url="http://www.ncbi.nlm.nih.gov/pubmed/36716082" } @Article{info:doi/10.2196/40922, author="Chin, Hyojin and Lima, Gabriel and Shin, Mingi and Zhunis, Assem and Cha, Chiyoung and Choi, Junghoi and Cha, Meeyoung", title="User-Chatbot Conversations During the COVID-19 Pandemic: Study Based on Topic Modeling and Sentiment Analysis", journal="J Med Internet Res", year="2023", month="Jan", day="27", volume="25", pages="e40922", keywords="chatbot", keywords="COVID-19", keywords="topic modeling", keywords="sentiment analysis", keywords="infodemiology", keywords="discourse", keywords="public perception", keywords="public health", keywords="infoveillance", keywords="conversational agent", keywords="global health", keywords="health information", abstract="Background: Chatbots have become a promising tool to support public health initiatives. Despite their potential, little research has examined how individuals interacted with chatbots during the COVID-19 pandemic. Understanding user-chatbot interactions is crucial for developing services that can respond to people's needs during a global health emergency. Objective: This study examined the COVID-19 pandemic--related topics online users discussed with a commercially available social chatbot and compared the sentiment expressed by users from 5 culturally different countries. Methods: We analyzed 19,782 conversation utterances related to COVID-19 covering 5 countries (the United States, the United Kingdom, Canada, Malaysia, and the Philippines) between 2020 and 2021, from SimSimi, one of the world's largest open-domain social chatbots. We identified chat topics using natural language processing methods and analyzed their emotional sentiments. Additionally, we compared the topic and sentiment variations in the COVID-19--related chats across countries. Results: Our analysis identified 18 emerging topics, which could be categorized into the following 5 overarching themes: ``Questions on COVID-19 asked to the chatbot'' (30.6\%), ``Preventive behaviors'' (25.3\%), ``Outbreak of COVID-19'' (16.4\%), ``Physical and psychological impact of COVID-19'' (16.0\%), and ``People and life in the pandemic'' (11.7\%). Our data indicated that people considered chatbots as a source of information about the pandemic, for example, by asking health-related questions. Users turned to SimSimi for conversation and emotional messages when offline social interactions became limited during the lockdown period. Users were more likely to express negative sentiments when conversing about topics related to masks, lockdowns, case counts, and their worries about the pandemic. In contrast, small talk with the chatbot was largely accompanied by positive sentiment. We also found cultural differences, with negative words being used more often by users in the United States than by those in Asia when talking about COVID-19. Conclusions: Based on the analysis of user-chatbot interactions on a live platform, this work provides insights into people's informational and emotional needs during a global health crisis. Users sought health-related information and shared emotional messages with the chatbot, indicating the potential use of chatbots to provide accurate health information and emotional support. Future research can look into different support strategies that align with the direction of public health policy. ", doi="10.2196/40922", url="https://www.jmir.org/2023/1/e40922", url="http://www.ncbi.nlm.nih.gov/pubmed/36596214" } @Article{info:doi/10.2196/41762, author="Brewer, R. Hannah and Hirst, Yasemin and Chadeau-Hyam, Marc and Johnson, Eric and Sundar, Sudha and Flanagan, M. James", title="Association Between Purchase of Over-the-Counter Medications and Ovarian Cancer Diagnosis in the Cancer Loyalty Card Study (CLOCS): Observational Case-Control Study", journal="JMIR Public Health Surveill", year="2023", month="Jan", day="26", volume="9", pages="e41762", keywords="ovarian cancer", keywords="early diagnosis", keywords="transactional data", keywords="health informatics", keywords="cancer risk", keywords="medication", keywords="self-medication", keywords="self-care", keywords="over-the-counter medication", keywords="nonspecific symptoms", keywords="pain medication", keywords="indigestion medication", abstract="Background: Over-the-counter (OTC) medications are frequently used to self-care for nonspecific ovarian cancer symptoms prior to diagnosis. Monitoring such purchases may provide an opportunity for earlier diagnosis. Objective: The aim of the Cancer Loyalty Card Study (CLOCS) was to investigate purchases of OTC pain and indigestion medications prior to ovarian cancer diagnosis in women with and without ovarian cancer in the United Kingdom using loyalty card data. Methods: An observational case-control study was performed comparing purchases of OTC pain and indigestion medications prior to diagnosis in women with (n=153) and without (n=120) ovarian cancer using loyalty card data from two UK-based high street retailers. Monthly purchases of pain and indigestion medications for cases and controls were compared using the Fisher exact test, conditional logistic regression, and receiver operating characteristic (ROC) curve analysis. Results: Pain and indigestion medication purchases were increased among cases 8 months before diagnosis, with maximum discrimination between cases and controls 8 months before diagnosis (Fisher exact odds ratio [OR] 2.9, 95\% CI 2.1-4.1). An increase in indigestion medication purchases was detected up to 9 months before diagnosis (adjusted conditional logistic regression OR 1.38, 95\% CI 1.04-1.83). The ROC analysis for indigestion medication purchases showed a maximum area under the curve (AUC) at 13 months before diagnosis (AUC=0.65, 95\% CI 0.57-0.73), which further improved when stratified to late-stage ovarian cancer (AUC=0.68, 95\% CI 0.59-0.78). Conclusions: There is a difference in purchases of pain and indigestion medications among women with and without ovarian cancer up to 8 months before diagnosis. Facilitating earlier presentation among those who self-care for symptoms using this novel data source could improve ovarian cancer patients' options for treatment and improve survival. Trial Registration: ClinicalTrials.gov NCT03994653; https://clinicaltrials.gov/ct2/show/NCT03994653 ", doi="10.2196/41762", url="https://publichealth.jmir.org/2023/1/e41762", url="http://www.ncbi.nlm.nih.gov/pubmed/36701184" } @Article{info:doi/10.2196/42162, author="Cuomo, Raphael and Purushothaman, Vidya and Calac, J. Alec and McMann, Tiana and Li, Zhuoran and Mackey, Tim", title="Estimating County-Level Overdose Rates Using Opioid-Related Twitter Data: Interdisciplinary Infodemiology Study", journal="JMIR Form Res", year="2023", month="Jan", day="25", volume="7", pages="e42162", keywords="overdose", keywords="mortality", keywords="geospatial analysis", keywords="social media", keywords="drug overuse", keywords="substance use", keywords="social media data", keywords="mortality estimates", keywords="real-time data", keywords="public health data", keywords="demographic variables", keywords="county-level", abstract="Background: There were an estimated 100,306 drug overdose deaths between April 2020 and April 2021, a three-quarter increase from the prior 12-month period. There is an approximate 6-month reporting lag for provisional counts of drug overdose deaths from the National Vital Statistics System, and the highest level of geospatial resolution is at the state level. By contrast, public social media data are available close to real-time and are often accessible with precise coordinates. Objective: The purpose of this study is to assess whether county-level overdose mortality burden could be estimated using opioid-related Twitter data. Methods: International Classification of Diseases (ICD) codes for poisoning or exposure to overdose at the county level were obtained from CDC WONDER. Demographics were collected from the American Community Survey. The Twitter Application Programming Interface was used to obtain tweets that contained any of the 36 terms with drug names. An unsupervised classification approach was used for clustering tweets. Population-normalized variables and polynomial population-normalized variables were produced. Furthermore, z scores of the Getis Ord Gi clustering statistic were produced, and both these scores and their polynomial counterparts were explored in regression modeling of county-level overdose mortality burden. A series of linear regression models were used for predictive modeling to explore the interpretability of the analytical output. Results: Modeling overdose mortality with normalized demographic variables alone explained only 7.4\% of the variability in county-level overdose mortality, whereas this was approximately doubled by the use of specific demographic and Twitter data covariates based on a backward selection approach. The highest adjusted R2 and lowest AIC (Akaike Info Criterion) were obtained for the model with normalized demographic variables, normalized z scores from geospatial analyses, and normalized topic counts (adjusted R2=0.133, AIC=8546.8). The z scores of the Getis Ord Gi statistic appeared to have improved utility over population-normalization alone. In this model, median age, female population, and tweets about web-based drug sales were positively associated with opioid mortality. Asian race and Hispanic ethnicity were significantly negatively associated with county-level burdens of overdose mortality. Conclusions: Social media data, when transformed using certain statistical approaches, may add utility to the goal of producing closer to real-time county-level estimates of overdose mortality. Prediction of opioid-related outcomes can be advanced to inform prevention and treatment decisions. This interdisciplinary approach can facilitate evidence-based funding decisions for various substance use disorder prevention and treatment programs. ", doi="10.2196/42162", url="https://formative.jmir.org/2023/1/e42162", url="http://www.ncbi.nlm.nih.gov/pubmed/36548118" } @Article{info:doi/10.2196/38390, author="Turner, Jason and Kantardzic, Mehmed and Vickers-Smith, Rachel and Brown, G. Andrew", title="Detecting Tweets Containing Cannabidiol-Related COVID-19 Misinformation Using Transformer Language Models and Warning Letters From Food and Drug Administration: Content Analysis and Identification", journal="JMIR Infodemiology", year="2023", month="Jan", day="23", volume="3", pages="e38390", keywords="transformer", keywords="misinformation", keywords="deep learning", keywords="COVID-19", keywords="infodemic", keywords="pandemic", keywords="language model", keywords="health information", keywords="social media", keywords="Twitter", keywords="content analysis", keywords="cannabidiol", keywords="sentence vector", keywords="infodemiology", abstract="Background: COVID-19 has introduced yet another opportunity to web-based sellers of loosely regulated substances, such as cannabidiol (CBD), to promote sales under false pretenses of curing the disease. Therefore, it has become necessary to innovate ways to identify such instances of misinformation. Objective: We sought to identify COVID-19 misinformation as it relates to the sales or promotion of CBD and used transformer-based language models to identify tweets semantically similar to quotes taken from known instances of misinformation. In this case, the known misinformation was the publicly available Warning Letters from Food and Drug Administration (FDA). Methods: We collected tweets using CBD- and COVID-19--related terms. Using a previously trained model, we extracted the tweets indicating commercialization and sales of CBD and annotated those containing COVID-19 misinformation according to the FDA definitions. We encoded the collection of tweets and misinformation quotes into sentence vectors and then calculated the cosine similarity between each quote and each tweet. This allowed us to establish a threshold to identify tweets that were making false claims regarding CBD and COVID-19 while minimizing the instances of false positives. Results: We demonstrated that by using quotes taken from Warning Letters issued by FDA to perpetrators of similar misinformation, we can identify semantically similar tweets that also contain misinformation. This was accomplished by identifying a cosine distance threshold between the sentence vectors of the Warning Letters and tweets. Conclusions: This research shows that commercial CBD or COVID-19 misinformation can potentially be identified and curbed using transformer-based language models and known prior instances of misinformation. Our approach functions without the need for labeled data, potentially reducing the time at which misinformation can be identified. Our approach shows promise in that it is easily adapted to identify other forms of misinformation related to loosely regulated substances. ", doi="10.2196/38390", url="https://infodemiology.jmir.org/2023/1/e38390", url="http://www.ncbi.nlm.nih.gov/pubmed/36844029" } @Article{info:doi/10.2196/37207, author="Omranian, Samaneh and Zolnoori, Maryam and Huang, Ming and Campos-Castillo, Celeste and McRoy, Susan", title="Predicting Patient Satisfaction With Medications for Treating Opioid Use Disorder: Case Study Applying Natural Language Processing to Reviews of Methadone and Buprenorphine/Naloxone on Health-Related Social Media", journal="JMIR Infodemiology", year="2023", month="Jan", day="23", volume="3", pages="e37207", keywords="machine learning", keywords="online forums", keywords="text classification", keywords="topic modeling", keywords="MetaMap", keywords="drug review", keywords="opioid treatment, opioid use disorder", keywords="patient-generated text", abstract="Background: Medication-assisted treatment (MAT) is an effective method for treating opioid use disorder (OUD), which combines behavioral therapies with one of three Food and Drug Administration--approved medications: methadone, buprenorphine, and naloxone. While MAT has been shown to be effective initially, there is a need for more information from the patient perspective about the satisfaction with medications. Existing research focuses on patient satisfaction with the entirety of the treatment, making it difficult to determine the unique role of medication and overlooking the views of those who may lack access to treatment due to being uninsured or concerns over stigma. Studies focusing on patients' perspectives are also limited by the lack of scales that can efficiently collect self-reports across domains of concerns. Objective: A broad survey of patients' viewpoints can be obtained through social media and drug review forums, which are then assessed using automated methods to discover factors associated with medication satisfaction. Because the text is unstructured, it may contain a mix of formal and informal language. The primary aim of this study was to use natural language processing methods on text posted on health-related social media to detect patients' satisfaction with two well-studied OUD medications: methadone and buprenorphine/naloxone. Methods: We collected 4353 patient reviews of methadone and buprenorphine/naloxone from 2008 to 2021 posted on WebMD and Drugs.com. To build our predictive models for detecting patient satisfaction, we first employed different analyses to build four input feature sets using the vectorized text, topic models, duration of treatment, and biomedical concepts by applying MetaMap. We then developed six prediction models: logistic regression, Elastic Net, least absolute shrinkage and selection operator, random forest classifier, Ridge classifier, and extreme gradient boosting to predict patients' satisfaction. Lastly, we compared the prediction models' performance over different feature sets. Results: Topics discovered included oral sensation, side effects, insurance, and doctor visits. Biomedical concepts included symptoms, drugs, and illnesses. The F-score of the predictive models across all methods ranged from 89.9\% to 90.8\%. The Ridge classifier model, a regression-based method, outperformed the other models. Conclusions: Assessment of patients' satisfaction with opioid dependency treatment medication can be predicted using automated text analysis. Adding biomedical concepts such as symptoms, drug name, and illness, along with the duration of treatment and topic models, had the most benefits for improving the prediction performance of the Elastic Net model compared to other models. Some of the factors associated with patient satisfaction overlap with domains covered in medication satisfaction scales (eg, side effects) and qualitative patient reports (eg, doctors' visits), while others (insurance) are overlooked, thereby underscoring the value added from processing text on online health forums to better understand patient adherence. ", doi="10.2196/37207", url="https://infodemiology.jmir.org/2023/1/e37207", url="http://www.ncbi.nlm.nih.gov/pubmed/37113381" } @Article{info:doi/10.2196/43521, author="Kim, Donghun and Jung, Woojin and Jiang, Ting and Zhu, Yongjun", title="An Exploratory Study of Medical Journal's Twitter Use: Metadata, Networks, and Content Analyses", journal="J Med Internet Res", year="2023", month="Jan", day="19", volume="25", pages="e43521", keywords="medical journals", keywords="social networks", keywords="Twitter", abstract="Background: An increasing number of medical journals are using social media to promote themselves and communicate with their readers. However, little is known about how medical journals use Twitter and what their social media management strategies are. Objective: This study aimed to understand how medical journals use Twitter from a global standpoint. We conducted a broad, in-depth analysis of all the available Twitter accounts of medical journals indexed by major indexing services, with a particular focus on their social networks and content. Methods: The Twitter profiles and metadata of medical journals were analyzed along with the social networks on their Twitter accounts. Results: The results showed that overall, publishers used different strategies regarding Twitter adoption, Twitter use patterns, and their subsequent decisions. The following specific findings were noted: journals with Twitter accounts had a significantly higher number of publications and a greater impact than their counterparts; subscription journals had a slightly higher Twitter adoption rate (2\%) than open access journals; journals with higher impact had more followers; and prestigious journals rarely followed other lesser-known journals on social media. In addition, an in-depth analysis of 2000 randomly selected tweets from 4 prestigious journals revealed that The Lancet had dedicated considerable effort to communicating with people about health information and fulfilling its social responsibility by organizing committees and activities to engage with a broad range of health-related issues; The New England Journal of Medicine and the Journal of the American Medical Association focused on promoting research articles and attempting to maximize the visibility of their research articles; and the British Medical Journal provided copious amounts of health information and discussed various health-related social problems to increase social awareness of the field of medicine. Conclusions: Our study used various perspectives to investigate how medical journals use Twitter and explored the Twitter management strategies of 4 of the most prestigious journals. Our study provides a detailed understanding of medical journals' use of Twitter from various perspectives and can help publishers, journals, and researchers to better use Twitter for their respective purposes. ", doi="10.2196/43521", url="https://www.jmir.org/2023/1/e43521", url="http://www.ncbi.nlm.nih.gov/pubmed/36656626" } @Article{info:doi/10.2196/44697, author="Ji-Xu, Antonio and Htet, Zin Kyaw and Leslie, S. Kieron", title="Monkeypox Content on TikTok: Cross-sectional Analysis", journal="J Med Internet Res", year="2023", month="Jan", day="17", volume="25", pages="e44697", keywords="TikTok", keywords="social media", keywords="monkeypox", keywords="mpox", keywords="pandemic", keywords="epidemic", keywords="infectious disease", keywords="outbreak", keywords="quality assessment", keywords="content analysis", doi="10.2196/44697", url="https://www.jmir.org/2023/1/e44697", url="http://www.ncbi.nlm.nih.gov/pubmed/36649057" } @Article{info:doi/10.2196/38112, author="Meyerson, U. William and Fineberg, K. Sarah and Song, Kyung Ye and Faber, Adam and Ash, Garrett and Andrade, C. Fernanda and Corlett, Philip and Gerstein, B. Mark and Hoyle, H. Rick", title="Estimation of Bedtimes of Reddit Users: Integrated Analysis of Time Stamps and Surveys", journal="JMIR Form Res", year="2023", month="Jan", day="17", volume="7", pages="e38112", keywords="social media", keywords="sleep", keywords="parametric models", keywords="Reddit", keywords="observational model", keywords="research tool", keywords="sleep patterns", keywords="usage data", keywords="model", keywords="bedtime", abstract="Background: Individuals with later bedtimes have an increased risk of difficulties with mood and substances. To investigate the causes and consequences of late bedtimes and other sleep patterns, researchers are exploring social media as a data source. Pioneering studies inferred sleep patterns directly from social media data. While innovative, these efforts are variously unscalable, context dependent, confined to specific sleep parameters, or rest on untested assumptions, and none of the reviewed studies apply to the popular Reddit platform or release software to the research community. Objective: This study builds on this prior work. We estimate the bedtimes of Reddit users from the times tamps of their posts, test inference validity against survey data, and release our model as an R package (The R Foundation). Methods: We included 159 sufficiently active Reddit users with known time zones and known, nonanomalous bedtimes, together with the time stamps of their 2.1 million posts. The model's form was chosen by visualizing the aggregate distribution of the timing of users' posts relative to their reported bedtimes. The chosen model represents a user's frequency of Reddit posting by time of day, with a flat portion before bedtime and a quadratic depletion that begins near the user's bedtime, with parameters fitted to the data. This model estimates the bedtimes of individual Reddit users from the time stamps of their posts. Model performance is assessed through k-fold cross-validation. We then apply the model to estimate the bedtimes of 51,372 sufficiently active, nonbot Reddit users with known time zones from the time stamps of their 140 million posts. Results: The Pearson correlation between expected and observed Reddit posting frequencies in our model was 0.997 on aggregate data. On average, posting starts declining 45 minutes before bedtime, reaches a nadir 4.75 hours after bedtime that is 87\% lower than the daytime rate, and returns to baseline 10.25 hours after bedtime. The Pearson correlation between inferred and reported bedtimes for individual users was 0.61 (P<.001). In 90 of 159 cases (56.6\%), our estimate was within 1 hour of the reported bedtime; 128 cases (80.5\%) were within 2 hours. There was equivalent accuracy in hold-out sets versus training sets of k-fold cross-validation, arguing against overfitting. The model was more accurate than a random forest approach. Conclusions: We uncovered a simple, reproducible relationship between Reddit users' reported bedtimes and the time of day when high daytime posting rates transition to low nighttime posting rates. We captured this relationship in a model that estimates users' bedtimes from the time stamps of their posts. Limitations include applicability only to users who post frequently, the requirement for time zone data, and limits on generalizability. Nonetheless, it is a step forward for inferring the sleep parameters of social media users passively at scale. Our model and precomputed estimated bedtimes of 50,000 Reddit users are freely available. ", doi="10.2196/38112", url="https://formative.jmir.org/2023/1/e38112", url="http://www.ncbi.nlm.nih.gov/pubmed/36649054" } @Article{info:doi/10.2196/42401, author="van Kessel, Robin and Kyriopoulos, Ilias and Wong, Han Brian Li and Mossialos, Elias", title="The Effect of the COVID-19 Pandemic on Digital Health--Seeking Behavior: Big Data Interrupted Time-Series Analysis of Google Trends", journal="J Med Internet Res", year="2023", month="Jan", day="16", volume="25", pages="e42401", keywords="digital health", keywords="healthcare seeking behaviour", keywords="big data", keywords="real-world data", keywords="data", keywords="COVID-19", keywords="pandemic", keywords="Google Trends", keywords="telehealth", abstract="Background: Due to the emergency responses early in the COVID-19 pandemic, the use of digital health in health care increased abruptly. However, it remains unclear whether this introduction was sustained in the long term, especially with patients being able to decide between digital and traditional health services once the latter regained their functionality throughout the COVID-19 pandemic. Objective: We aim to understand how the public interest in digital health changed as proxy for digital health--seeking behavior and to what extent this change was sustainable over time. Methods: We used an interrupted time-series analysis of Google Trends data with break points on March 11, 2020 (declaration of COVID-19 as a pandemic by the World Health Organization), and December 20, 2020 (the announcement of the first COVID-19 vaccines). Nationally representative time-series data from February 2019 to August 2021 were extracted from Google Trends for 6 countries with English as their dominant language: Canada, the United States, the United Kingdom, New Zealand, Australia, and Ireland. We measured the changes in relative search volumes of the keywords online doctor, telehealth, online health, telemedicine, and health app. In doing so, we capture the prepandemic trend, the immediate change due to the announcement of COVID-19 being a pandemic, and the gradual change after the announcement. Results: Digital health search volumes immediately increased in all countries under study after the announcement of COVID-19 being a pandemic. There was some variation in what keywords were used per country. However, searches declined after this immediate spike, sometimes reverting to prepandemic levels. The announcement of COVID-19 vaccines did not consistently impact digital health search volumes in the countries under study. The exception is the search volume of health app, which was observed as either being stable or gradually increasing during the pandemic. Conclusions: Our findings suggest that the increased public interest in digital health associated with the pandemic did not sustain, alluding to remaining structural barriers. Further building of digital health capacity and developing robust digital health governance frameworks remain crucial to facilitating sustainable digital health transformation. ", doi="10.2196/42401", url="https://www.jmir.org/2023/1/e42401", url="http://www.ncbi.nlm.nih.gov/pubmed/36603152" } @Article{info:doi/10.2196/44103, author="Zueger, Morgan and Nahod, Paige and Marroquin, A. Nathaniel and Szeto, D. Mindy and Ajmal, Hamza and Martini, Olnita and Burnette, Colin and Quinn, P. Alyssa and Furth, Garrett and Militello, Michelle and Dellavalle, P. Robert", title="Skin of Color Dermatology Representation in American College of Mohs Surgery Educational Cases on Instagram: Content Analysis", journal="JMIR Dermatol", year="2023", month="Jan", day="13", volume="6", pages="e44103", keywords="skin of color", keywords="inequality", keywords="color", keywords="skin", keywords="social media", keywords="content analysis", keywords="dermatology", keywords="cancer", keywords="diversity", keywords="equity", keywords="inclusion", keywords="representation, Mohs surgery", keywords="skin tone", keywords="dermatologic surgery", keywords="Instagram", keywords="education", keywords="medical education", doi="10.2196/44103", url="https://derma.jmir.org/2023/1/e44103", url="http://www.ncbi.nlm.nih.gov/pubmed/37632910" } @Article{info:doi/10.2196/34132, author="Li, Yongjie and Yan, Xiangyu and Wang, Zekun and Ma, Mingchang and Zhang, Bo and Jia, Zhongwei", title="Comparison of the Users' Attitudes Toward Cannabidiol on Social Media Platforms: Topic Modeling Study", journal="JMIR Public Health Surveill", year="2023", month="Jan", day="11", volume="9", pages="e34132", keywords="cannabidiol", keywords="drug policy", keywords="latent Dirichlet allocation", keywords="social media", keywords="sentiment analysis", abstract="Background: As one of the major constituents of the cannabis sativa plant, cannabidiol (CBD) is approved for use in medical treatment and cosmetics because of its potential health benefits. With the rapid growth of the CBD market, customers purchase these products, and relevant discussions are becoming more active on social media. Objective: In this study, we aimed to understand the users' attitudes toward CBD products in various countries by conducting text mining on social media in countries with different substance management policies. Methods: We collected posts from Reddit and Xiaohongshu, conducted topic mining using the latent Dirichlet allocation model, and analyzed the characteristics of topics on different social media. Subsequently, a co-occurrence network of high-frequency keywords was constructed to explore potential relationships among topics. Moreover, we conducted sentiment analysis on the posts' comments and compared users' attitudes toward CBD products on Reddit and Xiaohongshu using chi-square test. Results: CBD-related posts on social media have been rapidly increasing, especially on Xiaohongshu since 2019. A total of 1790 posts from Reddit and 1951 posts from Xiaohongshu were included in the final analysis. The posts on the 2 social media platforms, Reddit and Xiaohongshu, were categorized into 7 and 8 topics, respectively, by the latent Dirichlet allocation model, and these topics on the 2 social media were grouped into 5 themes. Our study showed that the themes on Reddit were mainly related to the therapeutic effects of CBD, whereas the themes on Xiaohongshu concentrated on cosmetics, such as facial masks. Theme 2 (CBD market information) and theme 3 (attitudes toward CBD) on Reddit had more connections with other themes in the co-occurrence network, and theme 3 and theme 1 (CBD therapeutic effects) had a high co-occurrence frequency (22,803/73,865, 30.87\%). Meanwhile, theme 1 (CBD cosmetics) on Xiaohongshu had various connections with others (169,961/384,575, 44.19\%), and the co-occurrence frequency of theme 4 (CBD ingredients) and theme 1 was relatively prominent (27,128/49,312, 55.01\%). Overall, users' comments tended to be positive for CBD-related information on both Reddit and Xiaohongshu, but the percentage was higher on Xiaohongshu (82.25\% vs 86.18\%; P<.001), especially in cosmetics and medical health care products. Conclusions: The CBD market has grown rapidly, and the topics related to CBD on social media have become active. There are apparent differences in users' attitudes toward CBD in countries with different substance management policies. Targeted CBD management measures should be formulated to suit the prevalence of CBD use of each country. ", doi="10.2196/34132", url="https://publichealth.jmir.org/2023/1/e34132", url="http://www.ncbi.nlm.nih.gov/pubmed/36630175" } @Article{info:doi/10.2196/40291, author="Kaplan, Samantha and von Isenburg, Megan and Waldrop, Lucy", title="Prepandemic Antivaccination Websites' COVID-19 Vaccine Behavior: Content Analysis of Archived Websites", journal="JMIR Form Res", year="2023", month="Jan", day="11", volume="7", pages="e40291", keywords="antivaccination behavior", keywords="web archiving", keywords="content analysis", keywords="COVID-19 vaccines", keywords="COVID-19", keywords="vaccine", keywords="website", keywords="web", keywords="pandemic", keywords="safety", keywords="science", keywords="content", abstract="Background: The onset of the COVID-19 pandemic and the concurrent development of vaccines offered a rare and somewhat unprecedented opportunity to study antivaccination behavior as it formed over time via the use of archived versions of websites. Objective: This study aims to assess how existing antivaccination websites modified their content to address COVID-19 vaccines and pandemic restrictions. Methods: Using a preexisting collection of 25 antivaccination websites curated by the IvyPlus Web Collection Program prior to the pandemic and crawled every 6 months via Archive-It, we conducted a content analysis to see how these websites acknowledged or ignored COVID-19 vaccines and pandemic restrictions. Websites were assessed for financial behaviors such as having storefronts, mention of COVID-19 vaccines in general or by manufacturer name, references to personal freedom such as masking, safety concerns like side effects, and skepticism of science. Results: The majority of websites addressed COVID-19 vaccines in a negative fashion, with more websites making appeals to personal freedom or expressing skepticism of science than questioning safety. This can potentially be attributed to the lack of available safety data about the vaccines at the time of data collection. Many of the antivaccination websites we evaluated actively sought donations and had a membership option, evidencing these websites have financial motivations and actively build a community around these issues. The content analysis also offered the opportunity to test the viability of archived websites for use in scholarly research. The archived versions of the websites had significant shortcomings, particularly in search functionality, and required supplementation with the live websites. For web archiving to be a viable source of stand-alone content for research, the technology needs to make significant improvements in its capture abilities. Conclusions: In summary, we found antivaccination websites existing prior to the COVID-19 pandemic largely adapted their messaging to address COVID-19 vaccines with very few sites ignoring the pandemic altogether. This study also demonstrated the timely and significant need for more robust web archiving capabilities as web-based environments become more ephemeral and unstable. ", doi="10.2196/40291", url="https://formative.jmir.org/2023/1/e40291", url="http://www.ncbi.nlm.nih.gov/pubmed/36548948" } @Article{info:doi/10.2196/39155, author="Kwan, Heng Yu and Phang, Kie Jie and Woon, Hui Ting and Liew, W. Jean and Dubreuil, Maureen and Proft, Fabian and Ramiro, Sofia and Molto, Anna and Navarro-Comp{\'a}n, Victoria and de Hooge, Manouk and Meghnathi, Bhowmik and Ziade, Nelly and Zhao, Steven Sizheng and Llop, Maria and Baraliakos, Xenofon and Fong, Warren", title="Social Media Use Among Members of the Assessment of Spondyloarthritis International Society: Results of a Web-Based Survey", journal="J Med Internet Res", year="2023", month="Jan", day="10", volume="25", pages="e39155", keywords="social media", keywords="spondyloarthritis", keywords="cross-sectional survey", abstract="Background: The use of social media in health care may serve as a beneficial tool for education, information dissemination, telemedicine, research, networking, and communications. To better leverage the benefits of social media, it is imperative to understand the patterns of its use and potential barriers to its implementation in health care. A previous study in 2016 that investigated social media use among young clinical rheumatologists (?45 years) and basic scientists showed that there was substantial social media use among them for social and professional reasons. However, there is a limited inquiry into social media use in different areas of rheumatology, such as spondyloarthritis. Objective: We aimed to explore the motivations, barriers, and patterns of social media use among an international group of experts in spondyloarthritis. Methods: We distributed a web-based survey via email from March 2021 to June 2021 to 198 members of the Assessment of Spondyloarthritis International Society. It contained 24 questions about demographic characteristics, patterns of current social media use, and perceptions of utility. Univariable and multivariable logistic regression analyses were performed to identify the characteristics associated with use trends. Results: The response rate was 78.8\% (156/198). Of these, 93.6\% (146/156) of participants used at least one social media platform. Apart from internet-based shopping and entertainment, the use of social media for clinical updates (odds ratio [OR] 6.25, 95\% CI 2.43-16.03) and research updates (OR 3.45, 95\% CI 1.35-8.78) were associated with higher social media consumption. Among the respondents, 66\% (103/156) used social media in a work-related manner. The use of social media for new web-based resources (OR 6.55, 95\% CI 2.01-21.37), interaction with international colleagues (OR 4.66, 95\% CI 1.21-17.90), and establishing a web-based presence (OR 4.05, 95\% CI 1.25-13.13) were associated with higher levels of consumption for work-related purposes. Time investment, confidentiality concerns, and security concerns were the top 3 challenges to a wider adoption of social media. Conclusions: Most respondents (103/156, 66\%) use social media in a work-related manner. Professional development, establishing a web-based presence, and international collaboration were associated with higher use. Challenges to social media adoption should be addressed to maximize its benefits. ", doi="10.2196/39155", url="https://www.jmir.org/2023/1/e39155", url="http://www.ncbi.nlm.nih.gov/pubmed/36626201" } @Article{info:doi/10.2196/41669, author="Szafran, Daria and G{\"o}rig, Tatiana and Vollst{\"a}dt-Klein, Sabine and Grundinger, Nadja and Mons, Ute and Lohner, Valerie and Schneider, Sven and Andreas, Marike", title="Addictive Potential of e-Cigarettes as Reported in e-Cigarette Online Forums: Netnographic Analysis of Subjective Experiences", journal="J Med Internet Res", year="2023", month="Jan", day="6", volume="25", pages="e41669", keywords="e-cigarette", keywords="addiction", keywords="netnographic analysis", keywords="smoking", keywords="tobacco", keywords="substance use", keywords="vaping", abstract="Background: While e-cigarettes usually contain nicotine, their addictive potential is not yet fully understood. We hypothesized that if e-cigarettes are addictive, users will experience typical symptoms of addiction. Objective: The aim of our study was to investigate whether and how e-cigarette users report signs of addiction. Methods: We identified 3 large German-language e-cigarette online forums via a systematic Google search. Based on a netnographic approach, we used deductive content analysis to investigate relevant posts in these forums. Netnography has the advantage of limiting the social desirability bias that prevails in face-to-face research, such as focus groups. The data were coded according to the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) criteria for tobacco use disorder, adapted for e-cigarettes. The DSM-5 criteria were used to portray a broad spectrum of possible experiences of addiction. Results: Overall, 5337 threads in 3 forums were screened, and 451 threads containing relevant information were included in the analysis. Users reported experiences consistent with the DSM-5 criteria, such as craving e-cigarettes, excessive time spent vaping, and health issues related to e-cigarette use. However, our analysis also showed that users reported the absence of typical tobacco use disorder criteria, such as successful attempts to reduce the nicotine dosage. For most themes, reports of their absence were more frequent than of their presence. The absence of perceived addiction was mostly reported in contrast to prior tobacco smoking. Conclusions: This is the first study to use a netnographic approach to explore unfiltered self-reports of experiences of e-cigarette addiction by users in online forums. As hypothesized, some but not all users reported subjective experiences that corresponded to the criteria of tobacco use disorder as defined by the DSM-5. Nevertheless, subjective reports also indicated that many e-cigarette users felt in control of their behavior, especially in contrast to their prior use of tobacco cigarettes. The finding that some e-cigarette users subjectively experience addiction highlights the need for effective cessation programs to support users who experience their e-cigarette use as burdensome. This research can guide the refinement of instruments to assess e-cigarette addiction and guide cessation programs. International Registered Report Identifier (IRRID): RR2-10.1186/s40359-021-00682-8 ", doi="10.2196/41669", url="https://www.jmir.org/2023/1/e41669", url="http://www.ncbi.nlm.nih.gov/pubmed/36607713" } @Article{info:doi/10.2196/38607, author="Sharma, E. Anjana and Khosla, Kiran and Potharaju, Kameswari and Mukherjea, Arnab and Sarkar, Urmimala", title="COVID-19--Associated Misinformation Across the South Asian Diaspora: Qualitative Study of WhatsApp Messages", journal="JMIR Infodemiology", year="2023", month="Jan", day="5", volume="3", pages="e38607", keywords="misinformation", keywords="COVID-19", keywords="South Asians", keywords="disparities", keywords="social media", keywords="infodemiology", keywords="WhatsApp", keywords="messages", keywords="apps", keywords="health information", keywords="reliability", keywords="communication", keywords="Asian", keywords="English", keywords="community", keywords="health", keywords="organization", keywords="public health", keywords="pandemic", abstract="Background: South Asians, inclusive of individuals originating in India, Pakistan, Maldives, Bangladesh, Sri Lanka, Bhutan, and Nepal, comprise the largest diaspora in the world, with large South Asian communities residing in the Caribbean, Africa, Europe, and elsewhere. There is evidence that South Asian communities have disproportionately experienced COVID-19 infections and mortality. WhatsApp, a free messaging app, is widely used in transnational communication within the South Asian diaspora. Limited studies exist on COVID-19--related misinformation specific to the South Asian community on WhatsApp. Understanding communication on WhatsApp may improve public health messaging to address COVID-19 disparities among South Asian communities worldwide. Objective: We developed the COVID-19--Associated misinfoRmation On Messaging apps (CAROM) study to identify messages containing misinformation about COVID-19 shared via WhatsApp. Methods: We collected messages forwarded globally through WhatsApp from self-identified South Asian community members between March 23 and June 3, 2021. We excluded messages that were in languages other than English, did not contain misinformation, or were not relevant to COVID-19. We deidentified each message and coded them for one or more content categories, media types (eg, video, image, text, web link, or a combination of these elements), and tone (eg, fearful, well intentioned, or pleading). We then performed a qualitative content analysis to arrive at key themes of COVID-19 misinformation. Results: We received 108 messages; 55 messages met the inclusion criteria for the final analytic sample; 32 (58\%) contained text, 15 (27\%) contained images, and 13 (24\%) contained video. Content analysis revealed the following themes: ``community transmission'' relating to misinformation on how COVID-19 spreads in the community; ``prevention'' and ``treatment,'' including Ayurvedic and traditional remedies for how to prevent or treat COVID-19 infection; and messaging attempting to sell ``products or services'' to prevent or cure COVID-19. Messages varied in audience from the general public to South Asians specifically; the latter included messages alluding to South Asian pride and solidarity. Scientific jargon and references to major organizations and leaders in health care were included to provide credibility. Messages with a pleading tone encouraged users to forward them to friends or family. Conclusions: Misinformation in the South Asian community on WhatsApp spreads erroneous ideas regarding disease transmission, prevention, and treatment. Content evoking solidarity, ``trustworthy'' sources, and encouragement to forward messages may increase the spread of misinformation. Public health outlets and social media companies must actively combat misinformation to address health disparities among the South Asian diaspora during the COVID-19 pandemic and in future public health emergencies. ", doi="10.2196/38607", url="https://infodemiology.jmir.org/2023/1/e38607", url="http://www.ncbi.nlm.nih.gov/pubmed/37113380" } @Article{info:doi/10.2196/36729, author="Pollack, C. Catherine and Emond, A. Jennifer and O'Malley, James A. and Byrd, Anna and Green, Peter and Miller, E. Katherine and Vosoughi, Soroush and Gilbert-Diamond, Diane and Onega, Tracy", title="Characterizing the Prevalence of Obesity Misinformation, Factual Content, Stigma, and Positivity on the Social Media Platform Reddit Between 2011 and 2019: Infodemiology Study", journal="J Med Internet Res", year="2022", month="Dec", day="30", volume="24", number="12", pages="e36729", keywords="obesity", keywords="misinformation", keywords="social stigma", keywords="social media", keywords="Reddit", keywords="natural language processing", abstract="Background: Reddit is a popular social media platform that has faced scrutiny for inflammatory language against those with obesity, yet there has been no comprehensive analysis of its obesity-related content. Objective: We aimed to quantify the presence of 4 types of obesity-related content on Reddit (misinformation, facts, stigma, and positivity) and identify psycholinguistic features that may be enriched within each one. Methods: All sentences (N=764,179) containing ``obese'' or ``obesity'' from top-level comments (n=689,447) made on non--age-restricted subreddits (ie, smaller communities within Reddit) between 2011 and 2019 that contained one of a series of keywords were evaluated. Four types of common natural language processing features were extracted: bigram term frequency--inverse document frequency, word embeddings derived from Bidirectional Encoder Representations from Transformers, sentiment from the Valence Aware Dictionary for Sentiment Reasoning, and psycholinguistic features from the Linguistic Inquiry and Word Count Program. These features were used to train an Extreme Gradient Boosting machine learning classifier to label each sentence as 1 of the 4 content categories or other. Two-part hurdle models for semicontinuous data (which use logistic regression to assess the odds of a 0 result and linear regression for continuous data) were used to evaluate whether select psycholinguistic features presented differently in misinformation (compared with facts) or stigma (compared with positivity). Results: After removing ambiguous sentences, 0.47\% (3610/764,179) of the sentences were labeled as misinformation, 1.88\% (14,366/764,179) were labeled as stigma, 1.94\% (14,799/764,179) were labeled as positivity, and 8.93\% (68,276/764,179) were labeled as facts. Each category had markers that distinguished it from other categories within the data as well as an external corpus. For example, misinformation had a higher average percent of negations ($\beta$=3.71, 95\% CI 3.53-3.90; P<.001) but a lower average number of words >6 letters ($\beta$=?1.47, 95\% CI ?1.85 to ?1.10; P<.001) relative to facts. Stigma had a higher proportion of swear words ($\beta$=1.83, 95\% CI 1.62-2.04; P<.001) but a lower proportion of first-person singular pronouns ($\beta$=?5.30, 95\% CI ?5.44 to ?5.16; P<.001) relative to positivity. Conclusions: There are distinct psycholinguistic properties between types of obesity-related content on Reddit that can be leveraged to rapidly identify deleterious content with minimal human intervention and provide insights into how the Reddit population perceives patients with obesity. Future work should assess whether these properties are shared across languages and other social media platforms. ", doi="10.2196/36729", url="https://www.jmir.org/2022/12/e36729", url="http://www.ncbi.nlm.nih.gov/pubmed/36583929" } @Article{info:doi/10.2196/41517, author="Maghsoudi, Arash and Nowakowski, Sara and Agrawal, Ritwick and Sharafkhaneh, Amir and Kunik, E. Mark and Naik, D. Aanand and Xu, Hua and Razjouyan, Javad", title="Sentiment Analysis of Insomnia-Related Tweets via a Combination of Transformers Using Dempster-Shafer Theory: Pre-- and Peri--COVID-19 Pandemic Retrospective Study", journal="J Med Internet Res", year="2022", month="Dec", day="27", volume="24", number="12", pages="e41517", keywords="COVID-19", keywords="coronavirus", keywords="sleep", keywords="Twitter", keywords="natural language processing", keywords="sentiment analysis", keywords="transformers", keywords="Dempster-Shafer theory", keywords="sleeping", keywords="social media", keywords="pandemic", keywords="effect", keywords="viral infection", abstract="Background: The COVID-19 pandemic has imposed additional stress on population health that may result in a change of sleeping behavior. Objective: In this study, we hypothesized that using natural language processing to explore social media would help with assessing the mental health conditions of people experiencing insomnia after the outbreak of COVID-19. Methods: We designed a retrospective study that used public social media content from Twitter. We categorized insomnia-related tweets based on time, using the following two intervals: the prepandemic (January 1, 2019, to January 1, 2020) and peripandemic (January 1, 2020, to January 1, 2021) intervals. We performed a sentiment analysis by using pretrained transformers in conjunction with Dempster-Shafer theory (DST) to classify the polarity of emotions as positive, negative, and neutral. We validated the proposed pipeline on 300 annotated tweets. Additionally, we performed a temporal analysis to examine the effect of time on Twitter users' insomnia experiences, using logistic regression. Results: We extracted 305,321 tweets containing the word insomnia (prepandemic tweets: n=139,561; peripandemic tweets: n=165,760). The best combination of pretrained transformers (combined via DST) yielded 84\% accuracy. By using this pipeline, we found that the odds of posting negative tweets (odds ratio [OR] 1.39, 95\% CI 1.37-1.41; P<.001) were higher in the peripandemic interval compared to those in the prepandemic interval. The likelihood of posting negative tweets after midnight was 21\% higher than that before midnight (OR 1.21, 95\% CI 1.19-1.23; P<.001). In the prepandemic interval, while the odds of posting negative tweets were 2\% higher after midnight compared to those before midnight (OR 1.02, 95\% CI 1.00-1.07; P=.008), they were 43\% higher (OR 1.43, 95\% CI 1.40-1.46; P<.001) in the peripandemic interval. Conclusions: The proposed novel sentiment analysis pipeline, which combines pretrained transformers via DST, is capable of classifying the emotions and sentiments of insomnia-related tweets. Twitter users shared more negative tweets about insomnia in the peripandemic interval than in the prepandemic interval. Future studies using a natural language processing framework could assess tweets about other types of psychological distress, habit changes, weight gain resulting from inactivity, and the effect of viral infection on sleep. ", doi="10.2196/41517", url="https://www.jmir.org/2022/12/e41517", url="http://www.ncbi.nlm.nih.gov/pubmed/36417585" } @Article{info:doi/10.2196/40825, author="Turvy, Alex", title="State-Level COVID-19 Symptom Searches and Case Data: Quantitative Analysis of Political Affiliation as a Predictor for Lag Time Using Google Trends and Centers for Disease Control and Prevention Data", journal="JMIR Form Res", year="2022", month="Dec", day="23", volume="6", number="12", pages="e40825", keywords="COVID-19", keywords="search trends", keywords="prediction", keywords="case", keywords="political", keywords="symptom", keywords="pandemic", keywords="data", keywords="google", keywords="disease", keywords="prevention", keywords="model", abstract="Background: Across each state, the emergence of the COVID-19 pandemic in the United States was marked by policies and rhetoric that often corresponded to the political party in power. These diverging responses have sparked broad ongoing discussion about how the political leadership of a state may affect not only the COVID-19 case numbers in a given state but also the subjective individual experience of the pandemic. Objective: This study leverages state-level data from Google Search Trends and Centers for Disease Control and Prevention (CDC) daily case data to investigate the temporal relationship between increases in relative search volume for COVID-19 symptoms and corresponding increases in case data. I aimed to identify whether there are state-level differences in patterns of lag time across each of the 4 spikes in the data (RQ1) and whether the political climate in a given state is associated with these differences (RQ2). Methods: Using publicly available data from Google Trends and the CDC, linear mixed modeling was utilized to account for random state-level intercepts. Lag time was operationalized as number of days between a peak (a sustained increase before a sustained decline) in symptom search data and a corresponding spike in case data and was calculated manually for each of the 4 spikes in individual states. Google offers a data set that tracks the relative search incidence of more than 400 potential COVID-19 symptoms, which is normalized on a 0-100 scale. I used the CDC's definition of the 11 most common COVID-19 symptoms and created a single construct variable that operationalizes symptom searches. To measure political climate, I considered the proportion of 2020 Trump popular votes in a state as well as a dummy variable for the political party that controls the governorship and a continuous variable measuring proportional party control of federal Congressional representatives. Results: The strongest overall fit was for a linear mixed model that included proportion of 2020 Trump votes as the predictive variable of interest and included controls for mean daily cases and deaths as well as population. Additional political climate variables were discarded for lack of model fit. Findings indicated evidence that there are statistically significant differences in lag time by state but that no individual variable measuring political climate was a statistically significant predictor of these differences. Conclusions: Given that there will likely be future pandemics within this political climate, it is important to understand how political leadership affects perceptions of and corresponding responses to public health crises. Although this study did not fully model this relationship, I believe that future research can build on the state-level differences that I identified by approaching the analysis with a different theoretical model, method for calculating lag time, or level of geographic modeling. ", doi="10.2196/40825", url="https://formative.jmir.org/2022/12/e40825", url="http://www.ncbi.nlm.nih.gov/pubmed/36446048" } @Article{info:doi/10.2196/41928, author="Kobayashi, Ryota and Takedomi, Yuka and Nakayama, Yuri and Suda, Towa and Uno, Takeaki and Hashimoto, Takako and Toyoda, Masashi and Yoshinaga, Naoki and Kitsuregawa, Masaru and Rocha, C. Luis E.", title="Evolution of Public Opinion on COVID-19 Vaccination in Japan: Large-Scale Twitter Data Analysis", journal="J Med Internet Res", year="2022", month="Dec", day="22", volume="24", number="12", pages="e41928", keywords="COVID-19", keywords="vaccine", keywords="vaccination", keywords="Twitter", keywords="public opinion", keywords="topic modeling", keywords="longitudinal study", keywords="topic dynamics", keywords="social events", keywords="interrupted time series regression", abstract="Background: Vaccines are promising tools to control the spread of COVID-19. An effective vaccination campaign requires government policies and community engagement, sharing experiences for social support, and voicing concerns about vaccine safety and efficiency. The increasing use of online social platforms allows us to trace large-scale communication and infer public opinion in real time. Objective: This study aimed to identify the main themes in COVID-19 vaccine-related discussions on Twitter in Japan and track how the popularity of the tweeted themes evolved during the vaccination campaign. Furthermore, we aimed to understand the impact of critical social events on the popularity of the themes. Methods: We collected more than 100 million vaccine-related tweets written in Japanese and posted by 8 million users (approximately 6.4\% of the Japanese population) from January 1 to October 31, 2021. We used Latent Dirichlet Allocation to perform automated topic modeling of tweet text during the vaccination campaign. In addition, we performed an interrupted time series regression analysis to evaluate the impact of 4 critical social events on public opinion. Results: We identified 15 topics grouped into the following 4 themes: (1) personal issue, (2) breaking news, (3) politics, and (4) conspiracy and humor. The evolution of the popularity of themes revealed a shift in public opinion, with initial sharing of attention over personal issues (individual aspect), collecting information from news (knowledge acquisition), and government criticism to focusing on personal issues. Our analysis showed that the Tokyo Olympic Games affected public opinion more than other critical events but not the course of vaccination. Public opinion about politics was significantly affected by various social events, positively shifting attention in the early stages of the vaccination campaign and negatively shifting attention later. Conclusions: This study showed a striking shift in public interest in Japan, with users splitting their attention over various themes early in the vaccination campaign and then focusing only on personal issues, as trust in vaccines and policies increased. An interrupted time series regression analysis showed that the vaccination rollout to the general population (under 65 years) increased the popularity of tweets about practical advice and personal vaccination experience, and the Tokyo Olympic Games disrupted public opinion but not the course of the vaccination campaign. The methodology developed here allowed us to monitor the evolution of public opinion and evaluate the impact of social events on public opinion, using large-scale Twitter data. ", doi="10.2196/41928", url="https://www.jmir.org/2022/12/e41928", url="http://www.ncbi.nlm.nih.gov/pubmed/36343186" } @Article{info:doi/10.2196/42781, author="Ravkin, D. Hersh and Yom-Tov, Elad and Nesher, Lior", title="The Effect of Nonpharmaceutical Interventions Implemented in Response to the COVID-19 Pandemic on Seasonal Respiratory Syncytial Virus: Analysis of Google Trends Data", journal="J Med Internet Res", year="2022", month="Dec", day="21", volume="24", number="12", pages="e42781", keywords="RSV", keywords="respiratory syncytial virus", keywords="search engine", keywords="Google Trends", keywords="Google", keywords="respiratory", keywords="children", keywords="pharmaceutical", keywords="intervention", keywords="COVID-19", keywords="pandemic", keywords="virus", keywords="infection", keywords="health", abstract="Background: Respiratory syncytial virus (RSV) is a major cause of respiratory infection in children. Despite usually following a consistent seasonal pattern, the 2020-2021 RSV season in many countries was delayed and changed in magnitude. Objective: This study aimed to test if these changes can be attributed to nonpharmaceutical interventions (NPIs) instituted around the world to combat SARS-CoV-2. Methods: We used the internet search volume for RSV, as obtained from Google Trends, as a proxy to investigate these abnormalities. Results: Our analysis shows a breakdown of the usual correlation between peak latency and magnitude during the year of the pandemic. Analyzing latency and magnitude separately, we found that the changes therein are associated with implemented NPIs. Among several important interventions, NPIs affecting population mobility are shown to be particularly relevant to RSV incidence. Conclusions: The 2020-2021 RSV season served as a natural experiment to test NPIs that are likely to restrict RSV spread, and our findings can be used to guide health authorities to possible interventions. ", doi="10.2196/42781", url="https://www.jmir.org/2022/12/e42781", url="http://www.ncbi.nlm.nih.gov/pubmed/36476385" } @Article{info:doi/10.2196/40198, author="DePaula, Nic and Hagen, Loni and Roytman, Stiven and Alnahass, Dana", title="Platform Effects on Public Health Communication: A Comparative and National Study of Message Design and Audience Engagement Across Twitter and Facebook", journal="JMIR Infodemiology", year="2022", month="Dec", day="20", volume="2", number="2", pages="e40198", keywords="platform effects", keywords="COVID-19", keywords="social media", keywords="health communication", keywords="message design", keywords="risk communication", keywords="Twitter", keywords="Facebook", keywords="user engagement", keywords="e-government", abstract="Background: Public health agencies widely adopt social media for health and risk communication. Moreover, different platforms have different affordances, which may impact the quality and nature of the messaging and how the public engages with the content. However, these platform effects are not often compared in studies of health and risk communication and not previously for the COVID-19 pandemic. Objective: This study measures the potential media effects of Twitter and Facebook on public health message design and engagement by comparing message elements and audience engagement in COVID-19--related posts by local, state, and federal public health agencies in the United States during the pandemic, to advance theories of public health messaging on social media and provide recommendations for tailored social media communication strategies. Methods: We retrieved all COVID-19--related posts from major US federal agencies related to health and infectious disease, all major state public health agencies, and selected local public health departments on Twitter and Facebook. A total of 100,785 posts related to COVID-19, from 179 different accounts of 96 agencies, were retrieved for the entire year of 2020. We adopted a framework of social media message elements to analyze the posts across Facebook and Twitter. For manual content analysis, we subsampled 1677 posts. We calculated the prevalence of various message elements across the platforms and assessed the statistical significance of differences. We also calculated and assessed the association between message elements with normalized measures of shares and likes for both Facebook and Twitter. Results: Distributions of message elements were largely similar across both sites. However, political figures (P<.001), experts (P=.01), and nonpolitical personalities (P=.01) were significantly more present on Facebook posts compared to Twitter. Infographics (P<.001), surveillance information (P<.001), and certain multimedia elements (eg, hyperlinks, P<.001) were more prevalent on Twitter. In general, Facebook posts received more (normalized) likes (0.19\%) and (normalized) shares (0.22\%) compared to Twitter likes (0.08\%) and shares (0.05\%). Elements with greater engagement on Facebook included expressives and collectives, whereas posts related to policy were more engaged with on Twitter. Science information (eg, scientific explanations) comprised 8.5\% (73/851) of Facebook and 9.4\% (78/826) of Twitter posts. Correctives of misinformation only appeared in 1.2\% (11/851) of Facebook and 1.4\% (12/826) of Twitter posts. Conclusions: In general, we find a data and policy orientation for Twitter messages and users and a local and personal orientation for Facebook, although also many similarities across platforms. Message elements that impact engagement are similar across platforms but with some notable distinctions. This study provides novel evidence for differences in COVID-19 public health messaging across social media sites, advancing knowledge of public health communication on social media and recommendations for health and risk communication strategies on these online platforms. ", doi="10.2196/40198", url="https://infodemiology.jmir.org/2022/2/e40198", url="http://www.ncbi.nlm.nih.gov/pubmed/36575712" } @Article{info:doi/10.2196/37582, author="Cai, Ruilie and Zhang, Jiajia and Li, Zhenlong and Zeng, Chengbo and Qiao, Shan and Li, Xiaoming", title="Using Twitter Data to Estimate the Prevalence of Symptoms of Mental Disorders in the United States During the COVID-19 Pandemic: Ecological Cohort Study", journal="JMIR Form Res", year="2022", month="Dec", day="20", volume="6", number="12", pages="e37582", keywords="mental health", keywords="anxiety disorder", keywords="depressive disorder", keywords="COVID-19", keywords="national survey", keywords="social media", keywords="Twitter", keywords="mixed model", keywords="anxiety", keywords="National Household Pulse survey", keywords="geospatial", abstract="Background: Existing research and national surveillance data suggest an increase of the prevalence of mental disorders during the COVID-19 pandemic. Social media platforms, such as Twitter, could be a source of data for estimation owing to its real-time nature, high availability, and large geographical coverage. However, there is a dearth of studies validating the accuracy of the prevalence of mental disorders on Twitter compared to that reported by the Centers for Disease Control and Prevention (CDC). Objective: This study aims to verify the feasibility of Twitter-based prevalence of mental disorders symptoms being an instrument for prevalence estimation, where feasibility is gauged via correlations between Twitter-based prevalence of mental disorder symptoms (ie, anxiety and depressive symptoms) and that based on national surveillance data. In addition, this study aims to identify how the correlations changed over time (ie, the temporal trend). Methods: State-level prevalence of anxiety and depressive symptoms was retrieved from the national Household Pulse Survey (HPS) of the CDC from April 2020 to July 2021. Tweets were retrieved from the Twitter streaming application programming interface during the same period and were used to estimate the prevalence of symptoms of mental disorders for each state using keyword analysis. Stratified linear mixed models were used to evaluate the correlations between the Twitter-based prevalence of symptoms of mental disorders and those reported by the CDC. The magnitude and significance of model parameters were considered to evaluate the correlations. Temporal trends of correlations were tested after adding the time variable to the model. Geospatial differences were compared on the basis of random effects. Results: Pearson correlation coefficients between the overall prevalence reported by the CDC and that on Twitter for anxiety and depressive symptoms were 0.587 (P<.001) and 0.368 (P<.001), respectively. Stratified by 4 phases (ie, April 2020, August 2020, October 2020, and April 2021) defined by the HPS, linear mixed models showed that Twitter-based prevalence for anxiety symptoms had a positive and significant correlation with CDC-reported prevalence in phases 2 and 3, while a significant correlation for depressive symptoms was identified in phases 1 and 3. Conclusions: Positive correlations were identified between Twitter-based and CDC-reported prevalence, and temporal trends of these correlations were found. Geospatial differences in the prevalence of symptoms of mental disorders were found between the northern and southern United States. Findings from this study could inform future investigation on leveraging social media platforms to estimate symptoms of mental disorders and the provision of immediate prevention measures to improve health outcomes. ", doi="10.2196/37582", url="https://formative.jmir.org/2022/12/e37582", url="http://www.ncbi.nlm.nih.gov/pubmed/36459569" } @Article{info:doi/10.2196/23422, author="Lin, Chen and Yousefi, Safoora and Kahoro, Elvis and Karisani, Payam and Liang, Donghai and Sarnat, Jeremy and Agichtein, Eugene", title="Detecting Elevated Air Pollution Levels by Monitoring Web Search Queries: Algorithm Development and Validation", journal="JMIR Form Res", year="2022", month="Dec", day="19", volume="6", number="12", pages="e23422", keywords="nowcasting of air pollution", keywords="web-based public health surveillance", keywords="neural network sequence modeling", keywords="search engine log analysis", keywords="air pollution exposure assessment", keywords="mobile phone", abstract="Background: Real-time air pollution monitoring is a valuable tool for public health and environmental surveillance. In recent years, there has been a dramatic increase in air pollution forecasting and monitoring research using artificial neural networks. Most prior work relied on modeling pollutant concentrations collected from ground-based monitors and meteorological data for long-term forecasting of outdoor ozone (O3), oxides of nitrogen, and fine particulate matter (PM2.5). Given that traditional, highly sophisticated air quality monitors are expensive and not universally available, these models cannot adequately serve those not living near pollutant monitoring sites. Furthermore, because prior models were built based on physical measurement data collected from sensors, they may not be suitable for predicting the public health effects of pollution exposure. Objective: This study aimed to develop and validate models to nowcast the observed pollution levels using web search data, which are publicly available in near real time from major search engines. Methods: We developed novel machine learning--based models using both traditional supervised classification methods and state-of-the-art deep learning methods to detect elevated air pollution levels at the US city level by using generally available meteorological data and aggregate web-based search volume data derived from Google Trends. We validated the performance of these methods by predicting 3 critical air pollutants (O3, nitrogen dioxide, and PM2.5) across 10 major US metropolitan statistical areas in 2017 and 2018. We also explore different variations of the long short-term memory model and propose a novel search term dictionary learner-long short-term memory model to learn sequential patterns across multiple search terms for prediction. Results: The top-performing model was a deep neural sequence model long short-term memory, using meteorological and web search data, and reached an accuracy of 0.82 (F1-score 0.51) for O3, 0.74 (F1-score 0.41) for nitrogen dioxide, and 0.85 (F1-score 0.27) for PM2.5, when used for detecting elevated pollution levels. Compared with using only meteorological data, the proposed method achieved superior accuracy by incorporating web search data. Conclusions: The results show that incorporating web search data with meteorological data improves the nowcasting performance for all 3 pollutants and suggest promising novel applications for tracking global physical phenomena using web search data. ", doi="10.2196/23422", url="https://formative.jmir.org/2022/12/e23422", url="http://www.ncbi.nlm.nih.gov/pubmed/36534457" } @Article{info:doi/10.2196/36755, author="Dirkson, Anne and den Hollander, Dide and Verberne, Suzan and Desar, Ingrid and Husson, Olga and van der Graaf, A. Winette T. and Oosten, Astrid and Reyners, L. Anna K. and Steeghs, Neeltje and van Loon, Wouter and van Oortmerssen, Gerard and Gelderblom, Hans and Kraaij, Wessel", title="Sample Bias in Web-Based Patient-Generated Health Data of Dutch Patients With Gastrointestinal Stromal Tumor: Survey Study", journal="JMIR Form Res", year="2022", month="Dec", day="15", volume="6", number="12", pages="e36755", keywords="social media", keywords="patient forum", keywords="sample bias", keywords="representativeness", keywords="pharmacovigilance", keywords="rare cancer", abstract="Background: Increasingly, social media is being recognized as a potential resource for patient-generated health data, for example, for pharmacovigilance. Although the representativeness of the web-based patient population is often noted as a concern, studies in this field are limited. Objective: This study aimed to investigate the sample bias of patient-centered social media in Dutch patients with gastrointestinal stromal tumor (GIST). Methods: A population-based survey was conducted in the Netherlands among 328 patients with GIST diagnosed 2-13 years ago to investigate their digital communication use with fellow patients. A logistic regression analysis was used to analyze clinical and demographic differences between forum users and nonusers. Results: Overall, 17.9\% (59/328) of survey respondents reported having contact with fellow patients via social media. Moreover, 78\% (46/59) of forum users made use of GIST patient forums. We found no statistically significant differences for age, sex, socioeconomic status, and time since diagnosis between forum users (n=46) and nonusers (n=273). Patient forum users did differ significantly in (self-reported) treatment phase from nonusers (P=.001). Of the 46 forum users, only 2 (4\%) were cured and not being monitored; 3 (7\%) were on adjuvant, curative treatment; 19 (41\%) were being monitored after adjuvant treatment; and 22 (48\%) were on palliative treatment. In contrast, of the 273 patients who did not use disease-specific forums to communicate with fellow patients, 56 (20.5\%) were cured and not being monitored, 31 (11.3\%) were on curative treatment, 139 (50.9\%) were being monitored after treatment, and 42 (15.3\%) were on palliative treatment. The odds of being on a patient forum were 2.8 times as high for a patient who is being monitored compared with a patient that is considered cured. The odds of being on a patient forum were 1.9 times as high for patients who were on curative (adjuvant) treatment and 10 times as high for patients who were in the palliative phase compared with patients who were considered cured. Forum users also reported a lower level of social functioning (84.8 out of 100) than nonusers (93.8 out of 100; P=.008). Conclusions: Forum users showed no particular bias on the most important demographic variables of age, sex, socioeconomic status, and time since diagnosis. This may reflect the narrowing digital divide. Overrepresentation and underrepresentation of patients with GIST in different treatment phases on social media should be taken into account when sourcing patient forums for patient-generated health data. A further investigation of the sample bias in other web-based patient populations is warranted. ", doi="10.2196/36755", url="https://formative.jmir.org/2022/12/e36755", url="http://www.ncbi.nlm.nih.gov/pubmed/36520526" } @Article{info:doi/10.2196/42179, author="de Vere Hunt, Isabella and Linos, Eleni", title="Social Media for Public Health: Framework for Social Media--Based Public Health Campaigns", journal="J Med Internet Res", year="2022", month="Dec", day="14", volume="24", number="12", pages="e42179", keywords="social media", keywords="digital heath", keywords="health communication", keywords="campaign", keywords="public health", keywords="framework", keywords="health promotion", keywords="public awareness", keywords="misinformation", keywords="tailored message", keywords="tailored messaging", keywords="information sharing", keywords="information exchange", keywords="advertise", keywords="advertising", doi="10.2196/42179", url="https://www.jmir.org/2022/12/e42179", url="http://www.ncbi.nlm.nih.gov/pubmed/36515995" } @Article{info:doi/10.2196/42619, author="Taira, Kazuya and Itaya, Takahiro and Fujita, Sumio", title="Predicting Smoking Prevalence in Japan Using Search Volumes in an Internet Search Engine: Infodemiology Study", journal="J Med Internet Res", year="2022", month="Dec", day="14", volume="24", number="12", pages="e42619", keywords="health policy", keywords="internet use", keywords="quality indicators", keywords="search engine", keywords="smoking", keywords="tobacco use", keywords="public health", keywords="infodemiology", keywords="smoking trend", keywords="health indicator", keywords="health promotion", abstract="Background: Tobacco smoking is an important public health issue and a core indicator of public health policy worldwide. However, global pandemics and natural disasters have prevented surveys from being conducted. Objective: The purpose of this study was to predict smoking prevalence by prefecture and sex in Japan using Internet search trends. Methods: This study used the infodemiology approach. The outcome variable was smoking prevalence by prefecture, obtained from national surveys. The predictor variables were the search volumes on Yahoo! Japan Search. We collected the search volumes for queries related to terms from the thesaurus of the Japanese medical article database Ichu-shi. Predictor variables were converted to per capita values and standardized as z scores. For smoking prevalence, the values for 2016 and 2019 were used, and for search volume, the values for the April 1 to March 31 fiscal year (FY) 1 year prior to the survey (ie, FY 2015 and FY 2018) were used. Partial correlation coefficients, adjusted for data year, were calculated between smoking prevalence and search volume, and a regression analysis using a generalized linear mixed model with random effects was conducted for each prefecture. Several models were tested, including a model that included all search queries, a variable reduction method, and one that excluded cigarette product names. The best model was selected with the Akaike information criterion corrected (AICC) for small sample size and the Bayesian information criterion (BIC). We compared the predicted and actual smoking prevalence in 2016 and 2019 based on the best model and predicted the smoking prevalence in 2022. Results: The partial correlation coefficients for men showed that 9 search queries had significant correlations with smoking prevalence, including cigarette (r=--0.417, P<.001), cigar in kanji (r=--0.412, P<.001), and cigar in katakana (r=-0.399, P<.001). For women, five search queries had significant correlations, including vape (r=0.335, P=.001), quitting smoking (r=0.288, P=.005), and cigar (r=0.286, P=.006). The models with all search queries were the best models for both AICC and BIC scores. Scatter plots of actual and estimated smoking prevalence in 2016 and 2019 confirmed a relatively high degree of agreement. The average estimated smoking prevalence in 2022 in the 47 prefectures for the total sample was 23.492\% (95\% CI 21.617\%-25.367\%), showing an increasing trend, with an average of 29.024\% (95\% CI 27.218\%-30.830\%) for men and 8.793\% (95\% CI 7.531\%-10.054\%) for women. Conclusions: This study suggests that the search volume of tobacco-related queries in internet search engines can predict smoking prevalence by prefecture and sex in Japan. These findings will enable the development of low-cost, timely, and crisis-resistant health indicators that will enable the evaluation of health measures and contribute to improved public health. ", doi="10.2196/42619", url="https://www.jmir.org/2022/12/e42619", url="http://www.ncbi.nlm.nih.gov/pubmed/36515993" } @Article{info:doi/10.2196/39460, author="Wu, Dezhi and Kasson, Erin and Singh, Kumar Avineet and Ren, Yang and Kaiser, Nina and Huang, Ming and Cavazos-Rehg, A. Patricia", title="Topics and Sentiment Surrounding Vaping on Twitter and Reddit During the 2019 e-Cigarette and Vaping Use--Associated Lung Injury Outbreak: Comparative Study", journal="J Med Internet Res", year="2022", month="Dec", day="13", volume="24", number="12", pages="e39460", keywords="vaping", keywords="e-cigarette", keywords="social media", keywords="Twitter", keywords="Reddit", keywords="e-cigarette and vaping use--associated lung injury", keywords="EVALI", keywords="sentiment analysis", keywords="topic analysis", abstract="Background: Vaping or e-cigarette use has become dramatically more popular in the United States in recent years. e-Cigarette and vaping use--associated lung injury (EVALI) cases caused an increase in hospitalizations and deaths in 2019, and many instances were later linked to unregulated products. Previous literature has leveraged social media data for surveillance of health topics. Individuals are willing to share mental health experiences and other personal stories on social media platforms where they feel a sense of community, reduced stigma, and empowerment. Objective: This study aimed to compare vaping-related content on 2 popular social media platforms (ie, Twitter and Reddit) to explore the context surrounding vaping during the 2019 EVALI outbreak and to support the feasibility of using data from both social platforms to develop in-depth and intelligent vaping detection models on social media. Methods: Data were extracted from both Twitter (316,620 tweets) and Reddit (17,320 posts) from July 2019 to September 2019 at the peak of the EVALI crisis. High-throughput computational analyses (sentiment analysis and topic analysis) were conducted. In addition, in-depth manual content analyses were performed and compared with computational analyses of content on both platforms (577 tweets and 613 posts). Results: Vaping-related posts and unique users on Twitter and Reddit increased from July 2019 to September 2019, with the average post per user increasing from 1.68 to 1.81 on Twitter and 1.19 to 1.21 on Reddit. Computational analyses found the number of positive sentiment posts to be higher on Reddit (P<.001, 95\% CI 0.4305-0.4475) and the number of negative posts to be higher on Twitter (P<.001, 95\% CI --0.4289 to ?0.4111). These results were consistent with the clinical content analyses results indicating that negative sentiment posts were higher on Twitter (273/577, 47.3\%) than Reddit (184/613, 30\%). Furthermore, topics prevalent on both platforms by keywords and based on manual post reviews included mentions of youth, marketing or regulation, marijuana, and interest in quitting. Conclusions: Post content and trending topics overlapped on both Twitter and Reddit during the EVALI period in 2019. However, crucial differences in user type and content keywords were also found, including more frequent mentions of health-related keywords on Twitter and more negative health outcomes from vaping mentioned on both Reddit and Twitter. Use of both computational and clinical content analyses is critical to not only identify signals of public health trends among vaping-related social media content but also to provide context for vaping risks and behaviors. By leveraging the strengths of both Twitter and Reddit as publicly available data sources, this research may provide technical and clinical insights to inform automatic detection of social media users who are vaping and may benefit from digital intervention and proactive outreach strategies on these platforms. ", doi="10.2196/39460", url="https://www.jmir.org/2022/12/e39460", url="http://www.ncbi.nlm.nih.gov/pubmed/36512403" } @Article{info:doi/10.2196/39340, author="Li, Chuqin and Jordan, Alexis and Song, Jun and Ge, Yaorong and Park, Albert", title="A Novel Approach to Characterize State-level Food Environment and Predict Obesity Rate Using Social Media Data: Correlational Study", journal="J Med Internet Res", year="2022", month="Dec", day="13", volume="24", number="12", pages="e39340", keywords="obesity", keywords="social media", keywords="machine learning", keywords="lifestyle", keywords="environment", keywords="food", keywords="correlation", keywords="modeling", keywords="predict", keywords="rates", keywords="outcome", keywords="category", keywords="dishes", keywords="popular", keywords="mobile phone", abstract="Background: Community obesity outcomes can reflect the food environment to which the community belongs. Recent studies have suggested that the local food environment can be measured by the degree of food accessibility, and survey data are normally used to calculate food accessibility. However, compared with survey data, social media data are organic, continuously updated, and cheaper to collect. Objective: The objective of our study was to use publicly available social media data to learn the relationship between food environment and obesity rates at the state level. Methods: To characterize the caloric information of the local food environment, we used food categories from Yelp and collected caloric information from MyFitnessPal for each category based on their popular dishes. We then calculated the average calories for each category and created a weighted score for each state. We also calculated 2 other dimensions from the concept of access, acceptability and affordability, to build obesity prediction models. Results: The local food environment characterized using only publicly available social media data had a statistically significant correlation with the state obesity rate. We achieved a Pearson correlation of 0.796 between the predicted obesity rate and the reported obesity rate from the Behavioral Risk Factor Surveillance System across US states and the District of Columbia. The model with 3 generated feature sets achieved the best performance. Conclusions: Our study proposed a method for characterizing state-level food environments only using continuously updated social media data. State-level food environments were accurately described using social media data, and the model also showed a disparity in the available food between states with different obesity rates. The proposed method should elastically apply to local food environments of different sizes and predict obesity rates effectively. ", doi="10.2196/39340", url="https://www.jmir.org/2022/12/e39340", url="http://www.ncbi.nlm.nih.gov/pubmed/36512396" } @Article{info:doi/10.2196/36197, author="Orumaa, Madleen and Campbell, Suzanne and St{\o}er, C. Nathalie and Castle, E. Philip and Sen, Sagar and Trop{\'e}, Ameli and Adedimeji, Adebola and Nyg{\aa}rd, Mari", title="Impact of the Mobile Game FightHPV on Cervical Cancer Screening Attendance: Retrospective Cohort Study", journal="JMIR Serious Games", year="2022", month="Dec", day="13", volume="10", number="4", pages="e36197", keywords="mobile app", keywords="gamification", keywords="empowering", keywords="health literacy", keywords="cervical cancer screening", keywords="cancer prevention", abstract="Background: The wide availability of mobile phones has made it easy to disseminate health-related information and make it accessible. With gamification, mobile apps can nudge people to make informed health choices, including attending cervical cancer screening. Objective: This matched retrospective cohort study examined the association between exposure to the FightHPV mobile app gamified educational content and having a cervical exam in the following year. Methods: Women aged 20 to 69 years who signed an electronic consent form after downloading the FightHPV app in 2017 (intervention group) were matched 1:6 with women of the same age and with the same screening history (reference group) in 2015. To estimate the impact of exposure to the FightHPV app, we estimated cumulative incidence and hazard ratios (HRs) with 95\% CIs. We used data from the Norwegian Cervical Cancer Screening Program database and Statistics Norway to determine screening participation and outcomes, respectively. Results: We matched 3860 women in the control group to 658 women in the intervention group; 6 months after enrollment, 29.6\% (195/658) of the women in the intervention group and 15.21\% (587/3860) of those in the reference group underwent a cervical exam (P<.01). Women exposed to the FightHPV app were 2 times more likely to attend screening (adjusted HR 2.3, 95\% CI 2.0-2.7), during which they were 13 times more likely to be diagnosed with high-grade abnormality (adjusted HR 12.7, 95\% CI 5.0-32.5) than the women in the reference group. Conclusions: Exposure to the FightHPV app significantly increased cervical cancer screening attendance across the various analyses and improved detection of women with high risk for cervical cancer. For the first time, we demonstrated the effectiveness of gamification combined with mobile technology in cancer prevention by empowering women to make active health-related decisions. Gamification can significantly improve the understanding of complicated scientific concepts behind interventions and increase the acceptance of proposed cancer control measures. ", doi="10.2196/36197", url="https://games.jmir.org/2022/4/e36197", url="http://www.ncbi.nlm.nih.gov/pubmed/36512401" } @Article{info:doi/10.2196/41198, author="Xu, Wayne Weiai and Tshimula, Marie Jean and Dub{\'e}, {\`E}ve and Graham, E. Janice and Greyson, Devon and MacDonald, E. Noni and Meyer, B. Samantha", title="Unmasking the Twitter Discourses on Masks During the COVID-19 Pandemic: User Cluster--Based BERT Topic Modeling Approach", journal="JMIR Infodemiology", year="2022", month="Dec", day="9", volume="2", number="2", pages="e41198", keywords="infoveillance", keywords="data analytics", keywords="Twitter", keywords="social media", keywords="user classification", keywords="COVID-19", abstract="Background: The COVID-19 pandemic has spotlighted the politicization of public health issues. A public health monitoring tool must be equipped to reveal a public health measure's political context and guide better interventions. In its current form, infoveillance tends to neglect identity and interest-based users, hence being limited in exposing how public health discourse varies by different political groups. Adopting an algorithmic tool to classify users and their short social media texts might remedy that limitation. Objective: We aimed to implement a new computational framework to investigate discourses and temporal changes in topics unique to different user clusters. The framework was developed to contextualize how web-based public health discourse varies by identity and interest-based user clusters. We used masks and mask wearing during the early stage of the COVID-19 pandemic in the English-speaking world as a case study to illustrate the application of the framework. Methods: We first clustered Twitter users based on their identities and interests as expressed through Twitter bio pages. Exploratory text network analysis reveals salient political, social, and professional identities of various user clusters. It then uses BERT Topic modeling to identify topics by the user clusters. It reveals how web-based discourse has shifted over time and varied by 4 user clusters: conservative, progressive, general public, and public health professionals. Results: This study demonstrated the importance of a priori user classification and longitudinal topical trends in understanding the political context of web-based public health discourse. The framework reveals that the political groups and the general public focused on the science of mask wearing and the partisan politics of mask policies. A populist discourse that pits citizens against elites and institutions was identified in some tweets. Politicians (such as Donald Trump) and geopolitical tensions with China were found to drive the discourse. It also shows limited participation of public health professionals compared with other users. Conclusions: We conclude by discussing the importance of a priori user classification in analyzing web-based discourse and illustrating the fit of BERT Topic modeling in identifying contextualized topics in short social media texts. ", doi="10.2196/41198", url="https://infodemiology.jmir.org/2022/2/e41198", url="http://www.ncbi.nlm.nih.gov/pubmed/36536763" } @Article{info:doi/10.2196/37331, author="Luo, Kai and Yang, Yang and Teo, Hai Hock", title="The Asymmetric Influence of Emotion in the Sharing of COVID-19 Science on Social Media: Observational Study", journal="JMIR Infodemiology", year="2022", month="Dec", day="8", volume="2", number="2", pages="e37331", keywords="COVID-19", keywords="science communication", keywords="emotion", keywords="COVID-19 science", keywords="online social networks", keywords="computational social science", keywords="social media", abstract="Background: Unlike past pandemics, COVID-19 is different to the extent that there is an unprecedented surge in both peer-reviewed and preprint research publications, and important scientific conversations about it are rampant on online social networks, even among laypeople. Clearly, this new phenomenon of scientific discourse is not well understood in that we do not know the diffusion patterns of peer-reviewed publications vis-{\`a}-vis preprints and what makes them viral. Objective: This paper aimed to examine how the emotionality of messages about preprint and peer-reviewed publications shapes their diffusion through online social networks in order to inform health science communicators' and policy makers' decisions on how to promote reliable sharing of crucial pandemic science on social media. Methods: We collected a large sample of Twitter discussions of early (January to May 2020) COVID-19 medical research outputs, which were tracked by Altmetric, in both preprint servers and peer-reviewed journals, and conducted statistical analyses to examine emotional valence, specific emotions, and the role of scientists as content creators in influencing the retweet rate. Results: Our large-scale analyses (n=243,567) revealed that scientific publication tweets with positive emotions were transmitted faster than those with negative emotions, especially for messages about preprints. Our results also showed that scientists' participation in social media as content creators could accentuate the positive emotion effects on the sharing of peer-reviewed publications. Conclusions: Clear communication of critical science is crucial in the nascent stage of a pandemic. By revealing the emotional dynamics in the social media sharing of COVID-19 scientific outputs, our study offers scientists and policy makers an avenue to shape the discussion and diffusion of emerging scientific publications through manipulation of the emotionality of tweets. Scientists could use emotional language to promote the diffusion of more reliable peer-reviewed articles, while avoiding using too much positive emotional language in social media messages about preprints if they think that it is too early to widely communicate the preprint (not peer reviewed) data to the public. ", doi="10.2196/37331", url="https://infodemiology.jmir.org/2022/2/e37331", url="http://www.ncbi.nlm.nih.gov/pubmed/36536762" } @Article{info:doi/10.2196/42245, author="Basch, H. Corey and Hillyer, C. Grace and Yalamanchili, Bhavya and Morris, Aldean", title="How TikTok Is Being Used to Help Individuals Cope With Breast Cancer: Cross-sectional Content Analysis", journal="JMIR Cancer", year="2022", month="Dec", day="6", volume="8", number="4", pages="e42245", keywords="TikTok", keywords="breast cancer", keywords="social media", keywords="short video apps", keywords="social support", keywords="content analysis", keywords="video", keywords="patient support", keywords="medical information", keywords="health information", keywords="peer support", keywords="online conversation", keywords="online health information", abstract="Background: Acknowledging the popularity of TikTok, how quickly medical information can spread, and how users seek support on social media, there is a clear lack of research on breast cancer conversations on TikTok. There is a paucity of information on how these videos can advocate for those impacted by breast cancer as a means to provide support and information as well as raise awareness. Objective: The purpose of this cross-sectional content analysis was to describe the content of videos from the hashtag \#breastcancer on TikTok. Content related to breast cancer support and coping, cancer education, and heightening the awareness of breast cancer early detection, prevention, and treatment was evaluated. Methods: This study included 100 of the most viewed TikTok videos related to breast cancer through June 30, 2022. Videos were excluded if they were not in the English language or relevant to the topic being studied. Content was deductively coded into categories related to video characteristics and content topics using a screener based on expert breast cancer information sheets. Univariable analyses were conducted to evaluate differences in video characteristics and content when stratified as advocating or not advocating for breast cancer (yes or no) support, education, and awareness. Results: The cumulative number of views of the videos included in this study was 369,504,590. The majority (n=81, 81\%) of videos were created by patients and loved ones of individuals with breast cancer, and the most commonly discussed topic was breast cancer support (n=88, 88\%), followed by coping with the myriad issues surrounding breast cancer (n=79, 79\%). Overall, <50\% of the videos addressed important issues such as body image (n=48, 48\%), surgery (n=46, 46\%), medication and therapy (n=41, 41\%), or the stigma associated with a breast cancer diagnosis (n=44, 44\%); however, in videos that were advocacy oriented, body image (40/62, 64\% vs 8/38, 21\%; P<.001), stigma associated with breast cancer (33/62, 53\% vs 11/38, 29\%; P=.02), and breast cancer surgery (36/62, 58\% vs 10/38, 26\%; P=.002) were discussed significantly more often than in videos that did not specifically advocate for breast cancer. Conclusions: The use of videos to display health journeys can facilitate engagement by patients, family members, and loved ones interested in information about challenging conditions. Collectively, these findings highlight the level of peer-to-peer involvement on TikTok and may provide insights for designing breast cancer educational campaigns. ", doi="10.2196/42245", url="https://cancer.jmir.org/2022/4/e42245", url="http://www.ncbi.nlm.nih.gov/pubmed/36472899" } @Article{info:doi/10.2196/37924, author="Germone, Monique and Wright, D. Casey and Kimmons, Royce and Coburn, Skelley Shayna", title="Twitter Trends for Celiac Disease and the Gluten-Free Diet: Cross-sectional Descriptive Analysis", journal="JMIR Infodemiology", year="2022", month="Dec", day="5", volume="2", number="2", pages="e37924", keywords="celiac disease", keywords="social media", keywords="Twitter", keywords="gluten-free", keywords="social networking site", keywords="diet", keywords="infodemiology", keywords="education", keywords="online", keywords="content", keywords="accuracy", keywords="credibility", abstract="Background: Few studies have systematically analyzed information regarding chronic medical conditions and available treatments on social media. Celiac disease (CD) is an exemplar of the need to investigate web-based educational sources. CD is an autoimmune condition wherein the ingestion of gluten causes intestinal damage and, if left untreated by a strict gluten-free diet (GFD), can result in significant nutritional deficiencies leading to cancer, bone disease, and death. Adherence to the GFD can be difficult owing to cost and negative stigma, including misinformation about what gluten is and who should avoid it. Given the significant impact that negative stigma and common misunderstandings have on the treatment of CD, this condition was chosen to systematically investigate the scope and nature of sources and information distributed through social media. Objective: To address concerns related to educational social media sources, this study explored trends on the social media platform Twitter about CD and the GFD to identify primary influencers and the type of information disseminated by these influencers. Methods: This cross-sectional study used data mining to collect tweets and users who used the hashtags \#celiac and \#glutenfree from an 8-month time frame. Tweets were then analyzed to describe who is disseminating information via this platform and the content, source, and frequency of such information. Results: More content was posted for \#glutenfree (1501.8 tweets per day) than for \#celiac (69 tweets per day). A substantial proportion of the content was produced by a small percentage of contributors (ie, ``Superuser''), who could be categorized as self-promotors (eg, bloggers, writers, authors; 13.9\% of \#glutenfree tweets and 22.7\% of \#celiac tweets), self-identified female family members (eg, mother; 4.3\% of \#glutenfree tweets and 8\% of \#celiac tweets), or commercial entities (eg, restaurants and bakeries). On the other hand, relatively few self-identified scientific, nonprofit, and medical provider users made substantial contributions on Twitter related to the GFD or CD (1\% of \#glutenfree tweets and 3.1\% of \#celiac tweets, respectively). Conclusions: Most material on Twitter was provided by self-promoters, commercial entities, or self-identified female family members, which may not have been supported by current medical and scientific practices. Researchers and medical providers could potentially benefit from contributing more to this space to enhance the web-based resources for patients and families. ", doi="10.2196/37924", url="https://infodemiology.jmir.org/2022/2/e37924", url="http://www.ncbi.nlm.nih.gov/pubmed/37113453" } @Article{info:doi/10.2196/42241, author="Tang, Qihang and Zhou, Runtao and Xie, Zidian and Li, Dongmei", title="Monitoring and Identifying Emerging e-Cigarette Brands and Flavors on Twitter: Observational Study", journal="JMIR Form Res", year="2022", month="Dec", day="5", volume="6", number="12", pages="e42241", keywords="e-cigarettes", keywords="brand", keywords="flavor", keywords="Twitter", abstract="Background: Flavored electronic cigarettes (e-cigarettes) have become very popular in recent years. e-Cigarette users like to share their e-cigarette products and e-cigarette use (vaping) experiences on social media. e-Cigarette marketing and promotions are also prevalent online. Objective: This study aims to develop a method to identify new e-cigarette brands and flavors mentioned on Twitter and to monitor e-cigarette brands and flavors mentioned on Twitter from May 2021 to December 2021. Methods: We collected 1.9 million tweets related to e-cigarettes between May 3, 2021, and December 31, 2021, by using the Twitter streaming application programming interface. Commercial and noncommercial tweets were characterized based on promotion-related keywords. We developed a depletion method to identify new e-cigarette brands by removing the keywords that already existed in the reference data set (Twitter data related to e-cigarettes from May 3, 2021, to August 31, 2021) or our previously identified brand list from the keywords in the target data set (e-cigarette--related Twitter data from September 1, 2021, to December 31, 2021), followed by a manual Google search to identify new e-cigarette brands. To identify new e-cigarette flavors, we constructed a flavor keyword list based on our previously collected e-cigarette flavor names, which were used to identify potential tweet segments that contain at least one of the e-cigarette flavor keywords. Tweets or tweet segments with flavor keywords but not any known flavor names were marked as potential new flavor candidates, which were further verified by a web-based search. The longitudinal trends in the number of tweets mentioning e-cigarette brands and flavors were examined in both commercial and noncommercial tweets. Results: Through our developed methods, we identified 34 new e-cigarette brands and 97 new e-cigarette flavors from commercial tweets as well as 56 new e-cigarette brands and 164 new e-cigarette flavors from noncommercial tweets. The longitudinal trend of the e-cigarette brands showed that JUUL was the most popular e-cigarette brand mentioned on Twitter; however, there was a decreasing trend in the mention of JUUL over time on Twitter. Menthol flavor was the most popular e-cigarette flavor mentioned in the commercial tweets, whereas mango flavor was the most popular e-cigarette flavor mentioned in the noncommercial tweets during our study period. Conclusions: Our proposed methods can successfully identify new e-cigarette brands and flavors mentioned on Twitter. Twitter data can be used for monitoring the dynamic changes in the popularity of e-cigarette brands and flavors. ", doi="10.2196/42241", url="https://formative.jmir.org/2022/12/e42241", url="http://www.ncbi.nlm.nih.gov/pubmed/36469415" } @Article{info:doi/10.2196/41527, author="Patton, Thomas and Abramovitz, Daniela and Johnson, Derek and Leas, Eric and Nobles, Alicia and Caputi, Theodore and Ayers, John and Strathdee, Steffanie and B{\'o}rquez, Annick", title="Characterizing Help-Seeking Searches for Substance Use Treatment From Google Trends and Assessing Their Use for Infoveillance: Longitudinal Descriptive and Validation Statistical Analysis", journal="J Med Internet Res", year="2022", month="Dec", day="1", volume="24", number="12", pages="e41527", keywords="internet", keywords="search", keywords="help-seeking", keywords="substance use treatment", keywords="surveillance", keywords="infoveillance", keywords="google trends", abstract="Background: There is no recognized gold standard method for estimating the number of individuals with substance use disorders (SUDs) seeking help within a given geographical area. This presents a challenge to policy makers in the effective deployment of resources for the treatment of SUDs. Internet search queries related to help seeking for SUDs using Google Trends may represent a low-cost, real-time, and data-driven infoveillance tool to address this shortfall in information. Objective: This paper assesses the feasibility of using search query data related to help seeking for SUDs as an indicator of unmet treatment needs, demand for treatment, and predictor of the health harms related to unmet treatment needs. We explore a continuum of hypotheses to account for different outcomes that might be expected to occur depending on the demand for treatment relative to the system capacity and the timing of help seeking in relation to trajectories of substance use and behavior change. Methods: We used negative binomial regression models to examine temporal trends in the annual SUD help-seeking internet search queries from Google Trends by US state for cocaine, methamphetamine, opioids, cannabis, and alcohol from 2010 to 2020. To validate the value of these data for surveillance purposes, we then used negative binomial regression models to investigate the relationship between SUD help-seeking searches and state-level outcomes across the continuum of care (including lack of care). We started by looking at associations with self-reported treatment need using data from the National Survey on Drug Use and Health, a national survey of the US general population. Next, we explored associations with treatment admission rates from the Treatment Episode Data Set, a national data system on SUD treatment facilities. Finally, we studied associations with state-level rates of people experiencing and dying from an opioid overdose, using data from the Agency for Healthcare Research and Quality and the CDC WONDER database. Results: Statistically significant differences in help-seeking searches were observed over time between 2010 and 2020 (based on P<.05 for the corresponding Wald tests). We were able to identify outlier states for each drug over time (eg, West Virginia for both opioids and methamphetamine), indicating significantly higher help-seeking behaviors compared to national trends. Results from our validation analyses across different outcomes showed positive, statistically significant associations for the models relating to treatment need for alcohol use, treatment admissions for opioid and methamphetamine use, emergency department visits related to opioid use, and opioid overdose mortality data (based on regression coefficients having P?.05). Conclusions: This study demonstrates the clear potential for using internet search queries from Google Trends as an infoveillance tool to predict the demand for substance use treatment spatially and temporally, especially for opioid use disorders. ", doi="10.2196/41527", url="https://www.jmir.org/2022/12/e41527", url="http://www.ncbi.nlm.nih.gov/pubmed/36454620" } @Article{info:doi/10.2196/40540, author="Kiszla, Matthew Benjamin and Harris, Broughton Mia", title="Trends in Tattoo-Related Google Search Data in the United States: Time-Series Analysis", journal="JMIR Dermatol", year="2022", month="Dec", day="1", volume="5", number="4", pages="e40540", keywords="big data", keywords="dermatoepidemiology", keywords="infodemiology", keywords="tattoo", keywords="United States", keywords="web search", keywords="dermatology", keywords="tattoo care", keywords="skin care", keywords="guidance seeking", keywords="tattoo removal", keywords="tattoo application", keywords="information seeking", keywords="internet search", keywords="web searches", keywords="adverse reactions", abstract="Background: Tattoos are becoming increasingly common in the United States. However, little information is available to help clinicians anticipate where, when, and on what topics patients will seek guidance regarding tattoo care, complications, and removal. Objective: The aim of this study was to model web searches concerning general interest in tattoo application, tattoo removal, and the geolocation of tattooing services. Methods: Relative search volumes (RSVs) were elicited from Google Trends, filtered to web searches made in the United States between January 15, 2008, and October 15, 2022. Longitudinal data were analyzed in GraphPad Prism and geospatial data were visualized with Datawrapper for general interest searches (tattoo and tattoo removal), aggregated geolocating searches (eg, tattoo shops near me), and symptomatic searches relating to adverse effects (eg, itchy tattoo). Results were compared to previous global literature and national surveys of tattoo prevalence. Results: In the United States, the search terms tattoo and tattoo removal have experienced stable RSVs over the past 14 years, with both showing peaks in the summer and troughs in the winter. RSVs for search terms that geolocate tattooing services have experienced a general increase in use since 2008. A compilation of results for all collated geolocating search terms localized these searches mainly to the American South, with lesser involvement in the eastern Midwest and inland West. Increased search interest in the Southeast at the expense of more populous coastal states and Great Plains or western Midwest states reflects the ongoing harmonization of tattoo prevalence across regions, as shown by national surveys. Searches for symptoms related to adverse reactions to tattooing experienced an increase over the period of interest, with the same distribution as previous global findings. Conclusions: Clinicians should be aware of an increase in search interest regarding tattoos and their removal, especially during the summer months in the Southeast and Midwest. This increase in interest is occurring together with increased tattoo prevalence and increased search interest for adverse reactions in a country lagging behind in tattoo ink regulation. ", doi="10.2196/40540", url="https://derma.jmir.org/2022/4/e40540", url="http://www.ncbi.nlm.nih.gov/pubmed/37632878" } @Article{info:doi/10.2196/41785, author="Galimov, Artur and Vassey, Julia and Galstyan, Ellen and Unger, B. Jennifer and Kirkpatrick, G. Matthew and Allem, Jon-Patrick", title="Ice Flavor--Related Discussions on Twitter: Content Analysis", journal="J Med Internet Res", year="2022", month="Nov", day="30", volume="24", number="11", pages="e41785", keywords="electronic cigarettes", keywords="Twitter", keywords="social media", keywords="ice flavors", keywords="tobacco policy", keywords="public health", keywords="infodemiology", keywords="FDA", keywords="tobacco", keywords="smoking", keywords="vaping", keywords="e-cigarette", keywords="public", abstract="Background: The US Food and Drug Administration (FDA) recently restricted characterizing flavors in tobacco products. As a result, ice hybrid--flavored e-cigarettes, which combine a cooling flavor with fruit or other flavors (eg, banana ice), emerged on the market. Like menthol, ice-flavored e-cigarettes produce a cooling sensory experience. It is unclear if ice hybrid--flavored e-cigarettes should be considered characterizing flavors or menthol, limiting regulatory action. Monitoring the public's conversations about ice-flavored e-cigarettes on Twitter may help inform the tobacco control community about these products and contribute to the US FDA policy targets in the future. Objective: This study documented the themes pertaining to vaping and ice flavor--related conversations on Twitter. Our goal was to identify key conversation trends and ascertain users' recent experiences with ice-flavored e-cigarette products. Methods: Posts containing vaping-related (eg, ``vape,'' ``ecig,'' ``e-juice,'' or ``e-cigarette'') and ice-related (ie, ``Ice,'' ``Cool,'' ``Frost,'' and ``Arctic'') terms were collected from Twitter's streaming application programming interface from January 1 to July 21, 2021. After removing retweets, a random sample of posts (N=2001) was selected, with 590 posts included in the content analysis. Themes were developed through an inductive approach. Theme co-occurrence was also examined. Results: Many of the 590 posts were marked as (or consisted of) marketing material (n=306, 51.9\%), contained positive personal testimonials (n=180, 30.5\%), and mentioned disposable pods (n=117, 19.8\%). Other themes had relatively low prevalence in the sample: neutral personal testimonials (n=45, 7.6\%), cannabidiol products (n=41, 7\%), negative personal testimonials (n=41, 7\%), ``official'' flavor description (n=37, 6.3\%), ice-flavored JUUL (n=19, 3.2\%), information seeking (n=14, 2.4\%), and comparison to combustible tobacco (n=10, 1.7\%). The most common co-occurring themes in a single tweet were related to marketing and disposable pods (n=73, 12.4\%). Conclusions: Our findings offer insight into the public's experience with and understanding of ice-flavored e-cigarette products. Ice-flavored e-cigarette products are actively marketed on Twitter, and the messages about them are positive. Public health education campaigns on the harms of flavored e-cigarettes may help to reduce positive social norms about ice-flavored products. Future studies should evaluate the relationship between exposure to personal testimonials of ice-flavored vaping products and curiosity, harm perceptions, and experimentation with these products among priority populations. ", doi="10.2196/41785", url="https://www.jmir.org/2022/11/e41785", url="http://www.ncbi.nlm.nih.gov/pubmed/36449326" } @Article{info:doi/10.2196/38398, author="Liu, Miao and Zhu, Yi and Gao, Haiqing and Li, Jialing", title="Examining Chinese Users' Feedback Comments on HIV Self-testing Kits From e-Commerce Platforms: Thematic and Content Analysis", journal="J Med Internet Res", year="2022", month="Nov", day="30", volume="24", number="11", pages="e38398", keywords="HIV self-testing", keywords="Chinese", keywords="feedback comments", keywords="e-commerce platforms", abstract="Background: HIV self-testing is preferred by many Chinese people for its convenience and confidentiality. However, most studies on HIV self-testing (HIVST) uptake in China overfocused on men who have sex with men and overrelied on obtrusive methods such as surveys and interviews to collect data. Objective: We aimed to explore Chinese HIVST kit users' authentic experience via their feedback comments posted on e-commerce platforms using an unobtrusive approach. Methods: In total, 21,018 feedback comments about buying and using HIVST kits posted on Chinese e-commerce platforms (Tmall and Pinduoduo) were collected. An inductive thematic analysis based on a random sample of 367 comments yielded several thematic features. These thematic features were developed into coding categories for a quantitative content analysis of another random sample of 1857 comments. Results: Four themes were identified in the first study, including the expression of positive and negative emotions after and before getting the test, respectively, calling for living a clean and moral life in the future, comments on the sellers and HIVST kits, and the reasons for buying HIVST kits. The results from the second study suggested that there were significant associations between different platforms and several thematic features. Nearly 50\% of the comments were related to the product itself and the disclosures of HIV-negative test results. More than 25\% of the comments showed users' feelings of gratefulness after receiving negative test results such as ``thank heavens for sparing my life.'' Conclusions: The results suggested that Chinese users relied on HIVST kits to reduce and prevent HIV infection, while they also considered HIV infection a punishment related to moral violation such as being sexually promiscuous. The traditional Chinese health belief that health is influenced by one's morality still persists among some Chinese users. Many users also lacked appropriate knowledge about HIV transmission and self-testing kits. ", doi="10.2196/38398", url="https://www.jmir.org/2022/11/e38398", url="http://www.ncbi.nlm.nih.gov/pubmed/36449327" } @Article{info:doi/10.2196/40380, author="Takats, Courtney and Kwan, Amy and Wormer, Rachel and Goldman, Dari and Jones, E. Heidi and Romero, Diana", title="Ethical and Methodological Considerations of Twitter Data for Public Health Research: Systematic Review", journal="J Med Internet Res", year="2022", month="Nov", day="29", volume="24", number="11", pages="e40380", keywords="systematic review", keywords="Twitter", keywords="social media", keywords="public health ethics", keywords="public health", keywords="ethics", keywords="ethical considerations", keywords="public health research", keywords="research topics", keywords="Twitter data", keywords="ethical framework", keywords="research ethics", abstract="Background: Much research is being carried out using publicly available Twitter data in the field of public health, but the types of research questions that these data are being used to answer and the extent to which these projects require ethical oversight are not clear. Objective: This review describes the current state of public health research using Twitter data in terms of methods and research questions, geographic focus, and ethical considerations including obtaining informed consent from Twitter handlers. Methods: We implemented a systematic review, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, of articles published between January 2006 and October 31, 2019, using Twitter data in secondary analyses for public health research, which were found using standardized search criteria on SocINDEX, PsycINFO, and PubMed. Studies were excluded when using Twitter for primary data collection, such as for study recruitment or as part of a dissemination intervention. Results: We identified 367 articles that met eligibility criteria. Infectious disease (n=80, 22\%) and substance use (n=66, 18\%) were the most common topics for these studies, and sentiment mining (n=227, 62\%), surveillance (n=224, 61\%), and thematic exploration (n=217, 59\%) were the most common methodologies employed. Approximately one-third of articles had a global or worldwide geographic focus; another one-third focused on the United States. The majority (n=222, 60\%) of articles used a native Twitter application programming interface, and a significant amount of the remainder (n=102, 28\%) used a third-party application programming interface. Only one-third (n=119, 32\%) of studies sought ethical approval from an institutional review board, while 17\% of them (n=62) included identifying information on Twitter users or tweets and 36\% of them (n=131) attempted to anonymize identifiers. Most studies (n=272, 79\%) included a discussion on the validity of the measures and reliability of coding (70\% for interreliability of human coding and 70\% for computer algorithm checks), but less attention was paid to the sampling frame, and what underlying population the sample represented. Conclusions: Twitter data may be useful in public health research, given its access to publicly available information. However, studies should exercise greater caution in considering the data sources, accession method, and external validity of the sampling frame. Further, an ethical framework is necessary to help guide future research in this area, especially when individual, identifiable Twitter users and tweets are shared and discussed. Trial Registration: PROSPERO CRD42020148170; https://www.crd.york.ac.uk/prospero/display\_record.php?RecordID=148170 ", doi="10.2196/40380", url="https://www.jmir.org/2022/11/e40380", url="http://www.ncbi.nlm.nih.gov/pubmed/36445739" } @Article{info:doi/10.2196/38441, author="Weeks, Rose and White, Sydney and Hartner, Anna-Maria and Littlepage, Shea and Wolf, Jennifer and Masten, Kristin and Tingey, Lauren", title="COVID-19 Messaging on Social Media for American Indian and Alaska Native Communities: Thematic Analysis of Audience Reach and Web Behavior", journal="JMIR Infodemiology", year="2022", month="Nov", day="25", volume="2", number="2", pages="e38441", keywords="COVID-19", keywords="American Indian or Alaska Native", keywords="social media", keywords="communication", keywords="tribal organization", keywords="community health", keywords="infodemiology", keywords="Twitter", keywords="online behavior", keywords="content analysis", keywords="thematic analysis", abstract="Background: During the COVID-19 pandemic, tribal and health organizations used social media to rapidly disseminate public health guidance highlighting protective behaviors such as masking and vaccination to mitigate the pandemic's disproportionate burden on American Indian and Alaska Native (AI/AN) communities. Objective: Seeking to provide guidance for future communication campaigns prioritizing AI/AN audiences, this study aimed to identify Twitter post characteristics associated with higher performance, measured by audience reach (impressions) and web behavior (engagement rate). Methods: We analyzed Twitter posts published by a campaign by the Johns Hopkins Center for Indigenous Health from July 2020 to June 2021. Qualitative analysis was informed by in-depth interviews with members of a Tribal Advisory Board and thematically organized according to the Health Belief Model. A general linearized model was used to analyze associations between Twitter post themes, impressions, and engagement rates. Results: The campaign published 162 Twitter messages, which organically generated 425,834 impressions and 6016 engagements. Iterative analysis of these Twitter posts identified 10 unique themes under theory- and culture-related categories of framing knowledge, cultural messaging, normalizing mitigation strategies, and interactive opportunities, which were corroborated by interviews with Tribal Advisory Board members. Statistical analysis of Twitter impressions and engagement rate by theme demonstrated that posts featuring culturally resonant community role models (P=.02), promoting web-based events (P=.002), and with messaging as part of Twitter Chats (P<.001) were likely to generate higher impressions. In the adjusted analysis controlling for the date of posting, only the promotion of web-based events (P=.003) and Twitter Chat messaging (P=.01) remained significant. Visual, explanatory posts promoting self-efficacy (P=.01; P=.01) and humorous posts (P=.02; P=.01) were the most likely to generate high--engagement rates in both the adjusted and unadjusted analysis. Conclusions: Results from the 1-year Twitter campaign provide lessons to inform organizations designing social media messages to reach and engage AI/AN social media audiences. The use of interactive events, instructional graphics, and Indigenous humor are promising practices to engage community members, potentially opening audiences to receiving important and time-sensitive guidance. ", doi="10.2196/38441", url="https://infodemiology.jmir.org/2022/2/e38441", url="http://www.ncbi.nlm.nih.gov/pubmed/36471705" } @Article{info:doi/10.2196/37559, author="Kim, Jung Sunny and Schiffelbein, E. Jenna and Imset, Inger and Olson, L. Ardis", title="Countering Antivax Misinformation via Social Media: Message-Testing Randomized Experiment for Human Papillomavirus Vaccination Uptake", journal="J Med Internet Res", year="2022", month="Nov", day="24", volume="24", number="11", pages="e37559", keywords="misinformation", keywords="vaccine hesitancy", keywords="vaccine communication", keywords="social media", keywords="human papillomavirus", keywords="HPV", keywords="HPV vaccine", abstract="Background: Suboptimal adolescent human papillomavirus (HPV) vaccination rates have been attributed to parental perceptions of the HPV vaccine. The internet has been cited as a setting where misinformation and controversy about HPV vaccination have been amplified. Objective: We aimed to test message effectiveness in changing parents' attitudes and behavioral intentions toward HPV vaccination. Methods: We conducted a web-based message-testing experiment with 6 control messages and 25 experimental messages and 5 from each of the 5 salient themes about HPV vaccination (theme 1: safety, side effects, risk, and ingredient concerns and long-term or major adverse events; theme 2: distrust of the health care system; theme 3: HPV vaccine effectiveness concerns; theme 4: connection to sexual activity; and theme 5: misinformation about HPV or HPV vaccine). Themes were identified from previous web-based focus group research with parents, and specific messages were developed by the study team using content from credible scientific sources. Through an iterative process of message development, the messages were crafted to be appropriate for presentation on a social media platform. Among the 1713 participants recruited via social media and crowdsourcing sites, 1043 eligible parents completed a pretest survey questionnaire. Participants were then randomly assigned to 1 of the 31 messages and asked to complete a posttest survey questionnaire that assessed attitudes toward the vaccine and perceived effectiveness of the viewed message. A subgroup of participants (189/995, 19\%) with unvaccinated children aged 9 to 14 years was also assessed for their behavioral intention to vaccinate their children against HPV. Results: Parents in the experimental group had increased positive attitudes toward HPV vaccination compared with those in the control group (t969=3.03, P=.003), which was associated with increased intention to vaccinate among parents of unvaccinated children aged 9 to 14 years (r=1.14, P=.05). At the thematic level, we identified 4 themes (themes 2-5) that were relatively effective in increasing behavioral intentions by positively influencing attitudes toward the HPV vaccine ($\chi$25=5.97, P=.31, root mean square error of approximation [RMSEA]=0.014, comparative fit index [CFI]=0.91, standardized root mean square residual [SRMR]=0.031). On the message level, messages that provided scientific evidence from government-related sources (eg, the Centers for Disease Control and Prevention) and corrected misinformation (eg, ``vaccines like the HPV vaccine are simply a way for pharmaceutical companies to make money. That isn't true'') were effective in forming positive perceptions toward the HPV vaccination messages. Conclusions: Evidence-based messages directly countering misinformation and promoting HPV vaccination in social media environments can positively influence parents' attitudes and behavioral intentions to vaccinate their children against HPV. ", doi="10.2196/37559", url="https://www.jmir.org/2022/11/e37559", url="http://www.ncbi.nlm.nih.gov/pubmed/36422887" } @Article{info:doi/10.2196/41288, author="Cohen Zion, Mairav and Gescheit, Iddo and Levy, Nir and Yom-Tov, Elad", title="Identifying Sleep Disorders From Search Engine Activity: Combining User-Generated Data With a Clinically Validated Questionnaire", journal="J Med Internet Res", year="2022", month="Nov", day="23", volume="24", number="11", pages="e41288", keywords="sleep disorders", keywords="search engine queries", keywords="search advertising", keywords="internet", keywords="Bing", keywords="sleep", keywords="machine learning", keywords="questionnaire", abstract="Background: Sleep disorders are experienced by up to 40\% of the population but their diagnosis is often delayed by the availability of specialists. Objective: We propose the use of search engine activity in conjunction with a validated web-based sleep questionnaire to facilitate wide-scale screening of prevalent sleep disorders. Methods: Search advertisements offering a web-based sleep disorder screening questionnaire were shown on the Bing search engine to individuals who indicated an interest in sleep disorders. People who clicked on the advertisements and completed the sleep questionnaire were identified as being at risk for 1 of 4 common sleep disorders. A machine learning algorithm was applied to previous search engine queries to predict their suspected sleep disorder, as identified by the questionnaire. Results: A total of 397 users consented to participate in the study and completed the questionnaire. Of them, 132 had sufficient past query data for analysis. Our findings show that diurnal patterns of people with sleep disorders were shifted by 2-3 hours compared to those of the controls. Past query activity was predictive of sleep disorders, approaching an area under the receiver operating characteristic curve of 0.62-0.69, depending on the sleep disorder. Conclusions: Targeted advertisements can be used as an initial screening tool for people with sleep disorders. However, search engine data are seemingly insufficient as a sole method for screening. Nevertheless, we believe that evaluable web-based information, easily collected and processed with little effort on part of the physician and with low burden on the individual, can assist in the diagnostic process and possibly drive people to seek sleep assessment and diagnosis earlier than they currently do. ", doi="10.2196/41288", url="https://www.jmir.org/2022/11/e41288", url="http://www.ncbi.nlm.nih.gov/pubmed/36416870" } @Article{info:doi/10.2196/39849, author="D{\'e}guilhem, Am{\'e}lia and Malaab, Joelle and Talmatkadi, Manissa and Renner, Simon and Foulqui{\'e}, Pierre and Fagherazzi, Guy and Loussikian, Paul and Marty, Tom and Mebarki, Adel and Texier, Nathalie and Schuck, Stephane", title="Identifying Profiles and Symptoms of Patients With Long COVID in France: Data Mining Infodemiology Study Based on Social Media", journal="JMIR Infodemiology", year="2022", month="Nov", day="22", volume="2", number="2", pages="e39849", keywords="long COVID", keywords="social media", keywords="Long Haulers", keywords="difficulties encountered", keywords="symptoms", keywords="infodemiology study", keywords="infodemiology", keywords="COVID-19", keywords="patient-reported outcomes", keywords="persistent", keywords="condition", keywords="topics", keywords="discussion", keywords="content", abstract="Background: Long COVID---a condition with persistent symptoms post COVID-19 infection---is the first illness arising from social media. In France, the French hashtag \#ApresJ20 described symptoms persisting longer than 20 days after contracting COVID-19. Faced with a lack of recognition from medical and official entities, patients formed communities on social media and described their symptoms as long-lasting, fluctuating, and multisystemic. While many studies on long COVID relied on traditional research methods with lengthy processes, social media offers a foundation for large-scale studies with a fast-flowing outburst of data. Objective: We aimed to identify and analyze Long Haulers' main reported symptoms, symptom co-occurrences, topics of discussion, difficulties encountered, and patient profiles. Methods: Data were extracted based on a list of pertinent keywords from public sites (eg, Twitter) and health-related forums (eg, Doctissimo). Reported symptoms were identified via the MedDRA dictionary, displayed per the volume of posts mentioning them, and aggregated at the user level. Associations were assessed by computing co-occurrences in users' messages, as pairs of preferred terms. Discussion topics were analyzed using the Biterm Topic Modeling; difficulties and unmet needs were explored manually. To identify patient profiles in relation to their symptoms, each preferred term's total was used to create user-level hierarchal clusters. Results: Between January 1, 2020, and August 10, 2021, overall, 15,364 messages were identified as originating from 6494 patients of long COVID or their caregivers. Our analyses revealed 3 major symptom co-occurrences: asthenia-dyspnea (102/289, 35.3\%), asthenia-anxiety (65/289, 22.5\%), and asthenia-headaches (50/289, 17.3\%). The main reported difficulties were symptom management (150/424, 35.4\% of messages), psychological impact (64/424,15.1\%), significant pain (51/424, 12.0\%), deterioration in general well-being (52/424, 12.3\%), and impact on daily and professional life (40/424, 9.4\% and 34/424, 8.0\% of messages, respectively). We identified 3 profiles of patients in relation to their symptoms: profile A (n=406 patients) reported exclusively an asthenia symptom; profile B (n=129) expressed anxiety (n=129, 100\%), asthenia (n=28, 21.7\%), dyspnea (n=15, 11.6\%), and ageusia (n=3, 2.3\%); and profile C (n=141) described dyspnea (n=141, 100\%), and asthenia (n=45, 31.9\%). Approximately 49.1\% of users (79/161) continued expressing symptoms after more than 3 months post infection, and 20.5\% (33/161) after 1 year. Conclusions: Long COVID is a lingering condition that affects people worldwide, physically and psychologically. It impacts Long Haulers' quality of life, everyday tasks, and professional activities. Social media played an undeniable role in raising and delivering Long Haulers' voices and can potentially rapidly provide large volumes of valuable patient-reported information. Since long COVID was a self-titled condition by patients themselves via social media, it is imperative to continuously include their perspectives in related research. Our results can help design patient-centric instruments to be further used in clinical practice to better capture meaningful dimensions of long COVID. ", doi="10.2196/39849", url="https://infodemiology.jmir.org/2022/2/e39849", url="http://www.ncbi.nlm.nih.gov/pubmed/36447795" } @Article{info:doi/10.2196/36871, author="Erturk, Sinan and Hudson, Georgie and Jansli, M. Sonja and Morris, Daniel and Odoi, M. Clarissa and Wilson, Emma and Clayton-Turner, Angela and Bray, Vanessa and Yourston, Gill and Cornwall, Andrew and Cummins, Nicholas and Wykes, Til and Jilka, Sagar", title="Codeveloping and Evaluating a Campaign to Reduce Dementia Misconceptions on Twitter: Machine Learning Study", journal="JMIR Infodemiology", year="2022", month="Nov", day="22", volume="2", number="2", pages="e36871", keywords="machine learning", keywords="patient and public involvement", keywords="codevelopment", keywords="misconceptions", keywords="stigma", keywords="Twitter", keywords="social media", abstract="Background: Dementia misconceptions on Twitter can have detrimental or harmful effects. Machine learning (ML) models codeveloped with carers provide a method to identify these and help in evaluating awareness campaigns. Objective: This study aimed to develop an ML model to distinguish between misconceptions and neutral tweets and to develop, deploy, and evaluate an awareness campaign to tackle dementia misconceptions. Methods: Taking 1414 tweets rated by carers from our previous work, we built 4 ML models. Using a 5-fold cross-validation, we evaluated them and performed a further blind validation with carers for the best 2 ML models; from this blind validation, we selected the best model overall. We codeveloped an awareness campaign and collected pre-post campaign tweets (N=4880), classifying them with our model as misconceptions or not. We analyzed dementia tweets from the United Kingdom across the campaign period (N=7124) to investigate how current events influenced misconception prevalence during this time. Results: A random forest model best identified misconceptions with an accuracy of 82\% from blind validation and found that 37\% of the UK tweets (N=7124) about dementia across the campaign period were misconceptions. From this, we could track how the prevalence of misconceptions changed in response to top news stories in the United Kingdom. Misconceptions significantly rose around political topics and were highest (22/28, 79\% of the dementia tweets) when there was controversy over the UK government allowing to continue hunting during the COVID-19 pandemic. After our campaign, there was no significant change in the prevalence of misconceptions. Conclusions: Through codevelopment with carers, we developed an accurate ML model to predict misconceptions in dementia tweets. Our awareness campaign was ineffective, but similar campaigns could be enhanced through ML to respond to current events that affect misconceptions in real time. ", doi="10.2196/36871", url="https://infodemiology.jmir.org/2022/2/e36871", url="http://www.ncbi.nlm.nih.gov/pubmed/37113444" } @Article{info:doi/10.2196/39312, author="Tan, Jin Rayner Kay and Lim, Mingjie Jane and Neo, Min Pearlyn Hui and Ong, Ee Suan", title="Reinterpretation of Health Information in the Context of an Emerging Infectious Disease: A Digital Focus Group Study", journal="JMIR Hum Factors", year="2022", month="Nov", day="22", volume="9", number="4", pages="e39312", keywords="health communication", keywords="infodemic", keywords="SARS-CoV-2", keywords="coronavirus", keywords="Singapore", keywords="WhatsApp", keywords="COVID-19", keywords="health information", keywords="misinformation", keywords="mobile health", keywords="smartphone", keywords="information quality", keywords="online health information", abstract="Background: Misinformation related to the COVID-19 pandemic has accelerated global public concern and panic. The glut of information, or ``infodemic,'' has caused concern for authorities due to its negative impacts on COVID-19 prevention and control, spurring calls for a greater scholarly focus on health literacy during the pandemic. Nevertheless, few studies have sought to qualitatively examine how individuals interpreted and assimilated health information at the initial wave of COVID-19 restrictions. Objective: We developed this qualitative study adopting chat-based focus group discussions to investigate how individuals interpreted COVID-19 health information during the first wave of COVID-19 restrictions. Methods: We conducted a qualitative study in Singapore to investigate how individuals perceive and interpret information that they receive on COVID-19. Data were generated through online focus group discussions conducted on the mobile messaging smartphone app WhatsApp. From March 28 to April 13, 2020, we held eight WhatsApp-based focus groups (N=60) with participants stratified by age groups, namely 21-30 years, 31-40 years, 41-50 years, and 51 years and above. Data were thematically analyzed. Results: A total of four types of COVID-19 health information were generated from the thematic analysis, labeled as formal health information, informal health information, suspicious health information, and fake health information, respectively. How participants interpreted these categories of information depended largely on the perceived trustworthiness of the information source as well as the perceived veracity of information. Both factors were instrumental in determining individuals' perceptions, and their subsequent treatment and assimilation of COVID-19--related information. Conclusions: Both perceived trustworthiness of the information source and perceived veracity of information were instrumental concepts in determining one's perception, and thus subsequent treatment and assimilation of such information for one's knowledge of COVID-19 or the onward propagation to their social networks. These findings have implications for how policymakers and health authorities communicate with the public and deal with fake health information in the context of COVID-19. ", doi="10.2196/39312", url="https://humanfactors.jmir.org/2022/4/e39312", url="http://www.ncbi.nlm.nih.gov/pubmed/36099011" } @Article{info:doi/10.2196/40764, author="Xue, Haoning and Zhang, Jingwen and Sagae, Kenji and Nishimine, Brian and Fukuoka, Yoshimi", title="Analyzing Public Conversations About Heart Disease and Heart Health on Facebook From 2016 to 2021: Retrospective Observational Study Applying Latent Dirichlet Allocation Topic Modeling", journal="JMIR Cardio", year="2022", month="Nov", day="22", volume="6", number="2", pages="e40764", keywords="heart health", keywords="heart disease", keywords="topic modeling", keywords="sentiment analysis", keywords="social media", keywords="Facebook", keywords="COVID-19", keywords="women's heart health", abstract="Background: Heart disease continues to be the leading cause of death in men and women in the United States. The COVID-19 pandemic has further led to increases in various long-term cardiovascular complications. Objective: This study analyzed public conversations related to heart disease and heart health on Facebook in terms of their thematic topics and sentiments. In addition, it provided in-depth analyses of 2 subtopics with important practical implications: heart health for women and heart health during the COVID-19 pandemic. Methods: We collected 34,885 posts and 51,835 comments spanning from June 2016 to June 2021 that were related to heart disease and health from public Facebook pages and groups. We used latent Dirichlet allocation topic modeling to extract discussion topics illuminating the public's interests and concerns regarding heart disease and heart health. We also used Linguistic Inquiry and Word Count (Pennebaker Conglomerates, Inc) to identify public sentiments regarding heart health. Results: We observed an increase in discussions related to heart health on Facebook. Posts and comments increased from 3102 and 3632 in 2016 to 8550 (176\% increase) and 14,617 (302\% increase) in 2021, respectively. Overall, 35.37\% (12,340/34,885) of the posts were created after January 2020, the start of the COVID-19 pandemic. In total, 39.21\% (13,677/34,885) of the posts were by nonprofit health organizations. We identified 6 topics in the posts (heart health promotion, personal experiences, risk-reduction education, heart health promotion for women, educational information, and physicians' live discussion sessions). We identified 6 topics in the comments (personal experiences, survivor stories, risk reduction, religion, medical questions, and appreciation of physicians and information on heart health). During the pandemic (from January 2020 to June 2021), risk reduction was a major topic in both posts and comments. Unverified information on alternative treatments and promotional content was also prevalent. Among all posts, 14.91\% (5200/34,885) were specifically about heart health for women centering on local event promotion and distinctive symptoms of heart diseases for women. Conclusions: Our results tracked the public's ongoing discussions on heart disease and heart health on one prominent social media platform, Facebook. The public's discussions and information sharing on heart health increased over time, especially since the start of the COVID-19 pandemic. Various levels of health organizations on Facebook actively promoted heart health information and engaged a large number of users. Facebook presents opportunities for more targeted heart health interventions that can reach and engage diverse populations. ", doi="10.2196/40764", url="https://cardio.jmir.org/2022/2/e40764", url="http://www.ncbi.nlm.nih.gov/pubmed/36318640" } @Article{info:doi/10.2196/40701, author="Wang, Dandan and Zhou, Yadong and Ma, Feicheng", title="Opinion Leaders and Structural Hole Spanners Influencing Echo Chambers in Discussions About COVID-19 Vaccines on Social Media in China: Network Analysis", journal="J Med Internet Res", year="2022", month="Nov", day="18", volume="24", number="11", pages="e40701", keywords="COVID-19", keywords="COVID-19 vaccine", keywords="echo chamber", keywords="opinion leader", keywords="structural hole spanner", keywords="topic", keywords="sentiment", keywords="social media", keywords="vaccine hesitancy", keywords="public health", keywords="vaccination", keywords="health promotion", keywords="online campaign", keywords="social network analysis", abstract="Background: Social media provide an ideal medium for breeding and reinforcing vaccine hesitancy, especially during public health emergencies. Algorithmic recommendation--based technology along with users' selective exposure and group pressure lead to online echo chambers, causing inefficiency in vaccination promotion. Avoiding or breaking echo chambers largely relies on key users' behavior. Objective: With the ultimate goal of eliminating the impact of echo chambers related to vaccine hesitancy on social media during public health emergencies, the aim of this study was to develop a framework to quantify the echo chamber effect in users' topic selection and attitude contagion about COVID-19 vaccines or vaccinations; detect online opinion leaders and structural hole spanners based on network attributes; and explore the relationships of their behavior patterns and network locations, as well as the relationships of network locations and impact on topic-based and attitude-based echo chambers. Methods: We called the Sina Weibo application programming interface to crawl tweets related to the COVID-19 vaccine or vaccination and user information on Weibo, a Chinese social media platform. Adopting social network analysis, we examined the low echo chamber effect based on topics in representational networks of information, according to attitude in communication flow networks of users under different interactive mechanisms (retweeting, commenting). Statistical and visual analyses were used to characterize behavior patterns of key users (opinion leaders, structural hole spanners), and to explore their function in avoiding or breaking topic-based and attitude-based echo chambers. Results: Users showed a low echo chamber effect in vaccine-related topic selection and attitude interaction. For the former, the homophily was more obvious in retweeting than in commenting, whereas the opposite trend was found for the latter. Speakers, replicators, and monologists tended to be opinion leaders, whereas common users, retweeters, and networkers tended to be structural hole spanners. Both leaders and spanners tended to be ``bridgers'' to disseminate diverse topics and communicate with users holding cross-cutting attitudes toward COVID-19 vaccines. Moreover, users who tended to echo a single topic could bridge multiple attitudes, while users who focused on diverse topics also tended to serve as bridgers for different attitudes. Conclusions: This study not only revealed a low echo chamber effect in vaccine hesitancy, but further elucidated the underlying reasons from the perspective of users, offering insights for research about the form, degree, and formation of echo chambers, along with depolarization, social capital, stakeholder theory, user portraits, dissemination pattern of topic, and sentiment. Therefore, this work can help to provide strategies for public health and public opinion managers to cooperate toward avoiding or correcting echo chamber chaos and effectively promoting online vaccine campaigns. ", doi="10.2196/40701", url="https://www.jmir.org/2022/11/e40701", url="http://www.ncbi.nlm.nih.gov/pubmed/36367965" } @Article{info:doi/10.2196/40160, author="Russell, M. Alex and Valdez, Danny and Chiang, C. Shawn and Montemayor, N. Ben and Barry, E. Adam and Lin, Hsien-Chang and Massey, M. Philip", title="Using Natural Language Processing to Explore ``Dry January'' Posts on Twitter: Longitudinal Infodemiology Study", journal="J Med Internet Res", year="2022", month="Nov", day="18", volume="24", number="11", pages="e40160", keywords="alcohol", keywords="drinking", keywords="social media", keywords="Twitter", keywords="Dry January", keywords="infodemiology", keywords="infoveillance", keywords="natural language processing", abstract="Background: Dry January, a temporary alcohol abstinence campaign, encourages individuals to reflect on their relationship with alcohol by temporarily abstaining from consumption during the month of January. Though Dry January has become a global phenomenon, there has been limited investigation into Dry January participants' experiences. One means through which to gain insights into individuals' Dry January-related experiences is by leveraging large-scale social media data (eg, Twitter chatter) to explore and characterize public discourse concerning Dry January. Objective: We sought to answer the following questions: (1) What themes are present within a corpus of tweets about Dry January, and is there consistency in the language used to discuss Dry January across multiple years of tweets (2020-2022)? (2) Do unique themes or patterns emerge in Dry January 2021 tweets after the onset of the COVID-19 pandemic? and (3) What is the association with tweet composition (ie, sentiment and human-authored vs bot-authored) and engagement with Dry January tweets? Methods: We applied natural language processing techniques to a large sample of tweets (n=222,917) containing the term ``dry january'' or ``dryjanuary'' posted from December 15 to February 15 across three separate years of participation (2020-2022). Term frequency inverse document frequency, k-means clustering, and principal component analysis were used for data visualization to identify the optimal number of clusters per year. Once data were visualized, we ran interpretation models to afford within-year (or within-cluster) comparisons. Latent Dirichlet allocation topic modeling was used to examine content within each cluster per given year. Valence Aware Dictionary and Sentiment Reasoner sentiment analysis was used to examine affect per cluster per year. The Botometer automated account check was used to determine average bot score per cluster per year. Last, to assess user engagement with Dry January content, we took the average number of likes and retweets per cluster and ran correlations with other outcome variables of interest. Results: We observed several similar topics per year (eg, Dry January resources, Dry January health benefits, updates related to Dry January progress), suggesting relative consistency in Dry January content over time. Although there was overlap in themes across multiple years of tweets, unique themes related to individuals' experiences with alcohol during the midst of the COVID-19 global pandemic were detected in the corpus of tweets from 2021. Also, tweet composition was associated with engagement, including number of likes, retweets, and quote-tweets per post. Bot-dominant clusters had fewer likes, retweets, or quote tweets compared with human-authored clusters. Conclusions: The findings underscore the utility for using large-scale social media, such as discussions on Twitter, to study drinking reduction attempts and to monitor the ongoing dynamic needs of persons contemplating, preparing for, or actively pursuing attempts to quit or cut down on their drinking. ", doi="10.2196/40160", url="https://www.jmir.org/2022/11/e40160", url="http://www.ncbi.nlm.nih.gov/pubmed/36343184" } @Article{info:doi/10.2196/42261, author="Ljaji{\'c}, Adela and Prodanovi{\'c}, Nikola and Medvecki, Darija and Ba{\vs}aragin, Bojana and Mitrovi{\'c}, Jelena", title="Uncovering the Reasons Behind COVID-19 Vaccine Hesitancy in Serbia: Sentiment-Based Topic Modeling", journal="J Med Internet Res", year="2022", month="Nov", day="17", volume="24", number="11", pages="e42261", keywords="topic modeling", keywords="sentiment analysis", keywords="LDA", keywords="NMF", keywords="BERT", keywords="vaccine hesitancy", keywords="COVID-19", keywords="Twitter", keywords="Serbian language processing", keywords="vaccine", keywords="public health", keywords="NLP", keywords="vaccination", keywords="Serbia", abstract="Background: Since the first COVID-19 vaccine appeared, there has been a growing tendency to automatically determine public attitudes toward it. In particular, it was important to find the reasons for vaccine hesitancy, since it was directly correlated with pandemic protraction. Natural language processing (NLP) and public health researchers have turned to social media (eg, Twitter, Reddit, and Facebook) for user-created content from which they can gauge public opinion on vaccination. To automatically process such content, they use a number of NLP techniques, most notably topic modeling. Topic modeling enables the automatic uncovering and grouping of hidden topics in the text. When applied to content that expresses a negative sentiment toward vaccination, it can give direct insight into the reasons for vaccine hesitancy. Objective: This study applies NLP methods to classify vaccination-related tweets by sentiment polarity and uncover the reasons for vaccine hesitancy among the negative tweets in the Serbian language. Methods: To study the attitudes and beliefs behind vaccine hesitancy, we collected 2 batches of tweets that mention some aspects of COVID-19 vaccination. The first batch of 8817 tweets was manually annotated as either relevant or irrelevant regarding the COVID-19 vaccination sentiment, and then the relevant tweets were annotated as positive, negative, or neutral. We used the annotated tweets to train a sequential bidirectional encoder representations from transformers (BERT)-based classifier for 2 tweet classification tasks to augment this initial data set. The first classifier distinguished between relevant and irrelevant tweets. The second classifier used the relevant tweets and classified them as negative, positive, or neutral. This sequential classifier was used to annotate the second batch of tweets. The combined data sets resulted in 3286 tweets with a negative sentiment: 1770 (53.9\%) from the manually annotated data set and 1516 (46.1\%) as a result of automatic classification. Topic modeling methods (latent Dirichlet allocation [LDA] and nonnegative matrix factorization [NMF]) were applied using the 3286 preprocessed tweets to detect the reasons for vaccine hesitancy. Results: The relevance classifier achieved an F-score of 0.91 and 0.96 for relevant and irrelevant tweets, respectively. The sentiment polarity classifier achieved an F-score of 0.87, 0.85, and 0.85 for negative, neutral, and positive sentiments, respectively. By summarizing the topics obtained in both models, we extracted 5 main groups of reasons for vaccine hesitancy: concern over vaccine side effects, concern over vaccine effectiveness, concern over insufficiently tested vaccines, mistrust of authorities, and conspiracy theories. Conclusions: This paper presents a combination of NLP methods applied to find the reasons for vaccine hesitancy in Serbia. Given these reasons, it is now possible to better understand the concerns of people regarding the vaccination process. ", doi="10.2196/42261", url="https://www.jmir.org/2022/11/e42261", url="http://www.ncbi.nlm.nih.gov/pubmed/36301673" } @Article{info:doi/10.2196/42441, author="Vassey, Julia and Donaldson, I. Scott and Dormanesh, Allison and Allem, Jon-Patrick", title="Themes in TikTok Videos Featuring Little Cigars and Cigarillos: Content Analysis", journal="J Med Internet Res", year="2022", month="Nov", day="16", volume="24", number="11", pages="e42441", keywords="cigarillo", keywords="little cigar", keywords="social media", keywords="TikTok", keywords="video", keywords="cigar", keywords="cigarette", keywords="smoker", keywords="smoking", keywords="tobacco", keywords="content analysis", keywords="youth", keywords="young adult", keywords="adolescent", keywords="user generated content", abstract="Background: Little cigars and cigarillos (LCCs) are popular tobacco products among youth (ie, adolescents and young adults). A variety of LCC-related promotional and user-generated content is present on social media. However, research on LCC-related posts on social media has been largely focused on platforms that are primarily text- or image-based, such as Twitter and Instagram. Objective: This study analyzed LCC-related content on TikTok, an audio and video--based platform popular among youth. Methods: Publicly available posts (N=811) that contained the LCC-related hashtags \#swishersweets or \#backwoods were collected on TikTok from January 2019 to May 2021. Metadata were also collected, including numbers of likes, comments, shares, and views per video. Using an inductive approach, a codebook consisting of 26 themes was developed to help summarize the underlying themes evident in the TikTok videos and corresponding captions. A pairwise co-occurrence analysis of themes was also conducted to evaluate connections among themes. Results: Among the 811 posts, the LCC presence theme (ie, a visible LCC) occurred in the most prominent number of posts (n=661, 81.5\%), followed by music (n=559, 68.9\%), youth (n=332, 40.9\%), humor (n=263, 32.4\%), LCC use (n=242, 29.8\%), flavors (n=232, 28.6\%), branding (n=182, 22.4\%), paraphernalia (n=137, 16.9\%), blunt rolling (n=94, 11.6\%), and price (n=84, 10.4\%). Product reviews had the highest engagement, with a median 44 (mean 2857, range 36,499) likes and median 491 (mean 15,711, range 193,590) views; followed by product comparisons, with a median 44 (mean 1920, range 36,500) likes and median 671 (mean 11,277, range 193,798) views. Promotions had the lowest engagement, with a median 4 (mean 8, range 34) likes and median 78 (mean 213, range 1131) views. The most prevalent themes co-occurring with LCC presence were (1) music (434/811, 53.5\%), (2) youth (264/811, 32.6\%), (3) humor (219/811, 27\%), (4) flavors (214/811, 26.4\%), and (5) LCC use (207/811, 25.5\%). Conclusions: LCC-related marketing and user-generated content was present on TikTok, including videos showing youth discussing, displaying, or using LCCs. Such content may be in violation of TikTok's community guidelines prohibiting the display, promotion, or posting of tobacco-related content on its platform, including the display of possession or consumption of tobacco by a minor. Further improvement in the enforcement of TikTok community guidelines and additional scrutiny from public health policy makers may be necessary for protecting youth from future exposure to tobacco-related posts. Observational and experimental studies are needed to understand the impact of exposure to LCC-related videos on attitudes and behaviors related to LCC use among youth. Finally, there may be a need for engaging antitobacco videos that appeal to youth on TikTok to counter the protobacco content on this platform. ", doi="10.2196/42441", url="https://www.jmir.org/2022/11/e42441", url="http://www.ncbi.nlm.nih.gov/pubmed/36383406" } @Article{info:doi/10.2196/35974, author="Khademi Habibabadi, Sedigheh and Hallinan, Christine and Bonomo, Yvonne and Conway, Mike", title="Consumer-Generated Discourse on Cannabis as a Medicine: Scoping Review of Techniques", journal="J Med Internet Res", year="2022", month="Nov", day="16", volume="24", number="11", pages="e35974", keywords="social media", keywords="data mining", keywords="internet and the web technology", keywords="consumer-generated data", keywords="medicinal cannabis", keywords="medical marijuana", abstract="Background: Medicinal cannabis is increasingly being used for a variety of physical and mental health conditions. Social media and web-based health platforms provide valuable, real-time, and cost-effective surveillance resources for gleaning insights regarding individuals who use cannabis for medicinal purposes. This is particularly important considering that the evidence for the optimal use of medicinal cannabis is still emerging. Despite the web-based marketing of medicinal cannabis to consumers, currently, there is no robust regulatory framework to measure clinical health benefits or individual experiences of adverse events. In a previous study, we conducted a systematic scoping review of studies that contained themes of the medicinal use of cannabis and used data from social media and search engine results. This study analyzed the methodological approaches and limitations of these studies. Objective: We aimed to examine research approaches and study methodologies that use web-based user-generated text to study the use of cannabis as a medicine. Methods: We searched MEDLINE, Scopus, Web of Science, and Embase databases for primary studies in the English language from January 1974 to April 2022. Studies were included if they aimed to understand web-based user-generated text related to health conditions where cannabis is used as a medicine or where health was mentioned in general cannabis-related conversations. Results: We included 42 articles in this review. In these articles, Twitter was used 3 times more than other computer-generated sources, including Reddit, web-based forums, GoFundMe, YouTube, and Google Trends. Analytical methods included sentiment assessment, thematic analysis (manual and automatic), social network analysis, and geographic analysis. Conclusions: This study is the first to review techniques used by research on consumer-generated text for understanding cannabis as a medicine. It is increasingly evident that consumer-generated data offer opportunities for a greater understanding of individual behavior and population health outcomes. However, research using these data has some limitations that include difficulties in establishing sample representativeness and a lack of methodological best practices. To address these limitations, deidentified annotated data sources should be made publicly available, researchers should determine the origins of posts (organizations, bots, power users, or ordinary individuals), and powerful analytical techniques should be used. ", doi="10.2196/35974", url="https://www.jmir.org/2022/11/e35974", url="http://www.ncbi.nlm.nih.gov/pubmed/36383417" } @Article{info:doi/10.2196/38862, author="Moyano, Luz Daniela and Lopez, Victoria Mar{\'i}a and Cavallo, Ana and Candia, Patricia Julia and Kaen, Aaron and Irazola, Vilma and Beratarrechea, Andrea", title="The Use of 2 e-Learning Modalities for Diabetes Education Using Facebook in 2 Cities of Argentina During the COVID-19 Pandemic: Qualitative Study", journal="JMIR Form Res", year="2022", month="Nov", day="16", volume="6", number="11", pages="e38862", keywords="COVID-19", keywords="social media", keywords="diabetes mellitus", keywords="public health", keywords="qualitative research", keywords="COVID-19 pandemic", keywords="teaching and learning settings", keywords="online learning", keywords="eHealth literacy", abstract="Background: The COVID-19 pandemic and the confinement that was implemented in Argentina generated a need to implement innovative tools for the strengthening of diabetes care. Diabetes self-management education (DSME) is a core element of diabetes care; however, because of COVID-19 restrictions, in-person diabetes educational activities were suspended. Social networks have played an instrumental role in this context to provide DSME in 2 cities of Argentina and help persons with diabetes in their daily self-management. Objective: The aim of this study is to evaluate 2 diabetes education modalities (synchronous and asynchronous) using the social media platform Facebook through the content of posts on diabetes educational sessions in 2 cities of Argentina during the COVID-19 pandemic. Methods: In this qualitative study, we explored 2 modalities of e-learning (synchronous and asynchronous) for diabetes education that used the Facebook pages of public health institutions in Chaco and La Rioja, Argentina, in the context of confinement. Social media metrics and the content of the messages posted by users were analyzed. Results: A total of 332 messages were analyzed. We found that in the asynchronous modality, there was a higher number of visualizations, while in the synchronous modality, there were more posts and interactions between educators and users. We also observed that the number of views increased when primary care clinics were incorporated as disseminators, sharing educational videos from the sessions via social media. Positive aspects were observed in the posts, consisting of messages of thanks and, to a lesser extent, reaffirmations, reflections or personal experiences, and consultations related to the subject treated. Another relevant finding was that the educator/moderator role had a greater presence in the synchronous modality, where posts were based on motivation for participation, help to resolve connectivity problems, and answers to specific user queries. Conclusions: Our findings show positive contributions of an educational intervention for diabetes care using the social media platform Facebook in the context of the COVID-19 pandemic. Although each modality (synchronous vs asynchronous) could have differential and particular advantages, we believe that these strategies have potential to be replicated and adapted to other contexts. However, more documented experiences are needed to explore their sustainability and long-term impact from the users' perspective. ", doi="10.2196/38862", url="https://formative.jmir.org/2022/11/e38862", url="http://www.ncbi.nlm.nih.gov/pubmed/36322794" } @Article{info:doi/10.2196/38232, author="Teodorowski, Piotr and Rodgers, E. Sarah and Fleming, Kate and Frith, Lucy", title="Use of the Hashtag \#DataSavesLives on Twitter: Exploratory and Thematic Analysis", journal="J Med Internet Res", year="2022", month="Nov", day="15", volume="24", number="11", pages="e38232", keywords="consumer involvement", keywords="patient participation", keywords="stakeholder participation", keywords="social media", keywords="public engagement", keywords="campaign", keywords="big data", keywords="research", keywords="trust", keywords="tweets", keywords="Twitter", keywords="perception", keywords="usage", keywords="users", keywords="data sharing", keywords="ethics", keywords="community", keywords="hashtag", abstract="Background: ``Data Saves Lives'' is a public engagement campaign that highlights the benefits of big data research and aims to establish public trust for this emerging research area. Objective: This study explores how the hashtag \#DataSavesLives is used on Twitter. We focused on the period when the UK government and its agencies adopted \#DataSavesLives in an attempt to support their plans to set up a new database holding National Health Service (NHS) users' medical data. Methods: Public tweets published between April 19 and July 15, 2021, using the hashtag \#DataSavesLives were saved using NCapture for NVivo 12. All tweets were coded twice. First, each tweet was assigned a positive, neutral, or negative attitude toward the campaign. Second, inductive thematic analysis was conducted. The results of the thematic analysis were mapped under 3 models of public engagement: deficit, dialogue, and participatory. Results: Of 1026 unique tweets available for qualitative analysis, discussion around \#DataSavesLives was largely positive (n=716, 69.8\%) or neutral (n=276, 26.9\%) toward the campaign with limited negative attitudes (n=34, 3.3\%). Themes derived from the \#DataSavesLives debate included ethical sharing, proactively engaging the public, coproducing knowledge with the public, harnessing potential, and gaining an understanding of big data research. The Twitter discourse was largely positive toward the campaign. The hashtag is predominantly used by similar-minded Twitter users to share information about big data projects and to spread positive messages about big data research when there are public controversies. The hashtag is generally used by organizations and people supportive of big data research. Tweet authors recognize that the public should be proactively engaged and involved in big data projects. The campaign remains UK centric. The results indicate that the communication around big data research is driven by the professional community and remains 1-way as members of the public rarely use the hashtag. Conclusions: The results demonstrate the potential of social media but draws attention to hashtag usage being generally confined to ``Twitter bubbles'': groups of similar-minded Twitter users. ", doi="10.2196/38232", url="https://www.jmir.org/2022/11/e38232", url="http://www.ncbi.nlm.nih.gov/pubmed/36378518" } @Article{info:doi/10.2196/39571, author="Yoon, Young Ho and You, Han Kyung and Kwon, Hye Jung and Kim, Sun Jung and Rha, Young Sun and Chang, Jung Yoon and Lee, Sang-Cheol", title="Understanding the Social Mechanism of Cancer Misinformation Spread on YouTube and Lessons Learned: Infodemiological Study", journal="J Med Internet Res", year="2022", month="Nov", day="14", volume="24", number="11", pages="e39571", keywords="cancer misinformation", keywords="social media health misinformation", keywords="fenbendazole", keywords="self-administration", keywords="complex contagion", keywords="YouTube", keywords="social media factual information delivery strategy", abstract="Background: A knowledge gap exists between the list of required actions and the action plan for countering cancer misinformation on social media. Little attention has been paid to a social media strategy for disseminating factual information while also disrupting misinformation on social media networks. Objective: The aim of this study was to, first, identify the spread structure of cancer misinformation on YouTube. We asked the question, ``How do YouTube videos play an important role in spreading information about the self-administration of anthelmintics for dogs as a cancer medicine for humans?'' Second, the study aimed to suggest an action strategy for disrupting misinformation diffusion on YouTube by exploiting the network logic of YouTube information flow and the recommendation system. We asked the question, ``What would be a feasible and effective strategy to block cancer misinformation diffusion on YouTube?'' Methods: The study used the YouTube case of the self-administration of anthelmintics for dogs as an alternative cancer medicine in South Korea. We gathered Korean YouTube videos about the self-administration of fenbendazole. Using the YouTube application programming interface for the query ``fenbendazole,'' 702 videos from 227 channels were compiled. Then, videos with at least 50,000 views, uploaded between September 2019 and September 2020, were selected from the collection, resulting in 90 videos. Finally, 10 recommended videos for each of the 90 videos were compiled, totaling 573 videos. Social network visualization for the recommended videos was used to identify three intervention strategies for disrupting the YouTube misinformation network. Results: The study found evidence of complex contagion by human and machine recommendation systems. By exposing stakeholders to multiple information sources on fenbendazole self-administration and by linking them through a recommendation algorithm, YouTube has become the perfect infrastructure for reinforcing the belief that fenbendazole can cure cancer, despite government warnings about the risks and dangers of self-administration. Conclusions: Health authorities should upload pertinent information through multiple channels and should exploit the existing YouTube recommendation algorithm to disrupt the misinformation network. Considering the viewing habits of patients and caregivers, the direct use of YouTube hospital channels is more effective than the indirect use of YouTube news media channels or government channels that report public announcements and statements. Reinforcing through multiple channels is the key. ", doi="10.2196/39571", url="https://www.jmir.org/2022/11/e39571", url="http://www.ncbi.nlm.nih.gov/pubmed/36374534" } @Article{info:doi/10.2196/37698, author="Chen, Xi and Yik, Michelle", title="The Emotional Anatomy of the Wuhan Lockdown: Sentiment Analysis Using Weibo Data", journal="JMIR Form Res", year="2022", month="Nov", day="14", volume="6", number="11", pages="e37698", keywords="Wuhan lockdown", keywords="COVID-19", keywords="public health emergency", keywords="emotion", keywords="circumplex model of affect", keywords="Weibo", keywords="jiayou", abstract="Background: On January 23, 2020, the city of Wuhan, China, was sealed off in response to the COVID-19 pandemic. Studies have found that the lockdown was associated with both positive and negative emotions, although their findings are not conclusive. In these studies, emotional responses to the Wuhan lockdown were identified using lexicons based on limited emotion types. Objective: This study aims to map Chinese people's emotional responses to the Wuhan lockdown and compare Wuhan residents' emotions with those of people elsewhere in China by analyzing social media data from Weibo using a lexicon based on the circumplex model of affect. Methods: Social media posts on Weibo from 2 weeks before to 2 weeks after the Wuhan lockdown was imposed (January 9, 2020, to February 6, 2020) were collected. Each post was coded using a valence score and an arousal score. To map emotional trajectories during the study period, we used a data set of 359,190 posts. To compare the immediate emotional responses to the lockdown and its longer-term emotional impact on Wuhan residents (n=1236) and non-Hubei residents (n=12,714), we used a second data set of 57,685 posts for multilevel modeling analyses. Results: Most posts (248,757/359,190, 69.25\%) made during the studied lockdown period indicated a pleasant mood with low arousal. A gradual increase in both valence and arousal before the lockdown was observed. The posts after the lockdown was imposed had higher valence and arousal than prelockdown posts. On the day of lockdown, the non-Hubei group had a temporarily boosted valence ($\gamma$20=0.118; SE 0.021; P<.001) and arousal ($\gamma$30=0.293; SE 0.022; P<.001). Compared with non-Hubei residents, the Wuhan group had smaller increases in valence ($\gamma$21=?0.172; SE 0.052; P<.001) and arousal ($\gamma$31=?0.262; SE 0.053; P<.001) on the day of lockdown. Weibo users' emotional valence ($\gamma$40=0.000; SE 0.001; P=.71) and arousal ($\gamma$40=0.001; SE 0.001; P=.56) remained stable over the 2 weeks after the lockdown was imposed regardless of geographical location (valence: $\gamma$41=?0.004, SE 0.003, and P=.16; arousal: $\gamma$41=0.003, SE 0.003, and P=.26). Conclusions: During the early stages of the pandemic, most Weibo posts indicated a pleasant mood with low arousal. The overall increase in the posts' valence and arousal after the lockdown announcement might indicate collective cohesion and mutual support in web-based communities during a public health crisis. Compared with the temporary increases in valence and arousal of non-Hubei users on the day of lockdown, Wuhan residents' emotions were less affected by the announcement. Overall, our data suggest that Weibo users were not influenced by the lockdown measures in the 2 weeks after the lockdown announcement. Our findings offer policy makers insights into the usefulness of social connections in maintaining the psychological well-being of people affected by a lockdown. ", doi="10.2196/37698", url="https://formative.jmir.org/2022/11/e37698", url="http://www.ncbi.nlm.nih.gov/pubmed/36166650" } @Article{info:doi/10.2196/38957, author="Fittler, Andr{\'a}s and Paczolai, P{\'e}ter and Ashraf, Reza Amir and Pourhashemi, Amir and Iv{\'a}nyi, P{\'e}ter", title="Prevalence of Poisoned Google Search Results of Erectile Dysfunction Medications Redirecting to Illegal Internet Pharmacies: Data Analysis Study", journal="J Med Internet Res", year="2022", month="Nov", day="8", volume="24", number="11", pages="e38957", keywords="internet pharmacies", keywords="search engine redirection", keywords="compromised websites", keywords="illegal medicines", keywords="patient safety", keywords="Europe", keywords="erectile dysfunction medications", abstract="Background: Illegal online pharmacies function as affiliate networks, in which search engine results pages (SERPs) are poisoned by several links redirecting site visitors to unlicensed drug distribution pages upon clicking on the link of a legitimate, yet irrelevant domain. This unfair online marketing practice is commonly referred to as search redirection attack, a most frequently used technique in the online illegal pharmaceutical marketplace. Objective: This study is meant to describe the mechanism of search redirection attacks in Google search results in relation to erectile dysfunction medications in European countries and also to determine the local and global scales of this problem. Methods: The search engine query results regarding 4 erectile dysfunction medications were documented using Google. The search expressions were ``active ingredient'' and ``buy'' in the language of 12 European countries, including Hungary. The final destination website legitimacy was checked at LegitScript, and the estimated number of monthly unique visitors was obtained from SEMrush traffic analytics. Compromised links leading to international illegal medicinal product vendors via redirection were analyzed using Gephi graph visualization software. Results: Compromised links redirecting to active online pharmacies were present in search query results of all evaluated countries. The prevalence was highest in Spain (62/160, 38.8\%), Hungary (52/160, 32.5\%), Italy (46/160, 28.8\%), and France (37/160, 23.1\%), whereas the lowest was in Finland (12/160, 7.5\%), Croatia (10/160, 6.3\%), and Bulgaria (2/160, 1.3\%), as per data recorded in November 2020. A decrease in the number of compromised sites linking visitors to illegitimate medicine sellers was observed in the Hungarian data set between 2019 and 2021, from 41\% (33/80) to 5\% (4/80), respectively. Out of 1920 search results in the international sample, 380 (19.79\%) search query results were compromised, with the majority (n=342, 90\%) of links redirecting individuals to 73 international illegal medicinal product vendors. Most of these illegal online pharmacies (41/73, 56\%) received only 1 or 2 compromised links, whereas the top 3 domains with the highest in-degree link value received more than one-third of all incoming links. Traffic analysis of 35 pharmacy specific domains, accessible via compromised links in search engine queries, showed a total of 473,118 unique visitors in November 2020. Conclusions: Although the number of compromised links in SERPs has shown a decreasing tendency in Hungary, an analysis of the European search query data set points to the global significance of search engine poisoning. Our research illustrates that search engine poisoning is a constant threat, as illegitimate affiliate networks continue to flourish while uncoordinated interventions by authorities and individual stakeholders remain insufficient. Ultimately, without a dedicated and comprehensive effort on the part of search engine providers for effectively monitoring and moderating SERPs, they may never be entirely free of compromised links leading to illegal online pharmacy networks. ", doi="10.2196/38957", url="https://www.jmir.org/2022/11/e38957", url="http://www.ncbi.nlm.nih.gov/pubmed/36346655" } @Article{info:doi/10.2196/38794, author="Ismail, Nashwa and Kbaier, Dhouha and Farrell, Tracie and Kane, Annemarie", title="The Experience of Health Professionals With Misinformation and Its Impact on Their Job Practice: Qualitative Interview Study", journal="JMIR Form Res", year="2022", month="Nov", day="2", volume="6", number="11", pages="e38794", keywords="health misinformation", keywords="social media", keywords="health professional", keywords="patients", keywords="trust", keywords="communication, COVID-19", keywords="intervention", keywords="qualitative research", keywords="interpretive phenomenological analysis", keywords="thematic analysis", keywords="misinformation", keywords="health practitioner", keywords="infodemiology", abstract="Background: Misinformation is often disseminated through social media, where information is spread rapidly and easily. Misinformation affects many patients' decisions to follow a treatment prescribed by health professionals (HPs). For example, chronic patients (eg, those with diabetes) may not follow their prescribed treatment plans. During the recent pandemic, misinformed people rejected COVID-19 vaccines and public health measures, such as masking and physical distancing, and used unproven treatments. Objective: This study investigated the impact of health-threatening misinformation on the practices of health care professionals in the United Kingdom, especially during the outbreaks of diseases where a great amount of health-threatening misinformation is produced and released. The study examined the misinformation surrounding the COVID-19 outbreak to determine how it may have impacted practitioners' perceptions of misinformation and how that may have influenced their practice. In particular, this study explored the answers to the following questions: How do HPs react when they learn that a patient has been misinformed? What misinformation do they believe has the greatest impact on medical practice? What aspects of change and intervention in HPs' practice are in response to misinformation? Methods: This research followed a qualitative approach to collect rich data from a smaller subset of health care practitioners working in the United Kingdom. Data were collected through 1-to-1 online interviews with 13 health practitioners, including junior and senior physicians and nurses in the United Kingdom. Results: Research findings indicated that HPs view misinformation in different ways according to the scenario in which it occurs. Some HPs consider it to be an acute incident exacerbated by the pandemic, while others see it as an ongoing phenomenon (always present) and address it as part of their daily work. HPs are developing pathways for dealing with misinformation. Two main pathways were identified: first, to educate the patient through coaching, advising, or patronizing and, second, to devote resources, such as time and effort, to facilitate 2-way communication between the patient and the health care provider through listening and talking to them. Conclusions: HPs do not receive the confidence they deserve from patients. The lack of trust in health care practitioners has been attributed to several factors, including (1) trusting alternative sources of information (eg, social media) (2) patients' doubts about HPs' experience (eg, a junior doctor with limited experience), and (3) limited time and availability for patients, especially during the pandemic. There are 2 dimensions of trust: patient-HP trust and patient-information trust. There are 2 necessary actions to address the issue of lack of trust in these dimensions: (1) building trust and (2) maintaining trust. The main recommendations of the HPs are to listen to patients, give them more time, and seek evidence-based resources. ", doi="10.2196/38794", url="https://formative.jmir.org/2022/11/e38794", url="http://www.ncbi.nlm.nih.gov/pubmed/36252133" } @Article{info:doi/10.2196/37258, author="Johnson, K. Amy and Bhaumik, Runa and Nandi, Debarghya and Roy, Abhishikta and Mehta, D. Supriya", title="Sexually Transmitted Disease--Related Reddit Posts During the COVID-19 Pandemic: Latent Dirichlet Allocation Analysis", journal="J Med Internet Res", year="2022", month="Oct", day="31", volume="24", number="10", pages="e37258", keywords="infodemiology", keywords="Latent Dirichlet Allocation", keywords="natural language processing", keywords="Reddit", keywords="sexually transmitted infections", keywords="surveillance", keywords="social media", keywords="COVID-19", keywords="social media content", keywords="content analysis", keywords="health outcome", keywords="infoveillance", keywords="health information", keywords="sexually transmitted disease", keywords="STD", abstract="Background: Sexually transmitted diseases (STDs) are common and costly, impacting approximately 1 in 5 people annually. Reddit, the sixth most used internet site in the world, is a user-generated social media discussion platform that may be useful in monitoring discussion about STD symptoms and exposure. Objective: This study sought to define and identify patterns and insights into STD-related discussions on Reddit over the course of the COVID-19 pandemic. Methods: We extracted posts from Reddit from March 2019 through July 2021. We used a topic modeling method, Latent Dirichlet Allocation, to identify the most common topics discussed in the Reddit posts. We then used word clouds, qualitative topic labeling, and spline regression to characterize the content and distribution of the topics observed. Results: Our extraction resulted in 24,311 total posts. Latent Dirichlet Allocation topic modeling showed that with 8 topics for each time period, we achieved high coherence values (pre--COVID-19=0.41, prevaccination=0.42, and postvaccination=0.44). Although most topic categories remained the same over time, the relative proportion of topics changed and new topics emerged. Spline regression revealed that some key terms had variability in the percentage of posts that coincided with pre--COVID-19 and post--COVID-19 periods, whereas others were uniform across the study periods. Conclusions: Our study's use of Reddit is a novel way to gain insights into STD symptoms experienced, potential exposures, testing decisions, common questions, and behavior patterns (eg, during lockdown periods). For example, reduction in STD screening may result in observed negative health outcomes due to missed cases, which also impacts onward transmission. As Reddit use is anonymous, users may discuss sensitive topics with greater detail and more freely than in clinical encounters. Data from anonymous Reddit posts may be leveraged to enhance the understanding of the distribution of disease and need for targeted outreach or screening programs. This study provides evidence in favor of establishing Reddit as having feasibility and utility to enhance the understanding of sexual behaviors, STD experiences, and needed health engagement with the public. ", doi="10.2196/37258", url="https://www.jmir.org/2022/10/e37258", url="http://www.ncbi.nlm.nih.gov/pubmed/36219757" } @Article{info:doi/10.2196/37861, author="Ke, Yang Si and Neeley-Tass, Shannon E. and Barnes, Michael and Hanson, L. Carl and Giraud-Carrier, Christophe and Snell, Quinn", title="COVID-19 Health Beliefs Regarding Mask Wearing and Vaccinations on Twitter: Deep Learning Approach", journal="JMIR Infodemiology", year="2022", month="Oct", day="31", volume="2", number="2", pages="e37861", keywords="COVID-19", keywords="Health Belief Model", keywords="deep learning", keywords="mask", keywords="vaccination", keywords="machine learning", keywords="vaccine data set", keywords="Twitter", keywords="content analysis", keywords="infodemic", keywords="infodemiology", keywords="misinformation", keywords="health belief", abstract="Background: Amid the global COVID-19 pandemic, a worldwide infodemic also emerged with large amounts of COVID-19--related information and misinformation spreading through social media channels. Various organizations, including the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC), and other prominent individuals issued high-profile advice on preventing the further spread of COVID-19. Objective: The purpose of this study is to leverage machine learning and Twitter data from the pandemic period to explore health beliefs regarding mask wearing and vaccines and the influence of high-profile cues to action. Methods: A total of 646,885,238 COVID-19--related English tweets were filtered, creating a mask-wearing data set and a vaccine data set. Researchers manually categorized a training sample of 3500 tweets for each data set according to their relevance to Health Belief Model (HBM) constructs and used coded tweets to train machine learning models for classifying each tweet in the data sets. Results: In total, 5 models were trained for both the mask-related and vaccine-related data sets using the XLNet transformer model, with each model achieving at least 81\% classification accuracy. Health beliefs regarding perceived benefits and barriers were most pronounced for both mask wearing and immunization; however, the strength of those beliefs appeared to vary in response to high-profile cues to action. Conclusions: During both the COVID-19 pandemic and the infodemic, health beliefs related to perceived benefits and barriers observed through Twitter using a big data machine learning approach varied over time and in response to high-profile cues to action from prominent organizations and individuals. ", doi="10.2196/37861", url="https://infodemiology.jmir.org/2022/2/e37861", url="http://www.ncbi.nlm.nih.gov/pubmed/36348979" } @Article{info:doi/10.2196/38153, author="Fuster-Casanovas, A{\"i}na and Das, Ronnie and Vidal-Alaball, Josep and Lopez Segui, Francesc and Ahmed, Wasim", title="The \#VaccinesWork Hashtag on Twitter in the Context of the COVID-19 Pandemic: Network Analysis", journal="JMIR Public Health Surveill", year="2022", month="Oct", day="28", volume="8", number="10", pages="e38153", keywords="Twitter", keywords="social media", keywords="COVID-19", keywords="misinformation", keywords="vaccination", keywords="public health", keywords="vaccine hesitancy", keywords="infodemiology", keywords="health campaign", keywords="content analysis", keywords="social network", keywords="layout algorithm", abstract="Background: Vaccination is one of the most successful public health interventions for the prevention of COVID-19. Toward the end of April 2021, UNICEF (United Nations International Children's Emergency Fund), alongside other organizations, were promoting the hashtag \#VaccinesWork. Objective: The aim of this paper is to analyze the \#VaccinesWork hashtag on Twitter in the context of the COVID-19 pandemic, analyzing the main messages shared and the organizations involved. Methods: The data set used in this study consists of 11,085 tweets containing the \#VaccinesWork hashtag from the 29th to the 30th of April 2021. The data set includes tweets that may not have the hashtag but were replies or mentions in those tweets. The data were retrieved using NodeXL, and the network graph was laid out using the Harel-Koren fast multiscale layout algorithm. Results: The study found that organizations such as the World Health Organization, UNICEF, and Gavi were the key opinion leaders and had a big influence on the spread of information among users. Furthermore, the most shared URLs belonged to academic journals with a high impact factor. Provaccination users had other vaccination-promoting hashtags in common, not only in the COVID-19 scenario. Conclusions: This study investigated the discussions surrounding the \#VaccinesWork hashtag. Social media networks containing conspiracy theories tend to contain dubious accounts leading the discussions and are often linked to unverified information. This kind of analysis can be useful to detect the optimal moment for launching health campaigns on Twitter. ", doi="10.2196/38153", url="https://publichealth.jmir.org/2022/10/e38153", url="http://www.ncbi.nlm.nih.gov/pubmed/36219832" } @Article{info:doi/10.2196/37790, author="Pickering, Gis{\`e}le and Mezouar, Linda and Kechemir, Hayet and Ebel-Bitoun, Caty", title="Paracetamol Use in Patients With Osteoarthritis and Lower Back Pain: Infodemiology Study and Observational Analysis of Electronic Medical Record Data", journal="JMIR Public Health Surveill", year="2022", month="Oct", day="27", volume="8", number="10", pages="e37790", keywords="osteoarthritis", keywords="lower back pain", keywords="general practice", keywords="rheumatology", keywords="paracetamol", keywords="real-world evidence", abstract="Background: Lower back pain (LBP) and osteoarthritis (OA) are common musculoskeletal disorders and account for around 17.0\% of years lived with disability worldwide; however, there is a lack of real-world data on these conditions. Paracetamol brands are frequently prescribed in France for musculoskeletal pain and include Doliprane, Dafalgan, and Ixprim (tramadol-paracetamol). Objective: The objective of this retrospective study was to understand the journey of patients with LBP or OA when treated with paracetamol. Methods: Three studies were undertaken. Two studies analyzed electronic medical records from general practitioners (GPs) and rheumatologists of patients with OA or LBP, who had received at least one paracetamol prescription between 2013 and 2018 in France. Data were extracted, anonymized, and stratified by gender, age, and provider specialty. The third study, an infodemiology study, analyzed associations between terms used on public medical forums and Twitter in France and the United States for OA only. Results: In the first 2 studies, among patients with LBP (98,998), most (n=92,068, 93.0\%) saw a GP, and Doliprane was a first-line therapy for 87.0\% (n=86,128) of patients (71.0\% [n=61,151] in combination with nonsteroidal anti-inflammatory drugs [NSAIDs] or opioids). Among patients with OA (99,997), most (n=84,997, 85.0\%) saw a GP, and Doliprane was a first-line therapy for 83.0\% (n=82,998) of patients (62.0\% [n=51,459] in combination). Overall, paracetamol monotherapy prescriptions decreased as episodes increased. In the third study, in line with available literature, the data confirmed that the prevalence of OA increases with age (91.5\% [212,875/232,650] above 41 years), OA is more predominant in females (46,530/232,650, 20.0\%), and paracetamol use varies between GPs and rheumatologists. Conclusions: This health surveillance analysis provides a better understanding of the journey for patients with LBP or OA. These data confirmed that although paracetamol remains the most common first-line analgesic for patients with LBP and OA, usage varies among patients and health care specialists, and there are concerns over efficacy. ", doi="10.2196/37790", url="https://publichealth.jmir.org/2022/10/e37790", url="http://www.ncbi.nlm.nih.gov/pubmed/36301591" } @Article{info:doi/10.2196/37029, author="Kokoska, E. Ryan and Kim, S. Lori and Szeto, D. Mindy and Aukerman, L. Erica and Dellavalle, P. Robert", title="Top Pediatric Dermatology Twitter Post Characteristics and Trends From 2020 to 2021: Content Analysis", journal="JMIR Dermatol", year="2022", month="Oct", day="26", volume="5", number="4", pages="e37029", keywords="pediatric dermatology", keywords="pediatrics", keywords="dermatology", keywords="Twitter", keywords="social media", keywords="social media engagement", keywords="content analysis", doi="10.2196/37029", url="https://derma.jmir.org/2022/4/e37029", url="http://www.ncbi.nlm.nih.gov/pubmed/37632885" } @Article{info:doi/10.2196/40049, author="Bacsu, Juanita-Dawne and Cammer, Allison and Ahmadi, Soheila and Azizi, Mehrnoosh and Grewal, S. Karl and Green, Shoshana and Gowda-Sookochoff, Rory and Berger, Corinne and Knight, Sheida and Spiteri, J. Raymond and O'Connell, E. Megan", title="Examining the Twitter Discourse on Dementia During Alzheimer's Awareness Month in Canada: Infodemiology Study", journal="JMIR Form Res", year="2022", month="Oct", day="26", volume="6", number="10", pages="e40049", keywords="Twitter", keywords="social media", keywords="dementia", keywords="Alzheimer disease", keywords="awareness", keywords="public health campaigns", abstract="Background: Twitter has become a primary platform for public health campaigns, ranging from mental health awareness week to diabetes awareness month. However, there is a paucity of knowledge about how Twitter is being used during health campaigns, especially for Alzheimer's Awareness Month. Objective: The purpose of our study was to examine dementia discourse during Canada's Alzheimer's Awareness Month in January to inform future awareness campaigns. Methods: We collected 1289 relevant tweets using the Twint application in Python from January 1 to January 31, 2022. Thematic analysis was used to analyze the data. Results: Guided by our analysis, 4 primary themes were identified: dementia education and advocacy, fundraising and promotion, experiences of dementia, and opportunities for future actions. Conclusions: Although our study identified many educational, promotional, and fundraising tweets to support dementia awareness, we also found numerous tweets with cursory messaging (ie, simply referencing January as Alzheimer's Awareness Month in Canada). While these tweets promoted general awareness, they also highlight an opportunity for targeted educational content to counter stigmatizing messages and misinformation about dementia. In addition, awareness strategies partnering with diverse stakeholders (such as celebrities, social media influencers, and people living with dementia and their care partners) may play a pivotal role in fostering dementia dialogue and education. Further research is needed to develop, implement, and evaluate dementia awareness strategies on Twitter. Increased knowledge, partnerships, and research are essential to enhancing dementia awareness during Canada's Alzheimer's Awareness Month and beyond. ", doi="10.2196/40049", url="https://formative.jmir.org/2022/10/e40049", url="http://www.ncbi.nlm.nih.gov/pubmed/36287605" } @Article{info:doi/10.2196/38316, author="van Kampen, Katherine and Laski, Jeremi and Herman, Gabrielle and Chan, M. Teresa", title="Investigating COVID-19 Vaccine Communication and Misinformation on TikTok: Cross-sectional Study", journal="JMIR Infodemiology", year="2022", month="Oct", day="25", volume="2", number="2", pages="e38316", keywords="TikTok", keywords="COVID-19 vaccines", keywords="vaccinations", keywords="misinformation", keywords="COVID-19", keywords="Infodemiology", keywords="social media", keywords="health information", keywords="content analysis", keywords="vaccine hesitancy", keywords="public health", keywords="web-based health information", abstract="Background: The COVID-19 pandemic has highlighted the need for reliable information, especially around vaccines. Vaccine hesitancy is a growing concern and a great threat to broader public health. The prevalence of social media within our daily lives emphasizes the importance of accurately analyzing how health information is being disseminated to the public. TikTok is of particular interest, as it is an emerging social media platform that young adults may be increasingly using to access health information. Objective: The objective of this study was to examine and describe the content within the top 100 TikToks trending with the hashtag \#covidvaccine. Methods: The top 250 most viewed TikToks with the hashtag \#covidvaccine were batch downloaded on July 1, 2021, with their respective metadata. Each TikTok was subsequently viewed and encoded by 2 independent reviewers. Coding continued until 100 TikToks could be included based on language and content. Descriptive features were recorded including health care professional (HCP) status of creator, verification of HCP status, genre, and misinformation addressed. Primary inclusion criteria were any TikToks in English with discussion of a COVID-19 vaccine. Results: Of 102 videos included, the median number of plays was 1,700,000, with median shares of 9224 and 62,200 followers. Upon analysis, 14.7\% (15/102) of TikToks included HCPs, of which 80\% (12/102) could be verified via social media or regulatory body search; 100\% (15/15) of HCP-created TikToks supported vaccine use, and overall, 81.3\% (83/102) of all TikToks (created by either a layperson or an HCP) supported vaccine use. Conclusions: As the pandemic continues, vaccine hesitancy poses a threat to lifting restrictions, and discovering reasons for this hesitancy is important to public health measures. This study summarizes the discourse around vaccine use on TikTok. Importantly, it opens a frank discussion about the necessity to incorporate new social media platforms into medical education, so we might ensure our trainees are ready to engage with patients on novel platforms. ", doi="10.2196/38316", url="https://infodemiology.jmir.org/2022/2/e38316", url="http://www.ncbi.nlm.nih.gov/pubmed/36338548" } @Article{info:doi/10.2196/42759, author="Gallo Marin, Benjamin and Ezemma, Ogechi and Frech, Stefano Fabio and Flores Servin, C. Julio and Rhee, S. Ben and Mulligan, M. Kathleen and O' Connell, A. Katie and Moseley, Isabelle and Wambier, G. Carlos", title="An Analysis of Information Sources of YouTube Videos Pertaining to Tattoo Removal: Cross-sectional Study", journal="JMIR Dermatol", year="2022", month="Oct", day="25", volume="5", number="4", pages="e42759", keywords="tattoo", keywords="tattoo removal", keywords="laser", keywords="internet", keywords="YouTube", keywords="misinformation", keywords="Food and Drug Administration", keywords="FDA", keywords="professional information", keywords="digital", keywords="research", keywords="skin", keywords="skin care", keywords="skincare", keywords="care", keywords="consultation", keywords="safe", keywords="evidence", keywords="dermatologist", abstract="Background: The American Academy of Dermatology and the Food and Drug Administration recommend consultation with a dermatologist prior to undergoing laser tattoo removal. However, non--health care professionals offer tattoo removal. Understanding the information available on the internet for patients regarding tattoo removal is important given that individuals are increasingly consulting digital sources to make decisions regarding skin care. Prior research has identified that YouTube contains misinformation on dermatologic health. Objective: Here, we present a cross-sectional study that determined the sources of information in YouTube videos that discuss tattoo removal and described the content presented to viewers. Methods: Using the query ``tattoo removal,'' we reviewed English-language YouTube videos that explicitly discussed tattoo removal. The following data were recorded: profession of the presenter, tattoo removal method discussed, whether an explicit recommendation to see a dermatologist or physician was present in the video, and number of views. Results: We analyzed 162 YouTube videos. We found that the majority were presented by non--health care professionals (n=125, 77\%), with only 4 (3.7\%) records of this subset recommending viewers to seek consultation from a dermatologist to ensure safe and adequate tattoo removal. Conclusions: Based on our findings, we recommend that dermatologists and other health care professionals provide high-quality, evidence-based information to viewers on tattoo removal and encourage dermatology societies to share via their social media platforms information about the importance of consulting a dermatologist for tattoo removal. ", doi="10.2196/42759", url="https://derma.jmir.org/2022/4/e42759", url="http://www.ncbi.nlm.nih.gov/pubmed/36419716" } @Article{info:doi/10.2196/39324, author="Lorenzo-Luaces, Lorenzo and Howard, Jacqueline and Edinger, Andy and Yan, Yaojun Harry and Rutter, A. Lauren and Valdez, Danny and Bollen, Johan", title="Sociodemographics and Transdiagnostic Mental Health Symptoms in SOCIAL (Studies of Online Cohorts for Internalizing Symptoms and Language) I and II: Cross-sectional Survey and Botometer Analysis", journal="JMIR Form Res", year="2022", month="Oct", day="20", volume="6", number="10", pages="e39324", keywords="depression", keywords="anxiety", keywords="pain", keywords="alcohol", keywords="social media", abstract="Background: Internalizing, externalizing, and somatoform disorders are the most common and disabling forms of psychopathology. Our understanding of these clinical problems is limited by a reliance on self-report along with research using small samples. Social media has emerged as an exciting channel for collecting a large sample of longitudinal data from individuals to study psychopathology. Objective: This study reported the results of 2 large ongoing studies in which we collected data from Twitter and self-reported clinical screening scales, the Studies of Online Cohorts for Internalizing Symptoms and Language (SOCIAL) I and II. Methods: The participants were a sample of Twitter-using adults (SOCIAL I: N=1123) targeted to be nationally representative in terms of age, sex assigned at birth, race, and ethnicity, as well as a sample of college students in the Midwest (SOCIAL II: N=1988), of which 61.78\% (1228/1988) were Twitter users. For all participants who were Twitter users, we asked for access to their Twitter handle, which we analyzed using Botometer, which rates the likelihood of an account belonging to a bot. We divided participants into 4 groups: Twitter users who did not give us their handle or gave us invalid handles (invalid), those who denied being Twitter users (no Twitter, only available for SOCIAL II), Twitter users who gave their handles but whose accounts had high bot scores (bot-like), and Twitter users who provided their handles and had low bot scores (valid). We explored whether there were significant differences among these groups in terms of their sociodemographic features, clinical symptoms, and aspects of social media use (ie, platforms used and time). Results: In SOCIAL I, most individuals were classified as valid (580/1123, 51.65\%), and a few were deemed bot-like (190/1123, 16.91\%). A total of 31.43\% (353/1123) gave no handle or gave an invalid handle (eg, entered ``N/A''). In SOCIAL II, many individuals were not Twitter users (760/1988, 38.23\%). Of the Twitter users in SOCIAL II (1228/1988, 61.78\%), most were classified as either invalid (515/1228, 41.94\%) or valid (484/1228, 39.41\%), with a smaller fraction deemed bot-like (229/1228, 18.65\%). Participants reported high rates of mental health diagnoses as well as high levels of symptoms, especially in SOCIAL II. In general, the differences between individuals who provided or did not provide their social media handles were small and not statistically significant. Conclusions: Triangulating passively acquired social media data and self-reported questionnaires offers new possibilities for large-scale assessment and evaluation of vulnerability to mental disorders. The propensity of participants to share social media handles is likely not a source of sample bias in subsequent social media analytics. ", doi="10.2196/39324", url="https://formative.jmir.org/2022/10/e39324", url="http://www.ncbi.nlm.nih.gov/pubmed/36264616" } @Article{info:doi/10.2196/36767, author="Blunck, Dominik and Kastner, Lena and Nissen, Michael and Winkler, Jacqueline", title="The Effectiveness of Patient Training in Inflammatory Bowel Disease Knowledge via Instagram: Randomized Controlled Trial", journal="J Med Internet Res", year="2022", month="Oct", day="19", volume="24", number="10", pages="e36767", keywords="social media", keywords="Instagram", keywords="patient training", keywords="patient education", keywords="disease-related knowledge", keywords="RCT", keywords="randomized controlled trial", keywords="Germany", keywords="inflammatory bowel disease", keywords="IBD-KNOW", abstract="Background: Patients' knowledge was found to be a key contributor to the success of therapy. Many efforts have been made to educate patients in their disease. However, research found that many patients still lack knowledge regarding their disease. Integrating patient education into social media platforms can bring materials closer to recipients. Objective: The aim of this study is to test the effectiveness of patient education via Instagram. Methods: A randomized controlled trial was conducted to test the effectiveness of patient education via Instagram among patients with inflammatory bowel disease. Participants were recruited online from the open Instagram page of a patient organization. The intervention group was educated via Instagram for 5 weeks by the research team; the control group did not receive any educational intervention. The knowledge about their disease was measured pre- and postintervention using the Inflammatory Bowel Disease Knowledge questionnaire. Data were analyzed by comparing mean knowledge scores and by regression analysis. The trial was purely web based. Results: In total, 49 participants filled out both questionnaires. The intervention group included 25 participants, and the control group included 24 participants. The preintervention knowledge level of the intervention group was reflected as a score of 18.67 out of 24 points; this improved by 3 points to 21.67 postintervention. The postintervention difference between the control and intervention groups was 3.59 points and was statistically significant (t32.88=--4.56, 95\% CI 1.98-5.19; P<.001). Results of the regression analysis, accounting for preintervention knowledge and group heterogeneity, indicated an increase of 3.33 points that was explained by the intervention (P<.001). Conclusions: Patient education via Instagram is an effective way to increase disease-related knowledge. Future studies are needed to assess the effects in other conditions and to compare different means of patient education. Trial Registration: German Clinical Trials Register DRKS00022935; https://tinyurl.com/bed4bzvh ", doi="10.2196/36767", url="https://www.jmir.org/2022/10/e36767", url="http://www.ncbi.nlm.nih.gov/pubmed/36260385" } @Article{info:doi/10.2196/40408, author="Melton, A. Chad and White, M. Brianna and Davis, L. Robert and Bednarczyk, A. Robert and Shaban-Nejad, Arash", title="Fine-tuned Sentiment Analysis of COVID-19 Vaccine--Related Social Media Data: Comparative Study", journal="J Med Internet Res", year="2022", month="Oct", day="17", volume="24", number="10", pages="e40408", keywords="sentiment analysis", keywords="DistilRoBERTa", keywords="natural language processing", keywords="social media", keywords="Twitter", keywords="Reddit", keywords="COVID-19", keywords="vaccination", keywords="vaccine", keywords="content analysis", keywords="public health", keywords="surveillance", keywords="misinformation", keywords="infodemiology", keywords="information quality", abstract="Background: The emergence of the novel coronavirus (COVID-19) and the necessary separation of populations have led to an unprecedented number of new social media users seeking information related to the pandemic. Currently, with an estimated 4.5 billion users worldwide, social media data offer an opportunity for near real-time analysis of large bodies of text related to disease outbreaks and vaccination. These analyses can be used by officials to develop appropriate public health messaging, digital interventions, educational materials, and policies. Objective: Our study investigated and compared public sentiment related to COVID-19 vaccines expressed on 2 popular social media platforms---Reddit and Twitter---harvested from January 1, 2020, to March 1, 2022. Methods: To accomplish this task, we created a fine-tuned DistilRoBERTa model to predict the sentiments of approximately 9.5 million tweets and 70 thousand Reddit comments. To fine-tune our model, our team manually labeled the sentiment of 3600 tweets and then augmented our data set through back-translation. Text sentiment for each social media platform was then classified with our fine-tuned model using Python programming language and the Hugging Face sentiment analysis pipeline. Results: Our results determined that the average sentiment expressed on Twitter was more negative (5,215,830/9,518,270, 54.8\%) than positive, and the sentiment expressed on Reddit was more positive (42,316/67,962, 62.3\%) than negative. Although the average sentiment was found to vary between these social media platforms, both platforms displayed similar behavior related to the sentiment shared at key vaccine-related developments during the pandemic. Conclusions: Considering this similar trend in shared sentiment demonstrated across social media platforms, Twitter and Reddit continue to be valuable data sources that public health officials can use to strengthen vaccine confidence and combat misinformation. As the spread of misinformation poses a range of psychological and psychosocial risks (anxiety and fear, etc), there is an urgency in understanding the public perspective and attitude toward shared falsities. Comprehensive educational delivery systems tailored to a population's expressed sentiments that facilitate digital literacy, health information--seeking behavior, and precision health promotion could aid in clarifying such misinformation. ", doi="10.2196/40408", url="https://www.jmir.org/2022/10/e40408", url="http://www.ncbi.nlm.nih.gov/pubmed/36174192" } @Article{info:doi/10.2196/38949, author="Jain, Shikha and Dhaon, R. Serena and Majmudar, Shivani and Zimmermann, J. Laura and Mordell, Lisa and Walker, Garth and Wallia, Amisha and Akbarnia, Halleh and Khan, Ali and Bloomgarden, Eve and Arora, M. Vineet", title="Empowering Health Care Workers on Social Media to Bolster Trust in Science and Vaccination During the Pandemic: Making IMPACT Using a Place-Based Approach", journal="J Med Internet Res", year="2022", month="Oct", day="17", volume="24", number="10", pages="e38949", keywords="misinformation", keywords="COVID-19", keywords="place-based", keywords="infodemic", keywords="infographic", keywords="social media", keywords="advocacy", keywords="infodemiology", keywords="vaccination", keywords="health care worker", keywords="policy maker", keywords="health policy", keywords="community health", abstract="Background: Given the widespread and concerted efforts to propagate health misinformation on social media, particularly centered around vaccination during the pandemic, many groups of clinicians and scientists were organized on social media to tackle misinformation and promote vaccination, using a national or international lens. Although documenting the impact of such social media efforts, particularly at the community level, can be challenging, a more hyperlocal or ``place-based approach'' for social media campaigns could be effective in tackling misinformation and improving public health outcomes at a community level. Objective: We aimed to describe and document the effectiveness of a place-based strategy for a coordinated group of Chicago health care workers on social media to tackle misinformation and improve vaccination rates in the communities they serve. Methods: The Illinois Medical Professionals Action Collaborative Team (IMPACT) was founded in March 2020 in response to the COVID-19 pandemic, with representatives from major academic teaching hospitals in Chicago (eg, University of Chicago, Northwestern University, University of Illinois, and Rush University) and community-based organizations. Through crowdsourcing on multiple social media platforms (eg, Facebook, Twitter, and Instagram) with a place-based approach, IMPACT engaged grassroots networks of thousands of Illinois health care workers and the public to identify gaps, needs, and viewpoints to improve local health care delivery during the pandemic. Results: To address vaccine misinformation, IMPACT created 8 ``myth debunking'' infographics and a ``vaccine information series'' of 14 infographics that have generated >340,000 impressions and informed the development of vaccine education for the Chicago Public Libraries. IMPACT delivered 13 policy letters focusing on different topics, such as health care worker personal protective equipment, universal masking, and vaccination, with >4000 health care workers signatures collected through social media and delivered to policy makers; it published over 50 op-eds on COVID-19 topics in high-impact news outlets and contributed to >200 local and national news features.Using the crowdsourcing approach on IMPACT social media channels, IMPACT mobilized health care and lay volunteers to staff >400 vaccine events for >120,000 individuals, many in Chicago's hardest-hit neighborhoods. The group's recommendations have influenced public health awareness campaigns and initiatives, as well as research, advocacy, and policy recommendations, and they have been recognized with local and national awards. Conclusions: A coordinated group of health care workers on social media, using a hyperlocal place-based approach, can not only work together to address misinformation but also collaborate to boost vaccination rates in their surrounding communities. ", doi="10.2196/38949", url="https://www.jmir.org/2022/10/e38949", url="http://www.ncbi.nlm.nih.gov/pubmed/35917489" } @Article{info:doi/10.2196/39676, author="Li, Minghui and Hua, Yining and Liao, Yanhui and Zhou, Li and Li, Xue and Wang, Ling and Yang, Jie", title="Tracking the Impact of COVID-19 and Lockdown Policies on Public Mental Health Using Social Media: Infoveillance Study", journal="J Med Internet Res", year="2022", month="Oct", day="13", volume="24", number="10", pages="e39676", keywords="COVID-19", keywords="mental health", keywords="social media", keywords="Twitter", keywords="topic model", keywords="health care workers", abstract="Background: The COVID-19 pandemic and its corresponding preventive and control measures have increased the mental burden on the public. Understanding and tracking changes in public mental status can facilitate optimizing public mental health intervention and control strategies. Objective: This study aimed to build a social media--based pipeline that tracks public mental changes and use it to understand public mental health status regarding the pandemic. Methods: This study used COVID-19--related tweets posted from February 2020 to April 2022. The tweets were downloaded using unique identifiers through the Twitter application programming interface. We created a lexicon of 4 mental health problems (depression, anxiety, insomnia, and addiction) to identify mental health--related tweets and developed a dictionary for identifying health care workers. We analyzed temporal and geographic distributions of public mental health status during the pandemic and further compared distributions among health care workers versus the general public, supplemented by topic modeling on their underlying foci. Finally, we used interrupted time series analysis to examine the statewide impact of a lockdown policy on public mental health in 12 states. Results: We extracted 4,213,005 tweets related to mental health and COVID-19 from 2,316,817 users. Of these tweets, 2,161,357 (51.3\%) were related to ``depression,'' whereas 1,923,635 (45.66\%), 225,205 (5.35\%), and 150,006 (3.56\%) were related to ``anxiety,'' ``insomnia,'' and ``addiction,'' respectively. Compared to the general public, health care workers had higher risks of all 4 types of problems (all P<.001), and they were more concerned about clinical topics than everyday issues (eg, ``students' pressure,'' ``panic buying,'' and ``fuel problems'') than the general public. Finally, the lockdown policy had significant associations with public mental health in 4 out of the 12 states we studied, among which Pennsylvania showed a positive association, whereas Michigan, North Carolina, and Ohio showed the opposite (all P<.05). Conclusions: The impact of COVID-19 and the corresponding control measures on the public's mental status is dynamic and shows variability among different cohorts regarding disease types, occupations, and regional groups. Health agencies and policy makers should primarily focus on depression (reported by 51.3\% of the tweets) and insomnia (which has had an ever-increasing trend since the beginning of the pandemic), especially among health care workers. Our pipeline timely tracks and analyzes public mental health changes, especially when primary studies and large-scale surveys are difficult to conduct. ", doi="10.2196/39676", url="https://www.jmir.org/2022/10/e39676", url="http://www.ncbi.nlm.nih.gov/pubmed/36191167" } @Article{info:doi/10.2196/40323, author="Alhuzali, Hassan and Zhang, Tianlin and Ananiadou, Sophia", title="Emotions and Topics Expressed on Twitter During the COVID-19 Pandemic in the United Kingdom: Comparative Geolocation and Text Mining Analysis", journal="J Med Internet Res", year="2022", month="Oct", day="5", volume="24", number="10", pages="e40323", keywords="Twitter", keywords="COVID-19", keywords="geolocation", keywords="emotion detection", keywords="sentiment analysis", keywords="topic modeling", keywords="social media", keywords="natural language processing", keywords="deep learning", abstract="Background: In recent years, the COVID-19 pandemic has brought great changes to public health, society, and the economy. Social media provide a platform for people to discuss health concerns, living conditions, and policies during the epidemic, allowing policymakers to use this content to analyze the public emotions and attitudes for decision-making. Objective: The aim of this study was to use deep learning--based methods to understand public emotions on topics related to the COVID-19 pandemic in the United Kingdom through a comparative geolocation and text mining analysis on Twitter. Methods: Over 500,000 tweets related to COVID-19 from 48 different cities in the United Kingdom were extracted, with the data covering the period of the last 2 years (from February 2020 to November 2021). We leveraged three advanced deep learning--based models for topic modeling to geospatially analyze the sentiment, emotion, and topics of tweets in the United Kingdom: SenticNet 6 for sentiment analysis, SpanEmo for emotion recognition, and combined topic modeling (CTM). Results: We observed a significant change in the number of tweets as the epidemiological situation and vaccination situation shifted over the 2 years. There was a sharp increase in the number of tweets from January 2020 to February 2020 due to the outbreak of COVID-19 in the United Kingdom. Then, the number of tweets gradually declined as of February 2020. Moreover, with identification of the COVID-19 Omicron variant in the United Kingdom in November 2021, the number of tweets grew again. Our findings reveal people's attitudes and emotions toward topics related to COVID-19. For sentiment, approximately 60\% of tweets were positive, 20\% were neutral, and 20\% were negative. For emotion, people tended to express highly positive emotions in the beginning of 2020, while expressing highly negative emotions over time toward the end of 2021. The topics also changed during the pandemic. Conclusions: Through large-scale text mining of Twitter, our study found meaningful differences in public emotions and topics regarding the COVID-19 pandemic among different UK cities. Furthermore, efficient location-based and time-based comparative analysis can be used to track people's thoughts and feelings, and to understand their behaviors. Based on our analysis, positive attitudes were common during the pandemic; optimism and anticipation were the dominant emotions. With the outbreak and epidemiological change, the government developed control measures and vaccination policies, and the topics also shifted over time. Overall, the proportion and expressions of emojis, sentiments, emotions, and topics varied geographically and temporally. Therefore, our approach of exploring public emotions and topics on the pandemic from Twitter can potentially lead to informing how public policies are received in a particular geographical area. ", doi="10.2196/40323", url="https://www.jmir.org/2022/10/e40323", url="http://www.ncbi.nlm.nih.gov/pubmed/36150046" } @Article{info:doi/10.2196/35034, author="Simonart, Thierry and Lam Hoai, Xu{\^a}n-Lan and de Maertelaer, Viviane", title="Worldwide Evolution of Vaccinable and Nonvaccinable Viral Skin Infections: Google Trends Analysis", journal="JMIR Dermatol", year="2022", month="Oct", day="4", volume="5", number="4", pages="e35034", keywords="big data", keywords="infodemiology", keywords="measles", keywords="varicella", keywords="rubella", keywords="hand", keywords="foot", keywords="mouth disease", keywords="skin infection", keywords="epidemic", keywords="wart", keywords="skin", keywords="dermatology", keywords="trend", keywords="Google search", keywords="web search", keywords="surveillance", keywords="vaccinable", keywords="incidence", keywords="viral epidemics", keywords="distribution", abstract="Background: Most common viral skin infections are not reportable conditions. Studying the population dynamics of these viral epidemics using traditional field methods is costly and time-consuming, especially over wide geographical areas. Objective: This study aimed to explore the evolution, seasonality, and distribution of vaccinable and nonvaccinable viral skin infections through an analysis of Google Trends. Methods: Worldwide search trends from January 2004 through May 2021 for viral skin infections were extracted from Google Trends, quantified, and analyzed. Results: Time series decomposition showed that the total search term volume for warts; zoster; roseola; measles; hand, foot, and mouth disease (HFMD); varicella; and rubella increased worldwide over the study period, whereas the interest for Pityriasis rosea and herpes simplex decreased. Internet searches for HFMD, varicella, and measles exhibited the highest seasonal patterns. The interest for measles and rubella was more pronounced in African countries, whereas the interest for HFMD and roseola was more pronounced in East Asia. Conclusions: Harnessing data generated by web searches may increase the efficacy of traditional surveillance systems and strengthens the suspicion that the incidence of some vaccinable viral skin infections such as varicella, measles, and rubella may be globally increasing, whereas the incidence of common nonvaccinable skin infections remains stable. ", doi="10.2196/35034", url="https://derma.jmir.org/2022/4/e35034", url="http://www.ncbi.nlm.nih.gov/pubmed/37632891" } @Article{info:doi/10.2196/39710, author="You, Yueyue and Yang-Huang, Junwen and Raat, Hein and Van Grieken, Amy", title="Social Media Use and Health-Related Quality of Life Among Adolescents: Cross-sectional Study", journal="JMIR Ment Health", year="2022", month="Oct", day="4", volume="9", number="10", pages="e39710", keywords="adolescents", keywords="social media platforms", keywords="social media", keywords="health-related quality of life", keywords="EuroQol 5-dimension questionnaire, youth version", abstract="Background: Using social media is a time-consuming activity of children and adolescents. Health authorities have warned that excessive use of social media can negatively affect adolescent social, physical, and psychological health. However, scientific findings regarding associations between time spent on social media and adolescent health-related quality of life (HRQoL) are not consistent. Adolescents typically use multiple social media platforms. Whether the use of multiple social media platforms impacts adolescent health is unclear. Objective: The aim of this study was to examine the relationship between social media use, including the number of social media platforms used and time spent on social media, and adolescent HRQoL. Methods: We analyzed the data of 3397 children (mean age 13.5, SD 0.4 years) from the Generation R Study, a population-based cohort study in the Netherlands. Children reported the number of social media platforms used and time spent on social media during weekdays and weekends separately. Children's HRQoL was self-reported with the EuroQol 5-dimension questionnaire--youth version. Data on social media use and HRQoL were collected from 2015 to 2019. Multiple logistic and linear regressions were applied. Results: In this study, 72.6\% (2466/3397) of the children used 3 or more social media platforms, and 37.7\% (1234/3276) and 58.3\% (1911/3277) of the children used social media at least 2 hours per day during weekdays and weekends, respectively. Children using more social media platforms (7 or more platforms) had a higher odds of reporting having some or a lot of problems on ``having pain or discomfort'' (OR 1.55, 95\% CI 1.20 to 1.99) and ``feeling worried, sad or unhappy'' (OR 1.99, 95\% CI 1.52 to 2.60) dimensions and reported lower self-rated health ($\beta$ --3.81, 95\% CI --5.54 to --2.09) compared with children who used 0 to 2 social media platforms. Both on weekdays and weekends, children spent more time on social media were more likely to report having some or a lot of problems on ``doing usual activities,'' ``having pain or discomfort,'' ``feeling worried, sad or unhappy,'' and report lower self-rated health (all P<.001). Conclusions: Our findings indicate that using more social media platforms and spending more time on social media were significantly related to lower HRQoL. We recommend future research to study the pathway between social media use and HRQoL among adolescents. ", doi="10.2196/39710", url="https://mental.jmir.org/2022/10/e39710", url="http://www.ncbi.nlm.nih.gov/pubmed/36194460" } @Article{info:doi/10.2196/39504, author="Ferawati, Kiki and Liew, Kongmeng and Aramaki, Eiji and Wakamiya, Shoko", title="Monitoring Mentions of COVID-19 Vaccine Side Effects on Japanese and Indonesian Twitter: Infodemiological Study", journal="JMIR Infodemiology", year="2022", month="Oct", day="4", volume="2", number="2", pages="e39504", keywords="COVID-19", keywords="vaccine", keywords="COVID-19 vaccine", keywords="Pfizer", keywords="Moderna", keywords="vaccine side effects", keywords="side effects", keywords="Twitter", keywords="logistic regression", abstract="Background: The year 2021 was marked by vaccinations against COVID-19, which spurred wider discussion among the general population, with some in favor and some against vaccination. Twitter, a popular social media platform, was instrumental in providing information about the COVID-19 vaccine and has been effective in observing public reactions. We focused on tweets from Japan and Indonesia, 2 countries with a large Twitter-using population, where concerns about side effects were consistently stated as a strong reason for vaccine hesitancy. Objective: This study aimed to investigate how Twitter was used to report vaccine-related side effects and to compare the mentions of these side effects from 2 messenger RNA (mRNA) vaccine types developed by Pfizer and Moderna, in Japan and Indonesia. Methods: We obtained tweet data from Twitter using Japanese and Indonesian keywords related to COVID-19 vaccines and their side effects from January 1, 2021, to December 31, 2021. We then removed users with a high frequency of tweets and merged the tweets from multiple users as a single sentence to focus on user-level analysis, resulting in a total of 214,165 users (Japan) and 12,289 users (Indonesia). Then, we filtered the data to select tweets mentioning Pfizer or Moderna only and removed tweets mentioning both. We compared the side effect counts to the public reports released by Pfizer and Moderna. Afterward, logistic regression models were used to compare the side effects for the Pfizer and Moderna vaccines for each country. Results: We observed some differences in the ratio of side effects between the public reports and tweets. Specifically, fever was mentioned much more frequently in tweets than would be expected based on the public reports. We also observed differences in side effects reported between Pfizer and Moderna vaccines from Japan and Indonesia, with more side effects reported for the Pfizer vaccine in Japanese tweets and more side effects with the Moderna vaccine reported in Indonesian tweets. Conclusions: We note the possible consequences of vaccine side effect surveillance on Twitter and information dissemination, in that fever appears to be over-represented. This could be due to fever possibly having a higher severity or measurability, and further implications are discussed. ", doi="10.2196/39504", url="https://infodemiology.jmir.org/2022/2/e39504", url="http://www.ncbi.nlm.nih.gov/pubmed/36277140" } @Article{info:doi/10.2196/39582, author="Koss, Jonathan and Bohnet-Joschko, Sabine", title="Social Media Mining of Long-COVID Self-Medication Reported by Reddit Users: Feasibility Study to Support Drug Repurposing", journal="JMIR Form Res", year="2022", month="Oct", day="3", volume="6", number="10", pages="e39582", keywords="social media mining", keywords="drug repurposing", keywords="long-COVID", keywords="crowdsourcing", keywords="COVID-19", keywords="Reddit", keywords="social media", keywords="content analysis", keywords="network analysis", keywords="recognition algorithm", keywords="treatment", abstract="Background: Since the beginning of the COVID-19 pandemic, over 480 million people have been infected and more than 6 million people have died from COVID-19 worldwide. In some patients with acute COVID-19, symptoms manifest over a longer period, which is also called ``long-COVID.'' Unmet medical needs related to long-COVID are high, since there are no treatments approved. Patients experiment with various medications and supplements hoping to alleviate their suffering. They often share their experiences on social media. Objective: The aim of this study was to explore the feasibility of social media mining methods to extract important compounds from the perspective of patients. The goal is to provide an overview of different medication strategies and important agents mentioned in Reddit users' self-reports to support hypothesis generation for drug repurposing, by incorporating patients' experiences. Methods: We used named-entity recognition to extract substances representing medications or supplements used to treat long-COVID from almost 70,000 posts on the ``/r/covidlonghaulers'' subreddit. We analyzed substances by frequency, co-occurrences, and network analysis to identify important substances and substance clusters. Results: The named-entity recognition algorithm achieved an F1 score of 0.67. A total of 28,447 substance entities and 5789 word co-occurrence pairs were extracted. ``Histamine antagonists,'' ``famotidine,'' ``magnesium,'' ``vitamins,'' and ``steroids'' were the most frequently mentioned substances. Network analysis revealed three clusters of substances, indicating certain medication patterns. Conclusions: This feasibility study indicates that network analysis can be used to characterize the medication strategies discussed in social media. Comparison with existing literature shows that this approach identifies substances that are promising candidates for drug repurposing, such as antihistamines, steroids, or antidepressants. In the context of a pandemic, the proposed method could be used to support drug repurposing hypothesis development by prioritizing substances that are important to users. ", doi="10.2196/39582", url="https://formative.jmir.org/2022/10/e39582", url="http://www.ncbi.nlm.nih.gov/pubmed/36007131" } @Article{info:doi/10.2196/40436, author="Nguyen, Jean Cassandra and Pham, Christian and Jackson, M. Alexandra and Ellison, Kamakahiolani Nicole Lee and Sinclair, Ka`imi", title="Online Food Security Discussion Before and During the COVID-19 Pandemic in Native Hawaiian and Pacific Islander Community Groups and Organizations: Content Analysis of Facebook Posts", journal="Asian Pac Isl Nurs J", year="2022", month="Sep", day="30", volume="6", number="1", pages="e40436", keywords="social media", keywords="oceanic ancestry group", keywords="food insecurity", keywords="social networking", keywords="COVID-19", keywords="Facebook", keywords="community", keywords="Hawaiian", keywords="Pacific Islander", keywords="online", keywords="food", keywords="risk factor", keywords="disease", keywords="cardiometabolic", keywords="diabetes", keywords="hypertension", keywords="food security", keywords="digital", keywords="support", keywords="culture", abstract="Background: The Native Hawaiian and Pacific Islander (NHPI) population experiences disproportionately higher rates of food insecurity, which is a risk factor for cardiometabolic diseases such as cardiovascular disease, type 2 diabetes, obesity, and hypertension, when compared to white individuals. Novel and effective approaches that address food insecurity are needed for the NHPI population, particularly in areas of the continental United States, which is a popular migration area for many NHPI families. Social media may serve as an opportune setting to reduce food insecurity and thus the risk factors for cardiometabolic diseases among NHPI people; however, it is unclear if and how food insecurity is discussed in online communities targeting NHPI individuals. Objective: The objective of this study was to characterize the quantity, nature, and audience engagement of messages related to food insecurity posted online in community groups and organizations that target NHPI audiences. Methods: Publicly accessible Facebook pages and groups focused on serving NHPI community members living in the states of Washington or Oregon served as the data source. Facebook posts between March and June 2019 (before the COVID-19 pandemic) and from March to June 2020 (during the COVID-19 pandemic) that were related to food security were identified using a set of 36 related keywords. Data on the post and any user engagement (ie, comments, shares, or digital reactions) were extracted for all relevant posts. A content analytical approach was used to identify and quantify the nature of the identified posts and any related comments. The codes resulting from the content analysis were described and compared by year, page type, and engagement. Results: Of the 1314 nonduplicated posts in the 7 relevant Facebook groups and pages, 88 were related to food security (8 in 2019 and 80 in 2020). The nature of posts was broadly classified into literature-based codes, food assistance (the most common), perspectives of food insecurity, community gratitude and support, and macrolevel contexts. Among the 88 posts, 74\% (n=65) had some form of engagement, and posts reflecting community gratitude and support or culture had more engagement than others (mean 19.9, 95\% CI 11.2-28.5 vs mean 6.1, 95\% CI 1.7-10.4; and mean 26.8, 95\% CI 12.7-40.9 vs mean 5.3, 95\% CI 3.0-7.7, respectively). Conclusions: Food security--related posts in publicly accessible Facebook groups targeting NHPI individuals living in Washington and Oregon largely focused on food assistance, although cultural values of gratitude, maintaining NHPI culture, and supporting children were also reflected. Future work should capitalize on social media as a potential avenue to reach a unique cultural group in the United States experiencing inequitably high rates of food insecurity and risk of cardiometabolic diseases. ", doi="10.2196/40436", url="https://apinj.jmir.org/2022/1/e40436", url="http://www.ncbi.nlm.nih.gov/pubmed/36212246" } @Article{info:doi/10.2196/30113, author="Hoque Tania, Marzia and Hossain, Razon Md and Jahanara, Nuzhat and Andreev, Ilya and Clifton, A. David", title="Thinking Aloud or Screaming Inside: Exploratory Study of Sentiment Around Work", journal="JMIR Form Res", year="2022", month="Sep", day="30", volume="6", number="9", pages="e30113", keywords="work-related mental health", keywords="sentiment analysis", keywords="natural language processing", keywords="occupational health", keywords="Bayesian inference", keywords="machine learning", keywords="artificial intelligence", keywords="mobile phone", abstract="Background: Millions of workers experience work-related ill health every year. The loss of working days often accounts for poor well-being because of discomfort and stress caused by the workplace. The ongoing pandemic and postpandemic shift in socioeconomic and work culture can continue to contribute to adverse work-related sentiments. Critically investigating state-of-the-art technologies, this study identifies the research gaps in recognizing workers' need for well-being support, and we aspire to understand how such evidence can be collected to transform the workforce and workplace. Objective: Building on recent advances in sentiment analysis, this study aims to closely examine the potential of social media as a tool to assess workers' emotions toward the workplace. Methods: This study collected a large Twitter data set comprising both pandemic and prepandemic tweets facilitated through a human-in-the-loop approach in combination with unsupervised learning and meta-heuristic optimization algorithms. The raw data preprocessed through natural language processing techniques were assessed using a generative statistical model and a lexicon-assisted rule-based model, mapping lexical features to emotion intensities. This study also assigned human annotations and performed work-related sentiment analysis. Results: A mixed methods approach, including topic modeling using latent Dirichlet allocation, identified the top topics from the corpus to understand how Twitter users engage with discussions on work-related sentiments. The sorted aspects were portrayed through overlapped clusters and low intertopic distances. However, further analysis comprising the Valence Aware Dictionary for Sentiment Reasoner suggested a smaller number of negative polarities among diverse subjects. By contrast, the human-annotated data set created for this study contained more negative sentiments. In this study, sentimental juxtaposition revealed through the labeled data set was supported by the n-gram analysis as well. Conclusions: The developed data set demonstrates that work-related sentiments are projected onto social media, which offers an opportunity to better support workers. The infrastructure of the workplace, the nature of the work, the culture within the industry and the particular organization, employers, colleagues, person-specific habits, and upbringing all play a part in the health and well-being of any working adult who contributes to the productivity of the organization. Therefore, understanding the origin and influence of the complex underlying factors both qualitatively and quantitatively can inform the next generation of workplaces to drive positive change by relying on empirically grounded evidence. Therefore, this study outlines a comprehensive approach to capture deeper insights into work-related health. ", doi="10.2196/30113", url="https://formative.jmir.org/2022/9/e30113", url="http://www.ncbi.nlm.nih.gov/pubmed/36178712" } @Article{info:doi/10.2196/38140, author="Yu, Deahan and Vydiswaran, Vinod V. G.", title="An Assessment of Mentions of Adverse Drug Events on Social Media With Natural Language Processing: Model Development and Analysis", journal="JMIR Med Inform", year="2022", month="Sep", day="28", volume="10", number="9", pages="e38140", keywords="natural language processing", keywords="machine learning", keywords="adverse drug event", keywords="pharmacovigilance", keywords="social media", keywords="drug", keywords="clinical", keywords="public health", keywords="health monitoring", keywords="surveillance", keywords="drug effects", keywords="drug safety", abstract="Background: Adverse reactions to drugs attract significant concern in both clinical practice and public health monitoring. Multiple measures have been put into place to increase postmarketing surveillance of the adverse effects of drugs and to improve drug safety. These measures include implementing spontaneous reporting systems and developing automated natural language processing systems based on data from electronic health records and social media to collect evidence of adverse drug events that can be further investigated as possible adverse reactions. Objective: While using social media for collecting evidence of adverse drug events has potential, it is not clear whether social media are a reliable source for this information. Our work aims to (1) develop natural language processing approaches to identify adverse drug events on social media and (2) assess the reliability of social media data to identify adverse drug events. Methods: We propose a collocated long short-term memory network model with attentive pooling and aggregated, contextual representation generated by a pretrained model. We applied this model on large-scale Twitter data to identify adverse drug event--related tweets. We conducted a qualitative content analysis of these tweets to validate the reliability of social media data as a means to collect such information. Results: The model outperformed a variant without contextual representation during both the validation and evaluation phases. Through the content analysis of adverse drug event tweets, we observed that adverse drug event--related discussions had 7 themes. Mental health--related, sleep-related, and pain-related adverse drug event discussions were most frequent. We also contrast known adverse drug reactions to those mentioned in tweets. Conclusions: We observed a distinct improvement in the model when it used contextual information. However, our results reveal weak generalizability of the current systems to unseen data. Additional research is needed to fully utilize social media data and improve the robustness and reliability of natural language processing systems. The content analysis, on the other hand, showed that Twitter covered a sufficiently wide range of adverse drug events, as well as known adverse reactions, for the drugs mentioned in tweets. Our work demonstrates that social media can be a reliable data source for collecting adverse drug event mentions. ", doi="10.2196/38140", url="https://medinform.jmir.org/2022/9/e38140", url="http://www.ncbi.nlm.nih.gov/pubmed/36170004" } @Article{info:doi/10.2196/39274, author="Korshakova, Elena and Marsh, K. Jessecae and Kleinberg, Samantha", title="Health Information Sourcing and Health Knowledge Quality: Repeated Cross-sectional Survey", journal="JMIR Form Res", year="2022", month="Sep", day="28", volume="6", number="9", pages="e39274", keywords="health knowledge", keywords="health information seeking", keywords="information dissemination", keywords="COVID-19", keywords="online health information", keywords="public health", keywords="health literacy", keywords="social media", keywords="information quality", keywords="infodemiology", abstract="Background: People's health-related knowledge influences health outcomes, as this knowledge may influence whether individuals follow advice from their doctors or public health agencies. Yet, little attention has been paid to where people obtain health information and how these information sources relate to the quality of knowledge. Objective: We aim to discover what information sources people use to learn about health conditions, how these sources relate to the quality of their health knowledge, and how both the number of information sources and health knowledge change over time. Methods: We surveyed 200 different individuals at 12 time points from March through September 2020. At each time point, we elicited participants' knowledge about causes, risk factors, and preventative interventions for 8 viral (Ebola, common cold, COVID-19, Zika) and nonviral (food allergies, amyotrophic lateral sclerosis [ALS], strep throat, stroke) illnesses. Participants were further asked how they learned about each illness and to rate how much they trust various sources of health information. Results: We found that participants used different information sources to obtain health information about common illnesses (food allergies, strep throat, stroke) compared to emerging illnesses (Ebola, common cold, COVID-19, Zika). Participants relied mainly on news media, government agencies, and social media for information about emerging illnesses, while learning about common illnesses from family, friends, and medical professionals. Participants relied on social media for information about COVID-19, with their knowledge accuracy of COVID-19 declining over the course of the pandemic. The number of information sources participants used was positively correlated with health knowledge quality, though there was no relationship with the specific source types consulted. Conclusions: Building on prior work on health information seeking and factors affecting health knowledge, we now find that people systematically consult different types of information sources by illness type and that the number of information sources people use affects the quality of individuals' health knowledge. Interventions to disseminate health information may need to be targeted to where individuals are likely to seek out information, and these information sources differ systematically by illness type. ", doi="10.2196/39274", url="https://formative.jmir.org/2022/9/e39274", url="http://www.ncbi.nlm.nih.gov/pubmed/35998198" } @Article{info:doi/10.2196/36941, author="Hu, Mengke and Conway, Mike", title="Perspectives of the COVID-19 Pandemic on Reddit: Comparative Natural Language Processing Study of the United States, the United Kingdom, Canada, and Australia", journal="JMIR Infodemiology", year="2022", month="Sep", day="27", volume="2", number="2", pages="e36941", keywords="COVID-19", keywords="social media", keywords="natural language processing", keywords="Reddit", abstract="Background: Since COVID-19 was declared a pandemic by the World Health Organization on March 11, 2020, the disease has had an unprecedented impact worldwide. Social media such as Reddit can serve as a resource for enhancing situational awareness, particularly regarding monitoring public attitudes and behavior during the crisis. Insights gained can then be utilized to better understand public attitudes and behaviors during the COVID-19 crisis, and to support communication and health-promotion messaging. Objective: The aim of this study was to compare public attitudes toward the 2020-2021 COVID-19 pandemic across four predominantly English-speaking countries (the United States, the United Kingdom, Canada, and Australia) using data derived from the social media platform Reddit. Methods: We utilized a topic modeling natural language processing method (more specifically latent Dirichlet allocation). Topic modeling is a popular unsupervised learning technique that can be used to automatically infer topics (ie, semantically related categories) from a large corpus of text. We derived our data from six country-specific, COVID-19--related subreddits (r/CoronavirusAustralia, r/CoronavirusDownunder, r/CoronavirusCanada, r/CanadaCoronavirus, r/CoronavirusUK, and r/coronavirusus). We used topic modeling methods to investigate and compare topics of concern for each country. Results: Our consolidated Reddit data set consisted of 84,229 initiating posts and 1,094,853 associated comments collected between February and November 2020 for the United States, the United Kingdom, Canada, and Australia. The volume of posting in COVID-19--related subreddits declined consistently across all four countries during the study period (February 2020 to November 2020). During lockdown events, the volume of posts peaked. The UK and Australian subreddits contained much more evidence-based policy discussion than the US or Canadian subreddits. Conclusions: This study provides evidence to support the contention that there are key differences between salient topics discussed across the four countries on the Reddit platform. Further, our approach indicates that Reddit data have the potential to provide insights not readily apparent in survey-based approaches. ", doi="10.2196/36941", url="https://infodemiology.jmir.org/2022/2/e36941", url="http://www.ncbi.nlm.nih.gov/pubmed/36196144" } @Article{info:doi/10.2196/39360, author="Liang, Jing and Wang, Linlin and Song, Shijie and Dong, Man and Xu, Yidan and Zuo, Xinyu and Zhang, Jingyi and Adrian Sherif, Akil and Ehsan, Jafree and Ma, Jianjun and Li, Pengyang", title="Quality and Audience Engagement of Takotsubo Syndrome--Related Videos on TikTok: Content Analysis", journal="J Med Internet Res", year="2022", month="Sep", day="26", volume="24", number="9", pages="e39360", keywords="TikTok", keywords="short video apps", keywords="information quality", keywords="Takotsubo syndrome", keywords="patient education", keywords="social media", keywords="audience engagement", abstract="Background: The incidence of Takotsubo syndrome (TTS), also known as the broken heart syndrome or stress cardiomyopathy, is increasing worldwide. The understanding of its prognosis has been progressively evolving and currently appears to be poorer than previously thought, which has attracted the attention of researchers. An attempt to recognize the awareness of this condition among the general population drove us to analyze the dissemination of this topic on TikTok, a popular short-video--based social media platform. We found a considerable number of videos on TTS on TikTok; however, the quality of the presented information remains unknown. Objective: The aim of this study was to analyze the quality and audience engagement of TTS-related videos on TikTok. Methods: Videos on the TikTok platform were explored on August 2, 2021 to identify those related to TTS by using 6 Chinese keywords. A total of 2549 videos were found, of which 80 met our inclusion criteria and were evaluated for their characteristics, content, quality, and reliability. The quality and reliability were rated using the DISCERN instrument and the Journal of the American Medical Association (JAMA) criteria by 2 reviewers independently, and a score was assigned. Descriptive statistics were generated, and the Kruskal-Wallis test was used for statistical analysis. Multiple linear regression was performed to evaluate the association between audience engagement and other factors such as video content, video quality, and author types. Results: The scores assigned to the selected video content were low with regard to the diagnosis (0.66/2) and management (0.34/2) of TTS. The evaluated videos were found to have an average score of 36.93 out of 80 on the DISCERN instrument and 1.51 out of 4 per the JAMA criteria. None of the evaluated videos met all the JAMA criteria. The quality of the relayed information varied by source (All P<.05). TTS-related videos made by health care professionals accounted for 28\% (22/80) of all the evaluated videos and had the highest DISCERN scores with an average of 40.59 out of 80. Multiple linear regression analysis showed that author types that identified as health professionals (exponentiated regression coefficient 17.48, 95\% CI 2.29-133.52; P=.006) and individual science communicators (exponentiated regression coefficient 13.38, 95\% CI 1.83-97.88; P=.01) were significant and independent determinants of audience engagement (in terms of the number of likes). Other author types of videos, video content, and DISCERN document scores were not associated with higher likes. Conclusions: We found that the quality of videos regarding TTS for patient education on TikTok is poor. Patients should be cautious about health-related information on TikTok. The formulation of a measure for video quality review is necessary, especially when the purpose of the published content is to educate and increase awareness on a health-related topic. ", doi="10.2196/39360", url="https://www.jmir.org/2022/9/e39360", url="http://www.ncbi.nlm.nih.gov/pubmed/36155486" } @Article{info:doi/10.2196/35648, author="Stafylis, Chrysovalantis and Vavala, Gabriella and Wang, Qiao and McLeman, Bethany and Lemley, M. Shea and Young, D. Sean and Xie, Haiyi and Matthews, G. Abigail and Oden, Neal and Revoredo, Leslie and Shmueli-Blumberg, Dikla and Hichborn, G. Emily and McKelle, Erin and Moran, M. Landhing and Jacobs, Petra and Marsch, A. Lisa and Klausner, D. Jeffrey", title="Relative Effectiveness of Social Media, Dating Apps, and Information Search Sites in Promoting HIV Self-testing: Observational Cohort Study", journal="JMIR Form Res", year="2022", month="Sep", day="23", volume="6", number="9", pages="e35648", keywords="HIV prevention", keywords="PrEP", keywords="home HIV test", keywords="social media", keywords="dating apps", keywords="search engines", keywords="HIV", keywords="human immunodeficiency virus", keywords="self-testing", keywords="infection", keywords="digital health", keywords="health promotion", keywords="MSM", keywords="pre-exposure prophylaxis", keywords="medical information", abstract="Background: Social media sites, dating apps, and information search sites have been used to reach individuals at high risk for HIV infection. However, it is not clear which platform is the most efficient in promoting home HIV self-testing, given that the users of various platforms may have different characteristics that impact their readiness for HIV testing. Objective: This study aimed to compare the relative effectiveness of social media sites, dating apps, and information search sites in promoting HIV self-testing among minority men who have sex with men (MSM) at an increased risk of HIV infection. Test kit order rates were used as a proxy to evaluate promotion effectiveness. In addition, we assessed differences in characteristics between participants who ordered and did not order an HIV test kit. Methods: Culturally appropriate advertisements were placed on popular sites of three different platforms: social media sites (Facebook, Instagram), dating apps (Grindr, Jack'D), and information search sites (Google, Bing). Advertisements targeted young (18-30 years old) and minority (Black or Latinx) MSM at risk of HIV exposure. Recruitment occurred in 2 waves, with each wave running advertisements on 1 platform of each type over the same period. Participants completed a baseline survey assessing sexual or injection use behavior, substance use including alcohol, psychological readiness to test, attitudes toward HIV testing and treatment, and HIV-related stigma. Participants received an electronic code to order a free home-based HIV self-test kit. Follow-up assessments were conducted to assess HIV self-test kit use and uptake of pre-exposure prophylaxis (PrEP) at 14 and 60 days post enrollment. Results: In total, 271 participants were enrolled, and 254 were included in the final analysis. Among these 254 participants, 177 (69.7\%) ordered a home HIV self-test kit. Most of the self-test kits were ordered by participants enrolled from dating apps. Due to waves with low enrollment, between wave statistical comparisons were not feasible. Within wave comparison revealed that Jack'D showed higher order rates (3.29 kits/day) compared to Instagram (0.34 kits/day) and Bing (0 kits/day). There were no associations among self-test kit ordering and HIV-related stigma, perceptions about HIV testing and treatment, and mistrust of medical organizations. Conclusions: Our findings show that using popular dating apps might be an efficient way to promote HIV self-testing. Stigma, perceptions about HIV testing and treatment, or mistrust of medical organizations may not affect order rates of HIV test kits promoted on the internet. Trial Registration: ClinicalTrials.gov NCT04155502; https://clinicaltrials.gov/ct2/show/NCT04155502 International Registered Report Identifier (IRRID): RR2-10.2196/20417 ", doi="10.2196/35648", url="https://formative.jmir.org/2022/9/e35648", url="http://www.ncbi.nlm.nih.gov/pubmed/36149729" } @Article{info:doi/10.2196/40331, author="Silver, Nathan and Kierstead, Elexis and Kostygina, Ganna and Tran, Hy and Briggs, Jodie and Emery, Sherry and Schillo, Barbara", title="The Influence of Provaping ``Gatewatchers'' on the Dissemination of COVID-19 Misinformation on Twitter: Analysis of Twitter Discourse Regarding Nicotine and the COVID-19 Pandemic", journal="J Med Internet Res", year="2022", month="Sep", day="22", volume="24", number="9", pages="e40331", keywords="social media", keywords="tobacco", keywords="COVID-19", keywords="nicotine", keywords="misinformation", keywords="Twitter", keywords="information", keywords="infodemiology", keywords="vaping", keywords="therapeutic", keywords="influence", keywords="environment", keywords="harmful", keywords="consequences", abstract="Background: There is a lot of misinformation about a potential protective role of nicotine against COVID-19 spread on Twitter despite significant evidence to the contrary. We need to examine the role of vape advocates in the dissemination of such information through the lens of the gatewatching framework, which posits that top users can amplify and exert a disproportionate influence over the dissemination of certain content through curating, sharing, or, in the case of Twitter, retweeting it, serving more as a vector for misinformation rather than the source. Objective: This research examines the Twitter discourse at the intersection of COVID-19 and tobacco (1) to identify the extent to which the most outspoken contributors to this conversation self-identify as vaping advocates and (2) to understand how and to what extent these vape advocates serve as gatewatchers through disseminating content about a therapeutic role of tobacco, nicotine, or vaping against COVID-19. Methods: Tweets about tobacco, nicotine, or vaping and COVID-19 (N=1,420,271) posted during the first 9 months of the pandemic (January-September 2020) were identified from within a larger corpus of tobacco-related tweets using validated keyword filters. The top posters (ie, tweeters and retweeters) were identified and characterized, along with the most shared Uniform Resource Locators (URLs), most used hashtags, and the 1000 most retweeted posts. Finally, we examined the role of both top users and vape advocates in retweeting the most retweeted posts about the therapeutic role of nicotine, tobacco, or vaping against COVID-19. Results: Vape advocates comprised between 49.7\% (n=81) of top 163 and 88\% (n=22) of top 25 users discussing COVID-19 and tobacco on Twitter. Content about the ability of tobacco, nicotine, or vaping to treat or prevent COVID-19 was disseminated broadly, accounting for 22.5\% (n=57) of the most shared URLs and 10\% (n=107) of the most retweeted tweets. Finally, among top users, retweets comprised an average of 78.6\% of the posts from vape advocates compared to 53.1\% from others (z=3.34, P<.001). Vape advocates were also more likely to retweet the top tweeted posts about a therapeutic role of nicotine, with 63\% (n=51) of vape advocates retweeting at least 1 post compared to 40.3\% (n=29) of other top users (z=2.80, P=.01). Conclusions: Provaping users dominated discussions of tobacco use during the COVID-19 pandemic on Twitter and were instrumental in disseminating the most retweeted posts about a potential therapeutic role of tobacco use against the virus. Subsequent research is needed to better understand the extent of this influence and how to mitigate the influence of vape advocates over the broader narrative of tobacco regulation on Twitter. ", doi="10.2196/40331", url="https://www.jmir.org/2022/9/e40331", url="http://www.ncbi.nlm.nih.gov/pubmed/36070451" } @Article{info:doi/10.2196/38839, author="Zhan, Kevin and Li, Yutong and Osmani, Rafay and Wang, Xiaoyu and Cao, Bo", title="Data Exploration and Classification of News Article Reliability: Deep Learning Study", journal="JMIR Infodemiology", year="2022", month="Sep", day="22", volume="2", number="2", pages="e38839", keywords="COVID-19", keywords="deep learning", keywords="news article reliability", keywords="false information", keywords="infodemic", keywords="ensemble model", abstract="Background: During the ongoing COVID-19 pandemic, we are being exposed to large amounts of information each day. This ``infodemic'' is defined by the World Health Organization as the mass spread of misleading or false information during a pandemic. This spread of misinformation during the infodemic ultimately leads to misunderstandings of public health orders or direct opposition against public policies. Although there have been efforts to combat misinformation spread, current manual fact-checking methods are insufficient to combat the infodemic. Objective: We propose the use of natural language processing (NLP) and machine learning (ML) techniques to build a model that can be used to identify unreliable news articles online. Methods: First, we preprocessed the ReCOVery data set to obtain 2029 English news articles tagged with COVID-19 keywords from January to May 2020, which are labeled as reliable or unreliable. Data exploration was conducted to determine major differences between reliable and unreliable articles. We built an ensemble deep learning model using the body text, as well as features, such as sentiment, Empath-derived lexical categories, and readability, to classify the reliability. Results: We found that reliable news articles have a higher proportion of neutral sentiment, while unreliable articles have a higher proportion of negative sentiment. Additionally, our analysis demonstrated that reliable articles are easier to read than unreliable articles, in addition to having different lexical categories and keywords. Our new model was evaluated to achieve the following performance metrics: 0.906 area under the curve (AUC), 0.835 specificity, and 0.945 sensitivity. These values are above the baseline performance of the original ReCOVery model. Conclusions: This paper identified novel differences between reliable and unreliable news articles; moreover, the model was trained using state-of-the-art deep learning techniques. We aim to be able to use our findings to help researchers and the public audience more easily identify false information and unreliable media in their everyday lives. ", doi="10.2196/38839", url="https://infodemiology.jmir.org/2022/2/e38839", url="http://www.ncbi.nlm.nih.gov/pubmed/36193330" } @Article{info:doi/10.2196/38944, author="Ahmed, Saifuddin and Rasul, Ehab Muhammad", title="Social Media News Use and COVID-19 Misinformation Engagement: Survey Study", journal="J Med Internet Res", year="2022", month="Sep", day="20", volume="24", number="9", pages="e38944", keywords="COVID-19", keywords="misinformation", keywords="personality", keywords="cognitive ability", keywords="social media", keywords="Singapore", abstract="Background: Social media is widely used as a source of news and information regarding COVID-19. However, the abundance of misinformation on social media platforms has raised concerns regarding the spreading infodemic. Accordingly, many have questioned the utility and impact of social media news use on users' engagement with (mis)information. Objective: This study offers a conceptual framework for how social media news use influences COVID-19 misinformation engagement. More specifically, we examined how news consumption on social media leads to COVID-19 misinformation sharing by inducing belief in such misinformation. We further explored if the effects of social media news use on COVID-19 misinformation engagement depend on individual differences in cognition and personality traits. Methods: We used data from an online survey panel administered by a survey agency (Qualtrics) in Singapore. The survey was conducted in March 2022, and 500 respondents answered the survey. All participants were older than 21 years and provided consent before taking part in the study. We used linear regression, mediation, and moderated mediation analyses to explore the proposed relationships between social media news use, cognitive ability, personality traits, and COVID-19 misinformation belief and sharing intentions. Results: The results suggested that those who frequently used social media for news consumption were more likely to believe COVID-19 misinformation and share it on social media. Further probing the mechanism suggested that social media news use translated into sharing intent via the perceived accuracy of misinformation. Simply put, social media news users shared COVID-19 misinformation because they believed it to be accurate. We also found that those with high levels of extraversion than those with low levels were more likely to perceive the misinformation to be accurate and share it. Those with high levels of neuroticism and openness than those with low levels were also likely to perceive the misinformation to be accurate. Finally, it was observed that personality traits did not significantly influence misinformation sharing at higher levels of cognitive ability, but low cognitive users largely drove misinformation sharing across personality traits. Conclusions: The reliance on social media platforms for news consumption during the COVID-19 pandemic has amplified, with dire consequences for misinformation sharing. This study shows that increased social media news consumption is associated with believing and sharing COVID-19 misinformation, with low cognitive users being the most vulnerable. We offer recommendations to newsmakers, social media moderators, and policymakers toward efforts in limiting COVID-19 misinformation propagation and safeguarding citizens. ", doi="10.2196/38944", url="https://www.jmir.org/2022/9/e38944", url="http://www.ncbi.nlm.nih.gov/pubmed/36067414" } @Article{info:doi/10.2196/37274, author="Jo, Soojung and Pituch, A. Keenan and Howe, Nancy", title="The Relationships Between Social Media and Human Papillomavirus Awareness and Knowledge: Cross-sectional Study", journal="JMIR Public Health Surveill", year="2022", month="Sep", day="20", volume="8", number="9", pages="e37274", keywords="papillomavirus infections", keywords="vaccination", keywords="social media", keywords="health promotion", keywords="public reporting of health care data", keywords="human papillomavirus", abstract="Background: Human papillomavirus (HPV) is the most common sexually transmitted infection. HPV can infect both females and males, and it can cause many cancers, including anal, cervical, vaginal, vulvar, and penile cancers. HPV vaccination rates are lower than vaccination rates within other national vaccination programs, despite its importance. Research literature indicates that people obtain health-related information from internet sources and social media; however, the association between such health-seeking behavior on social media and HPV-related behaviors has not been consistently demonstrated in the literature. Objective: This study aims to examine the association between social media usage and HPV knowledge and HPV awareness. Methods: This study analyzed public health data collected through the Health Information National Trends Survey (HINTS) conducted by the US National Cancer Institute. The analysis used data collected in 2020; in total, 2948 responses were included in the analysis. Six HPV-related questions were used to identify HPV awareness, HPV vaccine awareness, and HPV knowledge about HPV-related cancers. Four questions about social media usage and one question about online health information--seeking behavior were used to analyze the associations between social media usage and HPV-related behaviors. Initially, six logistic regressions were conducted using replicate weights. Based on the results, significant factors were included in a second set of regression analyses that also included demographic variables. Results: About half of the respondents were aware of HPV (68.40\%), the HPV vaccine (64.04\%), and the relationship between HPV and cervical cancer (48.00\%). However, fewer respondents were knowledgeable about the relationships between HPV and penile cancer (19.18\%), anal cancer (18.33\%), and oral cancer (19.86\%). Although social media usage is associated with HPV awareness, HPV vaccine awareness, and knowledge of cervical cancer, these associations were not significant after adjusting for demographic variables. Those less likely to report HPV awareness and knowledge included older participants, males, those with a household income of less than US \$20,000, those with a formal education equal to or less than high school, or those who resided in a household where adults are not fluent in English. Conclusions: After adjusting for demographic variables, social media use was not related to HPV knowledge and awareness, and survey respondents were generally not aware that HPV can lead to specific types of cancer, other than cervical cancer. These results suggest that perhaps a lack of high-quality information on social media may impede HPV awareness and knowledge. Efforts to educate the public about HPV via social media might be improved by using techniques like storytelling or infographics, especially targeting vulnerable populations, such as older participants, males, those with low incomes, those with less formal education, or those who reside in the United States but are not fluent in English. ", doi="10.2196/37274", url="https://publichealth.jmir.org/2022/9/e37274", url="http://www.ncbi.nlm.nih.gov/pubmed/36125858" } @Article{info:doi/10.2196/35121, author="Christensen, Bente and Laydon, Daniel and Chelkowski, Tadeusz and Jemielniak, Dariusz and Vollmer, Michaela and Bhatt, Samir and Krawczyk, Konrad", title="Quantifying Changes in Vaccine Coverage in Mainstream Media as a Result of the COVID-19 Outbreak: Text Mining Study", journal="JMIR Infodemiology", year="2022", month="Sep", day="20", volume="2", number="2", pages="e35121", keywords="data mining", keywords="COVID-19", keywords="vaccine", keywords="text mining", keywords="change", keywords="coverage", keywords="communication", keywords="media", keywords="social media", keywords="news", keywords="outbreak", keywords="acceptance", keywords="hesitancy", keywords="understanding", keywords="knowledge", keywords="sentiment", abstract="Background: Achieving herd immunity through vaccination depends upon the public's acceptance, which in turn relies on their understanding of its risks and benefits. The fundamental objective of public health messaging on vaccines is therefore the clear communication of often complex information and, increasingly, the countering of misinformation. The primary outlet shaping public understanding is mainstream online news media, where coverage of COVID-19 vaccines was widespread. Objective: We used text-mining analysis on the front pages of mainstream online news to quantify the volume and sentiment polarization of vaccine coverage. Methods: We analyzed 28 million articles from 172 major news sources across 11 countries between July 2015 and April 2021. We employed keyword-based frequency analysis to estimate the proportion of overall articles devoted to vaccines. We performed topic detection using BERTopic and named entity recognition to identify the leading subjects and actors mentioned in the context of vaccines. We used the Vader Python module to perform sentiment polarization quantification of all collated English-language articles. Results: The proportion of front-page articles mentioning vaccines increased from 0.1\% to 4\% with the outbreak of COVID-19. The number of negatively polarized articles increased from 6698 in 2015-2019 to 28,552 in 2020-2021. However, overall vaccine coverage before the COVID-19 pandemic was slightly negatively polarized (57\% negative), whereas coverage during the pandemic was positively polarized (38\% negative). Conclusions: Throughout the pandemic, vaccines have risen from a marginal to a widely discussed topic on the front pages of major news outlets. Mainstream online media has been positively polarized toward vaccines, compared with mainly negative prepandemic vaccine news. However, the pandemic was accompanied by an order-of-magnitude increase in vaccine news that, due to low prepandemic frequency, may contribute to a perceived negative sentiment. These results highlight important interactions between the volume of news and overall polarization. To the best of our knowledge, our work is the first systematic text mining study of front-page vaccine news headlines in the context of COVID-19. ", doi="10.2196/35121", url="https://infodemiology.jmir.org/2022/2/e35121", url="http://www.ncbi.nlm.nih.gov/pubmed/36348981" } @Article{info:doi/10.2196/37518, author="Renner, Simon and Loussikian, Paul and Foulqui{\'e}, Pierre and Arnould, Benoit and Marrel, Alexia and Barbier, Valentin and Mebarki, Adel and Sch{\"u}ck, St{\'e}phane and Bharmal, Murtuza", title="Perceived Unmet Needs in Patients Living With Advanced Bladder Cancer and Their Caregivers: Infodemiology Study Using Data From Social Media in the United States", journal="JMIR Cancer", year="2022", month="Sep", day="20", volume="8", number="3", pages="e37518", keywords="real-world evidence", keywords="unmet needs", keywords="quality of life", keywords="social media", keywords="bladder cancer", keywords="caregivers", abstract="Background: Locally advanced or metastatic bladder cancer (BC), which is generally termed advanced BC (aBC), has a very poor prognosis, and in addition to its physical symptoms, it is associated with emotional and social challenges. However, few studies have assessed the unmet needs and burden of aBC from patient and caregiver perspectives. Infodemiology, that is, epidemiology based on internet health-related content, can help obtain more insights on patients' and caregivers' experiences with aBC. Objective: The study aimed to identify the main discussion themes and the unmet needs of patients with aBC and their caregivers through a mixed methods analysis of social media posts. Methods: Social media posts were collected between January 2015 and April 2021 from US geolocalized sites using specific keywords for aBC. Automatic natural language processing (regular expressions and machine learning) methods were used to filter out irrelevant content and identify verbatim posts from patients and caregivers. The verbatim posts were analyzed to identify main discussion themes using biterm topic modeling. Difficulties or unmet needs were further explored using qualitative research methods by 2 independent annotators until saturation of concepts. Results: A total of 688 posts from 262 patients and 1214 posts from 679 caregivers discussing aBC were identified. Analysis of 340 randomly selected patient posts and 423 randomly selected caregiver posts uncovered 33 unique unmet need categories among patients and 36 among caregivers. The main unmet patient needs were related to challenges regarding adverse events (AEs; 28/95, 29\%) and the psychological impact of aBC (20/95, 21\%). Other patient unmet needs identified were prognosis or diagnosis errors (9/95, 9\%) and the need for better management of aBC symptoms (9/95, 9\%). The main unmet caregiver needs were related to the psychological impacts of aBC (46/177, 26.0\%), the need for support groups and to share experiences between peers (28/177, 15.8\%), and the fear and management of patient AEs (22/177, 12.4\%). Conclusions: The combination of manual and automatic methods allowed the extraction and analysis of several hundreds of social media posts from patients with aBC and their caregivers. The results highlighted the emotional burden of cancer for both patients and caregivers. Additional studies on patients with aBC and their caregivers are required to quantitatively explore the impact of this disease on quality of life. ", doi="10.2196/37518", url="https://cancer.jmir.org/2022/3/e37518", url="http://www.ncbi.nlm.nih.gov/pubmed/36125861" } @Article{info:doi/10.2196/38573, author="Charbonneau, Esther and Mellouli, Sehl and Chouikh, Arbi and Couture, Laurie-Jane and Desroches, Sophie", title="The Information Sharing Behaviors of Dietitians and Twitter Users in the Nutrition and COVID-19 Infodemic: Content Analysis Study of Tweets", journal="JMIR Infodemiology", year="2022", month="Sep", day="16", volume="2", number="2", pages="e38573", keywords="nutrition", keywords="COVID-19", keywords="dietitians", keywords="Twitter", keywords="public", keywords="themes", keywords="behavior", keywords="content accuracy", keywords="user engagement", keywords="content analysis", keywords="misinformation", keywords="disinformation", keywords="infodemic", abstract="Background: The COVID-19 pandemic has generated an infodemic, an overabundance of online and offline information. In this context, accurate information as well as misinformation and disinformation about the links between nutrition and COVID-19 have circulated on Twitter since the onset of the pandemic. Objective: The purpose of this study was to compare tweets on nutrition in times of COVID-19 published by 2 groups, namely, a preidentified group of dietitians and a group of general users of Twitter, in terms of themes, content accuracy, use of behavior change factors, and user engagement, in order to contrast their information sharing behaviors during the pandemic. Methods: Public English-language tweets published between December 31, 2019, and December 31, 2020, by 625 dietitians from Canada and the United States, and Twitter users were collected using hashtags and keywords related to nutrition and COVID-19. After filtration, tweets were coded against an original codebook of themes and the Theoretical Domains Framework (TDF) for identifying behavior change factors, and were compared to reliable nutritional recommendations pertaining to COVID-19. The numbers of likes, replies, and retweets per tweet were also collected to determine user engagement. Results: In total, 2886 tweets (dietitians, n=1417; public, n=1469) were included in the analyses. Differences in frequency between groups were found in 11 out of 15 themes. Grocery (271/1417, 19.1\%), and diets and dietary patterns (n=507, 34.5\%) were the most frequently addressed themes by dietitians and the public, respectively. For 9 out of 14 TDF domains, there were differences in the frequency of usage between groups. ``Skills'' was the most used domain by both groups, although they used it in different proportions (dietitians: 612/1417, 43.2\% vs public: 529/1469, 36.0\%; P<.001). A higher proportion of dietitians' tweets were accurate compared with the public's tweets (532/575, 92.5\% vs 250/382, 65.5\%; P<.001). The results for user engagement were mixed. While engagement by likes varied between groups according to the theme, engagement by replies and retweets was similar across themes but varied according to the group. Conclusions: Differences in tweets between groups, notably ones related to content accuracy, themes, and engagement in the form of likes, shed light on potentially useful and relevant elements to include in timely social media interventions aiming at fighting the COVID-19--related infodemic or future infodemics. ", doi="10.2196/38573", url="https://infodemiology.jmir.org/2022/2/e38573", url="http://www.ncbi.nlm.nih.gov/pubmed/36188421" } @Article{info:doi/10.2196/39547, author="Klein, Z. Ari and Magge, Arjun and O'Connor, Karen and Gonzalez-Hernandez, Graciela", title="Automatically Identifying Twitter Users for Interventions to Support Dementia Family Caregivers: Annotated Data Set and Benchmark Classification Models", journal="JMIR Aging", year="2022", month="Sep", day="16", volume="5", number="3", pages="e39547", keywords="natural language processing", keywords="social media", keywords="data mining", keywords="dementia", keywords="Alzheimer disease", keywords="caregivers", abstract="Background: More than 6 million people in the United States have Alzheimer disease and related dementias, receiving help from more than 11 million family or other informal caregivers. A range of traditional interventions has been developed to support family caregivers; however, most of them have not been implemented in practice and remain largely inaccessible. While recent studies have shown that family caregivers of people with dementia use Twitter to discuss their experiences, methods have not been developed to enable the use of Twitter for interventions. Objective: The objective of this study is to develop an annotated data set and benchmark classification models for automatically identifying a cohort of Twitter users who have a family member with dementia. Methods: Between May 4 and May 20, 2021, we collected 10,733 tweets, posted by 8846 users, that mention a dementia-related keyword, a linguistic marker that potentially indicates a diagnosis, and a select familial relationship. Three annotators annotated 1 random tweet per user to distinguish those that indicate having a family member with dementia from those that do not. Interannotator agreement was 0.82 (Fleiss kappa). We used the annotated tweets to train and evaluate support vector machine and deep neural network classifiers. To assess the scalability of our approach, we then deployed automatic classification on unlabeled tweets that were continuously collected between May 4, 2021, and March 9, 2022. Results: A deep neural network classifier based on a BERT (bidirectional encoder representations from transformers) model pretrained on tweets achieved the highest F1-score of 0.962 (precision=0.946 and recall=0.979) for the class of tweets indicating that the user has a family member with dementia. The classifier detected 128,838 tweets that indicate having a family member with dementia, posted by 74,290 users between May 4, 2021, and March 9, 2022---that is, approximately 7500 users per month. Conclusions: Our annotated data set can be used to automatically identify Twitter users who have a family member with dementia, enabling the use of Twitter on a large scale to not only explore family caregivers' experiences but also directly target interventions at these users. ", doi="10.2196/39547", url="https://aging.jmir.org/2022/3/e39547", url="http://www.ncbi.nlm.nih.gov/pubmed/36112408" } @Article{info:doi/10.2196/38749, author="Toussaint, A. Philipp and Renner, Maximilian and Lins, Sebastian and Thiebes, Scott and Sunyaev, Ali", title="Direct-to-Consumer Genetic Testing on Social Media: Topic Modeling and Sentiment Analysis of YouTube Users' Comments", journal="JMIR Infodemiology", year="2022", month="Sep", day="15", volume="2", number="2", pages="e38749", keywords="direct-to-consumer genetic testing", keywords="health information", keywords="social media", keywords="YouTube", keywords="sentiment analysis", keywords="topic modeling", keywords="content analysis", keywords="online health information", keywords="user discourse", keywords="infodemiology", abstract="Background: With direct-to-consumer (DTC) genetic testing enabling self-responsible access to novel information on ancestry, traits, or health, consumers often turn to social media for assistance and discussion. YouTube, the largest social media platform for videos, offers an abundance of DTC genetic testing--related videos. Nevertheless, user discourse in the comments sections of these videos is largely unexplored. Objective: This study aims to address the lack of knowledge concerning user discourse in the comments sections of DTC genetic testing--related videos on YouTube by exploring topics discussed and users' attitudes toward these videos. Methods: We employed a 3-step research approach. First, we collected metadata and comments of the 248 most viewed DTC genetic testing--related videos on YouTube. Second, we conducted topic modeling using word frequency analysis, bigram analysis, and structural topic modeling to identify topics discussed in the comments sections of those videos. Finally, we employed Bing (binary), National Research Council Canada (NRC) emotion, and 9-level sentiment analysis to identify users' attitudes toward these DTC genetic testing--related videos, as expressed in their comments. Results: We collected 84,082 comments from the 248 most viewed DTC genetic testing--related YouTube videos. With topic modeling, we identified 6 prevailing topics on (1) general genetic testing, (2) ancestry testing, (3) relationship testing, (4) health and trait testing, (5) ethical concerns, and (6) YouTube video reaction. Further, our sentiment analysis indicates strong positive emotions (anticipation, joy, surprise, and trust) and a neutral-to-positive attitude toward DTC genetic testing--related videos. Conclusions: With this study, we demonstrate how to identify users' attitudes on DTC genetic testing by examining topics and opinions based on YouTube video comments. Shedding light on user discourse on social media, our findings suggest that users are highly interested in DTC genetic testing and related social media content. Nonetheless, with this novel market constantly evolving, service providers, content providers, or regulatory authorities may still need to adapt their services to users' interests and desires. ", doi="10.2196/38749", url="https://infodemiology.jmir.org/2022/2/e38749", url="http://www.ncbi.nlm.nih.gov/pubmed/37113449" } @Article{info:doi/10.2196/37775, author="Yousef, Murooj and Dietrich, Timo and Rundle-Thiele, Sharyn", title="Actions Speak Louder Than Words: Sentiment and Topic Analysis of COVID-19 Vaccination on Twitter and Vaccine Uptake", journal="JMIR Form Res", year="2022", month="Sep", day="15", volume="6", number="9", pages="e37775", keywords="COVID-19", keywords="COVID-19 vaccination", keywords="sentiment analysis", keywords="public health campaigns", keywords="vaccine uptake", keywords="Twitter", keywords="social media", keywords="vaccines", abstract="Background: The lack of trust in vaccines is a major contributor to vaccine hesitancy. To overcome vaccine hesitancy for the COVID-19 vaccine, the Australian government launched multiple public health campaigns to encourage vaccine uptake. This sentiment analysis examines the effect of public health campaigns and COVID-19--related events on sentiment and vaccine uptake. Objective: This study aims to examine the relationship between sentiment and COVID-19 vaccine uptake and government actions that impacted public sentiment about the vaccine. Methods: Using machine learning methods, we collected 137,523 publicly available English language tweets published in Australia between February and October 2021 that contained COVID-19 vaccine--related keywords. Machine learning methods were used to extract topics and sentiments relating to COVID-19 vaccination. The relationship between public vaccination sentiment on Twitter and vaccine uptake was examined. Results: The majority of collected tweets expressed negative (n=91,052, 66\%) rather than positive (n=21,686, 16\%) or neutral (n=24,785, 18\%) sentiments. Topics discussed within the study time frame included the role of the government in the vaccination rollout, availability and accessibility of the vaccine, and vaccine efficacy. There was a significant positive correlation between negative sentiment and the number of vaccine doses administered daily (r267=.15, P<.05), with positive sentiment showing the inverse effect. Public health campaigns, lockdowns, and antivaccination protests were associated with increased negative sentiment, while vaccination mandates had no significant effect on sentiment. Conclusions: The study findings demonstrate that negative sentiment was more prevalent on Twitter during the Australian vaccination rollout but vaccine uptake remained high. Australians expressed anger at the slow rollout and limited availability of the vaccine during the study period. Public health campaigns, lockdowns, and antivaccination rallies increased negative sentiment. In contrast, news of increased vaccine availability for the public and government acquisition of more doses were key government actions that reduced negative sentiment. These findings can be used to inform government communication planning. ", doi="10.2196/37775", url="https://formative.jmir.org/2022/9/e37775", url="http://www.ncbi.nlm.nih.gov/pubmed/36007136" } @Article{info:doi/10.2196/38242, author="Marcon, R. Alessandro and Wagner, N. Darren and Giles, Carly and Isenor, Cynthia", title="Web-Based Perspectives of Deemed Consent Organ Donation Legislation in Nova Scotia: Thematic Analysis of Commentary in Facebook Groups", journal="JMIR Infodemiology", year="2022", month="Sep", day="14", volume="2", number="2", pages="e38242", keywords="organ donation", keywords="organ transplantation", keywords="deemed consent", keywords="presumed consent", keywords="social media", keywords="Facebook", keywords="public perceptions", keywords="public policy", keywords="thematic analysis", abstract="Background: The Canadian province of Nova Scotia recently became the first jurisdiction in North America to implement deemed consent organ donation legislation. Changing the consent models constituted one aspect of a larger provincial program to increase organ and tissue donation and transplantation rates. Deemed consent legislation can be controversial among the public, and public participation is integral to the successful implementation of the program. Objective: Social media constitutes key spaces where people express opinions and discuss topics, and social media discourse can influence public perceptions. This project aimed to examine how the public in Nova Scotia responded to legislative changes in Facebook groups. Methods: Using Facebook's search engine, we searched for posts in public Facebook groups using the terms ``deemed consent,'' ``presumed consent,'' ``opt out,'' or ``organ donation'' and ``Nova Scotia,'' appearing from January 1, 2020, to May 1, 2021. The finalized data set included 2337 comments on 26 relevant posts in 12 different public Nova Scotia--based Facebook groups. We conducted thematic and content analyses of the comments to determine how the public responded to the legislative changes and how the participants interacted with one another in the discussions. Results: Our thematic analysis revealed principal themes that supported and critiqued the legislation, raised specific issues, and reflected on the topic from a neutral perspective. Subthemes showed individuals presenting perspectives through a variety of themes, including compassion, anger, frustration, mistrust, and a range of argumentative tactics. The comments included personal narratives, beliefs about the government, altruism, autonomy, misinformation, and reflections on religion and death. Content analysis revealed that Facebook users reacted to popular comments with ``likes'' more than other reactions. Comments with the most reactions included both negative and positive perspectives about the legislation. Personal donation and transplantation success stories, as well as attempts to correct misinformation, were some of the most ``liked'' positive comments. Conclusions: The findings provide key insights into perspectives of individuals from Nova Scotia on deemed consent legislation, as well as organ donation and transplantation broadly. The insights derived from this analysis can contribute to public understanding, policy creation, and public outreach efforts that might occur in other jurisdictions considering the enactment of similar legislation. ", doi="10.2196/38242", url="https://infodemiology.jmir.org/2022/2/e38242", url="http://www.ncbi.nlm.nih.gov/pubmed/37113450" } @Article{info:doi/10.2196/37286, author="Abroms, C. Lorien and Yom-Tov, Elad", title="The Role of Information Boxes in Search Engine Results for Symptom Searches: Analysis of Archival Data", journal="JMIR Infodemiology", year="2022", month="Sep", day="14", volume="2", number="2", pages="e37286", keywords="health misinformation", keywords="search engine", keywords="internet search", keywords="information boxes", keywords="knowledge graph boxes", keywords="misinformation", keywords="health information", keywords="Microsoft", keywords="internet", keywords="data", keywords="symptoms", keywords="results", keywords="users", keywords="medical", keywords="Bing", keywords="USA", keywords="linear", keywords="logistic", keywords="regression", keywords="web", keywords="ads", keywords="behavior", abstract="Background: Search engines provide health information boxes as part of search results to address information gaps and misinformation for commonly searched symptoms. Few prior studies have sought to understand how individuals who are seeking information about health symptoms navigate different types of page elements on search engine results pages, including health information boxes. Objective: Using real-world search engine data, this study sought to investigate how users searching for common health-related symptoms with Bing interacted with health information boxes (info boxes) and other page elements. Methods: A sample of searches (N=28,552 unique searches) was compiled for the 17 most common medical symptoms queried on Microsoft Bing by users in the United States between September and November 2019. The association between the page elements that users saw, their characteristics, and the time spent on elements or clicks was investigated using linear and logistic regression. Results: The number of searches ranged by symptom type from 55 searches for cramps to 7459 searches for anxiety. Users searching for common health-related symptoms saw pages with standard web results (n=24,034, 84\%), itemized web results (n=23,354, 82\%), ads (n=13,171, 46\%), and info boxes (n=18,215, 64\%). Users spent on average 22 (SD 26) seconds on the search engine results page. Users who saw all page elements spent 25\% (7.1 s) of their time on the info box, 23\% (6.1 s) on standard web results, 20\% (5.7 s) on ads, and 10\% (10 s) on itemized web results, with significantly more time on the info box compared to other elements and the least amount of time on itemized web results. Info box characteristics such as reading ease and appearance of related conditions were associated with longer time on the info box. Although none of the info box characteristics were associated with clicks on standard web results, info box characteristics such as reading ease and related searches were negatively correlated with clicks on ads. Conclusions: Info boxes were attended most by users compared with other page elements, and their characteristics may influence future web searching. Future studies are needed that further explore the utility of info boxes and their influence on real-world health-seeking behaviors. ", doi="10.2196/37286", url="https://infodemiology.jmir.org/2022/2/e37286", url="http://www.ncbi.nlm.nih.gov/pubmed/37113445" } @Article{info:doi/10.2196/36525, author="Kinoshita, Takuya and Matsumoto, Takehiro and Taura, Naota and Usui, Tetsuya and Matsuya, Nemu and Nishiguchi, Mayumi and Horita, Hozumi and Nakao, Kazuhiko", title="Public Interest and Accessibility of Telehealth in Japan: Retrospective Analysis Using Google Trends and National Surveillance", journal="JMIR Form Res", year="2022", month="Sep", day="14", volume="6", number="9", pages="e36525", keywords="COVID-19", keywords="telehealth", keywords="telemedicine", keywords="public interest", keywords="mobile app", keywords="correlation", keywords="infodemiology, infoveillance", keywords="surveillance", keywords="Google Trends", abstract="Background: Recently, the use of telehealth for patient treatment under the COVID-19 pandemic has gained interest around the world. As a result, many infodemiology and infoveillance studies using web-based sources such as Google Trends were reported, focusing on the first wave of the COVID-19 pandemic. Although public interest in telehealth has increased in many countries during this time, the long-term interest has remained unknown among people living in Japan. Moreover, various mobile telehealth apps have become available for remote areas in the COVID-19 era, but the accessibility of these apps in epidemic versus nonepidemic regions is unknown. Objective: We aimed to investigate the public interest in telehealth during the first pandemic wave and after the wave in the first part of this study, and the accessibility of medical institutions using telehealth in the epidemic and nonepidemic regions, in the second part. Methods: We examined and compared the first wave and after the wave with regards to severe cases, number of deaths, relative search volume (RSV) of telehealth and COVID-19, and the correlation between RSV and COVID-19 cases, using open sources such as Google Trends and the Japanese Ministry of Health, Labour and Welfare (JMHLW) data. The weekly mean and the week-over-week change rates of RSV and COVID-19 cases were used to examine the correlation coefficients. In the second part, the prevalence of COVID-19 cases, severe cases, number of deaths, and the telehealth accessibility rate were compared between epidemic regions and nonepidemic regions, using the JMHLW data. We also examined the regional correlation between telehealth accessibility and the prevalence of COVID-19 cases. Results: Among the 83 weeks with 5 pandemic waves, the overall mean for the RSV of telehealth and COVID-19 was 11.3 (95\% CI 8.0-14.6) and 30.7 (95\% CI 27.2-34.2), respectively. The proportion of severe cases (26.54\% vs 18.16\%; P<.001), deaths (5.33\% vs 0.99\%; P<.001), RSV of telehealth (mean 33.1, 95\% CI 16.2-50.0 vs mean 7.3, 95\% CI 6.7-8.0; P<.001), and RSV of COVID-19 (mean 52.1, 95\% CI 38.3-65.9 vs mean 26.3, 95\% CI 24.4-29.2; P<.001) was significantly higher in the first wave compared to after the wave. In the correlation analysis, the public interest in telehealth was 0.899 in the first wave and --0.300 overall. In Japan, the accessibility of telehealth using mobile apps was significantly higher in epidemic regions compared to nonepidemic regions in both hospitals (3.8\% vs 2.0\%; P=.004) and general clinics (5.2\% vs 3.1\%; P<.001). In the regional correlation analysis, telehealth accessibility using mobile apps was 0.497 in hospitals and 0.629 in general clinics. Conclusions: Although there was no long-term correlation between the public interest in telehealth and COVID-19, there was a regional correlation between mobile telehealth app accessibility in Japan, especially for general clinics. We also revealed that epidemic regions had higher mobile telehealth app accessibility. Further studies about the actual use of telehealth and its effect after the COVID-19 pandemic are necessary. ", doi="10.2196/36525", url="https://formative.jmir.org/2022/9/e36525", url="http://www.ncbi.nlm.nih.gov/pubmed/36103221" } @Article{info:doi/10.2196/38541, author="Ceretti, Elisabetta and Covolo, Loredana and Cappellini, Francesca and Nanni, Alberto and Sorosina, Sara and Beatini, Andrea and Taranto, Mirella and Gasparini, Arianna and De Castro, Paola and Brusaferro, Silvio and Gelatti, Umberto", title="Evaluating the Effectiveness of Internet-Based Communication for Public Health: Systematic Review", journal="J Med Internet Res", year="2022", month="Sep", day="13", volume="24", number="9", pages="e38541", keywords="internet-based communication", keywords="websites", keywords="social media", keywords="public health", keywords="efficacy", keywords="systematic review", keywords="communication", keywords="internet-based", keywords="health information", keywords="exchange", keywords="health care", keywords="web-based", keywords="campaigns", abstract="Background: Communicating strategically is a key issue for health organizations. Over the past decade, health care communication via social media and websites has generated a great deal of studies examining different realities of communication strategies. However, when it comes to systematic reviews, there is fragmentary evidence on this type of communication. Objective: The aim of this systematic review was to summarize the evidence on web institutional health communication for public health authorities to evaluate possible aim-specific key points based on these existing studies. Methods: Guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement, we conducted a comprehensive review across 2 electronic databases (PubMed and Web of Science) from January 1, 2011, to October 7, 2021, searching for studies investigating institutional health communication. In total, 2 independent researchers (AN and SS) reviewed the articles for inclusion, and the assessment of methodological quality was based on the Kmet appraisal checklist. Results: A total of 78 articles were selected. Most studies (35/78, 45\%) targeted health promotion and disease prevention, followed by crisis communication (24/78, 31\%), general health (13/78, 17\%), and misinformation correction and health promotion (6/78, 8\%). Engagement and message framing were the most analyzed aspects. Few studies (14/78, 18\%) focused on campaign effectiveness. Only 23\% (18/78) of the studies had an experimental design. The Kmet evaluation was used to distinguish studies presenting a solid structure from lacking studies. In particular, considering the 0.75-point threshold, 36\% (28/78) of the studies were excluded. Studies above this threshold were used to identify a series of aim-specific and medium-specific suggestions as the communication strategies used differed greatly. Conclusions: Overall, the findings suggest that no single strategy works best in the case of web-based health care communication. The extreme variability of outcomes and the lack of a unitary measure for assessing the end points of a specific campaign or study lead us to reconsider the tools we use to evaluate the efficacy of web-based health communication. ", doi="10.2196/38541", url="https://www.jmir.org/2022/9/e38541", url="http://www.ncbi.nlm.nih.gov/pubmed/36098994" } @Article{info:doi/10.2196/37635, author="Stevens, Hannah and Rasul, Ehab Muhammad and Oh, Jung Yoo", title="Emotions and Incivility in Vaccine Mandate Discourse: Natural Language Processing Insights", journal="JMIR Infodemiology", year="2022", month="Sep", day="13", volume="2", number="2", pages="e37635", keywords="vaccine hesitancy", keywords="COVID-19", keywords="vaccine mandates", keywords="natural language processing", keywords="incivility", keywords="LIWC", keywords="Linguistic Inquiry and Word Count", keywords="Twitter", abstract="Background: Despite vaccine availability, vaccine hesitancy has inhibited public health officials' efforts to mitigate the COVID-19 pandemic in the United States. Although some US elected officials have responded by issuing vaccine mandates, others have amplified vaccine hesitancy by broadcasting messages that minimize vaccine efficacy. The politically polarized nature of COVID-19 information on social media has given rise to incivility, wherein health attitudes often hinge more on political ideology than science. Objective: To the best of our knowledge, incivility has not been studied in the context of discourse regarding COVID-19 vaccines and mandates. Specifically, there is little focus on the psychological processes that elicit uncivil vaccine discourse and behaviors. Thus, we investigated 3 psychological processes theorized to predict discourse incivility---namely, anxiety, anger, and sadness. Methods: We used 2 different natural language processing approaches: (1) the Linguistic Inquiry and Word Count computational tool and (2) the Google Perspective application programming interface (API) to analyze a data set of 8014 tweets containing terms related to COVID-19 vaccine mandates from September 14, 2021, to October 1, 2021. To collect the tweets, we used the Twitter API Tweet Downloader Tool (version 2). Subsequently, we filtered through a data set of 375,000 vaccine-related tweets using keywords to extract tweets explicitly focused on vaccine mandates. We relied on the Linguistic Inquiry and Word Count computational tool to measure the valence of linguistic anger, sadness, and anxiety in the tweets. To measure dimensions of post incivility, we used the Google Perspective API. Results: This study resolved discrepant operationalizations of incivility by introducing incivility as a multifaceted construct and explored the distinct emotional processes underlying 5 dimensions of discourse incivility. The findings revealed that 3 types of emotions---anxiety, anger, and sadness---were uniquely associated with dimensions of incivility (eg, toxicity, severe toxicity, insult, profanity, threat, and identity attacks). Specifically, the results showed that anger was significantly positively associated with all dimensions of incivility (all P<.001), whereas sadness was significantly positively related to threat (P=.04). Conversely, anxiety was significantly negatively associated with identity attack (P=.03) and profanity (P=.02). Conclusions: The results suggest that our multidimensional approach to incivility is a promising alternative to understanding and intervening in the psychological processes underlying uncivil vaccine discourse. Understanding specific emotions that can increase or decrease incivility such as anxiety, anger, and sadness can enable researchers and public health professionals to develop effective interventions against uncivil vaccine discourse. Given the need for real-time monitoring and automated responses to the spread of health information and misinformation on the web, social media platforms can harness the Google Perspective API to offer users immediate, automated feedback when it detects that a comment is uncivil. ", doi="10.2196/37635", url="https://infodemiology.jmir.org/2022/2/e37635", url="http://www.ncbi.nlm.nih.gov/pubmed/36188420" } @Article{info:doi/10.2196/39766, author="Ottwell, Ryan and Cox, Katherine and Dobson, Taylor and Shah, Muneeb and Hartwell, Micah", title="Evaluating the Public's Interest in Testicle Tanning: Observational Study", journal="JMIR Dermatol", year="2022", month="Sep", day="12", volume="5", number="3", pages="e39766", keywords="general dermatology", keywords="google trends", keywords="testicle tanning", keywords="UV radiation", keywords="public trends", keywords="skin cancer", keywords="cancer", keywords="harmful", keywords="internet", keywords="health trends", keywords="tanning", abstract="Background: A new and potentially dangerous health trend, testicle tanning, received extensive media attention following a popular television program where a health and fitness influencer touted that testicular tanning increases testosterone levels. It has been shown that the public has a particular interest in tanning wellness trends; thus, given the vague nomenclature of the practice, the abundance of misleading information and support for using UV light by other health influencers may lead to an increase in men exposing themselves to UV radiation and developing associated complications. Objective: The aim of this paper is to evaluate the public's interest in testicle tanning. Methods: Relative search interest was collected from Google Trends, and daily tweet volume was collected using Twitter via Sprout Social. The search was filtered to observe internet activity between February 1, 2022, and August 18, 2022. Autoregressive integrated moving average models were applied to forecast the predicted values through April 30 to compare to the actual observed values immediately following the airing of the show. Results: We found that the relative search interest for testicle tanning peaked (100) on April 19, 2022, following a discussion of the topic on a television program. Compared to the forecasted relative search interest of 1.36 (95\% CI --3.29 to 6.01), had the topic not been discussed, it showed a 7252\% increase in relative search interest. A similar spike was observed in the volume of tweets peaking on April 18 with 42,736. The expected number of tweets from the autoregressive integrated moving average model was 122 (95\% CI --154 to 397), representing a 35,053\% increase. Conclusions: Our results show that the promotion of testicle tanning generated significant public interest in an evidence-lacking and potentially dangerous health trend. Dermatologists and other health care professionals should be aware of these new viral health trends to best counsel patients and combat health misinformation. ", doi="10.2196/39766", url="https://derma.jmir.org/2022/3/e39766", url="http://www.ncbi.nlm.nih.gov/pubmed/37632896" } @Article{info:doi/10.2196/37984, author="Matharaarachchi, Surani and Domaratzki, Mike and Katz, Alan and Muthukumarana, Saman", title="Discovering Long COVID Symptom Patterns: Association Rule Mining and Sentiment Analysis in Social Media Tweets", journal="JMIR Form Res", year="2022", month="Sep", day="7", volume="6", number="9", pages="e37984", keywords="COVID-19", keywords="long COVID symptoms", keywords="social media analysis", keywords="association rule mining", keywords="bigram analysis", keywords="natural language processing", keywords="Twitter", keywords="content analysis", keywords="data mining", keywords="infodemiology", keywords="health information", abstract="Background: The COVID-19 pandemic is a substantial public health crisis that negatively affects human health and well-being. As a result of being infected with the coronavirus, patients can experience long-term health effects called long COVID syndrome. Multiple symptoms characterize this syndrome, and it is crucial to identify these symptoms as they may negatively impact patients' day-to-day lives. Breathlessness, fatigue, and brain fog are the 3 most common continuing and debilitating symptoms that patients with long COVID have reported, often months after the onset of COVID-19. Objective: This study aimed to understand the patterns and behavior of long COVID symptoms reported by patients on the Twitter social media platform, which is vital to improving our understanding of long COVID. Methods: Long COVID--related Twitter data were collected from May 1, 2020, to December 31, 2021. We used association rule mining techniques to identify frequent symptoms and establish relationships between symptoms among patients with long COVID in Twitter social media discussions. The highest confidence level--based detection was used to determine the most significant rules with 10\% minimum confidence and 0.01\% minimum support with a positive lift. Results: Among the 30,327 tweets included in our study, the most frequent symptoms were brain fog (n=7812, 25.8\%), fatigue (n=5284, 17.4\%), breathing/lung issues (n=4750, 15.7\%), heart issues (n=2900, 9.6\%), flu symptoms (n=2824, 9.3\%), depression (n=2256, 7.4\%) and general pains (n=1786, 5.9\%). Loss of smell and taste, cold, cough, chest pain, fever, headache, and arm pain emerged in 1.6\% (n=474) to 5.3\% (n=1616) of patients with long COVID. Furthermore, the highest confidence level--based detection successfully demonstrates the potential of association analysis and the Apriori algorithm to establish patterns to explore 57 meaningful relationship rules among long COVID symptoms. The strongest relationship revealed that patients with lung/breathing problems and loss of taste are likely to have a loss of smell with 77\% confidence. Conclusions: There are very active social media discussions that could support the growing understanding of COVID-19 and its long-term impact. These discussions enable a potential field of research to analyze the behavior of long COVID syndrome. Exploratory data analysis using natural language processing methods revealed the symptoms and medical conditions related to long COVID discussions on the Twitter social media platform. Using Apriori algorithm--based association rules, we determined interesting and meaningful relationships between symptoms. ", doi="10.2196/37984", url="https://formative.jmir.org/2022/9/e37984", url="http://www.ncbi.nlm.nih.gov/pubmed/36069846" } @Article{info:doi/10.2196/37752, author="Nakagawa, Keisuke and Yang, Tsang Nuen and Wilson, Machelle and Yellowlees, Peter", title="Twitter Usage Among Physicians From 2016 to 2020: Algorithm Development and Longitudinal Analysis Study", journal="J Med Internet Res", year="2022", month="Sep", day="6", volume="24", number="9", pages="e37752", keywords="social media", keywords="internet", keywords="health informatics", keywords="internet use", keywords="public health", keywords="Twitter", keywords="physician", abstract="Background: Physicians are increasingly using Twitter as a channel for communicating with colleagues and the public. Identifying physicians on Twitter is difficult due to the varied and imprecise ways that people self-identify themselves on the social media platform. This is the first study to describe a reliable, repeatable methodology for identifying physicians on Twitter. By using this approach, we characterized the longitudinal activity of US physicians on Twitter. Objective: We aimed to develop a reliable and repeatable methodology for identifying US physicians on Twitter and to characterize their activity on Twitter over 5 years by activity, tweeted topic, and account type. Methods: In this study, 5 years of Twitter data (2016-2020) were mined for physician accounts. US physicians on Twitter were identified by using a custom-built algorithm to screen for physician identifiers in the Twitter handles, user profiles, and tweeted content. The number of tweets by physician accounts from the 5-year period were counted and analyzed. The top 100 hashtags were identified, categorized into topics, and analyzed. Results: Approximately 1 trillion tweets were mined to identify 6,399,146 (<0.001\%) tweets originating from 39,084 US physician accounts. Over the 5-year period, the number of US physicians tweeting more than doubled (ie, increased by 112\%). Across all 5 years, the most popular themes were general health, medical education, and mental health, and in specific years, the number of tweets related to elections (2016 and 2020), Black Lives Matter (2020), and COVID-19 (2020) increased. Conclusions: Twitter has become an increasingly popular social media platform for US physicians over the past 5 years, and their use of Twitter has evolved to cover a broad range of topics, including science, politics, social activism, and COVID-19. We have developed an accurate, repeatable methodology for identifying US physicians on Twitter and have characterized their activity. ", doi="10.2196/37752", url="https://www.jmir.org/2022/9/e37752", url="http://www.ncbi.nlm.nih.gov/pubmed/36066939" } @Article{info:doi/10.2196/36986, author="Lejeune, Alban and Robaglia, Benoit-Marie and Walter, Michel and Berrouiguet, Sofian and Lemey, Christophe", title="Use of Social Media Data to Diagnose and Monitor Psychotic Disorders: Systematic Review", journal="J Med Internet Res", year="2022", month="Sep", day="6", volume="24", number="9", pages="e36986", keywords="schizophrenia", keywords="psychotic disorders", keywords="psychiatric disorders", keywords="artificial intelligence", keywords="AI", keywords="machine learning", keywords="neural network", keywords="social media", abstract="Background: Schizophrenia is a disease associated with high burden, and improvement in care is necessary. Artificial intelligence (AI) has been used to diagnose several medical conditions as well as psychiatric disorders. However, this technology requires large amounts of data to be efficient. Social media data could be used to improve diagnostic capabilities. Objective: The objective of our study is to analyze the current capabilities of AI to use social media data as a diagnostic tool for psychotic disorders. Methods: A systematic review of the literature was conducted using several databases (PubMed, Embase, Cochrane, PsycInfo, and IEEE Xplore) using relevant keywords to search for articles published as of November 12, 2021. We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) criteria to identify, select, and critically assess the quality of the relevant studies while minimizing bias. We critically analyzed the methodology of the studies to detect any bias and presented the results. Results: Among the 93 studies identified, 7 studies were included for analyses. The included studies presented encouraging results. Social media data could be used in several ways to care for patients with schizophrenia, including the monitoring of patients after the first episode of psychosis. We identified several limitations in the included studies, mainly lack of access to clinical diagnostic data, small sample size, and heterogeneity in study quality. We recommend using state-of-the-art natural language processing neural networks, called language models, to model social media activity. Combined with the synthetic minority oversampling technique, language models can tackle the imbalanced data set limitation, which is a necessary constraint to train unbiased classifiers. Furthermore, language models can be easily adapted to the classification task with a procedure called ``fine-tuning.'' Conclusions: The use of social media data for the diagnosis of psychotic disorders is promising. However, most of the included studies had significant biases; we therefore could not draw conclusions about accuracy in clinical situations. Future studies need to use more accurate methodologies to obtain unbiased results. ", doi="10.2196/36986", url="https://www.jmir.org/2022/9/e36986", url="http://www.ncbi.nlm.nih.gov/pubmed/36066938" } @Article{info:doi/10.2196/39805, author="Kong, Dexia and Chen, Anfan and Zhang, Jingwen and Xiang, Xiaoling and Lou, Vivian W. Q. and Kwok, Timothy and Wu, Bei", title="Public Discourse and Sentiment Toward Dementia on Chinese Social Media: Machine Learning Analysis of Weibo Posts", journal="J Med Internet Res", year="2022", month="Sep", day="2", volume="24", number="9", pages="e39805", keywords="dementia", keywords="public discourse", keywords="sentiment", keywords="Weibo", keywords="social media", keywords="machine learning", keywords="infodemiology", keywords="aging", keywords="elderly population", keywords="content analysis", keywords="topic modeling", keywords="thematic analysis", keywords="social support", keywords="sentiment analysis", abstract="Background: Dementia is a global public health priority due to rapid growth of the aging population. As China has the world's largest population with dementia, this debilitating disease has created tremendous challenges for older adults, family caregivers, and health care systems on the mainland nationwide. However, public awareness and knowledge of the disease remain limited in Chinese society. Objective: This study examines online public discourse and sentiment toward dementia among the Chinese public on a leading Chinese social media platform Weibo. Specifically, this study aims to (1) assess and examine public discourse and sentiment toward dementia among the Chinese public, (2) determine the extent to which dementia-related discourse and sentiment vary among different user groups (ie, government, journalists/news media, scientists/experts, and the general public), and (3) characterize temporal trends in public discourse and sentiment toward dementia among different user groups in China over the past decade. Methods: In total, 983,039 original dementia-related posts published by 347,599 unique users between 2010 and 2021, together with their user information, were analyzed. Machine learning analytical techniques, including topic modeling, sentiment analysis, and semantic network analyses, were used to identify salient themes/topics and their variations across different user groups (ie, government, journalists/news media, scientists/experts, and the general public). Results: Topic modeling results revealed that symptoms, prevention, and social support are the most prevalent dementia-related themes on Weibo. Posts about dementia policy/advocacy have been increasing in volume since 2018. Raising awareness is the least discussed topic over time. Sentiment analysis indicated that Weibo users generally attach negative attitudes/emotions to dementia, with the general public holding a more negative attitude than other user groups. Conclusions: Overall, dementia has received greater public attention on social media since 2018. In particular, discussions related to dementia advocacy and policy are gaining momentum in China. However, disparaging language is still used to describe dementia in China; therefore, a nationwide initiative is needed to alter the public discourse on dementia. The results contribute to previous research by providing a macrolevel understanding of the Chinese public's discourse and attitudes toward dementia, which is essential for building national education and policy initiatives to create a dementia-friendly society. Our findings indicate that dementia is associated with negative sentiments, and symptoms and prevention dominate public discourse. The development of strategies to address unfavorable perceptions of dementia requires policy and public health attention. The results further reveal that an urgent need exists to increase public knowledge about dementia. Social media platforms potentially could be leveraged for future dementia education interventions to increase dementia awareness and promote positive attitudes. ", doi="10.2196/39805", url="https://www.jmir.org/2022/9/e39805", url="http://www.ncbi.nlm.nih.gov/pubmed/36053565" } @Article{info:doi/10.2196/38976, author="Marcec, Robert and Stjepanovic, Josip and Likic, Robert", title="Seasonality of Hashimoto Thyroiditis: Infodemiology Study of Google Trends Data", journal="JMIR Bioinform Biotech", year="2022", month="Sep", day="1", volume="3", number="1", pages="e38976", keywords="Hashimoto disease", keywords="Hashimoto thyroiditis", keywords="infodemiology", keywords="search engine", keywords="Google Trends", keywords="seasonality", keywords="cosinor analysis", keywords="Google", keywords="thyroid", abstract="Background: Hashimoto thyroiditis (HT) is an autoimmune thyroid disease and the leading cause of hypothyroidism in areas with sufficient iodine intake. The quality-of-life impact and financial burden of hypothyroidism and HT highlight the need for additional research investigating the disease etiology with the aim of revealing potential modifiable risk factors. Objective: Implementation of measures against such risk factors, once identified, has the potential to lessen the financial burden while also improving the quality of life of many individuals. Therefore, we aimed to examine the potential seasonality of HT in Europe using the Google Trends data to explore whether there is a seasonal characteristic of Google searches regarding HT, examine the potential impact of the countries' geographic location on the potential seasonality, and identify potential modifiable risk factors for HT, thereby inspiring future research on the topic. Methods: Monthly Google Trends data on the search topic ``Hashimoto thyroiditis'' were retrieved in a 17-year time frame from January 2004 to December 2020 for 36 European countries. A cosinor model analysis was conducted to evaluate potential seasonality. Simple linear regression was used to estimate the potential effect of latitude and longitude on seasonal amplitude and phase of the model outputs. Results: Of 36 included European countries, significant seasonality was observed in 30 (83\%) countries. Most phase peaks occurred in spring (14/30, 46.7\%) and winter (8/30, 26.7\%). A statistically significant effect was observed regarding the effect of geographical latitude on cosinor model amplitude (y = --3.23 + 0.13 x; R2=0.29; P=.002). Seasonal increases in HT search volume may therefore be a consequence of an increased incidence or higher disease activity. It is particularly interesting that in most countries, a seasonal peak occurred in spring and winter months; when viewed in the context of the statistically significant impact of geographical latitude on seasonality amplitude, this may indicate the potential role of vitamin D levels in the seasonality of HT. Conclusions: Significant seasonality of HT Google Trends search volume was observed in our study, with seasonal peaks in most countries occurring in spring and winter and with a significant impact of latitude on seasonality amplitude. Further studies on the topic of seasonality in HT and factors impacting it are required. ", doi="10.2196/38976", url="https://bioinform.jmir.org/2022/1/e38976" } @Article{info:doi/10.2196/37862, author="Tong, Chau and Margolin, Drew and Chunara, Rumi and Niederdeppe, Jeff and Taylor, Teairah and Dunbar, Natalie and King, J. Andy", title="Search Term Identification Methods for Computational Health Communication: Word Embedding and Network Approach for Health Content on YouTube", journal="JMIR Med Inform", year="2022", month="Aug", day="30", volume="10", number="8", pages="e37862", keywords="health information retrieval", keywords="search term identification", keywords="social media", keywords="health communication", keywords="public health", keywords="computational textual analysis", keywords="natural language processing", keywords="NLP", keywords="word2vec", keywords="word embeddings", keywords="network analysis", abstract="Background: Common methods for extracting content in health communication research typically involve using a set of well-established queries, often names of medical procedures or diseases, that are often technical or rarely used in the public discussion of health topics. Although these methods produce high recall (ie, retrieve highly relevant content), they tend to overlook health messages that feature colloquial language and layperson vocabularies on social media. Given how such messages could contain misinformation or obscure content that circumvents official medical concepts, correctly identifying (and analyzing) them is crucial to the study of user-generated health content on social media platforms. Objective: Health communication scholars would benefit from a retrieval process that goes beyond the use of standard terminologies as search queries. Motivated by this, this study aims to put forward a search term identification method to improve the retrieval of user-generated health content on social media. We focused on cancer screening tests as a subject and YouTube as a platform case study. Methods: We retrieved YouTube videos using cancer screening procedures (colonoscopy, fecal occult blood test, mammogram, and pap test) as seed queries. We then trained word embedding models using text features from these videos to identify the nearest neighbor terms that are semantically similar to cancer screening tests in colloquial language. Retrieving more YouTube videos from the top neighbor terms, we coded a sample of 150 random videos from each term for relevance. We then used text mining to examine the new content retrieved from these videos and network analysis to inspect the relations between the newly retrieved videos and videos from the seed queries. Results: The top terms with semantic similarities to cancer screening tests were identified via word embedding models. Text mining analysis showed that the 5 nearest neighbor terms retrieved content that was novel and contextually diverse, beyond the content retrieved from cancer screening concepts alone. Results from network analysis showed that the newly retrieved videos had at least one total degree of connection (sum of indegree and outdegree) with seed videos according to YouTube relatedness measures. Conclusions: We demonstrated a retrieval technique to improve recall and minimize precision loss, which can be extended to various health topics on YouTube, a popular video-sharing social media platform. We discussed how health communication scholars can apply the technique to inspect the performance of the retrieval strategy before investing human coding resources and outlined suggestions on how such a technique can be extended to other health contexts. ", doi="10.2196/37862", url="https://medinform.jmir.org/2022/8/e37862", url="http://www.ncbi.nlm.nih.gov/pubmed/36040760" } @Article{info:doi/10.2196/35563, author="Lao, Cecilia and Lane, Jo and Suominen, Hanna", title="Analyzing Suicide Risk From Linguistic Features in Social Media: Evaluation Study", journal="JMIR Form Res", year="2022", month="Aug", day="30", volume="6", number="8", pages="e35563", keywords="evaluation study", keywords="interdisciplinary research", keywords="linguistics", keywords="machine learning", keywords="mental health", keywords="natural language processing", keywords="social media", keywords="suicide risk", abstract="Background: Effective suicide risk assessments and interventions are vital for suicide prevention. Although assessing such risks is best done by health care professionals, people experiencing suicidal ideation may not seek help. Hence, machine learning (ML) and computational linguistics can provide analytical tools for understanding and analyzing risks. This, therefore, facilitates suicide intervention and prevention. Objective: This study aims to explore, using statistical analyses and ML, whether computerized language analysis could be applied to assess and better understand a person's suicide risk on social media. Methods: We used the University of Maryland Suicidality Dataset comprising text posts written by users (N=866) of mental health--related forums on Reddit. Each user was classified with a suicide risk rating (no, low, moderate, or severe) by either medical experts or crowdsourced annotators, denoting their estimated likelihood of dying by suicide. In language analysis, the Linguistic Inquiry and Word Count lexicon assessed sentiment, thinking styles, and part of speech, whereas readability was explored using the TextStat library. The Mann-Whitney U test identified differences between at-risk (low, moderate, and severe risk) and no-risk users. Meanwhile, the Kruskal-Wallis test and Spearman correlation coefficient were used for granular analysis between risk levels and to identify redundancy, respectively. In the ML experiments, gradient boost, random forest, and support vector machine models were trained using 10-fold cross validation. The area under the receiver operator curve and F1-score were the primary measures. Finally, permutation importance uncovered the features that contributed the most to each model's decision-making. Results: Statistically significant differences (P<.05) were identified between the at-risk (671/866, 77.5\%) and no-risk groups (195/866, 22.5\%). This was true for both the crowd- and expert-annotated samples. Overall, at-risk users had higher median values for most variables (authenticity, first-person pronouns, and negation), with a notable exception of clout, which indicated that at-risk users were less likely to engage in social posturing. A high positive correlation ($\rho$>0.84) was present between the part of speech variables, which implied redundancy and demonstrated the utility of aggregate features. All ML models performed similarly in their area under the curve (0.66-0.68); however, the random forest and gradient boost models were noticeably better in their F1-score (0.65 and 0.62) than the support vector machine (0.52). The features that contributed the most to the ML models were authenticity, clout, and negative emotions. Conclusions: In summary, our statistical analyses found linguistic features associated with suicide risk, such as social posturing (eg, authenticity and clout), first-person singular pronouns, and negation. This increased our understanding of the behavioral and thought patterns of social media users and provided insights into the mechanisms behind ML models. We also demonstrated the applicative potential of ML in assisting health care professionals to assess and manage individuals experiencing suicide risk. ", doi="10.2196/35563", url="https://formative.jmir.org/2022/8/e35563", url="http://www.ncbi.nlm.nih.gov/pubmed/36040781" } @Article{info:doi/10.2196/37656, author="Bhagavathula, Srikanth Akshaya and Massey, M. Philip", title="Google Trends on Human Papillomavirus Vaccine Searches in the United States From 2010 to 2021: Infodemiology Study", journal="JMIR Public Health Surveill", year="2022", month="Aug", day="29", volume="8", number="8", pages="e37656", keywords="Google Trends", keywords="HPV vaccine", keywords="Google search", keywords="attitude", keywords="infodemiology", keywords="searches", keywords="United States of America", abstract="Background: The human papillomavirus (HPV) vaccine is recommended for adolescents and young adults to prevent HPV-related cancers and genital warts. However, HPV vaccine uptake among the target age groups is suboptimal. Objective: The aim of this infodemiology study was to examine public online searches in the United States related to the HPV vaccine from January 2010 to December 2021. Methods: Google Trends (GT) was used to explore online searches related to the HPV vaccine from January 1, 2010, to December 31, 2021. Online searches and queries on the HPV vaccine were investigated using relative search volumes (RSVs). Analysis of variance was performed to investigate quarterly differences in HPV vaccine searches in each year from 2010 to 2021. A joinpoint regression was used to identify statistically significant changes over time; the $\alpha$ level was set to .05. Results: The year-wise online search volume related to the HPV vaccine increased from 2010 to 2021, often following federal changes related to vaccine administration. Joinpoint regression analysis showed that HPV vaccine searches significantly increased on average by 8.6\% (95\% CI 5.9\%-11.4\%) across each year from 2010 to 2021. Moreover, HPV vaccine searches demonstrated a similar pattern across years, with search interest increasing through August nearly every year. At the state level, the highest 12-year mean RSV was observed in California (59.9, SD 14.3) and the lowest was observed in Wyoming (17.4, SD 8.5) during the period of 2010-2021. Conclusions: Online searches related to the HPV vaccine increased by an average of 8.6\% across each year from 2010 to 2021, with noticeable spikes corresponding to key changes in vaccine recommendations. We identified patterns across years and differences at the state level in the online search interest related to the HPV vaccine. Public health organizations can use GT as a tool to characterize the public interest in and promote the HPV vaccine in the United States. ", doi="10.2196/37656", url="https://publichealth.jmir.org/2022/8/e37656", url="http://www.ncbi.nlm.nih.gov/pubmed/36036972" } @Article{info:doi/10.2196/38174, author="Chen, Jiarui and Xue, Siyu and Xie, Zidian and Li, Dongmei", title="Perceptions and Discussions of Snus on Twitter: Observational Study", journal="JMIR Med Inform", year="2022", month="Aug", day="29", volume="10", number="8", pages="e38174", keywords="snus", keywords="Twitter", keywords="sentiment", keywords="topic modeling", keywords="smokeless tobacco products", abstract="Background: With the increasing popularity of snus, it is essential to understand the public perception of this oral tobacco product. Twitter---a popular social media platform that is being used to share personal experiences and opinions---provides an ideal data source for studying the public perception of snus. Objective: This study aims to examine public perceptions and discussions of snus on Twitter. Methods: Twitter posts (tweets) about snus were collected through the Twitter streaming application programming interface from March 11, 2021, to February 26, 2022. A temporal analysis was conducted to examine the change in number of snus-related tweets over time. A sentiment analysis was conducted to examine the sentiments of snus-related tweets. Topic modeling was applied to tweets to determine popular topics. Finally, a keyword search and hand-coding were used to understand the health symptoms mentioned in snus-related tweets. Results: The sentiment analysis showed that the proportion of snus-related tweets with a positive sentiment was significantly higher than the proportion of negative sentiment tweets (4341/11,631, 37.32\% vs 3094/11,631, 26.60\%; P<.001). The topic modeling analysis revealed that positive tweets focused on snus's harm reduction and snus use being an alternative to smoking, while negative tweets focused on health concerns related to snus. Mouth and respiratory symptoms were the most mentioned health symptoms in snus-related tweets. Conclusions: This study examined the public perception of snus and popular snus-related topics discussed on Twitter, thus providing a guide for policy makers with regard to the future formulation and adjustment of tobacco regulation policies. ", doi="10.2196/38174", url="https://medinform.jmir.org/2022/8/e38174", url="http://www.ncbi.nlm.nih.gov/pubmed/36036970" } @Article{info:doi/10.2196/38319, author="Singh, Lisa and Gresenz, Roan Carole and Wang, Yanchen and Hu, Sonya", title="Assessing Social Media Data as a Resource for Firearm Research: Analysis of Tweets Pertaining to Firearm Deaths", journal="J Med Internet Res", year="2022", month="Aug", day="25", volume="24", number="8", pages="e38319", keywords="firearms", keywords="fatalities", keywords="Twitter", keywords="firearm research", keywords="social media data", abstract="Background: Historic constraints on research dollars and reliable information have limited firearm research. At the same time, interest in the power and potential of social media analytics, particularly in health contexts, has surged. Objective: The aim of this study is to contribute toward the goal of establishing a foundation for how social media data may best be used, alone or in conjunction with other data resources, to improve the information base for firearm research. Methods: We examined the value of social media data for estimating a firearm outcome for which robust benchmark data exist---specifically, firearm mortality, which is captured in the National Vital Statistics System (NVSS). We hand curated tweet data from the Twitter application programming interface spanning January 1, 2017, to December 31, 2018. We developed machine learning classifiers to identify tweets that pertain to firearm deaths and develop estimates of the volume of Twitter firearm discussion by month. We compared within-state variation over time in the volume of tweets pertaining to firearm deaths with within-state trends in NVSS-based estimates of firearm fatalities using Pearson linear correlations. Results: The correlation between the monthly number of firearm fatalities measured by the NVSS and the monthly volume of tweets pertaining to firearm deaths was weak (median 0.081) and highly dispersed across states (range --0.31 to 0.535). The median correlation between month-to-month changes in firearm fatalities in the NVSS and firearm deaths discussed in tweets was moderate (median 0.30) and exhibited less dispersion among states (range --0.06 to 0.69). Conclusions: Our findings suggest that Twitter data may hold value for tracking dynamics in firearm-related outcomes, particularly for relatively populous cities that are identifiable through location mentions in tweet content. The data are likely to be particularly valuable for understanding firearm outcomes not currently measured, not measured well, or not measurable through other available means. This research provides an important building block for future work that continues to develop the usefulness of social media data for firearm research. ", doi="10.2196/38319", url="https://www.jmir.org/2022/8/e38319", url="http://www.ncbi.nlm.nih.gov/pubmed/36006693" } @Article{info:doi/10.2196/36085, author="Suarez-Lledo, Victor and Alvarez-Galvez, Javier", title="Assessing the Role of Social Bots During the COVID-19 Pandemic: Infodemic, Disagreement, and Criticism", journal="J Med Internet Res", year="2022", month="Aug", day="25", volume="24", number="8", pages="e36085", keywords="infodemics", keywords="social media", keywords="misinformation", keywords="epidemics", keywords="outbreaks", keywords="COVID-19", keywords="infodemiology", keywords="health promotion", keywords="pandemic", keywords="chatbot", keywords="social media bot", keywords="Twitter stream", keywords="Botometer", keywords="peer support", abstract="Background: Social media has changed the way we live and communicate, as well as offering unprecedented opportunities to improve many aspects of our lives, including health promotion and disease prevention. However, there is also a darker side to social media that is not always as evident as its possible benefits. In fact, social media has also opened the door to new social and health risks that are linked to health misinformation. Objective: This study aimed to study the role of social media bots during the COVID-19 outbreak. Methods: The Twitter streaming API was used to collect tweets regarding COVID-19 during the early stages of the outbreak. The Botometer tool was then used to obtain the likelihood of whether each account is a bot or not. Bot classification and topic-modeling techniques were used to interpret the Twitter conversation. Finally, the sentiment associated with the tweets was compared depending on the source of the tweet. Results: Regarding the conversation topics, there were notable differences between the different accounts. The content of nonbot accounts was associated with the evolution of the pandemic, support, and advice. On the other hand, in the case of self-declared bots, the content consisted mainly of news, such as the existence of diagnostic tests, the evolution of the pandemic, and scientific findings. Finally, in the case of bots, the content was mostly political. Above all, there was a general overriding tone of criticism and disagreement. In relation to the sentiment analysis, the main differences were associated with the tone of the conversation. In the case of self-declared bots, this tended to be neutral, whereas the conversation of normal users scored positively. In contrast, bots tended to score negatively. Conclusions: By classifying the accounts according to their likelihood of being bots and performing topic modeling, we were able to segment the Twitter conversation regarding COVID-19. Bot accounts tended to criticize the measures imposed to curb the pandemic, express disagreement with politicians, or question the veracity of the information shared on social media. ", doi="10.2196/36085", url="https://www.jmir.org/2022/8/e36085", url="http://www.ncbi.nlm.nih.gov/pubmed/35839385" } @Article{info:doi/10.2196/36210, author="Buller, David and Walkosz, Barbara and Henry, Kimberly and Woodall, Gill W. and Pagoto, Sherry and Berteletti, Julia and Kinsey, Alishia and Divito, Joseph and Baker, Katie and Hillhouse, Joel", title="Promoting Social Distancing and COVID-19 Vaccine Intentions to Mothers: Randomized Comparison of Information Sources in Social Media Messages", journal="JMIR Infodemiology", year="2022", month="Aug", day="23", volume="2", number="2", pages="e36210", keywords="social media", keywords="COVID-19", keywords="vaccination", keywords="nonpharmaceutical interventions", keywords="information source", keywords="misinformation", keywords="vaccine", keywords="public health", keywords="COVID-19 prevention", keywords="health promotion", abstract="Background: Social media disseminated information and spread misinformation during the COVID-19 pandemic that affected prevention measures, including social distancing and vaccine acceptance. Objective: In this study, we aimed to test the effect of a series of social media posts promoting COVID-19 nonpharmaceutical interventions (NPIs) and vaccine intentions and compare effects among 3 common types of information sources: government agency, near-peer parents, and news media. Methods: A sample of mothers of teen daughters (N=303) recruited from a prior trial were enrolled in a 3 (information source) {\texttimes} 4 (assessment period) randomized factorial trial from January to March 2021 to evaluate the effects of information sources in a social media campaign addressing NPIs (ie, social distancing), COVID-19 vaccinations, media literacy, and mother--daughter communication about COVID-19. Mothers received 1 social media post per day in 3 randomly assigned Facebook private groups, Monday-Friday, covering all 4 topics each week, plus 1 additional post on a positive nonpandemic topic to promote engagement. Posts in the 3 groups had the same messages but differed by links to information from government agencies, near-peer parents, or news media in the post. Mothers reported on social distancing behavior and COVID-19 vaccine intentions for self and daughter, theoretic mediators, and covariates in baseline and 3-, 6-, and 9-week postrandomization assessments. Views, reactions, and comments related to each post were counted to measure engagement with the messages. Results: Nearly all mothers (n=298, 98.3\%) remained in the Facebook private groups throughout the 9-week trial period, and follow-up rates were high (n=276, 91.1\%, completed the 3-week posttest; n=273, 90.1\%, completed the 6-week posttest; n=275, 90.8\%, completed the 9-week posttest; and n=244, 80.5\%, completed all assessments). In intent-to-treat analyses, social distancing behavior by mothers (b=--0.10, 95\% CI --0.12 to --0.08, P<.001) and daughters (b=--0.10, 95\% CI --0.18 to --0.03, P<.001) decreased over time but vaccine intentions increased (mothers: b=0.34, 95\% CI 0.19-0.49, P<.001; daughters: b=0.17, 95\% CI 0.04-0.29, P=.01). Decrease in social distancing by daughters was greater in the near-peer source group (b=--0.04, 95\% CI --0.07 to 0.00, P=.03) and lesser in the government agency group (b=0.05, 95\% CI 0.02-0.09, P=.003). The higher perceived credibility of the assigned information source increased social distancing (mothers: b=0.29, 95\% CI 0.09-0.49, P<.01; daughters: b=0.31, 95\% CI 0.11-0.51, P<.01) and vaccine intentions (mothers: b=4.18, 95\% CI 1.83-6.53, P<.001; daughters: b=3.36, 95\% CI 1.67-5.04, P<.001). Mothers' intentions to vaccinate self may have increased when they considered the near-peer source to be not credible (b=--0.50, 95\% CI --0.99 to --0.01, P=.05). Conclusions: Decreasing case counts, relaxation of government restrictions, and vaccine distribution during the study may explain the decreased social distancing and increased vaccine intentions. When promoting COVID-19 prevention, campaign planners may be more effective when selecting information sources that audiences consider credible, as no source was more credible in general. Trial Registration: ClinicalTrials.gov NCT02835807; https://clinicaltrials.gov/ct2/show/NCT02835807 ", doi="10.2196/36210", url="https://infodemiology.jmir.org/2022/2/e36210", url="http://www.ncbi.nlm.nih.gov/pubmed/36039372" } @Article{info:doi/10.2196/36244, author="Herbert, S. Amber and Hassan, Naeemul and Malik, D. Rena and Loeb, Stacy and Myrie, Akya", title="Exploring Urological Malignancies on Pinterest: Content Analysis", journal="JMIR Cancer", year="2022", month="Aug", day="22", volume="8", number="3", pages="e36244", keywords="bladder cancer", keywords="Pinterest", keywords="prostate cancer", keywords="kidney cancer", keywords="testicular cancer", keywords="urological cancer", keywords="misinformation", keywords="genitourinary", keywords="malignancy", keywords="oncology", keywords="content", keywords="information", keywords="social media", keywords="accuracy", keywords="quality", abstract="Background: Pinterest is a visually oriented social media platform with over 250 million monthly users. Previous studies have found misinformative content on genitourinary malignancies to be broadly disseminated on YouTube; however, no study has assessed the quality of this content on Pinterest. Objective: Our objective was to evaluate the quality, understandability, and actionability of genitourinary malignancy content on Pinterest. Methods: We examined 540 Pinterest posts or pins, using the following search terms: ``bladder cancer,'' ``kidney cancer,'' ``prostate cancer,'' and ``testicular cancer.'' The pins were limited to English language and topic-specific content, resulting in the following exclusions: bladder (n=88), kidney (n=4), prostate (n=79), and testicular cancer (n=10), leaving 359 pins as the final analytic sample. Pinterest pins were classified based on publisher and perceived race or ethnicity. Content was assessed using 2 validated grading systems: DISCERN quality criteria and the Patient Education Materials Assessment Tool. The presence of misinformation was evaluated using a published Likert scale ranging from 1=none to 5=high. Results: Overall, 359 pins with a total of 8507 repins were evaluated. The primary publisher of genitourinary malignancy pins were health and wellness groups (n=162, 45\%). Across all genitourinary malignancy pins with people, only 3\% (n=7) were perceived as Black. Additionally, Asian (n=2, 1\%) and Latinx (n=1, 0.5\%) individuals were underrepresented in all pins. Nearly 75\% (n=298) of the pins had moderate- to poor-quality information. Misinformative content was apparent in 4\%-26\% of all genitourinary cancer pins. Understandability and actionability were poor in 55\% (n=198) and 100\% (n=359) of the pins, respectively. Conclusions: On Pinterest, the majority of the urological oncology patient-centric content is of low quality and lacks diversity. This widely used, yet unregulated platform has the ability to influence consumers' health knowledge and decision-making. Ultimately, this can lead to consumers making suboptimal medical decisions. Moreover, our findings demonstrate underrepresentation across many racial and ethnic groups. Efforts should be made to ensure the dissemination of diverse, high-quality, and accurate health care information to the millions of users on Pinterest and other social media platforms. ", doi="10.2196/36244", url="https://cancer.jmir.org/2022/3/e36244", url="http://www.ncbi.nlm.nih.gov/pubmed/35994318" } @Article{info:doi/10.2196/37829, author="Singhal, Aditya and Baxi, Kaur Manmeet and Mago, Vijay", title="Synergy Between Public and Private Health Care Organizations During COVID-19 on Twitter: Sentiment and Engagement Analysis Using Forecasting Models", journal="JMIR Med Inform", year="2022", month="Aug", day="18", volume="10", number="8", pages="e37829", keywords="social media", keywords="health care", keywords="Twitter", keywords="content analysis", keywords="user engagement", keywords="sentiment forecasting", keywords="natural language processing", keywords="public health", keywords="pharmaceutical", keywords="public engagement", abstract="Background: Social media platforms (SMPs) are frequently used by various pharmaceutical companies, public health agencies, and nongovernment organizations (NGOs) for communicating health concerns, new advancements, and potential outbreaks. Although the benefits of using them as a tool have been extensively discussed, the online activity of various health care organizations on SMPs during COVID-19 in terms of engagement and sentiment forecasting has not been thoroughly investigated. Objective: The purpose of this research is to analyze the nature of information shared on Twitter, understand the public engagement generated on it, and forecast the sentiment score for various organizations. Methods: Data were collected from the Twitter handles of 5 pharmaceutical companies, 10 US and Canadian public health agencies, and the World Health Organization (WHO) from January 1, 2017, to December 31, 2021. A total of 181,469 tweets were divided into 2 phases for the analysis, before COVID-19 and during COVID-19, based on the confirmation of the first COVID-19 community transmission case in North America on February 26, 2020. We conducted content analysis to generate health-related topics using natural language processing (NLP)-based topic-modeling techniques, analyzed public engagement on Twitter, and performed sentiment forecasting using 16 univariate moving-average and machine learning (ML) models to understand the correlation between public opinion and tweet contents. Results: We utilized the topics modeled from the tweets authored by the health care organizations chosen for our analysis using nonnegative matrix factorization (NMF): cumass=--3.6530 and --3.7944 before and during COVID-19, respectively. The topics were chronic diseases, health research, community health care, medical trials, COVID-19, vaccination, nutrition and well-being, and mental health. In terms of user impact, WHO (user impact=4171.24) had the highest impact overall, followed by public health agencies, the Centers for Disease Control and Prevention (CDC; user impact=2895.87), and the National Institutes of Health (NIH; user impact=891.06). Among pharmaceutical companies, Pfizer's user impact was the highest at 97.79. Furthermore, for sentiment forecasting, autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) models performed best on the majority of the subsets of data (divided as per the health care organization and period), with the mean absolute error (MAE) between 0.027 and 0.084, the mean square error (MSE) between 0.001 and 0.011, and the root-mean-square error (RMSE) between 0.031 and 0.105. Conclusions: Our findings indicate that people engage more on topics such as COVID-19 than medical trials and customer experience. In addition, there are notable differences in the user engagement levels across organizations. Global organizations, such as WHO, show wide variations in engagement levels over time. The sentiment forecasting method discussed presents a way for organizations to structure their future content to ensure maximum user engagement. ", doi="10.2196/37829", url="https://medinform.jmir.org/2022/8/e37829", url="http://www.ncbi.nlm.nih.gov/pubmed/35849795" } @Article{info:doi/10.2196/37622, author="Park, Yoonseo and Park, Sewon and Lee, Munjae", title="Digital Health Care Industry Ecosystem: Network Analysis", journal="J Med Internet Res", year="2022", month="Aug", day="17", volume="24", number="8", pages="e37622", keywords="digital health care", keywords="industrial ecosystem", keywords="network analysis", keywords="topic modeling", keywords="South Korea", abstract="Background: As the need for digital health care based on mobile devices is increasing, with the rapid development of digital technologies, especially in the face of the COVID-19 pandemic, gaining a better understanding of the industrial structure is needed to activate the use of digital health care. Objective: The aim of this study was to suggest measures to revitalize the digital health care industry by deriving the stakeholders and major issues with respect to the ecosystem of the industry. Methods: A total of 1822 newspaper articles were collected using Big Kings, a big data system for news, for a limited period from 2016 to August 2021, when the mobile health care project was promoted in Korea centered on public health centers. The R and NetMiner programs were used for network analysis. Results: The Korean government and the Ministry of Health and Welfare showed the highest centrality and appeared as major stakeholders, and their common major issues were ``reviewing the introduction of telemedicine,'' ``concerns about bankruptcy of local clinics,'' and ``building an integrated platform for precision medicine.'' In addition, the major stakeholders of medical institutions and companies were Seoul National University Hospital, Kangbuk Samsung Hospital, Ajou University Hospital, Samsung, and Vuno Inc. Conclusions: This analysis confirmed that the issues related to digital health care are largely composed of telemedicine, data, and health care business. For digital health care to develop as a national innovative growth engine and to be institutionalized, the development of a digital health care fee model that can improve the regulatory system and the cost-effectiveness of patient care, centering on the Ministry of Health and Welfare as a key stakeholder, is essential. ", doi="10.2196/37622", url="https://www.jmir.org/2022/8/e37622", url="http://www.ncbi.nlm.nih.gov/pubmed/35976690" } @Article{info:doi/10.2196/34705, author="Metzler, Hannah and Baginski, Hubert and Niederkrotenthaler, Thomas and Garcia, David", title="Detecting Potentially Harmful and Protective Suicide-Related Content on Twitter: Machine Learning Approach", journal="J Med Internet Res", year="2022", month="Aug", day="17", volume="24", number="8", pages="e34705", keywords="suicide prevention", keywords="Twitter", keywords="social media", keywords="machine learning", keywords="deep learning", abstract="Background: Research has repeatedly shown that exposure to suicide-related news media content is associated with suicide rates, with some content characteristics likely having harmful and others potentially protective effects. Although good evidence exists for a few selected characteristics, systematic and large-scale investigations are lacking. Moreover, the growing importance of social media, particularly among young adults, calls for studies on the effects of the content posted on these platforms. Objective: This study applies natural language processing and machine learning methods to classify large quantities of social media data according to characteristics identified as potentially harmful or beneficial in media effects research on suicide and prevention. Methods: We manually labeled 3202 English tweets using a novel annotation scheme that classifies suicide-related tweets into 12 categories. Based on these categories, we trained a benchmark of machine learning models for a multiclass and a binary classification task. As models, we included a majority classifier, an approach based on word frequency (term frequency-inverse document frequency with a linear support vector machine) and 2 state-of-the-art deep learning models (Bidirectional Encoder Representations from Transformers [BERT] and XLNet). The first task classified posts into 6 main content categories, which are particularly relevant for suicide prevention based on previous evidence. These included personal stories of either suicidal ideation and attempts or coping and recovery, calls for action intending to spread either problem awareness or prevention-related information, reporting of suicide cases, and other tweets irrelevant to these 5 categories. The second classification task was binary and separated posts in the 11 categories referring to actual suicide from posts in the off-topic category, which use suicide-related terms in another meaning or context. Results: In both tasks, the performance of the 2 deep learning models was very similar and better than that of the majority or the word frequency classifier. BERT and XLNet reached accuracy scores above 73\% on average across the 6 main categories in the test set and F1-scores between 0.69 and 0.85 for all but the suicidal ideation and attempts category (F1=0.55). In the binary classification task, they correctly labeled around 88\% of the tweets as about suicide versus off-topic, with BERT achieving F1-scores of 0.93 and 0.74, respectively. These classification performances were similar to human performance in most cases and were comparable with state-of-the-art models on similar tasks. Conclusions: The achieved performance scores highlight machine learning as a useful tool for media effects research on suicide. The clear advantage of BERT and XLNet suggests that there is crucial information about meaning in the context of words beyond mere word frequencies in tweets about suicide. By making data labeling more efficient, this work has enabled large-scale investigations on harmful and protective associations of social media content with suicide rates and help-seeking behavior. ", doi="10.2196/34705", url="https://www.jmir.org/2022/8/e34705", url="http://www.ncbi.nlm.nih.gov/pubmed/35976193" } @Article{info:doi/10.2196/38268, author="Li, Yachao and Ashley, L. David and Popova, Lucy", title="Users' Modifications to Electronic Nicotine Delivery Systems: Content Analysis of YouTube Video Comments", journal="JMIR Infodemiology", year="2022", month="Aug", day="12", volume="2", number="2", pages="e38268", keywords="ENDS modifications", keywords="YouTube", keywords="comments", keywords="vaping", keywords="content analysis", abstract="Background: User modifications can alter the toxicity and addictiveness of electronic nicotine delivery systems (ENDSs). YouTube has been a major platform where ENDS users obtain and share information about ENDS modifications. Past research has examined the content and characteristics of ENDS modification videos. Objective: This study aims to analyze the video comments to understand the viewers' reactions to these videos. Methods: We identified 168 YouTube videos depicting ENDS modifications. Each video's top 20 most liked comments were retrieved. The final sample included 2859 comments. A content analysis identified major themes of the comment content. Results: Most comments were directed to creators and interacted with others: 952/2859 (33.30\%) expressed appreciation, 135/2859 (4.72\%) requested more videos, 462/2859 (16.16\%) asked for clarification, and 67/2859 (2.34\%) inquired about product purchases. In addition, comments mentioned viewers' experiences of ENDS modifications (430/2859, 15.04\%) and tobacco use (167/2859, 5.84\%); about 198/2859 (6.93\%) also indicated intentions to modify ENDSs and 34/2859 (1.19\%) mentioned that they were ``newbies.'' Moreover, comments included modification knowledge: 346/2859 (12.10\%) provided additional information, 227/2859 (7.94\%) mentioned newly learned knowledge, and 162/2859 (5.67\%) criticized the videos. Furthermore, few comments mentioned the dangers of ENDS modifications (136/2859, 4.76\%) and tobacco use (7/2859, 0.24\%). Lastly, among the 15 comments explicitly mentioning regulations, 13/2859 (0.45\%) were against and 2/2859 (0.07\%) were supportive of regulations. Conclusions: The results indicated acceptance and popularity of ENDS modifications and suggested that the videos might motivate current and new users to alter their devices. Few comments mentioned the risks and regulations. Regulatory research and agencies should be aware of online ENDS modification information and understand its impacts on users. ", doi="10.2196/38268", url="https://infodemiology.jmir.org/2022/2/e38268", url="http://www.ncbi.nlm.nih.gov/pubmed/35992739" } @Article{info:doi/10.2196/35937, author="Burgess, Raquel and Feliciano, T. Josemari and Lizbinski, Leonardo and Ransome, Yusuf", title="Trends and Characteristics of \#HIVPrevention Tweets Posted Between 2014 and 2019: Retrospective Infodemiology Study", journal="JMIR Public Health Surveill", year="2022", month="Aug", day="11", volume="8", number="8", pages="e35937", keywords="HIV", keywords="social media", keywords="Twitter", keywords="prevention", keywords="infodemiology", abstract="Background: Twitter is becoming an increasingly important avenue for people to seek information about HIV prevention. Tweets about HIV prevention may reflect or influence current norms about the acceptability of different HIV prevention methods. Therefore, it may be useful to empirically investigate trends in the level of attention paid to different HIV prevention topics on Twitter over time. Objective: The primary objective of this study was to investigate temporal trends in the frequency of tweets about different HIV prevention topics on Twitter between 2014 and 2019. Methods: We used the Twitter application programming interface to obtain English-language tweets employing \#HIVPrevention between January 1, 2014, and December 31, 2019 (n=69,197, globally). Using iterative qualitative content analysis on samples of tweets, we developed a keyword list to categorize the tweets into 10 prevention topics (eg, condom use, preexposure prophylaxis [PrEP]) and compared the frequency of tweets mentioning each topic over time. We assessed the overall change in the proportions of \#HIVPrevention tweets mentioning each prevention topic in 2019 as compared with 2014 using chi-square and Fisher exact tests. We also conducted descriptive analyses to identify the accounts posting the most original tweets, the accounts retweeted most frequently, the most frequently used word pairings, and the spatial distribution of tweets in the United States compared with the number of state-level HIV cases. Results: PrEP (13,895 tweets; 20.08\% of all included tweets) and HIV testing (7688, 11.11\%) were the most frequently mentioned topics, whereas condom use (2941, 4.25\%) and postexposure prophylaxis (PEP; 823, 1.19\%) were mentioned relatively less frequently. The proportions of tweets mentioning PrEP (327/2251, 14.53\%, in 2014, 5067/12,971, 39.1\%, in 2019; P?.001), HIV testing (208/2251, 9.24\%, in 2014, 2193/12,971, 16.91\% in 2019; P?.001), and PEP (25/2251, 1.11\%, in 2014, 342/12,971, 2.64\%, in 2019; P?.001) were higher in 2019 compared with 2014, whereas the proportions of tweets mentioning abstinence, condom use, circumcision, harm reduction, and gender inequity were lower in 2019 compared with 2014. The top retweeted accounts were mostly UN-affiliated entities; celebrities and HIV advocates were also represented. Geotagged \#HIVPrevention tweets in the United States between 2014 and 2019 (n=514) were positively correlated with the number of state-level HIV cases in 2019 (r=0.81, P?.01). Conclusions: Twitter may be a useful source for identifying HIV prevention trends. During our evaluation period (2014-2019), the most frequently mentioned prevention topics were PrEP and HIV testing in tweets using \#HIVPrevention. Strategic responses to these tweets that provide information about where to get tested or how to obtain PrEP may be potential approaches to reduce HIV incidence. ", doi="10.2196/35937", url="https://publichealth.jmir.org/2022/8/e35937", url="http://www.ncbi.nlm.nih.gov/pubmed/35969453" } @Article{info:doi/10.2196/37300, author="Yin, Dean-Chen Jason", title="Media Data and Vaccine Hesitancy: Scoping Review", journal="JMIR Infodemiology", year="2022", month="Aug", day="10", volume="2", number="2", pages="e37300", keywords="review", keywords="social media", keywords="traditional media", keywords="vaccine hesitancy", keywords="natural language processing", keywords="digital epidemiology", abstract="Background: Media studies are important for vaccine hesitancy research, as they analyze how the media shapes risk perceptions and vaccine uptake. Despite the growth in studies in this field owing to advances in computing and language processing and an expanding social media landscape, no study has consolidated the methodological approaches used to study vaccine hesitancy. Synthesizing this information can better structure and set a precedent for this growing subfield of digital epidemiology. Objective: This review aimed to identify and illustrate the media platforms and methods used to study vaccine hesitancy and how they build or contribute to the study of the media's influence on vaccine hesitancy and public health. Methods: This study followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A search was conducted on PubMed and Scopus for any studies that used media data (social media or traditional media), had an outcome related to vaccine sentiment (opinion, uptake, hesitancy, acceptance, or stance), were written in English, and were published after 2010. Studies were screened by only 1 reviewer and extracted for media platform, analysis method, the theoretical models used, and outcomes. Results: In total, 125 studies were included, of which 71 (56.8\%) used traditional research methods and 54 (43.2\%) used computational methods. Of the traditional methods, most used content analysis (43/71, 61\%) and sentiment analysis (21/71, 30\%) to analyze the texts. The most common platforms were newspapers, print media, and web-based news. The computational methods mostly used sentiment analysis (31/54, 57\%), topic modeling (18/54, 33\%), and network analysis (17/54, 31\%). Fewer studies used projections (2/54, 4\%) and feature extraction (1/54, 2\%). The most common platforms were Twitter and Facebook. Theoretically, most studies were weak. The following five major categories of studies arose: antivaccination themes centered on the distrust of institutions, civil liberties, misinformation, conspiracy theories, and vaccine-specific concerns; provaccination themes centered on ensuring vaccine safety using scientific literature; framing being important and health professionals and personal stories having the largest impact on shaping vaccine opinion; the coverage of vaccination-related data mostly identifying negative vaccine content and revealing deeply fractured vaccine communities and echo chambers; and the public reacting to and focusing on certain signals---in particular cases, deaths, and scandals---which suggests a more volatile period for the spread of information. Conclusions: The heterogeneity in the use of media to study vaccines can be better consolidated through theoretical grounding. Areas of suggested research include understanding how trust in institutions is associated with vaccine uptake, how misinformation and information signaling influence vaccine uptake, and the evaluation of government communications on vaccine rollouts and vaccine-related events. The review ends with a statement that media data analyses, though groundbreaking in approach, should supplement---not supplant---current practices in public health research. ", doi="10.2196/37300", url="https://infodemiology.jmir.org/2022/2/e37300", url="http://www.ncbi.nlm.nih.gov/pubmed/37113443" } @Article{info:doi/10.2196/38776, author="Hsu, Tze-Hou Jerome and Tsai, Tzong-Han Richard", title="Increased Online Aggression During COVID-19 Lockdowns: Two-Stage Study of Deep Text Mining and Difference-in-Differences Analysis", journal="J Med Internet Res", year="2022", month="Aug", day="9", volume="24", number="8", pages="e38776", keywords="natural language processing", keywords="lockdown", keywords="online aggression", keywords="infoveillance", keywords="causal relationship", keywords="social media", keywords="neural networks", keywords="computer", keywords="pandemic", keywords="COVID-19", keywords="emotions", keywords="internet", keywords="sentiment analysis", keywords="Twitter", keywords="content analysis", keywords="infodemiology", abstract="Background: The COVID-19 pandemic caused a critical public health crisis worldwide, and policymakers are using lockdowns to control the virus. However, there has been a noticeable increase in aggressive social behaviors that threaten social stability. Lockdown measures might negatively affect mental health and lead to an increase in aggressive emotions. Discovering the relationship between lockdown and increased aggression is crucial for formulating appropriate policies that address these adverse societal effects. We applied natural language processing (NLP) technology to internet data, so as to investigate the social and emotional impacts of lockdowns. Objective: This research aimed to understand the relationship between lockdown and increased aggression using NLP technology to analyze the following 3 kinds of aggressive emotions: anger, offensive language, and hate speech, in spatiotemporal ranges of tweets in the United States. Methods: We conducted a longitudinal internet study of 11,455 Twitter users by analyzing aggressive emotions in 1,281,362 tweets they posted from 2019 to 2020. We selected 3 common aggressive emotions (anger, offensive language, and hate speech) on the internet as the subject of analysis. To detect the emotions in the tweets, we trained a Bidirectional Encoder Representations from Transformers (BERT) model to analyze the percentage of aggressive tweets in every state and every week. Then, we used the difference-in-differences estimation to measure the impact of lockdown status on increasing aggressive tweets. Since most other independent factors that might affect the results, such as seasonal and regional factors, have been ruled out by time and state fixed effects, a significant result in this difference-in-differences analysis can not only indicate a concrete positive correlation but also point to a causal relationship. Results: In the first 6 months of lockdown in 2020, aggression levels in all users increased compared to the same period in 2019. Notably, users under lockdown demonstrated greater levels of aggression than those not under lockdown. Our difference-in-differences estimation discovered a statistically significant positive correlation between lockdown and increased aggression (anger: P=.002, offensive language: P<.001, hate speech: P=.005). It can be inferred from such results that there exist causal relations. Conclusions: Understanding the relationship between lockdown and aggression can help policymakers address the personal and societal impacts of lockdown. Applying NLP technology and using big data on social media can provide crucial and timely information for this effort. ", doi="10.2196/38776", url="https://www.jmir.org/2022/8/e38776", url="http://www.ncbi.nlm.nih.gov/pubmed/35943771" } @Article{info:doi/10.2196/37818, author="Cui, Bin and Wang, Jian and Lin, Hongfei and Zhang, Yijia and Yang, Liang and Xu, Bo", title="Emotion-Based Reinforcement Attention Network for Depression Detection on Social Media: Algorithm Development and Validation", journal="JMIR Med Inform", year="2022", month="Aug", day="9", volume="10", number="8", pages="e37818", keywords="depression detection", keywords="emotional semantic features", keywords="social media", keywords="sentence-level attention", keywords="emotion-based reinforcement", abstract="Background: Depression detection has recently received attention in the field of natural language processing. The task aims to detect users with depression based on their historical posts on social media. However, existing studies in this area use the entire historical posts of the users and select depression indicator posts. Moreover, these methods fail to effectively extract deep emotional semantic features or simply concatenate emotional representation. To solve this problem, we propose a model to extract deep emotional semantic features and select depression indicator posts based on the emotional states. Objective: This study aims to develop an emotion-based reinforcement attention network for depression detection of users on social media. Methods: The proposed model is composed of 2 components: the emotion extraction network, which is used to capture deep emotional semantic information, and the reinforcement learning (RL) attention network, which is used to select depression indicator posts based on the emotional states. Finally, we concatenated the output of these 2 parts and send them to the classification layer for depression detection. Results: Experimental results of our model on the multimodal depression data set outperform the state-of-the-art baselines. Specifically, the proposed model achieved accuracy, precision, recall, and F1-score of 90.6\%, 91.2\%, 89.7\%, and 90.4\%, respectively. Conclusions: The proposed model utilizes historical posts of users to effectively identify users' depression tendencies. The experimental results show that the emotion extraction network and the RL selection layer based on emotional states can effectively improve the accuracy of detection. In addition, sentence-level attention layer can capture core posts. ", doi="10.2196/37818", url="https://medinform.jmir.org/2022/8/e37818", url="http://www.ncbi.nlm.nih.gov/pubmed/35943770" } @Article{info:doi/10.2196/37339, author="Ma, Ming and Yin, Saifu and Zhu, Mengli and Fan, Yu and Wen, Xi and Lin, Tao and Song, Turun", title="Evaluation of Medical Information on Male Sexual Dysfunction on Baidu Encyclopedia and Wikipedia: Comparative Study", journal="J Med Internet Res", year="2022", month="Aug", day="9", volume="24", number="8", pages="e37339", keywords="sexual dysfunction", keywords="digital health", keywords="Baidu Encyclopedia", keywords="Wikipedia", keywords="internet", keywords="health information", keywords="DISCERN instrument", abstract="Background: Sexual dysfunction is a private set of disorders that may cause stigma for patients when discussing their private problems with doctors. They might also feel reluctant to initiate a face-to-face consultation. Internet searches are gradually becoming the first choice for people with sexual dysfunction to obtain health information. Globally, Wikipedia is the most popular and consulted validated encyclopedia website in the English-speaking world. Baidu Encyclopedia is becoming the dominant source in Chinese-speaking regions; however, the objectivity and readability of the content are yet to be evaluated. Objective: Hence, we aimed to evaluate the reliability, readability, and objectivity of male sexual dysfunction content on Wikipedia and Baidu Encyclopedia. Methods: The Chinese Baidu Encyclopedia and English Wikipedia were investigated. All possible synonymous and derivative keywords for the most common male sexual dysfunction, erectile dysfunction, premature ejaculation, and their most common complication, chronic prostatitis/chronic pelvic pain syndrome, were screened. Two doctors evaluated the articles on Chinese Baidu Encyclopedia and English Wikipedia. The Journal of the American Medical Association (JAMA) scoring system, DISCERN instrument, and Global Quality Score (GQS) were used to assess the quality of disease-related articles. Results: The total DISCERN scores (P=.002) and JAMA scores (P=.001) for Wikipedia were significantly higher than those of Baidu Encyclopedia; there was no statistical difference between the GQS scores (P=.31) for these websites. Specifically, the DISCERN Section 1 score (P<.001) for Wikipedia was significantly higher than that of Baidu Encyclopedia, while the differences between the DISCERN Section 2 and 3 scores (P=.14 and P=.17, respectively) were minor. Furthermore, Wikipedia had a higher proportion of high total DISCERN scores (P<.001) and DISCERN Section 1 scores (P<.001) than Baidu Encyclopedia. Baidu Encyclopedia and Wikipedia both had low DISCERN Section 2 and 3 scores (P=.49 and P=.99, respectively), and most of these scores were low quality. Conclusions: Wikipedia provides more reliable, higher quality, and more objective information than Baidu Encyclopedia. Yet, there are opportunities for both platforms to vastly improve their content quality. Moreover, both sites had similar poor quality content on treatment options. Joint efforts of physicians, physician associations, medical institutions, and internet platforms are needed to provide reliable, readable, and objective knowledge about diseases. ", doi="10.2196/37339", url="https://www.jmir.org/2022/8/e37339", url="http://www.ncbi.nlm.nih.gov/pubmed/35943768" } @Article{info:doi/10.2196/37367, author="Skafle, Ingjerd and Nordahl-Hansen, Anders and Quintana, S. Daniel and Wynn, Rolf and Gabarron, Elia", title="Misinformation About COVID-19 Vaccines on Social Media: Rapid Review", journal="J Med Internet Res", year="2022", month="Aug", day="4", volume="24", number="8", pages="e37367", keywords="social media", keywords="misinformation", keywords="COVID-19 vaccines", keywords="vaccination hesitancy", keywords="autism spectrum disorder", abstract="Background: The development of COVID-19 vaccines has been crucial in fighting the pandemic. However, misinformation about the COVID-19 pandemic and vaccines is spread on social media platforms at a rate that has made the World Health Organization coin the phrase infodemic. False claims about adverse vaccine side effects, such as vaccines being the cause of autism, were already considered a threat to global health before the outbreak of COVID-19. Objective: We aimed to synthesize the existing research on misinformation about COVID-19 vaccines spread on social media platforms and its effects. The secondary aim was to gain insight and gather knowledge about whether misinformation about autism and COVID-19 vaccines is being spread on social media platforms. Methods: We performed a literature search on September 9, 2021, and searched PubMed, PsycINFO, ERIC, EMBASE, Cochrane Library, and the Cochrane COVID-19 Study Register. We included publications in peer-reviewed journals that fulfilled the following criteria: original empirical studies, studies that assessed social media and misinformation, and studies about COVID-19 vaccines. Thematic analysis was used to identify the patterns (themes) of misinformation. Narrative qualitative synthesis was undertaken with the guidance of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 Statement and the Synthesis Without Meta-analysis reporting guideline. The risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal tool. Ratings of the certainty of evidence were based on recommendations from the Grading of Recommendations Assessment, Development and Evaluation Working Group. Results: The search yielded 757 records, with 45 articles selected for this review. We identified 3 main themes of misinformation: medical misinformation, vaccine development, and conspiracies. Twitter was the most studied social media platform, followed by Facebook, YouTube, and Instagram. A vast majority of studies were from industrialized Western countries. We identified 19 studies in which the effect of social media misinformation on vaccine hesitancy was measured or discussed. These studies implied that the misinformation spread on social media had a negative effect on vaccine hesitancy and uptake. Only 1 study contained misinformation about autism as a side effect of COVID-19 vaccines. Conclusions: To prevent these misconceptions from taking hold, health authorities should openly address and discuss these false claims with both cultural and religious awareness in mind. Our review showed that there is a need to examine the effect of social media misinformation on vaccine hesitancy with a more robust experimental design. Furthermore, this review also demonstrated that more studies are needed from the Global South and on social media platforms other than the major platforms such as Twitter and Facebook. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42021277524; https://www.crd.york.ac.uk/prospero/display\_record.php?ID=CRD42021277524 International Registered Report Identifier (IRRID): RR2-10.31219/osf.io/tyevj ", doi="10.2196/37367", url="https://www.jmir.org/2022/8/e37367", url="http://www.ncbi.nlm.nih.gov/pubmed/35816685" } @Article{info:doi/10.2196/34044, author="Williams, A. Kofoworola D. and Dougherty, A. Sharyn and Lattie, G. Emily and Guidry, D. Jeanine P. and Carlyle, E. Kellie", title="Examining Hashtag Use of \#blackboyjoy and \#theblackmancan and Related Content on Instagram: Descriptive Content Analysis", journal="JMIR Form Res", year="2022", month="Aug", day="1", volume="6", number="8", pages="e34044", keywords="Black/African American men", keywords="mental health prevention", keywords="social media", keywords="Instagram", keywords="hashtags", keywords="content analysis", keywords="Black masculinity", abstract="Background: Social media is widely accessible and increasingly utilized. Social media users develop hashtags and visual, text-based imagery to challenge misrepresentations, garner social support, and discuss a variety of mental health issues. Understanding how Black men are represented on social media and are using social media may be an avenue for promoting their engagement with and uptake of digital mental health interventions. Objective: The aim of this study was to conduct a content analysis of posts containing visual and text-based components related to representations of Black men's race, gender, and behaviors. Methods: An exploratory, descriptive content analysis was conducted for 500 Instagram posts to examine characteristics, content, and public engagement of posts containing the hashtags \#theblackmancan and \#blackboyjoy. Posts were selected randomly and extracted from Instagram using a social network mining tool during Fall 2018 and Spring 2019. A codebook was developed, and all posts were analyzed by 2 independent coders. Analyses included frequency counts and descriptive analysis to determine content and characteristics of posts. Mann-Whitney U tests and Kruskal-Wallis H tests were conducted to assess engagement associated with posts via likes, comments, and video views. Results: Of the 500 posts extracted, most were image based (368/500, 73.6\%), 272/500 (54.4\%) were posted by an individual and 135/500 (27.0\%) by a community organization, 269/500 (53.8\%) were posted by individuals from Black populations, and 177/500 (35.4\%) posts contained images of only males. Posts depicted images of Black men as fathers (100/500, 20.0\%), Black men being celebrated (101/500, 20.2\%), and Black men expressing joy (217/500, 43.4\%). Posts (127/500, 25.4\%) also depicted Black men in relation to gender atypical behavior, such as caring for children or styling their children's hair. Variables related to education and restrictive affection did not show up often in posts. Engagement via likes (median 1671, P<.001), comments (P<.001), and views (P<.001) for posts containing \#theblackmancan was significantly higher compared with posts containing \#blackboyjoy (median 140). Posts containing elements of celebrating Black men (P=.02) and gender atypical behavior (P<.001) also had significantly higher engagement. Conclusions: This is one of the first studies to look at hashtag use of \#blackboyjoy and \#theblackmancan. Posts containing \#blackboyjoy and \#theblackmancan promoted positive user-generated visual and text-based content on Instagram and promoted positive interactions among Black and diverse communities. With the popularity of social media and hashtag use increasing, researchers and future interventional research should investigate the potential for such imagery to serve as culturally relevant design components for digital mental health prevention efforts geared towards Black men and the communities they exist and engage with. ", doi="10.2196/34044", url="https://formative.jmir.org/2022/8/e34044", url="http://www.ncbi.nlm.nih.gov/pubmed/35916699" } @Article{info:doi/10.2196/38324, author="Royan, Regina and Pendergrast, Rae Tricia and Del Rios, Marina and Rotolo, M. Shannon and Trueger, Seth N. and Bloomgarden, Eve and Behrens, Deanna and Jain, Shikha and Arora, M. Vineet", title="Use of Twitter Amplifiers by Medical Professionals to Combat Misinformation During the COVID-19 Pandemic", journal="J Med Internet Res", year="2022", month="Jul", day="22", volume="24", number="7", pages="e38324", keywords="social media", keywords="combating disinformation", keywords="misinformation", keywords="infodemic", keywords="amplifier", keywords="COVID-19", keywords="advocacy", keywords="public health communication", keywords="disinformation", keywords="medical information", keywords="health professional amplifier", keywords="healthcare profession", keywords="health care profession", keywords="Twitter", keywords="public communication", keywords="health information", keywords="health promotion", doi="10.2196/38324", url="https://www.jmir.org/2022/7/e38324", url="http://www.ncbi.nlm.nih.gov/pubmed/35839387" } @Article{info:doi/10.2196/38332, author="Inoue, Mami and Shimoura, Kanako and Nagai-Tanima, Momoko and Aoyama, Tomoki", title="The Relationship Between Information Sources, Health Literacy, and COVID-19 Knowledge in the COVID-19 Infodemic: Cross-sectional Online Study in Japan", journal="J Med Internet Res", year="2022", month="Jul", day="22", volume="24", number="7", pages="e38332", keywords="COVID-19 infodemic", keywords="information source", keywords="health literacy", keywords="COVID-19 knowledge", keywords="social media", keywords="cross-sectional study", keywords="mass media", keywords="digital media", abstract="Background: The COVID-19 pandemic has caused not only a disease epidemic but also an infodemic. Due to the increased use of the internet and social media, along with the development of communication technology, information has spread faster and farther during the COVID-19 infodemic. Moreover, the increased choice of information sources has made it more difficult to make sound decisions regarding information. Although social media is the most common source of misinformation, other forms of media can also spread misinformation. However, the media sources used by people with high health literacy and COVID-19 knowledge to obtain information are unclear. Furthermore, the association between the use of multiple information sources and health literacy or COVID-19 knowledge is ill-defined. Objective: This study aims to examine the following 3 aspects regarding the COVID-19 infodemic: (1) the relationship between health literacy, COVID-19 knowledge, and the number of information sources used; (2) the impact of media use on health literacy; and (3) the impact of media use on COVID-19 knowledge. Methods: An online cross-sectional study was conducted in November 2021. Participants were 477 individuals aged 20-69 years. After obtaining consent to participate in the study, participants were asked about sociodemographic indicators, sources of health-related information, health literacy, and COVID-19 knowledge. Sources of health-related information were categorized into 4 types: mass media, digital media, social media, and face-to-face communication. The Spearman rank correlation test was conducted to determine the relationship between health literacy, the number of correct answers to COVID-19 knowledge, and the number of information sources used. Multiple regression analysis was conducted with health literacy and the number of correct answers as dependent variables, the 4 media types as independent variables, and age and sex as adjustment variables. Results: Mass media was the most frequently used source of information, followed by digital media, face-to-face communication, and social media. Social media use was significantly higher among individuals aged 20-29 years than among other age groups. Significant positive correlations were found between health literacy, the number of positive responses to COVID-19 knowledge, and the number of information sources used. Multiple linear regression analysis showed that health literacy is associated with access to information from digital media and face-to-face communication. Additionally, COVID-19 knowledge was associated with access to information from mass media, digital media, and face-to-face communication. Conclusions: Health literacy and COVID-19 knowledge could be improved using diverse information sources, especially by providing opportunities to use digital media and face-to-face communication. Furthermore, it may be important to improve health literacy and provide accurate knowledge about COVID-19 to young adults. ", doi="10.2196/38332", url="https://www.jmir.org/2022/7/e38332", url="http://www.ncbi.nlm.nih.gov/pubmed/35839380" } @Article{info:doi/10.2196/37412, author="Ezike, C. Nnamdi and Ames Boykin, Allison and Dobbs, D. Page and Mai, Huy and Primack, A. Brian", title="Exploring Factors That Predict Marketing of e-Cigarette Products on Twitter: Infodemiology Approach Using Time Series", journal="JMIR Infodemiology", year="2022", month="Jul", day="22", volume="2", number="2", pages="e37412", keywords="tobacco", keywords="electronic cigarettes", keywords="social media", keywords="marketing", keywords="time series", keywords="youth", keywords="young adults", keywords="infodemiology", keywords="infoveillance", keywords="digital marketing", keywords="advertising", keywords="Twitter", keywords="promote", keywords="e-cigarette", abstract="Background: Electronic nicotine delivery systems (known as electronic cigarettes or e-cigarettes) increase risk for adverse health outcomes among na{\"i}ve tobacco users, particularly youth and young adults. This vulnerable population is also at risk for exposed brand marketing and advertisement of e-cigarettes on social media. Understanding predictors of how e-cigarette manufacturers conduct social media advertising and marketing could benefit public health approaches to addressing e-cigarette use. Objective: This study documents factors that predict changes in daily frequency of commercial tweets about e-cigarettes using time series modeling techniques. Methods: We analyzed data on the daily frequency of commercial tweets about e-cigarettes collected between January 1, 2017, and December 31, 2020. We fit the data to an autoregressive integrated moving average (ARIMA) model and unobserved components model (UCM). Four measures assessed model prediction accuracy. Predictors in the UCM include days with events related to the US Food and Drug Administration (FDA), non-FDA-related events with significant importance such as academic or news announcements, weekday versus weekend, and the period when JUUL maintained an active Twitter account (ie, actively tweeting from their corporate Twitter account) versus when JUUL stopped tweeting. Results: When the 2 statistical models were fit to the data, the results indicate that the UCM was the best modeling technique for our data. All 4 predictors included in the UCM were significant predictors of the daily frequency of commercial tweets about e-cigarettes. On average, brand advertisement and marketing of e-cigarettes on Twitter was higher by more than 150 advertisements on days with FDA-related events compared to days without FDA events. Similarly, more than 40 commercial tweets about e-cigarettes were, on average, recorded on days with important non-FDA events compared to days without such events. We also found that there were more commercial tweets about e-cigarettes on weekdays than on weekends and more commercial tweets when JUUL maintained an active Twitter account. Conclusions: e-Cigarette companies promote their products on Twitter. Commercial tweets were significantly more likely to be posted on days with important FDA announcements, which may alter the narrative about information shared by the FDA. There remains a need for regulation of digital marketing of e-cigarette products in the United States. ", doi="10.2196/37412", url="https://infodemiology.jmir.org/2022/2/e37412", url="http://www.ncbi.nlm.nih.gov/pubmed/37113447" } @Article{info:doi/10.2196/33678, author="Baker, William and Colditz, B. Jason and Dobbs, D. Page and Mai, Huy and Visweswaran, Shyam and Zhan, Justin and Primack, A. Brian", title="Classification of Twitter Vaping Discourse Using BERTweet: Comparative Deep Learning Study", journal="JMIR Med Inform", year="2022", month="Jul", day="21", volume="10", number="7", pages="e33678", keywords="vaping", keywords="social media", keywords="deep learning", keywords="transformer models", keywords="infoveillance", abstract="Background: Twitter provides a valuable platform for the surveillance and monitoring of public health topics; however, manually categorizing large quantities of Twitter data is labor intensive and presents barriers to identify major trends and sentiments. Additionally, while machine and deep learning approaches have been proposed with high accuracy, they require large, annotated data sets. Public pretrained deep learning classification models, such as BERTweet, produce higher-quality models while using smaller annotated training sets. Objective: This study aims to derive and evaluate a pretrained deep learning model based on BERTweet that can identify tweets relevant to vaping, tweets (related to vaping) of commercial nature, and tweets with provape sentiment. Additionally, the performance of the BERTweet classifier will be compared against a long short-term memory (LSTM) model to show the improvements a pretrained model has over traditional deep learning approaches. Methods: Twitter data were collected from August to October 2019 using vaping-related search terms. From this set, a random subsample of 2401 English tweets was manually annotated for relevance (vaping related or not), commercial nature (commercial or not), and sentiment (positive, negative, or neutral). Using the annotated data, 3 separate classifiers were built using BERTweet with the default parameters defined by the Simple Transformer application programming interface (API). Each model was trained for 20 iterations and evaluated with a random split of the annotated tweets, reserving 10\% (n=165) of tweets for evaluations. Results: The relevance, commercial, and sentiment classifiers achieved an area under the receiver operating characteristic curve (AUROC) of 94.5\%, 99.3\%, and 81.7\%, respectively. Additionally, the weighted F1 scores of each were 97.6\%, 99.0\%, and 86.1\%, respectively. We found that BERTweet outperformed the LSTM model in the classification of all categories. Conclusions: Large, open-source deep learning classifiers, such as BERTweet, can provide researchers the ability to reliably determine if tweets are relevant to vaping; include commercial content; and include positive, negative, or neutral content about vaping with a higher accuracy than traditional natural language processing deep learning models. Such enhancement to the utilization of Twitter data can allow for faster exploration and dissemination of time-sensitive data than traditional methodologies (eg, surveys, polling research). ", doi="10.2196/33678", url="https://medinform.jmir.org/2022/7/e33678", url="http://www.ncbi.nlm.nih.gov/pubmed/35862172" } @Article{info:doi/10.2196/15055, author="Park, Albert", title="Tweets Related to Motivation and Physical Activity for Obesity-Related Behavior Change: Descriptive Analysis", journal="J Med Internet Res", year="2022", month="Jul", day="20", volume="24", number="7", pages="e15055", keywords="obesity", keywords="motivation", keywords="exercise", keywords="peer support", keywords="social network analysis", keywords="social computing", keywords="consumer health information", keywords="informatics", keywords="information science", keywords="social support", keywords="communications media", abstract="Background: Obesity is one of the greatest modern public health problems, due to the associated health and economic consequences. Decreased physical activity is one of the main societal changes driving the current obesity pandemic. Objective: Our goals are to fill a gap in the literature and study whether users organically utilize a social media platform, Twitter, for providing motivation. We examine the topics of messages and social network structures on Twitter. We discuss social media's potential for providing peer support and then draw insights to inform the development of interventions for long-term health-related behavior change. Methods: We examined motivational messages related to physical activity on Twitter. First, we collected tweets related to physical activity. Second, we analyzed them using (1) a lexicon-based approach to extract and characterize motivation-related tweets, (2) a thematic analysis to examine common themes in retweets, and (3) topic models to understand prevalent factors concerning motivation and physical activity on Twitter. Third, we created 2 social networks to investigate organically arising peer-support network structures for sustaining physical activity and to form a deeper understanding of the feasibility of these networks in a real-world context. Results: We collected over 1.5 million physical activity--related tweets posted from August 30 to November 6, 2018. A relatively small percentage of the tweets mentioned the term motivation; many of these were made on Mondays or during morning or late morning hours. The analysis of retweets showed that the following three themes were commonly conveyed on the platform: (1) using a number of different types of motivation (self, process, consolation, mental, or quotes), (2) promoting individuals or groups, and (3) sharing or requesting information. Topic models revealed that many of these users were weightlifters or people trying to lose weight. Twitter users also naturally forged relations, even though 98.12\% (2824/2878) of these users were in different physical locations. Conclusions: This study fills a knowledge gap on how individuals organically use social media to encourage and sustain physical activity. Elements related to peer support are found in the organic use of social media. Our findings suggest that geographical location is less important for providing peer support as long as the support provides motivation, despite users having few factors in common (eg, the weather) affecting their physical activity. This presents a unique opportunity to identify successful motivation-providing peer support groups in a large user base. However, further research on the effects in a real-world context, as well as additional design and usability features for improving user engagement, are warranted to develop a successful intervention counteracting the current obesity pandemic. This is especially important for young adults, the main user group for social media, as they develop lasting health-related behaviors. ", doi="10.2196/15055", url="https://www.jmir.org/2022/7/e15055", url="http://www.ncbi.nlm.nih.gov/pubmed/35857347" } @Article{info:doi/10.2196/37201, author="Ahne, Adrian and Khetan, Vivek and Tannier, Xavier and Rizvi, Hassan Md Imbesat and Czernichow, Thomas and Orchard, Francisco and Bour, Charline and Fano, Andrew and Fagherazzi, Guy", title="Extraction of Explicit and Implicit Cause-Effect Relationships in Patient-Reported Diabetes-Related Tweets From 2017 to 2021: Deep Learning Approach", journal="JMIR Med Inform", year="2022", month="Jul", day="19", volume="10", number="7", pages="e37201", keywords="causality", keywords="deep learning", keywords="natural language processing", keywords="diabetes", keywords="social media", keywords="causal relation extraction", keywords="social media data", keywords="machine learning", abstract="Background: Intervening in and preventing diabetes distress requires an understanding of its causes and, in particular, from a patient's perspective. Social media data provide direct access to how patients see and understand their disease and consequently show the causes of diabetes distress. Objective: Leveraging machine learning methods, we aim to extract both explicit and implicit cause-effect relationships in patient-reported diabetes-related tweets and provide a methodology to better understand the opinions, feelings, and observations shared within the diabetes online community from a causality perspective. Methods: More than 30 million diabetes-related tweets in English were collected between April 2017 and January 2021. Deep learning and natural language processing methods were applied to focus on tweets with personal and emotional content. A cause-effect tweet data set was manually labeled and used to train (1) a fine-tuned BERTweet model to detect causal sentences containing a causal relation and (2) a conditional random field model with Bidirectional Encoder Representations from Transformers (BERT)-based features to extract possible cause-effect associations. Causes and effects were clustered in a semisupervised approach and visualized in an interactive cause-effect network. Results: Causal sentences were detected with a recall of 68\% in an imbalanced data set. A conditional random field model with BERT-based features outperformed a fine-tuned BERT model for cause-effect detection with a macro recall of 68\%. This led to 96,676 sentences with cause-effect relationships. ``Diabetes'' was identified as the central cluster followed by ``death'' and ``insulin.'' Insulin pricing--related causes were frequently associated with death. Conclusions: A novel methodology was developed to detect causal sentences and identify both explicit and implicit, single and multiword cause, and the corresponding effect, as expressed in diabetes-related tweets leveraging BERT-based architectures and visualized as cause-effect network. Extracting causal associations in real life, patient-reported outcomes in social media data provide a useful complementary source of information in diabetes research. ", doi="10.2196/37201", url="https://medinform.jmir.org/2022/7/e37201", url="http://www.ncbi.nlm.nih.gov/pubmed/35852829" } @Article{info:doi/10.2196/34464, author="Gillis, Timber and Garrison, Scott", title="Confounding Effect of Undergraduate Semester--Driven ``Academic`` Internet Searches on the Ability to Detect True Disease Seasonality in Google Trends Data: Fourier Filter Method Development and Demonstration", journal="JMIR Infodemiology", year="2022", month="Jul", day="19", volume="2", number="2", pages="e34464", keywords="Google Trends", keywords="seasonality", keywords="Fast Fourier transform", keywords="FFT", keywords="pathogenic bacteria", keywords="depression", keywords="Google search", keywords="Google", keywords="health information", keywords="health information seeking", keywords="internet search", abstract="Background: Internet search volume for medical information, as tracked by Google Trends, has been used to demonstrate unexpected seasonality in the symptom burden of a variety of medical conditions. However, when more technical medical language is used (eg, diagnoses), we believe that this technique is confounded by the cyclic, school year--driven internet search patterns of health care students. Objective: This study aimed to (1) demonstrate that artificial ``academic cycling'' of Google Trends' search volume is present in many health care terms, (2) demonstrate how signal processing techniques can be used to filter academic cycling out of Google Trends data, and (3) apply this filtering technique to some clinically relevant examples. Methods: We obtained the Google Trends search volume data for a variety of academic terms demonstrating strong academic cycling and used a Fourier analysis technique to (1) identify the frequency domain fingerprint of this modulating pattern in one particularly strong example, and (2) filter that pattern out of the original data. After this illustrative example, we then applied the same filtering technique to internet searches for information on 3 medical conditions believed to have true seasonal modulation (myocardial infarction, hypertension, and depression), and all bacterial genus terms within a common medical microbiology textbook. Results: Academic cycling explains much of the seasonal variation in internet search volume for many technically oriented search terms, including the bacterial genus term [``Staphylococcus''], for which academic cycling explained 73.8\% of the variability in search volume (using the squared Spearman rank correlation coefficient, P<.001). Of the 56 bacterial genus terms examined, 6 displayed sufficiently strong seasonality to warrant further examination post filtering. This included (1) [``Aeromonas'' + ``Plesiomonas''] (nosocomial infections that were searched for more frequently during the summer), (2) [``Ehrlichia''] (a tick-borne pathogen that was searched for more frequently during late spring), (3) [``Moraxella''] and [``Haemophilus''] (respiratory infections that were searched for more frequently during late winter), (4) [``Legionella''] (searched for more frequently during midsummer), and (5) [``Vibrio''] (which spiked for 2 months during midsummer). The terms [``myocardial infarction''] and [``hypertension''] lacked any obvious seasonal cycling after filtering, whereas [``depression''] maintained an annual cycling pattern. Conclusions: Although it is reasonable to search for seasonal modulation of medical conditions using Google Trends' internet search volume and lay-appropriate search terms, the variation in more technical search terms may be driven by health care students whose search frequency varies with the academic school year. When this is the case, using Fourier analysis to filter out academic cycling is a potential means to establish whether additional seasonality is present. ", doi="10.2196/34464", url="https://infodemiology.jmir.org/2022/2/e34464", url="http://www.ncbi.nlm.nih.gov/pubmed/37113451" } @Article{info:doi/10.2196/37071, author="Shao, Yihan and Zou, Jonathan and Xie, Zidian and Mayne, Grana Rachel and Ossip, J. Deborah and Rahman, Irfan and McIntosh, Scott and Li, Dongmei", title="Perceptions of Oral Nicotine Pouches on Reddit: Observational Study", journal="J Med Internet Res", year="2022", month="Jul", day="15", volume="24", number="7", pages="e37071", keywords="oral nicotine pouches", keywords="Reddit", keywords="perception", keywords="nicotine", keywords="social media", keywords="sentiment", keywords="public opinion", keywords="user experience", keywords="attitude", keywords="content analysis", keywords="tobacco", keywords="smoking", keywords="cessation", keywords="quit", keywords="smoker", keywords="information seeking", keywords="information sharing", keywords="vaping", abstract="Background: Oral nicotine pouches are a new form of tobacco-free nicotine products launched in recent years with a variety of flavors. Objective: This study aims to examine the public perceptions and discussions of oral nicotine pouches on Reddit, a popular social media platform for sharing user experiences. Methods: Between February 15, 2019, and February 12, 2021, a total of 2410 Reddit posts related to oral nicotine pouches were obtained over a 2-year period. After the removal of unrelated or commercial posts, 653 Reddit posts related to oral nicotine pouches remained. Topics and sentiments related to oral nicotine pouches on Reddit were hand coded. Results: The number of Reddit posts related to oral nicotine pouches increased during the study period. Content analysis showed that the most popular topic was ``sharing product information and user experience'' (366/653, 56\%), in which sharing oral nicotine pouch products and user experiences were dominant. The next popular topic was ``asking product-related questions'' (product properties and product recommendations; 115/653, 17.6\%), followed by ``quitting nicotine products'' such as vaping or smoking through use of oral nicotine pouches or quitting the oral nicotine pouches themselves (83/653, 12.7\%) and ``discussing oral nicotine pouch--related health'' symptoms or concerns related to oral nicotine pouches (74/653, 11.3\%). The least popular topic was ``legality and permissions'' related to oral nicotine pouches (15/653, 2.3\%). In addition, a greater number of Reddit posts described positive attitudes compared to negative attitudes toward oral nicotine pouches (354/653, 54.2\% vs 101/653, 15.5\%; P<.001). Conclusions: Reddit posts overall had a positive attitude toward oral nicotine pouches and users were actively sharing product and user experiences. Our study provides the first insight on up-to-date oral nicotine pouch discussions on social media. ", doi="10.2196/37071", url="https://www.jmir.org/2022/7/e37071", url="http://www.ncbi.nlm.nih.gov/pubmed/35838764" } @Article{info:doi/10.2196/36268, author="Al-Rawi, Ahmed and Zemenchik, Kiana", title="Sex Workers' Lived Experiences With COVID-19 on Social Media: Content Analysis of Twitter Posts", journal="JMIR Form Res", year="2022", month="Jul", day="14", volume="6", number="7", pages="e36268", keywords="sex work", keywords="social media", keywords="COVID-19", keywords="pandemic", keywords="Twitter", keywords="infodemiology", keywords="social stigma", keywords="sex worker", keywords="risk", keywords="public health", abstract="Background: The COVID-19 pandemic has drawn attention to various inequalities in global societies, highlighting discrepancies in terms of safety, accessibility, and overall health. In particular, sex workers are disproportionately at risk due to the nature of their work and the social stigma that comes alongside it. Objective: This study examines how public social media can be used as a tool of professional and personal expression by sex workers during the COVID-19 pandemic. We aimed to explore an underresearched topic by focusing on sex workers' experiences with the ongoing COVID-19 pandemic on the social media platform Twitter. In particular, we aimed to find the main issues that sex workers discuss on social media in relation to the COVID-19 pandemic. Methods: A literature review followed by a qualitative analysis of 1458 (re)tweets from 22 sex worker Twitter accounts was used for this study. The tweets were qualitatively coded by theme through the use of intercoder reliability. Empirical, experimental, and observational studies were included in this review to provide context and support for our findings. Results: In total, 5 major categories were identified as a result of the content analysis used for this study: concerns (n=542, 37.2\%), solicitation (n=336, 23.0\%), herd mentality (n=231, 15.8\%), humor (n=190, 13.0\%), and blame (n=146, 10.0\%). The concerns category was the most prominent category, which could be due to its multifaceted nature of including individual concerns, health issues, concerns for essential workers and businesses, as well as concerns about inequalities or intersectionality. When using gender as a control factor, the majority of the results were not noteworthy, save for the blame category, in which sexual and gender minorities (SGMs) were more likely to post content. Conclusions: Though there has been an increase in the literature related to the experiences of sex workers, this paper recommends that future studies could benefit from further examining these 5 major categories through mixed methods research. Examining this phenomenon could recognize the challenges unique to this working community during the COVID-19 pandemic and potentially reduce the widespread stigma associated with sex work in general. ", doi="10.2196/36268", url="https://formative.jmir.org/2022/7/e36268", url="http://www.ncbi.nlm.nih.gov/pubmed/35767693" } @Article{info:doi/10.2196/37142, author="Leung, Tong Yue and Khalvati, Farzad", title="Exploring COVID-19--Related Stressors: Topic Modeling Study", journal="J Med Internet Res", year="2022", month="Jul", day="13", volume="24", number="7", pages="e37142", keywords="COVID-19", keywords="natural language processing", keywords="public health informatics", keywords="topic modeling", abstract="Background: The COVID-19 pandemic has affected the lives of people globally for over 2 years. Changes in lifestyles due to the pandemic may cause psychosocial stressors for individuals and could lead to mental health problems. To provide high-quality mental health support, health care organizations need to identify COVID-19--specific stressors and monitor the trends in the prevalence of those stressors. Objective: This study aims to apply natural language processing (NLP) techniques to social media data to identify the psychosocial stressors during the COVID-19 pandemic and to analyze the trend in the prevalence of these stressors at different stages of the pandemic. Methods: We obtained a data set of 9266 Reddit posts from the subreddit {\backslash}rCOVID19\_support, from February 14, 2020, to July 19, 2021. We used the latent Dirichlet allocation (LDA) topic model to identify the topics that were mentioned on the subreddit and analyzed the trends in the prevalence of the topics. Lexicons were created for each of the topics and were used to identify the topics of each post. The prevalences of topics identified by the LDA and lexicon approaches were compared. Results: The LDA model identified 6 topics from the data set: (1) ``fear of coronavirus,'' (2) ``problems related to social relationships,'' (3) ``mental health symptoms,'' (4) ``family problems,'' (5) ``educational and occupational problems,'' and (6) ``uncertainty on the development of pandemic.'' According to the results, there was a significant decline in the number of posts about the ``fear of coronavirus'' after vaccine distribution started. This suggests that the distribution of vaccines may have reduced the perceived risks of coronavirus. The prevalence of discussions on the uncertainty about the pandemic did not decline with the increase in the vaccinated population. In April 2021, when the Delta variant became prevalent in the United States, there was a significant increase in the number of posts about the uncertainty of pandemic development but no obvious effects on the topic of fear of the coronavirus. Conclusions: We created a dashboard to visualize the trend in the prevalence of topics about COVID-19--related stressors being discussed on a social media platform (Reddit). Our results provide insights into the prevalence of pandemic-related stressors during different stages of the COVID-19 pandemic. The NLP techniques leveraged in this study could also be applied to analyze event-specific stressors in the future. ", doi="10.2196/37142", url="https://www.jmir.org/2022/7/e37142", url="http://www.ncbi.nlm.nih.gov/pubmed/35731966" } @Article{info:doi/10.2196/33767, author="Allen, Gary and Garris, Jenna and Lawson, Luan and Reeder, Timothy and Crotty, Jennifer and Hannan, Johanna and Brewer, Kori", title="An Innovative Use of Twitter to Disseminate and Promote Medical Student Scholarship During the COVID-19 Pandemic: Usability Study", journal="JMIR Med Educ", year="2022", month="Jul", day="13", volume="8", number="3", pages="e33767", keywords="medical education", keywords="social media", keywords="web-based learning", keywords="innovation", keywords="Twitter", keywords="dissemination", keywords="scholarship", keywords="medical student", keywords="platform", keywords="academic promotion", keywords="COVID-19", abstract="Background: Due to the emergence of the COVID-19 pandemic in March 2020, the cancellation of in-person learning activities forced every aspect of medical education and student engagement to pivot to a web-based format, including activities supporting the performance and dissemination of scholarly work. At that time, social media had been used to augment in-person conference learning, but it had not been used as the sole platform for scholarly abstract presentations. Objective: Our aim was to assess the feasibility of using Twitter to provide a completely web-based forum for real-time dissemination of and engagement with student scholarly work as an alternative to a traditional in-person poster presentation session. Methods: The Brody School of Medicine at East Carolina University launched an online Medical Student Scholarship Forum, using Twitter as a platform for students to present scholarly work and prepare for future web-based presentations. A single student forum participant created posts using a standardized template that incorporated student research descriptions, uniform promotional hashtags, and individual poster presentations. Tweets were released over 5 days and analytic data were collected from the Twitter platform. Outcome measures included impressions, engagements, retweets, likes, media engagements, and average daily engagement rate. Results: During the conference, the student leader published 63 tweets promoting the work of 58 students (55 medical and 3 dental students) over 5 days. During the forum and the following week, tweets from the @BrodyDistinctly Twitter account received 63,142 impressions and 7487 engagements, including 187 retweets, 1427 likes, and 2082 media engagements. During the 5 days of the forum, the average daily engagement rate was 12.72\%. Conclusions: Using Twitter as a means of scholarly dissemination resulted in a larger viewing community compared to a traditional in-person event. Early evidence suggests that social media platforms may be an alternative to traditional scholarly presentations. Presenting via Twitter allowed students to receive instantaneous feedback and effectively network with wider academic communities. Additional research is needed to evaluate the effectiveness of knowledge uptake, feedback, and networking. ", doi="10.2196/33767", url="https://mededu.jmir.org/2022/3/e33767", url="http://www.ncbi.nlm.nih.gov/pubmed/35759753" } @Article{info:doi/10.2196/37134, author="Lohiniva, Anna-Leena and Nurzhynska, Anastasiya and Hudi, Al-hassan and Anim, Bridget and Aboagye, Costa Da", title="Infodemic Management Using Digital Information and Knowledge Cocreation to Address COVID-19 Vaccine Hesitancy: Case Study From Ghana", journal="JMIR Infodemiology", year="2022", month="Jul", day="12", volume="2", number="2", pages="e37134", keywords="COVID-19", keywords="infodemic management", keywords="misinformation", keywords="disinformation", keywords="social listening", keywords="pandemic preparedness", keywords="infodemiology", keywords="social media", keywords="Ghana", keywords="vaccination", keywords="qualitative methods", abstract="Background: Infodemic management is an integral part of pandemic management. Ghana Health Services (GHS) together with the UNICEF (United Nations International Children's Emergency Fund) Country Office have developed a systematic process that effectively identifies, analyzes, and responds to COVID-19 and vaccine-related misinformation in Ghana. Objective: This paper describes an infodemic management system workflow based on digital data collection, qualitative methodology, and human-centered systems to support the COVID-19 vaccine rollout in Ghana with examples of system implementation. Methods: The infodemic management system was developed by the Health Promotion Division of the GHS and the UNICEF Country Office. It uses Talkwalker, a social listening software platform, to collect misinformation on the web. The methodology relies on qualitative data analysis and interpretation as well as knowledge cocreation to verify the findings. Results: A multi-sectoral National Misinformation Task Force was established to implement and oversee the misinformation management system. Two members of the task force were responsible for carrying out the analysis. They used Talkwalker to find posts that include the keywords related to COVID-19 vaccine--related discussions. They then assessed the significance of the posts on the basis of the engagement rate and potential reach of the posts, negative sentiments, and contextual factors. The process continues by identifying misinformation within the posts, rating the risk of identified misinformation posts, and developing proposed responses to address them. The results of the analysis are shared weekly with the Misinformation Task Force for their review and verification to ensure that the risk assessment and responses are feasible, practical, and acceptable in the context of Ghana. Conclusions: The paper describes an infodemic management system workflow in Ghana based on qualitative data synthesis that can be used to manage real-time infodemic responses. ", doi="10.2196/37134", url="https://infodemiology.jmir.org/2022/2/e37134", url="http://www.ncbi.nlm.nih.gov/pubmed/35854815" } @Article{info:doi/10.2196/34114, author="Gao, Yankun and Xie, Zidian and Li, Dongmei", title="Investigating the Impact of the New York State Flavor Ban on e-Cigarette--Related Discussions on Twitter: Observational Study", journal="JMIR Public Health Surveill", year="2022", month="Jul", day="8", volume="8", number="7", pages="e34114", keywords="New York State flavor ban", keywords="e-cigarettes", keywords="twitter", keywords="topic modeling", keywords="sentiment analysis", abstract="Background: On May 18, 2020, the New York State Department of Health implemented a statewide flavor ban to prohibit the sales of all flavored vapor products, except for tobacco or any other authorized flavor. Objective: This study aims to investigate the discussion changes in e-cigarette--related tweets over time with the implementation of the New York State flavor ban. Methods: Through the Twitter streaming application programming interface, 59,883 e-cigarette--related tweets were collected within the New York State from February 6, 2020, to May 17, 2020 (period 1, before the implementation of the flavor ban), May 18, 2020-June 30, 2020 (period 2, between the implementation of the flavor ban and the online sales ban), July 1, 2020-September 15, 2020 (period 3, the short term after the online sales ban), and September 16, 2020-November 30, 2020 (period 4, the long term after the online sales ban). Sentiment analysis and topic modeling were conducted to investigate the changes in public attitudes and discussions in e-cigarette--related tweets. The popularity of different e-cigarette flavor categories was compared before and after the implementation of the New York State flavor ban. Results: Our results showed that the proportion of e-cigarette--related tweets with negative sentiment significantly decreased (4305/13,246, 32.5\% vs 3855/14,455, 26.67\%, P<.001), and tweets with positive sentiment significantly increased (5246/13,246, 39.6\% vs 7038/14,455, 48.69\%, P<.001) in period 4 compared to period 3. ``Teens and nicotine products'' was the most frequently discussed e-cigarette--related topic in the negative tweets. In contrast, ``nicotine products and quitting'' was more prevalent in positive tweets. The proportion of tweets mentioning mint and menthol flavors significantly increased right after the flavor ban and decreased to lower levels over time. The proportions of fruit and sweet flavors were most frequently mentioned in period 1, decreased in period 2, and dominated again in period 4. Conclusions: The proportion of e-cigarette--related tweets with different attitudes and frequently discussed flavor categories changed over time after the implementation of the New York State ban of flavored vaping products. This change indicated a potential impact of the flavor ban on public discussions of flavored e-cigarettes. ", doi="10.2196/34114", url="https://publichealth.jmir.org/2022/7/e34114", url="http://www.ncbi.nlm.nih.gov/pubmed/35802417" } @Article{info:doi/10.2196/37806, author="Ngai, Bik Cindy Sing and Singh, Gill Rita and Yao, Le", title="Impact of COVID-19 Vaccine Misinformation on Social Media Virality: Content Analysis of Message Themes and Writing Strategies", journal="J Med Internet Res", year="2022", month="Jul", day="6", volume="24", number="7", pages="e37806", keywords="antivaccine misinformation", keywords="content themes", keywords="writing strategies", keywords="COVID-19", keywords="virality", keywords="social media", keywords="content analysis", abstract="Background: Vaccines serve an integral role in containing pandemics, yet vaccine hesitancy is prevalent globally. One key reason for this hesitancy is the pervasiveness of misinformation on social media. Although considerable research attention has been drawn to how exposure to misinformation is closely associated with vaccine hesitancy, little scholarly attention has been given to the investigation or robust theorizing of the various content themes pertaining to antivaccine misinformation about COVID-19 and the writing strategies in which these content themes are manifested. Virality of such content on social media exhibited in the form of comments, shares, and reactions has practical implications for COVID-19 vaccine hesitancy. Objective: We investigated whether there were differences in the content themes and writing strategies used to disseminate antivaccine misinformation about COVID-19 and their impact on virality on social media. Methods: We constructed an antivaccine misinformation database from major social media platforms during September 2019-August 2021 to examine how misinformation exhibited in the form of content themes and how these themes manifested in writing were associated with virality in terms of likes, comments, and shares. Antivaccine misinformation was retrieved from two globally leading and widely cited fake news databases, COVID Global Misinformation Dashboard and International Fact-Checking Network Corona Virus Facts Alliance Database, which aim to track and debunk COVID-19 misinformation. We primarily focused on 140 Facebook posts, since most antivaccine misinformation posts on COVID-19 were found on Facebook. We then employed quantitative content analysis to examine the content themes (ie, safety concerns, conspiracy theories, efficacy concerns) and manifestation strategies of misinformation (ie, mimicking of news and scientific reports in terms of the format and language features, use of a conversational style, use of amplification) in these posts and their association with virality of misinformation in the form of likes, comments, and shares. Results: Our study revealed that safety concern was the most prominent content theme and a negative predictor of likes and shares. Regarding the writing strategies manifested in content themes, a conversational style and mimicking of news and scientific reports via the format and language features were frequently employed in COVID-19 antivaccine misinformation, with the latter being a positive predictor of likes. Conclusions: This study contributes to a richer research-informed understanding of which concerns about content theme and manifestation strategy need to be countered on antivaccine misinformation circulating on social media so that accurate information on COVID-19 vaccines can be disseminated to the public, ultimately reducing vaccine hesitancy. The liking of COVID-19 antivaccine posts that employ language features to mimic news or scientific reports is perturbing since a large audience can be reached on social media, potentially exacerbating the spread of misinformation and hampering global efforts to combat the virus. ", doi="10.2196/37806", url="https://www.jmir.org/2022/7/e37806", url="http://www.ncbi.nlm.nih.gov/pubmed/35731969" } @Article{info:doi/10.2196/27310, author="Deiner, S. Michael and Kaur, Gurbani and McLeod, D. Stephen and Schallhorn, M. Julie and Chodosh, James and Hwang, H. Daniel and Lietman, M. Thomas and Porco, C. Travis", title="A Google Trends Approach to Identify Distinct Diurnal and Day-of-Week Web-Based Search Patterns Related to Conjunctivitis and Other Common Eye Conditions: Infodemiology Study", journal="J Med Internet Res", year="2022", month="Jul", day="5", volume="24", number="7", pages="e27310", keywords="diurnal eye conditions", keywords="hebdomadal", keywords="online search", keywords="web-based search", keywords="eye conditions", keywords="infodemiology", keywords="dry eye", keywords="conjunctivitis", keywords="pink eye", keywords="information seeking", keywords="vision", abstract="Background: Studies suggest diurnal patterns of occurrence of some eye conditions. Leveraging new information sources such as web-based search data to learn more about such patterns could improve the understanding of patients' eye-related conditions and well-being, better inform timing of clinical and remote eye care, and improve precision when targeting web-based public health campaigns toward underserved populations. Objective: To investigate our hypothesis that the public is likely to consistently search about different ophthalmologic conditions at different hours of the day or days of week, we conducted an observational study using search data for terms related to ophthalmologic conditions such as conjunctivitis. We assessed whether search volumes reflected diurnal or day-of-week patterns and if those patterns were distinct from each other. Methods: We designed a study to analyze and compare hourly search data for eye-related and control search terms, using time series regression models with trend and periodicity terms to remove outliers and then estimate diurnal effects. We planned a Google Trends setting, extracting data from 10 US states for the entire year of 2018. The exposure was internet search, and the participants were populations who searched through Google's search engine using our chosen study terms. Our main outcome measures included cyclical hourly and day-of-week web-based search patterns. For statistical analyses, we considered P<.001 to be statistically significant. Results: Distinct diurnal (P<.001 for all search terms) and day-of-week search patterns for eye-related terms were observed but with differing peak time periods and cyclic strengths. Some diurnal patterns represented those reported from prior clinical studies. Of the eye-related terms, ``pink eye'' showed the largest diurnal amplitude-to-mean ratios. Stronger signal was restricted to and peaked in mornings, and amplitude was higher on weekdays. By contrast, ``dry eyes'' had a higher amplitude diurnal pattern on weekends, with stronger signal occurring over a broader evening-to-morning period and peaking in early morning. Conclusions: The frequency of web-based searches for various eye conditions can show cyclic patterns according to time of the day or week. Further studies to understand the reasons for these variations may help supplement the current clinical understanding of ophthalmologic symptom presentation and improve the timeliness of patient messaging and care interventions. ", doi="10.2196/27310", url="https://www.jmir.org/2022/7/e27310", url="http://www.ncbi.nlm.nih.gov/pubmed/35537041" } @Article{info:doi/10.2196/34285, author="Sigalo, Nekabari and St Jean, Beth and Frias-Martinez, Vanessa", title="Using Social Media to Predict Food Deserts in the United States: Infodemiology Study of Tweets", journal="JMIR Public Health Surveill", year="2022", month="Jul", day="5", volume="8", number="7", pages="e34285", keywords="social media", keywords="Twitter", keywords="food deserts", keywords="food insecurity", abstract="Background: The issue of food insecurity is becoming increasingly important to public health practitioners because of the adverse health outcomes and underlying racial disparities associated with insufficient access to healthy foods. Prior research has used data sources such as surveys, geographic information systems, and food store assessments to identify regions classified as food deserts but perhaps the individuals in these regions unknowingly provide their own accounts of food consumption and food insecurity through social media. Social media data have proved useful in answering questions related to public health; therefore, these data are a rich source for identifying food deserts in the United States. Objective: The aim of this study was to develop, from geotagged Twitter data, a predictive model for the identification of food deserts in the United States using the linguistic constructs found in food-related tweets. Methods: Twitter's streaming application programming interface was used to collect a random 1\% sample of public geolocated tweets across 25 major cities from March 2020 to December 2020. A total of 60,174 geolocated food-related tweets were collected across the 25 cities. Each geolocated tweet was mapped to its respective census tract using point-to-polygon mapping, which allowed us to develop census tract--level features derived from the linguistic constructs found in food-related tweets, such as tweet sentiment and average nutritional value of foods mentioned in the tweets. These features were then used to examine the associations between food desert status and the food ingestion language and sentiment of tweets in a census tract and to determine whether food-related tweets can be used to infer census tract--level food desert status. Results: We found associations between a census tract being classified as a food desert and an increase in the number of tweets in a census tract that mentioned unhealthy foods (P=.03), including foods high in cholesterol (P=.02) or low in key nutrients such as potassium (P=.01). We also found an association between a census tract being classified as a food desert and an increase in the proportion of tweets that mentioned healthy foods (P=.03) and fast-food restaurants (P=.01) with positive sentiment. In addition, we found that including food ingestion language derived from tweets in classification models that predict food desert status improves model performance compared with baseline models that only include socioeconomic characteristics. Conclusions: Social media data have been increasingly used to answer questions related to health and well-being. Using Twitter data, we found that food-related tweets can be used to develop models for predicting census tract food desert status with high accuracy and improve over baseline models. Food ingestion language found in tweets, such as census tract--level measures of food sentiment and healthiness, are associated with census tract--level food desert status. ", doi="10.2196/34285", url="https://publichealth.jmir.org/2022/7/e34285", url="http://www.ncbi.nlm.nih.gov/pubmed/35788108" } @Article{info:doi/10.2196/36315, author="Yashpal, Shahen and Raghunath, Ananditha and Gencerliler, Nihan and Burns, E. Lorel", title="Exploring Public Perceptions of Dental Care Affordability in the United States: Mixed Method Analysis via Twitter", journal="JMIR Form Res", year="2022", month="Jul", day="1", volume="6", number="7", pages="e36315", keywords="dentistry", keywords="oral health", keywords="social media", keywords="access to care", keywords="healthcare reform", keywords="COVID-19", keywords="dental care", keywords="health care service", keywords="twitter", keywords="public health", keywords="health communication", keywords="dental treatment", keywords="health policy", keywords="dental professional", keywords="thematic analysis", abstract="Background: Dental care expenses are reported to present higher financial barriers than any other type of health care service in the United States. Social media platforms such as Twitter have become a source of public health communication and surveillance. Previous studies have demonstrated the usefulness of Twitter in exploring public opinion on aspects of dental care. To date, no studies have leveraged Twitter to examine public sentiments regarding dental care affordability in the United States. Objective: The aim of this study is to understand public perceptions of dental care affordability in the United States on the social media site, Twitter. Methods: Tweets posted between September 1, 2017, and September 30, 2021, were collected using the Snscrape application. Query terms were selected a priori to represent dentistry and financial aspects associated with dental treatment. Data were analyzed qualitatively using both deductive and inductive approaches. In total, 8\% (440/5500) of all included tweets were coded to identify prominent themes and subthemes. The entire sample of included tweets were then independently coded into thematic categories. Quantitative data analyses included geographic distribution of tweets by state, volume analysis of tweets over time, and distribution of tweets by content theme. Results: A final sample of 5314 tweets were included in the study. Thematic analysis identified the following prominent themes: (1) general sentiments (1614 tweets, 30.4\%); (2) delaying or forgoing dental care (1190 tweets, 22.4\%); (3) payment strategies (1019 tweets, 19.2\%); (4) insurance (767 tweets, 14.4\%); and (5) policy statements (724 tweets, 13.6\%). Geographic distributions of the tweets established California, Texas, Florida, and New York as the states with the most tweets. Qualitative analysis revealed barriers faced by individuals to accessing dental care, strategies taken to cope with dental pain, and public perceptions on aspects of dental care policy. The volume and thematic trends of the tweets corresponded to relevant societal events, including the COVID-19 pandemic and debates on health care policy resulting from the election of President Joseph R. Biden. Conclusions: The findings illustrate the real-time sentiment of social media users toward the cost of dental treatment and suggest shortcomings in funding that may be representative of greater systemic failures in the provision of dental care. Thus, this study provides insights for policy makers and dental professionals who strive to increase access to dental care. ", doi="10.2196/36315", url="https://formative.jmir.org/2022/7/e36315", url="http://www.ncbi.nlm.nih.gov/pubmed/35658090" } @Article{info:doi/10.2196/34231, author="Hagen, Loni and Fox, Ashley and O'Leary, Heather and Dyson, DeAndre and Walker, Kimberly and Lengacher, A. Cecile and Hernandez, Raquel", title="The Role of Influential Actors in Fostering the Polarized COVID-19 Vaccine Discourse on Twitter: Mixed Methods of Machine Learning and Inductive Coding", journal="JMIR Infodemiology", year="2022", month="Jun", day="30", volume="2", number="1", pages="e34231", keywords="COVID-19, vaccine hesitancy, social media, influential actors", keywords="influencer", keywords="Twitter", abstract="Background: Since COVID-19 vaccines became broadly available to the adult population, sharp divergences in uptake have emerged along partisan lines. Researchers have indicated a polarized social media presence contributing to the spread of mis- or disinformation as being responsible for these growing partisan gaps in uptake. Objective: The major aim of this study was to investigate the role of influential actors in the context of the community structures and discourse related to COVID-19 vaccine conversations on Twitter that emerged prior to the vaccine rollout to the general population and discuss implications for vaccine promotion and policy. Methods: We collected tweets on COVID-19 between July 1, 2020, and July 31, 2020, a time when attitudes toward the vaccines were forming but before the vaccines were widely available to the public. Using network analysis, we identified different naturally emerging Twitter communities based on their internal information sharing. A PageRank algorithm was used to quantitively measure the level of ``influentialness'' of Twitter accounts and identifying the ``influencers,'' followed by coding them into different actor categories. Inductive coding was conducted to describe discourses shared in each of the 7 communities. Results: Twitter vaccine conversations were highly polarized, with different actors occupying separate ``clusters.'' The antivaccine cluster was the most densely connected group. Among the 100 most influential actors, medical experts were outnumbered both by partisan actors and by activist vaccine skeptics or conspiracy theorists. Scientists and medical actors were largely absent from the conservative network, and antivaccine sentiment was especially salient among actors on the political right. Conversations related to COVID-19 vaccines were highly polarized along partisan lines, with ``trust'' in vaccines being manipulated to the political advantage of partisan actors. Conclusions: These findings are informative for designing improved vaccine information communication strategies to be delivered on social media especially by incorporating influential actors. Although polarization and echo chamber effect are not new in political conversations in social media, it was concerning to observe these in health conversations on COVID-19 vaccines during the vaccine development process. ", doi="10.2196/34231", url="https://infodemiology.jmir.org/2022/1/e34231", url="http://www.ncbi.nlm.nih.gov/pubmed/35814809" } @Article{info:doi/10.2196/36771, author="Klein, Z. Ari and O'Connor, Karen and Levine, D. Lisa and Gonzalez-Hernandez, Graciela", title="Using Twitter Data for Cohort Studies of Drug Safety in Pregnancy: Proof-of-concept With $\beta$-Blockers", journal="JMIR Form Res", year="2022", month="Jun", day="30", volume="6", number="6", pages="e36771", keywords="natural language processing", keywords="social media", keywords="data mining", keywords="pregnancy", keywords="pharmacoepidemiology", abstract="Background: Despite the fact that medication is taken during more than 90\% of pregnancies, the fetal risk for most medications is unknown, and the majority of medications have no data regarding safety in pregnancy. Objective: Using $\beta$-blockers as a proof-of-concept, the primary objective of this study was to assess the utility of Twitter data for a cohort study design---in particular, whether we could identify (1) Twitter users who have posted tweets reporting that they took medication during pregnancy and (2) their associated pregnancy outcomes. Methods: We searched for mentions of $\beta$-blockers in 2.75 billion tweets posted by 415,690 users who announced their pregnancy on Twitter. We manually reviewed the matching tweets to first determine if the user actually took the $\beta$-blocker mentioned in the tweet. Then, to help determine if the $\beta$-blocker was taken during pregnancy, we used the time stamp of the tweet reporting intake and drew upon an automated natural language processing (NLP) tool that estimates the date of the user's prenatal time period. For users who posted tweets indicating that they took or may have taken the $\beta$-blocker during pregnancy, we drew upon additional NLP tools to help identify tweets that report their pregnancy outcomes. Adverse pregnancy outcomes included miscarriage, stillbirth, birth defects, preterm birth (<37 weeks gestation), low birth weight (<5 pounds and 8 ounces at delivery), and neonatal intensive care unit (NICU) admission. Normal pregnancy outcomes included gestational age ?37 weeks and birth weight ?5 pounds and 8 ounces. Results: We retrieved 5114 tweets, posted by 2339 users, that mention a $\beta$-blocker, and manually identified 2332 (45.6\%) tweets, posted by 1195 (51.1\%) of the users, that self-report taking the $\beta$-blocker. We were able to estimate the date of the prenatal time period for 356 pregnancies among 334 (27.9\%) of these 1195 users. Among these 356 pregnancies, we identified 257 (72.2\%) during which the $\beta$-blocker was or may have been taken. We manually verified an adverse pregnancy outcome---preterm birth, NICU admission, low birth weight, birth defects, or miscarriage---for 38 (14.8\%) of these 257 pregnancies. We manually verified a gestational age ?37 weeks for 198 (90.4\%) and a birth weight ?5 pounds and 8 ounces for 50 (22.8\%) of the 219 pregnancies for which we did not identify an adverse pregnancy outcome. Conclusions: Our ability to detect pregnancy outcomes for Twitter users who posted tweets reporting that they took or may have taken a $\beta$-blocker during pregnancy suggests that Twitter can be a complementary resource for cohort studies of drug safety in pregnancy. ", doi="10.2196/36771", url="https://formative.jmir.org/2022/6/e36771", url="http://www.ncbi.nlm.nih.gov/pubmed/35771614" } @Article{info:doi/10.2196/34834, author="Albalawi, Yahya and Nikolov, S. Nikola and Buckley, Jim", title="Pretrained Transformer Language Models Versus Pretrained Word Embeddings for the Detection of Accurate Health Information on Arabic Social Media: Comparative Study", journal="JMIR Form Res", year="2022", month="Jun", day="29", volume="6", number="6", pages="e34834", keywords="social media", keywords="machine learning", keywords="pretrained language models", keywords="bidirectional encoder representations from transformers", keywords="BERT", keywords="deep learning", keywords="health information", keywords="infodemiology", keywords="tweets", keywords="language model", keywords="health informatics", keywords="misinformation", abstract="Background: In recent years, social media has become a major channel for health-related information in Saudi Arabia. Prior health informatics studies have suggested that a large proportion of health-related posts on social media are inaccurate. Given the subject matter and the scale of dissemination of such information, it is important to be able to automatically discriminate between accurate and inaccurate health-related posts in Arabic. Objective: The first aim of this study is to generate a data set of generic health-related tweets in Arabic, labeled as either accurate or inaccurate health information. The second aim is to leverage this data set to train a state-of-the-art deep learning model for detecting the accuracy of health-related tweets in Arabic. In particular, this study aims to train and compare the performance of multiple deep learning models that use pretrained word embeddings and transformer language models. Methods: We used 900 health-related tweets from a previously published data set extracted between July 15, 2019, and August 31, 2019. Furthermore, we applied a pretrained model to extract an additional 900 health-related tweets from a second data set collected specifically for this study between March 1, 2019, and April 15, 2019. The 1800 tweets were labeled by 2 physicians as accurate, inaccurate, or unsure. The physicians agreed on 43.3\% (779/1800) of tweets, which were thus labeled as accurate or inaccurate. A total of 9 variations of the pretrained transformer language models were then trained and validated on 79.9\% (623/779 tweets) of the data set and tested on 20\% (156/779 tweets) of the data set. For comparison, we also trained a bidirectional long short-term memory model with 7 different pretrained word embeddings as the input layer on the same data set. The models were compared in terms of their accuracy, precision, recall, F1 score, and macroaverage of the F1 score. Results: We constructed a data set of labeled tweets, 38\% (296/779) of which were labeled as inaccurate health information, and 62\% (483/779) of which were labeled as accurate health information. We suggest that this was highly efficacious as we did not include any tweets in which the physician annotators were unsure or in disagreement. Among the investigated deep learning models, the Transformer-based Model for Arabic Language Understanding version 0.2 (AraBERTv0.2)-large model was the most accurate, with an F1 score of 87\%, followed by AraBERT version 2--large and AraBERTv0.2-base. Conclusions: Our results indicate that the pretrained language model AraBERTv0.2 is the best model for classifying tweets as carrying either inaccurate or accurate health information. Future studies should consider applying ensemble learning to combine the best models as it may produce better results. ", doi="10.2196/34834", url="https://formative.jmir.org/2022/6/e34834", url="http://www.ncbi.nlm.nih.gov/pubmed/35767322" } @Article{info:doi/10.2196/35663, author="Silva, Martha and Walker, Jonathan and Portillo, Erin and Dougherty, Leanne", title="Strengthening the Merci Mon H{\'e}ros Campaign Through Adaptive Management: Application of Social Listening Methodology", journal="JMIR Public Health Surveill", year="2022", month="Jun", day="28", volume="8", number="6", pages="e35663", keywords="social media", keywords="health communication", keywords="young people", keywords="reproductive health", abstract="Background: Between 2014 and 2018, the penetration of smartphones in sub-Saharan Africa increased from 10\% to 30\%, enabling increased access to the internet, Facebook, Twitter, Pinterest, and YouTube. These platforms engage users in multidirectional communication and provide public health programs with the tools to inform and engage diverse audiences on a range of public health issues, as well as monitor opinions and behaviors on health topics. Objective: This paper details the process used by the U.S. Agency for International Development--funded Breakthrough RESEARCH to apply social media monitoring and social listening techniques in Burkina Faso, C{\^o}te d'Ivoire, Niger, and Togo for the adaptive management of the Merci Mon H{\'e}ros campaign. We documented how these approaches were applied and how the lessons learned can be used to support future public health communication campaigns. Methods: The process involved 6 steps: (1) ensure there is a sufficient volume of topic-specific web-based conversation in the target countries; (2) develop measures to monitor the campaign's social media strategy; (3) identify search terms to assess campaign and related conversations; (4) quantitatively assess campaign audience demographics, campaign reach, and engagement through social media monitoring; (5) qualitatively assess audience attitudes, opinions, and behaviors and understand conversation context through social media listening; and (6) adapt campaign content and approach based on the analysis of social media data. Results: We analyzed posts across social media platforms from November 2019 to October 2020 based on identified key search terms related to family planning, reproductive health, menstruation, sexual activity, and gender. Based on the quantitative and qualitative assessments in steps 4 and 5, there were several adaptive shifts in the campaign's content and approach, of which the following 3 shifts are highlighted. (1) Social media monitoring identified that the Facebook campaign fans were primarily male, which prompted the campaign to target calls to action to the male audience already following the campaign and shift marketing approaches to increase the proportion of female followers. (2) Shorter videos had a higher chance of being viewed in their entirety. In response to this, the campaign shortened video lengths and created screenshot teasers to promote videos. (3) The most negative sentiment related to the campaign videos was associated with beliefs against premarital sex. In response to this finding, the campaign included videos and Facebook Live sessions with religious leaders who promoted talking openly with young people to support intergenerational discussion about reproductive health. Conclusions: Prior to launching health campaigns, programs should test the most relevant social media platforms and their limitations. Inherent biases to internet and social media access are important challenges, and ethical considerations around data privacy must continue to guide the advances in this technology's use for research. However, social listening and social media monitoring can be powerful monitoring and evaluation tools that can be used to aid the adaptive management of health campaigns that engage populations who have a digital presence. ", doi="10.2196/35663", url="https://publichealth.jmir.org/2022/6/e35663", url="http://www.ncbi.nlm.nih.gov/pubmed/35763319" } @Article{info:doi/10.2196/37077, author="Saini, Vipin and Liang, Li-Lin and Yang, Yu-Chen and Le, Mai Huong and Wu, Chun-Ying", title="The Association Between Dissemination and Characteristics of Pro-/Anti-COVID-19 Vaccine Messages on Twitter: Application of the Elaboration Likelihood Model", journal="JMIR Infodemiology", year="2022", month="Jun", day="27", volume="2", number="1", pages="e37077", keywords="COVID-19", keywords="Twitter", keywords="provaccine", keywords="antivaccine", keywords="elaboration likelihood model", keywords="infodemiology", keywords="dissemination", keywords="content analysis", keywords="emotional valence", keywords="social media", abstract="Background: Messages on one's stance toward vaccination on microblogging sites may affect the reader's decision on whether to receive a vaccine. Understanding the dissemination of provaccine and antivaccine messages relating to COVID-19 on social media is crucial; however, studies on this topic have remained limited. Objective: This study applies the elaboration likelihood model (ELM) to explore the characteristics of vaccine stance messages that may appeal to Twitter users. First, we examined the associations between the characteristics of vaccine stance tweets and the likelihood and number of retweets. Second, we identified the relative importance of the central and peripheral routes in decision-making on sharing a message. Methods: English-language tweets from the United States that contained provaccine and antivaccine hashtags (N=150,338) were analyzed between April 26 and August 26, 2021. Logistic and generalized negative binomial regressions were conducted to predict retweet outcomes. The content-related central-route predictors were measured using the numbers of hashtags and mentions, emotional valence, emotional intensity, and concreteness. The content-unrelated peripheral-route predictors were measured using the numbers of likes and followers and whether the source was a verified user. Results: Content-related characteristics played a prominent role in shaping decisions regarding whether to retweet antivaccine messages. Particularly, positive valence (incidence rate ratio [IRR]=1.32, P=.03) and concreteness (odds ratio [OR]=1.17, P=.01) were associated with higher numbers and likelihood of retweets of antivaccine messages, respectively; emotional intensity (subjectivity) was associated with fewer retweets of antivaccine messages (OR=0.78, P=.03; IRR=0.80, P=.04). However, these factors had either no or only small effects on the sharing of provaccine tweets. Retweets of provaccine messages were primarily determined by content-unrelated characteristics, such as the numbers of likes (OR=2.55, IRR=2.24, P<.001) and followers (OR=1.31, IRR=1.28, P<.001). Conclusions: The dissemination of antivaccine messages is associated with both content-related and content-unrelated characteristics. By contrast, the dissemination of provaccine messages is primarily driven by content-unrelated characteristics. These findings signify the importance of leveraging the peripheral route to promote the dissemination of provaccine messages. Because antivaccine tweets with positive emotions, objective content, and concrete words are more likely to be disseminated, policymakers should pay attention to antivaccine messages with such characteristics. ", doi="10.2196/37077", url="https://infodemiology.jmir.org/2022/1/e37077", url="http://www.ncbi.nlm.nih.gov/pubmed/35783451" } @Article{info:doi/10.2196/35324, author="Tripathi, D. Sanidhya and Parker, D. Pearman and Prabhu, V. Arpan and Thomas, Kevin and Rodriguez, Analiz", title="An Examination of Patients and Caregivers on Reddit Navigating Brain Cancer: Content Analysis of the Brain Tumor Subreddit", journal="JMIR Cancer", year="2022", month="Jun", day="22", volume="8", number="2", pages="e35324", keywords="brain tumor", keywords="internet", keywords="social media", keywords="Reddit", keywords="cancer", keywords="emotional support", keywords="self-management", abstract="Background: Occurring in up to 40\% of all patients with cancer, the incidence of brain tumors has caused limited survival, a high psychosocial burden, and an increase in the loss of decision-making capability for the unique population. Although specific symptoms depend on the type of brain tumor, a clinical team of physicians, nurses, and other individuals commonly assist patients and their caregivers with how to tackle the upcoming challenges of their diagnosis. Despite the support from clinical team members, many patients and caregivers may still seek outside support through social media to process their emotions and seek comfort outside of the clinical setting. Specifically, online resources such as Reddit are used where users are provided with the anonymity they need to show their true behavior without fear of judgment. In this study, we aimed to examine trends from Reddit discussion threads on brain tumors to identify areas of need in patient care. Objective: Our primary aims were to determine the type of Reddit user posting, classify the specific brain tumors that were the subject of the posts, and examine the content of the original posts. Methods: We used a qualitative descriptive design to understand patients' and caregivers' unmet and met needs. We selected posts from the top-rated 100 posts from the r/braincancer subreddit from February 2017 to June 2020 to identify common themes using content analysis. Results: The qualitative content analysis revealed how Reddit users primarily used the forum as a method to understand and process the emotions surrounding a brain tumor diagnosis. Three major topic areas from content analysis emerged as prominent themes, including (1) harnessing hope, (2) moving through the grief process, and (3) expressing gratitude toward other Reddit users. Most of the authors of the posts were patients with brain tumors (32/88, 36\%) who used Reddit as a reflective journaling tool to process the associated emotions of a challenging diagnosis. Conclusions: This study shows the potential of Reddit to serve as a unique group therapy platform for patients affected by brain tumors. Our results highlight the support provided by the Reddit community members as a unique mechanism to assist cancer survivors and caregivers with the emotional processing of living with brain tumors. Additionally, the results highlight the importance of recommending Reddit as a therapeutic virtual community and the need for implementing online resources as a part of a health care professional's repertoire to understand the level of support they can give their patients. ", doi="10.2196/35324", url="https://cancer.jmir.org/2022/2/e35324", url="http://www.ncbi.nlm.nih.gov/pubmed/35731559" } @Article{info:doi/10.2196/38423, author="Xue, Haoning and Gong, Xuanjun and Stevens, Hannah", title="COVID-19 Vaccine Fact-Checking Posts on Facebook: Observational Study", journal="J Med Internet Res", year="2022", month="Jun", day="21", volume="24", number="6", pages="e38423", keywords="COVID-19 vaccine", keywords="fact checking", keywords="misinformation correction", keywords="sentiment analysis", keywords="social media", keywords="COVID-19", keywords="vaccination", keywords="misinformation", keywords="health information", keywords="online information", keywords="infodemic", keywords="public sentiment", abstract="Background: Effective interventions aimed at correcting COVID-19 vaccine misinformation, known as fact-checking messages, are needed to combat the mounting antivaccine infodemic and alleviate vaccine hesitancy. Objective: This work investigates (1) the changes in the public's attitude toward COVID-19 vaccines over time, (2) the effectiveness of COVID-19 vaccine fact-checking information on social media engagement and attitude change, and (3) the emotional and linguistic features of the COVID-19 vaccine fact-checking information ecosystem. Methods: We collected a data set of 12,553 COVID-19 vaccine fact-checking Facebook posts and their associated comments (N=122,362) from January 2020 to March 2022 and conducted a series of natural language processing and statistical analyses to investigate trends in public attitude toward the vaccine in COVID-19 vaccine fact-checking posts and comments, and emotional and linguistic features of the COVID-19 fact-checking information ecosystem. Results: The percentage of fact-checking posts relative to all COVID-19 vaccine posts peaked in May 2020 and then steadily decreased as the pandemic progressed (r=--0.92, df=21, t=--10.94, 95\% CI --0.97 to --0.82, P<.001). The salience of COVID-19 vaccine entities was significantly lower in comments (mean 0.03, SD 0.03, t=39.28, P<.001) than in posts (mean 0.09, SD 0.11). Third-party fact checkers have been playing a more important role in more fact-checking over time (r=0.63, df=25, t=4.06, 95\% CI 0.33-0.82, P<.001). COVID-19 vaccine fact-checking posts continued to be more analytical (r=0.81, df=25, t=6.88, 95\% CI 0.62-0.91, P<.001) and more confident (r=0.59, df=25, t=3.68, 95\% CI 0.27-0.79, P=.001) over time. Although comments did not exhibit a significant increase in confidence over time, tentativeness in comments significantly decreased (r=--0.62, df=25, t=--3.94, 95\% CI --0.81 to --0.31, P=.001). In addition, although hospitals receive less engagement than other information sources, the comments expressed more positive attitudinal valence in comments compared to other information sources (b=0.06, 95\% CI 0.00-0.12, t=2.03, P=.04). Conclusions: The percentage of fact-checking posts relative to all posts about the vaccine steadily decreased after May 2020. As the pandemic progressed, third-party fact checkers played a larger role in posting fact-checking COVID-19 vaccine posts. COVID-19 vaccine fact-checking posts continued to be more analytical and more confident over time, reflecting increased confidence in posts. Similarly, tentativeness in comments decreased; this likewise suggests that public uncertainty diminished over time. COVID-19 fact-checking vaccine posts from hospitals yielded more positive attitudes toward vaccination than other information sources. At the same time, hospitals received less engagement than other information sources. This suggests that hospitals should invest more in generating engaging public health campaigns on social media. ", doi="10.2196/38423", url="https://www.jmir.org/2022/6/e38423", url="http://www.ncbi.nlm.nih.gov/pubmed/35671409" } @Article{info:doi/10.2196/35754, author="Ritschl, Valentin and Eibensteiner, Fabian and Mosor, Erika and Omara, Maisa and Sperl, Lisa and Nawaz, A. Faisal and Siva Sai, Chandragiri and Cenanovic, Merisa and Devkota, Prasad Hari and Hribersek, Mojca and De, Ronita and Klager, Elisabeth and Schaden, Eva and Kletecka-Pulker, Maria and V{\"o}lkl-Kernstock, Sabine and Willschke, Harald and Aufricht, Christoph and Atanasov, G. Atanas and Stamm, Tanja", title="Mandatory Vaccination Against COVID-19: Twitter Poll Analysis on Public Health Opinion", journal="JMIR Form Res", year="2022", month="Jun", day="21", volume="6", number="6", pages="e35754", keywords="COVID-19", keywords="SARS-CoV-2", keywords="vaccine", keywords="vaccination", keywords="Twitter", keywords="survey", keywords="mandatory vaccination", keywords="vaccination hesitancy", keywords="coronavirus", keywords="hesitancy", keywords="social media", keywords="questionnaire", keywords="mandatory", keywords="support", keywords="poll", keywords="opinion", keywords="public health", keywords="perception", abstract="Background: On January 30, 2020, the World Health Organization Emergency Committee declared the rapid worldwide spread of COVID-19 a global health emergency. By December 2020, the safety and efficacy of the first COVID-19 vaccines had been demonstrated. However, international vaccination coverage rates have remained below expectations (in Europe at the time of manuscript submission). Controversial mandatory vaccination is currently being discussed and has already been introduced in some countries (Austria, Greece, and Italy). We used the Twitter survey system as a viable method to quickly and comprehensively gather international public health insights on mandatory vaccination against COVID-19. Objective: The purpose of this study was to better understand the public's perception of mandatory COVID-19 vaccination in real time using Twitter polls. Methods: Two Twitter polls were developed (in the English language) to seek the public's opinion on the possibility of mandatory vaccination. The polls were pinned to the Digital Health and Patient Safety Platform's (based in Vienna, Austria) Twitter timeline for 1 week in mid-November 2021, 3 days after the official public announcement of mandatory COVID-19 vaccination in Austria. Twitter users were asked to participate and retweet the polls to reach the largest possible audience. Results: Our Twitter polls revealed two extremes on the topic of mandatory vaccination against COVID-19. Almost half of the 2545 respondents (n=1246, 49\%) favor mandatory vaccination, at least in certain areas. This attitude contrasts with the 45.7\% (n=1162) who categorically reject mandatory vaccination. Over one-quarter (n=621, 26.3\%) of participating Twitter users said they would never get vaccinated, as reflected by the current Western European and North American vaccination coverage rate. Concatenating interpretation of these two polls should be done cautiously as participating populations might substantially differ. Conclusions: Mandatory vaccination against COVID-19 (in at least certain areas) is favored by less than 50\%, whereas it is opposed by almost half of the surveyed Twitter users. Since (social) media strongly influences public perceptions and views, and social media discussions and surveys are specifically susceptible to the ``echo chamber effect,'' the results should be interpreted as a momentary snapshot. Therefore, the results of this study need to be complemented by long-term surveys to maintain their validity. ", doi="10.2196/35754", url="https://formative.jmir.org/2022/6/e35754", url="http://www.ncbi.nlm.nih.gov/pubmed/35617671" } @Article{info:doi/10.2196/37623, author="Zhao, Yuehua and Zhu, Sicheng and Wan, Qiang and Li, Tianyi and Zou, Chun and Wang, Hao and Deng, Sanhong", title="Understanding How and by Whom COVID-19 Misinformation is Spread on Social Media: Coding and Network Analyses", journal="J Med Internet Res", year="2022", month="Jun", day="20", volume="24", number="6", pages="e37623", keywords="health misinformation", keywords="COVID-19", keywords="social media", keywords="misinformation spread", keywords="infodemiology", keywords="global health crisis", keywords="misinformation", keywords="theoretical model", keywords="medical information", keywords="epidemic", keywords="pandemic", abstract="Background: During global health crises such as the COVID-19 pandemic, rapid spread of misinformation on social media has occurred. The misinformation associated with COVID-19 has been analyzed, but little attention has been paid to developing a comprehensive analytical framework to study its spread on social media. Objective: We propose an elaboration likelihood model--based theoretical model to understand the persuasion process of COVID-19--related misinformation on social media. Methods: The proposed model incorporates the central route feature (content feature) and peripheral features (including creator authority, social proof, and emotion). The central-level COVID-19--related misinformation feature includes five topics: medical information, social issues and people's livelihoods, government response, epidemic spread, and international issues. First, we created a data set of COVID-19 pandemic--related misinformation based on fact-checking sources and a data set of posts that contained this misinformation on real-world social media. Based on the collected posts, we analyzed the dissemination patterns. Results: Our data set included 11,450 misinformation posts, with medical misinformation as the largest category (n=5359, 46.80\%). Moreover, the results suggest that both the least (4660/11,301, 41.24\%) and most (2320/11,301, 20.53\%) active users are prone to sharing misinformation. Further, posts related to international topics that have the greatest chance of producing a profound and lasting impact on social media exhibited the highest distribution depth (maximum depth=14) and width (maximum width=2355). Additionally, 97.00\% (2364/2437) of the spread was characterized by radiation dissemination. Conclusions: Our proposed model and findings could help to combat the spread of misinformation by detecting suspicious users and identifying propagation characteristics. ", doi="10.2196/37623", url="https://www.jmir.org/2022/6/e37623", url="http://www.ncbi.nlm.nih.gov/pubmed/35671411" } @Article{info:doi/10.2196/35266, author="Li, Jingwei and Huang, Wei and Sia, Ling Choon and Chen, Zhuo and Wu, Tailai and Wang, Qingnan", title="Enhancing COVID-19 Epidemic Forecasting Accuracy by Combining Real-time and Historical Data From Multiple Internet-Based Sources: Analysis of Social Media Data, Online News Articles, and Search Queries", journal="JMIR Public Health Surveill", year="2022", month="Jun", day="16", volume="8", number="6", pages="e35266", keywords="SARS-CoV-2", keywords="COVID 19", keywords="epidemic forecasting", keywords="disease surveillance", keywords="infectious disease epidemiology", keywords="social medial", keywords="online news", keywords="search query", keywords="autoregression model", abstract="Background: The SARS-COV-2 virus and its variants pose extraordinary challenges for public health worldwide. Timely and accurate forecasting of the COVID-19 epidemic is key to sustaining interventions and policies and efficient resource allocation. Internet-based data sources have shown great potential to supplement traditional infectious disease surveillance, and the combination of different Internet-based data sources has shown greater power to enhance epidemic forecasting accuracy than using a single Internet-based data source. However, existing methods incorporating multiple Internet-based data sources only used real-time data from these sources as exogenous inputs but did not take all the historical data into account. Moreover, the predictive power of different Internet-based data sources in providing early warning for COVID-19 outbreaks has not been fully explored. Objective: The main aim of our study is to explore whether combining real-time and historical data from multiple Internet-based sources could improve the COVID-19 forecasting accuracy over the existing baseline models. A secondary aim is to explore the COVID-19 forecasting timeliness based on different Internet-based data sources. Methods: We first used core terms and symptom-related keyword-based methods to extract COVID-19--related Internet-based data from December 21, 2019, to February 29, 2020. The Internet-based data we explored included 90,493,912 online news articles, 37,401,900 microblogs, and all the Baidu search query data during that period. We then proposed an autoregressive model with exogenous inputs, incorporating real-time and historical data from multiple Internet-based sources. Our proposed model was compared with baseline models, and all the models were tested during the first wave of COVID-19 epidemics in Hubei province and the rest of mainland China separately. We also used lagged Pearson correlations for COVID-19 forecasting timeliness analysis. Results: Our proposed model achieved the highest accuracy in all 5 accuracy measures, compared with all the baseline models of both Hubei province and the rest of mainland China. In mainland China, except for Hubei, the COVID-19 epidemic forecasting accuracy differences between our proposed model (model i) and all the other baseline models were statistically significant (model 1, t198=--8.722, P<.001; model 2, t198=--5.000, P<.001, model 3, t198=--1.882, P=.06; model 4, t198=--4.644, P<.001; model 5, t198=--4.488, P<.001). In Hubei province, our proposed model's forecasting accuracy improved significantly compared with the baseline model using historical new confirmed COVID-19 case counts only (model 1, t198=--1.732, P=.09). Our results also showed that Internet-based sources could provide a 2- to 6-day earlier warning for COVID-19 outbreaks. Conclusions: Our approach incorporating real-time and historical data from multiple Internet-based sources could improve forecasting accuracy for epidemics of COVID-19 and its variants, which may help improve public health agencies' interventions and resource allocation in mitigating and controlling new waves of COVID-19 or other relevant epidemics. ", doi="10.2196/35266", url="https://publichealth.jmir.org/2022/6/e35266", url="http://www.ncbi.nlm.nih.gov/pubmed/35507921" } @Article{info:doi/10.2196/32912, author="Gauld, Christophe and Maquet, Julien and Micoulaud-Franchi, Jean-Arthur and Dumas, Guillaume", title="Popular and Scientific Discourse on Autism: Representational Cross-Cultural Analysis of Epistemic Communities to Inform Policy and Practice", journal="J Med Internet Res", year="2022", month="Jun", day="15", volume="24", number="6", pages="e32912", keywords="autism spectrum disorder", keywords="Twitter", keywords="natural language processing", keywords="network analysis", keywords="popular understanding of illness", keywords="knowledge translation", keywords="autism", keywords="tweets", keywords="psychiatry", keywords="text mining", abstract="Background: Social media provide a window onto the circulation of ideas in everyday folk psychiatry, revealing the themes and issues discussed both by the public and by various scientific communities. Objective: This study explores the trends in health information about autism spectrum disorder within popular and scientific communities through the systematic semantic exploration of big data gathered from Twitter and PubMed. Methods: First, we performed a natural language processing by text-mining analysis and with unsupervised (machine learning) topic modeling on a sample of the last 10,000 tweets in English posted with the term \#autism (January 2021). We built a network of words to visualize the main dimensions representing these data. Second, we performed precisely the same analysis with all the articles using the term ``autism'' in PubMed without time restriction. Lastly, we compared the results of the 2 databases. Results: We retrieved 121,556 terms related to autism in 10,000 tweets and 5.7x109 terms in 57,121 biomedical scientific articles. The 4 main dimensions extracted from Twitter were as follows: integration and social support, understanding and mental health, child welfare, and daily challenges and difficulties. The 4 main dimensions extracted from PubMed were as follows: diagnostic and skills, research challenges, clinical and therapeutical challenges, and neuropsychology and behavior. Conclusions: This study provides the first systematic and rigorous comparison between 2 corpora of interests, in terms of lay representations and scientific research, regarding the significant increase in information available on autism spectrum disorder and of the difficulty to connect fragments of knowledge from the general population. The results suggest a clear distinction between the focus of topics used in the social media and that of scientific communities. This distinction highlights the importance of knowledge mobilization and exchange to better align research priorities with personal concerns and to address dimensions of well-being, adaptation, and resilience. Health care professionals and researchers can use these dimensions as a framework in their consultations to engage in discussions on issues that matter to beneficiaries and develop clinical approaches and research policies in line with these interests. Finally, our study can inform policy makers on the health and social needs and concerns of individuals with autism and their caregivers, especially to define health indicators based on important issues for beneficiaries. ", doi="10.2196/32912", url="https://www.jmir.org/2022/6/e32912", url="http://www.ncbi.nlm.nih.gov/pubmed/35704359" } @Article{info:doi/10.2196/39180, author="Basch, H. Corey and Hillyer, C. Grace and Jacques, T. Erin", title="News Coverage of Colorectal Cancer on Google News: Descriptive Study", journal="JMIR Cancer", year="2022", month="Jun", day="15", volume="8", number="2", pages="e39180", keywords="colorectal cancer", keywords="internet", keywords="online news", keywords="screening", keywords="disparities", keywords="infodemiology", keywords="online health information", keywords="content analysis", keywords="public awareness", keywords="health news", keywords="cancer screening", keywords="health video", keywords="video content analysis", abstract="Background: Colorectal cancer (CRC) is one of the leading causes of cancer death in the United States. The incidence and prevalence of CRC have historically increased with age. Although rates of CRC in the United States have been decreasing over the past decades among those aged ?65 years, there has been an uptick among those in younger age brackets. Google News is one of the biggest traffic drivers to top news sites. It aggregates and shares news highlights from multiple sources worldwide and organizes them by content type. Despite the widespread use of Google News, research is lacking on the type of CRC content represented in this news source.? Objective: The purpose of this study was to analyze content related to CRC screening and prevention in Google News articles published during National Colorectal Cancer Awareness Month (March 2022). Methods: Data collection for this cross-sectional study was conducted in March 2022---National Colorectal Cancer Awareness Month.?Using the term colorectal cancer, 100 English-language Google News articles were extracted and coded for content. A combined approach---deductive and inductive coding---was utilized. Descriptive analyses were conducted, and frequency distributions were reported. Univariable analyses were performed to assess differences between articles that mentioned CRC screening and those that did not via chi-square tests. Results: Of the 100 articles reviewed, nearly half (n=49, 49\%) were created by health news organizations, and another 27\% (n=27) were created by television news services.?The predominant themes in the content included age at the onset of disease (n=59, 59\%), mortality related to CRC (n=57, 57\%), and the severity of disease (n=50, 50\%).?Only 18\% (n=18) of articles discussed CRC disparities, 23\% (n=23) mentioned that there are hereditary forms of the disease, 36\% (n=36) spoke of colonoscopy to screen for the disease, and 37\% (n=37) mentioned how the disease is treated.?Although most articles mentioned CRC screening (n=61, 61\%), it was striking that sex was only mentioned in 34\% (21/61) of these articles, colonoscopy was mentioned in 46\% (28/61), and diet was mentioned in 30\% (18/61). Conclusions: Heightening the public's awareness of this disease is important, but it is critical that messages related to how preventable this cancer is, who is the most likely to develop CRC, and what can be done to detect it in the early stages when the disease is the most curable be the critical elements of dialogue, particularly during National Colorectal Cancer Awareness Month. There is a need to disseminate information about early-onset CRC and the importance of screening, especially among populations with low rates of uptake. Web-based news is potentially an underutilized communication mechanism for promoting CRC screenings as secondary prevention measures for high-risk groups. ", doi="10.2196/39180", url="https://cancer.jmir.org/2022/2/e39180", url="http://www.ncbi.nlm.nih.gov/pubmed/35704377" } @Article{info:doi/10.2196/36445, author="Riddell, A. Corinne and Neumann, Krista and Santaularia, Jeanie N. and Farkas, Kriszta and Ahern, Jennifer and Mason, M. Susan", title="Excess Google Searches for Child Abuse and Intimate Partner Violence During the COVID-19 Pandemic: Infoveillance Approach", journal="J Med Internet Res", year="2022", month="Jun", day="13", volume="24", number="6", pages="e36445", keywords="child abuse", keywords="household violence", keywords="infoveillance", keywords="violence", keywords="domestic violence", keywords="abuse", keywords="Google", keywords="COVID-19", abstract="Background: The COVID-19 pandemic has created environments with increased risk factors for household violence, such as unemployment and financial uncertainty. At the same time, it led to the introduction of policies to mitigate financial uncertainty. Further, it hindered traditional measurements of household violence. Objective: Using an infoveillance approach, our goal was to determine if there were excess Google searches related to exposure to child abuse, intimate partner violence (IPV), and child-witnessed IPV during the COVID-19 pandemic and if any excesses are temporally related to shelter-in-place and economic policies. Methods: Data on relative search volume for each violence measure was extracted using the Google Health Trends application programming interface for each week from 2017 to 2020 for the United States. Using linear regression with restricted cubic splines, we analyzed data from 2017 to 2019 to characterize the seasonal variation shared across prepandemic years. Parameters from prepandemic years were used to predict the expected number of Google searches and 95\% prediction intervals (PI) for each week in 2020. Weeks with searches above the upper bound of the PI are in excess of the model's prediction. Results: Relative search volume for exposure to child abuse was greater than expected in 2020, with 19\% (10/52) of the weeks falling above the upper bound of the PI. These excesses in searches began a month after the Pandemic Unemployment Compensation program ended. Relative search volume was also heightened in 2020 for child-witnessed IPV, with 33\% (17/52) of the weeks falling above the upper bound of the PI. This increase occurred after the introduction of shelter-in-place policies. Conclusions: Social and financial disruptions, which are common consequences of major disasters such as the COVID-19 pandemic, may increase risks for child abuse and child-witnessed IPV. ", doi="10.2196/36445", url="https://www.jmir.org/2022/6/e36445", url="http://www.ncbi.nlm.nih.gov/pubmed/35700024" } @Article{info:doi/10.2196/34525, author="Ferrell, DaJuan and Campos-Castillo, Celeste", title="Factors Affecting Physicians' Credibility on Twitter When Sharing Health Information: Online Experimental Study", journal="JMIR Infodemiology", year="2022", month="Jun", day="13", volume="2", number="1", pages="e34525", keywords="source credibility", keywords="user engagement", keywords="social media", keywords="health communication", keywords="misinformation", keywords="Twitter", abstract="Background: Largely absent from research on how users appraise the credibility of professionals as sources for the information they find on social media is work investigating factors shaping credibility within a specific profession, such as physicians. Objective: We address debates about how physicians can show their credibility on social media depending on whether they employ a formal or casual appearance in their profile picture. Using prominence-interpretation theory, we posit that formal appearance will affect perceived credibility based on users' social context---specifically, whether they have a regular health care provider. Methods: For this experiment, we recruited 205 social media users using Amazon Mechanical Turk. We asked participants if they had a regular health care provider and then randomly assigned them to read 1 of 3 Twitter posts that varied only in the profile picture of the physician offering health advice. Next, we tasked participants with assessing the credibility of the physician and their likelihood of engaging with the tweet and the physician on Twitter. We used path analysis to assess whether participants having a regular health care provider impacted how the profile picture affected their ratings of the physician's credibility and their likelihood to engage with the tweet and physician on Twitter. Results: We found that the profile picture of a physician posting health advice in either formal or casual attire did not elicit significant differences in credibility, with ratings comparable to those having no profile image. Among participants assigned the formal appearance condition, those with a regular provider rated the physician higher on a credibility than those without, which led to stronger intentions to engage with the tweet and physician. Conclusions: The findings add to existing research by showing how the social context of information seeking on social media shapes the credibility of a given professional. Practical implications for professionals engaging with the public on social media and combating false information include moving past debates about casual versus formal appearances and toward identifying ways to segment audiences based on factors like their backgrounds (eg, experiences with health care providers). ", doi="10.2196/34525", url="https://infodemiology.jmir.org/2022/1/e34525", url="http://www.ncbi.nlm.nih.gov/pubmed/37113807" } @Article{info:doi/10.2196/35930, author="Chauhan, Jyoti and Aasaithambi, Sathyaraj and M{\'a}rquez-Rodas, Iv{\'a}n and Formisano, Luigi and Papa, Sophie and Meyer, Nicolas and Forschner, Andrea and Faust, Guy and Lau, Mike and Sagkriotis, Alexandros", title="Understanding the Lived Experiences of Patients With Melanoma: Real-World Evidence Generated Through a European Social Media Listening Analysis", journal="JMIR Cancer", year="2022", month="Jun", day="13", volume="8", number="2", pages="e35930", keywords="melanoma", keywords="social media", keywords="social media listening", keywords="real-world evidence", keywords="patient journey", keywords="cancer", keywords="mortality rate", keywords="health information", abstract="Background: Cutaneous melanoma is an aggressive malignancy that is proposed to account for 90\% of skin cancer--related mortality. Individuals with melanoma experience both physical and psychological impacts associated with their diagnosis and treatment. Health-related information is being increasingly accessed and shared by stakeholders on social media platforms. Objective: This study aimed to assess how individuals living with melanoma across 14 European countries use social media to discuss their needs and provide their perceptions of the disease. Methods: Social media sources including Twitter, forums, and blogs were searched using predefined search strings of keywords relating to melanoma. Manual and automated relevancy approaches filtered the extracted data for content that provided patient-centric insights. This contextualized data was then mined for insightful concepts around the symptoms, diagnosis, treatment, impacts, and lived experiences of melanoma. Results: A total of 182,400 posts related to melanoma were identified between November 2018 and November 2020. Following exclusion of irrelevant posts and using random sampling methodology, 864 posts were identified as relevant to the study objectives. Of the social media channels included, Twitter was the most commonly used, followed by forums and blogs. Most posts originated from the United Kingdom (n=328, 38\%) and Spain (n=138, 16\%). Of the relevant posts, 62\% (n=536) were categorized as originating from individuals with melanoma. The most frequently discussed melanoma-related topics were treatment (436/792, 55\%), diagnosis and tests (261/792, 33\%), and remission (190/792, 24\%). The majority of treatment discussions were about surgery (292/436, 67\%), followed by immunotherapy (52/436, 12\%). In total, 255 posts discussed the impacts of melanoma, which included emotional burden (n=179, 70\%), physical impacts (n=61, 24\%), effects on social life (n=43, 17\%), and financial impacts (n=10, 4\%). Conclusions: Findings from this study highlight how melanoma stakeholders discuss key concepts associated with the condition on social media, adding to the conceptual model of the patient journey. This social media listening approach is a powerful tool for exploring melanoma stakeholder perspectives, providing insights that can be used to corroborate existing data and inform future studies. ", doi="10.2196/35930", url="https://cancer.jmir.org/2022/2/e35930", url="http://www.ncbi.nlm.nih.gov/pubmed/35699985" } @Article{info:doi/10.2196/37466, author="Niu, Qian and Liu, Junyu and Kato, Masaya and Nagai-Tanima, Momoko and Aoyama, Tomoki", title="The Effect of Fear of Infection and Sufficient Vaccine Reservation Information on Rapid COVID-19 Vaccination in Japan: Evidence From a Retrospective Twitter Analysis", journal="J Med Internet Res", year="2022", month="Jun", day="9", volume="24", number="6", pages="e37466", keywords="COVID-19", keywords="vaccine hesitancy", keywords="Japan", keywords="social media", keywords="text mining", abstract="Background: The global public health and socioeconomic impacts of the COVID-19 pandemic have been substantial, rendering herd immunity by COVID-19 vaccination an important factor for protecting people and retrieving the economy. Among all the countries, Japan became one of the countries with the highest COVID-19 vaccination rates in several months, although vaccine confidence in Japan is the lowest worldwide. Objective: We attempted to find the reasons for rapid COVID-19 vaccination in Japan given its lowest vaccine confidence levels worldwide, through Twitter analysis.? Methods: We downloaded COVID-19--related Japanese tweets from a large-scale public COVID-19 Twitter chatter data set within the timeline of February 1 and September 30, 2021. The daily number of vaccination cases was collected from the official website of the Prime Minister's Office of Japan. After preprocessing, we applied unigram and bigram token analysis and then calculated the cross-correlation and Pearson correlation coefficient (r) between the term frequency and daily vaccination cases. We then identified vaccine sentiments and emotions of tweets and used the topic modeling to look deeper into the dominant emotions.? Results: We selected 190,697 vaccine-related tweets after filtering. Through n-gram token analysis, we discovered the top unigrams and bigrams over the whole period. In all the combinations of the top 6 unigrams, tweets with both keywords ``reserve'' and ``venue'' showed the largest correlation with daily vaccination cases (r=0.912; P<.001). On sentiment analysis, negative sentiment overwhelmed positive sentiment, and fear was the dominant emotion across the period. For the latent Dirichlet allocation model on tweets with fear emotion, the two topics were identified as ``infect'' and ``vaccine confidence.'' The expectation of the number of tweets generated from topic ``infect'' was larger than that generated from topic ``vaccine confidence.'' Conclusions: Our work indicates that awareness of the danger of COVID-19 might increase the willingness to get vaccinated. With a sufficient vaccine supply, effective delivery of vaccine reservation information may be an important factor for people to get vaccinated. We did not find evidence for increased vaccine confidence in Japan during the period of our study.?We recommend policy makers to share accurate and prompt information about the infectious diseases and vaccination and to make efforts on smoother delivery of vaccine reservation information. ", doi="10.2196/37466", url="https://www.jmir.org/2022/6/e37466", url="http://www.ncbi.nlm.nih.gov/pubmed/35649182" } @Article{info:doi/10.2196/34594, author="Kulkarni, Vishnutheertha and Okoye, A. Ginette and Garza, A. Luis and Wongvibulsin, Shannon", title="Geospatial Heterogeneity of Hidradenitis Suppurativa Searches in the United States: Infodemiology Study of Google Search Data", journal="JMIR Dermatol", year="2022", month="Jun", day="9", volume="5", number="2", pages="e34594", keywords="hidradenitis suppurativa", keywords="infodemiology", keywords="internet", keywords="digital dermatoepidemiology", keywords="epidemiology", keywords="big data", keywords="dermatology", doi="10.2196/34594", url="https://derma.jmir.org/2022/2/e34594", url="http://www.ncbi.nlm.nih.gov/pubmed/37632873" } @Article{info:doi/10.2196/37840, author="Watanabe, Tomomi and Yada, Shuntaro and Aramaki, Eiji and Yajima, Hiroshi and Kizaki, Hayato and Hori, Satoko", title="Extracting Multiple Worries From Breast Cancer Patient Blogs Using Multilabel Classification With the Natural Language Processing Model Bidirectional Encoder Representations From Transformers: Infodemiology Study of Blogs", journal="JMIR Cancer", year="2022", month="Jun", day="3", volume="8", number="2", pages="e37840", keywords="breast neoplasm", keywords="cancer", keywords="natural language processing", keywords="NLP", keywords="artificial intelligence", keywords="model", keywords="machine learning", keywords="content analysis", keywords="text mining", keywords="sentiment analysis", keywords="oncology", keywords="quality of life", keywords="social media", keywords="social support", keywords="breast cancer", keywords="BERT model", keywords="peer support", keywords="blog post", keywords="patient data", abstract="Background: Patients with breast cancer have a variety of worries and need multifaceted information support. Their accumulated posts on social media contain rich descriptions of their daily worries concerning issues such as treatment, family, and finances. It is important to identify these issues to help patients with breast cancer to resolve their worries and obtain reliable information. Objective: This study aimed to extract and classify multiple worries from text generated by patients with breast cancer using Bidirectional Encoder Representations From Transformers (BERT), a context-aware natural language processing model. Methods: A total of 2272 blog posts by patients with breast cancer in Japan were collected. Five worry labels, ``treatment,'' ``physical,'' ``psychological,'' ``work/financial,'' and ``family/friends,'' were defined and assigned to each post. Multiple labels were allowed. To assess the label criteria, 50 blog posts were randomly selected and annotated by two researchers with medical knowledge. After the interannotator agreement had been assessed by means of Cohen kappa, one researcher annotated all the blogs. A multilabel classifier that simultaneously predicts five worries in a text was developed using BERT. This classifier was fine-tuned by using the posts as input and adding a classification layer to the pretrained BERT. The performance was evaluated for precision using the average of 5-fold cross-validation results. Results: Among the blog posts, 477 included ``treatment,'' 1138 included ``physical,'' 673 included ``psychological,'' 312 included ``work/financial,'' and 283 included ``family/friends.'' The interannotator agreement values were 0.67 for ``treatment,'' 0.76 for ``physical,'' 0.56 for ``psychological,'' 0.73 for ``work/financial,'' and 0.73 for ``family/friends,'' indicating a high degree of agreement. Among all blog posts, 544 contained no label, 892 contained one label, and 836 contained multiple labels. It was found that the worries varied from user to user, and the worries posted by the same user changed over time. The model performed well, though prediction performance differed for each label. The values of precision were 0.59 for ``treatment,'' 0.82 for ``physical,'' 0.64 for ``psychological,'' 0.67 for ``work/financial,'' and 0.58 for ``family/friends.'' The higher the interannotator agreement and the greater the number of posts, the higher the precision tended to be. Conclusions: This study showed that the BERT model can extract multiple worries from text generated from patients with breast cancer. This is the first application of a multilabel classifier using the BERT model to extract multiple worries from patient-generated text. The results will be helpful to identify breast cancer patients' worries and give them timely social support. ", doi="10.2196/37840", url="https://cancer.jmir.org/2022/2/e37840", url="http://www.ncbi.nlm.nih.gov/pubmed/35657664" } @Article{info:doi/10.2196/34940, author="Gomaa, Basma and Houghton, F. Rebecca and Crocker, Nicole and Walsh-Buhi, R. Eric", title="Skin Cancer Narratives on Instagram: Content Analysis", journal="JMIR Infodemiology", year="2022", month="Jun", day="2", volume="2", number="1", pages="e34940", keywords="digital health", keywords="social media", keywords="skin cancer", keywords="Instagram", keywords="melanoma", keywords="oncology", keywords="cancer", keywords="skin", keywords="content analysis", keywords="narrative", keywords="information sharing", keywords="online platform", abstract="Background: Skin cancer is among the deadliest forms of cancer in the United States. The American Cancer Society reported that 3 million skin cancer cases could be avoided every year if individuals are more aware of the risk factors related to sun exposure and prevention. Social media platforms may serve as potential intervention modalities that can be used to raise public awareness of several diseases and health conditions, including skin cancer. Social media platforms are efficient, cost-effective tools for health-related content that can reach a broad number of individuals who are already using these spaces in their day-to-day personal lives. Instagram was launched in 2010, and it is now used by 1 billion users, of which 90\% are under the age of 35 years. Despite previous research highlighting the potential of image-based platforms in skin cancer prevention and leveraging Instagram's popularity among the priority population to raise awareness, there is still a lack of studies describing skin cancer--related content on Instagram. Objective: This study aims to describe skin cancer--related content on Instagram, including the type of account; the characteristics of the content, such as the kind of media used; and the type of skin cancer discussed. This study also seeks to reveal content themes in terms of skin cancer risks, treatment, and prevention. Methods: Through CrowdTangle, a Facebook-owned tool, we retrieved content from publicly available accounts on Instagram for the 30 days preceding May 14, 2021. Out of 2932 posts, we randomly selected 1000 posts for review. Of the 1000 posts, 592 (59.2\%) met the following inclusion criteria: (1) content was focused on human skin cancer, (2) written in English language only, and (3) originated from the United States. Guided by previous research and through an iterative process, 2 undergraduate students independently coded the remaining posts. The 2 coders and a moderator met several times to refine the codebook. Results: Of the 592 posts, profiles representing organizations (n=321, 54.2\%) were slightly more common than individual accounts (n=256, 43.2\%). The type of media included in the posts varied, with posts containing photos occurring more frequently (n=315, 53.2\%) than posts containing infographics (n=233, 39.4\%) or videos (n=85, 14.4\%). Melanoma was the most mentioned type of skin cancer (n=252, 42.6\%). Prevention methods (n=404, 68.2\%) were discussed in Instagram posts more often than risk factors (n=271, 45.8\%). Only 81 out of 592 (13.7\%) posts provided a citation. Conclusions: This study's findings highlight the potential role of Instagram as a platform for improving awareness of skin cancer risks and the benefits of prevention practices. We believe that social media is the most promising venue for researchers and dermatologists to dedicate their efforts and presence that can widely reach the public to educate about skin cancer and empower prevention. ", doi="10.2196/34940", url="https://infodemiology.jmir.org/2022/1/e34940", url="http://www.ncbi.nlm.nih.gov/pubmed/37113805" } @Article{info:doi/10.2196/37632, author="McMann, J. Tiana and Calac, Alec and Nali, Matthew and Cuomo, Raphael and Maroulis, James and Mackey, K. Tim", title="Synthetic Cannabinoids in Prisons: Content Analysis of TikToks", journal="JMIR Infodemiology", year="2022", month="May", day="31", volume="2", number="1", pages="e37632", keywords="social media", keywords="substance use disorder", keywords="synthetic drugs", keywords="prison", keywords="cannabinoid", keywords="synthetic", keywords="psychoactive", keywords="illicit", keywords="video", keywords="substance use", keywords="harmful", keywords="K2/Spice", keywords="TikTok", abstract="Background: Synthetic cannabinoids are a significant public health concern, especially among incarcerated populations due to increased reports of abuse. Recent news reports have highlighted the severe consequences of K2/Spice, a synthetic cannabinoid, among the prison population in the United States. Despite regulations against cell phone use, inmates use TikTok to post K2/Spice-related content. Objective: This study aimed to examine TikTok posts for use and illicit distribution of psychoactive substances (eg, K2/Spice) among incarcerated populations. Methods: The study collected TikTok videos associated with the \#k2spice hashtag and used a data collection approach similar to snowball sampling. Inductive coding was used to conduct content analysis of video characteristics. Videos were manually annotated to generate binary classifications related to the use of K2/Spice as well as selling and buying activities associated with it. Statistical analysis was used to determine associations between a video's user engagement and an intent to buy or sell K2/Spice. Results: A total of 89 TikTok videos with the hashtag \#k2spice were manually coded, with 40\% (n=36) identified as displaying the use, solicitation, or adverse effects of K2/Spice among the prison population. Of them, 44.44\% (n=16) were in a prison-based setting documenting adverse effects including possible overdose. Videos with higher user engagement were positively correlated with comments indicating an intent to buy or sell K2/Spice. Conclusions: K2/Spice is a drug subject to abuse among prison inmates in the United States, including depictions of its harmful effects being recorded and shared on TikTok. Lack of policy enforcement on TikTok and the need for better access to treatment services within the prison system may be exacerbating substance use among this highly vulnerable population. Minimizing the potential individual harm of this content on the incarcerated population should be a priority for social media platforms and the criminal justice system alike. ", doi="10.2196/37632", url="https://infodemiology.jmir.org/2022/1/e37632", url="http://www.ncbi.nlm.nih.gov/pubmed/37113804" } @Article{info:doi/10.2196/33577, author="Li, Peiyi and Chen, Bo and Deveaux, Genevieve and Luo, Yunmei and Tao, Wenjuan and Li, Weimin and Wen, Jin and Zheng, Yuan", title="Cross-Verification of COVID-19 Information Obtained From Unofficial Social Media Accounts and Associated Changes in Health Behaviors: Web-Based Questionnaire Study Among Chinese Netizens", journal="JMIR Public Health Surveill", year="2022", month="May", day="31", volume="8", number="5", pages="e33577", keywords="COVID-19", keywords="pandemic", keywords="social media", keywords="behavior change", keywords="information cross-verification", keywords="eHealth literacy", abstract="Background: As social media platforms have become significant sources of information during the pandemic, a significant volume of both factual and inaccurate information related to the prevention of COVID-19 has been disseminated through social media. Thus, disparities in COVID-19 information verification across populations have the potential to promote the dissemination of misinformation among clustered groups of people with similar characteristics. Objective: This study aimed to identify the characteristics of social media users who obtained COVID-19 information through unofficial social media accounts and were (1) most likely to change their health behaviors according to web-based information and (2) least likely to actively verify the accuracy of COVID-19 information, as these individuals may be susceptible to inaccurate prevention measures and may exacerbate transmission. Methods: An online questionnaire consisting of 17 questions was disseminated by West China Hospital via its official online platforms, between May 18, 2020, and May 31, 2020. The questionnaire collected the sociodemographic information of 14,509 adults, and included questions surveying Chinese netizens' knowledge about COVID-19, personal social media use, health behavioral change tendencies, and cross-verification behaviors for web-based information during the pandemic. Multiple stepwise regression models were used to examine the relationships between social media use, behavior changes, and information cross-verification. Results: Respondents who were most likely to change their health behaviors after obtaining web-based COVID-19 information from celebrity sources had the following characteristics: female sex (P=.004), age ?50 years (P=.009), higher COVID-19 knowledge and health literacy (P=.045 and P=.03, respectively), non--health care professional (P=.02), higher frequency of searching on social media (P<.001), better health conditions (P<.001), and a trust rating score of more than 3 for information released by celebrities on social media (P=.005). Furthermore, among participants who were most likely to change their health behaviors according to social media information released by celebrities, female sex (P<.001), living in a rural residence rather than first-tier city (P<.001), self-reported medium health status and lower health care literacy (P=.007 and P<.001, respectively), less frequent search for COVID-19 information on social media (P<.001), and greater level of trust toward celebrities' social media accounts with a trust rating score greater than 1 (P?.04) were associated with a lack of cross-verification of information. Conclusions: The findings suggest that governments, health care agencies, celebrities, and technicians should combine their efforts to decrease the risk in vulnerable groups that are inclined to change health behaviors according to web-based information but do not perform any fact-check verification of the accuracy of the unofficial information. Specifically, it is necessary to correct the false information related to COVID-19 on social media, appropriately apply celebrities' star power, and increase Chinese netizens' awareness of information cross-verification and eHealth literacy for evaluating the veracity of web-based information. ", doi="10.2196/33577", url="https://publichealth.jmir.org/2022/5/e33577", url="http://www.ncbi.nlm.nih.gov/pubmed/35486529" } @Article{info:doi/10.2196/36239, author="Cirillo, N. Madison and Halbert, P. Jennifer and Smith, Gomez Jessica and Alamiri, Sami Nour and Ingersoll, S. Karen", title="\#BingeDrinking---Using Social Media to Understand College Binge Drinking: Qualitative Study", journal="JMIR Hum Factors", year="2022", month="May", day="30", volume="9", number="2", pages="e36239", keywords="college students", keywords="binge drinking", keywords="social media", keywords="young adults", abstract="Background: Hazardous drinking among college students persists, despite ongoing university alcohol education and alcohol intervention programs. College students often post comments or pictures of drinking episodes on social media platforms. Objective: This study aimed to understand one university's student attitudes toward alcohol use by examining student posts about drinking on social media platforms and to identify opportunities to reduce alcohol-related harm and inform novel alcohol interventions. Methods: We analyzed social media posts from 7 social media platforms using qualitative inductive coding based on grounded theory to identify the contexts of student drinking and the attitudes and behaviors of students and peers during drinking episodes. We reviewed publicly available social media posts that referenced alcohol, collaborating with undergraduate students to select their most used platforms and develop locally relevant search terms; all posts in our data set were generated by students associated with a specific university. From the codes, we derived themes about student culture regarding alcohol use. Results: In total, 1151 social media posts were included in this study. These included 809 Twitter tweets, 113 Instagram posts, 100 Greekrank posts, 64 Reddit posts, 34 College Confidential posts, 23 Facebook posts, and 8 YouTube posts. Posts included both implicit and explicit portrayals of alcohol use. Across all types of posts reviewed, positive drinking attitudes were most common, followed by negative and then neutral attitudes, but valence varied by platform. Posts that portrayed drinking positively received positive peer feedback and indicate that drinking is viewed by students as an essential and positive part of university student culture. Conclusions: Social media provide a real-time picture of students' behavior during their own and others' heavy drinking. Posts portray heavy drinking as a normal part of student culture, reinforced by peers' positive feedback on posts. Interventions for college drinking should help students manage alcohol intake in real time, provide safety information during alcohol use episodes, and raise student awareness of web-based privacy concerns and reputation management. Additional interventions for students, alumni, and parents are needed to address positive attitudes about and traditions of drinking. ", doi="10.2196/36239", url="https://humanfactors.jmir.org/2022/2/e36239", url="http://www.ncbi.nlm.nih.gov/pubmed/35635740" } @Article{info:doi/10.2196/30371, author="Liu, Xiaohui and Kar, Bandana and Montiel Ishino, Alejandro Francisco and Onega, Tracy and Williams, Faustine", title="The Associations Between Racially/Ethnically Stratified COVID-19 Tweets and COVID-19 Cases and Deaths: Cross-sectional Study", journal="JMIR Form Res", year="2022", month="May", day="30", volume="6", number="5", pages="e30371", keywords="racial/ethnic stratification", keywords="geo-tagged COVID-19 tweets", keywords="racial/ethnic disparity", keywords="surveillance", abstract="Background: The COVID-19 pandemic exacerbated existing racial/ethnic health disparities in the United States. Monitoring nationwide Twitter conversations about COVID-19 and race/ethnicity could shed light on the impact of the pandemic on racial/ethnic minorities and help address health disparities. Objective: This paper aims to examine the association between COVID-19 tweet volume and COVID-19 cases and deaths, stratified by race/ethnicity, in the early onset of the pandemic. Methods: This cross-sectional study used geotagged COVID-19 tweets from within the United States posted in April 2020 on Twitter to examine the association between tweet volume, COVID-19 surveillance data (total cases and deaths in April), and population size. The studied time frame was limited to April 2020 because April was the earliest month when COVID-19 surveillance data on racial/ethnic groups were collected. Racially/ethnically stratified tweets were extracted using racial/ethnic group--related keywords (Asian, Black, Latino, and White) from COVID-19 tweets. Racially/ethnically stratified tweets, COVID-19 cases, and COVID-19 deaths were mapped to reveal their spatial distribution patterns. An ordinary least squares (OLS) regression model was applied to each stratified dataset. Results: The racially/ethnically stratified tweet volume was associated with surveillance data. Specifically, an increase of 1 Asian tweet was correlated with 288 Asian cases (P<.001) and 93.4 Asian deaths (P<.001); an increase of 1 Black tweet was linked to 47.6 Black deaths (P<.001); an increase of 1 Latino tweet was linked to 719 Latino deaths (P<.001); and an increase of 1 White tweet was linked to 60.2 White deaths (P<.001). Conclusions: Using racially/ethnically stratified Twitter data as a surveillance indicator could inform epidemiologic trends to help estimate future surges of COVID-19 cases and potential future outbreaks of a pandemic among racial/ethnic groups. ", doi="10.2196/30371", url="https://formative.jmir.org/2022/5/e30371", url="http://www.ncbi.nlm.nih.gov/pubmed/35537056" } @Article{info:doi/10.2196/32543, author="Hussain, Zain and Sheikh, Zakariya and Tahir, Ahsen and Dashtipour, Kia and Gogate, Mandar and Sheikh, Aziz and Hussain, Amir", title="Artificial Intelligence--Enabled Social Media Analysis for Pharmacovigilance of COVID-19 Vaccinations in the United Kingdom: Observational Study", journal="JMIR Public Health Surveill", year="2022", month="May", day="27", volume="8", number="5", pages="e32543", keywords="COVID-19", keywords="artificial intelligence", keywords="deep learning", keywords="Facebook", keywords="health informatics", keywords="natural language processing", keywords="public health", keywords="sentiment analysis", keywords="social media", keywords="Twitter", keywords="infodemiology", keywords="vaccination", abstract="Background: ?The rollout of vaccines for COVID-19 in the United Kingdom started in December 2020. Uptake has been high, and there has been a subsequent reduction in infections, hospitalizations, and deaths among vaccinated individuals. However, vaccine hesitancy remains a concern, in particular relating to adverse effects following immunization (AEFIs). Social media analysis has the potential to inform policy makers about AEFIs being discussed by the public as well as public attitudes toward the national immunization campaign. Objective: ?We sought to assess the frequency and nature of AEFI-related mentions on social media in the United Kingdom and to provide insights on public sentiments toward COVID-19 vaccines. Methods: ?We extracted and analyzed over 121,406 relevant Twitter and Facebook posts, from December 8, 2020, to April 30, 2021. These were thematically filtered using a 2-step approach, initially using COVID-19--related keywords and then using vaccine- and manufacturer-related keywords. We identified AEFI-related keywords and modeled their word frequency to monitor their trends over 2-week periods. We also adapted and utilized our recently developed hybrid ensemble model, which combines state-of-the-art lexicon rule--based and deep learning--based approaches, to analyze sentiment trends relating to the main vaccines available in the United Kingdom. Results: ?Our COVID-19 AEFI search strategy identified 46,762 unique Facebook posts by 14,346 users and 74,644 tweets (excluding retweets) by 36,446 users over the 4-month period. We identified an increasing trend in the number of mentions for each AEFI on social media over the study period. The most frequent AEFI mentions were found to be symptoms related to appetite (n=79,132, 14\%), allergy (n=53,924, 9\%), injection site (n=56,152, 10\%), and clots (n=43,907, 8\%). We also found some rarely reported AEFIs such as Bell palsy (n=11,909, 2\%) and Guillain-Barre syndrome (n=9576, 2\%) being discussed as frequently as more well-known side effects like headache (n=10,641, 2\%), fever (n=12,707, 2\%), and diarrhea (n=16,559, 3\%). Overall, we found public sentiment toward vaccines and their manufacturers to be largely positive (58\%), with a near equal split between negative (22\%) and neutral (19\%) sentiments. The sentiment trend was relatively steady over time and had minor variations, likely based on political and regulatory announcements and debates. Conclusions: ?The most frequently discussed COVID-19 AEFIs on social media were found to be broadly consistent with those reported in the literature and by government pharmacovigilance. We also detected potential safety signals from our analysis that have been detected elsewhere and are currently being investigated. As such, we believe our findings support the use of social media analysis to provide a complementary data source to conventional knowledge sources being used for pharmacovigilance purposes. ", doi="10.2196/32543", url="https://publichealth.jmir.org/2022/5/e32543", url="http://www.ncbi.nlm.nih.gov/pubmed/35144240" } @Article{info:doi/10.2196/34828, author="Kesler, R. Shelli and Henneghan, M. Ashley and Thurman, Whitney and Rao, Vikram", title="Identifying Themes for Assessing Cancer-Related Cognitive Impairment: Topic Modeling and Qualitative Content Analysis of Public Online Comments", journal="JMIR Cancer", year="2022", month="May", day="25", volume="8", number="2", pages="e34828", keywords="cognitive", keywords="natural language processing", keywords="cancer", keywords="oncology", abstract="Background: Cancer-related cognitive impairment (CRCI) is a common and significant adverse effect of cancer and its therapies. However, its definition and assessment remain difficult due to limitations of currently available measurement tools. Objective: This study aims to evaluate qualitative themes related to the cognitive effects of cancer to help guide development of assessments that are more specific than what is currently available. Methods: We applied topic modeling and inductive qualitative content analysis to 145 public online comments related to cognitive effects of cancer. Results: Topic modeling revealed 2 latent topics that we interpreted as representing internal and external factors related to cognitive effects. These findings lead us to hypothesize regarding the potential contribution of locus of control to CRCI. Content analysis suggested several major themes including symptoms, emotional/psychological impacts, coping, ``chemobrain'' is real, change over time, and function. There was some conceptual overlap between the 2 methods regarding internal and external factors related to patient experiences of cognitive effects. Conclusions: Our findings indicate that coping mechanisms and locus of control may be important themes to include in assessments of CRCI. Future directions in this field include prospective acquisition of free-text responses to guide development of assessments that are more sensitive and specific to cognitive function in patients with cancer. ", doi="10.2196/34828", url="https://cancer.jmir.org/2022/2/e34828", url="http://www.ncbi.nlm.nih.gov/pubmed/35612878" } @Article{info:doi/10.2196/37519, author="Lotto, Matheus and S{\'a} Menezes, Tamires and Zakir Hussain, Irfhana and Tsao, Shu-Feng and Ahmad Butt, Zahid and P Morita, Plinio and Cruvinel, Thiago", title="Characterization of False or Misleading Fluoride Content on Instagram: Infodemiology Study", journal="J Med Internet Res", year="2022", month="May", day="19", volume="24", number="5", pages="e37519", keywords="eHealth", keywords="fluorides", keywords="infodemiology", keywords="information seeking behavior", keywords="internet", keywords="misinformation", keywords="social media", keywords="infoveillance", keywords="health outcome", keywords="dental caries", keywords="health information", keywords="dental health", abstract="Background: Online false or misleading oral health--related content has been propagated on social media to deceive people against fluoride's economic and health benefits to prevent dental caries. Objective: The aim of this study was to characterize the false or misleading fluoride-related content on Instagram. Methods: A total of 3863 posts ranked by users' total interaction and published between August 2016 and August 2021 were retrieved by CrowdTangle, of which 641 were screened to obtain 500 final posts. Subsequently, two independent investigators analyzed posts qualitatively to define their authors' interests, profile characteristics, content type, and sentiment. Latent Dirichlet allocation analysis topic modeling was then applied to find salient terms and topics related to false or misleading content, and their similarity was calculated through an intertopic distance map. Data were evaluated by descriptive analysis, the Mann-Whitney U test, the Cramer V test, and multiple logistic regression models. Results: Most of the posts were categorized as misinformation and political misinformation. The overperforming score was positively associated with older messages (odds ratio [OR]=3.293, P<.001) and professional/political misinformation (OR=1.944, P=.05). In this context, time from publication, negative/neutral sentiment, author's profile linked to business/dental office/news agency, and social and political interests were related to the increment of performance of messages. Although political misinformation with negative/neutral sentiments was typically published by regular users, misinformation was linked to positive commercial posts. Overall messages focused on improving oral health habits, side effects, dentifrice containing natural ingredients, and fluoride-free products propaganda. Conclusions: False or misleading fluoride-related content found on Instagram was predominantly produced by regular users motivated by social, psychological, and/or financial interests. However, higher engagement and spreading metrics were associated with political misinformation. Most of the posts were related to the toxicity of fluoridated water and products frequently motivated by financial interests. ", doi="10.2196/37519", url="https://www.jmir.org/2022/5/e37519", url="http://www.ncbi.nlm.nih.gov/pubmed/35588055" } @Article{info:doi/10.2196/29183, author="Lloret-Pineda, Amanda and He, Yuelu and Haro, Maria Josep and Crist{\'o}bal-Narv{\'a}ez, Paula", title="Types of Racism and Twitter Users' Responses Amid the COVID-19 Outbreak: Content Analysis", journal="JMIR Form Res", year="2022", month="May", day="19", volume="6", number="5", pages="e29183", keywords="COVID-19", keywords="racism", keywords="Chinese", keywords="advocacy", keywords="Twitter", abstract="Background: When the first COVID-19 cases were noticed in China, many racist comments against Chinese individuals spread. As there is a huge need to better comprehend why all of these targeted comments and opinions developed specifically at the start of the outbreak, we sought to carefully examine racism and advocacy efforts on Twitter in the first quarter of 2020 (January 15 to March 3, 2020). Objective: The first research question aimed to understand the main type of racism displayed on Twitter during the first quarter of 2020. The second research question focused on evaluating Twitter users' positive and negative responses regarding racism toward Chinese individuals. Methods: Content analysis of tweets was utilized to address the two research questions. Using the NCapture browser link and NVivo software, tweets in English and Spanish were pulled from the Twitter data stream from January 15 to March 3, 2020. A total of 19,150 tweets were captured using the advanced Twitter search engine with the keywords and hashtags \#nosoyunvirus, \#imNotAVirus, \#ChineseDon'tComeToJapan, \#racism, ``No soy un virus,'' and ``Racismo Coronavirus.'' After cleaning the data, a total of 402 tweets were codified and analyzed. Results: The data confirmed clear sentiments of racism against Chinese individuals during the first quarter of 2020. The tweets displayed individual, cultural, and institutional racism. Individual racism was the most commonly reported form of racism, specifically displaying physical and verbal aggression. As a form of resistance, Twitter users created spaces for advocacy and activism. The hashtag ``I am not a virus'' helped to break stereotypes, prejudice, and discrimination on Twitter. Conclusions: Advocacy efforts were enormous both inside and outside the Chinese community; an allyship sentiment was fostered by some white users, and an identification with the oppression experienced by the Chinese population was expressed in the Black and Muslim worldwide communities. Activism through social media manifested through art, food sharing, and community support. ", doi="10.2196/29183", url="https://formative.jmir.org/2022/5/e29183", url="http://www.ncbi.nlm.nih.gov/pubmed/35446780" } @Article{info:doi/10.2196/38340, author="Basch, H. Corey and Donelle, Lorie and Fera, Joseph and Jaime, Christie", title="Deconstructing TikTok Videos on Mental Health: Cross-sectional, Descriptive Content Analysis", journal="JMIR Form Res", year="2022", month="May", day="19", volume="6", number="5", pages="e38340", keywords="TikTok", keywords="mental health", keywords="adolescent", keywords="social media", keywords="short video apps", keywords="content analysis", keywords="digital health", keywords="online health", keywords="visual media", keywords="descriptive content analysis", keywords="mental distress", keywords="health professional", keywords="health care professional", abstract="Background: Social media platforms that are based on the creation of visual media, such as TikTok, are increasingly popular with adolescents. Online social media networks provide valuable opportunities to connect with each other to share experiences and strategies for health and wellness. Objective: The aim of this study was to describe the content of the hashtag \#mentalhealth on TikTok. Methods: This cross-sectional, descriptive content analysis study included 100 videos with the hashtag \#mentalhealth on TikTok. All videos that included the hashtag \#mentalhealth were analyzed and coded for the presence of content categories. Additionally, the comments to each video were viewed and coded for content in the following themes: offering support or validation; mentioning experience with suicide or suicidal ideation; mentioning experience with self-harm; describing an experience with hospitalization for mental health issues; describing other mental health issues; and sharing coping strategies, experiences of healing, or ways to feel better. Results: Collectively, the 100 videos studied received 1,354,100,000 views; 266,900,000 likes; and 2,515,954 comments. On average, each video received 13,406,930.69 (SD 8,728,095.52) views; 2,657,425.74 (SD 1,449,920.45) likes; and 24,910.44 (SD 21,035.06) comments. The only content category observed in most (51/100, 51\%) of the videos included in the sample was ``general mental health.'' The remaining content categories appeared in less than 50\% of the sample. In total, 32\% (32/100) of the videos sampled received more than the overall average number of likes (ie, more that 2.67 million likes). Among these 32 videos, 23 (72\%) included comments offering support or validation and 20 (62\%) included comments that described other mental health issues or struggles. Conclusions: With over 1 billion cumulative views, almost half of the assessed TikTok videos included in this study reported or expressed symptoms of mental distress. Future research should focus on the potential role of intervention by health care professionals on social media. ", doi="10.2196/38340", url="https://formative.jmir.org/2022/5/e38340", url="http://www.ncbi.nlm.nih.gov/pubmed/35588057" } @Article{info:doi/10.2196/28063, author="Friedman, J. Vanessa and Wright, C. Cassandra J. and Molenaar, Annika and McCaffrey, Tracy and Brennan, Linda and Lim, C. Megan S.", title="The Use of Social Media as a Persuasive Platform to Facilitate Nutrition and Health Behavior Change in Young Adults: Web-Based Conversation Study", journal="J Med Internet Res", year="2022", month="May", day="18", volume="24", number="5", pages="e28063", keywords="young adults", keywords="nutrition", keywords="physical activity", keywords="mental health", keywords="social media", keywords="qualitative methods", keywords="health promotion", abstract="Background: Globally, suboptimal dietary choices are a leading cause of noncommunicable diseases. Evidence for effective interventions to address these behaviors, particularly in young adults, is limited. Given the substantial time young adults spend in using social media, there is interest in understanding the current and potential role of these platforms in shaping dietary behavior. Objective: This study aims to explore the influence of social media on young adults' dietary behaviors. Methods: We recruited 234 young adults aged 18-24 years and living in Australia, using market and social research panels. We applied a digital ethnography approach to collect data from web-based conversations in a series of forums, where participants responded to different health-themed questions related to health behavior change and persuasion on social media. We conducted a qualitative thematic analysis. Results: Participants described how social media influenced their decisions to change their health behaviors. Access to social support and health information through web-based communities was juxtaposed with exposure to highly persuasive fast-food advertisements. Some participants expressed that exposure to web-based health-focused content induced feelings of guilt about their behavior, which was more prominent among women. Fast-food advertisements were discussed as a contributor to poor health behaviors and indicated as a major barrier to change. Conclusions: Young adults reported that social media is highly persuasive toward dietary behavior through different pathways of social influence. This suggests that social norms on the web are an important aspect of changing young adults' health behaviors. The commercialization of social media also encourages poor health behaviors, largely through fast-food advertisements. Future social media--delivered dietary interventions should acknowledge the social and environmental factors that challenge the ability of young adults to make individual health behavior improvements. Care should also be taken to ensure that future interventions do not further elicit guilt in a way that contributes to poor mental health within this community. ", doi="10.2196/28063", url="https://www.jmir.org/2022/5/e28063", url="http://www.ncbi.nlm.nih.gov/pubmed/35583920" } @Article{info:doi/10.2196/31800, author="Garc{\'i}a-Mart{\'i}nez, Claudia and Oliv{\'a}n-Bl{\'a}zquez, B{\'a}rbara and Fabra, Javier and Mart{\'i}nez-Mart{\'i}nez, Bel{\'e}n Ana and P{\'e}rez-Yus, Cruz Mar{\'i}a and L{\'o}pez-Del-Hoyo, Yolanda", title="Exploring the Risk of Suicide in Real Time on Spanish Twitter: Observational Study", journal="JMIR Public Health Surveill", year="2022", month="May", day="17", volume="8", number="5", pages="e31800", keywords="suicide", keywords="prevention", keywords="social media", keywords="Twitter", keywords="emotional analysis", keywords="eHealth", keywords="big data", keywords="content analysis", keywords="emotional content", keywords="risk factors", keywords="mental health", keywords="public health", keywords="suicide prevention", abstract="Background: Social media is now a common context wherein people express their feelings in real time. These platforms are increasingly showing their potential to detect the mental health status of the population. Suicide prevention is a global health priority and efforts toward early detection are starting to develop, although there is a need for more robust research. Objective: We aimed to explore the emotional content of Twitter posts in Spanish and their relationships with severity of the risk of suicide at the time of writing the tweet. Methods: Tweets containing a specific lexicon relating to suicide were filtered through Twitter's public application programming interface. Expert psychologists were trained to independently evaluate these tweets. Each tweet was evaluated by 3 experts. Tweets were filtered by experts according to their relevance to the risk of suicide. In the tweets, the experts evaluated: (1) the severity of the general risk of suicide and the risk of suicide at the time of writing the tweet (2) the emotional valence and intensity of 5 basic emotions; (3) relevant personality traits; and (4) other relevant risk variables such as helplessness, desire to escape, perceived social support, and intensity of suicidal ideation. Correlation and multivariate analyses were performed. Results: Of 2509 tweets, 8.61\% (n=216) were considered to indicate suicidality by most experts. Severity of the risk of suicide at the time was correlated with sadness ($\rho$=0.266; P<.001), joy ($\rho$=--0.234; P=.001), general risk ($\rho$=0.908; P<.001), and intensity of suicidal ideation ($\rho$=0.766; P<.001). The severity of risk at the time of the tweet was significantly higher in people who expressed feelings of defeat and rejection (P=.003), a desire to escape (P<.001), a lack of social support (P=.03), helplessness (P=.001), and daily recurrent thoughts (P=.007). In the multivariate analysis, the intensity of suicide ideation was a predictor for the severity of suicidal risk at the time ($\beta$=0.311; P=.001), as well as being a predictor for fear ($\beta$=--0.009; P=.01) and emotional valence ($\beta$=0.007; P=.009). The model explained 75\% of the variance. Conclusions: These findings suggest that it is possible to identify emotional content and other risk factors in suicidal tweets with a Spanish sample. Emotional analysis and, in particular, the detection of emotional variations may be key for real-time suicide prevention through social media. ", doi="10.2196/31800", url="https://publichealth.jmir.org/2022/5/e31800", url="http://www.ncbi.nlm.nih.gov/pubmed/35579921" } @Article{info:doi/10.2196/37831, author="Sauvayre, Romy and Vernier, Jessica and Chauvi{\`e}re, C{\'e}dric", title="An Analysis of French-Language Tweets About COVID-19 Vaccines: Supervised Learning Approach", journal="JMIR Med Inform", year="2022", month="May", day="17", volume="10", number="5", pages="e37831", keywords="social media", keywords="natural language processing", keywords="public health", keywords="vaccine", keywords="machine learning", keywords="CamemBERT language model", keywords="method", keywords="epistemology", keywords="COVID-19", keywords="disinformation", keywords="language model", abstract="Background: As the COVID-19 pandemic progressed, disinformation, fake news, and conspiracy theories spread through many parts of society. However, the disinformation spreading through social media is, according to the literature, one of the causes of increased COVID-19 vaccine hesitancy. In this context, the analysis of social media posts is particularly important, but the large amount of data exchanged on social media platforms requires specific methods. This is why machine learning and natural language processing models are increasingly applied to social media data. Objective: The aim of this study is to examine the capability of the CamemBERT French-language model to faithfully predict the elaborated categories, with the knowledge that tweets about vaccination are often ambiguous, sarcastic, or irrelevant to the studied topic. Methods: A total of 901,908 unique French-language tweets related to vaccination published between July 12, 2021, and August 11, 2021, were extracted using Twitter's application programming interface (version 2; Twitter Inc). Approximately 2000 randomly selected tweets were labeled with 2 types of categorizations: (1) arguments for (pros) or against (cons) vaccination (health measures included) and (2) type of content (scientific, political, social, or vaccination status). The CamemBERT model was fine-tuned and tested for the classification of French-language tweets. The model's performance was assessed by computing the F1-score, and confusion matrices were obtained. Results: The accuracy of the applied machine learning reached up to 70.6\% for the first classification (pro and con tweets) and up to 90\% for the second classification (scientific and political tweets). Furthermore, a tweet was 1.86 times more likely to be incorrectly classified by the model if it contained fewer than 170 characters (odds ratio 1.86; 95\% CI 1.20-2.86). Conclusions: The accuracy of the model is affected by the classification chosen and the topic of the message examined. When the vaccine debate is jostled by contested political decisions, tweet content becomes so heterogeneous that the accuracy of the model drops for less differentiated classes. However, our tests showed that it is possible to improve the accuracy by selecting tweets using a new method based on tweet length. ", doi="10.2196/37831", url="https://medinform.jmir.org/2022/5/e37831", url="http://www.ncbi.nlm.nih.gov/pubmed/35512274" } @Article{info:doi/10.2196/36835, author="Gordejeva, Jelizaveta and Zowalla, Richard and Pobiruchin, Monika and Wiesner, Martin", title="Readability of English, German, and Russian Disease-Related Wikipedia Pages: Automated Computational Analysis", journal="J Med Internet Res", year="2022", month="May", day="16", volume="24", number="5", pages="e36835", keywords="readability", keywords="health literacy", keywords="health education", keywords="Wikipedia", abstract="Background: Wikipedia is a popular encyclopedia for health- and disease-related information in which patients seek advice and guidance on the web. Yet, Wikipedia articles can be unsuitable as patient education materials, as investigated in previous studies that analyzed specific diseases or medical topics with a comparatively small sample size. Currently, no data are available on the average readability levels of all disease-related Wikipedia pages for the different localizations of this particular encyclopedia. Objective: This study aimed to analyze disease-related Wikipedia pages written in English, German, and Russian using well-established readability metrics for each language. Methods: Wikipedia database snapshots and Wikidata metadata were chosen as resources for data collection. Disease-related articles were retrieved separately for English, German, and Russian starting with the main concept of Human Diseases and Disorders (German: Krankheit; Russian: ??????????? ????????). In the case of existence, the corresponding International Classification of Diseases, Tenth Revision (ICD-10), codes were retrieved for each article. Next, the raw texts were extracted and readability metrics were computed. Results: The number of articles included in this study for English, German, and Russian Wikipedia was n=6127, n=6024, and n=3314, respectively. Most disease-related articles had a Flesch Reading Ease (FRE) score <50.00, signaling difficult or very difficult educational material (English: 5937/6125, 96.93\%; German: 6004/6022, 99.7\%; Russian: 2647/3313, 79.9\%). In total, 70\% (7/10) of the analyzed articles could be assigned an ICD-10 code with certainty (English: 4235/6127, 69.12\%; German: 4625/6024, 76.78\%; Russian: 2316/3314, 69.89\%). For articles with ICD-10 codes, the mean FRE scores were 28.69 (SD 11.00), 20.33 (SD 9.98), and 38.54 (SD 13.51) for English, German, and Russian, respectively. A total of 9 English ICD-10 chapters (11 German and 10 Russian) showed significant differences: chapter F (FRE 23.88, SD 9.95; P<.001), chapter E (FRE 25.14, SD 9.88; P<.001), chapter H (FRE 30.04, SD 10.57; P=.049), chapter I (FRE 30.05, SD 9.07; P=.04), chapter M (FRE 31.17, 11.94; P<.001), chapter T (FRE 32.06, SD 10.51; P=.001), chapter A (FRE 32.63, SD 9.25; P<.001), chapter B (FRE 33.24, SD 9.07; P<.001), and chapter S (FRE 39.02, SD 8.22; P<.001). Conclusions: Disease-related English, German, and Russian Wikipedia articles cannot be recommended as patient education materials because a major fraction is difficult or very difficult to read. The authors of Wikipedia pages should carefully revise existing text materials for readers with a specific interest in a disease or its associated symptoms. Special attention should be given to articles on mental, behavioral, and neurodevelopmental disorders (ICD-10 chapter F) because these articles were most difficult to read in comparison with other ICD-10 chapters. Wikipedia readers should be supported by editors providing a short and easy-to-read summary for each article. ", doi="10.2196/36835", url="https://www.jmir.org/2022/5/e36835", url="http://www.ncbi.nlm.nih.gov/pubmed/35576562" } @Article{info:doi/10.2196/36215, author="Westmaas, Lee J. and Masters, Matthew and Bandi, Priti and Majmundar, Anuja and Asare, Samuel and Diver, Ryan W.", title="COVID-19 and Tweets About Quitting Cigarette Smoking: Topic Model Analysis of Twitter Posts 2018-2020", journal="JMIR Infodemiology", year="2022", month="May", day="16", volume="2", number="1", pages="e36215", keywords="COVID-19", keywords="machine learning", keywords="pandemic", keywords="quit smoking", keywords="topic model analysis", keywords="Twitter", keywords="social media", keywords="smoking cessation", keywords="latent Dirichlet allocation", keywords="tweet", keywords="public health", abstract="Background: The risk of infection and severity of illness by SARS-CoV-2 infection is elevated for people who smoke cigarettes and may motivate quitting. Organic public conversations on Twitter about quitting smoking could provide insight into quitting motivations or behaviors associated with the pandemic. Objective: This study explored key topics of conversation about quitting cigarette smoking and examined their trajectory during 2018-2020. Methods: Topic model analysis with latent Dirichlet allocation (LDA) identified themes in US tweets with the term ``quit smoking.'' The model was trained on posts from 2018 and was then applied to tweets posted in 2019 and 2020. Analysis of variance and follow-up pairwise tests were used to compare the daily frequency of tweets within and across years by quarter. Results: The mean numbers of daily tweets on quitting smoking in 2018, 2019, and 2020 were 133 (SD 36.2), 145 (SD 69.4), and 127 (SD 32.6), respectively. Six topics were extracted: (1) need to quit, (2) personal experiences, (3) electronic cigarettes (e-cigarettes), (4) advice/success, (5) quitting as a component of general health behavior change, and (6) clinics/services. Overall, the pandemic was not associated with changes in posts about quitting; instead, New Year's resolutions and the 2019 e-cigarette or vaping use--associated lung injury (EVALI) epidemic were more plausible explanations for observed changes within and across years. Fewer second-quarter posts in 2020 for the topic e-cigarettes may reflect lower pandemic-related quitting interest, whereas fourth-quarter increases in 2020 for other topics pointed to a late-year upswing. Conclusions: Twitter posts suggest that the pandemic did not generate greater interest in quitting smoking, but possibly a decrease in motivation when the rate of infections was increasing in the second quarter of 2020. Public health authorities may wish to craft messages for specific Twitter audiences (eg, using hashtags) to motivate quitting during pandemics. ", doi="10.2196/36215", url="https://infodemiology.jmir.org/2022/1/e36215", url="http://www.ncbi.nlm.nih.gov/pubmed/35611092" } @Article{info:doi/10.2196/37546, author="Chidambaram, Swathikan and Maheswaran, Yathukulan and Chan, Calvin and Hanna, Lydia and Ashrafian, Hutan and Markar, R. Sheraz and Sounderajah, Viknesh and Alverdy, C. John and Darzi, Ara", title="Misinformation About the Human Gut Microbiome in YouTube Videos: Cross-sectional Study", journal="JMIR Form Res", year="2022", month="May", day="16", volume="6", number="5", pages="e37546", keywords="microbiome", keywords="social media", keywords="YouTube", keywords="misinformation", keywords="content analysis", keywords="gut health", keywords="public", abstract="Background: Social media platforms such as YouTube are integral tools for disseminating information about health and wellness to the public. However, anecdotal reports have cited that the human gut microbiome has been a particular focus of dubious, misleading, and, on occasion, harmful media content. Despite these claims, there have been no published studies investigating this phenomenon within popular social media platforms. Objective: The aim of this study is to (1) evaluate the accuracy and reliability of the content in YouTube videos related to the human gut microbiome and (2) investigate the correlation between content engagement metrics and video quality, as defined by validated criteria. Methods: In this cross-sectional study, videos about the human gut microbiome were searched for on the United Kingdom version of YouTube on September 20, 2021. The 600 most-viewed videos were extracted and screened for relevance. The contents and characteristics of the videos were extracted and independently rated using the DISCERN quality criteria by 2 researchers. Results: Overall, 319 videos accounting for 62,354,628 views were included. Of the 319 videos, 73.4\% (n=234) were produced in North America and 78.7\% (n=251) were uploaded between 2019 and 2021. A total of 41.1\% (131/319) of videos were produced by nonprofit organizations. Of the videos, 16.3\% (52/319) included an advertisement for a product or promoted a health-related intervention for financial purposes. Videos by nonmedical education creators had the highest total and preferred viewership. Daily viewership was the highest for videos by internet media sources. The average DISCERN and Health on the Net Foundation Code of Conduct scores were 49.5 (SE 0.68) out of 80 and 5.05 (SE 2.52) out of 8, respectively. DISCERN scores for videos by medical professionals (mean 53.2, SE 0.17) were significantly higher than for videos by independent content creators (mean 39.1, SE 5.58; P<.001). Videos including promotional materials had significantly lower DISCERN scores than videos without any advertisements or product promotion (P<.001). There was no correlation between DISCERN scores and total viewership, daily viewership, or preferred viewership (number of likes). Conclusions: The overall quality and reliability of information about the human gut microbiome on YouTube is generally poor. Moreover, there was no correlation between the quality of a video and the level of public engagement. The significant disconnect between reliable sources of information and the public suggests that there is an immediate need for cross-sector initiatives to safeguard vulnerable viewers from the potentially harmful effects of misinformation. ", doi="10.2196/37546", url="https://formative.jmir.org/2022/5/e37546", url="http://www.ncbi.nlm.nih.gov/pubmed/35576578" } @Article{info:doi/10.2196/35115, author="Portelli, Beatrice and Scaboro, Simone and Tonino, Roberto and Chersoni, Emmanuele and Santus, Enrico and Serra, Giuseppe", title="Monitoring User Opinions and Side Effects on COVID-19 Vaccines in the Twittersphere: Infodemiology Study of Tweets", journal="J Med Internet Res", year="2022", month="May", day="13", volume="24", number="5", pages="e35115", keywords="adverse drug events", keywords="COVID-19", keywords="digital pharmacovigilance", keywords="opinion mining", keywords="vaccines", keywords="social media", keywords="machine learning", keywords="deep learning", keywords="learning models", keywords="sentiment analysis", keywords="Twitter analysis", keywords="Twitter", keywords="web portal", keywords="public health", abstract="Background: In the current phase of the COVID-19 pandemic, we are witnessing the most massive vaccine rollout in human history. Like any other drug, vaccines may cause unexpected side effects, which need to be investigated in a timely manner to minimize harm in the population. If not properly dealt with, side effects may also impact public trust in the vaccination campaigns carried out by national governments. Objective: Monitoring social media for the early identification of side effects, and understanding the public opinion on the vaccines are of paramount importance to ensure a successful and harmless rollout. The objective of this study was to create a web portal to monitor the opinion of social media users on COVID-19 vaccines, which can offer a tool for journalists, scientists, and users alike to visualize how the general public is reacting to the vaccination campaign. Methods: We developed a tool to analyze the public opinion on COVID-19 vaccines from Twitter, exploiting, among other techniques, a state-of-the-art system for the identification of adverse drug events on social media; natural language processing models for sentiment analysis; statistical tools; and open-source databases to visualize the trending hashtags, news articles, and their factuality. All modules of the system are displayed through an open web portal. Results: A set of 650,000 tweets was collected and analyzed in an ongoing process that was initiated in December 2020. The results of the analysis are made public on a web portal (updated daily), together with the processing tools and data. The data provide insights on public opinion about the vaccines and its change over time. For example, users show a high tendency to only share news from reliable sources when discussing COVID-19 vaccines (98\% of the shared URLs). The general sentiment of Twitter users toward the vaccines is negative/neutral; however, the system is able to record fluctuations in the attitude toward specific vaccines in correspondence with specific events (eg, news about new outbreaks). The data also show how news coverage had a high impact on the set of discussed topics. To further investigate this point, we performed a more in-depth analysis of the data regarding the AstraZeneca vaccine. We observed how media coverage of blood clot--related side effects suddenly shifted the topic of public discussions regarding both the AstraZeneca and other vaccines. This became particularly evident when visualizing the most frequently discussed symptoms for the vaccines and comparing them month by month. Conclusions: We present a tool connected with a web portal to monitor and display some key aspects of the public's reaction to COVID-19 vaccines. The system also provides an overview of the opinions of the Twittersphere through graphic representations, offering a tool for the extraction of suspected adverse events from tweets with a deep learning model. ", doi="10.2196/35115", url="https://www.jmir.org/2022/5/e35115", url="http://www.ncbi.nlm.nih.gov/pubmed/35446781" } @Article{info:doi/10.2196/34073, author="Faust, Guy and Booth, Alison and Merinopoulou, Evie and Halhol, Sonia and Tosar, Heena and Nawaz, Amir and Szlachetka, Magdalena and Chiu, Gavin", title="The Experiences of Patients With Adjuvant and Metastatic Melanoma Using Disease-Specific Social Media Communities in the Advent of Novel Therapies (Excite Project): Social Media Listening Study", journal="JMIR Cancer", year="2022", month="May", day="13", volume="8", number="2", pages="e34073", keywords="health-related social media", keywords="patient-centric", keywords="melanoma", keywords="adjuvant", keywords="metastatic", keywords="immunotherapy", keywords="targeted therapy", keywords="natural language processing", keywords="patient experience", keywords="cancer", keywords="cancer therapy", keywords="patient perspective", keywords="social media", keywords="caregiver experience", abstract="Background: Immunotherapy and targeted therapy treatments are novel treatments available for patients with metastatic and adjuvant melanoma. As recently approved treatments, information surrounding the patients' and caregivers' experience with these therapies, perceptions of treatments, and the effect the treatments have on their day-to-day life are lacking. Such insights would be valuable for any future decision-making with regard to treatment options. Objective: This study aims to use health-related social media data to understand the experience of patients with adjuvant and metastatic melanoma who are receiving either immunotherapy or targeted therapies. This study also included caregivers' perspectives. Methods: Publicly available social media forum posts by patients with self-reported adjuvant or metastatic melanoma (and their caregivers) between January 2014 to October 2019 were programmatically extracted, deidentified, cleaned, and analyzed using a combination of natural language processing and qualitative data analyses. This study identified spontaneously reported symptoms and their impacts, symptom duration, and the impact of treatment for both treatment groups. Results: Overall, 1037 users (9023 posts) and 114 users (442 posts) were included in the metastatic group and adjuvant group, respectively. The most identified symptoms in both groups were fatigue, pain, or exanthema (identified in 5\%-43\% of patients dependent on the treatment group). Symptom impacts reported by both groups were physical impacts, impacts on family, and impacts on work. Positive treatment impacts were reported in both groups and covered the areas of work, social and family life, and general health and quality of life. Conclusions: This study explored health-related social media to better understand the experience and perspectives of patients with melanoma receiving immunotherapy or targeted therapy treatments as well as the experience of their caregivers. This exploratory work uncovered the most discussed concerns among patients and caregivers on the forums including symptoms and their impacts, thus contributing to a deeper understanding of the patient/caregiver experience. ", doi="10.2196/34073", url="https://cancer.jmir.org/2022/2/e34073", url="http://www.ncbi.nlm.nih.gov/pubmed/35559986" } @Article{info:doi/10.2196/33184, author="Guendelman, Sylvia and Pleasants, Elizabeth and Cheshire, Coye and Kong, Ashley", title="Exploring Google Searches for Out-of-Clinic Medication Abortion in the United States During 2020: Infodemiology Approach Using Multiple Samples", journal="JMIR Infodemiology", year="2022", month="May", day="12", volume="2", number="1", pages="e33184", keywords="abortion", keywords="abortion access", keywords="internet", keywords="online information", keywords="Google searches", keywords="infodemiology", abstract="Background: As access barriers to in-person abortion care increase due to legal restrictions and COVID-19--related disruptions, individuals may be turning to the internet for information and services on out-of-clinic medication abortions. Google searches allow us to explore timely population-level interest in this topic and assess its implications. Objective: We examined the extent to which people searched for out-of-clinic medication abortions in the United States in 2020 through 3 initial search terms: home abortion, self abortion, and buy abortion pill online. Methods: Using the Google Trends website, we estimated the relative search index (RSI)---a comparative measure of search popularity---for each initial search term and determined trends and its peak value between January 1, 2020, and January 1, 2021. RSI scores also helped to identify the 10 states where these searches were most popular. We developed a master list of top search queries for each of the initial search terms using the Google Trends application programming interface (API). We estimated the relative search volume (RSV)---the search volume of each query relative to other associated terms---for each of the top queries using the Google Health Trends API. We calculated average RSIs and RSVs from multiple samples to account for low-frequency data. Using the Custom Search API, we determined the top webpages presented to people searching for each of the initial search terms, contextualizing the information found when searching them on Google. Results: Searches for home abortion had average RSIs that were 3 times higher than self abortion and almost 4 times higher than buy abortion pill online. Interest in home abortion peaked in November 2020, during the third pandemic wave, at a time when providers could dispense medication abortion using telemedicine and by mail. Home abortion was most frequently queried by searching for Planned Parenthood, abortion pill, and abortion clinic, presumably denoting varying degrees of clinical support. Consistently lower search popularity for self abortion and buy abortion pill online reflect less population interest in mostly or completely self-managed out-of-clinic abortions. We observed the highest interest for home abortion and self abortion in states hostile to abortion, suggesting that state restrictions encourage these online searches. Top webpages provided limited evidence-based clinical content on self-management of abortions, and several antiabortion sites presented health-related disinformation. Conclusions: During the pandemic in the United States, there has been considerably more interest in home abortions than in minimally or nonclinically supported self-abortions. While our study was mainly descriptive, showing how infrequent abortion-related search data can be analyzed through multiple resampling, future studies should explore correlations between the keywords denoting interest in out-of-clinic abortion and abortion care measures and test models that allow for improved monitoring and surveillance of abortion concerns in our rapidly evolving policy context. ", doi="10.2196/33184", url="https://infodemiology.jmir.org/2022/1/e33184", url="http://www.ncbi.nlm.nih.gov/pubmed/37113801" } @Article{info:doi/10.2196/32335, author="Niu, Qian and Liu, Junyu and Kato, Masaya and Shinohara, Yuki and Matsumura, Natsuki and Aoyama, Tomoki and Nagai-Tanima, Momoko", title="Public Opinion and Sentiment Before and at the Beginning of COVID-19 Vaccinations in Japan: Twitter Analysis", journal="JMIR Infodemiology", year="2022", month="May", day="9", volume="2", number="1", pages="e32335", keywords="COVID-19", keywords="Japan", keywords="vaccine", keywords="Twitter", keywords="sentiment", keywords="latent dirichlet allocation", keywords="natural language processing", abstract="Background: COVID-19 vaccines are considered one of the most effective ways for containing the COVID-19 pandemic, but Japan lagged behind other countries in vaccination in the early stages. A deeper understanding of the slow progress of vaccination in Japan can be instructive for COVID-19 booster vaccination and vaccinations during future pandemics. Objective: This retrospective study aims to analyze the slow progress of early-stage vaccination in Japan by exploring opinions and sentiment toward the COVID-19 vaccine in Japanese tweets before and at the beginning of vaccination. Methods: We collected 144,101 Japanese tweets containing COVID-19 vaccine-related keywords between August 1, 2020, and June 30, 2021. We visualized the trend of the tweets and sentiments and identified the critical events that may have triggered the surges. Correlations between sentiments and the daily infection, death, and vaccination cases were calculated. The latent dirichlet allocation model was applied to identify topics of negative tweets from the beginning of vaccination. We also conducted an analysis of vaccine brands (Pfizer, Moderna, AstraZeneca) approved in Japan. Results: The daily number of tweets continued with accelerating growth after the start of large-scale vaccinations in Japan. The sentiments of around 85\% of the tweets were neutral, and negative sentiment overwhelmed the positive sentiment in the other tweets. We identified 6 public-concerned topics related to the negative sentiment at the beginning of the vaccination process. Among the vaccines from the 3 manufacturers, the attitude toward Moderna was the most positive, and the attitude toward AstraZeneca was the most negative. Conclusions: Negative sentiment toward vaccines dominated positive sentiment in Japan, and the concerns about side effects might have outweighed fears of infection at the beginning of the vaccination process. Topic modeling on negative tweets indicated that the government and policy makers should take prompt actions in building a safe and convenient vaccine reservation and rollout system, which requires both flexibility of the medical care system and the acceleration of digitalization in Japan. The public showed different attitudes toward vaccine brands. Policy makers should provide more evidence about the effectiveness and safety of vaccines and rebut fake news to build vaccine confidence. ", doi="10.2196/32335", url="https://infodemiology.jmir.org/2022/1/e32335", url="http://www.ncbi.nlm.nih.gov/pubmed/35578643" } @Article{info:doi/10.2196/35788, author="Golder, Su and Stevens, Robin and O'Connor, Karen and James, Richard and Gonzalez-Hernandez, Graciela", title="Methods to Establish Race or Ethnicity of Twitter Users: Scoping Review", journal="J Med Internet Res", year="2022", month="Apr", day="29", volume="24", number="4", pages="e35788", keywords="twitter", keywords="social media", keywords="race", keywords="ethnicity", abstract="Background: A growing amount of health research uses social media data. Those critical of social media research often cite that it may be unrepresentative of the population; however, the suitability of social media data in digital epidemiology is more nuanced. Identifying the demographics of social media users can help establish representativeness. Objective: This study aims to identify the different approaches or combination of approaches to extract race or ethnicity from social media and report on the challenges of using these methods. Methods: We present a scoping review to identify methods used to extract the race or ethnicity of Twitter users from Twitter data sets. We searched 17 electronic databases from the date of inception to May 15, 2021, and carried out reference checking and hand searching to identify relevant studies. Sifting of each record was performed independently by at least two researchers, with any disagreement discussed. Studies were required to extract the race or ethnicity of Twitter users using either manual or computational methods or a combination of both. Results: Of the 1249 records sifted, we identified 67 (5.36\%) that met our inclusion criteria. Most studies (51/67, 76\%) have focused on US-based users and English language tweets (52/67, 78\%). A range of data was used, including Twitter profile metadata, such as names, pictures, information from bios (including self-declarations), or location or content of the tweets. A range of methodologies was used, including manual inference, linkage to census data, commercial software, language or dialect recognition, or machine learning or natural language processing. However, not all studies have evaluated these methods. Those that evaluated these methods found accuracy to vary from 45\% to 93\% with significantly lower accuracy in identifying categories of people of color. The inference of race or ethnicity raises important ethical questions, which can be exacerbated by the data and methods used. The comparative accuracies of the different methods are also largely unknown. Conclusions: There is no standard accepted approach or current guidelines for extracting or inferring the race or ethnicity of Twitter users. Social media researchers must carefully interpret race or ethnicity and not overpromise what can be achieved, as even manual screening is a subjective, imperfect method. Future research should establish the accuracy of methods to inform evidence-based best practice guidelines for social media researchers and be guided by concerns of equity and social justice. ", doi="10.2196/35788", url="https://www.jmir.org/2022/4/e35788", url="http://www.ncbi.nlm.nih.gov/pubmed/35486433" } @Article{info:doi/10.2196/35446, author="Xu, Qing and Nali, C. Matthew and McMann, Tiana and Godinez, Hector and Li, Jiawei and He, Yifan and Cai, Mingxiang and Lee, Christine and Merenda, Christine and Araojo, Richardae and Mackey, Ken Tim", title="Unsupervised Machine Learning to Detect and Characterize Barriers to Pre-exposure Prophylaxis Therapy: Multiplatform Social Media Study", journal="JMIR Infodemiology", year="2022", month="Apr", day="28", volume="2", number="1", pages="e35446", keywords="infoveillance", keywords="HIV", keywords="minority health", keywords="PrEP", keywords="social media", abstract="Background: Among racial and ethnic minority groups, the risk of HIV infection is an ongoing public health challenge. Pre-exposure prophylaxis (PrEP) is highly effective for preventing HIV when taken as prescribed. However, there is a need to understand the experiences, attitudes, and barriers of PrEP for racial and ethnic minority populations and sexual minority groups. Objective: This infodemiology study aimed to leverage big data and unsupervised machine learning to identify, characterize, and elucidate experiences and attitudes regarding perceived barriers associated with the uptake and adherence to PrEP therapy. This study also specifically examined shared experiences from racial or ethnic populations and sexual minority groups. Methods: The study used data mining approaches to collect posts from popular social media platforms such as Twitter, YouTube, Tumblr, Instagram, and Reddit. Posts were selected by filtering for keywords associated with PrEP, HIV, and approved PrEP therapies. We analyzed data using unsupervised machine learning, followed by manual annotation using a deductive coding approach to characterize PrEP and other HIV prevention--related themes discussed by users. Results: We collected 522,430 posts over a 60-day period, including 408,637 (78.22\%) tweets, 13,768 (2.63\%) YouTube comments, 8728 (1.67\%) Tumblr posts, 88,177 (16.88\%) Instagram posts, and 3120 (0.6\%) Reddit posts. After applying unsupervised machine learning and content analysis, 785 posts were identified that specifically related to barriers to PrEP, and they were grouped into three major thematic domains: provider level (13/785, 1.7\%), patient level (570/785, 72.6\%), and community level (166/785, 21.1\%). The main barriers identified in these categories included those associated with knowledge (lack of knowledge about PrEP), access issues (lack of insurance coverage, no prescription, and impact of COVID-19 pandemic), and adherence (subjective reasons for why users terminated PrEP or decided not to start PrEP, such as side effects, alternative HIV prevention measures, and social stigma). Among the 785 PrEP posts, we identified 320 (40.8\%) posts where users self-identified as racial or ethnic minority or as a sexual minority group with their specific PrEP barriers and concerns. Conclusions: Both objective and subjective reasons were identified as barriers reported by social media users when initiating, accessing, and adhering to PrEP. Though ample evidence supports PrEP as an effective HIV prevention strategy, user-generated posts nevertheless provide insights into what barriers are preventing people from broader adoption of PrEP, including topics that are specific to 2 different groups of sexual minority groups and racial and ethnic minority populations. Results have the potential to inform future health promotion and regulatory science approaches that can reach these HIV and AIDS communities that may benefit from PrEP. ", doi="10.2196/35446", url="https://infodemiology.jmir.org/2022/1/e35446", url="http://www.ncbi.nlm.nih.gov/pubmed/37113799" } @Article{info:doi/10.2196/35014, author="Kyabaggu, Ramona and Marshall, Deneice and Ebuwei, Patience and Ikenyei, Uche", title="Health Literacy, Equity, and Communication in the COVID-19 Era of Misinformation: Emergence of Health Information Professionals in Infodemic Management", journal="JMIR Infodemiology", year="2022", month="Apr", day="28", volume="2", number="1", pages="e35014", keywords="COVID-19", keywords="social media", keywords="infodemiology", keywords="infoveillance", keywords="equity", keywords="health literacy", keywords="digital literacy", keywords="health information management", keywords="pandemic", keywords="health information", keywords="public policy", keywords="infodemic", doi="10.2196/35014", url="https://infodemiology.jmir.org/2022/1/e35014", url="http://www.ncbi.nlm.nih.gov/pubmed/35529308" } @Article{info:doi/10.2196/37271, author="Han, Joseph and Kamat, Samir and Agarwal, Aneesh and O'Hagan, Ross and Tukel, Connor and Owji, Shayan and Ghalili, Sabrina and Luu, Yen and Dautriche Svidzinski, Cula and Abittan, J. Brian and Ungar, Jonathan and Gulati, Nicholas", title="Correlation Between Interest in COVID-19 Hair Loss and COVID-19 Surges: Analysis of Google Trends", journal="JMIR Dermatol", year="2022", month="Apr", day="27", volume="5", number="2", pages="e37271", keywords="COVID-19", keywords="SARS-CoV-2 virus", keywords="pandemic", keywords="hair loss", keywords="telogen effluvium", keywords="Google Trends", keywords="omicron", keywords="omicron variant", keywords="delta variant", keywords="public interest", keywords="stress", keywords="dermatology", keywords="public perception", keywords="social media", keywords="online health", keywords="digital dermatology", doi="10.2196/37271", url="https://derma.jmir.org/2022/2/e37271", url="http://www.ncbi.nlm.nih.gov/pubmed/35505684" } @Article{info:doi/10.2196/34321, author="Tahamtan, Iman and Potnis, Devendra and Mohammadi, Ehsan and Singh, Vandana and Miller, E. Laura", title="The Mutual Influence of the World Health Organization (WHO) and Twitter Users During COVID-19: Network Agenda-Setting Analysis", journal="J Med Internet Res", year="2022", month="Apr", day="26", volume="24", number="4", pages="e34321", keywords="COVID-19", keywords="agenda setting", keywords="network agenda setting", keywords="Twitter", keywords="social media", keywords="public opinion", keywords="content analysis", keywords="public health", keywords="WHO", abstract="Background: Little is known about the role of the World Health Organization (WHO) in communicating with the public on social media during a global health emergency. More specifically, there is no study about the relationship between the agendas of the WHO and Twitter users during the COVID-19 pandemic. Objective: This study utilizes the network agenda-setting model to investigate the mutual relationship between the agenda of the WHO's official Twitter account and the agenda of 7.5 million of its Twitter followers regarding COVID-19. Methods: Content analysis was applied to 7090 tweets posted by the WHO on Twitter from January 1, 2020, to July 31, 2020, to identify the topics of tweets. The quadratic assignment procedure (QAP) was used to investigate the relationship between the WHO agenda network and the agenda network of the 6 Twitter user categories, including ``health care professionals,'' ``academics,'' ``politicians,'' ``print and electronic media,'' ``legal professionals,'' and the ``private sector.'' Additionally, 98 Granger causality statistical tests were performed to determine which topic in the WHO agenda had an effect on the corresponding topic in each Twitter user category and vice versa. Results: Content analysis revealed 7 topics that reflect the WHO agenda related to the COVID-19 pandemic, including ``prevention,'' ``solidarity,'' ``charity,'' ``teamwork,'' ``ill-effect,'' ``surveillance,'' and ``credibility.'' Results of the QAP showed significant and strong correlations between the WHO agenda network and the agenda network of each Twitter user category. These results provide evidence that WHO had an overall effect on different types of Twitter users on the identified topics. For instance, the Granger causality tests indicated that the WHO tweets influenced politicians and print and electronic media about ``surveillance.'' The WHO tweets also influenced academics and the private sector about ``credibility'' and print and electronic media about ``ill-effect.'' Additionally, Twitter users affected some topics in the WHO. For instance, WHO followers affected ``charity'' and ``prevention'' in the WHO. Conclusions: This paper extends theorizing on agenda setting by providing empirical evidence that agenda-setting effects vary by topic and types of Twitter users. Although prior studies showed that network agenda setting is a ``one-way'' model, the novel findings of this research confirm a ``2-way'' or ``multiway'' effect of agenda setting on social media due to the interactions between the content creators and audiences. The WHO can determine which topics should be promoted on social media during different phases of a pandemic and collaborate with other public health gatekeepers to collectively make them salient in the public. ", doi="10.2196/34321", url="https://www.jmir.org/2022/4/e34321", url="http://www.ncbi.nlm.nih.gov/pubmed/35275836" } @Article{info:doi/10.2196/36830, author="Chen, Yen-Pin and Chen, Yi-Ying and Yang, Kai-Chou and Lai, Feipei and Huang, Chien-Hua and Chen, Yun-Nung and Tu, Yi-Chin", title="The Prevalence and Impact of Fake News on COVID-19 Vaccination in Taiwan: Retrospective Study of Digital Media", journal="J Med Internet Res", year="2022", month="Apr", day="26", volume="24", number="4", pages="e36830", keywords="misinformation", keywords="vaccine hesitancy", keywords="vaccination", keywords="infodemic", keywords="infodemiology", keywords="COVID-19", keywords="public immunity", keywords="social media", keywords="fake news", abstract="Background: Vaccination is an important intervention to prevent the incidence and spread of serious diseases. Many factors including information obtained from the internet influence individuals' decisions to vaccinate. Misinformation is a critical issue and can be hard to detect, although it can change people's minds, opinions, and decisions. The impact of misinformation on public health and vaccination hesitancy is well documented, but little research has been conducted on the relationship between the size of the population reached by misinformation and the vaccination decisions made by that population. A number of fact-checking services are available on the web, including the Islander news analysis system, a free web service that provides individuals with real-time judgment on web news. In this study, we used such services to estimate the amount of fake news available and used Google Trends levels to model the spread of fake news. We quantified this relationship using official public data on COVID-19 vaccination in Taiwan. Objective: In this study, we aimed to quantify the impact of the magnitude of the propagation of fake news on vaccination decisions. Methods: We collected public data about COVID-19 infections and vaccination from Taiwan's official website and estimated the popularity of searches using Google Trends. We indirectly collected news from 26 digital media sources, using the news database of the Islander system. This system crawls the internet in real time, analyzes the news, and stores it. The incitement and suspicion scores of the Islander system were used to objectively judge news, and a fake news percentage variable was produced. We used multivariable linear regression, chi-square tests, and the Johnson-Neyman procedure to analyze this relationship, using weekly data. Results: A total of 791,183 news items were obtained over 43 weeks in 2021. There was a significant increase in the proportion of fake news in 11 of the 26 media sources during the public vaccination stage. The regression model revealed a positive adjusted coefficient ($\beta$=0.98, P=.002) of vaccine availability on the following week's vaccination doses, and a negative adjusted coefficient ($\beta$=--3.21, P=.04) of the interaction term on the fake news percentage with the Google Trends level. The Johnson-Neiman plot of the adjusted effect for the interaction term showed that the Google Trends level had a significant negative adjustment effect on vaccination doses for the following week when the proportion of fake news exceeded 39.3\%. Conclusions: There was a significant relationship between the amount of fake news to which the population was exposed and the number of vaccination doses administered. Reducing the amount of fake news and increasing public immunity to misinformation will be critical to maintain public health in the internet age. ", doi="10.2196/36830", url="https://www.jmir.org/2022/4/e36830", url="http://www.ncbi.nlm.nih.gov/pubmed/35380546" } @Article{info:doi/10.2196/36218, author="Hong, Uk Brendan Jae and Woo, P. Benjamin K.", title="Investigating Turf Burn--Related Videos on TikTok: Cross-sectional Study", journal="JMIR Dermatol", year="2022", month="Apr", day="22", volume="5", number="2", pages="e36218", keywords="turf burn", keywords="skin", keywords="burn", keywords="turf", keywords="TikTok", keywords="misinformation", keywords="dermatologist", keywords="medical advice", keywords="peer support", keywords="companionship", keywords="web-based platform", keywords="sports medicine", keywords="dermatology", keywords="sports", keywords="sport", keywords="social media", keywords="mental health", keywords="sports injuries", keywords="athletic injuries", keywords="sport injury", keywords="athletic injury", keywords="athlete", keywords="injury", keywords="injuries", keywords="web-based video", keywords="psychiatry", abstract="Background: Due to the increased use of artificial turf, turf burn has become a common sports injury. Turf burn is caused by exposed skin sliding on artificial turf. Health complications, such as methicillin-resistant Staphylococcus aureus outbreaks, sepsis, and pneumonia, have been linked to untreated turf burns, and many athletes have been turning to social media for advice and companionship regarding their sports injuries. Objective: The goal of this study is to categorize and quantitatively assess the percentage of turf burn--related posts on TikTok based on creator type, content, athletes' experiences, and treatment and prevention methods. With these data, we not only investigate if there is room for health care professionals to assist in the distribution of evidence-based health education to athletes to counteract misinformation but also investigate if there is a potential audience of athletes on TikTok who have the potential to develop problematic responses to injuries. Methods: By using the Discover page on TikTok, we searched for the term turf burn on October 17, 2021. In total, 100 videos were analyzed. Videos were categorized and analyzed based on creator type, content, experiences of the athletes, and treatment and prevention methods. The number of likes and comments was recorded. Results: Most videos (98/100, 98\%) were created by athletes. A small number of videos (2/100, 2\%) were created by health care professionals. In terms of content, most videos (67/100, 67\%) displayed turf burns. A small amount of videos (15/100, 15\%) showed the incidents when turf burns were acquired, while around one-quarter of the videos (23/100, 23\%) demonstrated the treatment and prevention of turf burns. Of the 23 treatment and prevention videos, a minority (4/23, 17\%) showed the preferred treatment of turf burns, while most videos (19/23, 83\%) showed nonpreferred treatments. The smallest amount of videos (2/100, 2\%) were about turf burn education. Most of the videos created by athletes (56/98, 57\%) depicted the negative experiences that patients had with turf burns. Some videos (37/98, 38\%) depicted neutral experiences, while the smallest amount of videos (5/98, 5\%) depicted positive experiences. Conclusions: Our study suggests that there is a potential audience of athletes on TikTok who could develop problematic responses to sports injuries, such as turf burns, as most of the people who post videos are athletes, and many of the posts demonstrate negative experiences associated with turf burns. TikTok is a growing social media platform that should be studied to determine if it can be used to create a social support group for injured athletes to prevent the progression of negative emotional responses into problematic responses. Physicians should also have a role in establishing their social media presence on TikTok and offering evidence-based advice to athletes while disproving misinformation on TikTok. ", doi="10.2196/36218", url="https://derma.jmir.org/2022/2/e36218", url="http://www.ncbi.nlm.nih.gov/pubmed/37632852" } @Article{info:doi/10.2196/32386, author="Trevino, Jesus and Malik, Sanjeev and Schmidt, Michael", title="Integrating Google Trends Search Engine Query Data Into Adult Emergency Department Volume Forecasting: Infodemiology Study", journal="JMIR Infodemiology", year="2022", month="Apr", day="22", volume="2", number="1", pages="e32386", keywords="infodemiology", keywords="patient volume forecasting", keywords="emergency medicine", keywords="digital health", keywords="Google Trends", keywords="infoveillance", keywords="social media", keywords="prediction models", keywords="emergency department", abstract="Background: The search for health information from web-based resources raises opportunities to inform the service operations of health care systems. Google Trends search query data have been used to study public health topics, such as seasonal influenza, suicide, and prescription drug abuse; however, there is a paucity of literature using Google Trends data to improve emergency department patient-volume forecasting. Objective: We assessed the ability of Google Trends search query data to improve the performance of adult emergency department daily volume prediction models. Methods: Google Trends search query data related to chief complaints and health care facilities were collected from Chicago, Illinois (July 2015 to June 2017). We calculated correlations between Google Trends search query data and emergency department daily patient volumes from a tertiary care adult hospital in Chicago. A baseline multiple linear regression model of emergency department daily volume with traditional predictors was augmented with Google Trends search query data; model performance was measured using mean absolute error and mean absolute percentage error. Results: There were substantial correlations between emergency department daily volume and Google Trends ``hospital'' (r=0.54), combined terms (r=0.50), and ``Northwestern Memorial Hospital'' (r=0.34) search query data. The final Google Trends data--augmented model included the predictors Combined 3-day moving average and Hospital 3-day moving average and performed better (mean absolute percentage error 6.42\%) than the final baseline model (mean absolute percentage error 6.67\%)---an improvement of 3.1\%. Conclusions: The incorporation of Google Trends search query data into an adult tertiary care hospital emergency department daily volume prediction model modestly improved model performance. Further development of advanced models with comprehensive search query terms and complementary data sources may improve prediction performance and could be an avenue for further research. ", doi="10.2196/32386", url="https://infodemiology.jmir.org/2022/1/e32386", url="http://www.ncbi.nlm.nih.gov/pubmed/37113800" } @Article{info:doi/10.2196/30885, author="Grigsby-Toussaint, Diana and Champagne, Ashley and Uhr, Justin and Silva, Elizabeth and Noh, Madeline and Bradley, Adam and Rashleigh, Patrick", title="US Black Maternal Health Advocacy Topics and Trends on Twitter: Temporal Infoveillance Study", journal="JMIR Infodemiology", year="2022", month="Apr", day="20", volume="2", number="1", pages="e30885", keywords="Black maternal health", keywords="disparity", keywords="COVID-19", keywords="Twitter", keywords="topic modeling", keywords="digital humanities", keywords="infoveillance", keywords="maternal health", keywords="minority", keywords="women", keywords="advocacy", keywords="social media", keywords="model", keywords="trend", keywords="feasibility", abstract="Background: Black women in the United States disproportionately suffer adverse pregnancy and birth outcomes compared to White women. Economic adversity and implicit bias during clinical encounters may lead to physiological responses that place Black women at higher risk for adverse birth outcomes. The novel coronavirus disease of 2019 (COVID-19) further exacerbated this risk, as safety protocols increased social isolation in clinical settings, thereby limiting opportunities to advocate for unbiased care. Twitter, 1 of the most popular social networking sites, has been used to study a variety of issues of public interest, including health care. This study considers whether posts on Twitter accurately reflect public discourse during the COVID-19 pandemic and are being used in infodemiology studies by public health experts. Objective: This study aims to assess the feasibility of Twitter for identifying public discourse related to social determinants of health and advocacy that influence maternal health among Black women across the United States and to examine trends in sentiment between 2019 and 2020 in the context of the COVID-19 pandemic. Methods: Tweets were collected from March 1 to July 13, 2020, from 21 organizations and influencers and from 4 hashtags that focused on Black maternal health. Additionally, tweets from the same organizations and hashtags were collected from the year prior, from March 1 to July 13, 2019. Twint, a Python programming library, was used for data collection and analysis. We gathered the text of approximately 17,000 tweets, as well as all publicly available metadata. Topic modeling and k-means clustering were used to analyze the tweets. Results: A variety of trends were observed when comparing the 2020 data set to the 2019 data set from the same period. The percentages listed for each topic are probabilities of that topic occurring in our corpus. In our topic models, tweets on reproductive justice, maternal mortality crises, and patient care increased by 67.46\% in 2020 versus 2019. Topics on community, advocacy, and health equity increased by over 30\% in 2020 versus 2019. In contrast, tweet topics that decreased in 2020 versus 2019 were as follows: tweets on Medicaid and medical coverage decreased by 27.73\%, and discussions about creating space for Black women decreased by just under 30\%. Conclusions: The results indicate that the COVID-19 pandemic may have spurred an increased focus on advocating for improved reproductive health and maternal health outcomes among Black women in the United States. Further analyses are needed to capture a longer time frame that encompasses more of the pandemic, as well as more diverse voices to confirm the robustness of the findings. We also concluded that Twitter is an effective source for providing a snapshot of relevant topics to guide Black maternal health advocacy efforts. ", doi="10.2196/30885", url="https://infodemiology.jmir.org/2022/1/e30885", url="http://www.ncbi.nlm.nih.gov/pubmed/35578642" } @Article{info:doi/10.2196/35356, author="Rovetta, Alessandro", title="Google Trends as a Predictive Tool for COVID-19 Vaccinations in Italy: Retrospective Infodemiological Analysis", journal="JMIRx Med", year="2022", month="Apr", day="19", volume="3", number="2", pages="e35356", keywords="COVID-19", keywords="epidemiology", keywords="Google Trends", keywords="infodemiology", keywords="infoveillance", keywords="Italy", keywords="public health", keywords="SARS-CoV-2", keywords="vaccinations", keywords="vaccines", keywords="social media analysis", keywords="social media", abstract="Background: Google Trends is an infoveillance tool widely used by the scientific community to investigate different user behaviors related to COVID-19. However, several limitations regarding its adoption are reported in the literature. Objective: This paper aims to provide an effective and efficient approach to investigating vaccine adherence against COVID-19 via Google Trends. Methods: Through the cross-correlational analysis of well-targeted hypotheses, we investigate the predictive capacity of web searches related to COVID-19 toward vaccinations in Italy from November 2020 to November 2021. The keyword ``vaccine reservation'' query (VRQ) was chosen as it reflects a real intention of being vaccinated (V). Furthermore, the impact of the second most read Italian newspaper (vaccine-related headlines [VRH]) on vaccine-related web searches was investigated to evaluate the role of the mass media as a confounding factor. Fisher r-to-z transformation (z) and percentage difference ($\delta$) were used to compare Spearman coefficients. A regression model V=f(VRH, VRQ) was built to validate the results found. The Holm-Bonferroni correction was adopted (P*). SEs are reported. Results: Simple and generic keywords are more likely to identify the actual web interest in COVID-19 vaccines than specific and elaborated keywords. Cross-correlations between VRQ and V were very strong and significant (min r{\texttwosuperior}=0.460, P*<.001, lag 0 weeks; max r{\texttwosuperior}=0.903, P*<.001, lag 6 weeks). The remaining cross-correlations have been markedly lower ($\delta$>55.8\%; z>5.8; P*<.001). The regression model confirmed the greater significance of VRQ versus VRH (P*<.001 vs P=.03, P*=.29). Conclusions: This research provides preliminary evidence in favor of using Google Trends as a surveillance and prediction tool for vaccine adherence against COVID-19 in Italy. Further research is needed to establish the appropriate use and limits of Google Trends for vaccination tracking. However, these findings prove that the search for suitable keywords is a fundamental step to reduce confounding factors. Additionally, targeting hypotheses helps diminish the likelihood of spurious correlations. It is recommended that Google Trends be leveraged as a complementary infoveillance tool by government agencies to monitor and predict vaccine adherence in this and future crises by following the methods proposed in this paper. ", doi="10.2196/35356", url="https://med.jmirx.org/2022/2/e35356", url="http://www.ncbi.nlm.nih.gov/pubmed/35481982" } @Article{info:doi/10.2196/33909, author="Chandrasekaran, Ranganathan and Desai, Rashi and Shah, Harsh and Kumar, Vivek and Moustakas, Evangelos", title="Examining Public Sentiments and Attitudes Toward COVID-19 Vaccination: Infoveillance Study Using Twitter Posts", journal="JMIR Infodemiology", year="2022", month="Apr", day="15", volume="2", number="1", pages="e33909", keywords="coronavirus", keywords="infoveillance", keywords="COVID-19", keywords="vaccination", keywords="social media", keywords="Twitter study", keywords="text mining", keywords="sentiment analysis", keywords="topic modeling", keywords="tweets", keywords="content analysis", abstract="Background: A global rollout of vaccinations is currently underway to mitigate and protect people from the COVID-19 pandemic. Several individuals have been using social media platforms such as Twitter as an outlet to express their feelings, concerns, and opinions about COVID-19 vaccines and vaccination programs. This study examined COVID-19 vaccine--related tweets from January 1, 2020, to April 30, 2021, to uncover the topics, themes, and variations in sentiments of public Twitter users. Objective: The aim of this study was to examine key themes and topics from COVID-19 vaccine--related English tweets posted by individuals, and to explore the trends and variations in public opinions and sentiments. Methods: We gathered and assessed a corpus of 2.94 million COVID-19 vaccine--related tweets made by 1.2 million individuals. We used CoreX topic modeling to explore the themes and topics underlying the tweets, and used VADER sentiment analysis to compute sentiment scores and examine weekly trends. We also performed qualitative content analysis of the top three topics pertaining to COVID-19 vaccination. Results: Topic modeling yielded 16 topics that were grouped into 6 broader themes underlying the COVID-19 vaccination tweets. The most tweeted topic about COVID-19 vaccination was related to vaccination policy, specifically whether vaccines needed to be mandated or optional (13.94\%), followed by vaccine hesitancy (12.63\%) and postvaccination symptoms and effects (10.44\%) Average compound sentiment scores were negative throughout the 16 weeks for the topics postvaccination symptoms and side effects and hoax/conspiracy. However, consistent positive sentiment scores were observed for the topics vaccination disclosure, vaccine efficacy, clinical trials and approvals, affordability, regulation, distribution and shortage, travel, appointment and scheduling, vaccination sites, advocacy, opinion leaders and endorsement, and gratitude toward health care workers. Reversal in sentiment scores in a few weeks was observed for the topics vaccination eligibility and hesitancy. Conclusions: Identification of dominant themes, topics, sentiments, and changing trends about COVID-19 vaccination can aid governments and health care agencies to frame appropriate vaccination programs, policies, and rollouts. ", doi="10.2196/33909", url="https://infodemiology.jmir.org/2022/1/e33909", url="http://www.ncbi.nlm.nih.gov/pubmed/35462735" } @Article{info:doi/10.2196/33827, author="Mohammadi, Ehsan and Tahamtan, Iman and Mansourian, Yazdan and Overton, Holly", title="Identifying Frames of the COVID-19 Infodemic: Thematic Analysis of Misinformation Stories Across Media", journal="JMIR Infodemiology", year="2022", month="Apr", day="13", volume="2", number="1", pages="e33827", keywords="COVID-19", keywords="pandemic", keywords="misinformation", keywords="fake news", keywords="framing theory", keywords="social media", keywords="infodemic", keywords="thematic analysis", keywords="theme", keywords="pattern", keywords="prevalence", abstract="Background: The word ``infodemic'' refers to the deluge of false information about an event, and it is a global challenge for today's society. The sheer volume of misinformation circulating during the COVID-19 pandemic has been harmful to people around the world. Therefore, it is important to study different aspects of misinformation related to the pandemic. Objective: This paper aimed to identify the main subthemes related to COVID-19 misinformation on various platforms, from traditional outlets to social media. This paper aimed to place these subthemes into categories, track the changes, and explore patterns in prevalence, over time, across different platforms and contexts. Methods: From a theoretical perspective, this research was rooted in framing theory; it also employed thematic analysis to identify the main themes and subthemes related to COVID-19 misinformation. The data were collected from 8 fact-checking websites that formed a sample of 127 pieces of false COVID-19 news published from January 1, 2020 to March 30, 2020. Results: The findings revealed 4 main themes (attribution, impact, protection and solutions, and politics) and 19 unique subthemes within those themes related to COVID-19 misinformation. Governmental and political organizations (institutional level) and administrators and politicians (individual level) were the 2 most frequent subthemes, followed by origination and source, home remedies, fake statistics, treatments, drugs, and pseudoscience, among others. Results indicate that the prevalence of misinformation subthemes had altered over time between January 2020 and March 2020. For instance, false stories about the origin and source of the virus were frequent initially (January). Misinformation regarding home remedies became a prominent subtheme in the middle (February), while false information related to government organizations and politicians became popular later (March). Although conspiracy theory web pages and social media outlets were the primary sources of misinformation, surprisingly, results revealed trusted platforms such as official government outlets and news organizations were also avenues for creating COVID-19 misinformation. Conclusions: The identified themes in this study reflect some of the information attitudes and behaviors, such as denial, uncertainty, consequences, and solution-seeking, that provided rich information grounds to create different types of misinformation during the COVID-19 pandemic. Some themes also indicate that the application of effective communication strategies and the creation of timely content were used to persuade human minds with false stories in different phases of the crisis. The findings of this study can be beneficial for communication officers, information professionals, and policy makers to combat misinformation in future global health crises or related events. ", doi="10.2196/33827", url="https://infodemiology.jmir.org/2022/1/e33827", url="http://www.ncbi.nlm.nih.gov/pubmed/37113806" } @Article{info:doi/10.2196/26335, author="Sidani, E. Jaime and Hoffman, Beth and Colditz, B. Jason and Wolynn, Riley and Hsiao, Lily and Chu, Kar-Hai and Rose, J. Jason and Shensa, Ariel and Davis, Esa and Primack, Brian", title="Discussions and Misinformation About Electronic Nicotine Delivery Systems and COVID-19: Qualitative Analysis of Twitter Content", journal="JMIR Form Res", year="2022", month="Apr", day="13", volume="6", number="4", pages="e26335", keywords="COVID-19", keywords="coronavirus", keywords="e-cigarette", keywords="electronic nicotine delivery systems", keywords="Twitter", keywords="social media", keywords="misinformation", keywords="discussion", keywords="public health", keywords="communication", keywords="concern", keywords="severity", keywords="conspiracy", abstract="Background: Misinformation and conspiracy theories related to COVID-19 and electronic nicotine delivery systems (ENDS) are increasing. Some of this may stem from early reports suggesting a lower risk of severe COVID-19 in nicotine users. Additionally, a common conspiracy is that the e-cigarette or vaping product use--associated lung injury (EVALI) outbreak of 2019 was actually an early presentation of COVID-19. This may have important public health ramifications for both COVID-19 control and ENDS use. Objective: Twitter is an ideal tool for analyzing real-time public discussions related to both ENDS and COVID-19. This study seeks to collect and classify Twitter messages (``tweets'') related to ENDS and COVID-19 to inform public health messaging. Methods: Approximately 2.1 million tweets matching ENDS-related keywords were collected from March 1, 2020, through June 30, 2020, and were then filtered for COVID-19--related keywords, resulting in 67,321 original tweets. A 5\% (n=3366) subsample was obtained for human coding using a systematically developed codebook. Tweets were coded for relevance to the topic and four overarching categories. Results: A total of 1930 (57.3\%) tweets were coded as relevant to the research topic. Half (n=1008, 52.2\%) of these discussed a perceived association between ENDS use and COVID-19 susceptibility or severity, with 42.4\% (n=818) suggesting that ENDS use is associated with worse COVID-19 symptoms. One-quarter (n=479, 24.8\%) of tweets discussed the perceived similarity/dissimilarity of COVID-19 and EVALI, and 13.8\% (n=266) discussed ENDS use behavior. Misinformation and conspiracy theories were present throughout all coding categories. Conclusions: Discussions about ENDS use and COVID-19 on Twitter frequently highlight concerns about the susceptibility and severity of COVID-19 for ENDS users; however, many contain misinformation and conspiracy theories. Public health messaging should capitalize on these concerns and amplify accurate Twitter messaging. ", doi="10.2196/26335", url="https://formative.jmir.org/2022/4/e26335", url="http://www.ncbi.nlm.nih.gov/pubmed/35311684" } @Article{info:doi/10.2196/33680, author="Gunasekeran, Visva Dinesh and Chew, Alton and Chandrasekar, K. Eeshwar and Rajendram, Priyanka and Kandarpa, Vasundhara and Rajendram, Mallika and Chia, Audrey and Smith, Helen and Leong, Kit Choon", title="The Impact and Applications of Social Media Platforms for Public Health Responses Before and During the COVID-19 Pandemic: Systematic Literature Review", journal="J Med Internet Res", year="2022", month="Apr", day="11", volume="24", number="4", pages="e33680", keywords="digital health", keywords="social media", keywords="big data", keywords="population health", keywords="blockchain", keywords="COVID-19", keywords="review", keywords="benefit", keywords="challenge", keywords="public health", abstract="Background: ?Social media platforms have numerous potential benefits and drawbacks on public health, which have been described in the literature. The COVID-19 pandemic has exposed our limited knowledge regarding the potential health impact of these platforms, which have been detrimental to public health responses in many regions. Objective: This review aims to highlight a brief history of social media in health care and report its potential negative and positive public health impacts, which have been characterized in the literature. Methods: ?We searched electronic bibliographic databases including PubMed, including Medline and Institute of Electrical and Electronics Engineers Xplore, from December 10, 2015, to December 10, 2020. We screened the title and abstracts and selected relevant reports for review of full text and reference lists. These were analyzed thematically and consolidated into applications of social media platforms for public health. Results: ?The positive and negative impact of social media platforms on public health are catalogued on the basis of recent research in this report. These findings are discussed in the context of improving future public health responses and incorporating other emerging digital technology domains such as artificial intelligence. However, there is a need for more research with pragmatic methodology that evaluates the impact of specific digital interventions to inform future health policy. Conclusions: ?Recent research has highlighted the potential negative impact of social media platforms on population health, as well as potentially useful applications for public health communication, monitoring, and predictions. More research is needed to objectively investigate measures to mitigate against its negative impact while harnessing effective applications for the benefit of public health. ", doi="10.2196/33680", url="https://www.jmir.org/2022/4/e33680", url="http://www.ncbi.nlm.nih.gov/pubmed/35129456" } @Article{info:doi/10.2196/36489, author="Li, Ang and Jiao, Dongdong and Zhu, Tingshao", title="Stigmatizing Attitudes Across Cybersuicides and Offline Suicides: Content Analysis of Sina Weibo", journal="J Med Internet Res", year="2022", month="Apr", day="8", volume="24", number="4", pages="e36489", keywords="stigma", keywords="cybersuicide", keywords="livestreamed suicide", keywords="linguistic analysis", keywords="social media", abstract="Background: The new reality of cybersuicide raises challenges to ideologies about the traditional form of suicide that does not involve the internet (offline suicide), which may lead to changes in audience's attitudes. However, knowledge on whether stigmatizing attitudes differ between cybersuicides and offline suicides remains limited. Objective: This study aims to consider livestreamed suicide as a typical representative of cybersuicide and use social media data (Sina Weibo) to investigate the differences in stigmatizing attitudes across cybersuicides and offline suicides in terms of attitude types and linguistic characteristics. Methods: A total of 4393 cybersuicide-related and 2843 offline suicide-related Weibo posts were collected and analyzed. First, human coders were recruited and trained to perform a content analysis on the collected posts to determine whether each of them reflected stigma. Second, a text analysis tool was used to automatically extract a number of psycholinguistic features from each post. Subsequently, based on the selected features, a series of classification models were constructed for different purposes: differentiating the general stigma of cybersuicide from that of offline suicide and differentiating the negative stereotypes of cybersuicide from that of offline suicide. Results: In terms of attitude types, cybersuicide was observed to carry more stigma than offline suicide ($\chi$21=179.8; P<.001). Between cybersuicides and offline suicides, there were significant differences in the proportion of posts associated with five different negative stereotypes, including stupid and shallow ($\chi$21=28.9; P<.001), false representation ($\chi$21=144.4; P<.001), weak and pathetic ($\chi$21=20.4; P<.001), glorified and normalized ($\chi$21=177.6; P<.001), and immoral ($\chi$21=11.8; P=.001). Similar results were also found for different genders and regions. In terms of linguistic characteristics, the F-measure values of the classification models ranged from 0.81 to 0.85. Conclusions: The way people perceive cybersuicide differs from how they perceive offline suicide. The results of this study have implications for reducing the stigma against suicide. ", doi="10.2196/36489", url="https://www.jmir.org/2022/4/e36489", url="http://www.ncbi.nlm.nih.gov/pubmed/35394437" } @Article{info:doi/10.2196/36022, author="Rovetta, Alessandro and Bhagavathula, Srikanth Akshaya", title="The Impact of COVID-19 on Mortality in Italy: Retrospective Analysis of Epidemiological Trends", journal="JMIR Public Health Surveill", year="2022", month="Apr", day="7", volume="8", number="4", pages="e36022", keywords="COVID-19", keywords="deniers", keywords="excess deaths", keywords="epidemiology", keywords="infodemic", keywords="infodemiology", keywords="Italy", keywords="longitudinal analysis", keywords="mortality", keywords="time series", keywords="pandemic", keywords="public health", abstract="Background: Despite the available evidence on its severity, COVID-19 has often been compared with seasonal flu by some conspirators and even scientists. Various public discussions arose about the noncausal correlation between COVID-19 and the observed deaths during the pandemic period in Italy. Objective: This paper aimed to search for endogenous reasons for the mortality increase recorded in Italy during 2020 to test this controversial hypothesis. Furthermore, we provide a framework for epidemiological analyses of time series. Methods: We analyzed deaths by age, sex, region, and cause of death in Italy from 2011 to 2019. Ordinary least squares (OLS) linear regression analyses and autoregressive integrated moving average (ARIMA) were used to predict the best value for 2020. A Grubbs 1-sided test was used to assess the significance of the difference between predicted and observed 2020 deaths/mortality. Finally, a 1-sample t test was used to compare the population of regional excess deaths to a null mean. The relationship between mortality and predictive variables was assessed using OLS multiple regression models. Since there is no uniform opinion on multicomparison adjustment and false negatives imply great epidemiological risk, the less-conservative Siegel approach and more-conservative Holm-Bonferroni approach were employed. By doing so, we provided the reader with the means to carry out an independent analysis. Results: Both ARIMA and OLS linear regression models predicted the number of deaths in Italy during 2020 to be between 640,000 and 660,000 (range of 95\% CIs: 620,000-695,000) against the observed value of above 750,000. We found strong evidence supporting that the death increase in all regions (average excess=12.2\%) was not due to chance (t21=7.2; adjusted P<.001). Male and female national mortality excesses were 18.4\% (P<.001; adjusted P=.006) and 14.1\% (P=.005; adjusted P=.12), respectively. However, we found limited significance when comparing male and female mortality residuals' using the Mann-Whitney U test (P=.27; adjusted P=.99). Finally, mortality was strongly and positively correlated with latitude (R=0.82; adjusted P<.001). In this regard, the significance of the mortality increases during 2020 varied greatly from region to region. Lombardy recorded the highest mortality increase (38\% for men, adjusted P<.001; 31\% for women, P<.001; adjusted P=.006). Conclusions: Our findings support the absence of historical endogenous reasons capable of justifying the mortality increase observed in Italy during 2020. Together with the current knowledge on SARS-CoV-2, these results provide decisive evidence on the devastating impact of COVID-19. We suggest that this research be leveraged by government, health, and information authorities to furnish proof against conspiracy hypotheses that minimize COVID-19--related risks. Finally, given the marked concordance between ARIMA and OLS regression, we suggest that these models be exploited for public health surveillance. Specifically, meaningful information can be deduced by comparing predicted and observed epidemiological trends. ", doi="10.2196/36022", url="https://publichealth.jmir.org/2022/4/e36022", url="http://www.ncbi.nlm.nih.gov/pubmed/35238784" } @Article{info:doi/10.2196/35253, author="Wang, Alex and McCarron, Robert and Azzam, Daniel and Stehli, Annamarie and Xiong, Glen and DeMartini, Jeremy", title="Utilizing Big Data From Google Trends to Map Population Depression in the United States: Exploratory Infodemiology Study", journal="JMIR Ment Health", year="2022", month="Mar", day="31", volume="9", number="3", pages="e35253", keywords="depression", keywords="epidemiology", keywords="internet", keywords="google trends", keywords="big data", keywords="mental health", abstract="Background: The epidemiology of mental health disorders has important theoretical and practical implications for health care service and planning. The recent increase in big data storage and subsequent development of analytical tools suggest that mining search databases may yield important trends on mental health, which can be used to support existing population health studies. Objective: This study aimed to map depression search intent in the United States based on internet-based mental health queries. Methods: Weekly data on mental health searches were extracted from Google Trends for an 11-year period (2010-2021) and separated by US state for the following terms: ``feeling sad,'' ``depressed,'' ``depression,'' ``empty,'' ``insomnia,'' ``fatigue,'' ``guilty,'' ``feeling guilty,'' and ``suicide.'' Multivariable regression models were created based on geographic and environmental factors and normalized to the following control terms: ``sports,'' ``news,'' ``google,'' ``youtube,'' ``facebook,'' and ``netflix.'' Heat maps of population depression were generated based on search intent. Results: Depression search intent grew 67\% from January 2010 to March 2021. Depression search intent showed significant seasonal patterns with peak intensity during winter (adjusted P<.001) and early spring months (adjusted P<.001), relative to summer months. Geographic location correlated with depression search intent with states in the Northeast (adjusted P=.01) having higher search intent than states in the South. Conclusions: The trends extrapolated from Google Trends successfully correlate with known risk factors for depression, such as seasonality and increasing latitude. These findings suggest that Google Trends may be a valid novel epidemiological tool to map depression prevalence in the United States. ", doi="10.2196/35253", url="https://mental.jmir.org/2022/3/e35253", url="http://www.ncbi.nlm.nih.gov/pubmed/35357320" } @Article{info:doi/10.2196/32449, author="Marshall, Christopher and Lanyi, Kate and Green, Rhiannon and Wilkins, C. Georgina and Pearson, Fiona and Craig, Dawn", title="Using Natural Language Processing to Explore Mental Health Insights From UK Tweets During the COVID-19 Pandemic: Infodemiology Study", journal="JMIR Infodemiology", year="2022", month="Mar", day="31", volume="2", number="1", pages="e32449", keywords="Twitter", keywords="mental health", keywords="COVID-19", keywords="sentiment", keywords="lockdown", keywords="soft intelligence", keywords="artificial intelligence", keywords="machine learning", keywords="natural language processing", abstract="Background: There is need to consider the value of soft intelligence, leveraged using accessible natural language processing (NLP) tools, as a source of analyzed evidence to support public health research outputs and decision-making. Objective: The aim of this study was to explore the value of soft intelligence analyzed using NLP. As a case study, we selected and used a commercially available NLP platform to identify, collect, and interrogate a large collection of UK tweets relating to mental health during the COVID-19 pandemic. Methods: A search strategy comprised of a list of terms related to mental health, COVID-19, and lockdown restrictions was developed to prospectively collate relevant tweets via Twitter's advanced search application programming interface over a 24-week period. We deployed a readily and commercially available NLP platform to explore tweet frequency and sentiment across the United Kingdom and identify key topics of discussion. A series of keyword filters were used to clean the initial data retrieved and also set up to track specific mental health problems. All collated tweets were anonymized. Results: We identified and analyzed 286,902 tweets posted from UK user accounts from July 23, 2020 to January 6, 2021. The average sentiment score was 50\%, suggesting overall neutral sentiment across all tweets over the study period. Major fluctuations in volume (between 12,622 and 51,340) and sentiment (between 25\% and 49\%) appeared to coincide with key changes to any local and/or national social distancing measures. Tweets around mental health were polarizing, discussed with both positive and negative sentiment. Key topics of consistent discussion over the study period included the impact of the pandemic on people's mental health (both positively and negatively), fear and anxiety over lockdowns, and anger and mistrust toward the government. Conclusions: Using an NLP platform, we were able to rapidly mine and analyze emerging health-related insights from UK tweets into how the pandemic may be impacting people's mental health and well-being. This type of real-time analyzed evidence could act as a useful intelligence source that agencies, local leaders, and health care decision makers can potentially draw from, particularly during a health crisis. ", doi="10.2196/32449", url="https://infodemiology.jmir.org/2022/1/e32449", url="http://www.ncbi.nlm.nih.gov/pubmed/36406146" } @Article{info:doi/10.2196/35677, author="Bacsu, Juanita-Dawne and Fraser, Sarah and Chasteen, L. Alison and Cammer, Allison and Grewal, S. Karl and Bechard, E. Lauren and Bethell, Jennifer and Green, Shoshana and McGilton, S. Katherine and Morgan, Debra and O'Rourke, M. Hannah and Poole, Lisa and Spiteri, J. Raymond and O'Connell, E. Megan", title="Using Twitter to Examine Stigma Against People With Dementia During COVID-19: Infodemiology Study", journal="JMIR Aging", year="2022", month="Mar", day="31", volume="5", number="1", pages="e35677", keywords="coronavirus 2019", keywords="social media", keywords="stigma", keywords="dementia", keywords="ageism", keywords="COVID-19", keywords="Twitter", keywords="bias", keywords="infodemiology", keywords="attention", keywords="risk", keywords="impact", keywords="misinformation", keywords="belief", keywords="cognition", keywords="cognitive impairment", abstract="Background: During the pandemic, there has been significant social media attention focused on the increased COVID-19 risks and impacts for people with dementia and their care partners. However, these messages can perpetuate misconceptions, false information, and stigma. Objective: This study used Twitter data to understand stigma against people with dementia propagated during the COVID-19 pandemic. Methods: We collected 1743 stigma-related tweets using the GetOldTweets application in Python from February 15 to September 7, 2020. Thematic analysis was used to analyze the tweets. Results: Based on our analysis, 4 main themes were identified: (1) ageism and devaluing the lives of people with dementia, (2) misinformation and false beliefs about dementia and COVID-19, (3) dementia used as an insult for political ridicule, and (4) challenging stigma against dementia. Social media has been used to spread stigma, but it can also be used to challenge negative beliefs, stereotypes, and false information. Conclusions: Dementia education and awareness campaigns are urgently needed on social media to address COVID-19-related stigma. When stigmatizing discourse on dementia is widely shared and consumed amongst the public, it has public health implications. How we talk about dementia shapes how policymakers, clinicians, and the public value the lives of people with dementia. Stigma perpetuates misinformation, pejorative language, and patronizing attitudes that can lead to discriminatory actions, such as the limited provision of lifesaving supports and health services for people with dementia during the pandemic. COVID-19 policies and public health messages should focus on precautions and preventive measures rather than labeling specific population groups. ", doi="10.2196/35677", url="https://aging.jmir.org/2022/1/e35677", url="http://www.ncbi.nlm.nih.gov/pubmed/35290197" } @Article{info:doi/10.2196/34050, author="Purushothaman, Vidya and McMann, Tiana and Nali, Matthew and Li, Zhuoran and Cuomo, Raphael and Mackey, K. Tim", title="Content Analysis of Nicotine Poisoning (Nic Sick) Videos on TikTok: Retrospective Observational Infodemiology Study", journal="J Med Internet Res", year="2022", month="Mar", day="30", volume="24", number="3", pages="e34050", keywords="nic sick", keywords="vaping", keywords="tobacco", keywords="social media", keywords="TikTok", keywords="content analysis", keywords="smoking", keywords="nicotine", keywords="e-cigarette", keywords="adverse effects", keywords="public health", keywords="infodemiology", abstract="Background: TikTok is a microvideo social media platform currently experiencing rapid growth and with 60\% of its monthly users between the ages of 16 and 24 years. Increased exposure to e-cigarette content on social media may influence patterns of use, including the risk of overconsumption and possible nicotine poisoning, when users engage in trending challenges online. However, there is limited research assessing the characteristics of nicotine poisoning--related content posted on social media. Objective: We aimed to assess the characteristics of content on TikTok that is associated with a popular nicotine poisoning--related hashtag. Methods: We collected TikTok posts associated with the hashtag \#nicsick, using a Python programming package (Selenium) and used an inductive coding approach to analyze video content and characteristics of interest. Videos were manually annotated to generate a codebook of the nicotine sickness--related themes. Statistical analysis was used to compare user engagement characteristics and video length in content with and without active nicotine sickness TikTok topics. Results: A total of 132 TikTok videos associated with the hashtag \#nicsick were manually coded, with 52.3\% (69/132) identified as discussing firsthand and secondhand reports of suspected nicotine poisoning symptoms and experiences. More than one-third of nicotine poisoning--related content (26/69, 37.68\%) portrayed active vaping by users, which included content with vaping behavior such as vaping tricks and overconsumption, and 43\% (30/69) of recorded users self-reported experiencing nicotine sickness, poisoning, or adverse events such as vomiting following nicotine consumption. The average follower count of users posting content related to nicotine sickness was significantly higher than that for users posting content unrelated to nicotine sickness (W=2350.5, P=.03). Conclusions: TikTok users openly discuss experiences, both firsthand and secondhand, with nicotine adverse events via the \#nicsick hashtag including reports of overconsumption resulting in sickness. These study results suggest that there is a need to assess the utility of digital surveillance on emerging social media platforms for vaping adverse events, particularly on sites popular among youth and young adults. As vaping product use-patterns continue to evolve, digital adverse event detection likely represents an important tool to supplement traditional methods of public health surveillance (such as poison control center prevalence numbers). ", doi="10.2196/34050", url="https://www.jmir.org/2022/3/e34050", url="http://www.ncbi.nlm.nih.gov/pubmed/35353056" } @Article{info:doi/10.2196/33685, author="Stupinski, Marie Anne and Alshaabi, Thayer and Arnold, V. Michael and Adams, Lydia Jane and Minot, R. Joshua and Price, Matthew and Dodds, Sheridan Peter and Danforth, M. Christopher", title="Quantifying Changes in the Language Used Around Mental Health on Twitter Over 10 Years: Observational Study", journal="JMIR Ment Health", year="2022", month="Mar", day="30", volume="9", number="3", pages="e33685", keywords="mental health", keywords="stigma", keywords="natural language processing", abstract="Background: Mental health challenges are thought to affect approximately 10\% of the global population each year, with many of those affected going untreated because of the stigma and limited access to services. As social media lowers the barrier for joining difficult conversations and finding supportive groups, Twitter is an open source of language data describing the changing experience of a stigmatized group. Objective: By measuring changes in the conversation around mental health on Twitter, we aim to quantify the hypothesized increase in discussions and awareness of the topic as well as the corresponding reduction in stigma around mental health. Methods: We explored trends in words and phrases related to mental health through a collection of 1-, 2-, and 3-grams parsed from a data stream of approximately 10\% of all English tweets from 2010 to 2021. We examined temporal dynamics of mental health language and measured levels of positivity of the messages. Finally, we used the ratio of original tweets to retweets to quantify the fraction of appearances of mental health language that was due to social amplification. Results: We found that the popularity of the phrase mental health increased by nearly two orders of magnitude between 2012 and 2018. We observed that mentions of mental health spiked annually and reliably because of mental health awareness campaigns as well as unpredictably in response to mass shootings, celebrities dying by suicide, and popular fictional television stories portraying suicide. We found that the level of positivity of messages containing mental health, while stable through the growth period, has declined recently. Finally, we observed that since 2015, mentions of mental health have become increasingly due to retweets, suggesting that the stigma associated with the discussion of mental health on Twitter has diminished with time. Conclusions: These results provide useful texture regarding the growing conversation around mental health on Twitter and suggest that more awareness and acceptance has been brought to the topic compared with past years. ", doi="10.2196/33685", url="https://mental.jmir.org/2022/3/e33685", url="http://www.ncbi.nlm.nih.gov/pubmed/35353049" } @Article{info:doi/10.2196/25697, author="Lu, Xinyi and Sun, Li and Xie, Zidian and Li, Dongmei", title="Perception of the Food and Drug Administration Electronic Cigarette Flavor Enforcement Policy on Twitter: Observational Study", journal="JMIR Public Health Surveill", year="2022", month="Mar", day="29", volume="8", number="3", pages="e25697", keywords="electronic cigarette", keywords="FDA flavor enforcement policy", keywords="Twitter", keywords="Food and Drug Administration", keywords="enforcement", keywords="policy", keywords="e-cigarettes", keywords="e-cigarette flavor", keywords="tobacco flavors", keywords="prohibit", keywords="sale", abstract="Background: On January 2, 2020, the US Food and Drug Administration (FDA) released the electronic cigarette (e-cigarette) flavor enforcement policy to prohibit the sale of all flavored cartridge--based e-cigarettes, except for menthol and tobacco flavors. Objective: This research aimed to examine the public perception of this FDA flavor enforcement policy and its impact on the public perception of e-cigarettes on Twitter. Methods: A total of 2,341,660 e-cigarette--related tweets and 190,490 FDA flavor enforcement policy--related tweets in the United States were collected from Twitter before (between June 13 and August 22, 2019) and after (between January 2 and March 30, 2020) the announcement of the FDA flavor enforcement policy. Sentiment analysis was conducted to detect the changes in the public perceptions of the policy and e-cigarettes on Twitter. Topic modeling was used for finding frequently discussed topics about e-cigarettes. Results: The proportion of negative sentiment tweets about e-cigarettes significantly increased after the announcement of the FDA flavor enforcement policy compared with before the announcement of the policy. In contrast, the overall sentiment toward the FDA flavor enforcement policy became less negative. The FDA flavor enforcement policy was the most popular topic associated with e-cigarettes after the announcement of the FDA flavor enforcement policy. Twitter users who discussed about e-cigarettes started to talk about other alternative ways of getting e-cigarettes after the FDA flavor enforcement policy. Conclusions: Twitter users' perceptions of e-cigarettes became more negative after the announcement of the FDA flavor enforcement policy. ", doi="10.2196/25697", url="https://publichealth.jmir.org/2022/3/e25697", url="http://www.ncbi.nlm.nih.gov/pubmed/35348461" } @Article{info:doi/10.2196/35016, author="Jang, Hyeju and Rempel, Emily and Roe, Ian and Adu, Prince and Carenini, Giuseppe and Janjua, Zafar Naveed", title="Tracking Public Attitudes Toward COVID-19 Vaccination on Tweets in Canada: Using Aspect-Based Sentiment Analysis", journal="J Med Internet Res", year="2022", month="Mar", day="29", volume="24", number="3", pages="e35016", keywords="COVID-19", keywords="vaccination", keywords="Twitter", keywords="aspect-based sentiment analysis", keywords="Canada", keywords="social media", keywords="pandemic", keywords="content analysis", keywords="vaccine rollout", keywords="sentiment analysis", keywords="public sentiment", keywords="public health", keywords="health promotion", keywords="vaccination promotion", abstract="Background: The development and approval of COVID-19 vaccines have generated optimism for the end of the COVID-19 pandemic and a return to normalcy. However, vaccine hesitancy, often fueled by misinformation, poses a major barrier to achieving herd immunity. Objective: We aim to investigate Twitter users' attitudes toward COVID-19 vaccination in Canada after vaccine rollout. Methods: We applied a weakly supervised aspect-based sentiment analysis (ABSA) technique, which involves the human-in-the-loop system, on COVID-19 vaccination--related tweets in Canada. Automatically generated aspect and opinion terms were manually corrected by public health experts to ensure the accuracy of the terms and make them more domain-specific. Then, based on these manually corrected terms, the system inferred sentiments toward the aspects. We observed sentiments toward key aspects related to COVID-19 vaccination, and investigated how sentiments toward ``vaccination'' changed over time. In addition, we analyzed the most retweeted or liked tweets by observing most frequent nouns and sentiments toward key aspects. Results: After applying the ABSA system, we obtained 170 aspect terms (eg, ``immunity'' and ``pfizer'') and 6775 opinion terms (eg, ``trustworthy'' for the positive sentiment and ``jeopardize'' for the negative sentiment). While manually verifying or editing these terms, our public health experts selected 20 key aspects related to COVID-19 vaccination for analysis. The sentiment analysis results for the 20 key aspects revealed negative sentiments related to ``vaccine distribution,'' ``side effects,'' ``allergy,'' ``reactions,'' and ``anti-vaxxer,'' and positive sentiments related to ``vaccine campaign,'' ``vaccine candidates,'' and ``immune response.'' These results indicate that the Twitter users express concerns about the safety of vaccines but still consider vaccines as the option to end the pandemic. In addition, compared to the sentiment of the remaining tweets, the most retweeted or liked tweets showed more positive sentiment overall toward key aspects (P<.001), especially vaccines (P<.001) and vaccination (P=.009). Further investigation of the most retweeted or liked tweets revealed two opposing trends in Twitter users who showed negative sentiments toward vaccines: the ``anti-vaxxer'' population that used negative sentiments as a means to discourage vaccination and the ``Covid Zero'' population that used negative sentiments to encourage vaccinations while critiquing the public health response. Conclusions: Our study examined public sentiments toward COVID-19 vaccination on tweets over an extended period in Canada. Our findings could inform public health agencies to design and implement interventions to promote vaccination. ", doi="10.2196/35016", url="https://www.jmir.org/2022/3/e35016", url="http://www.ncbi.nlm.nih.gov/pubmed/35275835" } @Article{info:doi/10.2196/34421, author="Cresswell, Liam and Espin-Noboa, Lisette and Murphy, Q. Malia S. and Ramlawi, Serine and Walker, C. Mark and Karsai, M{\'a}rton and Corsi, J. Daniel", title="The Volume and Tone of Twitter Posts About Cannabis Use During Pregnancy: Protocol for a Scoping Review", journal="JMIR Res Protoc", year="2022", month="Mar", day="29", volume="11", number="3", pages="e34421", keywords="cannabis", keywords="pregnancy", keywords="health information", keywords="social media", keywords="Twitter", abstract="Background: Cannabis use has increased in Canada since its legalization in 2018, including among pregnant women who may be motivated to use cannabis to reduce symptoms of nausea and vomiting. However, a growing body of research suggests that cannabis use during pregnancy may harm the developing fetus. As a result, patients increasingly seek medical advice from online sources, but these platforms may also spread anecdotal descriptions or misinformation. Given the possible disconnect between online messaging and evidence-based research about the effects of cannabis use during pregnancy, there is a potential for advice taken from social media to affect the health of mothers and their babies. Objective: This study aims to quantify the volume and tone of English language posts related to cannabis use in pregnancy from January 2012 to December 2021. Methods: Modeling published frameworks for scoping reviews, we will collect publicly available posts from Twitter that mention cannabis use during pregnancy and use the Twitter Application Programming Interface for Academic Research to extract data from tweets, including public metrics such as the number of likes, retweets, and quotes, as well as health effect mentions, sentiment, location, and users' interests. These data will be used to quantify how cannabis use during pregnancy is discussed on Twitter and to build a qualitative profile of supportive and opposing posters. Results: The CHEO Research Ethics Board reviewed our project and granted an exemption in May 2021. As of December 2021, we have gained approval to use the Twitter Application Programming Interface for Academic Research and have developed a preliminary search strategy that returns over 3 million unique tweets posted between 2012 and 2021. Conclusions: Understanding how Twitter is being used to discuss cannabis use during pregnancy will help public health agencies and health care providers assess the messaging patients may be receiving and develop communication strategies to counter misinformation, especially in geographical regions where legalization is recent or imminent. Most importantly, we foresee that our findings will assist expecting families in making informed choices about where they choose to access advice about using cannabis during pregnancy. Trial Registration: Open Science Framework 10.17605/OSF.IO/BW8DA; www.osf.io/6fb2e International Registered Report Identifier (IRRID): PRR1-10.2196/34421 ", doi="10.2196/34421", url="https://www.researchprotocols.org/2022/3/e34421", url="http://www.ncbi.nlm.nih.gov/pubmed/35348465" } @Article{info:doi/10.2196/27894, author="Chu, Kar-Hai and Hershey, B. Tina and Hoffman, L. Beth and Wolynn, Riley and Colditz, B. Jason and Sidani, E. Jaime and Primack, A. Brian", title="Puff Bars, Tobacco Policy Evasion, and Nicotine Dependence: Content Analysis of Tweets", journal="J Med Internet Res", year="2022", month="Mar", day="25", volume="24", number="3", pages="e27894", keywords="tobacco", keywords="policy", keywords="social media", keywords="e-cigarette", keywords="twitter", keywords="mHealth", keywords="dependence", keywords="addiction", keywords="nicotine", abstract="Background: Puff Bars are e-cigarettes that continued marketing flavored products by exploiting the US Food and Drug Administration exemption for disposable devices. Objective: This study aimed to examine discussions related to Puff Bar on Twitter to identify tobacco regulation and policy themes as well as unanticipated outcomes of regulatory loopholes. Methods: Of 8519 original tweets related to Puff Bar collected from July 13, 2020, to August 13, 2020, a random 20\% subsample (n=2661) was selected for qualitative coding of topics related to nicotine dependence and tobacco policy. Results: Of the human-coded tweets, 2123 (80.2\%) were coded as relevant to Puff Bar as the main topic. Of those tweets, 698 (32.9\%) discussed tobacco policy, including flavors (n=320, 45.9\%), regulations (n=124, 17.8\%), purchases (n=117, 16.8\%), and other products (n=110, 15.8\%). Approximately 22\% (n=480) of the tweets referenced dependence, including lack of access (n=273, 56.9\%), appetite suppression (n=59, 12.3\%), frequent use (n=47, 9.8\%), and self-reported dependence (n=110, 22.9\%). Conclusions: This study adds to the growing evidence base that the US Food and Drug Administration ban of e-cigarette flavors did not reduce interest, but rather shifted the discussion to brands utilizing a loophole that allowed flavored products to continue to be sold in disposable devices. Until comprehensive tobacco policy legislation is developed, new products or loopholes will continue to supply nicotine demand. ", doi="10.2196/27894", url="https://www.jmir.org/2022/3/e27894", url="http://www.ncbi.nlm.nih.gov/pubmed/35333188" } @Article{info:doi/10.2196/31388, author="Grewal, Singh Udhayvir and Gupta, Arjun and Doggett, Jamie and Lou, Emil and Gusani, J. Niraj and Maitra, Anirban and Beg, Shaalan Muhammad and Ocean, J. Allyson", title="Twitter Conversations About Pancreatic Cancer by Health Care Providers and the General Public: Thematic Analysis", journal="JMIR Cancer", year="2022", month="Mar", day="24", volume="8", number="1", pages="e31388", keywords="pancreatic cancer", keywords="Twitter", keywords="general public", keywords="health care providers", keywords="social media", keywords="Creation Pinpoint", keywords="thematic analysis", abstract="Background: ?There is a growing interest in the pattern of consumption of health-related information on social media platforms. Objective: We evaluated the content of discussions around pancreatic cancer on Twitter to identify subtopics of greatest interest to health care providers and the general public.? Methods: ?We used an online analytical tool (Creation Pinpoint) to quantify Twitter mentions (tweets and retweets) related to pancreatic cancer between January 2018 and December 2019. Keywords, hashtags, word combinations, and phrases were used to identify mentions. Health care provider profiles were identified using machine learning and then verified by a human analyst. Remaining user profiles were classified as belonging to the general public. Data from conversations were stratified qualitatively into 5 domains: (1) prevention, (2) survivorship, (3) treatment, (4) research, and (5) policy. We compared the themes of conversations initiated by health care providers and the general public and analyzed the impact of the Pancreatic Cancer Awareness Month and announcements by public figures of pancreatic cancer diagnoses on the overall volume of conversations.? Results: ?Out of 1,258,028 mentions of pancreatic cancer, 313,668 unique mentions were classified into the 5 domains. We found that health care providers most commonly discussed pancreatic cancer research (10,640/27,031 mentions, 39.4\%), while the general public most commonly discussed treatment (154,484/307,449 mentions, 50.2\%). Health care providers were found to be more likely to initiate conversations related to research (odds ratio [OR] 1.75, 95\% CI 1.70-1.79, P<.001) and prevention (OR 1.49, 95\% CI 1.41-1.57, P<.001) whereas the general public took the lead in the domains of treatment (OR 1.63, 95\% CI 1.58-1.69, P<.001) and survivorship (OR 1.17, 95\% CI 1.13-1.21, P<.001). Pancreatic Cancer Awareness Month did not increase the number of mentions by health care providers in any of the 5 domains, but general public mentions increased temporarily in all domains except prevention and policy. Health care provider mentions did not increase with announcements by public figures of pancreatic cancer diagnoses. After Alex Trebek, host of the television show Jeopardy, received his diagnosis, general public mentions of survivorship increased, while Justice Ruth Bader Ginsburg's diagnosis increased conversations on treatment.? Conclusions: Health care provider conversations on Twitter are not aligned with the general public. Pancreatic Cancer Awareness Month temporarily increased general public conversations about treatment, research, and survivorship, but not prevention or policy. Future studies are needed to understand how conversations on social media platforms can be leveraged to increase health care awareness among the general public. ", doi="10.2196/31388", url="https://cancer.jmir.org/2022/1/e31388", url="http://www.ncbi.nlm.nih.gov/pubmed/35323123" } @Article{info:doi/10.2196/31088, author="Chen, Xi and Lin, Fen and Cheng, W. Edmund", title="Stratified Impacts of the Infodemic During the COVID-19 Pandemic: Cross-sectional Survey in 6 Asian Jurisdictions", journal="J Med Internet Res", year="2022", month="Mar", day="22", volume="24", number="3", pages="e31088", keywords="infodemic", keywords="information overload", keywords="psychological distress", keywords="protective behavior", keywords="cross-national survey", keywords="Asia", keywords="COVID-19", abstract="Background: Although timely and accurate information during the COVID-19 pandemic is essential for containing the disease and reducing mental distress, an infodemic, which refers to an overabundance of information, may trigger unpleasant emotions and reduce compliance. Prior research has shown the negative consequences of an infodemic during the pandemic; however, we know less about which subpopulations are more exposed to the infodemic and are more vulnerable to the adverse psychological and behavioral effects. Objective: This study aimed to examine how sociodemographic factors and information-seeking behaviors affect the perceived information overload during the COVID-19 pandemic. We also investigated the effect of perceived information overload on psychological distress and protective behavior and analyzed the socioeconomic differences in the effects. Methods: The data for this study were obtained from a cross-national survey of residents in 6 jurisdictions in Asia in May 2020. The survey targeted residents aged 18 years or older. A probability-based quota sampling strategy was adopted to ensure that the selected samples matched the population's geographical and demographic characteristics released by the latest available census in each jurisdiction. The final sample included 10,063 respondents. Information overload about COVID-19 was measured by asking the respondents to what extent they feel overwhelmed by news related to COVID-19. The measure of psychological distress was adapted from the Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders 5 (DSM-5). Protective behaviors included personal hygienic behavior and compliance with social distancing measures. Results: Younger respondents and women (b=0.20, 95\% CI 0.14 to 0.26) were more likely to perceive information overload. Participants self-perceived as upper or upper-middle class (b=0.19, 95\% CI 0.09 to 0.30) and those with full-time jobs (b=0.11, 95\% CI 0.04 to 0.17) tended to perceive higher information overload. Respondents who more frequently sought COVID-19 information from newspapers (b=0.12, 95\% CI 0.11 to 0.14), television (b=0.07, 95\% CI 0.05 to 0.09), and family and friends (b=0.11, 95\% CI 0.09 to 0.14) were more likely to feel overwhelmed. In contrast, obtaining COVID-19 information from online news outlets and social media was not associated with perceived information overload. There was a positive relationship between perceived information overload and psychological distress (b=2.18, 95\% CI 2.09 to 2.26). Such an association was stronger among urban residents, full-time employees, and those living in privately owned housing. The effect of perceived information overload on protective behavior was not significant. Conclusions: Our findings revealed that respondents who were younger, were female, had a higher socioeconomic status (SES), and had vulnerable populations in the household were more likely to feel overwhelmed by COVID-19 information. Perceived information overload tended to increase psychological distress, and people with higher SES were more vulnerable to this adverse psychological consequence. Effective policies and interventions should be promoted to target vulnerable populations who are more susceptible to the occurrence and negative psychological influence of perceived information overload. ", doi="10.2196/31088", url="https://www.jmir.org/2022/3/e31088", url="http://www.ncbi.nlm.nih.gov/pubmed/35103601" } @Article{info:doi/10.2196/31135, author="Metzler, Matthias Julian and Kalaitzopoulos, Rafail Dimitrios and Burla, Laurin and Schaer, Gabriel and Imesch, Patrick", title="Examining the Influence on Perceptions of Endometriosis via Analysis of Social Media Posts: Cross-sectional Study", journal="JMIR Form Res", year="2022", month="Mar", day="18", volume="6", number="3", pages="e31135", keywords="endometriosis", keywords="social media", keywords="Facebook", keywords="Instagram", keywords="influencer", keywords="engagement", abstract="Background: Social media platforms, such as Facebook and Instagram, are increasingly being used to share health-related information by ``influencers,'' regular users, and institutions alike. While patients may benefit in various ways from these interactions, little is known about the types of endometriosis-related information published on social media. As digital opinion leaders influence the perceptions of their followers, physicians need to be aware about ideas and beliefs that are available online, in order to address possible misconceptions and provide optimal patient care. Objective: The aim of this study was to identify and analyze frequent endometriosis-related discussion topics on social media in order to offer caregivers insight into commonly discussed subject matter and aspects. Methods: We performed a systematic search using predefined parameters. Using the term ``endometriosis'' in Facebook's search function and a social media search engine, a list of Facebook pages was generated. A list of Instagram accounts was generated using the terms ``endometriosis'' and ``endo'' in Instagram's search function. Pages and accounts in English with 5000 or more followers or likes were included. Nonpublic, unrelated, or inactive pages and accounts were excluded. For each account, the most recent 10 posts were identified and categorized by two independent examiners using qualitative content analysis. User engagement was calculated using the numbers of interactions (ie, shares, likes, and comments) for each post, stratified by the number of followers. Results: A total of 39 Facebook pages and 43 Instagram accounts with approximately 1.4 million followers were identified. Hospitals and medical centers made up 15\% (6/39) of the Facebook pages and 5\% (2/43) of the Instagram accounts. Top accounts had up to 111,600 (Facebook) and 41,400 (Instagram) followers. A total of 820 posts were analyzed. On Facebook, most posts were categorized as ``awareness'' (101/390, 25.9\% of posts), ``education and research'' (71/390, 18.2\%), and ``promotion'' (64/390, 16.4\%). On Instagram, the top categories were ``inspiration and support'' (120/430, 27.9\% of posts), ``awareness'' (72/430, 16.7\%), and ``personal story'' (72/430, 16.7\%). The frequency of most categories differed significantly between platforms. User engagement was higher on Instagram than on Facebook (3.20\% vs 0.97\% of followers per post). On Instagram, the highest percentage of users engaged with posts categorized as ``humor'' (mean 4.19\%, SD 4.53\%), ``personal story'' (mean 3.02\%, SD 4.95\%), and ``inspiration and support'' (mean 2.83\%, SD 3.08\%). On Facebook, posts in the categories ``awareness'' (mean 2.05\%, SD 15.56\%), ``humor'' (mean 0.91\%, SD 1.07\%), and ``inspiration and support'' (mean 0.56\%, SD 1.37\%) induced the most user engagement. Posts made by hospitals and medical centers generated higher user engagement than posts by regular accounts on Facebook (mean 1.44\%, SD 1.11\% vs mean 0.88\%, SD 2.71\% of followers per post) and Instagram (mean 3.33\%, SD 1.21\% vs mean 3.19\%, SD 2.52\% of followers per post). Conclusions: Facebook and Instagram are widely used to share endometriosis-related information among a large number of users. Most posts offer inspiration or support, spread awareness about the disease, or cover personal issues. Followers mostly engage with posts with a humoristic, supportive, and awareness-generating nature. Health care providers should be aware about the topics discussed online, as this may lead to an increased understanding of the needs and demands of digitally proficient patients with endometriosis. ", doi="10.2196/31135", url="https://formative.jmir.org/2022/3/e31135", url="http://www.ncbi.nlm.nih.gov/pubmed/35302501" } @Article{info:doi/10.2196/31732, author="Deiner, S. Michael and Seitzman, D. Gerami and Kaur, Gurbani and McLeod, D. Stephen and Chodosh, James and Lietman, M. Thomas and Porco, C. Travis", title="Sustained Reductions in Online Search Interest for Communicable Eye and Other Conditions During the COVID-19 Pandemic: Infodemiology Study", journal="JMIR Infodemiology", year="2022", month="Mar", day="16", volume="2", number="1", pages="e31732", keywords="COVID-19", keywords="pandemic", keywords="communicable disease", keywords="social distancing", keywords="infodemiology", keywords="Google Trends", keywords="influenza", keywords="conjunctivitis", keywords="ocular symptoms", keywords="seasonality", keywords="trend", keywords="online health information", keywords="information-seeking", abstract="Background: In a prior study at the start of the pandemic, we reported reduced numbers of Google searches for the term ``conjunctivitis'' in the United States in March and April 2020 compared with prior years. As one explanation, we conjectured that reduced information-seeking may have resulted from social distancing reducing contagious conjunctivitis cases. Here, after 1 year of continued implementation of social distancing, we asked if there have been persistent reductions in searches for ``conjunctivitis,'' and similarly for other communicable disease terms, compared to control terms. Objective: The aim of this study was to determine if reduction in searches in the United States for terms related to conjunctivitis and other common communicable diseases occurred in the spring-winter season of the COVID-19 pandemic, and to compare this outcome to searches for terms representing noncommunicable conditions, COVID-19, and to seasonality. Methods: Weekly relative search frequency volume data from Google Trends for 68 search terms in English for the United States were obtained for the weeks of March 2011 through February 2021. Terms were classified a priori as 16 terms related to COVID-19, 29 terms representing communicable conditions, and 23 terms representing control noncommunicable conditions. To reduce bias, all analyses were performed while masked to term names, classifications, and locations. To test for the significance of changes during the pandemic, we detrended and compared postpandemic values to those expected based on prepandemic trends, per season, computing one- and two-sided P values. We then compared these P values between term groups using Wilcoxon rank-sum and Fisher exact tests to assess if non-COVID-19 terms representing communicable diseases were more likely to show significant reductions in searches in 2020-2021 than terms not representing such diseases. We also assessed any relationship between a term's seasonality and a reduced search trend for the term in 2020-2021 seasons. P values were subjected to false discovery rate correction prior to reporting. Data were then unmasked. Results: Terms representing conjunctivitis and other communicable conditions showed a sustained reduced search trend in the first 4 seasons of the 2020-2021 COVID-19 pandemic compared to prior years. In comparison, the search for noncommunicable condition terms was significantly less reduced (Wilcoxon and Fisher exact tests, P<.001; summer, autumn, winter). A significant correlation was also found between reduced search for a term in 2020-2021 and seasonality of that term (Theil-Sen, P<.001; summer, autumn, winter). Searches for COVID-19--related conditions were significantly elevated compared to those in prior years, and searches for influenza-related terms were significantly lower than those for prior years in winter 2020-2021 (P<.001). Conclusions: We demonstrate the low-cost and unbiased use of online search data to study how a wide range of conditions may be affected by large-scale interventions or events such as social distancing during the COVID-19 pandemic. Our findings support emerging clinical evidence implicating social distancing and the COVID-19 pandemic in the reduction of communicable disease and on ocular conditions. ", doi="10.2196/31732", url="https://infodemiology.jmir.org/2022/1/e31732", url="http://www.ncbi.nlm.nih.gov/pubmed/35320981" } @Article{info:doi/10.2196/33587, author="Calac, J. Alec and Haupt, R. Michael and Li, Zhuoran and Mackey, Tim", title="Spread of COVID-19 Vaccine Misinformation in the Ninth Inning: Retrospective Observational Infodemic Study", journal="JMIR Infodemiology", year="2022", month="Mar", day="16", volume="2", number="1", pages="e33587", keywords="infoveillance", keywords="infodemiology", keywords="COVID-19", keywords="vaccine", keywords="Twitter", keywords="social listening", keywords="social media", keywords="misinformation", keywords="spread", keywords="observational", keywords="hesitancy", keywords="communication", keywords="discourse", abstract="Background: Shortly after Pfizer and Moderna received emergency use authorizations from the Food and Drug Administration, there were increased reports of COVID-19 vaccine-related deaths in the Vaccine Adverse Event Reporting System (VAERS). In January 2021, Major League Baseball legend and Hall of Famer, Hank Aaron, passed away at the age of 86 years from natural causes, just 2 weeks after he received the COVID-19 vaccine. Antivaccination groups attempted to link his death to the Moderna vaccine, similar to other attempts misrepresenting data from the VAERS to spread COVID-19 misinformation. Objective: This study assessed the spread of misinformation linked to erroneous claims about Hank Aaron's death on Twitter and then characterized different vaccine misinformation and hesitancy themes generated from users who interacted with this misinformation discourse. Methods: An initial sample of tweets from January 31, 2021, to February 6, 2021, was queried from the Twitter Search Application Programming Interface using the keywords ``Hank Aaron'' and ``vaccine.'' The sample was manually annotated for misinformation, reporting or news media, and public reaction. Nonmedia user accounts were also classified if they were verified by Twitter. A second sample of tweets, representing direct comments or retweets to misinformation-labeled content, was also collected. User sentiment toward misinformation, positive (agree) or negative (disagree), was recorded. The Strategic Advisory Group of Experts Vaccine Hesitancy Matrix from the World Health Organization was used to code the second sample of tweets for factors influencing vaccine confidence. Results: A total of 436 tweets were initially sampled from the Twitter Search Application Programming Interface. Misinformation was the most prominent content type (n=244, 56\%) detected, followed by public reaction (n=122, 28\%) and media reporting (n=69, 16\%). No misinformation-related content reviewed was labeled as misleading by Twitter at the time of the study. An additional 1243 comments on misinformation-labeled tweets from 973 unique users were also collected, with 779 comments deemed relevant to study aims. Most of these comments expressed positive sentiment (n=612, 78.6\%) to misinformation and did not refute it. Based on the World Health Organization Strategic Advisory Group of Experts framework, the most common vaccine hesitancy theme was individual or group influences (n=508, 65\%), followed by vaccine or vaccination-specific influences (n=110, 14\%) and contextual influences (n=93, 12\%). Common misinformation themes observed included linking the death of Hank Aaron to ``suspicious'' elderly deaths following vaccination, claims about vaccines being used for depopulation, death panels, federal officials targeting Black Americans, and misinterpretation of VAERS reports. Four users engaging with or posting misinformation were verified on Twitter at the time of data collection. Conclusions: Our study found that the death of a high-profile ethnic minority celebrity led to the spread of misinformation on Twitter. This misinformation directly challenged the safety and effectiveness of COVID-19 vaccines at a time when ensuring vaccine coverage among minority populations was paramount. Misinformation targeted at minority groups and echoed by other verified Twitter users has the potential to generate unwarranted vaccine hesitancy at the expense of people such as Hank Aaron who sought to promote public health and community immunity. ", doi="10.2196/33587", url="https://infodemiology.jmir.org/2022/1/e33587", url="http://www.ncbi.nlm.nih.gov/pubmed/35320982" } @Article{info:doi/10.2196/32452, author="Quinn, K. Emma and Fenton, Shelby and Ford-Sahibzada, A. Chelsea and Harper, Andrew and Marcon, R. Alessandro and Caulfield, Timothy and Fazel, S. Sajjad and Peters, E. Cheryl", title="COVID-19 and Vitamin D Misinformation on YouTube: Content Analysis", journal="JMIR Infodemiology", year="2022", month="Mar", day="14", volume="2", number="1", pages="e32452", keywords="COVID-19", keywords="vitamin D", keywords="misinformation", keywords="YouTube", keywords="content analysis", keywords="social media", keywords="video", keywords="infodemic", keywords="risk", keywords="prevention", keywords="health information", keywords="immunity", keywords="immune system", keywords="supplements", keywords="natural medicine", abstract="Background: The ``infodemic'' accompanying the SARS-CoV-2 virus pandemic has the potential to increase avoidable spread as well as engagement in risky health behaviors. Although social media platforms, such as YouTube, can be an inexpensive and effective method of sharing accurate health information, inaccurate and misleading information shared on YouTube can be dangerous for viewers. The confusing nature of data and claims surrounding the benefits of vitamin D, particularly in the prevention or cure of COVID-19, influences both viewers and the general ``immune boosting'' commercial interest. Objective: The aim of this study was to ascertain how information on vitamin D and COVID-19 was presented on YouTube in 2020. Methods: YouTube video results for the search terms ``COVID,'' ``coronavirus,'' and ``vitamin D'' were collected and analyzed for content themes and deemed useful or misleading based on the accuracy or inaccuracy of the content. Qualitative content analysis and simple statistical analysis were used to determine the prevalence and frequency of concerning content, such as confusing correlation with causation regarding vitamin D benefits. Results: In total, 77 videos with a combined 10,225,763 views (at the time of data collection) were included in the analysis, with over three-quarters of them containing misleading content about COVID-19 and vitamin D. In addition, 45 (58\%) of the 77 videos confused the relationship between vitamin D and COVID-19, with 46 (85\%) of 54 videos stating that vitamin D has preventative or curative abilities. The major contributors to these videos were medical professionals with YouTube accounts. Vitamin D recommendations that do not align with the current literature were frequently suggested, including taking supplementation higher than the recommended safe dosage or seeking intentional solar UV radiation exposure. Conclusions: The spread of misinformation is particularly alarming when spread by medical professionals, and existing data suggesting vitamin D has immune-boosting abilities can add to viewer confusion or mistrust in health information. Further, the suggestions made in the videos may increase the risks of other poor health outcomes, such as skin cancer from solar UV radiation. ", doi="10.2196/32452", url="https://infodemiology.jmir.org/2022/1/e32452", url="http://www.ncbi.nlm.nih.gov/pubmed/35310014" } @Article{info:doi/10.2196/31687, author="Ni, Congning and Wan, Zhiyu and Yan, Chao and Liu, Yongtai and Clayton, Wright Ellen and Malin, Bradley and Yin, Zhijun", title="The Public Perception of the \#GeneEditedBabies Event Across Multiple Social Media Platforms: Observational Study", journal="J Med Internet Res", year="2022", month="Mar", day="11", volume="24", number="3", pages="e31687", keywords="CRISPR/Cas9", keywords="gene-edited babies", keywords="social media", keywords="stance learning", keywords="text mining", keywords="content analysis", abstract="Background: In November 2018, a Chinese researcher reported that his team had applied clustered regularly interspaced palindromic repeats or associated protein 9 to delete the gene C-C chemokine receptor type 5 from embryos and claimed that the 2 newborns would have lifetime immunity from HIV infection, an event referred to as \#GeneEditedBabies on social media platforms. Although this event stirred a worldwide debate on ethical and legal issues regarding clinical trials with embryonic gene sequences, the focus has mainly been on academics and professionals. However, how the public, especially stratified by geographic region and culture, reacted to these issues is not yet well-understood. Objective: The aim of this study is to examine web-based posts about the \#GeneEditedBabies event and characterize and compare the public's stance across social media platforms with different user bases. Methods: We used a set of relevant keywords to search for web-based posts in 4 worldwide or regional mainstream social media platforms: Sina Weibo (China), Twitter, Reddit, and YouTube. We applied structural topic modeling to analyze the main discussed topics and their temporal trends. On the basis of the topics we found, we designed an annotation codebook to label 2000 randomly sampled posts from each platform on whether a supporting, opposing, or neutral stance toward this event was expressed and what the major considerations of those posts were if a stance was described. The annotated data were used to compare stances and the language used across the 4 web-based platforms. Results: We collected >220,000 posts published by approximately 130,000 users regarding the \#GeneEditedBabies event. Our results indicated that users discussed a wide range of topics, some of which had clear temporal trends. Our results further showed that although almost all experts opposed this event, many web-based posts supported this event. In particular, Twitter exhibited the largest number of posts in opposition (701/816, 85.9\%), followed by Sina Weibo (968/1140, 84.91\%), Reddit (550/898, 61.2\%), and YouTube (567/1078, 52.6\%). The primary opposing reason was rooted in ethical concerns, whereas the primary supporting reason was based on the expectation that such technology could prevent the occurrence of diseases in the future. Posts from these 4 platforms had different language uses and patterns when they expressed stances on the \#GeneEditedBabies event. Conclusions: This research provides evidence that posts on web-based platforms can offer insights into the public's stance on gene editing techniques. However, these stances vary across web-based platforms and often differ from those raised by academics and policy makers. ", doi="10.2196/31687", url="https://www.jmir.org/2022/3/e31687", url="http://www.ncbi.nlm.nih.gov/pubmed/35275077" } @Article{info:doi/10.2196/25614, author="Tian, Hao and Gaines, Christy and Launi, Lori and Pomales, Ana and Vazquez, Germaine and Goharian, Amanda and Goodnight, Bradley and Haney, Erica and Reh, M. Christopher and Rogers, D. Rachel", title="Understanding Public Perceptions of Per- and Polyfluoroalkyl Substances: Infodemiology Study of Social Media", journal="J Med Internet Res", year="2022", month="Mar", day="11", volume="24", number="3", pages="e25614", keywords="PFAS", keywords="per- and polyfluoroalkyl substances", keywords="social media", keywords="public perceptions", abstract="Background: Per- and polyfluoroalkyl substances (PFAS) are environmental contaminants that have received significant public attention. PFAS are a large group of human-made chemicals that have been used in industry and consumer products worldwide since the 1950s. Human exposure to PFAS is a growing public health concern. Studies suggest that exposure to PFAS may increase the risk of some cancers and have negative health impacts on the endocrine, metabolic, and immune systems. Federal and state health partners are investigating the exposure to and possible health effects associated with PFAS. Government agencies can observe social media discourse on PFAS to better understand public concerns and develop targeted communication and outreach efforts. Objective: The primary objective of this study is to understand how social media is used to share, disseminate, and engage in public discussions of PFAS-related information in the United States. Methods: We investigated PFAS-related content across 2 social media platforms between May 1, 2017, and April 30, 2019, to identify how social media is used in the United States to seek and disseminate PFAS-related information. Our key variable of interest was posts that mentioned ``PFAS,'' ``PFOA,'' ``PFOS,'' and their hashtag variations across social media platforms. Additional variables included post type, time, PFAS event, and geographic location. We examined term use and post type differences across platforms. We used descriptive statistics and regression analysis to assess the incidence of PFAS discussions and to identify the date, event, and geographic patterns. We qualitatively analyzed social media content to determine the most prevalent themes discussed on social media platforms. Results: Our analysis revealed that Twitter had a significantly greater volume of PFAS-related posts compared with Reddit (98,264 vs 3126 posts). PFAS-related social media posts increased by 670\% over 2 years, indicating a marked increase in social media users' interest in and awareness of PFAS. Active engagement varied across platforms, with Reddit posts demonstrating more in-depth discussions compared with passive likes and reposts among Twitter users. Spikes in PFAS discussions were evident and connected to the discovery of contamination events, media coverage, and scientific publications. Thematic analysis revealed that social media users see PFAS as a significant public health concern and seek a trusted source of information about PFAS-related public health efforts. Conclusions: The analysis identified a prevalent theme---on social media, PFAS are perceived as an immediate public health concern, which demonstrates a growing sense of urgency to understand this emerging contaminant and its potential health impacts. Government agencies can continue using social media research to better understand the changing community sentiment on PFAS and disseminate targeted information and then use social media as a forum for dispelling misinformation, communicating scientific findings, and providing resources for relevant public health services. ", doi="10.2196/25614", url="https://www.jmir.org/2022/3/e25614", url="http://www.ncbi.nlm.nih.gov/pubmed/35275066" } @Article{info:doi/10.2196/29819, author="Okunoye, Babatunde and Ning, Shaoyang and Jemielniak, Dariusz", title="Searching for HIV and AIDS Health Information in South Africa, 2004-2019: Analysis of Google and Wikipedia Search Trends", journal="JMIR Form Res", year="2022", month="Mar", day="11", volume="6", number="3", pages="e29819", keywords="HIV/AIDS", keywords="web search", keywords="big data", keywords="public health", keywords="Wikipedia", keywords="information seeking behavior", keywords="online behavior", keywords="online health information", keywords="Google Trends", abstract="Background: AIDS, caused by HIV, is a leading cause of mortality in Africa. HIV/AIDS is among the greatest public health challenges confronting health authorities, with South Africa having the greatest prevalence of the disease in the world. There is little research into how Africans meet their health information needs on HIV/AIDS online, and this research gap impacts programming and educational responses to the HIV/AIDS pandemic. Objective: This paper reports on how, in general, interest in the search terms ``HIV'' and ``AIDS'' mirrors the increase in people living with HIV and the decline in AIDS cases in South Africa. Methods: Data on search trends for HIV and AIDS for South Africa were found using the search terms ``HIV'' and ``AIDS'' (categories: health, web search) on Google Trends. This was compared with data on estimated adults and children living with HIV, and AIDS-related deaths in South Africa, from the Joint United Nations Programme on HIV/AIDS, and also with search interest in the topics ``HIV'' and ``AIDS'' on Wikipedia Afrikaans, the most developed local language Wikipedia service in South Africa. Nonparametric statistical tests were conducted to support the trends and associations identified in the data. Results: Google Trends shows a statistically significant decline (P<.001) in search interest for AIDS relative to HIV in South Africa. This trend mirrors progress on the ground in South Africa and is significantly associated (P<.001) with a decline in AIDS-related deaths and people living longer with HIV. This trend was also replicated on Wikipedia Afrikaans, where there was a greater interest in HIV than AIDS. Conclusions: This statistically significant (P<.001) association between interest in the search terms ``HIV'' and ``AIDS'' in South Africa (2004-2019) and the number of people living with HIV and AIDS in the country (2004-2019) might be an indicator that multilateral efforts at combating HIV/AIDS---particularly through awareness raising and behavioral interventions in South Africa---are bearing fruit, and this is not only evident on the ground, but is also reflected in the online information seeking on the HIV/AIDS pandemic. We acknowledge the limitation that in studying the association between Google search interests on HIV/AIDS and cases/deaths, causal relationships should not be drawn due to the limitations of the data. ", doi="10.2196/29819", url="https://formative.jmir.org/2022/3/e29819", url="http://www.ncbi.nlm.nih.gov/pubmed/35275080" } @Article{info:doi/10.2196/34040, author="Blane, T. Janice and Bellutta, Daniele and Carley, M. Kathleen", title="Social-Cyber Maneuvers During the COVID-19 Vaccine Initial Rollout: Content Analysis of Tweets", journal="J Med Internet Res", year="2022", month="Mar", day="7", volume="24", number="3", pages="e34040", keywords="social cybersecurity", keywords="social-cyber maneuvers", keywords="social network analysis", keywords="disinformation", keywords="BEND maneuvers", keywords="COVID-19", keywords="coronavirus", keywords="social media", keywords="vaccine", keywords="anti-vaccine", keywords="pro-vaccine", keywords="ORA-PRO", keywords="cybersecurity", keywords="security", keywords="Twitter", keywords="community", keywords="communication", keywords="health information", keywords="manipulation", keywords="belief", abstract="Background: During the time surrounding the approval and initial distribution of Pfizer-BioNTech's COVID-19 vaccine, large numbers of social media users took to using their platforms to voice opinions on the vaccine. They formed pro- and anti-vaccination groups toward the purpose of influencing behaviors to vaccinate or not to vaccinate. The methods of persuasion and manipulation for convincing audiences online can be characterized under a framework for social-cyber maneuvers known as the BEND maneuvers. Previous studies have been conducted on the spread of COVID-19 vaccine disinformation. However, these previous studies lacked comparative analyses over time on both community stances and the competing techniques of manipulating both the narrative and network structure to persuade target audiences. Objective: This study aimed to understand community response to vaccination by dividing Twitter data from the initial Pfizer-BioNTech COVID-19 vaccine rollout into pro-vaccine and anti-vaccine stances, identifying key actors and groups, and evaluating how the different communities use social-cyber maneuvers, or BEND maneuvers, to influence their target audiences and the network as a whole. Methods: COVID-19 Twitter vaccine data were collected using the Twitter application programming interface (API) for 1-week periods before, during, and 6 weeks after the initial Pfizer-BioNTech rollout (December 2020 to January 2021). Bot identifications and linguistic cues were derived for users and tweets, respectively, to use as metrics for evaluating social-cyber maneuvers. Organization Risk Analyzer (ORA)-PRO software was then used to separate the vaccine data into pro-vaccine and anti-vaccine communities and to facilitate identification of key actors, groups, and BEND maneuvers for a comparative analysis between each community and the entire network. Results: Both the pro-vaccine and anti-vaccine communities used combinations of the 16 BEND maneuvers to persuade their target audiences of their particular stances. Our analysis showed how each side attempted to build its own community while simultaneously narrowing and neglecting the opposing community. Pro-vaccine users primarily used positive maneuvers such as excite and explain messages to encourage vaccination and backed leaders within their group. In contrast, anti-vaccine users relied on negative maneuvers to dismay and distort messages with narratives on side effects and death and attempted to neutralize the effectiveness of the leaders within the pro-vaccine community. Furthermore, nuking through platform policies showed to be effective in reducing the size of the anti-vaccine online community and the quantity of anti-vaccine messages. Conclusions: Social media continues to be a domain for manipulating beliefs and ideas. These conversations can ultimately lead to real-world actions such as to vaccinate or not to vaccinate against COVID-19. Moreover, social media policies should be further explored as an effective means for curbing disinformation and misinformation online. ", doi="10.2196/34040", url="https://www.jmir.org/2022/3/e34040", url="http://www.ncbi.nlm.nih.gov/pubmed/35044302" } @Article{info:doi/10.2196/25552, author="Li, Chuqin and Ademiluyi, Adesoji and Ge, Yaorong and Park, Albert", title="Using Social Media to Understand Web-Based Social Factors Concerning Obesity: Systematic Review", journal="JMIR Public Health Surveill", year="2022", month="Mar", day="7", volume="8", number="3", pages="e25552", keywords="obesity", keywords="web-based social factors", keywords="systematic review", keywords="social-ecological model", abstract="Background: Evidence in the literature surrounding obesity suggests that social factors play a substantial role in the spread of obesity. Although social ties with a friend who is obese increase the probability of becoming obese, the role of social media in this dynamic remains underexplored in obesity research. Given the rapid proliferation of social media in recent years, individuals socialize through social media and share their health-related daily routines, including dieting and exercising. Thus, it is timely and imperative to review previous studies focused on social factors in social media and obesity. Objective: This study aims to examine web-based social factors in relation to obesity research. Methods: We conducted a systematic review. We searched PubMed, Association for Computing Machinery, and ScienceDirect for articles published by July 5, 2019. Web-based social factors that are related to obesity behaviors were studied and analyzed. Results: In total, 1608 studies were identified from the selected databases. Of these 1608 studies, 50 (3.11\%) studies met the eligibility criteria. In total, 10 types of web-based social factors were identified, and a socioecological model was adopted to explain their potential impact on an individual from varying levels of web-based social structure to social media users' connection to the real world. Conclusions: We found 4 levels of interaction in social media. Gender was the only factor found at the individual level, and it affects user's web-based obesity-related behaviors. Social support was the predominant factor identified, which benefits users in their weight loss journey at the interpersonal level. Some factors, such as stigma were also found to be associated with a healthy web-based social environment. Understanding the effectiveness of these factors is essential to help users create and maintain a healthy lifestyle. ", doi="10.2196/25552", url="https://publichealth.jmir.org/2022/3/e25552", url="http://www.ncbi.nlm.nih.gov/pubmed/35254279" } @Article{info:doi/10.2196/32752, author="Gabarron, Elia and Dechsling, Anders and Skafle, Ingjerd and Nordahl-Hansen, Anders", title="Discussions of Asperger Syndrome on Social Media: Content and Sentiment Analysis on Twitter", journal="JMIR Form Res", year="2022", month="Mar", day="7", volume="6", number="3", pages="e32752", keywords="social media", keywords="autism spectrum disorder", keywords="health literacy", keywords="famous persons", keywords="Asperger", keywords="Elon Musk", keywords="twitter", keywords="tweets", keywords="mental health", keywords="autism", keywords="sentiment analysis", abstract="Background: On May 8, 2021, Elon Musk, a well-recognized entrepreneur and business magnate, revealed on a popular television show that he has Asperger syndrome. Research has shown that people's perceptions of a condition are modified when influential individuals in society publicly disclose their diagnoses. It was anticipated that Musk's disclosure would contribute to discussions on the internet about the syndrome, and also to a potential change in the perception of this condition. Objective: The objective of this study was to compare the types of information contained in popular tweets about Asperger syndrome as well as their engagement and sentiment before and after Musk's disclosure. Methods: We extracted tweets that were published 1 week before and after Musk's disclosure that had received >30 likes and included the terms ``Aspergers'' or ``Aspie.'' The content of each post was classified by 2 independent coders as to whether the information provided was valid, contained misinformation, or was neutral. Furthermore, we analyzed the engagement on these posts and the expressed sentiment by using the AFINN sentiment analysis tool. Results: We extracted a total of 227 popular tweets (34 posted the week before Musk's announcement and 193 posted the week after). We classified 210 (92.5\%) of the tweets as neutral, 13 (5.7\%) tweets as informative, and 4 (1.8\%) as containing misinformation. Both informative and misinformative tweets were posted after Musk's disclosure. Popular tweets posted before Musk's disclosure were significantly more engaging (received more comments, retweets, and likes) than the tweets posted the week after. We did not find a significant difference in the sentiment expressed in the tweets posted before and after the announcement. Conclusions: The use of social media platforms by health authorities, autism associations, and other stakeholders has the potential to increase the awareness and acceptance of knowledge about autism and Asperger syndrome. When prominent figures disclose their diagnoses, the number of posts about their particular condition tends to increase and thus promote a potential opportunity for greater outreach to the general public about that condition. ", doi="10.2196/32752", url="https://formative.jmir.org/2022/3/e32752", url="http://www.ncbi.nlm.nih.gov/pubmed/35254265" } @Article{info:doi/10.2196/25243, author="Rivera, M. Yonaira and Moran, B. Meghan and Thrul, Johannes and Joshu, Corinne and Smith, C. Katherine", title="Contextualizing Engagement With Health Information on Facebook: Using the Social Media Content and Context Elicitation Method", journal="J Med Internet Res", year="2022", month="Mar", day="4", volume="24", number="3", pages="e25243", keywords="mixed methods", keywords="data collection", keywords="social media", keywords="cancer", keywords="health information", keywords="Facebook", keywords="digital health", abstract="Background: Most of what is known regarding health information engagement on social media stems from quantitative methodologies. Public health literature often quantifies engagement by measuring likes, comments, and/or shares of posts within health organizations' Facebook pages. However, this content may not represent the health information (and misinformation) generally available to and consumed by platform users. Furthermore, some individuals may prefer to engage with information without leaving quantifiable digital traces. Mixed methods approaches may provide a way of surpassing the constraints of assessing engagement with health information by using only currently available social media metrics. Objective: This study aims to discuss the limitations of current approaches in assessing health information engagement on Facebook and presents the social media content and context elicitation method, a qualitatively driven, mixed methods approach to understanding engagement with health information and how engagement may lead to subsequent actions. Methods: Data collection, management, and analysis using the social media content and context elicitation method are presented. This method was developed for a broader study exploring how and why US Latinos and Latinas engage with cancer prevention and screening information on Facebook. The study included 20 participants aged between 40 and 75 years without cancer who participated in semistructured, in-depth interviews to discuss their Facebook use and engagement with cancer information on the platform. Participants accessed their Facebook account alongside the researcher, typed cancer in the search bar, and discussed cancer-related posts they engaged with during the previous 12 months. Engagement was defined as liking, commenting, and/or sharing a post; clicking on a post link; reading an article in a post; and/or watching a video within a post. Content engagement prompted questions regarding the reasons for engagement and whether engagement triggered further action. Data were managed using MAXQDA (VERBI GmbH) and analyzed using thematic and content analyses. Results: Data emerging from the social media content and context elicitation method demonstrated that participants mainly engaged with cancer prevention and screening information by viewing and/or reading content (48/66, 73\%) without liking, commenting, or sharing it. This method provided rich content regarding how US Latinos and Latinas engage with and act upon cancer prevention and screening information on Facebook. We present 2 emblematic cases from the main study to exemplify the additional information and context elicited from this methodology, which is currently lacking from quantitative approaches. Conclusions: The social media content and context elicitation method allows a better representation and deeper contextualization of how people engage with and act upon health information and misinformation encountered on social media. This method may be applied to future studies regarding how to best communicate health information on social media, including how these affect assessments of message credibility and accuracy, which can influence health outcomes. ", doi="10.2196/25243", url="https://www.jmir.org/2022/3/e25243", url="http://www.ncbi.nlm.nih.gov/pubmed/35254266" } @Article{info:doi/10.2196/24787, author="Young, D. Sean and Zhang, Qingpeng and Zeng, Dajun Daniel and Zhan, Yongcheng and Cumberland, William", title="Social Media Images as an Emerging Tool to Monitor Adherence to COVID-19 Public Health Guidelines: Content Analysis", journal="J Med Internet Res", year="2022", month="Mar", day="3", volume="24", number="3", pages="e24787", keywords="internet", keywords="social media", keywords="health informatics", keywords="tool", keywords="monitor", keywords="adherence", keywords="COVID-19", keywords="public health", keywords="guidelines", keywords="content analysis", keywords="policy", abstract="Background: Innovative surveillance methods are needed to assess adherence to COVID-19 recommendations, especially methods that can provide near real-time or highly geographically targeted data. Use of location-based social media image data (eg, Instagram images) is one possible approach that could be explored to address this problem. Objective: We seek to evaluate whether publicly available near real-time social media images might be used to monitor COVID-19 health policy adherence. Methods: We collected a sample of 43,487 Instagram images in New York from February 7 to April 11, 2020, from the following location hashtags: \#Centralpark (n=20,937), \#Brooklyn Bridge (n=14,875), and \#Timesquare (n=7675). After manually reviewing images for accuracy, we counted and recorded the frequency of valid daily posts at each of these hashtag locations over time, as well as rated and counted whether the individuals in the pictures at these location hashtags were social distancing (ie, whether the individuals in the images appeared to be distanced from others vs next to or touching each other). We analyzed the number of images posted over time and the correlation between trends among hashtag locations. Results: We found a statistically significant decline in the number of posts over time across all regions, with an approximate decline of 17\% across each site (P<.001). We found a positive correlation between hashtags (\#Centralpark and \#Brooklynbridge: r=0.40; \#BrooklynBridge and \#Timesquare: r=0.41; and \#Timesquare and \#Centralpark: r=0.33; P<.001 for all correlations). The logistic regression analysis showed a mild statistically significant increase in the proportion of posts over time with people appearing to be social distancing at Central Park (P=.004) and Brooklyn Bridge (P=.02) but not for Times Square (P=.16). Conclusions: Results suggest the potential of using location-based social media image data as a method for surveillance of COVID-19 health policy adherence. Future studies should further explore the implementation and ethical issues associated with this approach. ", doi="10.2196/24787", url="https://www.jmir.org/2022/3/e24787", url="http://www.ncbi.nlm.nih.gov/pubmed/34995205" } @Article{info:doi/10.2196/32364, author="Cai, Owen and Sousa-Pinto, Bernardo", title="United States Influenza Search Patterns Since the Emergence of COVID-19: Infodemiology Study", journal="JMIR Public Health Surveill", year="2022", month="Mar", day="3", volume="8", number="3", pages="e32364", keywords="COVID-19", keywords="influenza", keywords="surveillance", keywords="media coverage", keywords="Google Trends", keywords="infodemiology", keywords="monitoring", keywords="trend", keywords="United States", keywords="information-seeking", keywords="online health information", abstract="Background: The emergence and media coverage of COVID-19 may have affected influenza search patterns, possibly affecting influenza surveillance results using Google Trends. Objective: We aimed to investigate if the emergence of COVID-19 was associated with modifications in influenza search patterns in the United States. Methods: We retrieved US Google Trends data (relative number of searches for specified terms) for the topics influenza, Coronavirus disease 2019, and symptoms shared between influenza and COVID-19. We calculated the correlations between influenza and COVID-19 search data for a 1-year period after the first COVID-19 diagnosis in the United States (January 21, 2020 to January 20, 2021). We constructed a seasonal autoregressive integrated moving average model and compared predicted search volumes, using the 4 previous years, with Google Trends relative search volume data. We built a similar model for shared symptoms data. We also assessed correlations for the past 5 years between Google Trends influenza data, US Centers for Diseases Control and Prevention influenza-like illness data, and influenza media coverage data. Results: We observed a nonsignificant weak correlation ($\rho$= --0.171; P=0.23) between COVID-19 and influenza Google Trends data. Influenza search volumes for 2020-2021 distinctly deviated from values predicted by seasonal autoregressive integrated moving average models---for 6 weeks within the first 13 weeks after the first COVID-19 infection was confirmed in the United States, the observed volume of searches was higher than the upper bound of 95\% confidence intervals for predicted values. Similar results were observed for shared symptoms with influenza and COVID-19 data. The correlation between Google Trends influenza data and CDC influenza-like-illness data decreased after the emergence of COVID-19 (2020-2021: $\rho$=0.643; 2019-2020: $\rho$=0.902), while the correlation between Google Trends influenza data and influenza media coverage volume remained stable (2020-2021: $\rho$=0.746; 2019-2020: $\rho$=0.707). Conclusions: Relevant differences were observed between predicted and observed influenza Google Trends data the year after the onset of the COVID-19 pandemic in the United States. Such differences are possibly due to media coverage, suggesting limitations to the use of Google Trends as a flu surveillance tool. ", doi="10.2196/32364", url="https://publichealth.jmir.org/2022/3/e32364", url="http://www.ncbi.nlm.nih.gov/pubmed/34878996" } @Article{info:doi/10.2196/31813, author="Jalali, Niloofar and Tran, Ken N. and Sen, Anindya and Morita, Pelegrini Plinio", title="Identifying the Socioeconomic, Demographic, and Political Determinants of Social Mobility and Their Effects on COVID-19 Cases and Deaths: Evidence From US Counties", journal="JMIR Infodemiology", year="2022", month="Mar", day="3", volume="2", number="1", pages="e31813", keywords="COVID-19", keywords="cases", keywords="deaths", keywords="mobility", keywords="Google mobility data", keywords="clustering", abstract="Background: The spread of COVID-19 at the local level is significantly impacted by population mobility. The U.S. has had extremely high per capita COVID-19 case and death rates. Efficient nonpharmaceutical interventions to control the spread of COVID-19 depend on our understanding of the determinants of public mobility. Objective: This study used publicly available Google data and machine learning to investigate population mobility across a sample of US counties. Statistical analysis was used to examine the socioeconomic, demographic, and political determinants of mobility and the corresponding patterns of per capita COVID-19 case and death rates. Methods: Daily Google population mobility data for 1085 US counties from March 1 to December 31, 2020, were clustered based on differences in mobility patterns using K-means clustering methods. Social mobility indicators (retail, grocery and pharmacy, workplace, and residence) were compared across clusters. Statistical differences in socioeconomic, demographic, and political variables between clusters were explored to identify determinants of mobility. Clusters were matched with daily per capita COVID-19 cases and deaths. Results: Our results grouped US counties into 4 Google mobility clusters. Clusters with more population mobility had a higher percentage of the population aged 65 years and over, a greater population share of Whites with less than high school and college education, a larger percentage of the population with less than a college education, a lower percentage of the population using public transit to work, and a smaller share of voters who voted for Clinton during the 2016 presidential election. Furthermore, clusters with greater population mobility experienced a sharp increase in per capita COVID-19 case and death rates from November to December 2020. Conclusions: Republican-leaning counties that are characterized by certain demographic characteristics had higher increases in social mobility and ultimately experienced a more significant incidence of COVID-19 during the latter part of 2020. ", doi="10.2196/31813", url="https://infodemiology.jmir.org/2022/1/e31813", url="http://www.ncbi.nlm.nih.gov/pubmed/35287305" } @Article{info:doi/10.2196/32871, author="Arshonsky, Josh and Krawczyk, Noa and Bunting, M. Amanda and Frank, David and Friedman, R. Samuel and Bragg, A. Marie", title="Informal Coping Strategies Among People Who Use Opioids During COVID-19: Thematic Analysis of Reddit Forums", journal="JMIR Form Res", year="2022", month="Mar", day="3", volume="6", number="3", pages="e32871", keywords="opioid use", keywords="Reddit", keywords="coping strategies", keywords="COVID-19", keywords="opioid", keywords="drug", keywords="coping", keywords="social media", keywords="strategy", keywords="content analysis", keywords="abstain", keywords="addiction", keywords="data mining", keywords="support", abstract="Background: The COVID-19 pandemic has transformed how people seeking to reduce opioid use access treatment services and navigate efforts to abstain from using opioids. Social distancing policies have drastically reduced access to many forms of social support, but they may have also upended some perceived barriers to reducing or abstaining from opioid use. Objective: This qualitative study aims to identify informal coping strategies for reducing and abstaining from opioid use among Reddit users who have posted in opioid-related subreddits at the beginning of the COVID-19 pandemic. Methods: We extracted data from 2 major opioid-related subreddits. Thematic data analysis was used to evaluate subreddit posts dated from March 5 to May 13, 2020, that referenced COVID-19 and opioid use, resulting in a final sample of 300 posts that were coded and analyzed. Results: Of the 300 subreddit posts, 100 (33.3\%) discussed at least 1 type of informal coping strategy. Those strategies included psychological and behavioral coping skills, adoption of healthy habits, and use of substances to manage withdrawal symptoms. In addition, 12 (4\%) subreddit posts explicitly mentioned using social distancing as an opportunity for cessation of or reduction in opioid use. Conclusions: Reddit discussion forums have provided a community for people to share strategies for reducing opioid use and support others during the COVID-19 pandemic. Future research needs to assess the impact of COVID-19 on opioid use behaviors, especially during periods of limited treatment access and isolation, as these can inform future efforts in curbing the opioid epidemic and other substance-related harms. ", doi="10.2196/32871", url="https://formative.jmir.org/2022/3/e32871", url="http://www.ncbi.nlm.nih.gov/pubmed/35084345" } @Article{info:doi/10.2196/34831, author="Mourali, Mehdi and Drake, Carly", title="The Challenge of Debunking Health Misinformation in Dynamic Social Media Conversations: Online Randomized Study of Public Masking During COVID-19", journal="J Med Internet Res", year="2022", month="Mar", day="2", volume="24", number="3", pages="e34831", keywords="misinformation", keywords="debunking", keywords="correction", keywords="social media", keywords="truth objectivity", keywords="COVID-19", keywords="infodemiology", keywords="health information", keywords="digital health", keywords="public health", keywords="health professional", abstract="Background: The spread of false and misleading health information on social media can cause individual and social harm. Research on debunking has shown that properly designed corrections can mitigate the impact of misinformation, but little is known about the impact of correction in the context of prolonged social media debates. For example, when a social media user takes to Facebook to make a false claim about a health-related practice and a health expert subsequently refutes the claim, the conversation rarely ends there. Often, the social media user proceeds by rebuking the critic and doubling down on the claim. Objective: The aim of this study was to examine the impact of such extended back and forth between false claims and debunking attempts on observers' dispositions toward behavior that science favors. We tested competing predictions about the effect of extended exposure on people's attitudes and intentions toward masking in public during the early days of the COVID-19 pandemic and explored several psychological processes potentially underlying this effect. Methods: A total of 500 US residents took part in an online experiment in October 2020. They reported on their attitudes and intentions toward wearing masks in public. They were then randomly assigned to one of four social media exposure conditions (misinformation only vs misinformation+correction vs misinformation+correction+rebuke vs misinformation+correction+rebuke+second correction), and reported their attitudes and intentions for a second time. They also indicated whether they would consider sharing the thread if they were to see it on social media and answered questions on potential mediators and covariates. Results: Exposure to misinformation had a negative impact on attitudes and intentions toward masking ($\beta$=--.35, 95\% CI --.42 to --.29; P<.001). Moreover, initial debunking of a false claim generally improved attitudes and intentions toward masking ($\beta$=.35, 95\% CI .16 to .54; P<.001). However, this improvement was washed out by further exposure to false claims and debunking attempts ($\beta$=--.53, 95\% CI --.72 to --.34; P<.001). The latter result is partially explained by a decrease in the perceived objectivity of truth. That is, extended exposure to false claims and debunking attempts appear to weaken the belief that there is an objectively correct answer to how people ought to behave in this situation, which in turn leads to less positive reactions toward masking as the prescribed behavior. Conclusions: Health professionals and science advocates face an underappreciated challenge in attempting to debunk misinformation on social media. Although engaging in extended debates with science deniers and other purveyors of bunk appears necessary, more research is needed to address the unintended consequences of such engagement. ", doi="10.2196/34831", url="https://www.jmir.org/2022/3/e34831", url="http://www.ncbi.nlm.nih.gov/pubmed/35156933" } @Article{info:doi/10.2196/34870, author="MacLeod, Spencer and Singh, Paul Nikhi and Boyd, Joseph Carter", title="The Unclear Role of the Physician on Social Media During the COVID-19 Pandemic. Comment on ``Emergency Physician Twitter Use in the COVID-19 Pandemic as a Potential Predictor of Impending Surge: Retrospective Observational Study''", journal="J Med Internet Res", year="2022", month="Mar", day="2", volume="24", number="3", pages="e34870", keywords="COVID-19 pandemic", keywords="emergency medicine", keywords="disaster medicine", keywords="crisis standards of care", keywords="latent Dirichlet allocation", keywords="topic modeling", keywords="Twitter", keywords="sentiment analysis", keywords="surge capacity", keywords="physician wellness", keywords="social media", keywords="internet", keywords="infodemiology", keywords="COVID-19", doi="10.2196/34870", url="https://www.jmir.org/2022/3/e34870", url="http://www.ncbi.nlm.nih.gov/pubmed/35120018" } @Article{info:doi/10.2196/35234, author="Struik, Laura and Rodberg, Danielle and Sharma, H. Ramona", title="The Behavior Change Techniques Used in Canadian Online Smoking Cessation Programs: Content Analysis", journal="JMIR Ment Health", year="2022", month="Mar", day="1", volume="9", number="3", pages="e35234", keywords="content analysis", keywords="smoking cessation", keywords="internet", keywords="behavior change technique", keywords="mental health", keywords="smoking", keywords="online program", keywords="website", keywords="government", keywords="federal", keywords="provincial", abstract="Background: Smoking rates in Canada remain unacceptably high, and cessation rates have stalled in recent years. Online cessation programs, touted for their ability to reach many different populations anytime, have shown promise in their efficacy. The Government of Canada has therefore funded provincial and national smoking cessation websites countrywide. However, little is known about the behavior change techniques (BCTs) that underpin the content of these websites, which is key to establishing the quality of the websites, as well as a way forward for evaluation. Objective: The purpose of this study, therefore, is to apply the BCTTv1 taxonomy to Canadian provincial and federal websites, and to determine which BCTs they use. Methods: A total of 12 government-funded websites across Canada were included for analysis. Using deductive content analysis and through training in applying the BCTTv1 taxonomy, the website content was coded according to the 93 BCTs across the 16 BCT categories. Results: Of the 16 BCT categories, 14 were present within the websites. The most widely represented BCT categories (used in all 12 websites) included goals and planning, social support, natural consequences, and regulation. Implementation of BCTs within these categories varied across the sites. Conclusions: Analyzing the content of online smoking cessation websites using the BCTTv1 taxonomy is an appropriate method for identifying the behavior change content of these programs. The findings offer programmers and researchers tangible directions for prioritizing and enhancing provincial and national smoking cessation programs, and an evaluation framework to assess smoking cessation outcomes in relation to the web-based content. ", doi="10.2196/35234", url="https://mental.jmir.org/2022/3/e35234", url="http://www.ncbi.nlm.nih.gov/pubmed/35230253" } @Article{info:doi/10.2196/30397, author="Hasan, Abul and Levene, Mark and Weston, David and Fromson, Renate and Koslover, Nicolas and Levene, Tamara", title="Monitoring COVID-19 on Social Media: Development of an End-to-End Natural Language Processing Pipeline Using a Novel Triage and Diagnosis Approach", journal="J Med Internet Res", year="2022", month="Feb", day="28", volume="24", number="2", pages="e30397", keywords="COVID-19", keywords="conditional random fields", keywords="disease detection and surveillance", keywords="medical social media", keywords="natural language processing", keywords="severity and prevalence", keywords="support vector machines", keywords="triage and diagnosis", abstract="Background: The COVID-19 pandemic has created a pressing need for integrating information from disparate sources in order to assist decision makers. Social media is important in this respect; however, to make sense of the textual information it provides and be able to automate the processing of large amounts of data, natural language processing methods are needed. Social media posts are often noisy, yet they may provide valuable insights regarding the severity and prevalence of the disease in the population. Here, we adopt a triage and diagnosis approach to analyzing social media posts using machine learning techniques for the purpose of disease detection and surveillance. We thus obtain useful prevalence and incidence statistics to identify disease symptoms and their severities, motivated by public health concerns. Objective: This study aims to develop an end-to-end natural language processing pipeline for triage and diagnosis of COVID-19 from patient-authored social media posts in order to provide researchers and public health practitioners with additional information on the symptoms, severity, and prevalence of the disease rather than to provide an actionable decision at the individual level. Methods: The text processing pipeline first extracted COVID-19 symptoms and related concepts, such as severity, duration, negations, and body parts, from patients' posts using conditional random fields. An unsupervised rule-based algorithm was then applied to establish relations between concepts in the next step of the pipeline. The extracted concepts and relations were subsequently used to construct 2 different vector representations of each post. These vectors were separately applied to build support vector machine learning models to triage patients into 3 categories and diagnose them for COVID-19. Results: We reported macro- and microaveraged F1 scores in the range of 71\%-96\% and 61\%-87\%, respectively, for the triage and diagnosis of COVID-19 when the models were trained on human-labeled data. Our experimental results indicated that similar performance can be achieved when the models are trained using predicted labels from concept extraction and rule-based classifiers, thus yielding end-to-end machine learning. In addition, we highlighted important features uncovered by our diagnostic machine learning models and compared them with the most frequent symptoms revealed in another COVID-19 data set. In particular, we found that the most important features are not always the most frequent ones. Conclusions: Our preliminary results show that it is possible to automatically triage and diagnose patients for COVID-19 from social media natural language narratives, using a machine learning pipeline in order to provide information on the severity and prevalence of the disease for use within health surveillance systems. ", doi="10.2196/30397", url="https://www.jmir.org/2022/2/e30397", url="http://www.ncbi.nlm.nih.gov/pubmed/35142636" } @Article{info:doi/10.2196/32689, author="Kim, Maryanne and Noh, Youran and Yamada, Akihiko and Hong, Hee Song", title="Comparison of the Erectile Dysfunction Drugs Sildenafil and Tadalafil Using Patient Medication Reviews: Topic Modeling Study", journal="JMIR Med Inform", year="2022", month="Feb", day="28", volume="10", number="2", pages="e32689", keywords="topic modeling", keywords="patient preference", keywords="patient-centered communication", keywords="erectile dysfunction", keywords="PDE5 inhibitor", keywords="phosphodiesterase type 5 inhibitor", abstract="Background: Topic modeling of patient medication reviews of erectile dysfunction (ED) drugs can help identify patient preferences regarding ED treatment options. The identification of a set of topics important to the patient from social network service drug reviews would inform the design of patient-centered medication counseling. Objective: This study aimed to (1) identify the distinctive topics from patient medication reviews unique to tadalafil versus sildenafil; (2) determine if the primary topics are distributed differently for each drug and for each patient characteristic (age and time on ED drug therapy); and (3) test if the primary topics affect satisfaction with ED drug therapy controlling for patient characteristics. Methods: Data were collected from the patient medication reviews of sildenafil and tadalafil posted on WebMD and Ask a Patient. The latent Dirichlet allocation method of natural language processing was used to identify 5 distinctive topics from the patient medication reviews on each drug. Analysis of variance and a 2-sample t test were conducted to compare the topic distribution and assess whether patient satisfaction varies with the primary topics, age, and time on medication for each ED drug. Statistical significance was tested at an alpha of .05. Results: The patient medication reviews of sildenafil (N=463) had 2 topics on treatment benefit and 1 each on medication safety, marketing claim, and treatment comparison, while the patient medication reviews of tadalafil (N=919) had 2 topics on medication safety and 1 each on the remaining subjects. Sildenafil's reviewers quite frequently (94/463, 20.4\%) mentioned erection sustainability as their primary topic, whereas tadalafil's reviewers were more concerned about severe medication safety. Those who mentioned erection sustainability as their primary topic were quite satisfied with their treatment as opposed to those who mentioned severe medication safety as their primary topic (score 3.85 vs 2.44). The discovered topics reflected the marketing claims of blue magic and amber romance for sildenafil and tadalafil, respectively. The topic of blue magic was preferred among younger patients, while the topic of amber romance was preferred among older patients. The topic alternative choices, which appeared for both the ED drugs, reflected patient interest in the comparative effectiveness and price outside the drug labeling information. Conclusions: The patient medication reviews of ED drugs reflect patient preferences regarding drug labeling information, marketing claims, and alternative treatment choices. The patient preferences concerning ED treatment attributes inform the design of patient-centered communication for improved ED drug therapy. ", doi="10.2196/32689", url="https://medinform.jmir.org/2022/2/e32689", url="http://www.ncbi.nlm.nih.gov/pubmed/35225813" } @Article{info:doi/10.2196/35027, author="Allem, Jon-Patrick and Majmundar, Anuja and Dormanesh, Allison and Donaldson, I. Scott", title="Identifying Health-Related Discussions of Cannabis Use on Twitter by Using a Medical Dictionary: Content Analysis of Tweets", journal="JMIR Form Res", year="2022", month="Feb", day="25", volume="6", number="2", pages="e35027", keywords="cannabis", keywords="marijuana", keywords="Twitter", keywords="social media", keywords="adverse event", keywords="cannabis safety", keywords="dictionary", keywords="rule-based classifier", keywords="medical", keywords="health-related", keywords="conversation", keywords="codebook", abstract="Background: The cannabis product and regulatory landscape is changing in the United States. Against the backdrop of these changes, there have been increasing reports on health-related motives for cannabis use and adverse events from its use. The use of social media data in monitoring cannabis-related health conversations may be useful to state- and federal-level regulatory agencies as they grapple with identifying cannabis safety signals in a comprehensive and scalable fashion. Objective: This study attempted to determine the extent to which a medical dictionary---the Unified Medical Language System Consumer Health Vocabulary---could identify cannabis-related motivations for use and health consequences of cannabis use based on Twitter posts in 2020. Methods: Twitter posts containing cannabis-related terms were obtained from January 1 to August 31, 2020. Each post from the sample (N=353,353) was classified into at least 1 of 17 a priori categories of common health-related topics by using a rule-based classifier. Each category was defined by the terms in the medical dictionary. A subsample of posts (n=1092) was then manually annotated to help validate the rule-based classifier and determine if each post pertained to health-related motivations for cannabis use, perceived adverse health effects from its use, or neither. Results: The validation process indicated that the medical dictionary could identify health-related conversations in 31.2\% (341/1092) of posts. Specifically, 20.4\% (223/1092) of posts were accurately identified as posts related to a health-related motivation for cannabis use, while 10.8\% (118/1092) of posts were accurately identified as posts related to a health-related consequence from cannabis use. The health-related conversations about cannabis use included those about issues with the respiratory system, stress to the immune system, and gastrointestinal issues, among others. Conclusions: The mining of social media data may prove helpful in improving the surveillance of cannabis products and their adverse health effects. However, future research needs to develop and validate a dictionary and codebook that capture cannabis use--specific health conversations on Twitter. ", doi="10.2196/35027", url="https://formative.jmir.org/2022/2/e35027", url="http://www.ncbi.nlm.nih.gov/pubmed/35212637" } @Article{info:doi/10.2196/31793, author="Kreps, Sarah and George, Julie and Watson, Noah and Cai, Gloria and Ding, Keyi", title="(Mis)Information on Digital Platforms: Quantitative and Qualitative Analysis of Content From Twitter and Sina Weibo in the COVID-19 Pandemic", journal="JMIR Infodemiology", year="2022", month="Feb", day="24", volume="2", number="1", pages="e31793", keywords="internet", keywords="social media", keywords="misinformation", keywords="COVID-19", keywords="Twitter", keywords="Weibo", keywords="prevalence", keywords="discourse", keywords="content", keywords="communication", keywords="public health", keywords="context", keywords="content analysis", abstract="Background: Misinformation about COVID-19 on social media has presented challenges to public health authorities during the pandemic. This paper leverages qualitative and quantitative content analysis on cross-platform, cross-national discourse and misinformation in the context of COVID-19. Specifically, we investigated COVID-19-related content on Twitter and Sina Weibo---the largest microblogging sites in the United States and China, respectively. Objective: Using data from 2 prominent microblogging platform, Twitter, based in the United States, and Sina Weibo, based in China, we compared the content and relative prevalence of misinformation to better understand public discourse of public health issues across social media and cultural contexts. Methods: A total of 3,579,575 posts were scraped from both Sina Weibo and Twitter, focusing on content from January 30, 2020, within 24 hours of when WHO declared COVID-19 a ``public health emergency of international concern,'' and a week later, on February 6, 2020. We examined how the use and engagement measured by keyword frequencies and hashtags differ across the 2 platforms. A 1\% random sample of tweets that contained both the English keywords ``coronavirus'' and ``covid-19'' and the equivalent Chinese characters was extracted and analyzed based on changes in the frequencies of keywords and hashtags and the Viterbi algorithm. We manually coded a random selection of 5\%-7\% of the content to identify misinformation on each platform and compared posts using the WHO fact-check page to adjudicate accuracy of content. Results: Both platforms posted about the outbreak and transmission, but posts on Sina Weibo were less likely to reference topics such as WHO, Hong Kong, and death and more likely to cite themes of resisting, fighting, and cheering against coronavirus. Misinformation constituted 1.1\% of Twitter content and 0.3\% of Sina Weibo content---almost 4 times as much on Twitter compared to Sina Weibo. Conclusions: Quantitative and qualitative analysis of content on both platforms points to lower degrees of misinformation, more content designed to bolster morale, and less reference to topics such as WHO, death, and Hong Kong on Sina Weibo than on Twitter. ", doi="10.2196/31793", url="https://infodemiology.jmir.org/2022/1/e31793", url="http://www.ncbi.nlm.nih.gov/pubmed/36406147" } @Article{info:doi/10.2196/31978, author="Cummins, Alexander Jack", title="Getting a Vaccine, Jab, or Vax Is More Than a Regular Expression. Comment on ``COVID-19 Vaccine-Related Discussion on Twitter: Topic Modeling and Sentiment Analysis''", journal="J Med Internet Res", year="2022", month="Feb", day="23", volume="24", number="2", pages="e31978", keywords="COVID-19", keywords="vaccine", keywords="vaccination", keywords="Twitter", keywords="infodemiology", keywords="infoveillance", keywords="topic", keywords="sentiment", keywords="opinion", keywords="discussion", keywords="communication", keywords="social media", keywords="perception", keywords="concern", keywords="emotion", keywords="natural language processing", doi="10.2196/31978", url="https://www.jmir.org/2022/2/e31978", url="http://www.ncbi.nlm.nih.gov/pubmed/35195531" } @Article{info:doi/10.2196/31259, author="Santarossa, Sara and Rapp, Ashley and Sardinas, Saily and Hussein, Janine and Ramirez, Alex and Cassidy-Bushrow, E. Andrea and Cheng, Philip and Yu, Eunice", title="Understanding the \#longCOVID and \#longhaulers Conversation on Twitter: Multimethod Study", journal="JMIR Infodemiology", year="2022", month="Feb", day="22", volume="2", number="1", pages="e31259", keywords="COVID-19", keywords="postacute sequela of COVID-19", keywords="PASC", keywords="patient-centered care", keywords="social media", keywords="social network analysis", keywords="long term", keywords="symptom", keywords="Twitter", keywords="communication", keywords="insight", keywords="perception", keywords="experience", keywords="patient-centered", abstract="Background: The scientific community is just beginning to uncover the potential long-term effects of COVID-19, and one way to start gathering information is by examining the present discourse on the topic. The conversation about long COVID-19 on Twitter provides insight into related public perception and personal experiences. Objective: The aim of this study was to investigate the \#longCOVID and \#longhaulers conversations on Twitter by examining the combined effects of topic discussion and social network analysis for discovery on long COVID-19. Methods: A multipronged approach was used to analyze data (N=2500 records from Twitter) about long COVID-19 and from people experiencing long COVID-19. A text analysis was performed by both human coders and Netlytic, a cloud-based text and social networks analyzer. The social network analysis generated Name and Chain networks that showed connections and interactions between Twitter users. Results: Among the 2010 tweets about long COVID-19 and 490 tweets by COVID-19 long haulers, 30,923 and 7817 unique words were found, respectively. For both conversation types, ``\#longcovid'' and ``covid'' were the most frequently mentioned words; however, through visually inspecting the data, words relevant to having long COVID-19 (ie, symptoms, fatigue, pain) were more prominent in tweets by COVID-19 long haulers. When discussing long COVID-19, the most prominent frames were ``support'' (1090/1931, 56.45\%) and ``research'' (435/1931, 22.53\%). In COVID-19 long haulers conversations, ``symptoms'' (297/483, 61.5\%) and ``building a community'' (152/483, 31.5\%) were the most prominent frames. The social network analysis revealed that for both tweets about long COVID-19 and tweets by COVID-19 long haulers, networks are highly decentralized, fragmented, and loosely connected. Conclusions: This study provides a glimpse into the ways long COVID-19 is framed by social network users. Understanding these perspectives may help generate future patient-centered research questions. ", doi="10.2196/31259", url="https://infodemiology.jmir.org/2022/1/e31259", url="http://www.ncbi.nlm.nih.gov/pubmed/35229074" } @Article{info:doi/10.2196/32372, author="Engel-Rebitzer, Eden and Stokes, C. Daniel and Meisel, F. Zachary and Purtle, Jonathan and Doyle, Rebecca and Buttenheim, M. Alison", title="Partisan Differences in Legislators' Discussion of Vaccination on Twitter During the COVID-19 Era: Natural Language Processing Analysis", journal="JMIR Infodemiology", year="2022", month="Feb", day="18", volume="2", number="1", pages="e32372", keywords="social media", keywords="Twitter", keywords="vaccination", keywords="partisanship", keywords="COVID-19", keywords="vaccine", keywords="natural language processing", keywords="NLP", keywords="hesitancy", keywords="politicization", keywords="communication", keywords="linguistic", keywords="pattern", abstract="Background: The COVID-19 era has been characterized by the politicization of health-related topics. This is especially concerning given evidence that politicized discussion of vaccination may contribute to vaccine hesitancy. No research, however, has examined the content and politicization of legislator communication with the public about vaccination during the COVID-19 era. Objective: The aim of this study was to examine vaccine-related tweets produced by state and federal legislators during the COVID-19 era to (1) describe the content of vaccine-related tweets; (2) examine the differences in vaccine-related tweet content between Democrats and Republicans; and (3) quantify (and describe trends over time in) partisan differences in vaccine-related communication. Methods: We abstracted all vaccine-related tweets produced by state and federal legislators between February 01, 2020, and December 11, 2020. We used latent Dirichlet allocation to define the tweet topics and used descriptive statistics to describe differences by party in the use of topics and changes in political polarization over time. Results: We included 14,519 tweets generated by 1463 state legislators and 521 federal legislators. Republicans were more likely to use words (eg, ``record time,'' ``launched,'' and ``innovation'') and topics (eg, Operation Warp Speed success) that were focused on the successful development of a SARS-CoV-2 vaccine. Democrats used a broader range of words (eg, ``anti-vaxxers,'' ``flu,'' and ``free'') and topics (eg, vaccine prioritization, influenza, and antivaxxers) that were more aligned with public health messaging related to the vaccine. Polarization increased over most of the study period. Conclusions: Republican and Democratic legislators used different language in their Twitter conversations about vaccination during the COVID-19 era, leading to increased political polarization of vaccine-related tweets. These communication patterns have the potential to contribute to vaccine hesitancy. ", doi="10.2196/32372", url="https://infodemiology.jmir.org/2022/1/e32372", url="http://www.ncbi.nlm.nih.gov/pubmed/35229075" } @Article{info:doi/10.2196/34385, author="Spitale, Giovanni and Biller-Andorno, Nikola and Germani, Federico", title="Concerns Around Opposition to the Green Pass in Italy: Social Listening Analysis by Using a Mixed Methods Approach", journal="J Med Internet Res", year="2022", month="Feb", day="16", volume="24", number="2", pages="e34385", keywords="green pass", keywords="COVID-19", keywords="COVID-19 pandemic", keywords="vaccines", keywords="vaccination hesitancy", keywords="freedom", keywords="social listening", keywords="social media", keywords="infodemic", keywords="bioethics", keywords="telegram", abstract="Background: The recent introduction of COVID-19 certificates in several countries, including the introduction of the European green pass, has been met with protests and concerns by a fraction of the population. In Italy, the green pass has been used as a nudging measure to incentivize vaccinations because a valid green pass is needed to enter restaurants, bars, museums, or stadiums. As of December 2021, a valid green pass can be obtained by being fully vaccinated with an approved vaccine, recovered from COVID-19, or tested. However, a green pass obtained with a test has a short validity (48 hours for the rapid test, 72 hours for the polymerase chain reaction test) and does not allow access to several indoor public places. Objective: This study aims to understand and describe the concerns of individuals opposed to the green pass in Italy, the main arguments of their discussions, and their characterization. Methods: We collected data from Telegram chats and analyzed the arguments and concerns that were raised by the users by using a mixed methods approach. Results: Most individuals opposing the green pass share antivaccine views, but doubts and concerns about vaccines are generally not among the arguments raised to oppose the green pass. Instead, the discussion revolves around the legal aspects and the definition of personal freedom. We explain the differences and similarities between antivaccine and anti--green pass discourses, and we discuss the ethical ramifications of our research, focusing on the use of Telegram chats as a social listening tool for public health. Conclusions: A large portion of individuals opposed to the green pass share antivaccine views. We suggest public health and political institutions to provide a legal explanation and a context for the use of the green pass, as well as to continue focusing on vaccine communication to inform vaccine-hesitant individuals. Further work is needed to define a consensual ethical framework for social listening for public health. ", doi="10.2196/34385", url="https://www.jmir.org/2022/2/e34385", url="http://www.ncbi.nlm.nih.gov/pubmed/35156930" } @Article{info:doi/10.2196/33959, author="Sarabadani, Sarah and Baruah, Gaurav and Fossat, Yan and Jeon, Jouhyun", title="Longitudinal Changes of COVID-19 Symptoms in Social Media: Observational Study", journal="J Med Internet Res", year="2022", month="Feb", day="16", volume="24", number="2", pages="e33959", keywords="COVID-19", keywords="symptom", keywords="diagnosis", keywords="treatment", keywords="social media", keywords="Reddit", keywords="longitudinal", keywords="observational", keywords="machine learning", abstract="Background: In December 2019, the COVID-19 outbreak started in China and rapidly spread around the world. Many studies have been conducted to understand the clinical characteristics of COVID-19, and recently postinfection sequelae of this disease have begun to be investigated. However, there is little consensus on the longitudinal changes of lasting physical or psychological symptoms from prior COVID-19 infection. Objective: This study aims to investigate and analyze public social media data from Reddit to understand the longitudinal impact of COVID-19 symptoms before and after recovery from COVID-19. Methods: We collected 22,890 Reddit posts that were generated by 14,401 authors from March 14 to December 16, 2020. Using active learning and intensive manual inspection, 292 (2.03\%) active authors, who were infected by COVID-19 and frequently reported disease progress on Reddit, along with their 2213 (9.67\%) longitudinal posts, were identified. Machine learning tools to extract biomedical information were applied to identify COVID-19 symptoms mentioned in the Reddit posts. We then examined longitudinal changes in individual physiological and psychological characteristics before and after recovery from COVID-19 infection. Results: In total, 58 physiological and 3 psychological symptoms were identified in social media before and after recovery from COVID-19 infection. From the analyses, we found that symptoms of patients with COVID-19 lasted 2.5 months. On average, symptoms appeared around a month before recovery and remained for 1.5 months after recovery. Well-known COVID-19 symptoms, such as fever, cough, and chest congestion, appeared relatively earlier in patient journeys and were frequently observed before recovery from COVID-19. Meanwhile, mental discomfort or distress, such as brain fog or stress, fatigue, and manifestations on toes or fingers, were frequently mentioned after recovery and remained as intermediate- and longer-term sequelae. Conclusions: In this study, we showed the dynamic changes in COVID-19 symptoms during the infection and recovery phases of the disease. Our findings suggest the feasibility of using social media data for investigating disease states and understanding the evolution of the physiological and psychological characteristics of COVID-19 infection over time. ", doi="10.2196/33959", url="https://www.jmir.org/2022/2/e33959", url="http://www.ncbi.nlm.nih.gov/pubmed/35076400" } @Article{info:doi/10.2196/32355, author="Shakeri Hossein Abad, Zahra and Butler, P. Gregory and Thompson, Wendy and Lee, Joon", title="Physical Activity, Sedentary Behavior, and Sleep on Twitter: Multicountry and Fully Labeled Public Data Set for Digital Public Health Surveillance Research", journal="JMIR Public Health Surveill", year="2022", month="Feb", day="14", volume="8", number="2", pages="e32355", keywords="digital public health surveillance", keywords="social media analysis", keywords="physical activity", keywords="sedentary behavior", keywords="sleep", keywords="machine learning", keywords="online health information", keywords="infodemiology", keywords="public health database", abstract="Background: Advances in automated data processing and machine learning (ML) models, together with the unprecedented growth in the number of social media users who publicly share and discuss health-related information, have made public health surveillance (PHS) one of the long-lasting social media applications. However, the existing PHS systems feeding on social media data have not been widely deployed in national surveillance systems, which appears to stem from the lack of practitioners and the public's trust in social media data. More robust and reliable data sets over which supervised ML models can be trained and tested reliably is a significant step toward overcoming this hurdle. The health implications of daily behaviors (physical activity, sedentary behavior, and sleep [PASS]), as an evergreen topic in PHS, are widely studied through traditional data sources such as surveillance surveys and administrative databases, which are often several months out-of-date by the time they are used, costly to collect, and thus limited in quantity and coverage. Objective: The main objective of this study is to present a large-scale, multicountry, longitudinal, and fully labeled data set to enable and support digital PASS surveillance research in PHS. To support high-quality surveillance research using our data set, we have conducted further analysis on the data set to supplement it with additional PHS-related metadata. Methods: We collected the data of this study from Twitter using the Twitter livestream application programming interface between November 28, 2018, and June 19, 2020. To obtain PASS-related tweets for manual annotation, we iteratively used regular expressions, unsupervised natural language processing, domain-specific ontologies, and linguistic analysis. We used Amazon Mechanical Turk to label the collected data to self-reported PASS categories and implemented a quality control pipeline to monitor and manage the validity of crowd-generated labels. Moreover, we used ML, latent semantic analysis, linguistic analysis, and label inference analysis to validate the different components of the data set. Results: LPHEADA (Labelled Digital Public Health Dataset) contains 366,405 crowd-generated labels (3 labels per tweet) for 122,135 PASS-related tweets that originated in Australia, Canada, the United Kingdom, or the United States, labeled by 708 unique annotators on Amazon Mechanical Turk. In addition to crowd-generated labels, LPHEADA provides details about the three critical components of any PHS system: place, time, and demographics (ie, gender and age range) associated with each tweet. Conclusions: Publicly available data sets for digital PASS surveillance are usually isolated and only provide labels for small subsets of the data. We believe that the novelty and comprehensiveness of the data set provided in this study will help develop, evaluate, and deploy digital PASS surveillance systems. LPHEADA will be an invaluable resource for both public health researchers and practitioners. ", doi="10.2196/32355", url="https://publichealth.jmir.org/2022/2/e32355", url="http://www.ncbi.nlm.nih.gov/pubmed/35156938" } @Article{info:doi/10.2196/18539, author="Delanys, Sarah and Benamara, Farah and Moriceau, V{\'e}ronique and Olivier, Fran{\c{c}}ois and Mothe, Josiane", title="Psychiatry on Twitter: Content Analysis of the Use of Psychiatric Terms in French", journal="JMIR Form Res", year="2022", month="Feb", day="14", volume="6", number="2", pages="e18539", keywords="social media analysis", keywords="psychiatric term use", keywords="social stigma", keywords="Twitter", keywords="social media", keywords="mental health", abstract="Background: With the advent of digital technology and specifically user-generated contents in social media, new ways emerged for studying possible stigma of people in relation with mental health. Several pieces of work studied the discourse conveyed about psychiatric pathologies on Twitter considering mostly tweets in English and a limited number of psychiatric disorders terms. This paper proposes the first study to analyze the use of a wide range of psychiatric terms in tweets in French. Objective: Our aim is to study how generic, nosographic, and therapeutic psychiatric terms are used on Twitter in French. More specifically, our study has 3 complementary goals: (1) to analyze the types of psychiatric word use (medical, misuse, or irrelevant), (2) to analyze the polarity conveyed in the tweets that use these terms (positive, negative, or neural), and (3) to compare the frequency of these terms to those observed in related work (mainly in English). Methods: Our study was conducted on a corpus of tweets in French posted from January 1, 2016, to December 31, 2018, and collected using dedicated keywords. The corpus was manually annotated by clinical psychiatrists following a multilayer annotation scheme that includes the type of word use and the opinion orientation of the tweet. A qualitative analysis was performed to measure the reliability of the produced manual annotation, and then a quantitative analysis was performed considering mainly term frequency in each layer and exploring the interactions between them. Results: One of the first results is a resource as an annotated dataset. The initial dataset is composed of 22,579 tweets in French containing at least one of the selected psychiatric terms. From this set, experts in psychiatry randomly annotated 3040 tweets that corresponded to the resource resulting from our work. The second result is the analysis of the annotations showing that terms are misused in 45.33\% (1378/3040) of the tweets and that their associated polarity is negative in 86.21\% (1188/1378) of the cases. When considering the 3 types of term use, 52.14\% (1585/3040) of the tweets are associated with a negative polarity. Misused terms related to psychotic disorders (721/1300, 55.46\%) were more frequent to those related to depression (15/280, 5.4\%). Conclusions: Some psychiatric terms are misused in the corpora we studied, which is consistent with the results reported in related work in other languages. Thanks to the great diversity of studied terms, this work highlighted a disparity in the representations and ways of using psychiatric terms. Moreover, our study is important to help psychiatrists to be aware of the term use in new communication media such as social networks that are widely used. This study has the huge advantage to be reproducible thanks to the framework and guidelines we produced so that the study could be renewed in order to analyze the evolution of term usage. While the newly build dataset is a valuable resource for other analytical studies, it could also serve to train machine learning algorithms to automatically identify stigma in social media. ", doi="10.2196/18539", url="https://formative.jmir.org/2022/2/e18539", url="http://www.ncbi.nlm.nih.gov/pubmed/35156925" } @Article{info:doi/10.2196/29246, author="Stevens, Hannah and Palomares, A. Nicholas", title="Constituents' Inferences of Local Governments' Goals and the Relationship Between Political Party and Belief in COVID-19 Misinformation: Cross-sectional Survey of Twitter Followers of State Public Health Departments", journal="JMIR Infodemiology", year="2022", month="Feb", day="10", volume="2", number="1", pages="e29246", keywords="COVID-19", keywords="outbreak", keywords="mass communication", keywords="Twitter", keywords="goal inferences", keywords="political agendas", keywords="misinformation", keywords="infodemic", keywords="partisanship", keywords="health information", abstract="Background: Amid the COVID-19 pandemic, social media have influenced the circulation of health information. Public health agencies often use Twitter to disseminate and amplify the propagation of such information. Still, exposure to local government--endorsed COVID-19 public health information does not make one immune to believing misinformation. Moreover, not all health information on Twitter is accurate, and some users may believe misinformation and disinformation just as much as those who endorse more accurate information. This situation is complicated, given that elected officials may pursue a political agenda of re-election by downplaying the need for COVID-19 restrictions. The politically polarized nature of information and misinformation on social media in the United States has fueled a COVID-19 infodemic. Because pre-existing political beliefs can both facilitate and hinder persuasion, Twitter users' belief in COVID-19 misinformation is likely a function of their goal inferences about their local government agencies' motives for addressing the COVID-19 pandemic. Objective: We shed light on the cognitive processes of goal understanding that underlie the relationship between partisanship and belief in health misinformation. We investigate how the valence of Twitter users' goal inferences of local governments' COVID-19 efforts predicts their belief in COVID-19 misinformation as a function of their political party affiliation. Methods: We conducted a web-based cross-sectional survey of US Twitter users who followed their state's official Department of Public Health Twitter account (n=258) between August 10 and December 23, 2020. Inferences about local governments' goals, demographics, and belief in COVID-19 misinformation were measured. State political affiliation was controlled. Results: Participants from all 50 states were included in the sample. An interaction emerged between political party affiliation and goal inference valence for belief in COVID-19 misinformation (?R2=0.04; F8,249=4.78; P<.001); positive goal inference valence predicted increased belief in COVID-19 misinformation among Republicans ($\beta$=.47; t249=2.59; P=.01) but not among Democrats ($\beta$=.07; t249=0.84; P=.40). Conclusions: Our results reveal that favorable inferences about local governments' COVID-19 efforts can accelerate belief in misinformation among Republican-identifying constituents. In other words, accurate COVID-19 transmission knowledge is a function of constituents' sentiment toward politicians rather than science, which has significant implications on public health efforts for minimizing the spread of the disease, as convincing misinformed constituents to practice safety measures might be a political issue just as much as it is a health one. Our work suggests that goal understanding processes matter for misinformation about COVID-19 among Republicans. Those responsible for future COVID-19 public health messaging aimed at increasing belief in valid information about COVID-19 should recognize the need to test persuasive appeals that address partisans' pre-existing political views in order to prevent individuals' goal inferences from interfering with public health messaging. ", doi="10.2196/29246", url="https://infodemiology.jmir.org/2022/1/e29246", url="http://www.ncbi.nlm.nih.gov/pubmed/37113808" } @Article{info:doi/10.2196/31473, author="Lwin, O. May and Sheldenkar, Anita and Lu, Jiahui and Schulz, Johannes Peter and Shin, Wonsun and Panchapakesan, Chitra and Gupta, Kumar Raj and Yang, Yinping", title="The Evolution of Public Sentiments During the COVID-19 Pandemic: Case Comparisons of India, Singapore, South Korea, the United Kingdom, and the United States", journal="JMIR Infodemiology", year="2022", month="Feb", day="10", volume="2", number="1", pages="e31473", keywords="COVID-19", keywords="public sentiment", keywords="Twitter", keywords="crisis communication", keywords="cross-country comparison", keywords="sentiment", keywords="social media", keywords="communication", keywords="public health", keywords="health information", keywords="emotion", keywords="perception", keywords="health literacy", keywords="information literacy", keywords="digital literacy", keywords="community health", abstract="Background: Public sentiments are an important indicator of crisis response, with the need to balance exigency without adding to panic or projecting overconfidence. Given the rapid spread of the COVID-19 pandemic, governments have enacted various nationwide measures against the disease with social media platforms providing the previously unparalleled communication space for the global populations. Objective: This research aims to examine and provide a macro-level narrative of the evolution of public sentiments on social media at national levels, by comparing Twitter data from India, Singapore, South Korea, the United Kingdom, and the United States during the current pandemic. Methods: A total of 67,363,091 Twitter posts on COVID-19 from January 28, 2020, to April 28, 2021, were analyzed from the 5 countries with ``wuhan,'' ``corona,'' ``nCov,'' and ``covid'' as search keywords. Change in sentiments (``very negative,'' ``negative,'' ``neutral or mixed,'' ``positive,'' ``very positive'') were compared between countries in connection with disease milestones and public health directives. Results: Country-specific assessments show that negative sentiments were predominant across all 5 countries during the initial period of the global pandemic. However, positive sentiments encompassing hope, resilience, and support arose at differing intensities across the 5 countries, particularly in Asian countries. In the next stage of the pandemic, India, Singapore, and South Korea faced escalating waves of COVID-19 cases, resulting in negative sentiments, but positive sentiments appeared simultaneously. In contrast, although negative sentiments in the United Kingdom and the United States increased substantially after the declaration of a national public emergency, strong parallel positive sentiments were slow to surface. Conclusions: Our findings on sentiments across countries facing similar outbreak concerns suggest potential associations between government response actions both in terms of policy and communications, and public sentiment trends. Overall, a more concerted approach to government crisis communication appears to be associated with more stable and less volatile public sentiments over the evolution of the COVID-19 pandemic. ", doi="10.2196/31473", url="https://infodemiology.jmir.org/2022/1/e31473", url="http://www.ncbi.nlm.nih.gov/pubmed/37113803" } @Article{info:doi/10.2196/35164, author="Tang, Hao and Kim, Sungwoo and Laforet, E. Priscila and Tettey, Naa-Solo and Basch, H. Corey", title="Loss of Weight Gained During the COVID-19 Pandemic: Content Analysis of YouTube Videos", journal="JMIR Form Res", year="2022", month="Feb", day="9", volume="6", number="2", pages="e35164", keywords="COVID-19", keywords="quarantine", keywords="weight loss", keywords="weight gain", keywords="social media", keywords="YouTube", abstract="Background: Many people experienced unintended weight gain during the COVID-19 pandemic, which has been discussed widely on social media. Objective: This study aims to describe the content of weight loss videos on YouTube (Google LLC) during the COVID-19 pandemic. Methods: By using the keywords weight loss during quarantine, the 100 most viewed English-language videos were identified and coded for content related to losing weight gained during the COVID-19 pandemic. Results: In total, 9 videos were excluded due to having non-English content or posting data before the COVID-19 pandemic. The 91 videos included in the study sample acquired 407,326 views at the time of study and were roughly 14 minutes long. A total of 48\% (44/91) of the sample videos included graphic comparisons to illustrate weight change. Videos that included a graphic comparison were more likely to have content related to trigger warnings ($\chi$21=6.05; P=.01), weight loss ($\chi$21=13.39; P<.001), negative feelings during quarantine ($\chi$21=4.75; P=.03), instructions for losing weight ($\chi$21=9.17; P=.002), self-love ($\chi$21=6.01; P=.01), body shaming ($\chi$21=4.36; P=.04), and special dietary practices ($\chi$21=11.10; P<.001) but were less likely to include food recipes ($\chi$21=5.05; P=.03). Our regression analysis results suggested that mentioning quarantine (P=.05), fat-gaining food (P=.04), self-care and self-love (P=.05), and body shaming (P=.008) and having presenters from both sexes (P<.001) are significant predictors for a higher number of views. Our adjusted regression model suggested that videos with content about routine change have significantly lower view counts (P=.03) than those of videos without such content. Conclusions: The findings of this study indicate the ways in which YouTube is being used to showcase COVID-19--related weight loss in a pre-post fashion. The use of graphic comparisons garnered a great deal of attention. Additional studies are needed to understand the role of graphic comparisons in social media posts. Further studies that focus on people's attitudes and behaviors toward weight change during the COVID-19 pandemic and the implications of social media on these attitudes and behaviors are warranted. ", doi="10.2196/35164", url="https://formative.jmir.org/2022/2/e35164", url="http://www.ncbi.nlm.nih.gov/pubmed/34978534" } @Article{info:doi/10.2196/31726, author="Huangfu, Luwen and Mo, Yiwen and Zhang, Peijie and Zeng, Dajun Daniel and He, Saike", title="COVID-19 Vaccine Tweets After Vaccine Rollout: Sentiment--Based Topic Modeling", journal="J Med Internet Res", year="2022", month="Feb", day="8", volume="24", number="2", pages="e31726", keywords="COVID-19", keywords="COVID-19 vaccine", keywords="sentiment evolution", keywords="topic modeling", keywords="social media", keywords="text mining", abstract="Background: COVID-19 vaccines are one of the most effective preventive strategies for containing the pandemic. Having a better understanding of the public's conceptions of COVID-19 vaccines may aid in the effort to promptly and thoroughly vaccinate the community. However, because no empirical research has yet fully explored the public's vaccine awareness through sentiment--based topic modeling, little is known about the evolution of public attitude since the rollout of COVID-19 vaccines. Objective: In this study, we specifically focused on tweets about COVID-19 vaccines (Pfizer, Moderna, AstraZeneca, and Johnson \& Johnson) after vaccines became publicly available. We aimed to explore the overall sentiments and topics of tweets about COVID-19 vaccines, as well as how such sentiments and main concerns evolved. Methods: We collected 1,122,139 tweets related to COVID-19 vaccines from December 14, 2020, to April 30, 2021, using Twitter's application programming interface. We removed retweets and duplicate tweets to avoid data redundancy, which resulted in 857,128 tweets. We then applied sentiment--based topic modeling by using the compound score to determine sentiment polarity and the coherence score to determine the optimal topic number for different sentiment polarity categories. Finally, we calculated the topic distribution to illustrate the topic evolution of main concerns. Results: Overall, 398,661 (46.51\%) were positive, 204,084 (23.81\%) were negative, 245,976 (28.70\%) were neutral, 6899 (0.80\%) were highly positive, and 1508 (0.18\%) were highly negative sentiments. The main topics of positive and highly positive tweets were planning for getting vaccination (251,979/405,560, 62.13\%), getting vaccination (76,029/405,560, 18.75\%), and vaccine information and knowledge (21,127/405,560, 5.21\%). The main concerns in negative and highly negative tweets were vaccine hesitancy (115,206/205,592, 56.04\%), extreme side effects of the vaccines (19,690/205,592, 9.58\%), and vaccine supply and rollout (17,154/205,592, 8.34\%). During the study period, negative sentiment trends were stable, while positive sentiments could be easily influenced. Topic heatmap visualization demonstrated how main concerns changed during the current widespread vaccination campaign. Conclusions: To the best of our knowledge, this is the first study to evaluate public COVID-19 vaccine awareness and awareness trends on social media with automated sentiment--based topic modeling after vaccine rollout. Our results can help policymakers and research communities track public attitudes toward COVID-19 vaccines and help them make decisions to promote the vaccination campaign. ", doi="10.2196/31726", url="https://www.jmir.org/2022/2/e31726", url="http://www.ncbi.nlm.nih.gov/pubmed/34783665" } @Article{info:doi/10.2196/32378, author="Chen, Emily and Jiang, Julie and Chang, Herbert Ho-Chun and Muric, Goran and Ferrara, Emilio", title="Charting the Information and Misinformation Landscape to Characterize Misinfodemics on Social Media: COVID-19 Infodemiology Study at a Planetary Scale", journal="JMIR Infodemiology", year="2022", month="Feb", day="8", volume="2", number="1", pages="e32378", keywords="social media", keywords="social networks", keywords="Twitter", keywords="COVID-19", keywords="infodemics", keywords="misinfodemics", keywords="infodemiology", keywords="misinformation", abstract="Background: The novel coronavirus, also known as SARS-CoV-2, has come to define much of our lives since the beginning of 2020. During this time, countries around the world imposed lockdowns and social distancing measures. The physical movements of people ground to a halt, while their online interactions increased as they turned to engaging with each other virtually. As the means of communication shifted online, information consumption also shifted online. Governing authorities and health agencies have intentionally shifted their focus to use social media and online platforms to spread factual and timely information. However, this has also opened the gate for misinformation, contributing to and accelerating the phenomenon of misinfodemics. Objective: We carried out an analysis of Twitter discourse on over 1 billion tweets related to COVID-19 over a year to identify and investigate prevalent misinformation narratives and trends. We also aimed to describe the Twitter audience that is more susceptible to health-related misinformation and the network mechanisms driving misinfodemics. Methods: We leveraged a data set that we collected and made public, which contained over 1 billion tweets related to COVID-19 between January 2020 and April 2021. We created a subset of this larger data set by isolating tweets that included URLs with domains that had been identified by Media Bias/Fact Check as being prone to questionable and misinformation content. By leveraging clustering and topic modeling techniques, we identified major narratives, including health misinformation and conspiracies, which were present within this subset of tweets. Results: Our focus was on a subset of 12,689,165 tweets that we determined were representative of COVID-19 misinformation narratives in our full data set. When analyzing tweets that shared content from domains known to be questionable or that promoted misinformation, we found that a few key misinformation narratives emerged about hydroxychloroquine and alternative medicines, US officials and governing agencies, and COVID-19 prevention measures. We further analyzed the misinformation retweet network and found that users who shared both questionable and conspiracy-related content were clustered more closely in the network than others, supporting the hypothesis that echo chambers can contribute to the spread of health misinfodemics. Conclusions: We presented a summary and analysis of the major misinformation discourse surrounding COVID-19 and those who promoted and engaged with it. While misinformation is not limited to social media platforms, we hope that our insights, particularly pertaining to health-related emergencies, will help pave the way for computational infodemiology to inform health surveillance and interventions. ", doi="10.2196/32378", url="https://infodemiology.jmir.org/2022/1/e32378", url="http://www.ncbi.nlm.nih.gov/pubmed/35190798" } @Article{info:doi/10.2196/31569, author="Neely, Stephen and Eldredge, Christina and Sanders, Ronald", title="Authors' Reply: Understanding the Impact of Social Media Information and Misinformation Producers on Health Information Seeking. Comment on ``Health Information Seeking Behaviors on Social Media During the COVID-19 Pandemic Among American Social Networking Site Users: Survey Study''", journal="J Med Internet Res", year="2022", month="Feb", day="4", volume="24", number="2", pages="e31569", keywords="social media", keywords="internet", keywords="communication", keywords="public health", keywords="COVID-19", keywords="usage", keywords="United States", keywords="information seeking", keywords="web-based health information", keywords="online health information", keywords="survey", keywords="mistrust", keywords="vaccination", keywords="misinformation", doi="10.2196/31569", url="https://www.jmir.org/2022/2/e31569", url="http://www.ncbi.nlm.nih.gov/pubmed/35119376" } @Article{info:doi/10.2196/31415, author="Boudreau, Hunter and Singh, Nikhi and Boyd, J. Carter", title="Understanding the Impact of Social Media Information and Misinformation Producers on Health Information Seeking. Comment on ``Health Information Seeking Behaviors on Social Media During the COVID-19 Pandemic Among American Social Networking Site Users: Survey Study''", journal="J Med Internet Res", year="2022", month="Feb", day="4", volume="24", number="2", pages="e31415", keywords="social media", keywords="internet", keywords="communication", keywords="public health", keywords="COVID-19", keywords="usage", keywords="United States", keywords="information seeking", keywords="web-based health information", keywords="online health information", keywords="survey", keywords="mistrust", keywords="vaccination", keywords="misinformation", doi="10.2196/31415", url="https://www.jmir.org/2022/2/e31415", url="http://www.ncbi.nlm.nih.gov/pubmed/35076408" } @Article{info:doi/10.2196/25216, author="Sun, Li and Lu, Xinyi and Xie, Zidian and Li, Dongmei", title="Public Reactions to the New York State Policy on Flavored Electronic Cigarettes on Twitter: Observational Study", journal="JMIR Public Health Surveill", year="2022", month="Feb", day="3", volume="8", number="2", pages="e25216", keywords="New York State policy", keywords="flavored e-cigarettes", keywords="Twitter", keywords="social media", keywords="vaping", keywords="e-cigarette", abstract="Background: Flavored electronic cigarettes (e-cigarettes) have become popular in recent years, especially among youth and young adults. To address the epidemic of e-cigarettes, New York State approved a ban on sales of most flavored vaping products other than tobacco and menthol flavors on September 17, 2019. Objective: This study aims to examine the attitude of Twitter users to the policy on flavored e-cigarettes in New York State and the impact of this policy on public perceptions of e-cigarettes. This study also compares the attitudes and topics between New York Twitter users and Twitter users from other states who were not directly affected by this policy. Methods: Tweets related to e-cigarettes and the New York State policy on flavored e-cigarettes were collected using the Twitter streaming application programming interface from June 2019 to December 2019. Tweets from New York State and those from other states that did not have a flavored e-cigarette policy were extracted. Sentiment analysis was applied to analyze the proportion of negative and positive tweets about e-cigarettes or the flavor policy. Topic modeling was applied to e-cigarette--related data sets and New York flavor policy--related data sets to identify the most frequent topics before and after the announcement of the New York State policy. Results: We found that the average number of tweets related to e-cigarettes and the New York State policy on flavored e-cigarettes increased in both New York State and other states after the flavor policy announcement. Sentiment analysis revealed that after the announcement of the New York State flavor policy, in both New York State and other states, the proportion of negative tweets on e-cigarettes increased from 34.07\% (4531/13,299) to 44.58\% (18,451/41,390) and from 32.48\% (14,320/44,090) to 44.40\% (64,262/144,734), respectively, while positive tweets decreased significantly from 39.03\% (5191/13,299) to 32.86\% (13,601/41,390) and from 42.78\% (18,863/44,090) to 33.93\% (49,105/144,734), respectively. The majority of tweets related to the New York State flavor policy were negative both before and after the announcement of this policy in both New York (87/98, 89\% and 3810/4565, 83.46\%, respectively) and other states (200/255, 78.4\% and 12,695/15,569, 81.54\%, respectively), while New York State had a higher proportion of negative tweets than other states. Topic modeling results demonstrated that teenage vaping and health problems were the most discussed topics associated with e-cigarettes. Conclusions: Public attitudes toward e-cigarettes became more negative on Twitter after New York State announced the policy on flavored e-cigarettes. Twitter users in other states that did not have such a policy on flavored e-cigarettes paid close attention to the New York State flavor policy. This study provides some valuable information about the potential impact of the flavored e-cigarettes policy in New York State on public attitudes toward flavored e-cigarettes. ", doi="10.2196/25216", url="https://publichealth.jmir.org/2022/2/e25216", url="http://www.ncbi.nlm.nih.gov/pubmed/35113035" } @Article{info:doi/10.2196/33941, author="Wakamiya, Shoko and Morimoto, Osamu and Omichi, Katsuhiro and Hara, Hideyuki and Kawase, Ichiro and Koshiba, Ryuji and Aramaki, Eiji", title="Exploring Relationships Between Tweet Numbers and Over-the-counter Drug Sales for Allergic Rhinitis: Retrospective Analysis", journal="JMIR Form Res", year="2022", month="Feb", day="2", volume="6", number="2", pages="e33941", keywords="infoveillance", keywords="social media", keywords="Twitter", keywords="over-the-counter drugs", keywords="allergic rhinitis", keywords="hay fever", keywords="drug", keywords="treatment", keywords="allergy", keywords="immunology", keywords="surveillance", keywords="monitoring", keywords="prevalence", keywords="motivation", keywords="Japan", keywords="symptom", abstract="Background: Health-related social media data are increasingly being used in disease surveillance studies. In particular, surveillance of infectious diseases such as influenza has demonstrated high correlations between the number of social media posts mentioning the disease and the number of patients who went to the hospital and were diagnosed with the disease. However, the prevalence of some diseases, such as allergic rhinitis, cannot be estimated based on the number of patients alone. Specifically, individuals with allergic rhinitis typically self-medicate by taking over-the-counter (OTC) medications without going to the hospital. Although allergic rhinitis is not a life-threatening disease, it represents a major social problem because it reduces people's quality of life, making it essential to understand its prevalence and people's motives for self-medication behavior. Objective: This study aims to explore the relationship between the number of social media posts mentioning the main symptoms of allergic rhinitis and the sales volume of OTC rhinitis medications in Japan. Methods: We collected tweets over 4 years (from 2017 to 2020) that included keywords corresponding to the main nasal symptoms of allergic rhinitis: ``sneezing,'' ``runny nose,'' and ``stuffy nose.'' We also obtained the sales volume of OTC drugs, including oral medications and nasal sprays, for the same period. We then calculated the Pearson correlation coefficient between time series data on the number of tweets per week and time series data on the sales volume of OTC drugs per week. Results: The results showed a much higher correlation (r=0.8432) between the time series data on the number of tweets mentioning ``stuffy nose'' and the time series data on the sales volume of nasal sprays than for the other two symptoms. There was also a high correlation (r=0.9317) between the seasonal components of these time series data. Conclusions: We investigated the relationships between social media data and behavioral patterns, such as OTC drug sales volume. Exploring these relationships can help us understand the prevalence of allergic rhinitis and the motives for self-care treatment using social media data, which would be useful as a marketing indicator to reduce the number of out-of-stocks in stores, provide (sell) rhinitis medicines to consumers in a stable manner, and reduce the loss of sales opportunities. In the future, in-depth investigations are required to estimate sales volume using social media data, and future research could investigate other diseases and countries. ", doi="10.2196/33941", url="https://formative.jmir.org/2022/2/e33941", url="http://www.ncbi.nlm.nih.gov/pubmed/35107434" } @Article{info:doi/10.2196/35552, author="Gisondi, A. Michael and Barber, Rachel and Faust, Samuel Jemery and Raja, Ali and Strehlow, C. Matthew and Westafer, M. Lauren and Gottlieb, Michael", title="A Deadly Infodemic: Social Media and the Power of COVID-19 Misinformation", journal="J Med Internet Res", year="2022", month="Feb", day="1", volume="24", number="2", pages="e35552", keywords="COVID-19", keywords="social media", keywords="misinformation", keywords="disinformation", keywords="infodemic", keywords="ethics", keywords="vaccination", keywords="vaccine hesitancy", keywords="infoveillance", keywords="vaccine", doi="10.2196/35552", url="https://www.jmir.org/2022/2/e35552", url="http://www.ncbi.nlm.nih.gov/pubmed/35007204" } @Article{info:doi/10.2196/32309, author="Kim, Hyun-Yong and Kang, Kyung-Ah and Han, Suk-Jung and Chun, Jiyoung", title="Web-Based Research Trends on Child and Adolescent Cancer Survivors Over the Last 5 Years: Text Network Analysis and Topic Modeling Study", journal="J Med Internet Res", year="2022", month="Feb", day="1", volume="24", number="2", pages="e32309", keywords="text network analysis", keywords="topic modeling", keywords="cancer survivors", keywords="child", keywords="adolescent", keywords="research trends", keywords="knowledge structures", abstract="Background: Being diagnosed with cancer during childhood or adolescence can disrupt important periods in an individual's physical, psychosocial, and spiritual development and potentially reduce the quality of life (QOL) after treatment. Research is urgently required to improve the QOL for child and adolescent cancer survivors, and it is necessary to analyze the trends in prior research reported in international academic journals to identify knowledge structures. Objective: This study aims to identify the main keywords based on network centrality, subgroups (clusters) of keyword networks by using a cohesion analysis method, and the main theme of child and adolescent cancer survivor--related research abstracts through topic modeling. This study also aims to label the subgroups by comparing the results of the cohesion and topic modeling. Methods: A text network analysis method and topic modeling were used to explore the main trends in child and adolescent cancer survivor research by structuring a network of keyword (semantic morphemes) co-occurrence in the abstracts of articles published in 5 major web-based databases from 2016 to 2020. A total of 1677 child and adolescent cancer survivor--related studies were used for data analyses. Data selection, processing, and analyses were also conducted. Results: The top 5 keywords in terms of degree and eigenvector centrality were risk, control interval, radiation, childhood cancer treatment, and diagnosis. Of the 1677 studies used for data analyses, cluster 1 included 780 (46.51\%) documents under risk management, cluster 2 contained 557 (33.21\%) articles under health-related QOL and supportive care, and cluster 3 consisted of 340 (20.27\%) studies under cancer treatment and complications. Conclusions: This study is significant in that it confirms the knowledge structure based on the main keywords and cross-disciplinary trends in child and adolescent cancer survivor research published in the last 5 years worldwide. The primary goal of child and adolescent cancer survivor research is to prevent and manage the various aspects of the problems encountered during the transition to a normal life and to improve the overall QOL. To this end, it is necessary to further revitalize the study of the multidisciplinary team approach for the promotion of age-specific health behaviors and the development of intervention strategies with increased feasibility for child and adolescent cancer survivors. ", doi="10.2196/32309", url="https://www.jmir.org/2022/2/e32309", url="http://www.ncbi.nlm.nih.gov/pubmed/35103615" } @Article{info:doi/10.2196/31528, author="Renner, Simon and Marty, Tom and Khadhar, Micka{\"i}l and Foulqui{\'e}, Pierre and Voillot, Pam{\'e}la and Mebarki, Adel and Montagni, Ilaria and Texier, Nathalie and Sch{\"u}ck, St{\'e}phane", title="A New Method to Extract Health-Related Quality of Life Data From Social Media Testimonies: Algorithm Development and Validation", journal="J Med Internet Res", year="2022", month="Jan", day="28", volume="24", number="1", pages="e31528", keywords="health-related quality of life", keywords="social media use", keywords="measures", keywords="real world", keywords="natural language processing", keywords="social media", keywords="NLP", keywords="infoveillance", keywords="quality of life", keywords="digital health", keywords="social listening", abstract="Background: Monitoring social media has been shown to be a useful means to capture patients' opinions and feelings about medical issues, ranging from diseases to treatments. Health-related quality of life (HRQoL) is a useful indicator of overall patients' health, which can be captured online. Objective: This study aimed to describe a social media listening algorithm able to detect the impact of diseases or treatments on specific dimensions of HRQoL based on posts written by patients in social media and forums. Methods: Using a web crawler, 19 forums in France were harvested, and messages related to patients' experience with disease or treatment were specifically collected. The SF-36 (Short Form Health Survey) and EQ-5D (Euro Quality of Life 5 Dimensions) HRQoL surveys were mixed and adapted for a tailored social media listening system. This was carried out to better capture the variety of expression on social media, resulting in 5 dimensions of the HRQoL, which are physical, psychological, activity-based, social, and financial. Models were trained using cross-validation and hyperparameter optimization. Oversampling was used to increase the infrequent dimension: after annotation, SMOTE (synthetic minority oversampling technique) was used to balance the proportions of the dimensions among messages. Results: The training set was composed of 1399 messages, randomly taken from a batch of 20,000 health-related messages coming from forums. The algorithm was able to detect a general impact on HRQoL (sensitivity of 0.83 and specificity of 0.74), a physical impact (0.67 and 0.76), a psychic impact (0.82 and 0.60), an activity-related impact (0.73 and 0.78), a relational impact (0.73 and 0.70), and a financial impact (0.79 and 0.74). Conclusions: The development of an innovative method to extract health data from social media as real time assessment of patients' HRQoL is useful to a patient-centered medical care. As a source of real-world data, social media provide a complementary point of view to understand patients' concerns and unmet needs, as well as shedding light on how diseases and treatments can be a burden in their daily lives. ", doi="10.2196/31528", url="https://www.jmir.org/2022/1/e31528", url="http://www.ncbi.nlm.nih.gov/pubmed/35089152" } @Article{info:doi/10.2196/31140, author="Voillot, Pam{\'e}la and Riche, Brigitte and Portafax, Michel and Foulqui{\'e}, Pierre and Gedik, Ana{\"i}s and Barbarot, S{\'e}bastien and Misery, Laurent and H{\'e}as, St{\'e}phane and Mebarki, Adel and Texier, Nathalie and Sch{\"u}ck, St{\'e}phane", title="Social Media Platforms Listening Study on Atopic Dermatitis: Quantitative and Qualitative Findings", journal="J Med Internet Res", year="2022", month="Jan", day="28", volume="24", number="1", pages="e31140", keywords="atopic dermatitis", keywords="Atopic Dermatitis Control Tool", keywords="health-related quality of life", keywords="social media use", keywords="real world", keywords="dermatology", keywords="skin disease", keywords="social media", keywords="online health information", keywords="online health", keywords="health care", abstract="Background: Atopic dermatitis (AD) is a chronic, pruritic, inflammatory disease that occurs most frequently in children but also affects many adults. Social media have become key tools for finding and disseminating medical information. Objective: The aims of this study were to identify the main themes of discussion, the difficulties encountered by patients with respect to AD, the impact of the pathology on quality of life (QoL; physical, psychological, social, or financial), and to study the perception of patients regarding their treatment. Methods: A retrospective study was carried out by collecting social media posts in French language written by internet users mentioning their experience with AD, their QoL, and their treatments. Messages related to AD discomfort posted between July 1, 2010, and October 23, 2020, were extracted from French-speaking publicly available online forums. Automatic and manual extractions were implemented to create a general corpus and 2 subcorpuses depending on the level of control of the disease. Results: A total of 33,115 messages associated with AD were included in the analysis corpus after extraction and cleaning. These messages were posted by 15,857 separate web users, most of them being women younger than 40 years. Tips to manage AD and everyday hygiene/treatments were among the most discussed topics for controlled AD subcorpus, while baby-related topics and therapeutic failure were among the most discussed topics for insufficiently controlled AD subcorpus. QoL was discussed in both subcorpuses with a higher proportion in the controlled AD subcorpus. Treatments and their perception were also discussed by web users. Conclusions: More than just emotional or peer support, patients with AD turn to online forums to discuss their health. Our findings show the need for an intersection between social media and health care and the importance of developing new approaches such as the Atopic Dermatitis Control Tool, which is a patient-related disease severity assessment tool focused on patients with AD. ", doi="10.2196/31140", url="https://www.jmir.org/2022/1/e31140", url="http://www.ncbi.nlm.nih.gov/pubmed/35089160" } @Article{info:doi/10.2196/35286, author="Girardi, Abdias and Singh, Paul Nikhi and Boyd, Joseph Carter", title="Using Social Media in Health Care Research Should Proceed With Caution. Comment on ``The Use of Social Media for Health Research Purposes: Scoping Review''", journal="J Med Internet Res", year="2022", month="Jan", day="28", volume="24", number="1", pages="e35286", keywords="public health", keywords="epidemiology", keywords="research", keywords="health", keywords="medical", keywords="social networking", keywords="infodemiology", keywords="eHealth", keywords="text mining", keywords="medical education", keywords="social media", keywords="information technology", keywords="health care", keywords="HIPAA", keywords="education", doi="10.2196/35286", url="https://www.jmir.org/2022/1/e35286", url="http://www.ncbi.nlm.nih.gov/pubmed/35089149" } @Article{info:doi/10.2196/32731, author="Lekkas, Damien and Gyorda, A. Joseph and Price, D. George and Wortzman, Zoe and Jacobson, C. Nicholas", title="Using the COVID-19 Pandemic to Assess the Influence of News Affect on Online Mental Health-Related Search Behavior Across the United States: Integrated Sentiment Analysis and the Circumplex Model of Affect", journal="J Med Internet Res", year="2022", month="Jan", day="27", volume="24", number="1", pages="e32731", keywords="affect", keywords="sentiment", keywords="circumplex", keywords="news", keywords="mental health", keywords="online search behavior", keywords="generalized mixed models", keywords="natural language processing", keywords="anxiety", keywords="depression", keywords="coronavirus", keywords="internet", keywords="information seeking", keywords="behavior", keywords="online health information", keywords="COVID-19", abstract="Background: The digital era has ushered in an unprecedented volume of readily accessible information, including news coverage of current events. Research has shown that the sentiment of news articles can evoke emotional responses from readers on a daily basis with specific evidence for increased anxiety and depression in response to coverage of the recent COVID-19 pandemic. Given the primacy and relevance of such information exposure, its daily impact on the mental health of the general population within this modality warrants further nuanced investigation. Objective: Using the COVID-19 pandemic as a subject-specific example, this work aimed to profile and examine associations between the dynamics of semantic affect in online local news headlines and same-day online mental health term search behavior over time across the United States. Methods: Using COVID-19--related news headlines from a database of online news stories in conjunction with mental health--related online search data from Google Trends, this paper first explored the statistical and qualitative affective properties of state-specific COVID-19 news coverage across the United States from January 23, 2020, to October 22, 2020. The resultant operationalizations and findings from the joint application of dictionary-based sentiment analysis and the circumplex theory of affect informed the construction of subsequent hypothesis-driven mixed effects models. Daily state-specific counts of mental health search queries were regressed on circumplex-derived features of semantic affect, time, and state (as a random effect) to model the associations between the dynamics of news affect and search behavior throughout the pandemic. Search terms were also grouped into depression symptoms, anxiety symptoms, and nonspecific depression and anxiety symptoms to model the broad impact of news coverage on mental health. Results: Exploratory efforts revealed patterns in day-to-day news headline affect variation across the first 9 months of the pandemic. In addition, circumplex mapping of the most frequently used words in state-specific headlines uncovered time-agnostic similarities and differences across the United States, including the ubiquitous use of negatively valenced and strongly arousing language. Subsequent mixed effects modeling implicated increased consistency in affective tone (SpinVA $\beta$=--.207; P<.001) as predictive of increased depression-related search term activity, with emotional language patterns indicative of affective uncontrollability (FluxA $\beta$=.221; P<.001) contributing generally to an increase in online mental health search term frequency. Conclusions: This study demonstrated promise in applying the circumplex model of affect to written content and provided a practical example for how circumplex theory can be integrated with sentiment analysis techniques to interrogate mental health--related associations. The findings from pandemic-specific news headlines highlighted arousal, flux, and spin as potentially significant affect-based foci for further study. Future efforts may also benefit from more expansive sentiment analysis approaches to more broadly test the practical application and theoretical capabilities of the circumplex model of affect on text-based data. ", doi="10.2196/32731", url="https://www.jmir.org/2022/1/e32731", url="http://www.ncbi.nlm.nih.gov/pubmed/34932494" } @Article{info:doi/10.2196/30679, author="Dashtian, Hassan and Murthy, Dhiraj and Kong, Grace", title="An Exploration of e-Cigarette--Related Search Items on YouTube: Network Analysis", journal="J Med Internet Res", year="2022", month="Jan", day="27", volume="24", number="1", pages="e30679", keywords="electronic nicotine delivery systems", keywords="vaping", keywords="social media", keywords="search engine", keywords="natural language processing", keywords="social network analysis", abstract="Background: e-Cigarette use among youth is high, which may be due in part to pro--e-cigarette content on social media such as YouTube. YouTube is also a valuable resource for learning about e-cigarette use, trends, marketing, and e-cigarette user perceptions. However, there is a lack of understanding on how similar e-cigarette--related search items result in similar or relatively mutually exclusive search results. This study uses novel methods to evaluate the relationship between e-cigarette--related search items and results. Objective: The aim of this study is to apply network modeling and rule-based classification to characterize the relationships between e-cigarette--related search items on YouTube and gauge the level of importance of each search item as part of an e-cigarette information network on YouTube. Methods: We used 16 fictitious YouTube profiles to retrieve 4201 distinct videos from 18 keywords related to e-cigarettes. We used network modeling to represent the relationships between the search items. Moreover, we developed a rule-based classification approach to classify videos. We used betweenness centrality (BC) and correlations between nodes (ie, search items) to help us gain knowledge of the underlying structure of the information network. Results: By modeling search items and videos as a network, we observed that broad search items such as e-cig had the most connections to other search items, and specific search items such as cigalike had the least connections. Search items with similar words (eg, vape and vaping) and search items with similar meaning (eg, e-liquid and e-juice) yielded a high degree of connectedness. We also found that each node had 18 (SD 34.8) connections (common videos) on average. BC indicated that general search items such as electronic cigarette and vaping had high importance in the network (BC=0.00836). Our rule-based classification sorted videos into four categories: e-cigarette devices (34\%-57\%), cannabis vaping (16\%-28\%), e-liquid (14\%-37\%), and other (8\%-22\%). Conclusions: Our findings indicate that search items on YouTube have unique relationships that vary in strength and importance. Our methods can not only be used to successfully identify the important, overlapping, and unique e-cigarette--related search items but also help determine which search items are more likely to act as a gateway to e-cigarette--related content. ", doi="10.2196/30679", url="https://www.jmir.org/2022/1/e30679", url="http://www.ncbi.nlm.nih.gov/pubmed/35084353" } @Article{info:doi/10.2196/29894, author="Chejfec-Ciociano, Matias Jonathan and Mart{\'i}nez-Herrera, Pablo Juan and Parra-Guerra, Darianna Alexa and Chejfec, Ricardo and Barbosa-Camacho, Jos{\'e} Francisco and Ibarrola-Pe{\~n}a, Carlos Juan and Cervantes-Guevara, Gabino and Cervantes-Cardona, Alonso Guillermo and Fuentes-Orozco, Clotilde and Cervantes-P{\'e}rez, Enrique and Garc{\'i}a-Reyna, Benjam{\'i}n and Gonz{\'a}lez-Ojeda, Alejandro", title="Misinformation About and Interest in Chlorine Dioxide During the COVID-19 Pandemic in Mexico Identified Using Google Trends Data: Infodemiology Study", journal="JMIR Infodemiology", year="2022", month="Jan", day="27", volume="2", number="1", pages="e29894", keywords="coronavirus", keywords="COVID-19", keywords="Google Trends", keywords="chlorine dioxide", keywords="COVID-19 misinformation", keywords="public health surveillance", keywords="infodemiology", keywords="internet behavior", keywords="digital epidemiology", keywords="internet", keywords="mHealth", keywords="mobile health", keywords="pandemic", keywords="tele-epidemiology", abstract="Background: The COVID-19 pandemic has prompted the increasing popularity of several emerging therapies or preventives that lack scientific evidence or go against medical directives. One such therapy involves the consumption of chlorine dioxide, which is commonly used in the cleaning industry and is available commercially as a mineral solution. This substance has been promoted as a preventive or treatment agent for several diseases, including SARS-CoV-2 infection. As interest in chlorine dioxide has grown since the start of the pandemic, health agencies, institutions, and organizations worldwide have tried to discourage and restrict the consumption of this substance. Objective: The aim of this study is to analyze search engine trends in Mexico to evaluate changes in public interest in chlorine dioxide since the beginning of the COVID-19 pandemic. Methods: We retrieved public query data for the Spanish equivalent of the term ``chlorine dioxide'' from the Google Trends platform. The location was set to Mexico, and the time frame was from March 3, 2019, to February 21, 2021. A descriptive analysis was performed. The Kruskal-Wallis and Dunn tests were used to identify significant changes in search volumes for this term between four consecutive time periods, each of 13 weeks, from March 1, 2020, to February 27, 2021. Results: From the start of the pandemic in Mexico (February 2020), an upward trend was observed in the number of searches compared with that in 2019. Maximum volume trends were recorded during the week of July 19-25, 2020. The search volumes declined between September and November 2020, but another peak was registered in December 2020 through February 2021, which reached a maximum value on January 10. Percentage change from the first to the fourth time periods was +312.85, --71.35, and +228.18, respectively. Pairwise comparisons using the Kruskal-Wallis and Dunn tests showed significant differences between the four periods (P<.001). Conclusions: Misinformation is a public health risk because it can lower compliance with the recommended measures and encourage the use of therapies that have not been proven safe. The ingestion of chlorine dioxide presents a danger to the population, and several adverse reactions have been reported. Programs should be implemented to direct those interested in this substance to accurate medical information. ", doi="10.2196/29894", url="https://infodemiology.jmir.org/2022/1/e29894", url="http://www.ncbi.nlm.nih.gov/pubmed/35155994" } @Article{info:doi/10.2196/26781, author="Gonzalez, Gabriela and Vaculik, Kristina and Khalil, Carine and Zektser, Yuliya and Arnold, Corey and Almario, V. Christopher and Spiegel, Brennan and Anger, Jennifer", title="Using Large-scale Social Media Analytics to Understand Patient Perspectives About Urinary Tract Infections: Thematic Analysis", journal="J Med Internet Res", year="2022", month="Jan", day="25", volume="24", number="1", pages="e26781", keywords="female urology", keywords="urinary tract infections", keywords="health services research", keywords="social media", keywords="online community", keywords="online forum", keywords="latent Dirichlet allocation", keywords="data mining", keywords="digital ethnography", abstract="Background: Current qualitative literature about the experiences of women dealing with urinary tract infections (UTIs) is limited to patients recruited from tertiary centers and medical clinics. However, traditional focus groups and interviews may limit what patients share. Using digital ethnography, we analyzed free-range conversations of an online community. Objective: This study aimed to investigate and characterize the patient perspectives of women dealing with UTIs using digital ethnography. Methods: A data-mining service was used to identify online posts. A thematic analysis was conducted on a subset of the identified posts. Additionally, a latent Dirichlet allocation (LDA) probabilistic topic modeling method was applied to review the entire data set using a semiautomatic approach. Each identified topic was generated as a discrete distribution over the words in the collection, which can be thought of as a word cloud. We also performed a thematic analysis of the word cloud topic model results. Results: A total of 83,589 posts by 53,460 users from 859 websites were identified. Our hand-coding inductive analysis yielded the following 7 themes: quality-of-life impact, knowledge acquisition, support of the online community, health care utilization, risk factors and prevention, antibiotic treatment, and alternative therapies. Using the LDA topic model method, 105 themes were identified and consolidated into 9 categories. Of the LDA-derived themes, 25.7\% (27/105) were related to online community support, and 22\% (23/105) focused on UTI risk factors and prevention strategies. Conclusions: Our large-scale social media analysis supports the importance and reproducibility of using online data to comprehend women's UTI experience. This inductive thematic analysis highlights patient behavior, self-empowerment, and online media utilization by women to address their health concerns in a safe, anonymous way. ", doi="10.2196/26781", url="https://www.jmir.org/2022/1/e26781", url="http://www.ncbi.nlm.nih.gov/pubmed/35076404" } @Article{info:doi/10.2196/28152, author="Yeung, Kan Andy Wai and Tosevska, Anela and Klager, Elisabeth and Eibensteiner, Fabian and Tsagkaris, Christos and Parvanov, D. Emil and Nawaz, A. Faisal and V{\"o}lkl-Kernstock, Sabine and Schaden, Eva and Kletecka-Pulker, Maria and Willschke, Harald and Atanasov, G. Atanas", title="Medical and Health-Related Misinformation on Social Media: Bibliometric Study of the Scientific Literature", journal="J Med Internet Res", year="2022", month="Jan", day="25", volume="24", number="1", pages="e28152", keywords="COVID-19", keywords="Twitter", keywords="health", keywords="social media", keywords="bibliometric", keywords="dissemination", keywords="knowledge exchange", abstract="Background: Social media has been extensively used for the communication of health-related information and consecutively for the potential spread of medical misinformation. Conventional systematic reviews have been published on this topic to identify original articles and to summarize their methodological approaches and themes. A bibliometric study could complement their findings, for instance, by evaluating the geographical distribution of the publications and determining if they were well cited and disseminated in high-impact journals. Objective: The aim of this study was to perform a bibliometric analysis of the current literature to discover the prevalent trends and topics related to medical misinformation on social media. Methods: The Web of Science Core Collection electronic database was accessed to identify relevant papers with the following search string: ALL=(misinformati* OR ``wrong informati*'' OR disinformati* OR ``misleading informati*'' OR ``fake news*'') AND ALL=(medic* OR illness* OR disease* OR health* OR pharma* OR drug* OR therap*) AND ALL=(``social media*'' OR Facebook* OR Twitter* OR Instagram* OR YouTube* OR Weibo* OR Whatsapp* OR Reddit* OR TikTok* OR WeChat*). Full records were exported to a bibliometric software, VOSviewer, to link bibliographic information with citation data. Term and keyword maps were created to illustrate recurring terms and keywords. Results: Based on an analysis of 529 papers on medical and health-related misinformation on social media, we found that the most popularly investigated social media platforms were Twitter (n=90), YouTube (n=67), and Facebook (n=57). Articles targeting these 3 platforms had higher citations per paper (>13.7) than articles covering other social media platforms (Instagram, Weibo, WhatsApp, Reddit, and WeChat; citations per paper <8.7). Moreover, social media platform--specific papers accounted for 44.1\% (233/529) of all identified publications. Investigations on these platforms had different foci. Twitter-based research explored cyberchondria and hypochondriasis, YouTube-based research explored tobacco smoking, and Facebook-based research studied vaccine hesitancy related to autism. COVID-19 was a common topic investigated across all platforms. Overall, the United States contributed to half of all identified papers, and 80\% of the top 10 most productive institutions were based in this country. The identified papers were mostly published in journals of the categories public environmental and occupational health, communication, health care sciences services, medical informatics, and medicine general internal, with the top journal being the Journal of Medical Internet Research. Conclusions: There is a significant platform-specific topic preference for social media investigations on medical misinformation. With a large population of internet users from China, it may be reasonably expected that Weibo, WeChat, and TikTok (and its Chinese version Douyin) would be more investigated in future studies. Currently, these platforms present research gaps that leave their usage and information dissemination warranting further evaluation. Future studies should also include social platforms targeting non-English users to provide a wider global perspective. ", doi="10.2196/28152", url="https://www.jmir.org/2022/1/e28152", url="http://www.ncbi.nlm.nih.gov/pubmed/34951864" } @Article{info:doi/10.2196/30388, author="Hudson, Georgie and Jansli, M. Sonja and Erturk, Sinan and Morris, Daniel and Odoi, M. Clarissa and Clayton-Turner, Angela and Bray, Vanessa and Yourston, Gill and Clouden, Doreen and Proudfoot, David and Cornwall, Andrew and Waldron, Claire and Wykes, Til and Jilka, Sagar", title="Investigation of Carers' Perspectives of Dementia Misconceptions on Twitter: Focus Group Study", journal="JMIR Aging", year="2022", month="Jan", day="24", volume="5", number="1", pages="e30388", keywords="patient and public involvement", keywords="dementia", keywords="co-production", keywords="misconceptions", keywords="stigma", keywords="Twitter", keywords="social media", keywords="Alzheimer's Disease", abstract="Background: Dementia misconceptions on social media are common, with negative effects on people with the condition, their carers, and those who know them. This study codeveloped a thematic framework with carers to understand the forms these misconceptions take on Twitter. Objective: The aim of this study is to identify and analyze types of dementia conversations on Twitter using participatory methods. Methods: A total of 3 focus groups with dementia carers were held to develop a framework of dementia misconceptions based on their experiences. Dementia-related tweets were collected from Twitter's official application programming interface using neutral and negative search terms defined by the literature and by carers (N=48,211). A sample of these tweets was selected with equal numbers of neutral and negative words (n=1497), which was validated in individual ratings by carers. We then used the framework to analyze, in detail, a sample of carer-rated negative tweets (n=863). Results: A total of 25.94\% (12,507/48,211) of our tweet corpus contained negative search terms about dementia. The carers' framework had 3 negative and 3 neutral categories. Our thematic analysis of carer-rated negative tweets found 9 themes, including the use of weaponizing language to insult politicians (469/863, 54.3\%), using dehumanizing or outdated words or statements about members of the public (n=143, 16.6\%), unfounded claims about the cures or causes of dementia (n=11, 1.3\%), or providing armchair diagnoses of dementia (n=21, 2.4\%). Conclusions: This is the first study to use participatory methods to develop a framework that identifies dementia misconceptions on Twitter. We show that misconceptions and stigmatizing language are not rare. They manifest through minimizing and underestimating language. Web-based campaigns aiming to reduce discrimination and stigma about dementia could target those who use negative vocabulary and reduce the misconceptions that are being propagated, thus improving general awareness. ", doi="10.2196/30388", url="https://aging.jmir.org/2022/1/e30388", url="http://www.ncbi.nlm.nih.gov/pubmed/35072637" } @Article{info:doi/10.2196/32394, author="Zhang, Chunyan and Xu, Songhua and Li, Zongfang and Liu, Ge and Dai, Duwei and Dong, Caixia", title="The Evolution and Disparities of Online Attitudes Toward COVID-19 Vaccines: Year-long Longitudinal and Cross-sectional Study", journal="J Med Internet Res", year="2022", month="Jan", day="21", volume="24", number="1", pages="e32394", keywords="COVID-19", keywords="vaccine", keywords="attitude", keywords="Twitter", keywords="data mining", keywords="pandemic", keywords="population group", keywords="evolution", keywords="disparity", abstract="Background: Due to the urgency caused by the COVID-19 pandemic worldwide, vaccine manufacturers have to shorten and parallel the development steps to accelerate COVID-19 vaccine production. Although all usual safety and efficacy monitoring mechanisms remain in place, varied attitudes toward the new vaccines have arisen among different population groups. Objective: This study aimed to discern the evolution and disparities of attitudes toward COVID-19 vaccines among various population groups through the study of large-scale tweets spanning over a whole year. Methods: We collected over 1.4 billion tweets from June 2020 to July 2021, which cover some critical phases concerning the development and inoculation of COVID-19 vaccines worldwide. We first developed a data mining model that incorporates a series of deep learning algorithms for inferring a range of individual characteristics, both in reality and in cyberspace, as well as sentiments and emotions expressed in tweets. We further conducted an observational study, including an overall analysis, a longitudinal study, and a cross-sectional study, to collectively explore the attitudes of major population groups. Results: Our study derived 3 main findings. First, the whole population's attentiveness toward vaccines was strongly correlated (Pearson r=0.9512) with official COVID-19 statistics, including confirmed cases and deaths. Such attentiveness was also noticeably influenced by major vaccine-related events. Second, after the beginning of large-scale vaccine inoculation, the sentiments of all population groups stabilized, followed by a considerably pessimistic trend after June 2021. Third, attitude disparities toward vaccines existed among population groups defined by 8 different demographic characteristics. By crossing the 2 dimensions of attitude, we found that among population groups carrying low sentiments, some had high attentiveness ratios, such as males and individuals aged ?40 years, while some had low attentiveness ratios, such as individuals aged ?18 years, those with occupations of the 3rd category, those with account age <5 years, and those with follower number <500. These findings can be used as a guide in deciding who should be given more attention and what kinds of help to give to alleviate the concerns about vaccines. Conclusions: This study tracked the year-long evolution of attitudes toward COVID-19 vaccines among various population groups defined by 8 demographic characteristics, through which significant disparities in attitudes along multiple dimensions were revealed. According to these findings, it is suggested that governments and public health organizations should provide targeted interventions to address different concerns, especially among males, older people, and other individuals with low levels of education, low awareness of news, low income, and light use of social media. Moreover, public health authorities may consider cooperating with Twitter users having high levels of social influence to promote the acceptance of COVID-19 vaccines among all population groups. ", doi="10.2196/32394", url="https://www.jmir.org/2022/1/e32394", url="http://www.ncbi.nlm.nih.gov/pubmed/34878410" } @Article{info:doi/10.2196/27805, author="Taira, Kazuya and Fujita, Sumio", title="Prediction of Age-Adjusted Mortality From Stroke in Japanese Prefectures: Ecological Study Using Search Engine Queries", journal="JMIR Form Res", year="2022", month="Jan", day="20", volume="6", number="1", pages="e27805", keywords="stroke", keywords="age-adjusted mortality", keywords="search engine query", keywords="Japan", keywords="random forest", keywords="generalized linear mixed model", keywords="search engine", keywords="GLMM", keywords="information-seeking behavior", abstract="Background: Stroke is a major cause of death and the need for nursing care in Japan, with large regional disparities. Objective: The purpose of this study was to clarify the association between stroke-related information retrieval behavior and age-adjusted mortality in each prefecture in Japan. Methods: Age-adjusted mortality from stroke and aging rates were obtained from publicly available Japanese government statistics. A total of 9476 abstracts of Japanese articles related to symptoms and signs of stroke were identified in Ichushi-Web, a Japanese web-based database of biomedical articles, and 100 highly frequent words (hereafter referred to as the Stroke 100) were extracted. Using data from 2014 to 2019, a random forest analysis was carried out using the age-adjusted mortality from stroke in 47 prefectures as the outcome variable and the standardized retrieval numbers of the Stroke 100 words in the log data of Yahoo! JAPAN Search as predictive variables. Regression analysis was performed using a generalized linear mixed model (GLMM) with the number of standardized searches for Stroke 100 words with high importance scores in the random forest model as the predictive variable. In the GLMM, the aging rate and data year were used as control variables, and the random slope of data year and random intercept were calculated by prefecture. Results: The mean age-adjusted mortality from stroke was 28.07 (SD 4.55) deaths per 100,000 for all prefectures in all data years. The accuracy score of the random forest analysis was 89.94\%, the average error was 2.79 degrees, and the mean squared error was 13.57 degrees. The following 9 variables with high importance scores in the random forest analysis were selected as predictive variables for the regression analysis: male, age, hospitalization, enforcement, progress, stroke, abnormal, use, and change. As a result of the regression analysis with GLMM, the standardized partial regression coefficients ($\beta$) and 95\% confidence intervals showed that the following internet search terms were significantly associated with age-adjusted mortality from stroke: male ($\beta$=?5.83, 95\% CI ?8.67 to ?3.29), age ($\beta$=?5.83, 95\% CI ?8.67 to ?3.29), hospitalization ($\beta$=?5.83, 95\% CI ?8.67 to ?3.29), and abnormal ($\beta$=3.83, 95\% CI 1.14 to 6.56). Conclusions: Stroke-related search behavior was associated with age-adjusted mortality from stroke in each prefecture in Japan. Query terms that were strongly associated with age-adjusted mortality rates of stroke suggested the possibility that individual characteristics, such as sex and age, have an impact on stroke-associated mortality and that it is important to receive medical care early after stroke onset. Further studies on the criteria and timing of alerting are needed by monitoring information-seeking behavior to identify queries that are strongly associated with stroke mortality. ", doi="10.2196/27805", url="https://formative.jmir.org/2022/1/e27805", url="http://www.ncbi.nlm.nih.gov/pubmed/35049512" } @Article{info:doi/10.2196/28749, author="Shakeri Hossein Abad, Zahra and Butler, P. Gregory and Thompson, Wendy and Lee, Joon", title="Crowdsourcing for Machine Learning in Public Health Surveillance: Lessons Learned From Amazon Mechanical Turk", journal="J Med Internet Res", year="2022", month="Jan", day="18", volume="24", number="1", pages="e28749", keywords="crowdsourcing", keywords="machine learning", keywords="digital public health surveillance", keywords="public health database", keywords="social media analysis", abstract="Background: Crowdsourcing services, such as Amazon Mechanical Turk (AMT), allow researchers to use the collective intelligence of a wide range of web users for labor-intensive tasks. As the manual verification of the quality of the collected results is difficult because of the large volume of data and the quick turnaround time of the process, many questions remain to be explored regarding the reliability of these resources for developing digital public health systems. Objective: This study aims to explore and evaluate the application of crowdsourcing, generally, and AMT, specifically, for developing digital public health surveillance systems. Methods: We collected 296,166 crowd-generated labels for 98,722 tweets, labeled by 610 AMT workers, to develop machine learning (ML) models for detecting behaviors related to physical activity, sedentary behavior, and sleep quality among Twitter users. To infer the ground truth labels and explore the quality of these labels, we studied 4 statistical consensus methods that are agnostic of task features and only focus on worker labeling behavior. Moreover, to model the meta-information associated with each labeling task and leverage the potential of context-sensitive data in the truth inference process, we developed 7 ML models, including traditional classifiers (offline and active), a deep learning--based classification model, and a hybrid convolutional neural network model. Results: Although most crowdsourcing-based studies in public health have often equated majority vote with quality, the results of our study using a truth set of 9000 manually labeled tweets showed that consensus-based inference models mask underlying uncertainty in data and overlook the importance of task meta-information. Our evaluations across 3 physical activity, sedentary behavior, and sleep quality data sets showed that truth inference is a context-sensitive process, and none of the methods studied in this paper were consistently superior to others in predicting the truth label. We also found that the performance of the ML models trained on crowd-labeled data was sensitive to the quality of these labels, and poor-quality labels led to incorrect assessment of these models. Finally, we have provided a set of practical recommendations to improve the quality and reliability of crowdsourced data. Conclusions: Our findings indicate the importance of the quality of crowd-generated labels in developing ML models designed for decision-making purposes, such as public health surveillance decisions. A combination of inference models outlined and analyzed in this study could be used to quantitatively measure and improve the quality of crowd-generated labels for training ML models. ", doi="10.2196/28749", url="https://www.jmir.org/2022/1/e28749", url="http://www.ncbi.nlm.nih.gov/pubmed/35040794" } @Article{info:doi/10.2196/23656, author="Lim, C. Megan S. and Molenaar, Annika and Brennan, Linda and Reid, Mike and McCaffrey, Tracy", title="Young Adults' Use of Different Social Media Platforms for Health Information: Insights From Web-Based Conversations", journal="J Med Internet Res", year="2022", month="Jan", day="18", volume="24", number="1", pages="e23656", keywords="social media", keywords="Facebook", keywords="Instagram", keywords="YouTube", keywords="health information", keywords="health communication", keywords="young adults", abstract="Background: Social media--delivered health promotion has demonstrated limited uptake and effectiveness among young adults. Understanding how young adults interact with existing social media platforms for health might provide insight for future health promotion interventions. Objective: The aim of this study is to describe how young adults interact with different social media platforms for health and health information. Methods: We used a web-based conversation methodology to collect data from 165 young adults aged 18 to 24 years. Participants participated in an extended conversation with moderators and other participants about health and social media. They were prompted to discuss how they find health information, how they use different social media platforms, and how they evaluate the trustworthiness of information. A thematic qualitative analysis was applied to the data. Results: Young adults spent a lot of time scrolling through Facebook newsfeeds, which often resulted in seeing health-related content either from their friends, news sources, or advertisements. Some actively sought out information about specific health areas by joining groups or following relevant pages. YouTube was considered a useful source for learning about everything and was often the go-to when searching for information or advice (after Google). Young adults found the video format easy to learn from. They stated that they could identify accurate YouTube health content by cross-checking multiple videos, by feeling that the presenter was real and relatable, or just through instinctively judging a video's credibility. Instagram was a source of inspiration for health and wellness from those whose lives were dedicated to healthy lifestyles and fitness. Twitter, Tumblr, and Snapchat were rarely used for health information. Conclusions: Most young adults obtain health information from social media, both actively and through passive exposure. Participants indicated looking to social media influencers for health and lifestyle inspiration and judged the credibility of sources by appearance and instinct. Health experts should try to use the channels in the way that young adults already use them; use relatable role models on Instagram and YouTube, eye-catching headlines and support groups on Facebook, and easy to follow instruction videos via YouTube. International Registered Report Identifier (IRRID): RR2-10.1111/1747-0080.12448 ", doi="10.2196/23656", url="https://www.jmir.org/2022/1/e23656", url="http://www.ncbi.nlm.nih.gov/pubmed/35040796" } @Article{info:doi/10.2196/32140, author="Schluter, J. Philip and G{\'e}n{\'e}reux, M{\'e}lissa and Hung, KC Kevin and Landaverde, Elsa and Law, P. Ronald and Mok, Yin Catherine Pui and Murray, Virginia and O'Sullivan, Tracey and Qadar, Zeeshan and Roy, Mathieu", title="Patterns of Suicide Ideation Across Eight Countries in Four Continents During the COVID-19 Pandemic Era: Repeated Cross-sectional Study", journal="JMIR Public Health Surveill", year="2022", month="Jan", day="17", volume="8", number="1", pages="e32140", keywords="pandemic", keywords="infodemic", keywords="psychosocial impacts", keywords="sense of coherence", keywords="suicide ideation", keywords="epidemiology", keywords="suicide", keywords="pattern", keywords="COVID-19", keywords="cross-sectional", keywords="mental health", keywords="misinformation", keywords="risk", keywords="prevalence", keywords="gender", keywords="age", keywords="sociodemographic", abstract="Background: The COVID-19 pandemic and countries' response measures have had a globally significant mental health impact. This mental health burden has also been fueled by an infodemic: an information overload that includes misinformation and disinformation. Suicide, the worst mental health outcome, is a serious public health problem that can be prevented with timely, evidence-based, and often low-cost interventions. Suicide ideation, one important risk factor for suicide, is thus important to measure and monitor, as are the factors that may impact on it. Objective: This investigation had 2 primary aims: (1) to estimate and compare country-specific prevalence of suicide ideation at 2 different time points, overall and by gender and age groups, and (2) to investigate the influence of sociodemographic and infodemic variables on suicide ideation. Methods: A repeated, online, 8-country (Canada, the United States, England, Switzerland, Belgium, Hong Kong, Philippines, and New Zealand), cross-sectional study was undertaken with adults aged ?18 years, with measurement wave 1 conducted from May 29, 2020 to June 12, 2020 and measurement wave 2 conducted November 6-18, 2021. Self-reported suicide ideation was derived from item 9 of the Patient Health Questionnaire-9 (PHQ-9). Age-standardized suicide ideation rates were reported, a binomial regression model was used to estimate suicide ideation indication rates for each country and measurement wave, and logistic regression models were then employed to relate sociodemographic, pandemic, and infodemic variables to suicide ideation. Results: The final sample totaled 17,833 adults: 8806 (49.4\%) from measurement wave 1 and 9027 (50.6\%) from wave 2. Overall, 24.2\% (2131/8806) and 27.5\% (2486/9027) of participants reported suicide ideation at measurement waves 1 and 2, respectively, a difference that was significant (P<.001). Considerable variability was observed in suicide ideation age-standardized rates between countries, ranging from 15.6\% in Belgium (wave 1) to 42.9\% in Hong Kong (wave 2). Frequent social media usage was associated with increased suicide ideation at wave 2 (adjusted odds ratio [AOR] 1.47, 95\% CI 1.25-1.72; P<.001) but not wave 1 (AOR 1.11, 95\% CI 0.96-1.23; P=.16). However, having a weaker sense of coherence (SOC; AOR 3.80, 95\% CI 3.18-4.55 at wave 1 and AOR 4.39, 95\% CI 3.66-5.27 at wave 2; both P<.001) had the largest overall effect size. Conclusions: Suicide ideation is prevalent and significantly increasing over time in this COVID-19 pandemic era, with considerable variability between countries. Younger adults and those residing in Hong Kong carried disproportionately higher rates. Social media appears to have an increasingly detrimental association with suicide ideation, although having a stronger SOC had a larger protective effect. Policies and promotion of SOC, together with disseminating health information that explicitly tackles the infodemic's misinformation and disinformation, may importantly reduce the rising mental health morbidity and mortality triggered by this pandemic. ", doi="10.2196/32140", url="https://publichealth.jmir.org/2022/1/e32140", url="http://www.ncbi.nlm.nih.gov/pubmed/34727524" } @Article{info:doi/10.2196/34429, author="Struik, Laura and Khan, Shaheer and Assoiants, Artem and Sharma, H. Ramona", title="Assessment of Social Support and Quitting Smoking in an Online Community Forum: Study Involving Content Analysis", journal="JMIR Form Res", year="2022", month="Jan", day="13", volume="6", number="1", pages="e34429", keywords="qualitative research", keywords="smoking cessation", keywords="social media", keywords="social support", keywords="smoking", keywords="tobacco use", keywords="tobacco", keywords="online forum", abstract="Background: A key factor in successfully reducing and quitting smoking, as well as preventing smoking relapse is access to and engagement with social support. Recent technological advances have made it possible for smokers to access social support via online community forums. While community forums associated with smoking cessation interventions are now common practice, there is a gap in understanding how and when the different types of social support identified by Cutrona and Suhr (1992) (emotional, esteem, informational, tangible, and network) are exchanged on such forums. Community forums that entail ``superusers'' (a key marker of a successful forum), like QuitNow, are ripe for exploring and leveraging promising social support exchanges on these platforms. Objective: The purpose of this study was to characterize the posts made on the QuitNow community forum at different stages in the quit journey, and determine when and how the social support constructs are present within the posts. Methods: A total of 506 posts (including original and response posts) were collected. Using conventional content analysis, the original posts were coded inductively to generate categories and subcategories, and the responses were coded deductively according to the 5 types of social support. Data were analyzed using Microsoft Excel software. Results: Overall, individuals were most heavily engaged on the forum during the first month of quitting, which then tapered off in the subsequent months. In relation to the original posts, the majority of them fit into the categories of sharing quit successes, quit struggles, updates, quit strategies, and desires to quit. Asking for advice and describing smoke-free benefits were the least represented categories. In relation to the responses, encouragement (emotional), compliment (esteem), and suggestion/advice (informational) consistently remained the most prominent types of support throughout all quit stages. Companionship (network) maintained a steady downward trajectory over time. Conclusions: The findings of this study highlight the complexity of how and when different types of social support are exchanged on the QuitNow community forum. These findings provide directions for how social support can be more strategically employed and leveraged in these online contexts to support smoking cessation. ", doi="10.2196/34429", url="https://formative.jmir.org/2022/1/e34429", url="http://www.ncbi.nlm.nih.gov/pubmed/35023834" } @Article{info:doi/10.2196/31175, author="Pereira-Sanchez, Victor and Alvarez-Mon, Angel Miguel and Horinouchi, Toru and Kawagishi, Ryo and Tan, J. Marcus P. and Hooker, R. Elizabeth and Alvarez-Mon, Melchor and Teo, R. Alan", title="Examining Tweet Content and Engagement of Users With Tweets About Hikikomori in Japanese: Mixed Methods Study of Social Withdrawal", journal="J Med Internet Res", year="2022", month="Jan", day="11", volume="24", number="1", pages="e31175", keywords="hikikomori", keywords="loneliness", keywords="social isolation", keywords="social withdrawal", keywords="Twitter", keywords="hidden youth", keywords="mobile phone", abstract="Background: Hikikomori is a form of severe social withdrawal that is particularly prevalent in Japan. Social media posts offer insight into public perceptions of mental health conditions and may also inform strategies to identify, engage, and support hard-to-reach patient populations such as individuals affected by hikikomori. Objective: In this study, we seek to identify the types of content on Twitter related to hikikomori in the Japanese language and to assess Twitter users' engagement with that content. Methods: We conducted a mixed methods analysis of a random sample of 4940 Japanese tweets from February to August 2018 using a hashtag (\#hikikomori). Qualitative content analysis included examination of the text of each tweet, development of a codebook, and categorization of tweets into relevant codes. For quantitative analysis (n=4859 tweets), we used bivariate and multivariate logistic regression models, adjusted for multiple comparisons, and estimated the predicted probabilities of tweets receiving engagement (likes or retweets). Results: Our content analysis identified 9 codes relevant to tweets about hikikomori: personal anecdotes, social support, marketing, advice, stigma, educational opportunities, refuge (ibasho), employment opportunities, and medicine and science. Tweets about personal anecdotes were the most common (present in 2747/4859, 56.53\% of the tweets), followed by social support (902/4859, 18.56\%) and marketing (624/4859, 12.84\%). In the adjusted models, tweets coded as stigma had a lower predicted probability of likes (?33 percentage points, 95\% CI ?42 to ?23 percentage points; P<.001) and retweets (?11 percentage points, 95\% CI ?18 to ?4 percentage points; P<.001), personal anecdotes had a lower predicted probability of retweets (?8 percentage points, 95\% CI ?14 to ?3 percentage points; P=.002), marketing had a lower predicted probability of likes (?13 percentage points, 95\% CI ?21 to ?6 percentage points; P<.001), and social support had a higher predicted probability of retweets (+15 percentage points, 95\% CI 6-24 percentage points; P=.001), compared with all tweets without each of these codes. Conclusions: Japanese tweets about hikikomori reflect a unique array of topics, many of which have not been identified in prior research and vary in their likelihood of receiving engagement. Tweets often contain personal stories of hikikomori, suggesting the potential to identify individuals with hikikomori through Twitter. ", doi="10.2196/31175", url="https://www.jmir.org/2022/1/e31175", url="http://www.ncbi.nlm.nih.gov/pubmed/35014971" } @Article{info:doi/10.2196/27000, author="Cheng, Cecilia and Ebrahimi, V. Omid and Luk, W. Jeremy", title="Heterogeneity of Prevalence of Social Media Addiction Across Multiple Classification Schemes: Latent Profile Analysis", journal="J Med Internet Res", year="2022", month="Jan", day="10", volume="24", number="1", pages="e27000", keywords="behavioral addiction", keywords="compulsive social media use", keywords="information technology addiction", keywords="mental health", keywords="psychological assessment", keywords="sensitivity", keywords="social network site", keywords="social networking", keywords="well-being", abstract="Background: As social media is a major channel of interpersonal communication in the digital age, social media addiction has emerged as a novel mental health issue that has raised considerable concerns among researchers, health professionals, policy makers, mass media, and the general public. Objective: The aim of this study is to examine the prevalence of social media addiction derived from 4 major classification schemes (strict monothetic, strict polythetic, monothetic, and polythetic), with latent profiles embedded in the empirical data adopted as the benchmark for comparison. The extent of matching between the classification of each scheme and the actual data pattern was evaluated using sensitivity and specificity analyses. The associations between social media addiction and 2 comorbid mental health conditions---depression and anxiety---were investigated. Methods: A cross-sectional web-based survey was conducted, and the replicability of findings was assessed in 2 independent samples comprising 573 adults from the United Kingdom (261/573, 45.6\% men; mean age 43.62 years, SD 12.24 years) and 474 adults from the United States (224/474, 47.4\% men; mean age 44.67 years, SD 12.99 years). The demographic characteristics of both samples were similar to those of their respective populations. Results: The prevalence estimates of social media addiction varied across the classification schemes, ranging from 1\% to 15\% for the UK sample and 0\% to 11\% for the US sample. The latent profile analysis identified 3 latent groups for both samples: low-risk, at-risk, and high-risk. The sensitivity, specificity, and negative predictive values were high (83\%-100\%) for all classification schemes, except for the relatively lower sensitivity (73\%-74\%) for the polythetic scheme. However, the polythetic scheme had high positive predictive values (88\%-94\%), whereas such values were low (2\%-43\%) for the other 3 classification schemes. The group membership yielded by the polythetic scheme was largely consistent (95\%-96\%) with that of the benchmark. Conclusions: Among the classification schemes, the polythetic scheme is more well-balanced across all 4 indices. ", doi="10.2196/27000", url="https://www.jmir.org/2022/1/e27000", url="http://www.ncbi.nlm.nih.gov/pubmed/35006084" } @Article{info:doi/10.2196/33792, author="Klein, Z. Ari and O'Connor, Karen and Gonzalez-Hernandez, Graciela", title="Toward Using Twitter Data to Monitor COVID-19 Vaccine Safety in Pregnancy: Proof-of-Concept Study of Cohort Identification", journal="JMIR Form Res", year="2022", month="Jan", day="6", volume="6", number="1", pages="e33792", keywords="natural language processing", keywords="social media", keywords="COVID-19", keywords="data mining", keywords="COVID-19 vaccine", keywords="pregnancy outcomes", abstract="Background: COVID-19 during pregnancy is associated with an increased risk of maternal death, intensive care unit admission, and preterm birth; however, many people who are pregnant refuse to receive COVID-19 vaccination because of a lack of safety data. Objective: The objective of this preliminary study was to assess whether Twitter data could be used to identify a cohort for epidemiologic studies of COVID-19 vaccination in pregnancy. Specifically, we examined whether it is possible to identify users who have reported (1) that they received COVID-19 vaccination during pregnancy or the periconception period, and (2) their pregnancy outcomes. Methods: We developed regular expressions to search for reports of COVID-19 vaccination in a large collection of tweets posted through the beginning of July 2021 by users who have announced their pregnancy on Twitter. To help determine if users were vaccinated during pregnancy, we drew upon a natural language processing (NLP) tool that estimates the timeframe of the prenatal period. For users who posted tweets with a timestamp indicating they were vaccinated during pregnancy, we drew upon additional NLP tools to help identify tweets that reported their pregnancy outcomes. Results: We manually verified the content of tweets detected automatically, identifying 150 users who reported on Twitter that they received at least one dose of COVID-19 vaccination during pregnancy or the periconception period. We manually verified at least one reported outcome for 45 of the 60 (75\%) completed pregnancies. Conclusions: Given the limited availability of data on COVID-19 vaccine safety in pregnancy, Twitter can be a complementary resource for potentially increasing the acceptance of COVID-19 vaccination in pregnant populations. The results of this preliminary study justify the development of scalable methods to identify a larger cohort for epidemiologic studies. ", doi="10.2196/33792", url="https://formative.jmir.org/2022/1/e33792", url="http://www.ncbi.nlm.nih.gov/pubmed/34870607" } @Article{info:doi/10.2196/33311, author="Park, Susan and Choi, Hyun So and Song, Yun-Kyoung and Kwon, Jin-Won", title="Comparison of Online Patient Reviews and National Pharmacovigilance Data for Tramadol-Related Adverse Events: Comparative Observational Study", journal="JMIR Public Health Surveill", year="2022", month="Jan", day="4", volume="8", number="1", pages="e33311", keywords="drug safety", keywords="pharmacovigilance", keywords="tramadol", keywords="social media", keywords="adverse effect", abstract="Background: Tramadol is known to cause fewer adverse events (AEs) than other opioids. However, recent research has raised concerns about various safety issues. Objective: We aimed to explore these new AEs related to tramadol using social media and conventional pharmacovigilance data. Methods: This study used 2 data sets, 1 from patients' drug reviews on WebMD (January 2007 to January 2021) and 1 from the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS; January 2016 to December 2020). We analyzed 2062 and 29,350 patient reports from WebMD and FAERS, respectively. Patient posts on WebMD were manually assigned the preferred terms of the Medical Dictionary for Regulatory Activities. To analyze AEs from FAERS, a disproportionality analysis was performed with 3 measures: proportional reporting ratio, reporting odds ratio, and information component. Results: From the 869 AEs reported, we identified 125 new signals related to tramadol use not listed on the drug label that satis?ed all 3 signal detection criteria. In addition, 20 serious AEs were selected from new signals. Among new serious AEs, vascular disorders had the largest signal detection criteria value. Based on the disproportionality analysis and patients' symptom descriptions, tramadol-induced pain might also be an unexpected AE. Conclusions: This study detected several novel signals related to tramadol use, suggesting newly identified possible AEs. Additionally, this study indicates that unexpected AEs can be detected using social media analysis alongside traditional pharmacovigilance data. ", doi="10.2196/33311", url="https://publichealth.jmir.org/2022/1/e33311", url="http://www.ncbi.nlm.nih.gov/pubmed/34982723" } @Article{info:doi/10.2196/31671, author="Kahanek, Alexander and Yu, Xinchen and Hong, Lingzi and Cleveland, Ana and Philbrick, Jodi", title="Temporal Variations and Spatial Disparities in Public Sentiment Toward COVID-19 and Preventive Practices in the United States: Infodemiology Study of Tweets", journal="JMIR Infodemiology", year="2021", month="Dec", day="30", volume="1", number="1", pages="e31671", keywords="COVID-19", keywords="preventive practices", keywords="temporal variations", keywords="spatial disparities", keywords="Twitter", keywords="public sentiment", keywords="socioeconomic factors", abstract="Background: During the COVID-19 pandemic, US public health authorities and county, state, and federal governments recommended or ordered certain preventative practices, such as wearing masks, to reduce the spread of the disease. However, individuals had divergent reactions to these preventive practices. Objective: The purpose of this study was to understand the variations in public sentiment toward COVID-19 and the recommended or ordered preventive practices from the temporal and spatial perspectives, as well as how the variations in public sentiment are related to geographical and socioeconomic factors. Methods: The authors leveraged machine learning methods to investigate public sentiment polarity in COVID-19--related tweets from January 21, 2020 to June 12, 2020. The study measured the temporal variations and spatial disparities in public sentiment toward both general COVID-19 topics and preventive practices in the United States. Results: In the temporal analysis, we found a 4-stage pattern from high negative sentiment in the initial stage to decreasing and low negative sentiment in the second and third stages, to the rebound and increase in negative sentiment in the last stage. We also identified that public sentiment to preventive practices was significantly different in urban and rural areas, while poverty rate and unemployment rate were positively associated with negative sentiment to COVID-19 issues. Conclusions: The differences between public sentiment toward COVID-19 and the preventive practices imply that actions need to be taken to manage the initial and rebound stages in future pandemics. The urban and rural differences should be considered in terms of the communication strategies and decision making during a pandemic. This research also presents a framework to investigate time-sensitive public sentiment at the county and state levels, which could guide local and state governments and regional communities in making decisions and developing policies in crises. ", doi="10.2196/31671", url="https://infodemiology.jmir.org/2021/1/e31671", url="http://www.ncbi.nlm.nih.gov/pubmed/35013722" } @Article{info:doi/10.2196/28042, author="Lu, Jiahui and Lee, J. Edmund W.", title="Examining Twitter Discourse on Electronic Cigarette and Tobacco Consumption During National Cancer Prevention Month in 2018: Topic Modeling and Geospatial Analysis", journal="J Med Internet Res", year="2021", month="Dec", day="29", volume="23", number="12", pages="e28042", keywords="electronic cigarette", keywords="smoking", keywords="lung cancer", keywords="Twitter", keywords="national cancer prevention month", keywords="policy", keywords="topic modeling", keywords="cessation", keywords="e-cigarette", keywords="cancer", keywords="social media", keywords="eHealth", keywords="cancer prevention", keywords="tweets", keywords="public health", abstract="Background: Examining public perception of tobacco products is critical for effective tobacco policy making and public education outreach. While the link between traditional tobacco products and lung cancer is well established, it is not known how the public perceives the association between electronic cigarettes (e-cigarettes) and lung cancer. In addition, it is unclear how members of the public interact with official messages during cancer campaigns on tobacco consumption and lung cancer. Objective: In this study, we aimed to analyze e-cigarette and smoking tweets in the context of lung cancer during National Cancer Prevention Month in 2018 and examine how e-cigarette and traditional tobacco product discussions relate to implementation of tobacco control policies across different states in the United States. Methods: We mined tweets that contained the term ``lung cancer'' on Twitter from February to March 2018. The data set contained 13,946 publicly available tweets that occurred during National Cancer Prevention Month (February 2018), and 10,153 tweets that occurred during March 2018. E-cigarette--related and smoking-related tweets were retrieved, using topic modeling and geospatial analysis. Results: Debates on harmfulness (454/915, 49.7\%), personal experiences (316/915, 34.5\%), and e-cigarette risks (145/915, 15.8\%) were the major themes of e-cigarette tweets related to lung cancer. Policy discussions (2251/3870, 58.1\%), smoking risks (843/3870, 21.8\%), and personal experiences (776/3870, 20.1\%) were the major themes of smoking tweets related to lung cancer. Geospatial analysis showed that discussion on e-cigarette risks was positively correlated with the number of state-level smoke-free policies enacted for e-cigarettes. In particular, the number of indoor and on campus smoke-free policies was related to the number of tweets on e-cigarette risks (smoke-free indoor, r49=0.33, P=.02; smoke-free campus, r49=0.32, P=.02). The total number of e-cigarette policies was also positively related to the number of tweets on e-cigarette risks (r49=0.32, P=.02). In contrast, the number of smoking policies was not significantly associated with any of the smoking themes in the lung cancer discourse (P>.13). Conclusions: Though people recognized the importance of traditional tobacco control policies in reducing lung cancer incidences, their views on e-cigarette risks were divided, and discussions on the importance of e-cigarette policy control were missing from public discourse. Findings suggest the need for health organizations to continuously engage the public in discussions on the potential health risks of e-cigarettes and raise awareness of the insidious lobbying efforts from the tobacco industry. ", doi="10.2196/28042", url="https://www.jmir.org/2021/12/e28042", url="http://www.ncbi.nlm.nih.gov/pubmed/34964716" } @Article{info:doi/10.2196/31540, author="Beliga, Slobodan and Martin{\v c}i{\'c}-Ip{\vs}i{\'c}, Sanda and Mate{\vs}i{\'c}, Mihaela and Petrijev{\v c}anin Vuksanovi{\'c}, Irena and Me{\vs}trovi{\'c}, Ana", title="Infoveillance of the Croatian Online Media During the COVID-19 Pandemic: One-Year Longitudinal Study Using Natural Language Processing", journal="JMIR Public Health Surveill", year="2021", month="Dec", day="24", volume="7", number="12", pages="e31540", keywords="COVID-19", keywords="pandemic", keywords="online media", keywords="news coverage", keywords="infoveillance", keywords="infodemic", keywords="infodemiology", keywords="natural language processing", keywords="name entity recognition", keywords="longitudinal study", abstract="Background: Online media play an important role in public health emergencies and serve as essential communication platforms. Infoveillance of online media during the COVID-19 pandemic is an important step toward gaining a better understanding of crisis communication. Objective: The goal of this study was to perform a longitudinal analysis of the COVID-19--related content on online media based on natural language processing. Methods: We collected a data set of news articles published by Croatian online media during the first 13 months of the pandemic. First, we tested the correlations between the number of articles and the number of new daily COVID-19 cases. Second, we analyzed the content by extracting the most frequent terms and applied the Jaccard similarity coefficient. Third, we compared the occurrence of the pandemic-related terms during the two waves of the pandemic. Finally, we applied named entity recognition to extract the most frequent entities and tracked the dynamics of changes during the observation period. Results: The results showed no significant correlation between the number of articles and the number of new daily COVID-19 cases. Furthermore, there were high overlaps in the terminology used in all articles published during the pandemic with a slight shift in the pandemic-related terms between the first and the second waves. Finally, the findings indicate that the most influential entities have lower overlaps for the identified people and higher overlaps for locations and institutions. Conclusions: Our study shows that online media have a prompt response to the pandemic with a large number of COVID-19--related articles. There was a high overlap in the frequently used terms across the first 13 months, which may indicate the narrow focus of reporting in certain periods. However, the pandemic-related terminology is well-covered. ", doi="10.2196/31540", url="https://publichealth.jmir.org/2021/12/e31540", url="http://www.ncbi.nlm.nih.gov/pubmed/34739388" } @Article{info:doi/10.2196/33331, author="Yang, S. Joshua and Cuomo, E. Raphael and Purushothaman, Vidya and Nali, Matthew and Shah, Neal and Bardier, Cortni and Obradovich, Nick and Mackey, Tim", title="Campus Smoking Policies and Smoking-Related Twitter Posts Originating From California Public Universities: Retrospective Study", journal="JMIR Form Res", year="2021", month="Dec", day="24", volume="5", number="12", pages="e33331", keywords="tobacco-free policies", keywords="social media", keywords="colleges and universities", keywords="smoking", keywords="smoking policy", keywords="campus policy", keywords="tobacco use", keywords="Twitter analysis", keywords="smoke-free", keywords="tobacco-free", keywords="Twitter", keywords="college students", keywords="students", keywords="campus", keywords="health policy", abstract="Background: The number of colleges and universities with smoke- or tobacco-free campus policies has been increasing. The effects of campus smoking policies on overall sentiment, particularly among young adult populations, are more difficult to assess owing to the changing tobacco and e-cigarette product landscape and differential attitudes toward policy implementation and enforcement. Objective: The goal of the study was to retrospectively assess the campus climate toward tobacco use by comparing tweets from California universities with and those without smoke- or tobacco-free campus policies. Methods: Geolocated Twitter posts from 2015 were collected using the Twitter public application programming interface in combination with cloud computing services on Amazon Web Services. Posts were filtered for tobacco products and behavior-related keywords. A total of 42,877,339 posts were collected from 2015, with 2837 originating from a University of California or California State University system campus, and 758 of these manually verified as being about smoking. Chi-square tests were conducted to determine if there were significant differences in tweet user sentiments between campuses that were smoke- or tobacco-free (all University of California campuses and California State University, Fullerton) compared to those that were not. A separate content analysis of tweets included in chi-square tests was conducted to identify major themes by campus smoking policy status. Results: The percentage of positive sentiment tweets toward tobacco use was higher on campuses without a smoke- or tobacco-free campus policy than on campuses with a smoke- or tobacco-free campus policy (76.7\% vs 66.4\%, P=.03). Higher positive sentiment on campuses without a smoke- or tobacco-free campus policy may have been driven by general comments about one's own smoking behavior and comments about smoking as a general behavior. Positive sentiment tweets originating from campuses without a smoke- or tobacco-free policy had greater variation in tweet type, which may have also contributed to differences in sentiment among universities. Conclusions: Our study introduces preliminary data suggesting that campus smoke- and tobacco-free policies are associated with a reduction in positive sentiment toward smoking. However, continued expressions and intentions to smoke and reports of one's own smoking among Twitter users suggest a need for more research to better understand the dynamics between implementation of smoke- and tobacco-free policies and resulting tobacco behavioral sentiment. ", doi="10.2196/33331", url="https://formative.jmir.org/2021/12/e33331", url="http://www.ncbi.nlm.nih.gov/pubmed/34951597" } @Article{info:doi/10.2196/27339, author="Ning, Peishan and Cheng, Peixia and Li, Jie and Zheng, Ming and Schwebel, C. David and Yang, Yang and Lu, Peng and Mengdi, Li and Zhang, Zhuo and Hu, Guoqing", title="COVID-19--Related Rumor Content, Transmission, and Clarification Strategies in China: Descriptive Study", journal="J Med Internet Res", year="2021", month="Dec", day="23", volume="23", number="12", pages="e27339", keywords="COVID-19", keywords="rumor", keywords="strategy", keywords="China", keywords="social media", abstract="Background: Given the permeation of social media throughout society, rumors spread faster than ever before, which significantly complicates government responses to public health emergencies such as the COVID-19 pandemic. Objective: We aimed to examine the characteristics and propagation of rumors during the early months of the COVID-19 pandemic in China and evaluated the effectiveness of health authorities' release of correction announcements. Methods: We retrieved rumors widely circulating on social media in China during the early stages of the COVID-19 pandemic and assessed the effectiveness of official government clarifications and popular science articles refuting those rumors. Results: We show that the number of rumors related to the COVID-19 pandemic fluctuated widely in China between December 1, 2019 and April 15, 2020. Rumors mainly occurred in 3 provinces: Hubei, Zhejiang, and Guangxi. Personal social media accounts constituted the major source of media reports of the 4 most widely distributed rumors (the novel coronavirus can be prevented with ``Shuanghuanglian'': 7648/10,664, 71.7\%; the novel coronavirus is the SARS coronavirus: 14,696/15,902, 92.4\%; medical supplies intended for assisting Hubei were detained by the local government: 3911/3943, 99.2\%; asymptomatically infected persons were regarded as diagnosed COVID-19 patients with symptoms in official counts: 322/323, 99.7\%). The number of rumors circulating was positively associated with the severity of the COVID-19 epidemic ($\rho$=0.88, 95\% CI 0.81-0.93). The release of correction articles was associated with a substantial decrease in the proportion of rumor reports compared to accurate reports. The proportions of negative sentiments appearing among comments by citizens in response to media articles disseminating rumors and disseminating correct information differ insignificantly (both correct reports: $\chi$12=0.315, P=.58; both rumors: $\chi$12=0.025, P=.88; first rumor and last correct report: $\chi$12=1.287, P=.26; first correct report and last rumor: $\chi$12=0.033, P=.86). Conclusions: Our results highlight the importance and urgency of monitoring and correcting false or misleading reports on websites and personal social media accounts. The circulation of rumors can influence public health, and government bodies should establish guidelines to monitor and mitigate the negative impact of such rumors. ", doi="10.2196/27339", url="https://www.jmir.org/2021/12/e27339", url="http://www.ncbi.nlm.nih.gov/pubmed/34806992" } @Article{info:doi/10.2196/26093, author=" and Delir Haghighi, Pari and Burstein, Frada and Urquhart, Donna and Cicuttini, Flavia", title="Investigating Individuals' Perceptions Regarding the Context Around the Low Back Pain Experience: Topic Modeling Analysis of Twitter Data", journal="J Med Internet Res", year="2021", month="Dec", day="23", volume="23", number="12", pages="e26093", keywords="low back pain", keywords="Twitter", keywords="content analysis", keywords="social media", keywords="topic modeling", keywords="patient-centered approach", keywords="pain experience", keywords="context of pain", abstract="Background: Low back pain (LBP) remains the leading cause of disability worldwide. A better understanding of the beliefs regarding LBP and impact of LBP on the individual is important in order to improve outcomes. Although personal experiences of LBP have traditionally been explored through qualitative studies, social media allows access to data from a large, heterogonous, and geographically distributed population, which is not possible using traditional qualitative or quantitative methods. As data on social media sites are collected in an unsolicited manner, individuals are more likely to express their views and emotions freely and in an unconstrained manner as compared to traditional data collection methods. Thus, content analysis of social media provides a novel approach to understanding how problems such as LBP are perceived by those who experience it and its impact. Objective: The objective of this study was to identify contextual variables of the LBP experience from a first-person perspective to provide insights into individuals' beliefs and perceptions. Methods: We analyzed 896,867 cleaned tweets about LBP between January 1, 2014, and December 31, 2018. We tested and compared latent Dirichlet allocation (LDA), Dirichlet multinomial mixture (DMM), GPU-DMM, biterm topic model, and nonnegative matrix factorization for identifying topics associated with tweets. A coherence score was determined to identify the best model. Two domain experts independently performed qualitative content analysis of the topics with the strongest coherence score and grouped them into contextual categories. The experts met and reconciled any differences and developed the final labels. Results: LDA outperformed all other algorithms, resulting in the highest coherence score. The best model was LDA with 60 topics, with a coherence score of 0.562. The 60 topics were grouped into 19 contextual categories. ``Emotion and beliefs'' had the largest proportion of total tweets (157,563/896,867, 17.6\%), followed by ``physical activity'' (124,251/896,867, 13.85\%) and ``daily life'' (80,730/896,867, 9\%), while ``food and drink,'' ``weather,'' and ``not being understood'' had the smallest proportions (11,551/896,867, 1.29\%; 10,109/896,867, 1.13\%; and 9180/896,867, 1.02\%, respectively). Of the 11 topics within ``emotion and beliefs,'' 113,562/157,563 (72\%) had negative sentiment. Conclusions: The content analysis of tweets in the area of LBP identified common themes that are consistent with findings from conventional qualitative studies but provide a more granular view of individuals' perspectives related to LBP. This understanding has the potential to assist with developing more effective and personalized models of care to improve outcomes in those with LBP. ", doi="10.2196/26093", url="https://www.jmir.org/2021/12/e26093", url="http://www.ncbi.nlm.nih.gov/pubmed/36260398" } @Article{info:doi/10.2196/34218, author="Tan, YQ Edina and Wee, RE Russell and Saw, Ern Young and Heng, JQ Kylie and Chin, WE Joseph and Tong, MW Eddie and Liu, CJ Jean", title="Tracking Private WhatsApp Discourse About COVID-19 in Singapore: Longitudinal Infodemiology Study", journal="J Med Internet Res", year="2021", month="Dec", day="23", volume="23", number="12", pages="e34218", keywords="social media", keywords="WhatsApp", keywords="infodemiology", keywords="misinformation", keywords="COVID-19", keywords="tracking", keywords="surveillance", keywords="app", keywords="longitudinal", keywords="Singapore", keywords="characteristic", keywords="usage", keywords="pattern", keywords="well-being", keywords="communication", keywords="risk", abstract="Background: Worldwide, social media traffic increased following the onset of the COVID-19 pandemic. Although the spread of COVID-19 content has been described for several social media platforms (eg, Twitter and Facebook), little is known about how such content is spread via private messaging platforms, such as WhatsApp (WhatsApp LLC). Objective: In this study, we documented (1) how WhatsApp is used to transmit COVID-19 content, (2) the characteristics of WhatsApp users based on their usage patterns, and (3) how usage patterns link to COVID-19 concerns. Methods: We used the experience sampling method to track day-to-day WhatsApp usage during the COVID-19 pandemic. For 1 week, participants reported each day the extent to which they had received, forwarded, or discussed COVID-19 content. The final data set comprised 924 data points, which were collected from 151 participants. Results: During the weeklong monitoring process, most participants (143/151, 94.7\%) reported at least 1 COVID-19--related use of WhatsApp. When a taxonomy was generated based on usage patterns, around 1 in 10 participants (21/151, 13.9\%) were found to have received and shared a high volume of forwarded COVID-19 content, akin to super-spreaders identified on other social media platforms. Finally, those who engaged with more COVID-19 content in their personal chats were more likely to report having COVID-19--related thoughts throughout the day. Conclusions: Our findings provide a rare window into discourse on private messaging platforms. Such data can be used to inform risk communication strategies during the pandemic. Trial Registration: ClinicalTrials.gov NCT04367363; https://clinicaltrials.gov/ct2/show/NCT04367363 ", doi="10.2196/34218", url="https://www.jmir.org/2021/12/e34218", url="http://www.ncbi.nlm.nih.gov/pubmed/34881720" } @Article{info:doi/10.2196/30753, author="ElSherief, Mai and Sumner, A. Steven and Jones, M. Christopher and Law, K. Royal and Kacha-Ochana, Akadia and Shieber, Lyna and Cordier, LeShaundra and Holton, Kelly and De Choudhury, Munmun", title="Characterizing and Identifying the Prevalence of Web-Based Misinformation Relating to Medication for Opioid Use Disorder: Machine Learning Approach", journal="J Med Internet Res", year="2021", month="Dec", day="22", volume="23", number="12", pages="e30753", keywords="opioid use disorder", keywords="substance use", keywords="addiction treatment", keywords="misinformation", keywords="social media", keywords="machine learning", keywords="natural language processing", abstract="Background: Expanding access to and use of medication for opioid use disorder (MOUD) is a key component of overdose prevention. An important barrier to the uptake of MOUD is exposure to inaccurate and potentially harmful health misinformation on social media or web-based forums where individuals commonly seek information. There is a significant need to devise computational techniques to describe the prevalence of web-based health misinformation related to MOUD to facilitate mitigation efforts. Objective: By adopting a multidisciplinary, mixed methods strategy, this paper aims to present machine learning and natural language analysis approaches to identify the characteristics and prevalence of web-based misinformation related to MOUD to inform future prevention, treatment, and response efforts. Methods: The team harnessed public social media posts and comments in the English language from Twitter (6,365,245 posts), YouTube (99,386 posts), Reddit (13,483,419 posts), and Drugs-Forum (5549 posts). Leveraging public health expert annotations on a sample of 2400 of these social media posts that were found to be semantically most similar to a variety of prevailing opioid use disorder--related myths based on representational learning, the team developed a supervised machine learning classifier. This classifier identified whether a post's language promoted one of the leading myths challenging addiction treatment: that the use of agonist therapy for MOUD is simply replacing one drug with another. Platform-level prevalence was calculated thereafter by machine labeling all unannotated posts with the classifier and noting the proportion of myth-indicative posts over all posts. Results: Our results demonstrate promise in identifying social media postings that center on treatment myths about opioid use disorder with an accuracy of 91\% and an area under the curve of 0.9, including how these discussions vary across platforms in terms of prevalence and linguistic characteristics, with the lowest prevalence on web-based health communities such as Reddit and Drugs-Forum and the highest on Twitter. Specifically, the prevalence of the stated MOUD myth ranged from 0.4\% on web-based health communities to 0.9\% on Twitter. Conclusions: This work provides one of the first large-scale assessments of a key MOUD-related myth across multiple social media platforms and highlights the feasibility and importance of ongoing assessment of health misinformation related to addiction treatment. ", doi="10.2196/30753", url="https://www.jmir.org/2021/12/e30753", url="http://www.ncbi.nlm.nih.gov/pubmed/34941555" } @Article{info:doi/10.2196/34178, author="Husnayain, Atina and Shim, Eunha and Fuad, Anis and Su, Chia-Yu Emily", title="Predicting New Daily COVID-19 Cases and Deaths Using Search Engine Query Data in South Korea From 2020 to 2021: Infodemiology Study", journal="J Med Internet Res", year="2021", month="Dec", day="22", volume="23", number="12", pages="e34178", keywords="prediction", keywords="internet search", keywords="COVID-19", keywords="South Korea", keywords="infodemiology", abstract="Background: Given the ongoing COVID-19 pandemic situation, accurate predictions could greatly help in the health resource management for future waves. However, as a new entity, COVID-19's disease dynamics seemed difficult to predict. External factors, such as internet search data, need to be included in the models to increase their accuracy. However, it remains unclear whether incorporating online search volumes into models leads to better predictive performances for long-term prediction. Objective: The aim of this study was to analyze whether search engine query data are important variables that should be included in the models predicting new daily COVID-19 cases and deaths in short- and long-term periods. Methods: We used country-level case-related data, NAVER search volumes, and mobility data obtained from Google and Apple for the period of January 20, 2020, to July 31, 2021, in South Korea. Data were aggregated into four subsets: 3, 6, 12, and 18 months after the first case was reported. The first 80\% of the data in all subsets were used as the training set, and the remaining data served as the test set. Generalized linear models (GLMs) with normal, Poisson, and negative binomial distribution were developed, along with linear regression (LR) models with lasso, adaptive lasso, and elastic net regularization. Root mean square error values were defined as a loss function and were used to assess the performance of the models. All analyses and visualizations were conducted in SAS Studio, which is part of the SAS OnDemand for Academics. Results: GLMs with different types of distribution functions may have been beneficial in predicting new daily COVID-19 cases and deaths in the early stages of the outbreak. Over longer periods, as the distribution of cases and deaths became more normally distributed, LR models with regularization may have outperformed the GLMs. This study also found that models performed better when predicting new daily deaths compared to new daily cases. In addition, an evaluation of feature effects in the models showed that NAVER search volumes were useful variables in predicting new daily COVID-19 cases, particularly in the first 6 months of the outbreak. Searches related to logistical needs, particularly for ``thermometer'' and ``mask strap,'' showed higher feature effects in that period. For longer prediction periods, NAVER search volumes were still found to constitute an important variable, although with a lower feature effect. This finding suggests that search term use should be considered to maintain the predictive performance of models. Conclusions: NAVER search volumes were important variables in short- and long-term prediction, with higher feature effects for predicting new daily COVID-19 cases in the first 6 months of the outbreak. Similar results were also found for death predictions. ", doi="10.2196/34178", url="https://www.jmir.org/2021/12/e34178", url="http://www.ncbi.nlm.nih.gov/pubmed/34762064" } @Article{info:doi/10.2196/23210, author="Taylor, A. Kimberly and Humphrey Jr, F. William", title="Impact of Medical Blog Reading and Information Presentation on Readers' Preventative Health Intentions: Mixed Methods, Multistudy Investigation", journal="J Med Internet Res", year="2021", month="Dec", day="22", volume="23", number="12", pages="e23210", keywords="health blogs", keywords="patient blogs", keywords="preventative care", keywords="cancer", keywords="caregivers", keywords="perceived risk", abstract="Background: Medical blogs have become valuable information sources for patients and caregivers. Most research has focused on patients' creation of blogs as therapy. But we know less about how these blogs affect their readers and what format of information influences readers to take preventative health actions. Objective: This study aimed to identify how reading patient medical blogs influences readers' perceived health risk and their intentions to engage in preventative health actions. Further, we aimed to examine the format of the medical blog and the reader's response. Methods: We surveyed 99 university participants and a general-population, online panel of 167 participants. Both studies randomly assigned participants to conditions and measured blog evaluation, intentions for preventative health action, and evaluation of health risk and beliefs, and allowed open-ended comments. The second study used a different sample and added a control condition. A third study used a convenience sample of blog readers to evaluate the link between reading medical blogs and taking preventative health action. Results: Across 3 studies, participants indicated a desire to take future preventative health action after reading patient blogs. Studies 1 and 2 used experimental scenario-based designs, while Study 3 employed a qualitative design with real blog readers. The 2 experimental studies showed that the type of blog impacted intentions to engage in future preventative health actions (Study 1: F2,96=6.08, P=.003; Study 2: F3,166=2.59, P=.06), with a statistical blog being most effective in both studies and a personal narrative blog showing similar effectiveness in Study 2, contrary to some prior research. The readers' perceptions of their own health risk did not impact the relationship between the blog type and health intentions. In contrast, in one study, participants' judgments about the barriers they might face to accessing care improved the fit of the model (F2,95=13.57, P<.001). In Study 3's sample of medical blog readers, 53\% (24/45) reported taking preventative health action after reading a health blog, including performing a self-check, asking a doctor about their health risk, or requesting a screening test. Additionally, these readers expressed that they read the blogs to follow the author (patient) and to learn general health information. All studies demonstrated the blogs were somewhat sad and emotional but also informative and well-written. They noted that the blogs made them appreciate life more and motivated them to consider taking some action regarding their health.? Conclusions: Reading patient blogs influences intentions to take future health actions. However, blog formats show different efficacy, and the readers' disease risk perceptions do not. Physicians, medical practitioners, and health organizations may find it useful to curate or promote selected medical blogs to influence patient behavior. ", doi="10.2196/23210", url="https://www.jmir.org/2021/12/e23210", url="http://www.ncbi.nlm.nih.gov/pubmed/34941543" } @Article{info:doi/10.2196/31834, author="Drescher, S. Larissa and Roosen, Jutta and Aue, Katja and Dressel, Kerstin and Sch{\"a}r, Wiebke and G{\"o}tz, Anne", title="The Spread of COVID-19 Crisis Communication by German Public Authorities and Experts on Twitter: Quantitative Content Analysis", journal="JMIR Public Health Surveill", year="2021", month="Dec", day="22", volume="7", number="12", pages="e31834", keywords="COVID-19", keywords="crisis communication", keywords="content analysis", keywords="Twitter", keywords="experts", keywords="authorities", keywords="Germany", keywords="negative binomial regression", keywords="social media", keywords="communication", keywords="crisis", keywords="information", keywords="development", abstract="Background: The COVID-19 pandemic led to the necessity of immediate crisis communication by public health authorities. In Germany, as in many other countries, people choose social media, including Twitter, to obtain real-time information and understanding of the pandemic and its consequences. Next to authorities, experts such as virologists and science communicators were very prominent at the beginning of German Twitter COVID-19 crisis communication. Objective: The aim of this study was to detect similarities and differences between public authorities and individual experts in COVID-19 crisis communication on Twitter during the first year of the pandemic. Methods: Descriptive analysis and quantitative content analysis were carried out on 8251 original tweets posted from January 1, 2020, to January 15, 2021. COVID-19--related tweets of 21 authorities and 18 experts were categorized into structural, content, and style components. Negative binomial regressions were performed to evaluate tweet spread measured by the retweet and like counts of COVID-19--related tweets. Results: Descriptive statistics revealed that authorities and experts increasingly tweeted about COVID-19 over the period under study. Two experts and one authority were responsible for 70.26\% (544,418/774,865) of all retweets, thus representing COVID-19 influencers. Altogether, COVID-19 tweets by experts reached a 7-fold higher rate of retweeting (t8,249=26.94, P<.001) and 13.9 times the like rate (t8,249=31.27, P<.001) compared with those of authorities. Tweets by authorities were much more designed than those by experts, with more structural and content components; for example, 91.99\% (4997/5432) of tweets by authorities used hashtags in contrast to only 19.01\% (536/2819) of experts' COVID-19 tweets. Multivariate analysis revealed that such structural elements reduce the spread of the tweets, and the incidence rate of retweets for authorities' tweets using hashtags was approximately 0.64 that of tweets without hashtags (Z=--6.92, P<.001). For experts, the effect of hashtags on retweets was insignificant (Z=1.56, P=.12). Conclusions: Twitter data are a powerful information source and suitable for crisis communication in Germany. COVID-19 tweet activity mirrors the development of COVID-19 cases in Germany. Twitter users retweet and like communications regarding COVID-19 by experts more than those delivered by authorities. Tweets have higher coverage for both authorities and experts when they are plain and for authorities when they directly address people. For authorities, it appears that it was difficult to win recognition during COVID-19. For all stakeholders studied, the association between number of followers and number of retweets was highly significantly positive (authorities Z=28.74, P<.001; experts Z=25.99, P<.001). Updated standards might be required for successful crisis communication by authorities. ", doi="10.2196/31834", url="https://publichealth.jmir.org/2021/12/e31834", url="http://www.ncbi.nlm.nih.gov/pubmed/34710054" } @Article{info:doi/10.2196/26644, author="Ming, Wai-kit and Huang, Fengqiu and Chen, Qiuyi and Liang, Beiting and Jiao, Aoao and Liu, Taoran and Wu, Huailiang and Akinwunmi, Babatunde and Li, Jia and Liu, Guan and Zhang, P. Casper J. and Huang, Jian and Liu, Qian", title="Understanding Health Communication Through Google Trends and News Coverage for COVID-19: Multinational Study in Eight Countries", journal="JMIR Public Health Surveill", year="2021", month="Dec", day="21", volume="7", number="12", pages="e26644", keywords="COVID-19", keywords="Google Trends", keywords="search peaks", keywords="news coverage", keywords="public concerns", abstract="Background: Due to the COVID-19 pandemic, health information related to COVID-19 has spread across news media worldwide. Google is among the most used internet search engines, and the Google Trends tool can reflect how the public seeks COVID-19--related health information during the pandemic. Objective: The aim of this study was to understand health communication through Google Trends and news coverage and to explore their relationship with prevention and control of COVID-19 at the early epidemic stage. Methods: To achieve the study objectives, we analyzed the public's information-seeking behaviors on Google and news media coverage on COVID-19. We collected data on COVID-19 news coverage and Google search queries from eight countries (ie, the United States, the United Kingdom, Canada, Singapore, Ireland, Australia, South Africa, and New Zealand) between January 1 and April 29, 2020. We depicted the characteristics of the COVID-19 news coverage trends over time, as well as the search query trends for the topics of COVID-19--related ``diseases,'' ``treatments and medical resources,'' ``symptoms and signs,'' and ``public measures.'' The search query trends provided the relative search volume (RSV) as an indicator to represent the popularity of a specific search term in a specific geographic area over time. Also, time-lag correlation analysis was used to further explore the relationship between search terms trends and the number of new daily cases, as well as the relationship between search terms trends and news coverage. Results: Across all search trends in eight countries, almost all search peaks appeared between March and April 2020, and declined in April 2020. Regarding COVID-19--related ``diseases,'' in most countries, the RSV of the term ``coronavirus'' increased earlier than that of ``covid-19''; however, around April 2020, the search volume of the term ``covid-19'' surpassed that of ``coronavirus.'' Regarding the topic ``treatments and medical resources,'' the most and least searched terms were ``mask'' and ``ventilator,'' respectively. Regarding the topic ``symptoms and signs,'' ``fever'' and ``cough'' were the most searched terms. The RSV for the term ``lockdown'' was significantly higher than that for ``social distancing'' under the topic ``public health measures.'' In addition, when combining search trends with news coverage, there were three main patterns: (1) the pattern for Singapore, (2) the pattern for the United States, and (3) the pattern for the other countries. In the time-lag correlation analysis between the RSV for the topic ``treatments and medical resources'' and the number of new daily cases, the RSV for all countries except Singapore was positively correlated with new daily cases, with a maximum correlation of 0.8 for the United States. In addition, in the time-lag correlation analysis between the overall RSV for the topic ``diseases'' and the number of daily news items, the overall RSV was positively correlated with the number of daily news items, the maximum correlation coefficient was more than 0.8, and the search behavior occurred 0 to 17 days earlier than the news coverage. Conclusions: Our findings revealed public interest in masks, disease control, and public measures, and revealed the potential value of Google Trends in the face of the emergence of new infectious diseases. Also, Google Trends combined with news media can achieve more efficient health communication. Therefore, both news media and Google Trends can contribute to the early prevention and control of epidemics. ", doi="10.2196/26644", url="https://publichealth.jmir.org/2021/12/e26644", url="http://www.ncbi.nlm.nih.gov/pubmed/34591781" } @Article{info:doi/10.2196/27183, author="Liu, Jessica and Wright, Caroline and Williams, Philippa and Elizarova, Olga and Dahne, Jennifer and Bian, Jiang and Zhao, Yunpeng and Tan, L. Andy S.", title="Smokers' Likelihood to Engage With Information and Misinformation on Twitter About the Relative Harms of e-Cigarette Use: Results From a Randomized Controlled Trial", journal="JMIR Public Health Surveill", year="2021", month="Dec", day="21", volume="7", number="12", pages="e27183", keywords="e-cigarettes", keywords="misinformation", keywords="Twitter", keywords="social media", abstract="Background: Information and misinformation on the internet about e-cigarette harms may increase smokers' misperceptions of e-cigarettes. There is limited research on smokers' engagement with information and misinformation about e-cigarettes on social media. Objective: This study assessed smokers' likelihood to engage with---defined as replying, retweeting, liking, and sharing---tweets that contain information and misinformation and uncertainty about the harms of e-cigarettes. Methods: We conducted a web-based randomized controlled trial among 2400 UK and US adult smokers who did not vape in the past 30 days. Participants were randomly assigned to view four tweets in one of four conditions: (1) e-cigarettes are as harmful or more harmful than smoking, (2) e-cigarettes are completely harmless, (3) uncertainty about e-cigarette harms, or (4) control (physical activity). The outcome measure was participants' likelihood of engaging with tweets, which comprised the sum of whether they would reply, retweet, like, and share each tweet. We fitted Poisson regression models to predict the likelihood of engagement with tweets among 974 Twitter users and 1287 non-Twitter social media users, adjusting for covariates and stratified by UK and US participants. Results: Among Twitter users, participants were more likely to engage with tweets in condition 1 (e-cigarettes are as harmful or more harmful than smoking) than in condition 2 (e-cigarettes are completely harmless). Among other social media users, participants were more likely to likely to engage with tweets in condition 1 than in conditions 2 and 3 (e-cigarettes are completely harmless and uncertainty about e-cigarette harms). Conclusions: Tweets stating information and misinformation that e-cigarettes were as harmful or more harmful than smoking regular cigarettes may receive higher engagement than tweets indicating e-cigarettes were completely harmless. Trial Registration: International Standard Randomized Controlled Trial Number (ISRCTN) 16082420; https://doi.org/10.1186/ISRCTN16082420 ", doi="10.2196/27183", url="https://publichealth.jmir.org/2021/12/e27183", url="http://www.ncbi.nlm.nih.gov/pubmed/34931999" } @Article{info:doi/10.2196/32427, author="Fedoruk, Benjamin and Nelson, Harrison and Frost, Russell and Fucile Ladouceur, Kai", title="The Plebeian Algorithm: A Democratic Approach to Censorship and Moderation", journal="JMIR Form Res", year="2021", month="Dec", day="21", volume="5", number="12", pages="e32427", keywords="infodemiology", keywords="misinformation", keywords="algorithm", keywords="social media", keywords="plebeian", keywords="natural language processing", keywords="sentiment analysis", keywords="sentiment", keywords="trust", keywords="decision-making", keywords="COVID-19", abstract="Background: The infodemic created by the COVID-19 pandemic has created several societal issues, including a rise in distrust between the public and health experts, and even a refusal of some to accept vaccination; some sources suggest that 1 in 4 Americans will refuse the vaccine. This social concern can be traced to the level of digitization today, particularly in the form of social media. Objective: The goal of the research is to determine an optimal social media algorithm, one which is able to reduce the number of cases of misinformation and which also ensures that certain individual freedoms (eg, the freedom of expression) are maintained. After performing the analysis described herein, an algorithm was abstracted. The discovery of a set of abstract aspects of an optimal social media algorithm was the purpose of the study. Methods: As social media was the most significant contributing factor to the spread of misinformation, the team decided to examine infodemiology across various text-based platforms (Twitter, 4chan, Reddit, Parler, Facebook, and YouTube). This was done by using sentiment analysis to compare general posts with key terms flagged as misinformation (all of which concern COVID-19) to determine their verity. In gathering the data sets, both application programming interfaces (installed using Python's pip) and pre-existing data compiled by standard scientific third parties were used. Results: The sentiment can be described using bimodal distributions for each platform, with a positive and negative peak, as well as a skewness. It was found that in some cases, misinforming posts can have up to 92.5\% more negative sentiment skew compared to accurate posts. Conclusions: From this, the novel Plebeian Algorithm is proposed, which uses sentiment analysis and post popularity as metrics to flag a post as misinformation. This algorithm diverges from that of the status quo, as the Plebeian Algorithm uses a democratic process to detect and remove misinformation. A method was constructed in which content deemed as misinformation to be removed from the platform is determined by a randomly selected jury of anonymous users. This not only prevents these types of infodemics but also guarantees a more democratic way of using social media that is beneficial for repairing social trust and encouraging the public's evidence-informed decision-making. ", doi="10.2196/32427", url="https://formative.jmir.org/2021/12/e32427", url="http://www.ncbi.nlm.nih.gov/pubmed/34854812" } @Article{info:doi/10.2196/29187, author="Black, Joshua and Margolin, R. Zachary and Bau, Gabrielle and Olson, Richard and Iwanicki, L. Janetta and Dart, C. Richard", title="Web-Based Discussion and Illicit Street Sales of Tapentadol and Oxycodone in Australia: Epidemiological Surveillance Study", journal="JMIR Public Health Surveill", year="2021", month="Dec", day="20", volume="7", number="12", pages="e29187", keywords="Australia", keywords="opioids", keywords="web-based discussion", keywords="diversion", abstract="Background: Opioid use disorder and its consequences are a persistent public health concern for Australians. Web activity has been used to understand the perception of drug safety and diversion of drugs in contexts outside of Australia. The anonymity of the internet offers several advantages for surveilling and inquiring about specific covert behaviors, such as diversion or discussion of sensitive subjects where traditional surveillance approaches might be limited. Objective: This study aims to characterize the content of web posts and compare reports of illicit sales of tapentadol and oxycodone from sources originating in Australia. First, post content is evaluated to determine whether internet discussion encourages or discourages proper therapeutic use of the drugs. Second, we hypothesize that tapentadol would have lower street price and fewer illicit sales than oxycodone. Methods: Web posts originating in Australia between 2017 and 2019 were collected using the Researched Abuse, Diversion, and Addiction-Related Surveillance System Web Monitoring Program. Using a manual coding process, unstructured post content from social media, blogs, and forums was categorized into topics of discussion related to the harms and behaviors that could lead to harm. Illicit sales data in a structured format were collected through a crowdsourcing website between 2016 and 2019 using the Researched Abuse, Diversion, and Addiction-Related Surveillance System StreetRx Program. In total, 2 multivariable regression models assessed the differences in illicit price and number of sales. Results: A total of 4.7\% (28/600) of tapentadol posts discussed an adverse event, whereas 10.27\% (95\% CI 9.32-11.21) of oxycodone posts discussed this topic. A total of 10\% (60/600) of tapentadol posts discussed unsafe use or side effects, whereas 20.17\% (95\% CI 18.92-21.41) of oxycodone posts discussed unsafe use or side effects. There were 31 illicit sales reports for tapentadol (geometric mean price per milligram: Aus \$0.12 [US \$0.09]) and 756 illicit sales reports for oxycodone (Aus \$1.28 [US \$0.91]). Models detected no differences in the street price or number of sales between the drugs when covariates were included, although the potency of the pill significantly predicted the street price (P<.001) and availability predicted the number of sales (P=.03). Conclusions: Australians searching the web for opinions could judge tapentadol as safer than oxycodone because of the web post content. The illicit sales market for tapentadol was smaller than that of oxycodone, and drug potency and licit availability are likely important factors influencing the illicit market. ", doi="10.2196/29187", url="https://publichealth.jmir.org/2021/12/e29187", url="http://www.ncbi.nlm.nih.gov/pubmed/34932012" } @Article{info:doi/10.2196/27307, author="Turner, Jason and Kantardzic, Mehmed and Vickers-Smith, Rachel", title="Infodemiological Examination of Personal and Commercial Tweets About Cannabidiol: Term and Sentiment Analysis", journal="J Med Internet Res", year="2021", month="Dec", day="20", volume="23", number="12", pages="e27307", keywords="social media", keywords="social networks", keywords="text mining", keywords="CBD", keywords="cannabidiol", keywords="cannabis", keywords="public health", keywords="drug regulation", keywords="Twitter", keywords="sentiment analysis", keywords="unregulated substances", abstract="Background: In the absence of official clinical trial information, data from social networks can be used by public health and medical researchers to assess public claims about loosely regulated substances such as cannabidiol (CBD). For example, this can be achieved by comparing the medical conditions targeted by those selling CBD against the medical conditions patients commonly treat with CBD. Objective: The objective of this study was to provide a framework for public health and medical researchers to use for identifying and analyzing the consumption and marketing of unregulated substances. Specifically, we examined CBD, which is a substance that is often presented to the public as medication despite complete evidence of efficacy and safety. Methods: We collected 567,850 tweets by searching Twitter with the Tweepy Python package using the terms ``CBD'' and ``cannabidiol.'' We trained two binary text classifiers to create two corpora of 167,755 personal use and 143,322 commercial/sales tweets. Using medical, standard, and slang dictionaries, we identified and compared the most frequently occurring medical conditions, symptoms, side effects, body parts, and other substances referenced in both corpora. In addition, to assess popular claims about the efficacy of CBD as a medical treatment circulating on Twitter, we performed sentiment analysis via the VADER (Valence Aware Dictionary for Sentiment Reasoning) model on the personal CBD tweets. Results: We found references to medically relevant terms that were unique to either personal or commercial CBD tweet classes, as well as medically relevant terms that were common to both classes. When we calculated the average sentiment scores for both personal and commercial CBD tweets referencing at least one of 17 medical conditions/symptoms terms, an overall positive sentiment was observed in both personal and commercial CBD tweets. We observed instances of negative sentiment conveyed in personal CBD tweets referencing autism, whereas CBD was also marketed multiple times as a treatment for autism within commercial tweets. Conclusions: Our proposed framework provides a tool for public health and medical researchers to analyze the consumption and marketing of unregulated substances on social networks. Our analysis showed that most users of CBD are satisfied with it in regard to the condition that it is being advertised for, with the exception of autism. ", doi="10.2196/27307", url="https://www.jmir.org/2021/12/e27307", url="http://www.ncbi.nlm.nih.gov/pubmed/34932014" } @Article{info:doi/10.2196/32161, author="B{\'e}rub{\'e}, Caterina and Kovacs, Ferenc Zsolt and Fleisch, Elgar and Kowatsch, Tobias", title="Reliability of Commercial Voice Assistants' Responses to Health-Related Questions in Noncommunicable Disease Management: Factorial Experiment Assessing Response Rate and Source of Information", journal="J Med Internet Res", year="2021", month="Dec", day="20", volume="23", number="12", pages="e32161", keywords="voice assistants", keywords="conversational agents", keywords="health literacy", keywords="noncommunicable diseases", keywords="mobile phone", keywords="smart speaker", keywords="smart display", keywords="evaluation", keywords="protocol", keywords="assistant", keywords="agent", keywords="literacy", keywords="audio", keywords="health information", keywords="management", keywords="factorial", keywords="information source", abstract="Background: Noncommunicable diseases (NCDs) constitute a burden on public health. These are best controlled through self-management practices, such as self-information. Fostering patients' access to health-related information through efficient and accessible channels, such as commercial voice assistants (VAs), may support the patients' ability to make health-related decisions and manage their chronic conditions. Objective: This study aims to evaluate the reliability of the most common VAs (ie, Amazon Alexa, Apple Siri, and Google Assistant) in responding to questions about management of the main NCD. Methods: We generated health-related questions based on frequently asked questions from health organization, government, medical nonprofit, and other recognized health-related websites about conditions associated with Alzheimer's disease (AD), lung cancer (LCA), chronic obstructive pulmonary disease, diabetes mellitus (DM), cardiovascular disease, chronic kidney disease (CKD), and cerebrovascular accident (CVA). We then validated them with practicing medical specialists, selecting the 10 most frequent ones. Given the low average frequency of the AD-related questions, we excluded such questions. This resulted in a pool of 60 questions. We submitted the selected questions to VAs in a 3{\texttimes}3{\texttimes}6 fractional factorial design experiment with 3 developers (ie, Amazon, Apple, and Google), 3 modalities (ie, voice only, voice and display, display only), and 6 diseases. We assessed the rate of error-free voice responses and classified the web sources based on previous research (ie, expert, commercial, crowdsourced, or not stated). Results: Google showed the highest total response rate, followed by Amazon and Apple. Moreover, although Amazon and Apple showed a comparable response rate in both voice-and-display and voice-only modalities, Google showed a slightly higher response rate in voice only. The same pattern was observed for the rate of expert sources. When considering the response and expert source rate across diseases, we observed that although Google remained comparable, with a slight advantage for LCA and CKD, both Amazon and Apple showed the highest response rate for LCA. However, both Google and Apple showed most often expert sources for CVA, while Amazon did so for DM. Conclusions: Google showed the highest response rate and the highest rate of expert sources, leading to the conclusion that Google Assistant would be the most reliable tool in responding to questions about NCD management. However, the rate of expert sources differed across diseases. We urge health organizations to collaborate with Google, Amazon, and Apple to allow their VAs to consistently provide reliable answers to health-related questions on NCD management across the different diseases. ", doi="10.2196/32161", url="https://www.jmir.org/2021/12/e32161", url="http://www.ncbi.nlm.nih.gov/pubmed/34932003" } @Article{info:doi/10.2196/28318, author="Song, Shijie and Xue, Xiang and Zhao, Chris Yuxiang and Li, Jinhao and Zhu, Qinghua and Zhao, Mingming", title="Short-Video Apps as a Health Information Source for Chronic Obstructive Pulmonary Disease: Information Quality Assessment of TikTok Videos", journal="J Med Internet Res", year="2021", month="Dec", day="20", volume="23", number="12", pages="e28318", keywords="COPD", keywords="information quality", keywords="social media", keywords="short-video apps", keywords="TikTok", abstract="Background: Chronic obstructive pulmonary disease (COPD) has become one of the most critical public health problems worldwide. Because many COPD patients are using video-based social media to search for health information, there is an urgent need to assess the information quality of COPD videos on social media. Recently, the short-video app TikTok has demonstrated huge potential in disseminating health information and there are currently many COPD videos available on TikTok; however, the information quality of these videos remains unknown. Objective: The aim of this study was to investigate the information quality of COPD videos on TikTok. Methods: In December 2020, we retrieved and screened 300 videos from TikTok and collected a sample of 199 COPD-related videos in Chinese for data extraction. We extracted the basic video information, coded the content, and identified the video sources. Two independent raters assessed the information quality of each video using the DISCERN instrument. Results: COPD videos on TikTok came mainly from two types of sources: individual users (n=168) and organizational users (n=31). The individual users included health professionals, individual science communicators, and general TikTok users, whereas the organizational users consisted of for-profit organizations, nonprofit organizations, and news agencies. For the 199 videos, the mean scores of the DISCERN items ranged from 3.42 to 4.46, with a total mean score of 3.75. Publication reliability (P=.04) and overall quality (P=.02) showed significant differences across the six types of sources, whereas the quality of treatment choices showed only a marginally significant difference (P=.053) across the different sources. Conclusions: The overall information quality of COPD videos on TikTok is satisfactory, although the quality varies across different sources and according to specific quality dimensions. Patients should be selective and cautious when watching COPD videos on TikTok. ", doi="10.2196/28318", url="https://www.jmir.org/2021/12/e28318", url="http://www.ncbi.nlm.nih.gov/pubmed/34931996" } @Article{info:doi/10.2196/19183, author="Lei, Yuqi and Xu, Songhua and Zhou, Linyun", title="User Behaviors and User-Generated Content in Chinese Online Health Communities: Comparative Study", journal="J Med Internet Res", year="2021", month="Dec", day="15", volume="23", number="12", pages="e19183", keywords="online health community", keywords="user behaviors", keywords="user-generated content", keywords="social network analysis", keywords="weighted knowledge network", abstract="Background: Online health communities (OHCs) have increasingly gained traction with patients, caregivers, and supporters globally. Chinese OHCs are no exception. However, user-generated content (UGC) and the associated user behaviors in Chinese OHCs are largely underexplored and rarely analyzed systematically, forfeiting valuable opportunities for optimizing treatment design and care delivery with insights gained from OHCs. Objective: This study aimed to reveal both the shared and distinct characteristics of 2 popular OHCs in China by systematically and comprehensively analyzing their UGC and the associated user behaviors. Methods: We concentrated on studying the lung cancer forum (LCF) and breast cancer forum (BCF) on Mijian, and the diabetes consultation forum (DCF) on Sweet Home, because of the importance of the 3 diseases among Chinese patients and their prevalence on Chinese OHCs in general. Our analysis explored the key user activities, small-world effect, and scale-free characteristics of each social network. We examined the UGC of these forums comprehensively and adopted the weighted knowledge network technique to discover salient topics and latent relations among these topics on each forum. Finally, we discussed the public health implications of our analysis findings. Results: Our analysis showed that the number of reads per thread on each forum followed gamma distribution (HL=0, HB=0, and HD=0); the number of replies on each forum followed exponential distribution (adjusted RL2=0.946, adjusted RB2=0.958, and adjusted RD2=0.971); and the number of threads a user is involved with (adjusted RL2=0.978, adjusted RB2=0.964, and adjusted RD2=0.970), the number of followers of a user (adjusted RL2=0.989, adjusted RB2=0.962, and adjusted RD2=0.990), and a user's degrees (adjusted RL2=0.997, adjusted RB2=0.994, and adjusted RD2=0.968) all followed power-law distribution. The study further revealed that users are generally more active during weekdays, as commonly witnessed in all 3 forums. In particular, the LCF and DCF exhibited high temporal similarity ($\rho$=0.927; P<.001) in terms of the relative thread posting frequencies during each hour of the day. Besides, the study showed that all 3 forums exhibited the small-world effect (mean $\sigma$L=517.15, mean $\sigma$B=275.23, and mean $\sigma$D=525.18) and scale-free characteristics, while the global clustering coefficients were lower than those of counterpart international OHCs. The study also discovered several hot topics commonly shared among the 3 disease forums, such as disease treatment, disease examination, and diagnosis. In particular, the study found that after the outbreak of COVID-19, users on the LCF and BCF were much more likely to bring up COVID-19--related issues while discussing their medical issues. Conclusions: UGC and related online user behaviors in Chinese OHCs can be leveraged as important sources of information to gain insights regarding individual and population health conditions. Effective and timely mining and utilization of such content can continuously provide valuable firsthand clues for enhancing the situational awareness of health providers and policymakers. ", doi="10.2196/19183", url="https://www.jmir.org/2021/12/e19183", url="http://www.ncbi.nlm.nih.gov/pubmed/34914615" } @Article{info:doi/10.2196/29086, author="Parker, K. Jane and Kelly, E. Christine and Smith, C. Barry and Kirkwood, F. Aidan and Hopkins, Claire and Gane, Simon", title="Patients' Perspectives on Qualitative Olfactory Dysfunction: Thematic Analysis of Social Media Posts", journal="JMIR Form Res", year="2021", month="Dec", day="14", volume="5", number="12", pages="e29086", keywords="olfactory dysfunction", keywords="parosmia", keywords="phantosmia", keywords="olfactory perseveration", keywords="trigger foods", keywords="mental health", keywords="COVID-19", keywords="patients' perspective", keywords="thematic analysis", keywords="social media", keywords="perspective", keywords="smell", keywords="nose", keywords="symptom", keywords="concern", keywords="support", abstract="Background: The impact of qualitative olfactory disorders is underestimated. Parosmia, the distorted perception of familiar odors, and phantosmia, the experience of odors in the absence of a stimulus, can arise following postinfectious anosmia, and the incidences of both have increased substantially since the outbreak of COVID-19. Objective: The aims of this study are to explore the symptoms and sequalae of postinfectious olfactory dysfunction syndrome using unstructured and unsolicited threads from social media, and to articulate the perspectives and concerns of patients affected by these debilitating olfactory disorders. Methods: A thematic analysis and content analysis of posts in the AbScent Parosmia and Phantosmia Support group on Facebook was conducted between June and December 2020. Results: In this paper, we identify a novel symptom, olfactory perseveration, which is a triggered, identifiable, and usually unpleasant olfactory percept that persists in the absence of an ongoing stimulus. We also observe fluctuations in the intensity and duration of symptoms of parosmia, phantosmia, and olfactory perseveration. In addition, we identify a group of the most common items (coffee, meat, onion, and toothpaste) that trigger distortions; however, people have difficulty describing these distortions, using words associated with disgust and revulsion. The emotional aspect of living with qualitative olfactory dysfunction was evident and highlighted the detrimental impact on mental health. Conclusions: Qualitative and unsolicited data acquired from social media has provided useful insights into the patient experience of parosmia and phantosmia, which can inform rehabilitation strategies and ongoing research into understanding the molecular triggers associated with parosmic distortions and research into patient benefit. ", doi="10.2196/29086", url="https://formative.jmir.org/2021/12/e29086", url="http://www.ncbi.nlm.nih.gov/pubmed/34904953" } @Article{info:doi/10.2196/29737, author="Vallury, Dee Kari and Baird, Barbara and Miller, Emma and Ward, Paul", title="Going Viral: Researching Safely on Social Media", journal="J Med Internet Res", year="2021", month="Dec", day="13", volume="23", number="12", pages="e29737", keywords="cyber bullying", keywords="online bullying", keywords="research activities", keywords="occupational safety", keywords="research ethics", keywords="students", keywords="bullying", keywords="social media", doi="10.2196/29737", url="https://www.jmir.org/2021/12/e29737", url="http://www.ncbi.nlm.nih.gov/pubmed/34898450" } @Article{info:doi/10.2196/25963, author="Link, Elena and Baumann, Eva and Klimmt, Christoph", title="Explaining Online Information Seeking Behaviors in People With Different Health Statuses: German Representative Cross-sectional Survey", journal="J Med Internet Res", year="2021", month="Dec", day="10", volume="23", number="12", pages="e25963", keywords="online health information seeking behavior", keywords="Planned Risk Information Seeking Model", keywords="health status", keywords="theory building", keywords="personal survey", abstract="Background: Worldwide, the internet is an increasingly important channel for health information. Many theories have been applied in research on online health information seeking behaviors (HISBs), with each model integrating a different set of predictors; thus, a common understanding of the predictors of (online) HISB is still missing. Another shortcoming of the theories explaining (online) HISB is that most existing models, so far, focus on very specific health contexts such as cancer. Therefore, the assumptions of the Planned Risk Information Seeking Model (PRISM) as the latest integrative model are applied to study online HISB, because this model identifies the general cognitive and sociopsychological factors that explain health information seeking intention. We shift away from single diseases and explore cross-thematic patterns of online HISB intention and compare predictors concerning different health statuses as it can be assumed that groups of people perceiving themselves as ill or healthy will differ concerning their drivers of online HISB. Considering the specifics of online HISB and variation in individual context factors is key for the development of generalizable theories. Objective: The objective of our study was to contribute to the development of the concept of online HISB in 2 areas. First, this study aimed to explore individual-level predictors of individuals' online HISB intention by applying the postulates of PRISM. Second, we compared relevant predictors of online HISB in groups of people with different health statuses to identify cross-thematic central patterns of online HISB. Methods: Data from a representative sample of German internet users (n=822) served to explain online HISB intentions and influencing patterns in different groups of people. The applicability of the PRISM to online HISB intention was tested by structural equation modeling and multigroup comparison. Results: Our results revealed PRISM to be an effective framework for explaining online HISB intention. For online HISB, attitudes toward seeking health information online provided the most important explanatory power followed by risk perceptions and affective risk responses. The multigroup comparison revealed differences both regarding the explanatory power of the model and the relevance of predictors of online HISB. The online HISB intention could be better explained for people facing a health threat, suggesting that the predictors adopted from PRISM were more suitable to explain a problem-driven type of information-seeking behavior. Conclusions: Our findings indicate that attitudes toward seeking health information online and risk perceptions are of central importance for online HISB across different health-conditional contexts. Predictors such as self-efficacy and perceived knowledge insufficiency play a context-dependent role---they are more influential when individuals are facing health threats and the search for health information is of higher personal relevance and urgency. These findings can be understood as the first step to develop a generalized theory of online HISB. ", doi="10.2196/25963", url="https://www.jmir.org/2021/12/e25963", url="http://www.ncbi.nlm.nih.gov/pubmed/34890348" } @Article{info:doi/10.2196/31358, author="Koren, Ainat and Alam, Ul Mohammad Arif and Koneru, Sravani and DeVito, Alexa and Abdallah, Lisa and Liu, Benyuan", title="Nursing Perspectives on the Impacts of COVID-19: Social Media Content Analysis", journal="JMIR Form Res", year="2021", month="Dec", day="10", volume="5", number="12", pages="e31358", keywords="mental health", keywords="information retrieval", keywords="coronavirus", keywords="COVID-19", keywords="nursing", keywords="nurses", keywords="health care workers", keywords="pandemic", keywords="impact", keywords="social media analytics", abstract="Background: Nurses are at the forefront of the COVID-19 pandemic. During the pandemic, nurses have faced an elevated risk of exposure and have experienced the hazards related to a novel virus. While being heralded as lifesaving heroes on the front lines of the pandemic, nurses have experienced more physical, mental, and psychosocial problems as a consequence of the COVID-19 outbreak. Social media discussions by nursing professionals participating in publicly formed Facebook groups constitute a valuable resource that offers longitudinal insights. Objective: This study aimed to explore how COVID-19 impacted nurses through capturing public sentiments expressed by nurses on a social media discussion platform and how these sentiments changed over time. Methods: We collected over 110,993 Facebook discussion posts and comments in an open COVID-19 group for nurses from March 2020 until the end of November 2020. Scraping of deidentified offline HTML tags on social media posts and comments was performed. Using subject-matter expert opinions and social media analytics (ie, topic modeling, information retrieval, and sentiment analysis), we performed a human-in-a-loop analysis of nursing professionals' key perspectives to identify trends of the COVID-19 impact among at-risk nursing communities. We further investigated the key insights of the trends of the nursing professionals' perspectives by detecting temporal changes of comments related to emotional effects, feelings of frustration, impacts of isolation, shortage of safety equipment, and frequency of safety equipment uses. Anonymous quotes were highlighted to add context to the data. Results: We determined that COVID-19 impacted nurses' physical, mental, and psychosocial health as expressed in the form of emotional distress, anger, anxiety, frustration, loneliness, and isolation. Major topics discussed by nurses were related to work during a pandemic, misinformation spread by the media, improper personal protective equipment (PPE), PPE side effects, the effects of testing positive for COVID-19, and lost days of work related to illness. Conclusions: Public Facebook nursing groups are venues for nurses to express their experiences, opinions, and concerns and can offer researchers an important insight into understanding the COVID-19 impact on health care workers. ", doi="10.2196/31358", url="https://formative.jmir.org/2021/12/e31358", url="http://www.ncbi.nlm.nih.gov/pubmed/34623957" } @Article{info:doi/10.2196/30323, author="Keselman, Alla and Arnott Smith, Catherine and Leroy, Gondy and Kaufman, R. David", title="Factors Influencing Willingness to Share Health Misinformation Videos on the Internet: Web-Based Survey", journal="J Med Internet Res", year="2021", month="Dec", day="9", volume="23", number="12", pages="e30323", keywords="misinformation", keywords="information literacy", keywords="science literacy", keywords="webcasts as topic", keywords="YouTube", abstract="Background: The rapidly evolving digital environment of the social media era has increased the reach of both quality health information and misinformation. Platforms such as YouTube enable easy sharing of attractive, if not always evidence-based, videos with large personal networks and the public. Although much research has focused on characterizing health misinformation on the internet, it has not sufficiently focused on describing and measuring individuals' information competencies that build resilience. Objective: This study aims to assess individuals' willingness to share a non--evidence-based YouTube video about strengthening the immune system; to describe types of evidence that individuals view as supportive of the claim by the video; and to relate information-sharing behavior to several information competencies, namely, information literacy, science literacy, knowledge of the immune system, interpersonal trust, and trust in health authority. Methods: A web-based survey methodology with 150 individuals across the United States was used. Participants were asked to watch a YouTube excerpt from a morning TV show featuring a wellness pharmacy representative promoting an immunity-boosting dietary supplement produced by his company; answer questions about the video and report whether they would share it with a cousin who was frequently sick; and complete instruments pertaining to the information competencies outlined in the objectives. Results: Most participants (105/150, 70\%) said that they would share the video with their cousins. Their confidence in the supplement would be further boosted by a friend's recommendations, positive reviews on a crowdsourcing website, and statements of uncited effectiveness studies on the producer's website. Although all information literacy competencies analyzed in this study had a statistically significant relationship with the outcome, each competency was also highly correlated with the others. Information literacy and interpersonal trust independently predicted the largest amount of variance in the intention to share the video (17\% and 16\%, respectively). Interpersonal trust was negatively related to the willingness to share the video. Science literacy explained 7\% of the variance. Conclusions: People are vulnerable to web-based misinformation and are likely to propagate it on the internet. Information literacy and science literacy are associated with less vulnerability to misinformation and a lower propensity to spread it. Of the two, information literacy holds a greater promise as an intervention target. Understanding the role of different kinds of trust in information sharing merits further research. ", doi="10.2196/30323", url="https://www.jmir.org/2021/12/e30323", url="http://www.ncbi.nlm.nih.gov/pubmed/34889750" } @Article{info:doi/10.2196/27613, author="Sakib, Shahriar Ahmed and Mukta, Hossain Md Saddam and Huda, Rowshan Fariha and Islam, Najmul A. K. M. and Islam, Tohedul and Ali, Eunus Mohammed", title="Identifying Insomnia From Social Media Posts: Psycholinguistic Analyses of User Tweets", journal="J Med Internet Res", year="2021", month="Dec", day="9", volume="23", number="12", pages="e27613", keywords="insomnia", keywords="Twitter", keywords="word embedding", keywords="Big 5 personality traits", keywords="classification", keywords="social media", keywords="prediction model", keywords="psycholinguistics", abstract="Background: Many people suffer from insomnia, a sleep disorder characterized by difficulty falling and staying asleep during the night. As social media have become a ubiquitous platform to share users' thoughts, opinions, activities, and preferences with their friends and acquaintances, the shared content across these platforms can be used to diagnose different health problems, including insomnia. Only a few recent studies have examined the prediction of insomnia from Twitter data, and we found research gaps in predicting insomnia from word usage patterns and correlations between users' insomnia and their Big 5 personality traits as derived from social media interactions. Objective: The purpose of this study is to build an insomnia prediction model from users' psycholinguistic patterns, including the elements of word usage, semantics, and their Big 5 personality traits as derived from tweets. Methods: In this paper, we exploited both psycholinguistic and personality traits derived from tweets to identify insomnia patients. First, we built psycholinguistic profiles of the users from their word choices and the semantic relationships between the words of their tweets. We then determined the relationship between a users' personality traits and insomnia. Finally, we built a double-weighted ensemble classification model to predict insomnia from both psycholinguistic and personality traits as derived from user tweets. Results: Our classification model showed strong prediction potential (78.8\%) to predict insomnia from tweets. As insomniacs are generally ill-tempered and feel more stress and mental exhaustion, we observed significant correlations of certain word usage patterns among them. They tend to use negative words (eg, ``no,'' ``not,'' ``never''). Some people frequently use swear words (eg, ``damn,'' ``piss,'' ``fuck'') with strong temperament. They also use anxious (eg, ``worried,'' ``fearful,'' ``nervous'') and sad (eg, ``crying,'' ``grief,'' ``sad'') words in their tweets. We also found that the users with high neuroticism and conscientiousness scores for the Big 5 personality traits likely have strong correlations with insomnia. Additionally, we observed that users with high conscientiousness scores have strong correlations with insomnia patterns, while negative correlation between extraversion and insomnia was also found. Conclusions: Our model can help predict insomnia from users' social media interactions. Thus, incorporating our model into a software system can help family members detect insomnia problems in individuals before they become worse. The software system can also help doctors to diagnose possible insomnia in patients. ", doi="10.2196/27613", url="https://www.jmir.org/2021/12/e27613", url="http://www.ncbi.nlm.nih.gov/pubmed/34889758" } @Article{info:doi/10.2196/28305, author="Ng, Reuben", title="Anti-Asian Sentiments During the COVID-19 Pandemic Across 20 Countries: Analysis of a 12-Billion-Word News Media Database", journal="J Med Internet Res", year="2021", month="Dec", day="8", volume="23", number="12", pages="e28305", keywords="racism", keywords="COVID-19", keywords="anti-Asian sentiments", keywords="psychomics", keywords="quantitative social science", keywords="culture", keywords="text as data", keywords="xenophobia", keywords="digital humanities", abstract="Background: US president Joe Biden signed an executive action directing federal agencies to combat hate crimes and racism against Asians, which have percolated during the COVID-19 pandemic. This is one of the first known empirical studies to dynamically test whether global societal sentiments toward Asians have become more negative during the COVID-19 pandemic. Objective: This study aimed to investigate whether global societal sentiments toward Asians across 20 countries have become more negative, month by month, from before the pandemic (October 2019) to May 2020, along with the pandemic (incidence and mortality rates) and cultural (Hofstede's cultural dimensions) predictors of this trend. Methods: We leveraged a 12-billion-word web-based media database, with over 30 million newspaper and magazine articles taken from over 7000 sites across 20 countries, and identified 6 synonyms of ``Asian'' that are related to the coronavirus. We compiled their most frequently used descriptors (collocates) from October 2019 to May 2020 across 20 countries, culminating in 85,827 collocates that were rated by 2 independent researchers to provide a Cumulative Asian Sentiment Score (CASS) per month. This allowed us to track significant shifts in societal sentiments toward Asians from a baseline period (October to December 2019) to the onset of the pandemic (January to May 2020). We tested the competing predictors of this trend: pandemic variables of incidence and mortality rates measured monthly for all 20 countries taken from the Oxford COVID-19 Government Response Tracker, and Hofstede's Cultural Dimensions of Individualism, Power Distance, Uncertainty Avoidance, and Masculinity for the 20 countries. Results: Before the pandemic in December 2019, Jamaica and New Zealand evidenced the most negative societal sentiments toward Asians; when news about the coronavirus was released in January 2020, the United States and Nigeria evidenced the most negative sentiments toward Asians among 20 countries. Globally, sentiments of Asians became more negative---a significant linear decline during the COVID-19 pandemic. CASS trended neutral before the pandemic during the baseline period of October to November 2019 and then plummeted in February 2020. CASS were, ironically, not predicted by COVID-19's incidence and mortality rates, but rather by Hofstede's cultural dimensions: individualism, power distance, and uncertainty avoidance---as shown by mixed models (N=28,494). Specifically, higher power distance, individualism, and uncertainty avoidance were associated with negative societal sentiments toward Asians. Conclusions: Racism, in the form of Anti-Asian sentiments, are deep-seated, and predicated on structural undercurrents of culture. The COVID-19 pandemic may have indirectly and inadvertently exacerbated societal tendencies for racism. Our study lays the important groundwork to design interventions and policy communications to ameliorate Anti-Asian racism, which are culturally nuanced and contextually appropriate. ", doi="10.2196/28305", url="https://www.jmir.org/2021/12/e28305", url="http://www.ncbi.nlm.nih.gov/pubmed/34678754" } @Article{info:doi/10.2196/31645, author="Syed Abdul, Shabbir and Ramaswamy, Meghna and Fernandez-Luque, Luis and John, Oommen and Pitti, Thejkiran and Parashar, Babita", title="The Pandemic, Infodemic, and People's Resilience in India: Viewpoint", journal="JMIR Public Health Surveill", year="2021", month="Dec", day="8", volume="7", number="12", pages="e31645", keywords="pandemic", keywords="COVID-19", keywords="India", keywords="digital health", keywords="infodemics", keywords="Sustainable Development Goals", keywords="SDGs", doi="10.2196/31645", url="https://publichealth.jmir.org/2021/12/e31645", url="http://www.ncbi.nlm.nih.gov/pubmed/34787574" } @Article{info:doi/10.2196/31961, author="Mukka, Milla and Pes{\"a}l{\"a}, Samuli and Hammer, Charlotte and Mustonen, Pekka and Jormanainen, Vesa and Pelttari, Hanna and Kaila, Minna and Helve, Otto", title="Analyzing Citizens' and Health Care Professionals' Searches for Smell/Taste Disorders and Coronavirus in Finland During the COVID-19 Pandemic: Infodemiological Approach Using Database Logs", journal="JMIR Public Health Surveill", year="2021", month="Dec", day="7", volume="7", number="12", pages="e31961", keywords="COVID-19", keywords="SARS-CoV-2", keywords="smell disorders", keywords="taste disorders", keywords="information-seeking behavior", keywords="health personnel", keywords="statistical models", keywords="medical informatics", abstract="Background: The COVID-19 pandemic has prevailed over a year, and log and register data on coronavirus have been utilized to establish models for detecting the pandemic. However, many sources contain unreliable health information on COVID-19 and its symptoms, and platforms cannot characterize the users performing searches. Prior studies have assessed symptom searches from general search engines (Google/Google Trends). Little is known about how modeling log data on smell/taste disorders and coronavirus from the dedicated internet databases used by citizens and health care professionals (HCPs) could enhance disease surveillance. Our material and method provide a novel approach to analyze web-based information seeking to detect infectious disease outbreaks. Objective: The aim of this study was (1) to assess whether citizens' and professionals' searches for smell/taste disorders and coronavirus relate to epidemiological data on COVID-19 cases, and (2) to test our negative binomial regression modeling (ie, whether the inclusion of the case count could improve the model). Methods: We collected weekly log data on searches related to COVID-19 (smell/taste disorders, coronavirus) between December 30, 2019, and November 30, 2020 (49 weeks). Two major medical internet databases in Finland were used: Health Library (HL), a free portal aimed at citizens, and Physician's Database (PD), a database widely used among HCPs. Log data from databases were combined with register data on the numbers of COVID-19 cases reported in the Finnish National Infectious Diseases Register. We used negative binomial regression modeling to assess whether the case numbers could explain some of the dynamics of searches when plotting database logs. Results: We found that coronavirus searches drastically increased in HL (0 to 744,113) and PD (4 to 5375) prior to the first wave of COVID-19 cases between December 2019 and March 2020. Searches for smell disorders in HL doubled from the end of December 2019 to the end of March 2020 (2148 to 4195), and searches for taste disorders in HL increased from mid-May to the end of November (0 to 1980). Case numbers were significantly associated with smell disorders (P<.001) and taste disorders (P<.001) in HL, and with coronavirus searches (P<.001) in PD. We could not identify any other associations between case numbers and searches in either database. Conclusions: Novel infodemiological approaches could be used in analyzing database logs. Modeling log data from web-based sources was seen to improve the model only occasionally. However, search behaviors among citizens and professionals could be used as a supplementary source of information for infectious disease surveillance. Further research is needed to apply statistical models to log data of the dedicated medical databases. ", doi="10.2196/31961", url="https://publichealth.jmir.org/2021/12/e31961", url="http://www.ncbi.nlm.nih.gov/pubmed/34727525" } @Article{info:doi/10.2196/29011, author="Valdez, Danny and Unger, B. Jennifer", title="Difficulty Regulating Social Media Content of Age-Restricted Products: Comparing JUUL's Official Twitter Timeline and Social Media Content About JUUL", journal="JMIR Infodemiology", year="2021", month="Dec", day="7", volume="1", number="1", pages="e29011", keywords="social media", keywords="JUUL", keywords="underage marketing", keywords="LDA", keywords="Latent Dirichlet Allocation", keywords="topic models", abstract="Background: In 2018, JUUL Labs Inc, a popular e-cigarette manufacturer, announced it would substantially limit its social media presence in compliance with the Food and Drug Administration's (FDA) call to curb underage e-cigarette use. However, shortly after the announcement, a series of JUUL-related hashtags emerged on various social media platforms, calling the effectiveness of the FDA's regulations into question. Objective: The purpose of this study is to determine whether hashtags remain a common venue to market age-restricted products on social media. Methods: We used Twitter's standard application programming interface to download the 3200 most-recent tweets originating from JUUL Labs Inc's official Twitter Account (@JUULVapor), and a series of tweets (n=28,989) from other Twitter users containing either \#JUUL or mentioned JUUL in the tweet text. We ran exploratory (10{\texttimes}10) and iterative Latent Dirichlet Allocation (LDA) topic models to compare @JUULVapor's content versus our hashtag corpus. We qualitatively deliberated topic meanings and substantiated our interpretations with tweets from either corpus. Results: The topic models generated for @JUULVapor's timeline seemingly alluded to compliance with the FDA's call to prohibit marketing of age-restricted products on social media. However, the topic models generated for the hashtag corpus of tweets from other Twitter users contained several references to flavors, vaping paraphernalia, and illicit drugs, which may be appealing to younger audiences. Conclusions: Our findings underscore the complicated nature of social media regulation. Although JUUL Labs Inc seemingly complied with the FDA to limit its social media presence, JUUL and other e-cigarette manufacturers are still discussed openly in social media spaces. Much discourse about JUUL and e-cigarettes is spread via hashtags, which allow messages to reach a wide audience quickly. This suggests that social media regulations on manufacturers cannot prevent e-cigarette users, influencers, or marketers from spreading information about e-cigarette attributes that appeal to the youth, such as flavors. Stricter protocols are needed to regulate discourse about age-restricted products on social media. ", doi="10.2196/29011", url="https://infodemiology.jmir.org/2021/1/e29011", url="http://www.ncbi.nlm.nih.gov/pubmed/37114198" } @Article{info:doi/10.2196/31774, author="Bohnhoff, James and Davis, Alexander and Bruine de Bruin, W{\"a}ndi and Krishnamurti, Tamar", title="COVID-19 Information Sources and Health Behaviors During Pregnancy: Results From a Prenatal App-Embedded Survey", journal="JMIR Infodemiology", year="2021", month="Dec", day="7", volume="1", number="1", pages="e31774", keywords="COVID-19", keywords="health behavior", keywords="health behaviour", keywords="pregnancy", keywords="obstetrics", keywords="perinatal", keywords="preventive", keywords="preventative", keywords="mHealth", keywords="risk", keywords="information source", keywords="medical literacy", keywords="media literacy", keywords="information literacy", keywords="protection", keywords="protective", keywords="harm", keywords="women", keywords="engagement", keywords="online health information", keywords="behavior", keywords="information-seeking", keywords="critical appraisal", keywords="communication", abstract="Background: Pregnancy is a time of heightened COVID-19 risk. Pregnant individuals' choice of specific protective health behaviors during pregnancy may be affected by information sources. Objective: This study examined the association between COVID-19 information sources and engagement in protective health behaviors among a pregnant population in a large academic medical system. Methods: Pregnant patients completed an app-based questionnaire about their sources of COVID-19 information and engagement in protective health behaviors. The voluntary questionnaire was made available to patients using a pregnancy app as part of their routine prenatal care between April 21 and November 27, 2020. Results: In total, 637 pregnant responders routinely accessed a median of 5 sources for COVID-19 information. The most cited source (79\%) was the Centers for Disease Control and Prevention (CDC). Self-reporting evidence-based protective actions was relatively common, although 14\% self-reported potentially harmful behaviors to avoid COVID-19 infection. The CDC and other sources were positively associated with engaging in protective behaviors while others (eg, US president Donald Trump) were negatively associated with protective behaviors. Participation in protective behaviors was not associated with refraining from potentially harmful behaviors (P=.93). Moreover, participation in protective behaviors decreased (P=.03) and participation in potentially harmful actions increased (P=.001) over the course of the pandemic. Conclusions: Pregnant patients were highly engaged in COVID-19--related information-seeking and health behaviors. Clear, targeted, and regular communication from commonly accessed health organizations about which actions may be harmful, in addition to which actions offer protection, may offer needed support to the pregnant population. ", doi="10.2196/31774", url="https://infodemiology.jmir.org/2021/1/e31774", url="http://www.ncbi.nlm.nih.gov/pubmed/34926994" } @Article{info:doi/10.2196/31791, author="Abdel-Razig, Sawsan and Anglade, Pascale and Ibrahim, Halah", title="Impact of the COVID-19 Pandemic on a Physician Group's WhatsApp Chat: Qualitative Content Analysis", journal="JMIR Form Res", year="2021", month="Dec", day="7", volume="5", number="12", pages="e31791", keywords="WhatsApp", keywords="social media", keywords="physician", keywords="pandemic", keywords="COVID-19", keywords="qualitative", keywords="communication", keywords="misinformation", keywords="information-seeking behavior", keywords="information seeking", keywords="information sharing", keywords="content analysis", keywords="community", abstract="Background: Social media has emerged as an effective means of information sharing and community building among health professionals. The utility of these platforms is likely heightened during times of health system crises and global uncertainty. Studies have demonstrated that physicians' social media platforms serve to bridge the gap of information between on-the-ground experiences of health care workers and emerging knowledge. Objective: The primary aim of this study was to characterize the use of a physician WhatsApp (WhatsApp LLC) group chat during the early months of the COVID-19 pandemic. Methods: Through the lens of the social network theory, we performed a qualitative content analysis of the posts of a women physician WhatsApp group located in the United Arab Emirates between February 1, 2020, and May 31, 2020, that is, during the initial surge of COVID-19 cases. Results: There were 6101 posts during the study period, which reflected a 2.6-fold increase in platform use when compared with platform use in the year prior. A total of 8 themes and 9 subthemes were described. The top 3 uses of the platform were requests for information (posts: 2818/6101, 46.2\%), member support and promotion (posts: 988/6101, 16.2\%), and information sharing (posts: 896/6101, 14.7\%). A substantial proportion of posts were related to COVID-19 (2653/6101, 43.5\%), with the most popular theme being requests for logistical (nonmedical) information. Among posts containing COVID-19--related medical information, it was notable that two-thirds (571/868, 65.8\%) of these posts were from public mass media or unverified sources. Conclusions: Health crises can potentiate the use of social media platforms among physicians. This reflects physicians' tendency to turn to these platforms for information sharing and community building purposes. However, important questions remain regarding the accuracy and credibility of the information shared. Our findings suggest that the training of physicians in social media practices and information dissemination may be needed. ", doi="10.2196/31791", url="https://formative.jmir.org/2021/12/e31791", url="http://www.ncbi.nlm.nih.gov/pubmed/34784291" } @Article{info:doi/10.2196/29127, author="Cruickshank, Iain and Ginossar, Tamar and Sulskis, Jason and Zheleva, Elena and Berger-Wolf, Tanya", title="Content and Dynamics of Websites Shared Over Vaccine-Related Tweets in COVID-19 Conversations: Computational Analysis", journal="J Med Internet Res", year="2021", month="Dec", day="3", volume="23", number="12", pages="e29127", keywords="COVID-19", keywords="agenda setting", keywords="antivaccination", keywords="cross-platform", keywords="data mining of social media", keywords="misinformation", keywords="social media", keywords="Twitter", keywords="vaccinations", keywords="vaccine hesitancy", abstract="Background: The onset of the COVID-19 pandemic and the consequent ``infodemic'' increased concerns about Twitter's role in advancing antivaccination messages, even before a vaccine became available to the public. New computational methods allow for analysis of cross-platform use by tracking links to websites shared over Twitter, which, in turn, can uncover some of the content and dynamics of information sources and agenda-setting processes. Such understanding can advance theory and efforts to reduce misinformation. Objective: Informed by agenda-setting theory, this study aimed to identify the content and temporal patterns of websites shared in vaccine-related tweets posted to COVID-19 conversations on Twitter between February and June 2020. Methods: We used triangulation of data analysis methods. Data mining consisted of the screening of around 5 million tweets posted to COVID-19 conversations to identify tweets that related to vaccination and including links to websites shared within these tweets. We further analyzed the content the 20 most-shared external websites using a mixed methods approach. Results: Of 841,896 vaccination-related tweets identified, 185,994 (22.1\%) contained links to specific websites. A wide range of websites were shared, with the 20 most-tweeted websites constituting 14.5\% (27,060/185,994) of the shared websites and typically being shared for only 2 to 3 days. Traditional media constituted the majority of these 20 websites, along with other social media and governmental sources. We identified markers of inauthentic propagation for some of these links. Conclusions: The topic of vaccination was prevalent in tweets about COVID-19 early in the pandemic. Sharing websites was a common communication strategy, and its ``bursty'' pattern and inauthentic propagation strategies pose challenges for health promotion efforts. Future studies should consider cross-platform use in dissemination of health information and in counteracting misinformation. ", doi="10.2196/29127", url="https://www.jmir.org/2021/12/e29127", url="http://www.ncbi.nlm.nih.gov/pubmed/34665760" } @Article{info:doi/10.2196/32814, author="Zhang, Jueman and Wang, Yi and Shi, Molu and Wang, Xiuli", title="Factors Driving the Popularity and Virality of COVID-19 Vaccine Discourse on Twitter: Text Mining and Data Visualization Study", journal="JMIR Public Health Surveill", year="2021", month="Dec", day="3", volume="7", number="12", pages="e32814", keywords="COVID-19", keywords="vaccine", keywords="topic modeling", keywords="LDA", keywords="valence", keywords="share", keywords="viral", keywords="Twitter", keywords="social media", abstract="Background: COVID-19 vaccination is considered a critical prevention measure to help end the pandemic. Social media platforms such as Twitter have played an important role in the public discussion about COVID-19 vaccines. Objective: The aim of this study was to investigate message-level drivers of the popularity and virality of tweets about COVID-19 vaccines using machine-based text-mining techniques. We further aimed to examine the topic communities of the most liked and most retweeted tweets using network analysis and visualization. Methods: We collected US-based English-language public tweets about COVID-19 vaccines from January 1, 2020, to April 30, 2021 (N=501,531). Topic modeling and sentiment analysis were used to identify latent topics and valence, which together with autoextracted information about media presence, linguistic features, and account verification were used in regression models to predict likes and retweets. Among the 2500 most liked tweets and 2500 most retweeted tweets, network analysis and visualization were used to detect topic communities and present the relationship between the topics and the tweets. Results: Topic modeling yielded 12 topics. The regression analyses showed that 8 topics positively predicted likes and 7 topics positively predicted retweets, among which the topic of vaccine development and people's views and that of vaccine efficacy and rollout had relatively larger effects. Network analysis and visualization revealed that the 2500 most liked and most retweeted retweets clustered around the topics of vaccine access, vaccine efficacy and rollout, vaccine development and people's views, and vaccination status. The overall valence of the tweets was positive. Positive valence increased likes, but valence did not affect retweets. Media (photo, video, gif) presence and account verification increased likes and retweets. Linguistic features had mixed effects on likes and retweets. Conclusions: This study suggests the public interest in and demand for information about vaccine development and people's views, and about vaccine efficacy and rollout. These topics, along with the use of media and verified accounts, have enhanced the popularity and virality of tweets. These topics could be addressed in vaccine campaigns to help the diffusion of content on Twitter. ", doi="10.2196/32814", url="https://publichealth.jmir.org/2021/12/e32814", url="http://www.ncbi.nlm.nih.gov/pubmed/34665761" } @Article{info:doi/10.2196/34016, author="Taira, Kazuya and Hosokawa, Rikuya and Itatani, Tomoya and Fujita, Sumio", title="Predicting the Number of Suicides in Japan Using Internet Search Queries: Vector Autoregression Time Series Model", journal="JMIR Public Health Surveill", year="2021", month="Dec", day="3", volume="7", number="12", pages="e34016", keywords="suicide", keywords="internet search engine", keywords="infoveillance", keywords="query", keywords="time series analysis", keywords="vector autoregression model", keywords="COVID-19", keywords="suicide-related terms", keywords="internet", keywords="information seeking", keywords="time series", keywords="model", keywords="loneliness", keywords="mental health", keywords="prediction", keywords="Japan", keywords="behavior", keywords="trend", abstract="Background: The number of suicides in Japan increased during the COVID-19 pandemic. Predicting the number of suicides is important to take timely preventive measures. Objective: This study aims to clarify whether the number of suicides can be predicted by suicide-related search queries used before searching for the keyword ``suicide.'' Methods: This study uses the infoveillance approach for suicide in Japan by search trends in search engines. The monthly number of suicides by gender, collected and published by the National Police Agency, was used as an outcome variable. The number of searches by gender with queries associated with ``suicide'' on ``Yahoo! JAPAN Search'' from January 2016 to December 2020 was used as a predictive variable. The following five phrases highly relevant to suicide were used as search terms before searching for the keyword ``suicide'' and extracted and used for analyses: ``abuse''; ``work, don't want to go''; ``company, want to quit''; ``divorce''; and ``no money.'' The augmented Dickey-Fuller and Johansen tests were performed for the original series and to verify the existence of unit roots and cointegration for each variable, respectively. The vector autoregression model was applied to predict the number of suicides. The Breusch-Godfrey Lagrangian multiplier (BG-LM) test, autoregressive conditional heteroskedasticity Lagrangian multiplier (ARCH-LM) test, and Jarque-Bera (JB) test were used to confirm model convergence. In addition, a Granger causality test was performed for each predictive variable. Results: In the original series, unit roots were found in the trend model, whereas in the first-order difference series, both men (minimum tau 3: ?9.24; max tau 3: ?5.38) and women (minimum tau 3: ?9.24; max tau 3: ?5.38) had no unit roots for all variables. In the Johansen test, a cointegration relationship was observed among several variables. The queries used in the converged models were ``divorce'' for men (BG-LM test: P=.55; ARCH-LM test: P=.63; JB test: P=.66) and ``no money'' for women (BG-LM test: P=.17; ARCH-LM test: P=.15; JB test: P=.10). In the Granger causality test for each variable, ``divorce'' was significant for both men (F104=3.29; P=.04) and women (F104=3.23; P=.04). Conclusions: The number of suicides can be predicted by search queries related to the keyword ``suicide.'' Previous studies have reported that financial poverty and divorce are associated with suicide. The results of this study, in which search queries on ``no money'' and ``divorce'' predicted suicide, support the findings of previous studies. Further research on the economic poverty of women and those with complex problems is necessary. ", doi="10.2196/34016", url="https://publichealth.jmir.org/2021/12/e34016", url="http://www.ncbi.nlm.nih.gov/pubmed/34823225" } @Article{info:doi/10.2196/30855, author="Xiong, Zihui and Zhang, Liang and Li, Zhong and Xu, Wanchun and Zhang, Yan and Ye, Ting", title="Frequency of Online Health Information Seeking and Types of Information Sought Among the General Chinese Population: Cross-sectional Study", journal="J Med Internet Res", year="2021", month="Dec", day="2", volume="23", number="12", pages="e30855", keywords="online health information seeking", keywords="sociodemographic factors", keywords="information types", keywords="Chinese population", keywords="information seeking behavior", keywords="demography", keywords="China", keywords="online health information", abstract="Background: The internet is one of the most popular health information resources, and the Chinese constitute one-fifth of the online users worldwide. As internet penetration continues to rise, more details on the Chinese population seeking online health information need to be known based on the current literature. Objective: This study aims to explore the sociodemographic differences in online health information seeking (OHIS), including the frequency of OHIS and the types of online health information sought among the general Chinese population in mainland China. Methods: A cross-sectional study for assessing the residents' health care needs with self-administered questionnaires was implemented in 4 counties and districts in China from July 2018 to August 2018. Pearson's chi-square test was used to identify the sociodemographic differences between infrequent and frequent online health information seekers. We also performed binary logistic regression for the 4 types of online health information as the dependent variables and the sociodemographic factors as the independent variables. Results: Compared with infrequent online health information seekers, frequent seekers were more likely to be female (infrequent: 1654/3318; 49.85\%; frequent: 1015/1831, 55.43\%), older (over 60 years old; infrequent: 454/3318, 13.68\%; frequent: 282/1831, 15.40\%), married (infrequent: 2649/3318, 79.84\%; frequent: 1537/1831, 83.94\%), and better educated (bachelor's or above; infrequent: 834/3318, 25.14\%; frequent: 566/1831, 30.91\%). They were also more likely to earn a higher income (over RMB {\textyen}50k [RMB {\textyen}1=US \$0.15641]; infrequent: 1139/3318, 34.33\%; frequent: 710/1831, 34.78\%), have commercial health insurance (infrequent: 628/3318, 18.93\%; frequent: 470/1831, 25.67\%), and have reported illness in the past 12 months (infrequent: 659/3318, 19.86\%; frequent: 415/1831, 22.67\%). Among the 4 health information types, health science popularization was the most searched for information by Chinese online health information seekers (3654/5149, 70.79\%), followed by healthy behaviors (3567/5149, 69.28\%), traditional Chinese medicine (1931/5149, 37.50\%), and medical concerns (1703/5149, 33.07\%). The binary logistic regression models showed that males were less likely to seek information on healthy behaviors (adjusted odds ratio [AOR] 0.69, 95\% CI 0.61-0.78) and traditional Chinese medicine (AOR 0.64, 95\% CI 0.57-0.73), and respondents who had at least 1 chronic disease were more likely to seek information on medical concerns (AOR 1.27, 95\% CI 1.07-1.51) and traditional Chinese medicine (AOR 1.26, 95\% CI 1.06-1.49). Conclusions: Sociodemographic factors were associated with the frequency of OHIS and types of information sought among the general Chinese population. The results remind providers of online health information to consider the needs of specific population groups when tailoring and presenting health information to the target population. ", doi="10.2196/30855", url="https://www.jmir.org/2021/12/e30855", url="http://www.ncbi.nlm.nih.gov/pubmed/34860676" } @Article{info:doi/10.2196/29768, author="Dey, Vishal and Krasniak, Peter and Nguyen, Minh and Lee, Clara and Ning, Xia", title="A Pipeline to Understand Emerging Illness Via Social Media Data Analysis: Case Study on Breast Implant Illness", journal="JMIR Med Inform", year="2021", month="Nov", day="29", volume="9", number="11", pages="e29768", keywords="breast implant illness", keywords="social media", keywords="natural language processing", keywords="topic modeling", abstract="Background: A new illness can come to public attention through social media before it is medically defined, formally documented, or systematically studied. One example is a condition known as breast implant illness (BII), which has been extensively discussed on social media, although it is vaguely defined in the medical literature. Objective: The objective of this study is to construct a data analysis pipeline to understand emerging illnesses using social media data and to apply the pipeline to understand the key attributes of BII. Methods: We constructed a pipeline of social media data analysis using natural language processing and topic modeling. Mentions related to signs, symptoms, diseases, disorders, and medical procedures were extracted from social media data using the clinical Text Analysis and Knowledge Extraction System. We mapped the mentions to standard medical concepts and then summarized these mapped concepts as topics using latent Dirichlet allocation. Finally, we applied this pipeline to understand BII from several BII-dedicated social media sites. Results: Our pipeline identified topics related to toxicity, cancer, and mental health issues that were highly associated with BII. Our pipeline also showed that cancers, autoimmune disorders, and mental health problems were emerging concerns associated with breast implants, based on social media discussions. Furthermore, the pipeline identified mentions such as rupture, infection, pain, and fatigue as common self-reported issues among the public, as well as concerns about toxicity from silicone implants. Conclusions: Our study could inspire future studies on the suggested symptoms and factors of BII. Our study provides the first analysis and derived knowledge of BII from social media using natural language processing techniques and demonstrates the potential of using social media information to better understand similar emerging illnesses. ", doi="10.2196/29768", url="https://medinform.jmir.org/2021/11/e29768", url="http://www.ncbi.nlm.nih.gov/pubmed/34847064" } @Article{info:doi/10.2196/30529, author="Jarynowski, Andrzej and Semenov, Alexander and Kami?ski, Miko?aj and Belik, Vitaly", title="Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning", journal="J Med Internet Res", year="2021", month="Nov", day="29", volume="23", number="11", pages="e30529", keywords="adverse events", keywords="Sputnik V", keywords="Gam-COVID-Vac", keywords="social media", keywords="Telegram", keywords="COVID-19", keywords="Sars-CoV-2", keywords="deep learning", keywords="vaccine safety", abstract="Background: There is a limited amount of data on the safety profile of the COVID-19 vector vaccine Gam-COVID-Vac (Sputnik V). Previous infodemiology studies showed that social media discourse could be analyzed to assess the most concerning adverse events (AE) caused by drugs. Objective: We aimed to investigate mild AEs of Sputnik V based on a participatory trial conducted on Telegram in the Russian language. We compared AEs extracted from Telegram with other limited databases on Sputnik V and other COVID-19 vaccines. We explored symptom co-occurrence patterns and determined how counts of administered doses, age, gender, and sequence of shots could confound the reporting of AEs. Methods: We collected a unique dataset consisting of 11,515 self-reported Sputnik V vaccine AEs posted on the Telegram group, and we utilized natural language processing methods to extract AEs. Specifically, we performed multilabel classifications using the deep neural language model Bidirectional Encoder Representations from Transformers (BERT) ``DeepPavlov,'' which was pretrained on a Russian language corpus and applied to the Telegram messages. The resulting area under the curve score was 0.991. We chose symptom classes that represented the following AEs: fever, pain, chills, fatigue, nausea/vomiting, headache, insomnia, lymph node enlargement, erythema, pruritus, swelling, and diarrhea. Results: Telegram users complained mostly about pain (5461/11,515, 47.43\%), fever (5363/11,515, 46.57\%), fatigue (3862/11,515, 33.54\%), and headache (2855/11,515, 24.79\%). Women reported more AEs than men (1.2-fold, P<.001). In addition, there were more AEs from the first dose than from the second dose (1.1-fold, P<.001), and the number of AEs decreased with age ($\beta$=.05 per year, P<.001). The results also showed that Sputnik V AEs were more similar to other vector vaccines (132 units) than with messenger RNA vaccines (241 units) according to the average Euclidean distance between the vectors of AE frequencies. Elderly Telegram users reported significantly more (5.6-fold on average) systemic AEs than their peers, according to the results of the phase 3 clinical trials published in The Lancet. However, the AEs reported in Telegram posts were consistent (Pearson correlation r=0.94, P=.02) with those reported in the Argentinian postmarketing AE registry. Conclusions: After the Sputnik V vaccination, Russian Telegram users reported mostly pain, fever, and fatigue. The Sputnik V AE profile was comparable with other vector COVID-19 vaccines. Discussion on social media could provide meaningful information about the AE profile of novel vaccines. ", doi="10.2196/30529", url="https://www.jmir.org/2021/11/e30529", url="http://www.ncbi.nlm.nih.gov/pubmed/34662291" } @Article{info:doi/10.2196/29958, author="Reuter, Katja and Angyan, Praveen and Le, NamQuyen and Buchanan, A. Thomas", title="Using Patient-Generated Health Data From Twitter to Identify, Engage, and Recruit Cancer Survivors in Clinical Trials in Los Angeles County: Evaluation of a Feasibility Study", journal="JMIR Form Res", year="2021", month="Nov", day="26", volume="5", number="11", pages="e29958", keywords="breast cancer", keywords="cancer", keywords="clinical research", keywords="clinical trial", keywords="colon cancer", keywords="infoveillance", keywords="kidney cancer", keywords="lung cancer", keywords="lymphoma", keywords="patient engagement", keywords="prostate cancer", keywords="recruitment", keywords="Twitter", keywords="social media", abstract="Background: Failure to find and attract clinical trial participants remains a persistent barrier to clinical research. Researchers increasingly complement recruitment methods with social media--based methods. We hypothesized that user-generated data from cancer survivors and their family members and friends on the social network Twitter could be used to identify, engage, and recruit cancer survivors for cancer trials. Objective: This pilot study aims to examine the feasibility of using user-reported health data from cancer survivors and family members and friends on Twitter in Los Angeles (LA) County to enhance clinical trial recruitment. We focus on 6 cancer conditions (breast cancer, colon cancer, kidney cancer, lymphoma, lung cancer, and prostate cancer). Methods: The social media intervention involved monitoring cancer-specific posts about the 6 cancer conditions by Twitter users in LA County to identify cancer survivors and their family members and friends and contacting eligible Twitter users with information about open cancer trials at the University of Southern California (USC) Norris Comprehensive Cancer Center. We reviewed both retrospective and prospective data published by Twitter users in LA County between July 28, 2017, and November 29, 2018. The study enrolled 124 open clinical trials at USC Norris. We used descriptive statistics to report the proportion of Twitter users who were identified, engaged, and enrolled. Results: We analyzed 107,424 Twitter posts in English by 25,032 unique Twitter users in LA County for the 6 cancer conditions. We identified and contacted 1.73\% (434/25,032) of eligible Twitter users (127/434, 29.3\% cancer survivors; 305/434, 70.3\% family members and friends; and 2/434, 0.5\% Twitter users were excluded). Of them, 51.4\% (223/434) were female and approximately one-third were male. About one-fifth were people of color, whereas most of them were White. Approximately one-fifth (85/434, 19.6\%) engaged with the outreach messages (cancer survivors: 33/85, 38\% and family members and friends: 52/85, 61\%). Of those who engaged with the messages, one-fourth were male, the majority were female, and approximately one-fifth were people of color, whereas the majority were White. Approximately 12\% (10/85) of the contacted users requested more information and 40\% (4/10) set up a prescreening. Two eligible candidates were transferred to USC Norris for further screening, but neither was enrolled. Conclusions: Our findings demonstrate the potential of identifying and engaging cancer survivors and their family members and friends on Twitter. Optimization of downstream recruitment efforts such as screening for digital populations on social media may be required. Future research could test the feasibility of the approach for other diseases, locations, languages, social media platforms, and types of research involvement (eg, survey research). Computer science methods could help to scale up the analysis of larger data sets to support more rigorous testing of the intervention. Trial Registration: ClinicalTrials.gov NCT03408561; https://clinicaltrials.gov/ct2/show/NCT03408561 ", doi="10.2196/29958", url="https://formative.jmir.org/2021/11/e29958", url="http://www.ncbi.nlm.nih.gov/pubmed/34842538" } @Article{info:doi/10.2196/29600, author="Gao, Yankun and Xie, Zidian and Sun, Li and Xu, Chenliang and Li, Dongmei", title="Characteristics of and User Engagement With Antivaping Posts on Instagram: Observational Study", journal="JMIR Public Health Surveill", year="2021", month="Nov", day="25", volume="7", number="11", pages="e29600", keywords="anti-vaping", keywords="Instagram", keywords="user engagement", keywords="e-cigarettes", keywords="vaping", keywords="social media", keywords="content analysis", keywords="public health", keywords="lung health", abstract="Background: Although government agencies acknowledge that messages about the adverse health effects of e-cigarette use should be promoted on social media, effectively delivering those health messages is challenging. Instagram is one of the most popular social media platforms among US youth and young adults, and it has been used to educate the public about the potential harm of vaping through antivaping posts. Objective: We aim to analyze the characteristics of and user engagement with antivaping posts on Instagram to inform future message development and information delivery. Methods: A total of 11,322 Instagram posts were collected from November 18, 2019, to January 2, 2020, by using antivaping hashtags including \#novape, \#novaping, \#stopvaping, \#dontvape, \#antivaping, \#quitvaping, \#antivape, \#stopjuuling, \#dontvapeonthepizza, and \#escapethevape. Among those posts, 1025 posts were randomly selected and 500 antivaping posts were further identified by hand coding. The image type, image content, and account type of antivaping posts were hand coded, the text information in the caption was explored by topic modeling, and the user engagement of each category was compared. Results: Analyses found that antivaping images of the educational/warning type were the most common (253/500; 50.6\%). The average likes of the educational/warning type (15 likes/post) were significantly lower than the catchphrase image type (these emphasized a slogan such as ``athletesdontvape'' in the image; 32.5 likes/post; P<.001). The majority of the antivaping posts contained the image content element text (n=332, 66.4\%), followed by the image content element people/person (n=110, 22\%). The images containing people/person elements (32.8 likes/post) had more likes than the images containing other elements (13.8-21.1 likes/post). The captions of the antivaping Instagram posts covered topics including ``lung health,'' ``teen vaping,'' ``stop vaping,'' and ``vaping death cases.'' Among the 500 antivaping Instagram posts, while most posts were from the antivaping community (n=177, 35.4\%) and personal account types (n=182, 36.4\%), the antivaping community account type had the highest average number of posts (1.69 posts/account). However, there was no difference in the number of likes among different account types. Conclusions: Multiple features of antivaping Instagram posts may be related to user engagement and perception. This study identified the critical elements associated with high user engagement, which could be used to design antivaping posts to deliver health-related information more efficiently. ", doi="10.2196/29600", url="https://publichealth.jmir.org/2021/11/e29600", url="http://www.ncbi.nlm.nih.gov/pubmed/34842553" } @Article{info:doi/10.2196/31366, author="Tan, Yi Ming and Goh, Enhui Charlene and Tan, Hon Hee", title="Contemporary English Pain Descriptors as Detected on Social Media Using Artificial Intelligence and Emotion Analytics Algorithms: Cross-sectional Study", journal="JMIR Form Res", year="2021", month="Nov", day="25", volume="5", number="11", pages="e31366", keywords="pain descriptors", keywords="social media", keywords="artificial intelligence", keywords="emotion analytics", keywords="McGill Pain Questionnaire", abstract="Background: Pain description is fundamental to health care. The McGill Pain Questionnaire (MPQ) has been validated as a tool for the multidimensional measurement of pain; however, its use relies heavily on language proficiency. Although the MPQ has remained unchanged since its inception, the English language has evolved significantly since then. The advent of the internet and social media has allowed for the generation of a staggering amount of publicly available data, allowing linguistic analysis at a scale never seen before. Objective: The aim of this study is to use social media data to examine the relevance of pain descriptors from the existing MPQ, identify novel contemporary English descriptors for pain among users of social media, and suggest a modification for a new MPQ for future validation and testing. Methods: All posts from social media platforms from January 1, 2019, to December 31, 2019, were extracted. Artificial intelligence and emotion analytics algorithms (Crystalace and CrystalFeel) were used to measure the emotional properties of the text, including sarcasm, anger, fear, sadness, joy, and valence. Word2Vec was used to identify new pain descriptors associated with the original descriptors from the MPQ. Analysis of count and pain intensity formed the basis for proposing new pain descriptors and determining the order of pain descriptors within each subclass. Results: A total of 118 new associated words were found via Word2Vec. Of these 118 words, 49 (41.5\%) words had a count of at least 110, which corresponded to the count of the bottom 10\% (8/78) of the original MPQ pain descriptors. The count and intensity of pain descriptors were used to formulate the inclusion criteria for a new pain questionnaire. For the suggested new pain questionnaire, 11 existing pain descriptors were removed, 13 new descriptors were added to existing subclasses, and a new Psychological subclass comprising 9 descriptors was added. Conclusions: This study presents a novel methodology using social media data to identify new pain descriptors and can be repeated at regular intervals to ensure the relevance of pain questionnaires. The original MPQ contains several potentially outdated pain descriptors and is inadequate for reporting the psychological aspects of pain. Further research is needed to examine the reliability and validity of the revised MPQ. ", doi="10.2196/31366", url="https://formative.jmir.org/2021/11/e31366", url="http://www.ncbi.nlm.nih.gov/pubmed/34842554" } @Article{info:doi/10.2196/25287, author="Assaf, Elias and Bond, M. Robert and Cranmer, J. Skyler and Kaizar, E. Eloise and Ratliff Santoro, Lauren and Shikano, Susumu and Sivakoff, J. David", title="Understanding the Relationship Between Official and Social Information About Infectious Disease: Experimental Analysis", journal="J Med Internet Res", year="2021", month="Nov", day="23", volume="23", number="11", pages="e25287", keywords="disease", keywords="social information", keywords="official information", keywords="network experiments", abstract="Background: Communicating official public health information about infectious diseases is complicated by the fact that individuals receive much of their information from their social contacts, either via interpersonal interaction or social media, which can be prone to bias and misconception. Objective: This study aims to evaluate the effect of public health campaigns and the effect of socially communicated health information on learning about diseases simultaneously. Although extant literature addresses the effect of one source of information (official or social) or the other, it has not addressed the simultaneous interaction of official information (OI) and social information (SI) in an experimental setting. Methods: We used a series of experiments that exposed participants to both OI and structured SI about the symptoms and spread of hepatitis C over a series of 10 rounds of computer-based interactions. Participants were randomly assigned to receive a high, low, or control intensity of OI and to receive accurate or inaccurate SI about the disease. Results: A total of 195 participants consented to participate in the study. Of these respondents, 186 had complete responses across all ten experimental rounds, which corresponds to a 4.6\% (9/195) nonresponse rate. The OI high intensity treatment increases learning over the control condition for all symptom and contagion questions when individuals have lower levels of baseline knowledge (all P values ?.04). The accurate SI condition increased learning across experimental rounds over the inaccurate condition (all P values ?.01). We find limited evidence of an interaction between official and SI about infectious diseases. Conclusions: This project demonstrates that exposure to official public health information increases individuals' knowledge of the spread and symptoms of a disease. Socially shared information also facilitates the learning of accurate and inaccurate information, though to a lesser extent than exposure to OI. Although the effect of OI persists, preliminary results suggest that it can be degraded by persistent contradictory SI over time. ", doi="10.2196/25287", url="https://www.jmir.org/2021/11/e25287", url="http://www.ncbi.nlm.nih.gov/pubmed/34817389" } @Article{info:doi/10.2196/30467, author="Wang, W. Andrea and Lan, Jo-Yu and Wang, Ming-Hung and Yu, Chihhao", title="The Evolution of Rumors on a Closed Social Networking Platform During COVID-19: Algorithm Development and Content Study", journal="JMIR Med Inform", year="2021", month="Nov", day="23", volume="9", number="11", pages="e30467", keywords="COVID-19", keywords="rumors", keywords="rumor diffusion", keywords="rumor propagation", keywords="social listening", keywords="infodemic", keywords="social media", keywords="closed platform", keywords="natural language processing", keywords="machine learning", keywords="unsupervised learning", keywords="computers and society", abstract="Background: In 2020, the COVID-19 pandemic put the world in a crisis regarding both physical and psychological health. Simultaneously, a myriad of unverified information flowed on social media and online outlets. The situation was so severe that the World Health Organization identified it as an infodemic in February 2020. Objective: The aim of this study was to examine the propagation patterns and textual transformation of COVID-19--related rumors on a closed social media platform. Methods: We obtained a data set of suspicious text messages collected on Taiwan's most popular instant messaging platform, LINE, between January and July 2020. We proposed a classification-based clustering algorithm that could efficiently cluster messages into groups, with each group representing a rumor. For ease of understanding, a group is referred to as a ``rumor group.'' Messages in a rumor group could be identical or could have limited textual differences between them. Therefore, each message in a rumor group is a form of the rumor. Results: A total of 936 rumor groups with at least 10 messages each were discovered among 114,124 text messages collected from LINE. Among 936 rumors, 396 (42.3\%) were related to COVID-19. Of the 396 COVID-19--related rumors, 134 (33.8\%) had been fact-checked by the International Fact-Checking Network--certified agencies in Taiwan and determined to be false or misleading. By studying the prevalence of simplified Chinese characters or phrases in the messages that originated in China, we found that COVID-19--related messages, compared to non--COVID-19--related messages, were more likely to have been written by non-Taiwanese users. The association was statistically significant, with P<.001, as determined by the chi-square independence test. The qualitative investigations of the three most popular COVID-19 rumors revealed that key authoritative figures, mostly medical personnel, were often misquoted in the messages. In addition, these rumors resurfaced multiple times after being fact-checked, usually preceded by major societal events or textual transformations. Conclusions: To fight the infodemic, it is crucial that we first understand why and how a rumor becomes popular. While social media has given rise to an unprecedented number of unverified rumors, it also provides a unique opportunity for us to study the propagation of rumors and their interactions with society. Therefore, we must put more effort into these areas. ", doi="10.2196/30467", url="https://medinform.jmir.org/2021/11/e30467", url="http://www.ncbi.nlm.nih.gov/pubmed/34623954" } @Article{info:doi/10.2196/26660, author="Chang, Angela and Schulz, Johannes Peter and Jiao, Wen and Liu, Tingchi Matthew", title="Obesity-Related Communication in Digital Chinese News From Mainland China, Hong Kong, and Taiwan: Automated Content Analysis", journal="JMIR Public Health Surveill", year="2021", month="Nov", day="23", volume="7", number="11", pages="e26660", keywords="public health", keywords="computational content", keywords="digital research methods", keywords="obesity discourse", keywords="gene disorders", keywords="noncommunicable disease", abstract="Background: The fact that the number of individuals with obesity has increased worldwide calls into question media efforts for informing the public. This study attempts to determine the ways in which the mainstream digital news covers the etiology of obesity and diseases associated with the burden of obesity. Objective: The dual objectives of this study are to obtain an understanding of what the news reports on obesity and to explore meaning in data by extending the preconceived grounded theory. Methods: The 10 years of news text from 2010 to 2019 compared the development of obesity-related coverage and its potential impact on its perception in Mainland China, Hong Kong, and Taiwan. Digital news stories on obesity along with affliction and inferences in 9 Chinese mainstream newspapers were sampled. An automatic content analysis tool, DiVoMiner was proposed. This computer-aided platform is designed to organize and filter large sets of data on the basis of the patterns of word occurrence and term discovery. Another programming language, Python 3, was used to explore connections and patterns created by the aggregated interactions. Results: A total of 30,968 news stories were identified with increasing attention since 2016. The highest intensity of newspaper coverage of obesity communication was observed in Taiwan. Overall, a stronger focus on 2 shared causative attributes of obesity is on stress (n=4483, 33.0\%) and tobacco use (n=3148, 23.2\%). The burdens of obesity and cardiovascular diseases are implied to be the most, despite the aggregated interaction of edge centrality showing the highest link between the ``cancer'' and obesity. This study goes beyond traditional journalism studies by extending the framework of computational and customizable web-based text analysis. This could set a norm for researchers and practitioners who work on data projects largely for an innovative attempt. Conclusions: Similar to previous studies, the discourse between the obesity epidemic and personal afflictions is the most emphasized approach. Our study also indicates that the inclination of blaming personal attributes for health afflictions potentially limits social and governmental responsibility for addressing this issue. ", doi="10.2196/26660", url="https://publichealth.jmir.org/2021/11/e26660", url="http://www.ncbi.nlm.nih.gov/pubmed/34817383" } @Article{info:doi/10.2196/30642, author="Muric, Goran and Wu, Yusong and Ferrara, Emilio", title="COVID-19 Vaccine Hesitancy on Social Media: Building a Public Twitter Data Set of Antivaccine Content, Vaccine Misinformation, and Conspiracies", journal="JMIR Public Health Surveill", year="2021", month="Nov", day="17", volume="7", number="11", pages="e30642", keywords="vaccine hesitancy", keywords="COVID-19 vaccines", keywords="dataset", keywords="COVID-19", keywords="SARS-CoV-2", keywords="social media", keywords="network analysis", keywords="hesitancy", keywords="vaccine", keywords="Twitter", keywords="misinformation", keywords="conspiracy", keywords="trust", keywords="public health", keywords="utilization", abstract="Background: False claims about COVID-19 vaccines can undermine public trust in ongoing vaccination campaigns, posing a threat to global public health. Misinformation originating from various sources has been spreading on the web since the beginning of the COVID-19 pandemic. Antivaccine activists have also begun to use platforms such as Twitter to promote their views. To properly understand the phenomenon of vaccine hesitancy through the lens of social media, it is of great importance to gather the relevant data. Objective: In this paper, we describe a data set of Twitter posts and Twitter accounts that publicly exhibit a strong antivaccine stance. The data set is made available to the research community via our AvaxTweets data set GitHub repository. We characterize the collected accounts in terms of prominent hashtags, shared news sources, and most likely political leaning. Methods: We started the ongoing data collection on October 18, 2020, leveraging the Twitter streaming application programming interface (API) to follow a set of specific antivaccine-related keywords. Then, we collected the historical tweets of the set of accounts that engaged in spreading antivaccination narratives between October 2020 and December 2020, leveraging the Academic Track Twitter API. The political leaning of the accounts was estimated by measuring the political bias of the media outlets they shared. Results: We gathered two curated Twitter data collections and made them publicly available: (1) a streaming keyword--centered data collection with more than 1.8 million tweets, and (2) a historical account--level data collection with more than 135 million tweets. The accounts engaged in the antivaccination narratives lean to the right (conservative) direction of the political spectrum. The vaccine hesitancy is fueled by misinformation originating from websites with already questionable credibility. Conclusions: The vaccine-related misinformation on social media may exacerbate the levels of vaccine hesitancy, hampering progress toward vaccine-induced herd immunity, and could potentially increase the number of infections related to new COVID-19 variants. For these reasons, understanding vaccine hesitancy through the lens of social media is of paramount importance. Because data access is the first obstacle to attain this goal, we published a data set that can be used in studying antivaccine misinformation on social media and enable a better understanding of vaccine hesitancy. ", doi="10.2196/30642", url="https://publichealth.jmir.org/2021/11/e30642", url="http://www.ncbi.nlm.nih.gov/pubmed/34653016" } @Article{info:doi/10.2196/32707, author="Raffaelli, Bianca and Kull, Pia and Mecklenburg, Jasper and Overeem, Hendrik Lucas and Storch, Elisabeth and Terhart, Maria and Neeb, Lars and Reuter, Uwe", title="Patients' and Health Care Workers' Perception of Migraine Images on the Internet: Cross-sectional Survey Study", journal="J Med Internet Res", year="2021", month="Nov", day="12", volume="23", number="11", pages="e32707", keywords="migraine", keywords="stigma", keywords="mass media", keywords="stock photos", keywords="advocacy", keywords="internet", keywords="perception", keywords="headache", keywords="pain", keywords="cross-sectional", keywords="survey", keywords="stereotype", keywords="media", keywords="awareness", abstract="Background: The representation of migraine in the media is stereotypical. Standard images of migraine attacks display stylish young women holding their head in a pain pose. This representation may contribute to the social stigmatization of patients with migraine. Objective: We aimed to analyze how patients with migraine and health care workers perceive online images of migraine. Methods: The study consisted of an anonymous web-based survey of patients with migraine at the Headache Center of Charit{\'e} -- Universit{\"a}tsmedizin Berlin (migraine group) and employees and students at our university (health care group). A total of 10 frequently used Adobe Stock photos of migraine attacks were presented to the participants. Each photo was rated on a scale of 0\% to 100\% based on how closely it resembled a realistic migraine attack (realism score). Patients with migraine also indicated how much each photo corresponded to their own experience of migraine as a percentage (representation score). We calculated the mean realism and representation scores for all photos and conducted further analyses using the categories male or female models, younger or older models, and unilateral or bilateral pain pose. Results: A total of 367 patients with migraine and 331 health care employees and students completed the survey. In both groups, the mean realism score was <50\% (migraine group: 47.8\%, SD 18.3\%; health care group: 46.0\%, SD 16.2\%). Patients with migraine identified their own migraine experience in these photos to a lesser degree (mean representation score 44.4\%, SD 19.8\%; P<.001 when compared to the realism score). Patients and health care workers considered photos with male models to be more realistic than photos with females (P<.001) and photos with older models to be more realistic than those with younger people (P<.001). In the health care group only, a bilateral pain posture was deemed more realistic than a unilateral pose (P<.001). Conclusions: Standard images of migraine attacks are considered only slightly or moderately realistic by patients and health care workers. Some characteristics perceived as more realistic such as male sex or older age are in contrast with migraine epidemiology. A more accurate representation of migraine in the media could help to raise awareness for migraine and reduce the associated stigma. ", doi="10.2196/32707", url="https://www.jmir.org/2021/11/e32707", url="http://www.ncbi.nlm.nih.gov/pubmed/34766918" } @Article{info:doi/10.2196/28237, author="Hendriks, Hanneke and de Nooy, Wouter and Gebhardt, A. Winifred and van den Putte, Bas", title="Causal Effects of Alcohol-Related Facebook Posts on Drinking Behavior: Longitudinal Experimental Study", journal="J Med Internet Res", year="2021", month="Nov", day="11", volume="23", number="11", pages="e28237", keywords="social media", keywords="social networking site (SNS)", keywords="alcohol-related posts", keywords="alcoholposts", keywords="alcohol consumption", abstract="Background: Adolescents and young adults frequently post alcohol-related content (ie, alcoholposts) on social media. This is problematic because both social norms theory and social learning theory suggest that viewing alcoholposts of peers could increase drinking behavior. It is therefore paramount to understand the effects of exposure to alcoholposts on viewers. Objective: This study aimed to investigate the causal effects of exposure to alcoholposts on alcohol consumption by using a rigorous design. Methods: We conducted a 6-week longitudinal study during which alcoholposts were measured by a newly developed app that copied Facebook posts shared by participants (n=281) to a new social media environment. In addition, daily questionnaires assessed alcohol use. Effects of natural alcoholposts (ie, posted by the participants) were assessed in phase 1, and effects of experimental posts (ie, posted by fake participants) were explored in phase 2. Results: Results showed that natural alcoholposts increased the occurrence and quantity of drinking the following day. That is, exposure to a single additional alcoholpost increased the log odds of drinking the next day by 0.27 (b=.27, credible interval [CI] .18 to .35). Furthermore, the number of natural alcoholposts had a positive (predictive) effect on the number of glasses drunk the next day (b=.21, CI .14 to .29). In phase 2 when experimental posts were also present, these effects decreased. Experimental posts themselves had hardly any effects. Conclusions: This study illustrates clear and direct effects of exposure to alcoholposts on next-day alcohol consumption and suggests that alcoholposts represent an important societal problem that interventions need to address. ", doi="10.2196/28237", url="https://www.jmir.org/2021/11/e28237", url="http://www.ncbi.nlm.nih.gov/pubmed/34762061" } @Article{info:doi/10.2196/26272, author="Freeman, Benjamin Tobe Che and Rodriguez-Esteban, Raul and Gottowik, Juergen and Yang, Xing and Erpenbeck, Johannes Veit and Leddin, Mathias", title="A Neural Network Approach for Understanding Patient Experiences of Chronic Obstructive Pulmonary Disease (COPD): Retrospective, Cross-sectional Study of Social Media Content", journal="JMIR Med Inform", year="2021", month="Nov", day="11", volume="9", number="11", pages="e26272", keywords="outcomes research", keywords="natural language processing", keywords="neural networks (computer)", keywords="social media", keywords="exercise", keywords="sleep deprivation", keywords="social media listening", keywords="drug development", abstract="Background: The abundance of online content contributed by patients is a rich source of insight about the lived experience of disease. Patients share disease experiences with other members of the patient and caregiver community and do so using their own lexicon of words and phrases. This lexicon and the topics that are communicated using words and phrases belonging to the lexicon help us better understand disease burden. Insights from social media may ultimately guide clinical development in ways that ensure that future treatments are fit for purpose from the patient's perspective. Objective: We sought insights into the patient experience of chronic obstructive pulmonary disease (COPD) by analyzing a substantial corpus of social media content. The corpus was sufficiently large to make manual review and manual coding all but impossible to perform in a consistent and systematic fashion. Advanced analytics were applied to the corpus content in the search for associations between symptoms and impacts across the entire text corpus. Methods: We conducted a retrospective, cross-sectional study of 5663 posts sourced from open blogs and online forum posts published by COPD patients between February 2016 and August 2019. We applied a novel neural network approach to identify a lexicon of community words and phrases used by patients to describe their symptoms. We used this lexicon to explore the relationship between COPD symptoms and disease-related impacts. Results: We identified a diverse lexicon of community words and phrases for COPD symptoms, including gasping, wheezy, mucus-y, and muck. These symptoms were mentioned in association with specific words and phrases for disease impact such as frightening, breathing discomfort, and difficulty exercising. Furthermore, we found an association between mucus hypersecretion and moderate disease severity, which distinguished mucus from the other main COPD symptoms, namely breathlessness and cough. Conclusions: We demonstrated the potential of neural networks and advanced analytics to gain patient-focused insights about how each distinct COPD symptom contributes to the burden of chronic and acute respiratory illness. Using a neural network approach, we identified words and phrases for COPD symptoms that were specific to the patient community. Identifying patterns in the association between symptoms and impacts deepened our understanding of the patient experience of COPD. This approach can be readily applied to other disease areas. ", doi="10.2196/26272", url="https://medinform.jmir.org/2021/11/e26272", url="http://www.ncbi.nlm.nih.gov/pubmed/34762056" } @Article{info:doi/10.2196/32936, author="Zhang, Zizheng and Feng, Guanrui and Xu, Jiahong and Zhang, Yimin and Li, Jinhui and Huang, Jian and Akinwunmi, Babatunde and Zhang, P. Casper J. and Ming, Wai-kit", title="The Impact of Public Health Events on COVID-19 Vaccine Hesitancy on Chinese Social Media: National Infoveillance Study", journal="JMIR Public Health Surveill", year="2021", month="Nov", day="9", volume="7", number="11", pages="e32936", keywords="COVID-19", keywords="vaccine", keywords="hesitancy", keywords="social media", keywords="China", keywords="sentiment analysis", keywords="infoveillance", keywords="public health", keywords="surveillance", keywords="Weibo", keywords="data mining", keywords="sentiment", keywords="attitude", abstract="Background: The ongoing COVID-19 pandemic has brought unprecedented challenges to every country worldwide. A call for global vaccination for COVID-19 plays a pivotal role in the fight against this virus. With the development of COVID-19 vaccines, public willingness to get vaccinated has become an important public health concern, considering the vaccine hesitancy observed worldwide. Social media is powerful in monitoring public attitudes and assess the dissemination, which would provide valuable information for policy makers. Objective: This study aimed to investigate the responses of vaccine positivity on social media when major public events (major outbreaks) or major adverse events related to vaccination (COVID-19 or other similar vaccines) were reported. Methods: A total of 340,783 vaccine-related posts were captured with the poster's information on Weibo, the largest social platform in China. After data cleaning, 156,223 posts were included in the subsequent analysis. Using pandas and SnowNLP Python libraries, posts were classified into 2 categories, positive and negative. After model training and sentiment analysis, the proportion of positive posts was computed to measure the public positivity toward the COVID-19 vaccine. Results: The positivity toward COVID-19 vaccines in China tends to fluctuate over time in the range of 45.7\% to 77.0\% and is intuitively correlated with public health events. In terms of gender, males were more positive (70.0\% of the time) than females. In terms of region, when regional epidemics arose, not only the region with the epidemic and surrounding regions but also the whole country showed more positive attitudes to varying degrees. When the epidemic subsided temporarily, positivity decreased with varying degrees in each region. Conclusions: In China, public positivity toward COVID-19 vaccines fluctuates over time and a regional epidemic or news on social media may cause significant variations in willingness to accept a vaccine. Furthermore, public attitudes toward COVID-19 vaccination vary from gender and region. It is crucial for policy makers to adjust their policies through the use of positive incentives with prompt responses to pandemic-related news to promote vaccination acceptance. ", doi="10.2196/32936", url="https://publichealth.jmir.org/2021/11/e32936", url="http://www.ncbi.nlm.nih.gov/pubmed/34591782" } @Article{info:doi/10.2196/30150, author="Wawrzuta, Dominik and Jaworski, Mariusz and Gotlib, Joanna and Panczyk, Mariusz", title="Social Media Sharing of Articles About Measles in a European Context: Text Analysis Study", journal="J Med Internet Res", year="2021", month="Nov", day="8", volume="23", number="11", pages="e30150", keywords="measles", keywords="Facebook", keywords="Twitter", keywords="Pinterest", keywords="social media", keywords="vaccine", keywords="infodemiology", keywords="public health", abstract="Background: Despite the existence of an effective vaccine, measles still threatens the health and lives of many Europeans. Notably, during the COVID-19 pandemic, measles vaccine uptake declined; as a result, after the pandemic, European countries will have to increase vaccination rates to restore the extent of vaccination coverage among the population. Because information obtained from social media are one of the main causes of vaccine hesitancy, knowledge of the nature of information pertaining to measles that is shared on social media may help create educational campaigns. Objective: In this study, we aim to define the characteristics of European news about measles shared on social media platforms (ie, Facebook, Twitter, and Pinterest) from 2017 to 2019. Methods: We downloaded and translated (into English) 10,305 articles on measles published in European Union countries. Using latent Dirichlet allocation, we identified main topics and estimated the sentiments expressed in these articles. Furthermore, we used linear regression to determine factors related to the number of times a given article was shared on social media. Results: We found that, in most European social media posts, measles is only discussed in the context of local European events. Articles containing educational information and describing world outbreaks appeared less frequently. The most common emotions identified from the study's news data set were fear and trust. Yet, it was found that readers were more likely to share information on educational topics and the situation in Germany, Ukraine, Italy, and Samoa. A high amount of anger, joy, and sadness expressed within the text was also associated with a higher number of shares. Conclusions: We identified which features of news articles were related to increased social media shares. We found that social media users prefer sharing educational news to sharing informational news. Appropriate emotional content can also increase the willingness of social media users to share an article. Effective media content that promotes measles vaccinations should contain educational or scientific information, as well as specific emotions (such as anger, joy, or sadness). Articles with this type of content may offer the best chance of disseminating vital messages to a broad social media audience. ", doi="10.2196/30150", url="https://www.jmir.org/2021/11/e30150", url="http://www.ncbi.nlm.nih.gov/pubmed/34570715" } @Article{info:doi/10.2196/29789, author="Liew, Ming Tau and Lee, Sin Cia", title="Examining the Utility of Social Media in COVID-19 Vaccination: Unsupervised Learning of 672,133 Twitter Posts", journal="JMIR Public Health Surveill", year="2021", month="Nov", day="3", volume="7", number="11", pages="e29789", keywords="social media", keywords="COVID-19", keywords="vaccine hesitancy", keywords="natural language processing", keywords="machine learning", keywords="infodemiology", abstract="Background: Although COVID-19 vaccines have recently become available, efforts in global mass vaccination can be hampered by the widespread issue of vaccine hesitancy. Objective: The aim of this study was to use social media data to capture close-to-real-time public perspectives and sentiments regarding COVID-19 vaccines, with the intention to understand the key issues that have captured public attention, as well as the barriers and facilitators to successful COVID-19 vaccination. Methods: Twitter was searched for tweets related to ``COVID-19'' and ``vaccine'' over an 11-week period after November 18, 2020, following a press release regarding the first effective vaccine. An unsupervised machine learning approach (ie, structural topic modeling) was used to identify topics from tweets, with each topic further grouped into themes using manually conducted thematic analysis as well as guided by the theoretical framework of the COM-B (capability, opportunity, and motivation components of behavior) model. Sentiment analysis of the tweets was also performed using the rule-based machine learning model VADER (Valence Aware Dictionary and Sentiment Reasoner). Results: Tweets related to COVID-19 vaccines were posted by individuals around the world (N=672,133). Six overarching themes were identified: (1) emotional reactions related to COVID-19 vaccines (19.3\%), (2) public concerns related to COVID-19 vaccines (19.6\%), (3) discussions about news items related to COVID-19 vaccines (13.3\%), (4) public health communications about COVID-19 vaccines (10.3\%), (5) discussions about approaches to COVID-19 vaccination drives (17.1\%), and (6) discussions about the distribution of COVID-19 vaccines (20.3\%). Tweets with negative sentiments largely fell within the themes of emotional reactions and public concerns related to COVID-19 vaccines. Tweets related to facilitators of vaccination showed temporal variations over time, while tweets related to barriers remained largely constant throughout the study period. Conclusions: The findings from this study may facilitate the formulation of comprehensive strategies to improve COVID-19 vaccine uptake; they highlight the key processes that require attention in the planning of COVID-19 vaccination and provide feedback on evolving barriers and facilitators in ongoing vaccination drives to allow for further policy tweaks. The findings also illustrate three key roles of social media in COVID-19 vaccination, as follows: surveillance and monitoring, a communication platform, and evaluation of government responses. ", doi="10.2196/29789", url="https://publichealth.jmir.org/2021/11/e29789", url="http://www.ncbi.nlm.nih.gov/pubmed/34583316" } @Article{info:doi/10.2196/29390, author="Meleo-Erwin, C. Zoe and Basch, H. Corey and Fera, Joseph and Smith, Bonnie", title="Discussion of Weight Loss Surgery in Instagram Posts: Successive Sampling Study ", journal="JMIR Perioper Med", year="2021", month="Nov", day="1", volume="4", number="2", pages="e29390", keywords="bariatric surgery", keywords="social media", keywords="Instagram", keywords="health promotion", keywords="post-operative medicine", keywords="online health information", keywords="information accuracy", keywords="surgery", keywords="information quality", abstract="Background: The majority of American adults search for health and illness information on the internet. However, the quality and accuracy of this information are notoriously variable. With the advent of social media, US individuals have increasingly shared their own health and illness experiences, including those related to bariatric surgery, on social media platforms. Previous research has found that peer-to-peer requesting and giving of advice related to bariatric surgery on social media is common, that such advice is often presented in stark terms, and that the advice may not reflect patient standards of care. These previous investigations have helped to map bariatric surgery content on Facebook and YouTube. Objective: This objective of this study was to document and compare weight loss surgery (WLS)--related content on Instagram in the months leading up to the COVID-19 pandemic and 1 year later. Methods: We analyzed a total of 300 Instagram posts (50 posts per week for 3 consecutive weeks in late February and early March in both 2020 and 2021) uploaded using the hashtag \#wls. Descriptive statistics were reported, and independent 1-tailed chi-square tests were used to determine if a post's publication year statistically affected its inclusion of a particular type of content. Results: Overall, advice giving and personal responsibility for outcomes were emphasized by WLS posters on Instagram. However, social support was less emphasized. The safety, challenges, and risks associated with WLS were rarely discussed. The majority of posts did not contain references to facts from reputable medical sources. Posts published in 2021 were more likely to mention stress/hardships of living with WLS (45/150, 30\%, vs 29/150, 19.3\%; P=.03); however, those published in 2020 more often identified the importance of ongoing support for WLS success (35/150, 23.3\%, vs 16/150, 10.7\%; P=.004). Conclusions: Given that bariatric patients have low rates of postoperative follow-up, yet post-operative care and yet support are associated with improved health and weight loss outcomes, and given that health content on the web is of mixed accuracy, bariatric professionals may wish to consider including an online support forum moderated by a professional as a routine part of postoperative care. Doing so may not only improve follow-up rates but may offer providers the opportunity to counter inaccuracies encountered on social media. ", doi="10.2196/29390", url="https://periop.jmir.org/2021/2/e29390", url="http://www.ncbi.nlm.nih.gov/pubmed/34723828" } @Article{info:doi/10.2196/33653, author="Spitale, Giovanni and Merten, Sonja and Jafflin, Kristen and Schwind, Bettina and Kaiser-Grolimund, Andrea and Biller-Andorno, Nikola", title="A Novel Risk and Crisis Communication Platform to Bridge the Gap Between Policy Makers and the Public in the Context of the COVID-19 Crisis (PubliCo): Protocol for a Mixed Methods Study", journal="JMIR Res Protoc", year="2021", month="Nov", day="1", volume="10", number="11", pages="e33653", keywords="disease outbreaks", keywords="coronavirus", keywords="COVID-19 surveys", keywords="COVID-19 questionnaires", keywords="qualitative methods", keywords="health literacy", keywords="policy making", keywords="risk and crisis communication", keywords="COVID-19", abstract="Background: Since the end of 2019, COVID-19 has had a significant impact on people around the globe. As governments institute more restrictive measures, public adherence could decrease and discontent may grow. Providing high-quality information and countering fake news are important. However, we also need feedback loops so that government officials can refine preventive measures and communication strategies. Policy makers need information---preferably based on real-time data---on people's cognitive, emotional, and behavioral reactions to public health messages and restrictive measures. PubliCo aims to foster effective and tailored risk and crisis communication as well as provide an assessment of the risks and benefits of prevention and control measures, since their effectiveness depends on public trust and cooperation. Objective: Our project aims to develop a tool that helps tackle the COVID-19 infodemic, with a focus on enabling a nuanced and in-depth understanding of public perception. The project adopts a transdisciplinary multistakeholder approach, including participatory citizen science. Methods: We aim to combine a literature and media review and analysis as well as empirical research using mixed methods, including an online survey and diary-based research, both of which are ongoing and continuously updated. Building on real-time data and continuous data collection, our research results will be highly adaptable to the evolving situation. Results: As of September 2021, two-thirds of the proposed tool is operational. The current development cycles are focusing on analytics, user experience, and interface refinement. We have collected a total of 473 responses through PubliCo Survey and 22 diaries through PubliCo Diaries. Conclusions: Pilot data show that PubliCo is a promising and efficient concept for bidirectional risk and crisis communication in the context of public health crises. Further data are needed to assess its function at a larger scale or in the context of an issue other than COVID-19. International Registered Report Identifier (IRRID): DERR1-10.2196/33653 ", doi="10.2196/33653", url="https://www.researchprotocols.org/2021/11/e33653", url="http://www.ncbi.nlm.nih.gov/pubmed/34612823" } @Article{info:doi/10.2196/28069, author="Haupt, Robert Michael and Xu, Qing and Yang, Joshua and Cai, Mingxiang and Mackey, K. Tim", title="Characterizing Vaping Industry Political Influence and Mobilization on Facebook: Social Network Analysis", journal="J Med Internet Res", year="2021", month="Oct", day="29", volume="23", number="10", pages="e28069", keywords="vaping", keywords="alternative tobacco industry", keywords="e-cigarettes", keywords="Facebook", keywords="social network analysis", keywords="social networks", keywords="ehealth", keywords="health policy", abstract="Background: In response to recent policy efforts to regulate tobacco and vaping products, the vaping industry has been aggressive in mobilizing opposition by using a network of manufacturers, trade associations, and tobacco user communities, and by appealing to the general public. One strategy the alternative tobacco industry uses to mobilize political action is coordinating on social media platforms, such as the social networking site Facebook. However, few studies have specifically assessed how platforms such as Facebook are used to influence public sentiment and attitudes towards tobacco control policy. Objective: This study used social network analysis to examine how the alternative tobacco industry uses Facebook to mobilize online users to influence tobacco control policy outcomes with a focus on the state of California. Methods: Data were collected from local and national alternative tobacco Facebook groups that had affiliations with activities in the state of California. Network ties were constructed based on users' reactions to posts (eg, ``like'' and ``love'') and comments to characterize political mobilization networks. Results: Findings show that alternative tobacco industry employees were more likely to engage within these networks and that these employees were also more likely to be influential members (ie, be more active) in the network. Comparisons between subnetworks show that communication within the local alternative tobacco advocacy group network was less dense and more centralized in contrast to a national advocacy group that had overall higher levels of engagement among members. A timeline analysis found that a higher number of influential posts that disseminated widely across networks occurred during e-cigarette--related legislative events, suggesting strategic online engagement and increased mobilization of online activity for the purposes of influencing policy outcomes. Conclusions: Results from this study provide important insights into how tobacco industry--related advocacy groups leverage the Facebook platform to mobilize their online constituents in an effort to influence public perceptions and coordinate to defeat tobacco control efforts at the local, state, and federal level. Study results reveal one part of a vast network of socially enabled alternative tobacco industry actors and constituents that use Facebook as a mobilization point to support goals of the alternative tobacco industry. ", doi="10.2196/28069", url="https://www.jmir.org/2021/10/e28069", url="http://www.ncbi.nlm.nih.gov/pubmed/34714245" } @Article{info:doi/10.2196/32005, author="Kamba, Masaru and Manabe, Masae and Wakamiya, Shoko and Yada, Shuntaro and Aramaki, Eiji and Odani, Satomi and Miyashiro, Isao", title="Medical Needs Extraction for Breast Cancer Patients from Question and Answer Services: Natural Language Processing-Based Approach", journal="JMIR Cancer", year="2021", month="Oct", day="28", volume="7", number="4", pages="e32005", keywords="natural language processing", keywords="internet use", keywords="patient generated health data", keywords="neoplasms", abstract="Background: A large number of patient narratives are available on various web services. As for web question and answer services, patient questions often relate to medical needs, and we expect these questions to provide clues for a better understanding of patients' medical needs. Objective: This study aimed to extract patients' needs and classify them into thematic categories. Clarifying patient needs is the first step in solving social issues that patients with cancer encounter. Methods: For this study, we used patient question texts containing the key phrase ``breast cancer,`` available at the Yahoo! Japan question and answer service, Yahoo! Chiebukuro, which contains over 60,000 questions on cancer. First, we converted the question text into a vector representation. Next, the relevance between patient needs and existing cancer needs categories was calculated based on cosine similarity. Results: The proportion of correct classifications in our proposed method was approximately 70\%. Considering the results of classifying questions, we found the variation and the number of needs. Conclusions: We created 3 corpora to classify the problems of patients with cancer. The proposed method was able to classify the problems considering the question text. Moreover, as an application example, the question text that included the side effect signaling of drugs and the unmet needs of cancer patients could be extracted. Revealing these needs is important to fulfill the medical needs of patients with cancer. ", doi="10.2196/32005", url="https://cancer.jmir.org/2021/4/e32005", url="http://www.ncbi.nlm.nih.gov/pubmed/34709187" } @Article{info:doi/10.2196/32856, author="Silangcruz, Krixie and Nishimura, Yoshito and Czech, Torrey and Kimura, Nobuhiko and Hagiya, Hideharu and Koyama, Toshihiro and Otsuka, Fumio", title="Impact of the World Inflammatory Bowel Disease Day and Crohn's and Colitis Awareness Week on Population Interest Between 2016 and 2020: Google Trends Analysis", journal="JMIR Infodemiology", year="2021", month="Oct", day="28", volume="1", number="1", pages="e32856", keywords="inflammatory bowel disease", keywords="ulcerative colitis", keywords="Crohn disease", keywords="google trends", keywords="trend analysis", keywords="online health information", keywords="awareness", keywords="chronic disease", keywords="gastrointestinal", keywords="trend", keywords="impact", keywords="public health", keywords="United States", abstract="Background: More than 6 million people are affected by inflammatory bowel disease (IBD) globally. The World IBD Day (WID, May 19) and Crohn's and Colitis Awareness Week (CCAW, December 1-7) occur yearly as national health observances to raise public awareness of IBD, but their effects are unclear. Objective: The aim of this study was to analyze the relationship between WID or CCAW and the public health awareness on IBD represented by the Google search engine query data. Methods: This study evaluates the impact of WID and CCAW on the public awareness of IBD in the United States and worldwide from 2016 to 2020 by using the relative search volume of ``IBD,'' ``ulcerative colitis,'' and ``Crohn's disease'' in Google Trends. To identify significant time points of trend changes (joinpoints), we performed joinpoint regression analysis. Results: No joinpoints were noted around the time of WID or CCAW during the study period in the search results of the United States. Worldwide, joinpoints were noted around WID in 2020 with the search for ``IBD'' and around CCAW in 2017 and 2019 with the search for ``ulcerative colitis.'' However, the extents of trend changes were modest without statistically significant increases. Conclusions: These results posed a question that WID and CCAW might not have worked as expected to raise public awareness of IBD. Additional studies are needed to precisely estimate the impact of health observances to raise the awareness of IBD. ", doi="10.2196/32856", url="https://infodemiology.jmir.org/2021/1/e32856", url="http://www.ncbi.nlm.nih.gov/pubmed/37114197" } @Article{info:doi/10.2196/29820, author="Monzani, Dario and Vergani, Laura and Pizzoli, Maria Silvia Francesca and Marton, Giulia and Pravettoni, Gabriella", title="Emotional Tone, Analytical Thinking, and Somatosensory Processes of a Sample of Italian Tweets During the First Phases of the COVID-19 Pandemic: Observational Study", journal="J Med Internet Res", year="2021", month="Oct", day="27", volume="23", number="10", pages="e29820", keywords="internet", keywords="mHealth", keywords="infodemiology", keywords="infoveillance", keywords="pandemic", keywords="public health", keywords="COVID-19", keywords="Twitter", keywords="psycholinguistic analysis", keywords="trauma", abstract="Background: The COVID-19 pandemic is a traumatic individual and collective chronic experience, with tremendous consequences on mental and psychological health that can also be reflected in people's use of words. Psycholinguistic analysis of tweets from Twitter allows obtaining information about people's emotional expression, analytical thinking, and somatosensory processes, which are particularly important in traumatic events contexts. Objective: We aimed to analyze the influence of official Italian COVID-19 daily data (new cases, deaths, and hospital discharges) and the phase of managing the pandemic on how people expressed emotions and their analytical thinking and somatosensory processes in Italian tweets written during the first phases of the COVID-19 pandemic in Italy. Methods: We retrieved 1,697,490 Italian COVID-19--related tweets written from February 24, 2020 to June 14, 2020 and analyzed them using LIWC2015 to calculate 3 summary psycholinguistic variables: emotional tone, analytical thinking, and somatosensory processes. Official daily data about new COVID-19 cases, deaths, and hospital discharges were retrieved from the Italian Prime Minister's Office and Civil Protection Department GitHub page. We considered 3 phases of managing the COVID-19 pandemic in Italy. We performed 3 general models, 1 for each summary variable as the dependent variable and with daily data and phase of managing the pandemic as independent variables. Results: General linear models to assess differences in daily scores of emotional tone, analytical thinking, and somatosensory processes were significant (F6,104=21.53, P<.001, R2= .55; F5,105=9.20, P<.001, R2= .30; F6,104=6.15, P<.001, R2=.26, respectively). Conclusions: The COVID-19 pandemic affects how people express emotions, analytical thinking, and somatosensory processes in tweets. Our study contributes to the investigation of pandemic psychological consequences through psycholinguistic analysis of social media textual data. ", doi="10.2196/29820", url="https://www.jmir.org/2021/10/e29820", url="http://www.ncbi.nlm.nih.gov/pubmed/34516386" } @Article{info:doi/10.2196/24336, author="Alvarez-Mon, Angel Miguel and Llavero-Valero, Maria and Asunsolo del Barco, Angel and Zaragoz{\'a}, Cristina and Ortega, A. Miguel and Lahera, Guillermo and Quintero, Javier and Alvarez-Mon, Melchor", title="Areas of Interest and Attitudes Toward Antiobesity Drugs: Thematic and Quantitative Analysis Using Twitter", journal="J Med Internet Res", year="2021", month="Oct", day="26", volume="23", number="10", pages="e24336", keywords="obesity", keywords="social media", keywords="Twitter", keywords="drug therapy", keywords="pharmacotherapy", keywords="attitude", keywords="thematic analysis", keywords="quantitative analysis", keywords="drug", abstract="Background: Antiobesity drugs are prescribed for the treatment of obesity in conjunction with healthy eating, physical activity, and behavior modification. However, poor adherence rates have been reported. Attitudes or beliefs toward medications are important to ascertain because they may be associated with patient behavior. The analysis of tweets has become a tool for health research. Objective: The aim of this study is to investigate the content and key metrics of tweets referring to antiobesity drugs. Methods: In this observational quantitative and qualitative study, we focused on tweets containing hashtags related to antiobesity drugs between September 20, 2019, and October 31, 2019. Tweets were first classified according to whether they described medical issues or not. Tweets with medical content were classified according to the topic they referred to: side effects, efficacy, or adherence. We additionally rated it as positive or negative. Furthermore, we classified any links included within a tweet as either scientific or nonscientific. Finally, the number of retweets generated as well as the dissemination and sentiment score obtained by the antiobesity drugs analyzed were also measured. Results: We analyzed a total of 2045 tweets, 945 of which were excluded according to the criteria of the study. Finally, 320 out of the 1,100 remaining tweets were also excluded because their content, although related to drugs for obesity treatment, did not address the efficacy, side effects, or adherence to medication. Liraglutide and semaglutide accumulated the majority of tweets (682/780, 87.4\%). Notably, the content that generated the highest frequency of tweets was related to treatment efficacy, with liraglutide-, semaglutide-, and lorcaserin-related tweets accumulating the highest proportion of positive consideration. We found the highest percentages of tweets with scientific links in those posts related to liraglutide and semaglutide. Semaglutide-related tweets obtained the highest probability of likes and were the most disseminated within the Twitter community. Conclusions: This analysis of posted tweets related to antiobesity drugs shows that the interest, beliefs, and experiences regarding these pharmacological treatments are heterogeneous. The efficacy of the treatment accounts for the majority of interest among Twitter users. ", doi="10.2196/24336", url="https://www.jmir.org/2021/10/e24336", url="http://www.ncbi.nlm.nih.gov/pubmed/34698653" } @Article{info:doi/10.2196/31101, author="Ainley, Esther and Witwicki, Cara and Tallett, Amy and Graham, Chris", title="Using Twitter Comments to Understand People's Experiences of UK Health Care During the COVID-19 Pandemic: Thematic and Sentiment Analysis", journal="J Med Internet Res", year="2021", month="Oct", day="25", volume="23", number="10", pages="e31101", keywords="patient experience", keywords="COVID-19", keywords="remote health care", keywords="phone consultation", keywords="video consultation", keywords="Twitter", keywords="sentiment analysis", keywords="social media", keywords="digital health", keywords="public health", keywords="public opinion", abstract="Background: The COVID-19 pandemic has led to changes in health service utilization patterns and a rapid rise in care being delivered remotely. However, there has been little published research examining patients' experiences of accessing remote consultations since COVID-19. Such research is important as remote methods for delivering some care may be maintained in the future. Objective: The aim of this study was to use content from Twitter to understand discourse around health and care delivery in the United Kingdom as a result of COVID-19, focusing on Twitter users' views on and attitudes toward care being delivered remotely. Methods: Tweets posted from the United Kingdom between January 2018 and October 2020 were extracted using the Twitter application programming interface. A total of 1408 tweets across three search terms were extracted into Excel; 161 tweets were removed following deduplication and 610 were identified as irrelevant to the research question. The remaining relevant tweets (N=637) were coded into categories using NVivo software, and assigned a positive, neutral, or negative sentiment. To examine views of remote care over time, the coded data were imported back into Excel so that each tweet was associated with both a theme and sentiment. Results: The volume of tweets on remote care delivery increased markedly following the COVID-19 outbreak. Five main themes were identified in the tweets: access to remote care (n=267), quality of remote care (n=130), anticipation of remote care (n=39), online booking and asynchronous communication (n=85), and publicizing changes to services or care delivery (n=160). Mixed public attitudes and experiences to the changes in service delivery were found. The proportion of positive tweets regarding access to, and quality of, remote care was higher in the immediate period following the COVID-19 outbreak (March-May 2020) when compared to the time before COVID-19 onset and the time when restrictions from the first lockdown eased (June-October 2020). Conclusions: Using Twitter data to address our research questions proved beneficial for providing rapid access to Twitter users' attitudes to remote care delivery at a time when it would have been difficult to conduct primary research due to COVID-19. This approach allowed us to examine the discourse on remote care over a relatively long period and to explore shifting attitudes of Twitter users at a time of rapid changes in care delivery. The mixed attitudes toward remote care highlight the importance for patients to have a choice over the type of consultation that best suits their needs, and to ensure that the increased use of technology for delivering care does not become a barrier for some. The finding that overall sentiment about remote care was more positive in the early stages of the pandemic but has since declined emphasizes the need for a continued examination of people's preference, particularly if remote appointments are likely to remain central to health care delivery. ", doi="10.2196/31101", url="https://www.jmir.org/2021/10/e31101", url="http://www.ncbi.nlm.nih.gov/pubmed/34469327" } @Article{info:doi/10.2196/30681, author="Basch, H. Corey and Fera, Joseph and Pellicane, Alessia and Basch, E. Charles", title="Videos With the Hashtag \#vaping on TikTok and Implications for Informed Decision-making by Adolescents: Descriptive Study", journal="JMIR Pediatr Parent", year="2021", month="Oct", day="25", volume="4", number="4", pages="e30681", keywords="vaping", keywords="TikTok", keywords="social media", keywords="misinformation", keywords="decision-making", keywords="adolescents", keywords="young adults", keywords="e-cigarettes", keywords="public health", keywords="informed decision-making", abstract="Background: Despite the public health importance of vaping and the widespread use of TikTok by adolescents and young adults, research is lacking on the nature and scope of vaping content on this networking service. Objective: The purpose of this study is to describe the content of TikTok videos related to vaping. Methods: By searching the hashtag \#vaping in the discover feature, {\textasciitilde}478.4 million views were seen during the time of data collection. The first 100 relevant videos under that hashtag were used in this study. Relevance was determined by simply noting if the video was related in any way to vaping. Coding consisted of several categories directly related to vaping and additional categories, including the number of likes, comments, and views, and if the video involved music, humor, or dance. Results: The 100 videos included in the sample garnered 156,331,347 views; 20,335,800 likes; and 296,460 comments. The majority of the videos (n=59) used music and over one-third (n=37) used humor. The only content category observed in the majority of the videos sampled was the promotion of vaping, which was included in 57 videos that garnered over 74 million views (47.5\% of cumulative views). A total of 42\% (n=42) of the 100 videos sampled featured someone vaping or in the presence of vape pens, and these videos garnered over 22\% (>35 million) of the total views. Conclusions: It is necessary for public health agencies to improve understanding of the nature and content of videos that attract viewers' attention and harness the strength of this communication channel to promote informed decision-making about vaping. ", doi="10.2196/30681", url="https://pediatrics.jmir.org/2021/4/e30681", url="http://www.ncbi.nlm.nih.gov/pubmed/34694231" } @Article{info:doi/10.2196/30217, author="Elyashar, Aviad and Plochotnikov, Ilia and Cohen, Idan-Chaim and Puzis, Rami and Cohen, Odeya", title="The State of Mind of Health Care Professionals in Light of the COVID-19 Pandemic: Text Analysis Study of Twitter Discourses", journal="J Med Internet Res", year="2021", month="Oct", day="22", volume="23", number="10", pages="e30217", keywords="health care professionals", keywords="Twitter", keywords="COVID-19", keywords="topic analysis", keywords="emotion analysis", keywords="sentiment analysis", keywords="social media", keywords="machine learning", keywords="active learning", abstract="Background: The COVID-19 pandemic has affected populations worldwide, with extreme health, economic, social, and political implications. Health care professionals (HCPs) are at the core of pandemic response and are among the most crucial factors in maintaining coping capacities. Yet, they are also vulnerable to mental health effects caused by managing a long-lasting emergency with a lack of resources and under complicated personal concerns. However, there are a lack of longitudinal studies that investigate the HCP population. Objective: The aim of this study was to analyze the state of mind of HCPs as expressed in online discussions published on Twitter in light of the COVID-19 pandemic, from the onset of the pandemic until the end of 2020. Methods: The population for this study was selected from followers of a few hundred Twitter accounts of health care organizations and common HCP points of interest. We used active learning, a process that iteratively uses machine learning and manual data labeling, to select the large-scale population of Twitter accounts maintained by English-speaking HCPs, focusing on individuals rather than official organizations. We analyzed the topics and emotions in their discourses during 2020. The topic distributions were obtained using the latent Dirichlet allocation algorithm. We defined a measure of topic cohesion and described the most cohesive topics. The emotions expressed in tweets during 2020 were compared to those in 2019. Finally, the emotion intensities were cross-correlated with the pandemic waves to explore possible associations between the pandemic development and emotional response. Results: We analyzed the timelines of 53,063 Twitter profiles, 90\% of which were maintained by individual HCPs. Professional topics accounted for 44.5\% of tweets by HCPs from January 1, 2019, to December 6, 2020. Events such as the pandemic waves, US elections, or the George Floyd case affected the HCPs' discourse. The levels of joy and sadness exceeded their minimal and maximal values from 2019, respectively, 80\% of the time (P=.001). Most interestingly, fear preceded the pandemic waves, in terms of the differences in confirmed cases, by 2 weeks with a Spearman correlation coefficient of $\rho$(47 pairs)=0.340 (P=.03). Conclusions: Analyses of longitudinal data over the year 2020 revealed that a large fraction of HCP discourse is directly related to professional content, including the increase in the volume of discussions following the pandemic waves. The changes in emotional patterns (ie, decrease in joy and increase in sadness, fear, and disgust) during the year 2020 may indicate the utmost importance in providing emotional support for HCPs to prevent fatigue, burnout, and mental health disorders during the postpandemic period. The increase in fear 2 weeks in advance of pandemic waves indicates that HCPs are in a position, and with adequate qualifications, to anticipate pandemic development, and could serve as a bottom-up pathway for expressing morbidity and clinical situations to health agencies. ", doi="10.2196/30217", url="https://www.jmir.org/2021/10/e30217", url="http://www.ncbi.nlm.nih.gov/pubmed/34550899" } @Article{info:doi/10.2196/28689, author="Bragg, Marie and Lutfeali, Samina and Greene, Tenay and Osterman, Jessica and Dalton, Madeline", title="How Food Marketing on Instagram Shapes Adolescents' Food Preferences: Online Randomized Trial", journal="J Med Internet Res", year="2021", month="Oct", day="22", volume="23", number="10", pages="e28689", keywords="food marketing", keywords="traditional media", keywords="social media", keywords="adolescents", keywords="Instagram", abstract="Background: Worldwide obesity rates have prompted 16 countries to enact policies to reduce children's exposure to unhealthy food marketing, but few policies address online advertising practices or protect adolescents from being targeted. Given adolescents spend so much time online, it is critical to understand how persuasive Instagram food advertisements (ads) are compared with traditional food ads. To strengthen online food marketing policies, more evidence is needed on whether social media ads are more persuasive than other types of ads in shaping adolescents' preferences. Objective: This study examined whether adolescents could identify food companies' Instagram posts as ads, and the extent to which Instagram versus traditional food ads shape adolescents' preferences. Methods: In Part 1, participants aged 13-17 years (N=832) viewed 8 pairs of ads and were asked to identify which ads originated from Instagram. One ad in each pair was selected from traditional sources (eg, print; online banner ad), and the other ad was selected from Instagram, but we removed the Instagram frame---which includes the logo, comments, and ``likes.'' In Part 2, participants were randomized to rate food ads that ostensibly originated from (1) Instagram (ie, we photoshopped the Instagram frame onto ads); or (2) traditional sources. Unbeknownst to participants, half of the ads in their condition originated from Instagram and half originated from traditional sources. Results: In Part 1, adolescents performed worse than chance when asked to identify Instagram ads (P<.001). In Part 2, there were no differences on 4 of 5 outcomes in the ``labeled ad condition.'' In the ``unlabeled ad condition,'' however, they preferred Instagram ads to traditional ads on 3 of 5 outcomes (ie, trendiness, P=.001; artistic appeal, P=.001; likeability, P=.001). Conclusions: Adolescents incorrectly identified traditional ads as Instagram posts, suggesting the artistic appearance of social media ads may not be perceived as marketing. Further, the mere presence of Instagram features caused adolescents to rate food ads more positively than ads without Instagram features. ", doi="10.2196/28689", url="https://www.jmir.org/2021/10/e28689", url="http://www.ncbi.nlm.nih.gov/pubmed/34677136" } @Article{info:doi/10.2196/28262, author="Moon, C. Khatiya and Van Meter, R. Anna and Kirschenbaum, A. Michael and Ali, Asra and Kane, M. John and Birnbaum, L. Michael", title="Internet Search Activity of Young People With Mood Disorders Who Are Hospitalized for Suicidal Thoughts and Behaviors: Qualitative Study of Google Search Activity", journal="JMIR Ment Health", year="2021", month="Oct", day="22", volume="8", number="10", pages="e28262", keywords="suicide", keywords="mood disorders", keywords="depression", keywords="internet", keywords="search engine", keywords="Google search", keywords="digital health", keywords="mobile health", keywords="adolescent", keywords="young adult", abstract="Background: Little is known about the internet search activity of people with suicidal thoughts and behaviors (STBs). This data source has the potential to inform both clinical and public health efforts, such as suicide risk assessment and prevention. Objective: We aimed to evaluate the internet search activity of suicidal young people to find evidence of suicidal ideation and behavioral health--related content. Methods: Individuals aged between 15 and 30 years (N=43) with mood disorders who were hospitalized for STBs provided access to their internet search history. Searches that were conducted in the 3-month period prior to hospitalization were extracted and manually evaluated for search themes related to suicide and behavioral health. Results: A majority (27/43, 63\%) of participants conducted suicide-related searches. Participants searched for information that exactly matched their planned or chosen method of attempting suicide in 21\% (9/43) of cases. Suicide-related search queries also included unusual suicide methods and references to suicide in popular culture. A majority of participants (33/43, 77\%) had queries related to help-seeking themes, including how to find inpatient and outpatient behavioral health care. Queries related to mood and anxiety symptoms were found among 44\% (19/43) of participants and included references to panic disorder, the inability to focus, feelings of loneliness, and despair. Queries related to substance use were found among 44\% (19/43) of participants. Queries related to traumatic experiences were present among 33\% (14/43) of participants. Few participants conducted searches for crisis hotlines (n=3). Conclusions: Individuals search the internet for information related to suicide prior to hospitalization for STBs. The improved understanding of the search activity of suicidal people could inform outreach, assessment, and intervention strategies for people at risk. Access to search data may also benefit the ongoing care of suicidal patients. ", doi="10.2196/28262", url="https://mental.jmir.org/2021/10/e28262", url="http://www.ncbi.nlm.nih.gov/pubmed/34677139" } @Article{info:doi/10.2196/30765, author="Monselise, Michal and Chang, Chia-Hsuan and Ferreira, Gustavo and Yang, Rita and Yang, C. Christopher", title="Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis", journal="J Med Internet Res", year="2021", month="Oct", day="21", volume="23", number="10", pages="e30765", keywords="health care informatics", keywords="topic detection", keywords="unsupervised sentiment analysis", keywords="COVID-19", keywords="vaccine hesitancy", keywords="sentiment", keywords="concern", keywords="vaccine", keywords="social media", keywords="trend", keywords="trust", keywords="health information", keywords="Twitter", keywords="discussion", keywords="communication", keywords="hesitancy", keywords="emotion", keywords="fear", abstract="Background: As a number of vaccines for COVID-19 are given emergency use authorization by local health agencies and are being administered in multiple countries, it is crucial to gain public trust in these vaccines to ensure herd immunity through vaccination. One way to gauge public sentiment regarding vaccines for the goal of increasing vaccination rates is by analyzing social media such as Twitter. Objective: The goal of this research was to understand public sentiment toward COVID-19 vaccines by analyzing discussions about the vaccines on social media for a period of 60 days when the vaccines were started in the United States. Using the combination of topic detection and sentiment analysis, we identified different types of concerns regarding vaccines that were expressed by different groups of the public on social media. Methods: To better understand public sentiment, we collected tweets for exactly 60 days starting from December 16, 2020 that contained hashtags or keywords related to COVID-19 vaccines. We detected and analyzed different topics of discussion of these tweets as well as their emotional content. Vaccine topics were identified by nonnegative matrix factorization, and emotional content was identified using the Valence Aware Dictionary and sEntiment Reasoner sentiment analysis library as well as by using sentence bidirectional encoder representations from transformer embeddings and comparing the embedding to different emotions using cosine similarity. Results: After removing all duplicates and retweets, 7,948,886 tweets were collected during the 60-day time period. Topic modeling resulted in 50 topics; of those, we selected 12 topics with the highest volume of tweets for analysis. Administration and access to vaccines were some of the major concerns of the public. Additionally, we classified the tweets in each topic into 1 of the 5 emotions and found fear to be the leading emotion in the tweets, followed by joy. Conclusions: This research focused not only on negative emotions that may have led to vaccine hesitancy but also on positive emotions toward the vaccine. By identifying both positive and negative emotions, we were able to identify the public's response to the vaccines overall and to news events related to the vaccines. These results are useful for developing plans for disseminating authoritative health information and for better communication to build understanding and trust. ", doi="10.2196/30765", url="https://www.jmir.org/2021/10/e30765", url="http://www.ncbi.nlm.nih.gov/pubmed/34581682" } @Article{info:doi/10.2196/27714, author="Lavertu, Adam and Hamamsy, Tymor and Altman, B. Russ", title="Quantifying the Severity of Adverse Drug Reactions Using Social Media: Network Analysis", journal="J Med Internet Res", year="2021", month="Oct", day="21", volume="23", number="10", pages="e27714", keywords="social media for health", keywords="pharmacovigilance", keywords="adverse drug reactions", keywords="machine learning", keywords="network analysis", keywords="word embeddings", keywords="drug safety", keywords="social media", abstract="Background: Adverse drug reactions (ADRs) affect the health of hundreds of thousands of individuals annually in the United States, with associated costs of hundreds of billions of dollars. The monitoring and analysis of the severity of ADRs is limited by the current qualitative and categorical systems of severity classification. Previous efforts have generated quantitative estimates for a subset of ADRs but were limited in scope because of the time and costs associated with the efforts. Objective: The aim of this study is to increase the number of ADRs for which there are quantitative severity estimates while improving the quality of these severity estimates. Methods: We present a semisupervised approach that estimates ADR severity by using social media word embeddings to construct a lexical network of ADRs and perform label propagation. We used this method to estimate the severity of 28,113 ADRs, representing 12,198 unique ADR concepts from the Medical Dictionary for Regulatory Activities. Results: Our Severity of Adverse Events Derived from Reddit (SAEDR) scores have good correlations with real-world outcomes. The SAEDR scores had Spearman correlations of 0.595, 0.633, and ?0.748 for death, serious outcome, and no outcome, respectively, with ADR case outcomes in the Food and Drug Administration Adverse Event Reporting System. We investigated different methods for defining initial seed term sets and evaluated their impact on the severity estimates. We analyzed severity distributions for ADRs based on their appearance in boxed warning drug label sections, as well as for ADRs with sex-specific associations. We found that ADRs discovered in the postmarketing period had significantly greater severity than those discovered during the clinical trial (P<.001). We created quantitative drug-risk profile (DRIP) scores for 968 drugs that had a Spearman correlation of 0.377 with drugs ranked by the Food and Drug Administration Adverse Event Reporting System cases resulting in death, where the given drug was the primary suspect. Conclusions: Our SAEDR and DRIP scores are well correlated with the real-world outcomes of the entities they represent and have demonstrated utility in pharmacovigilance research. We make the SAEDR scores for 12,198 ADRs and the DRIP scores for 968 drugs publicly available to enable more quantitative analysis of pharmacovigilance data. ", doi="10.2196/27714", url="https://www.jmir.org/2021/10/e27714", url="http://www.ncbi.nlm.nih.gov/pubmed/34673524" } @Article{info:doi/10.2196/30695, author="Soltys, Coyle Frank and Spilo, Kimi and Politi, C. Mary", title="The Content and Quality of Publicly Available Information About Congenital Diaphragmatic Hernia: Descriptive Study", journal="JMIR Pediatr Parent", year="2021", month="Oct", day="19", volume="4", number="4", pages="e30695", keywords="congenital diaphragmatic hernia", keywords="prenatal counseling", keywords="fetal care", keywords="online information", keywords="parental decision making", abstract="Background: Congenital diaphragmatic hernia (CDH) diagnosis in an infant is distressing for parents. Parents often feel unable to absorb the complexities of CDH during prenatal consultations and use the internet to supplement their knowledge and decision making. Objective: We aimed to examine the content and quality of publicly available, internet-based CDH information. Methods: We conducted internet searches across 2 popular search engines (Google and Bing). Websites were included if they contained CDH information and were publicly available. We developed a coding instrument to evaluate websites. Two coders (FS and KS) were trained, achieved interrater reliability, and rated remaining websites independently. Descriptive statistics were performed. Results: Searches yielded 520 websites; 91 met inclusion criteria and were analyzed. Most websites provided basic CDH information including describing the defect (86/91, 95\%), need for neonatal intensive care (77/91, 85\%), and surgical correction (79/91, 87\%). Few mentioned palliative care, decisions about pregnancy termination (13/91, 14\%), or support resources (21/91, 23\%). Conclusions: Findings highlight the variability of information about CDH on the internet. Clinicians should work to develop or identify reliable, comprehensive information about CDH to support parents. ", doi="10.2196/30695", url="https://pediatrics.jmir.org/2021/4/e30695", url="http://www.ncbi.nlm.nih.gov/pubmed/34665147" } @Article{info:doi/10.2196/19307, author="Fang, Yang and Shepherd, A. Thomas and Smith, E. Helen", title="Examining the Trends in Online Health Information--Seeking Behavior About Chronic Obstructive Pulmonary Disease in Singapore: Analysis of Data From Google Trends and the Global Burden of Disease Study", journal="J Med Internet Res", year="2021", month="Oct", day="18", volume="23", number="10", pages="e19307", keywords="online health information seeking", keywords="infodemiology", keywords="Google Trends", keywords="Global Burden of Disease study", keywords="chronic obstructive pulmonary disease", keywords="respiratory health", abstract="Background: Chronic obstructive pulmonary disease (COPD) is the third leading cause of death globally, and timely health care seeking is imperative for its prevention, early detection, and management. While online health information--seeking behavior (OHISB) is increasingly popular due to widespread internet connectivity, little is known about how OHISB for COPD has changed in comparison with the COPD disease burden, particularly at a country-specific level. Objective: This study aimed to examine the trends in OHISB for COPD and how that compared with the estimates of COPD disease burden in Singapore, a highly wired country with a steadily increasing COPD disease burden. Methods: To examine the trends in OHISB for COPD, we performed Prais-Winsten regression analyses on monthly search volume data for COPD from January 2004 to June 2020 downloaded from Google Trends. We then conducted cross-correlational analyses to examine the relationship between annualized search volume on COPD topics and estimates of COPD morbidity and mortality reported in the Global Burden of Disease study from 2004 to 2017. Results: From 2004 to 2020, the trend in COPD search volume was curvilinear ($\beta$=1.69, t194=6.64, P<.001), with a slope change around the end of 2006. There was a negative linear trend ($\beta$=--0.53, t33=--3.57, P=.001) from 2004 to 2006 and a positive linear trend ($\beta$=0.51, t159=7.43, P<.001) from 2007 to 2020. Cross-correlation analyses revealed positive associations between COPD search volume and COPD disease burden indicators: positive correlations between search volume and prevalence, incidence, years living with disability (YLD) at lag 0, and positive correlations between search volume and prevalence, YLD at lag 1. Conclusions: Google search volume on COPD increased from 2007 to 2020; this trend correlated with the upward trajectory of several COPD morbidity estimates, suggesting increasing engagement in OHISB for COPD in Singapore. These findings underscore the importance of making high-quality, web-based information accessible to the public, particularly COPD patients and their carers. ", doi="10.2196/19307", url="https://www.jmir.org/2021/10/e19307", url="http://www.ncbi.nlm.nih.gov/pubmed/34661539" } @Article{info:doi/10.2196/32233, author="Rovetta, Alessandro and Castaldo, Lucia", title="Influence of Mass Media on Italian Web Users During the COVID-19 Pandemic: Infodemiological Analysis", journal="JMIRx Med", year="2021", month="Oct", day="18", volume="2", number="4", pages="e32233", keywords="COVID-19", keywords="Google Trends", keywords="infodemiology", keywords="infoveillance", keywords="infodemic", keywords="media coverage", keywords="mass media influence", keywords="mass media", keywords="social media", abstract="Background: Concurrently with the COVID-19 pandemic, the world has been facing a growing infodemic, which has caused severe damage to economic and health systems and has often compromised the effectiveness of infection containment regulations. Although this infodemic has spread mainly through social media, there are numerous occasions on which mass media outlets have shared dangerous information, giving resonance to statements without a scientific basis. For these reasons, infoveillance and infodemiology methods are increasingly exploited to monitor information traffic on the web and make epidemiological predictions. Objective: The purpose of this paper is to estimate the impact of Italian mass media on users' web searches to understand the role of press and television channels in both the infodemic and the interest of Italian netizens in COVID-19. Methods: We collected the headlines published from January 2020 to March 2021 containing specific COVID-19--related keywords published on PubMed, Google, the Italian Ministry of Health website, and the most-read newspapers in Italy. We evaluated the percentages of infodemic terms on these platforms. Through Google Trends, we searched for cross-correlations between newspaper headlines and COVID-19--related web searches. Finally, we analyzed the web interest in infodemic content posted on YouTube. Results: During the first wave of COVID-19, the Italian press preferred to draw on infodemic terms (rate of adoption: 1.6\%-6.3\%) and moderately infodemic terms (rate of adoption: 88\%-94\%), while scientific sources favored the correct names (rate of adoption: 65\%-88\%). The correlational analysis showed that the press heavily influenced users in adopting terms to identify the novel coronavirus (cross-correlations of ?0.74 to ?0.89, P value <.001; maximum lag=1 day). The use of scientific denominations by the press reached acceptable values only during the third wave (approximately 80\%, except for the television services Rai and Mediaset). Web queries about COVID-19 symptoms also appeared to be influenced by the press (best average correlation=0.92, P<.007). Furthermore, web users showed pronounced interest in YouTube videos of an infodemic nature. Finally, the press gave resonance to serious ``fake news'' on COVID-19, which caused pronounced spikes of interest from web users. Conclusions: Our results suggest that the Italian mass media have played a decisive role in spreading the COVID-19 infodemic and addressing netizens' web interest, thus favoring the adoption of terms that are unsuitable for identifying COVID-19. Therefore, the directors of news channels and newspapers should be more cautious, and government dissemination agencies should exert more control over such news stories. ", doi="10.2196/32233", url="https://med.jmirx.org/2021/4/e32233", url="http://www.ncbi.nlm.nih.gov/pubmed/34842858" } @Article{info:doi/10.2196/30756, author="Bedford-Petersen, Cianna and Weston, J. Sara", title="Mapping Individual Differences on the Internet: Case Study of the Type 1 Diabetes Community", journal="JMIR Diabetes", year="2021", month="Oct", day="15", volume="6", number="4", pages="e30756", keywords="type 1 diabetes", keywords="diabetes community", keywords="social media", keywords="Twitter", keywords="natural language processing", keywords="social network analysis", keywords="Latent Dirichlet Allocation", keywords="diabetes", keywords="data scraping", keywords="sentiment analysis", abstract="Background: Social media platforms, such as Twitter, are increasingly popular among communities of people with chronic conditions, including those with type 1 diabetes (T1D). There is some evidence that social media confers emotional and health-related benefits to people with T1D, including emotional support and practical information regarding health maintenance. Research on social media has primarily relied on self-reports of web-based behavior and qualitative assessment of web-based content, which can be expensive and time-consuming. Meanwhile, recent advances in natural language processing have allowed for large-scale assessment of social media behavior. Objective: This study attempts to document the major themes of Twitter posts using a natural language processing method to identify topics of interest in the T1D web-based community. We also seek to map social relations on Twitter as they relate to these topics of interest, to determine whether Twitter users in the T1D community post in ``echo chambers,'' which reflect their own topics back to them, or whether users typically see a mix of topics on the internet. Methods: Through Twitter scraping, we gathered a data set of 691,691 tweets from 8557 accounts, spanning a date range from 2008 to 2020, which includes people with T1D, their caregivers, health practitioners, and advocates. Tweet content was analyzed for sentiment and topic, using Latent Dirichlet Allocation. We used social network analysis to examine the degree to which identified topics are siloed within specific groups or disseminated through the broader T1D web-based community. Results: Tweets were, on average, positive in sentiment. Through topic modeling, we identified 6 broad-bandwidth topics, ranging from clinical to advocacy to daily management to emotional health, which can inform researchers and practitioners interested in the needs of people with T1D. These analyses also replicate prior work using machine learning methods to map social behavior on the internet. We extend these results through social network analysis, indicating that users are likely to see a mix of these topics discussed by the accounts they follow. Conclusions: Twitter communities are sources of information for people with T1D and members related to that community. Topics identified reveal key concerns of the T1D community and may be useful to practitioners and researchers alike. The methods used are efficient (low cost) while providing researchers with enormous amounts of data. We provide code to facilitate the use of these methods with other populations. ", doi="10.2196/30756", url="https://diabetes.jmir.org/2021/4/e30756", url="http://www.ncbi.nlm.nih.gov/pubmed/34652277" } @Article{info:doi/10.2196/29809, author="Clavier, Thomas and Occhiali, Emilie and Demailly, Zo{\'e} and Comp{\`e}re, Vincent and Veber, Benoit and Selim, Jean and Besnier, Emmanuel", title="The Association Between Professional Accounts on Social Networks Twitter and ResearchGate and the Number of Scientific Publications and Citations Among Anesthesia Researchers: Observational Study", journal="J Med Internet Res", year="2021", month="Oct", day="15", volume="23", number="10", pages="e29809", keywords="social network", keywords="anesthesia", keywords="publication", keywords="Twitter", keywords="ResearchGate", keywords="citation", keywords="social media", keywords="academic", keywords="researcher", keywords="bibliometrics", keywords="research output", abstract="Background: Social networks are now essential tools for promoting research and researchers. However, there is no study investigating the link between presence or not on professional social networks and scientific publication or citation for a given researcher. Objective: The objective of this study was to study the link between professional presence on social networks and scientific publications/citations among anesthesia researchers. Methods: We included all the French full professors and associate professors of anesthesia. We analyzed their presence on the social networks Twitter (professional account with ?1 tweet over the 6 previous months) and ResearchGate. We extracted their bibliometric parameters for the 2016-2020 period via the Web of Science Core Collection (Clarivate Analytics) database in the Science Citation Index-Expanded index. Results: A total of 162 researchers were analyzed; 42 (25.9\%) had an active Twitter account and 110 (67.9\%) a ResearchGate account. There was no difference between associate professors and full professors regarding active presence on Twitter (8/23 [35\%] vs. 34/139 [24.5\%], respectively; P=.31) or ResearchGate (15/23 [65\%] vs. 95/139 [68.3\%], respectively; P=.81). Researchers with an active Twitter account (median [IQR]) had more scientific publications (45 [28-61] vs. 26 [12-41]; P<.001), a higher h-index (12 [8-16] vs. 8 [5-11]; P<.001), a higher number of citations per publication (12.54 [9.65-21.8] vs. 10.63 [5.67-16.10]; P=.01), and a higher number of citations (563 [321-896] vs. 263 [105-484]; P<.001). Researchers with a ResearchGate account (median [IQR]) had more scientific publications (33 [17-47] vs. 26 [9-43]; P=.03) and a higher h-index (9 [6-13] vs. 8 [3-11]; P=.03). There was no difference between researchers with a ResearchGate account and those without it concerning the number of citations per publication and overall number of citations. In multivariate analysis including sex, academic status, and presence on social networks, the presence on Twitter was associated with the number of publications ($\beta$=20.2; P<.001), the number of citations ($\beta$=494.5; P<.001), and the h-index ($\beta$=4.5; P<.001). Conclusions: Among French anesthesia researchers, an active presence on Twitter is associated with higher scientific publication and citations. ", doi="10.2196/29809", url="https://www.jmir.org/2021/10/e29809", url="http://www.ncbi.nlm.nih.gov/pubmed/34652279" } @Article{info:doi/10.2196/32425, author="Agley, Jon and Xiao, Yunyu and Thompson, E. Esi and Chen, Xiwei and Golzarri-Arroyo, Lilian", title="Intervening on Trust in Science to Reduce Belief in COVID-19 Misinformation and Increase COVID-19 Preventive Behavioral Intentions: Randomized Controlled Trial", journal="J Med Internet Res", year="2021", month="Oct", day="14", volume="23", number="10", pages="e32425", keywords="infodemic", keywords="misinformation", keywords="trust in science", keywords="COVID-19", keywords="RCT", keywords="randomized controlled trial", abstract="Background: Trust in science meaningfully contributes to our understanding of people's belief in misinformation and their intentions to take actions to prevent COVID-19. However, no experimental research has sought to intervene on this variable to develop a scalable response to the COVID-19 infodemic. Objective: Our study examined whether brief exposure to an infographic about the scientific process might increase trust in science and thereby affect belief in misinformation and intention to take preventive actions for COVID-19. Methods: This two-arm, parallel-group, randomized controlled trial aimed to recruit a US representative sample of 1000 adults by age, race/ethnicity, and gender using the Prolific platform. Participants were randomly assigned to view either an intervention infographic about the scientific process or a control infographic. The intervention infographic was designed through a separate pilot study. Primary outcomes were trust in science, COVID-19 narrative belief profile, and COVID-19 preventive behavioral intentions. We also collected 12 covariates and incorporated them into all analyses. All outcomes were collected using web-based assessment. Results: From January 22, 2021 to January 24, 2021, 1017 participants completed the study. The intervention slightly improved trust in science (difference-in-difference 0.03, SE 0.01, t1000=2.16, P=.031). No direct intervention effect was observed on belief profile membership, but there was some evidence of an indirect intervention effect mediated by trust in science (adjusted odds ratio 1.06, SE 0.03, 95\% CI 1.00-1.12, z=2.01, P=.045) on membership in the ``scientific'' profile compared with the others. No direct nor indirect effects on preventive behaviors were observed. Conclusions: Briefly viewing an infographic about science appeared to cause a small aggregate increase in trust in science, which may have, in turn, reduced the believability of COVID-19 misinformation. The effect sizes were small but commensurate with our 60-second, highly scalable intervention approach. Researchers should study the potential for truthful messaging about how science works to serve as misinformation inoculation and test how best to do so. Trial Registration: NCT04557241; https://clinicaltrials.gov/ct2/show/NCT04557241 International Registered Report Identifier (IRRID): RR2-10.2196/24383 ", doi="10.2196/32425", url="https://www.jmir.org/2021/10/e32425", url="http://www.ncbi.nlm.nih.gov/pubmed/34581678" } @Article{info:doi/10.2196/29584, author="Kummervold, E. Per and Martin, Sam and Dada, Sara and Kilich, Eliz and Denny, Chermain and Paterson, Pauline and Larson, J. Heidi", title="Categorizing Vaccine Confidence With a Transformer-Based Machine Learning Model: Analysis of Nuances of Vaccine Sentiment in Twitter Discourse", journal="JMIR Med Inform", year="2021", month="Oct", day="8", volume="9", number="10", pages="e29584", keywords="computer science", keywords="information technology", keywords="public health", keywords="health humanities", keywords="vaccines", keywords="machine learning", abstract="Background: Social media has become an established platform for individuals to discuss and debate various subjects, including vaccination. With growing conversations on the web and less than desired maternal vaccination uptake rates, these conversations could provide useful insights to inform future interventions. However, owing to the volume of web-based posts, manual annotation and analysis are difficult and time consuming. Automated processes for this type of analysis, such as natural language processing, have faced challenges in extracting complex stances such as attitudes toward vaccination from large amounts of text. Objective: The aim of this study is to build upon recent advances in transposer-based machine learning methods and test whether transformer-based machine learning could be used as a tool to assess the stance expressed in social media posts toward vaccination during pregnancy. Methods: A total of 16,604 tweets posted between November 1, 2018, and April 30, 2019, were selected using keyword searches related to maternal vaccination. After excluding irrelevant tweets, the remaining tweets were coded by 3 individual researchers into the categories Promotional, Discouraging, Ambiguous, and Neutral or No Stance. After creating a final data set of 2722 unique tweets, multiple machine learning techniques were trained on a part of this data set and then tested and compared with the human annotators. Results: We found the accuracy of the machine learning techniques to be 81.8\% (F score=0.78) compared with the agreed score among the 3 annotators. For comparison, the accuracies of the individual annotators compared with the final score were 83.3\%, 77.9\%, and 77.5\%. Conclusions: This study demonstrates that we are able to achieve close to the same accuracy in categorizing tweets using our machine learning models as could be expected from a single human coder. The potential to use this automated process, which is reliable and accurate, could free valuable time and resources for conducting this analysis, in addition to informing potentially effective and necessary interventions. ", doi="10.2196/29584", url="https://medinform.jmir.org/2021/10/e29584", url="http://www.ncbi.nlm.nih.gov/pubmed/34623312" } @Article{info:doi/10.2196/27472, author="Heyerdahl, W. Leonardo and Lana, Benedetta and Giles-Vernick, Tamara", title="The Impact of the Online COVID-19 Infodemic on French Red Cross Actors' Field Engagement and Protective Behaviors: Mixed Methods Study", journal="JMIR Infodemiology", year="2021", month="Oct", day="6", volume="1", number="1", pages="e27472", keywords="COVID-19", keywords="infodemics", keywords="social listening", keywords="epidemics", keywords="medical anthropology", keywords="nongovernmental organizations", abstract="Background: The COVID-19 pandemic has been widely described as an infodemic, an excess of rapidly circulating information in social and traditional media in which some information may be erroneous, contradictory, or inaccurate. One key theme cutting across many infodemic analyses is that it stymies users' capacities to identify appropriate information and guidelines, encourages them to take inappropriate or even harmful actions, and should be managed through multiple transdisciplinary approaches. Yet, investigations demonstrating how the COVID-19 information ecosystem influences complex public decision making and behavior offline are relatively few. Objective: The aim of this study was to investigate whether information reported through the social media channel Twitter, linked articles and websites, and selected traditional media affected the risk perception, engagement in field activities, and protective behaviors of French Red Cross (FRC) volunteers and health workers in the Paris region of France from June to October 2020. Methods: We used a hybrid approach that blended online and offline data. We tracked daily Twitter discussions and selected traditional media in France for 7 months, qualitatively evaluating COVID-19 claims and debates about nonpharmaceutical protective measures. We conducted 24 semistructured interviews with FRC workers and volunteers. Results: Social and traditional media debates about viral risks and nonpharmaceutical interventions fanned anxieties among FRC volunteers and workers. Decisions to continue conducting FRC field activities and daily protective practices were also influenced by other factors unrelated to the infodemic: familial and social obligations, gender expectations, financial pressures, FRC rules and communications, state regulations, and relationships with coworkers. Some respondents developed strategies for ``tuning out'' social and traditional media. Conclusions: This study suggests that during the COVID-19 pandemic, the information ecosystem may be just one among multiple influences on one group's offline perceptions and behavior. Measures to address users who have disengaged from online sources of health information and who rely on social relationships to obtain information are needed. Tuning out can potentially lead to less informed decision making, leading to worse health outcomes. ", doi="10.2196/27472", url="https://infodemiology.jmir.org/2021/1/e27472", url="http://www.ncbi.nlm.nih.gov/pubmed/34661065" } @Article{info:doi/10.2196/28957, author="Sharp, J. Kendall and Vitagliano, A. Julia and Weitzman, R. Elissa and Fitzgerald, Susan and Dahlberg, E. Suzanne and Austin, Bryn S.", title="Peer-to-Peer Social Media Communication About Dietary Supplements Used for Weight Loss and Sports Performance Among Military Personnel: Pilot Content Analysis of 11 Years of Posts on Reddit", journal="JMIR Form Res", year="2021", month="Oct", day="4", volume="5", number="10", pages="e28957", keywords="dietary supplements", keywords="social media", keywords="Reddit", keywords="OPSS", abstract="Background: Over 60\% of military personnel in the United States currently use dietary supplements. Two types of dietary supplements, weight loss and sports performance (WLSP) supplements, are commonly used by military personnel despite the associated serious adverse effects such as dehydration and stroke. Objective: To understand peer-to-peer communication about WLSP supplements among military personnel, we conducted a pilot study using the social media website, Reddit. Methods: A total of 64 relevant posts and 243 comments from 2009 to 2019 were collected from 6 military subreddits. The posts were coded for year of posting, subreddit, and content consistent with the following themes: resources about supplement safety and regulation, discernability of supplement use through drug testing, serious adverse effects, brand names or identifiers, and reasons for supplement use. Results: A primary concern posted by personnel who used supplements was uncertainty about the supplements that were not detectable on a drug test. Supplements to improve workout performance were the most frequently used. Conclusions: Our pilot study suggests that military personnel may seek out peer advice about WLSP supplements on Reddit and spread misinformation about the safety and effectiveness of these products through this platform. Future directions for the monitoring of WLSP supplement use in military personnel are discussed. ", doi="10.2196/28957", url="https://formative.jmir.org/2021/10/e28957", url="http://www.ncbi.nlm.nih.gov/pubmed/34605769" } @Article{info:doi/10.2196/29025, author="Usher, Kim and Durkin, Joanne and Martin, Sam and Vanderslott, Samantha and Vindrola-Padros, Cecilia and Usher, Luke and Jackson, Debra", title="Public Sentiment and Discourse on Domestic Violence During the COVID-19 Pandemic in Australia: Analysis of Social Media Posts", journal="J Med Internet Res", year="2021", month="Oct", day="1", volume="23", number="10", pages="e29025", keywords="COVID-19", keywords="domestic violence", keywords="social media", keywords="Twitter", keywords="sentiment analysis", keywords="discourse analysis", keywords="keyword analysis", keywords="pandemic", keywords="sentiment", keywords="public health", keywords="public expression", abstract="Background: Measuring public response during COVID-19 is an important way of ensuring the suitability and effectiveness of epidemic response efforts. An analysis of social media provides an approximation of public sentiment during an emergency like the current pandemic. The measures introduced across the globe to help curtail the spread of the coronavirus have led to the development of a situation labeled as a ``perfect storm,'' triggering a wave of domestic violence. As people use social media to communicate their experiences, analyzing public discourse and sentiment on social platforms offers a way to understand concerns and issues related to domestic violence during the COVID-19 pandemic. Objective: This study was based on an analysis of public discourse and sentiment related to domestic violence during the stay-at-home periods of the COVID-19 pandemic in Australia in 2020. It aimed to understand the more personal self-reported experiences, emotions, and reactions toward domestic violence that were not always classified or collected by official public bodies during the pandemic. Methods: We searched social media and news posts in Australia using key terms related to domestic violence and COVID-19 during 2020 via digital analytics tools to determine sentiments related to domestic violence during this period. Results: The study showed that the use of sentiment and discourse analysis to assess social media data is useful in measuring the public expression of feelings and sharing of resources in relation to the otherwise personal experience of domestic violence. There were a total of 63,800 posts across social media and news media. Within these posts, our analysis found that domestic violence was mentioned an average of 179 times a day. There were 30,100 tweets, 31,700 news reports, 1500 blog posts, 548 forum posts, and 7 comments (posted on news and blog websites). Negative or neutral sentiment centered on the sharp rise in domestic violence during different lockdown periods of the 2020 pandemic, and neutral and positive sentiments centered on praise for efforts that raised awareness of domestic violence as well as the positive actions of domestic violence charities and support groups in their campaigns. There were calls for a positive and proactive handling (rather than a mishandling) of the pandemic, and results indicated a high level of public discontent related to the rising rates of domestic violence and the lack of services during the pandemic. Conclusions: This study provided a timely understanding of public sentiment related to domestic violence during the COVID-19 lockdown periods in Australia using social media analysis. Social media represents an important avenue for the dissemination of information; posts can be widely dispersed and easily accessed by a range of different communities who are often difficult to reach. An improved understanding of these issues is important for future policy direction. Heightened awareness of this could help agencies tailor and target messaging to maximize impact. ", doi="10.2196/29025", url="https://www.jmir.org/2021/10/e29025", url="http://www.ncbi.nlm.nih.gov/pubmed/34519659" } @Article{info:doi/10.2196/28765, author="Dysthe, K. Kim and Haavet, R. Ole and R{\o}ssberg, I. Jan and Brandtzaeg, B. Petter and F{\o}lstad, Asbj{\o}rn and Klovning, Atle", title="Finding Relevant Psychoeducation Content for Adolescents Experiencing Symptoms of Depression: Content Analysis of User-Generated Online Texts", journal="J Med Internet Res", year="2021", month="Sep", day="30", volume="23", number="9", pages="e28765", keywords="adolescent", keywords="depression", keywords="internet", keywords="education", keywords="preventive psychiatry", keywords="early medical intervention", keywords="self-report", keywords="psychoeducation", keywords="information content", keywords="online", keywords="digital health", keywords="e-health", abstract="Background: Symptoms of depression are frequent in youth and may develop into more severe mood disorders, suggesting interventions should take place during adolescence. However, young people tend not to share mental problems with friends, family, caregivers, or professionals. Many receive misleading information when searching the internet. Among several attempts to create mental health services for adolescents, technological information platforms based on psychoeducation show promising results. Such development rests on established theories and therapeutic models. To fulfill the therapeutic potential of psychoeducation in health technologies, we lack data-driven research on young peoples' demand for information about depression. Objective: Our objective is to gain knowledge about what information is relevant to adolescents with symptoms of depression. From this knowledge, we can develop a population-specific psychoeducation for use in different technology platforms. Methods: We conducted a qualitative, constructivist-oriented content analysis of questions submitted by adolescents aged 16-20 years to an online public information service. A sample of 100 posts containing questions on depression were randomly selected from a total of 870. For analysis, we developed an a priori codebook from the main information topics of existing psychoeducational programs on youth depression. The distribution of topic prevalence in the total volume of posts containing questions on depression was calculated. Results: With a 95\% confidence level and a {\textpm}9.2\% margin of error, the distribution analysis revealed the following categories to be the most prevalent among adolescents seeking advice about depression: self-management (33\%, 61/180), etiology (20\%, 36/180), and therapy (20\%, 36/180). Self-management concerned subcategories on coping in general and how to open to friends, family, and caregivers. The therapy topic concerned therapy options, prognosis, where to seek help, and how to open up to a professional. We also found young people dichotomizing therapy and self-management as opposite entities. The etiology topic concerned stressors and risk factors. The diagnosis category was less frequently referred to (9\%, 17/180). Conclusions: Self-management, etiology, and therapy are the most prevalent categories among adolescents seeking advice about depression. Young people also dichotomize therapy and self-management as opposite entities. Future research should focus on measures to promote self-management, measures to stimulate expectations of self-efficacy, information about etiology, and information about diagnosis to improve self-monitoring skills, enhancing relapse prevention. ", doi="10.2196/28765", url="https://www.jmir.org/2021/9/e28765", url="http://www.ncbi.nlm.nih.gov/pubmed/34591021" } @Article{info:doi/10.2196/20975, author="Sager, A. Monique and Kashyap, M. Aditya and Tamminga, Mila and Ravoori, Sadhana and Callison-Burch, Christopher and Lipoff, B. Jules", title="Identifying and Responding to Health Misinformation on Reddit Dermatology Forums With Artificially Intelligent Bots Using Natural Language Processing: Design and Evaluation Study", journal="JMIR Dermatol", year="2021", month="Sep", day="30", volume="4", number="2", pages="e20975", keywords="bots", keywords="natural language processing", keywords="artificial intelligence", keywords="Reddit, medical misinformation", keywords="health misinformation", keywords="detecting misinformation", keywords="dermatology", keywords="misinformation", abstract="Background: Reddit, the fifth most popular website in the United States, boasts a large and engaged user base on its dermatology forums where users crowdsource free medical opinions. Unfortunately, much of the advice provided is unvalidated and could lead to the provision of inappropriate care. Initial testing has revealed that artificially intelligent bots can detect misinformation regarding tanning and essential oils on Reddit dermatology forums and may be able to produce responses to posts containing misinformation. Objective: To analyze the ability of bots to find and respond to tanning and essential oil--related health misinformation on Reddit's dermatology forums in a controlled test environment. Methods: Using natural language processing techniques, we trained bots to target misinformation, using relevant keywords and to post prefabricated responses. By evaluating different model architectures across a held-out test set, we compared performances. Results: Our models yielded data test accuracies ranging 95\%-100\%, with a Bidirectional Encoder Representations from Transformers (BERT) fine-tuned model resulting in the highest level of test accuracy. Bots were then able to post corrective prefabricated responses to misinformation in a test environment. Conclusions: Using a limited data set, bots accurately detected examples of health misinformation within Reddit dermatology forums. Given that these bots can then post prefabricated responses, this technique may allow for interception of misinformation. Providing correct information does not mean that users will be receptive or find such interventions persuasive. Further studies should investigate this strategy's effectiveness to inform future deployment of bots as a technique in combating health misinformation. ", doi="10.2196/20975", url="https://derma.jmir.org/2021/2/e20975", url="http://www.ncbi.nlm.nih.gov/pubmed/37632809" } @Article{info:doi/10.2196/24554, author="Yuan, Kai and Huang, Guangrui and Wang, Lepeng and Wang, Ting and Liu, Wenbin and Jiang, Haixu and Yang, C. Albert", title="Predicting Norovirus in the United States Using Google Trends: Infodemiology Study", journal="J Med Internet Res", year="2021", month="Sep", day="29", volume="23", number="9", pages="e24554", keywords="norovirus", keywords="Google Trends", keywords="correlation", keywords="outbreak", keywords="predictors", abstract="Background: Norovirus is a contagious disease. The transmission of norovirus spreads quickly and easily in various ways. Because effective methods to prevent or treat norovirus have not been discovered, it is important to rapidly recognize and report norovirus outbreaks in the early phase. Internet search has been a useful method for people to access information immediately. With the precise record of internet search trends, internet search has been a useful tool to manifest infectious disease outbreaks. Objective: In this study, we tried to discover the correlation between internet search terms and norovirus infection. Methods: The internet search trend data of norovirus were obtained from Google Trends. We used cross-correlation analysis to discover the temporal correlation between norovirus and other terms. We also used multiple linear regression with the stepwise method to recognize the most important predictors of internet search trends and norovirus. In addition, we evaluated the temporal correlation between actual norovirus cases and internet search terms in New York, California, and the United States as a whole. Results: Some Google search terms such as gastroenteritis, watery diarrhea, and stomach bug coincided with norovirus Google Trends. Some Google search terms such as contagious, travel, and party presented earlier than norovirus Google Trends. Some Google search terms such as dehydration, bar, and coronavirus presented several months later than norovirus Google Trends. We found that fever, gastroenteritis, poison, cruise, wedding, and watery diarrhea were important factors correlated with norovirus Google Trends. In actual norovirus cases from New York, California, and the United States as a whole, some Google search terms presented with, earlier, or later than actual norovirus cases. Conclusions: Our study provides novel strategy-based internet search evidence regarding the epidemiology of norovirus. ", doi="10.2196/24554", url="https://www.jmir.org/2021/9/e24554", url="http://www.ncbi.nlm.nih.gov/pubmed/34586079" } @Article{info:doi/10.2196/27741, author="Geronikolou, Styliani and Drosatos, George and Chrousos, George", title="Emotional Analysis of Twitter Posts During the First Phase of the COVID-19 Pandemic in Greece: Infoveillance Study", journal="JMIR Form Res", year="2021", month="Sep", day="29", volume="5", number="9", pages="e27741", keywords="emotional analysis", keywords="COVID-19", keywords="Twitter", keywords="Greece", keywords="infodemics", keywords="emotional contagion", keywords="epidemiology", keywords="pandemic", keywords="mental health", abstract="Background: The effectiveness of public health measures depends upon a community's compliance as well as on its positive or negative emotions. Objective: The purpose of this study was to perform an analysis of the expressed emotions in English tweets by Greek Twitter users during the first phase of the COVID-19 pandemic in Greece. Methods: The period of this study was from January 25, 2020 to June 30, 2020. Data collection was performed by using appropriate search words with the filter-streaming application programming interface of Twitter. The emotional analysis of the tweets that satisfied the inclusion criteria was achieved using a deep learning approach that performs better by utilizing recurrent neural networks on sequences of characters. Emotional epidemiology tools such as the 6 basic emotions, that is, joy, sadness, disgust, fear, surprise, and anger based on the Paul Ekman classification were adopted. Results: The most frequent emotion that was detected in the tweets was ``surprise'' at the emerging contagion, while the imposed isolation resulted mostly in ``anger'' (odds ratio 2.108, 95\% CI 0.986-4.506). Although the Greeks felt rather safe during the first phase of the COVID-19 pandemic, their positive and negative emotions reflected a masked ``flight or fight'' or ``fear versus anger'' response to the contagion. Conclusions: The findings of our study show that emotional analysis emerges as a valid tool for epidemiology evaluations, design, and public health strategy and surveillance. ", doi="10.2196/27741", url="https://formative.jmir.org/2021/9/e27741", url="http://www.ncbi.nlm.nih.gov/pubmed/34469328" } @Article{info:doi/10.2196/29885, author="Li, Jinhui and Zheng, Han and Duan, Xu", title="Factors Influencing the Popularity of a Health-Related Answer on a Chinese Question-and-Answer Website: Case Study", journal="J Med Internet Res", year="2021", month="Sep", day="28", volume="23", number="9", pages="e29885", keywords="answer-response behaviors", keywords="Zhihu", keywords="HPV vaccine information", keywords="content features", keywords="context features", keywords="contributor features", abstract="Background: Social question-and-answer (Q\&A) sites have become an important venue for individuals to obtain and share human papillomavirus (HPV) vaccine knowledge. Objective: This study aims to examine how different features of an HPV vaccine--related answer are associated with users' response behaviors on social Q\&A websites. Methods: A total of 2953 answers and 270 corresponding questions regarding the HPV vaccine were collected from a leading Chinese social Q\&A platform, Zhihu. Three types of key features, including content, context, and contributor, were extracted and coded. Negative binomial regression models were used to examine their impact on the vote and comment count of an HPV vaccine--related answer. Results: The findings showed that both content length and vividness were positively related to the response behaviors of HPV vaccine--related answers. In addition, compared with answers under the question theme benefits and risks, answers under the question theme vaccination experience received fewer votes and answers under the theme news opinions received more votes but fewer comments. The effects of characteristics of contributors were also supported, suggesting that answers from a male contributor with more followers and no professional identity would attract more votes and comments from community members. The significant interaction effect between content and context features further showed that long and vivid answers about HPV vaccination experience were more likely to receive votes and comments of users than those about benefits and risks. Conclusions: The study provides a complete picture of the underlying mechanism behind response behaviors of users toward HPV vaccine--related answers on social Q\&A websites. The results help health community organizers develop better strategies for building and maintaining a vibrant web-based community for communicating HPV vaccine knowledge. ", doi="10.2196/29885", url="https://www.jmir.org/2021/9/e29885", url="http://www.ncbi.nlm.nih.gov/pubmed/34581675" } @Article{info:doi/10.2196/29413, author="Ding, Qinglan and Massey, Daisy and Huang, Chenxi and Grady, B. Connor and Lu, Yuan and Cohen, Alina and Matzner, Pini and Mahajan, Shiwani and Caraballo, C{\'e}sar and Kumar, Navin and Xue, Yuchen and Dreyer, Rachel and Roy, Brita and Krumholz, M. Harlan", title="Tracking Self-reported Symptoms and Medical Conditions on Social Media During the COVID-19 Pandemic: Infodemiological Study", journal="JMIR Public Health Surveill", year="2021", month="Sep", day="28", volume="7", number="9", pages="e29413", keywords="health conditions", keywords="symptoms", keywords="mental health", keywords="social media", keywords="infoveillance", keywords="public health surveillance", keywords="COVID-19", keywords="pandemic", keywords="natural language processing", abstract="Background: Harnessing health-related data posted on social media in real time can offer insights into how the pandemic impacts the mental health and general well-being of individuals and populations over time. Objective: This study aimed to obtain information on symptoms and medical conditions self-reported by non-Twitter social media users during the COVID-19 pandemic, to determine how discussion of these symptoms and medical conditions changed over time, and to identify correlations between frequency of the top 5 commonly mentioned symptoms post and daily COVID-19 statistics (new cases, new deaths, new active cases, and new recovered cases) in the United States. Methods: We used natural language processing (NLP) algorithms to identify symptom- and medical condition--related topics being discussed on social media between June 14 and December 13, 2020. The sample posts were geotagged by NetBase, a third-party data provider. We calculated the positive predictive value and sensitivity to validate the classification of posts. We also assessed the frequency of health-related discussions on social media over time during the study period, and used Pearson correlation coefficients to identify statistically significant correlations between the frequency of the 5 most commonly mentioned symptoms and fluctuation of daily US COVID-19 statistics. Results: Within a total of 9,807,813 posts (nearly 70\% were sourced from the United States), we identified a discussion of 120 symptom-related topics and 1542 medical condition--related topics. Our classification of the health-related posts had a positive predictive value of over 80\% and an average classification rate of 92\% sensitivity. The 5 most commonly mentioned symptoms on social media during the study period were anxiety (in 201,303 posts or 12.2\% of the total posts mentioning symptoms), generalized pain (189,673, 11.5\%), weight loss (95,793, 5.8\%), fatigue (91,252, 5.5\%), and coughing (86,235, 5.2\%). The 5 most discussed medical conditions were COVID-19 (in 5,420,276 posts or 66.4\% of the total posts mentioning medical conditions), unspecified infectious disease (469,356, 5.8\%), influenza (270,166, 3.3\%), unspecified disorders of the central nervous system (253,407, 3.1\%), and depression (151,752, 1.9\%). Changes in posts in the frequency of anxiety, generalized pain, and weight loss were significant but negatively correlated with daily new COVID-19 cases in the United States (r=-0.49, r=-0.46, and r=-0.39, respectively; P<.05). Posts on the frequency of anxiety, generalized pain, weight loss, fatigue, and the changes in fatigue positively and significantly correlated with daily changes in both new deaths and new active cases in the United States (r ranged=0.39-0.48; P<.05). Conclusions: COVID-19 and symptoms of anxiety were the 2 most commonly discussed health-related topics on social media from June 14 to December 13, 2020. Real-time monitoring of social media posts on symptoms and medical conditions may help assess the population's mental health status and enhance public health surveillance for infectious disease. ", doi="10.2196/29413", url="https://publichealth.jmir.org/2021/9/e29413", url="http://www.ncbi.nlm.nih.gov/pubmed/34517338" } @Article{info:doi/10.2196/32685, author="Yan, Cathy and Law, Melanie and Nguyen, Stephanie and Cheung, Janelle and Kong, Jude", title="Comparing Public Sentiment Toward COVID-19 Vaccines Across Canadian Cities: Analysis of Comments on Reddit", journal="J Med Internet Res", year="2021", month="Sep", day="24", volume="23", number="9", pages="e32685", keywords="COVID-19", keywords="public sentiment", keywords="social media", keywords="Reddit", keywords="Canada", keywords="communication", keywords="sentiment", keywords="opinion", keywords="emotion", keywords="concern", keywords="pandemic", keywords="vaccine", keywords="hesitancy", abstract="Background: Social media enables the rapid consumption of news related to COVID-19 and serves as a platform for discussions. Its richness in text-based data in the form of posts and comments allows researchers to identify popular topics and assess public sentiment. Nonetheless, the vast majority of topic extraction and sentiment analysis based on social media is performed on the platform or country level and does not account for local culture and policies. Objective: The aim of this study is to use location-based subreddits on Reddit to study city-level variations in sentiments toward vaccine-related topics. Methods: Comments on posts providing regular updates on COVID-19 statistics in the Vancouver (r/vancouver, n=49,291), Toronto (r/toronto, n=20,764), and Calgary (r/calgary, n=21,277) subreddits between July 13, 2020, and June 14, 2021, were extracted. Latent Dirichlet allocation was used to identify frequently discussed topics. Sentiment (joy, sadness, fear, and anger) scores were assigned to comments through random forest regression. Results: The number of comments on the 250 posts from the Vancouver subreddit positively correlated with the number of new daily COVID-19 cases in British Columbia (R=0.51, 95\% CI for slope 0.18-0.29; P<.001). From the comments, 13 topics were identified. Two were related to vaccines, 1 regarding vaccine uptake and the other about vaccine supply. The levels of discussion for both topics were linked to the total number of vaccines administered (Granger test for causality, P<.001). Comments pertaining to either topic displayed higher scores for joy than for other topics (P<.001). Calgary and Toronto also discussed vaccine uptake. Sentiment scores for this topic differed across the 3 cities (P<.001). Conclusions: Our work demonstrates that data from city-specific subreddits can be used to better understand concerns and sentiments around COVID-19 vaccines at the local level. This can potentially lead to more targeted and publicly acceptable policies based on content on social media. ", doi="10.2196/32685", url="https://www.jmir.org/2021/9/e32685", url="http://www.ncbi.nlm.nih.gov/pubmed/34519654" } @Article{info:doi/10.2196/27283, author="Siedlikowski, Sophia and No{\"e}l, Louis-Philippe and Moynihan, Anne Stephanie and Robin, Marc", title="Chloe for COVID-19: Evolution of an Intelligent Conversational Agent to Address Infodemic Management Needs During the COVID-19 Pandemic", journal="J Med Internet Res", year="2021", month="Sep", day="21", volume="23", number="9", pages="e27283", keywords="chatbot", keywords="COVID-19", keywords="conversational agents", keywords="public health", keywords="artificial intelligence", keywords="infodemic", keywords="infodemiology", keywords="misinformation", keywords="digital health", keywords="virtual care", doi="10.2196/27283", url="https://www.jmir.org/2021/9/e27283", url="http://www.ncbi.nlm.nih.gov/pubmed/34375299" } @Article{info:doi/10.2196/28635, author="Huemer, Matthias and Jahn-Kuch, Daniela and Hofmann, Guenter and Andritsch, Elisabeth and Farkas, Clemens and Schaupp, Walter and Masel, Katharina Eva and Jost, J. Philipp and Pichler, Martin", title="Trends and Patterns in the Public Awareness of Palliative Care, Euthanasia, and End-of-Life Decisions in 3 Central European Countries Using Big Data Analysis From Google: Retrospective Analysis", journal="J Med Internet Res", year="2021", month="Sep", day="20", volume="23", number="9", pages="e28635", keywords="Google Trends", keywords="end-of-life decisions", keywords="assisted suicide", keywords="euthanasia", keywords="palliative care", keywords="health care policy", abstract="Background: End-of-life decisions, specifically the provision of euthanasia and assisted suicide services, challenge traditional medical and ethical principles. Austria and Germany have decided to liberalize their laws restricting assisted suicide, thus reigniting the debate about a meaningful framework in which the practice should be embedded. Evidence of the relevance of assisted suicide and euthanasia for the general population in Germany and Austria is limited. Objective: The aim of this study is to examine whether the public awareness documented by search activities in the most frequently used search engine, Google, on the topics of palliative care, euthanasia, and advance health care directives changed with the implementation of palliative care services and new governmental regulations concerning end-of-life decisions. Methods: We searched for policies, laws, and regulations promulgated or amended in Austria, Germany, and Switzerland between 2004 and 2020 and extracted data on the search volume for each search term topic from Google Trends as a surrogate of public awareness and interest. Annual averages were analyzed using the Joinpoint Regression Program. Results: Important policy changes yielded significant changes in search trends for the investigated topics. The enactment of laws regulating advance health care directives coincided with a significant drop in the volume of searches for the topic of euthanasia in all 3 countries (Austria: ?24.48\%, P=.02; Germany: ?14.95\%, P<.001; Switzerland: ?11.75\%, P=.049). Interest in palliative care increased with the availability of care services and the implementation of laws and policies to promote palliative care (Austria: 22.69\%, P=.01; Germany: 14.39, P<.001; Switzerland: 17.59\%, P<.001). The search trends for advance health care directives showed mixed results. While interest remained steady in Austria within the study period, it increased by 3.66\% (P<.001) in Switzerland and decreased by 2.85\% (P<.001) in Germany. Conclusions: Our results demonstrate that legal measures securing patients' autonomy at the end of life may lower the search activities for topics related to euthanasia and assisted suicide. Palliative care may be a meaningful way to raise awareness of the different options for end-of-life care and to guide patients in their decision-making process regarding the same. ", doi="10.2196/28635", url="https://www.jmir.org/2021/9/e28635", url="http://www.ncbi.nlm.nih.gov/pubmed/34542419" } @Article{info:doi/10.2196/27063, author="Loeb, Stacy and Massey, Philip and Leader, E. Amy and Thakker, Sameer and Falge, Emily and Taneja, Sabina and Byrne, Nataliya and Rose, Meredith and Joy, Matthew and Walter, Dawn and Katz, S. Matthew and Wong, L. Risa and Selvan, Preethi and Keith, W. Scott and Giri, N. Veda", title="Gaps in Public Awareness About BRCA and Genetic Testing in Prostate Cancer: Social Media Landscape Analysis", journal="JMIR Cancer", year="2021", month="Sep", day="20", volume="7", number="3", pages="e27063", keywords="genetic testing", keywords="BRCA", keywords="prostate cancer", keywords="breast cancer", keywords="social media", keywords="infodemiology", abstract="Background: Genetic testing, particularly for BRCA1/2, is increasingly important in prostate cancer (PCa) care, with impact on PCa management and hereditary cancer risk. However, the extent of public awareness and online discourse on social media is unknown, and presents opportunities to identify gaps and enhance population awareness and uptake of advances in PCa precision medicine. Objective: The objective of this study was to characterize activity and engagement across multiple social media platforms (Twitter, Facebook, and YouTube) regarding BRCA and genetic testing for PCa compared with breast cancer, which has a long history of public awareness, advocacy, and prominent social media presence. Methods: The Symplur Signals online analytics platform was used to obtain metrics for tweets about (1) \#BRCA and \#breastcancer, (2) \#BRCA and \#prostatecancer, (3) \#genetictesting and \#breastcancer, and (4) \#genetictesting and \#prostatecancer from 2016 to 2020. We examined the total number of tweets, users, and reach for each hashtag, and performed content analysis for a subset of tweets. Facebook and YouTube were queried using analogous search terms, and engagement metrics were calculated. Results: During a 5-year period, there were 10,005 tweets for \#BRCA and \#breastcancer, versus 1008 tweets about \#BRCA and \#prostatecancer. There were also more tweets about \#genetictesting and \#breastcancer (n=1748), compared with \#genetic testing and \#prostatecancer (n=328). Tweets about genetic testing (12,921,954) and BRCA (75,724,795) in breast cancer also had substantially greater reach than those about PCa (1,463,777 and 4,849,905, respectively). Facebook groups and pages regarding PCa and BRCA/genetic testing had fewer average members, new members, and new posts, as well as fewer likes and followers, compared with breast cancer. Facebook videos had more engagement than YouTube videos across both PCa and breast cancer content. Conclusions: There is substantially less social media engagement about BRCA and genetic testing in PCa compared with breast cancer. This landscape analysis provides insights into strategies for leveraging social media platforms to increase public awareness about PCa germline testing, including use of Facebook to share video content and Twitter for discussions with health professionals. ", doi="10.2196/27063", url="https://cancer.jmir.org/2021/3/e27063", url="http://www.ncbi.nlm.nih.gov/pubmed/34542414" } @Article{info:doi/10.2196/30161, author="Teodoro, Douglas and Ferdowsi, Sohrab and Borissov, Nikolay and Kashani, Elham and Vicente Alvarez, David and Copara, Jenny and Gouareb, Racha and Naderi, Nona and Amini, Poorya", title="Information Retrieval in an Infodemic: The Case of COVID-19 Publications", journal="J Med Internet Res", year="2021", month="Sep", day="17", volume="23", number="9", pages="e30161", keywords="information retrieval", keywords="multistage retrieval", keywords="neural search", keywords="deep learning", keywords="COVID-19", keywords="coronavirus", keywords="infodemic", keywords="infodemiology", keywords="literature", keywords="online information", abstract="Background: The COVID-19 global health crisis has led to an exponential surge in published scientific literature. In an attempt to tackle the pandemic, extremely large COVID-19--related corpora are being created, sometimes with inaccurate information, which is no longer at scale of human analyses. Objective: In the context of searching for scientific evidence in the deluge of COVID-19--related literature, we present an information retrieval methodology for effective identification of relevant sources to answer biomedical queries posed using natural language. Methods: Our multistage retrieval methodology combines probabilistic weighting models and reranking algorithms based on deep neural architectures to boost the ranking of relevant documents. Similarity of COVID-19 queries is compared to documents, and a series of postprocessing methods is applied to the initial ranking list to improve the match between the query and the biomedical information source and boost the position of relevant documents. Results: The methodology was evaluated in the context of the TREC-COVID challenge, achieving competitive results with the top-ranking teams participating in the competition. Particularly, the combination of bag-of-words and deep neural language models significantly outperformed an Okapi Best Match 25--based baseline, retrieving on average, 83\% of relevant documents in the top 20. Conclusions: These results indicate that multistage retrieval supported by deep learning could enhance identification of literature for COVID-19--related questions posed using natural language. ", doi="10.2196/30161", url="https://www.jmir.org/2021/9/e30161", url="http://www.ncbi.nlm.nih.gov/pubmed/34375298" } @Article{info:doi/10.2196/27670, author="Alsudias, Lama and Rayson, Paul", title="Social Media Monitoring of the COVID-19 Pandemic and Influenza Epidemic With Adaptation for Informal Language in Arabic Twitter Data: Qualitative Study", journal="JMIR Med Inform", year="2021", month="Sep", day="17", volume="9", number="9", pages="e27670", keywords="Arabic", keywords="COVID-19", keywords="infectious disease", keywords="influenza", keywords="infodemiology", keywords="infoveillance", keywords="social listening", keywords="informal language", keywords="multilabel classification", keywords="natural language processing", keywords="named entity recognition", keywords="Twitter", abstract="Background: Twitter is a real-time messaging platform widely used by people and organizations to share information on many topics. Systematic monitoring of social media posts (infodemiology or infoveillance) could be useful to detect misinformation outbreaks as well as to reduce reporting lag time and to provide an independent complementary source of data compared with traditional surveillance approaches. However, such an analysis is currently not possible in the Arabic-speaking world owing to a lack of basic building blocks for research and dialectal variation. Objective: We collected around 4000 Arabic tweets related to COVID-19 and influenza. We cleaned and labeled the tweets relative to the Arabic Infectious Diseases Ontology, which includes nonstandard terminology, as well as 11 core concepts and 21 relations. The aim of this study was to analyze Arabic tweets to estimate their usefulness for health surveillance, understand the impact of the informal terms in the analysis, show the effect of deep learning methods in the classification process, and identify the locations where the infection is spreading. Methods: We applied the following multilabel classification techniques: binary relevance, classifier chains, label power set, adapted algorithm (multilabel adapted k-nearest neighbors [MLKNN]), support vector machine with naive Bayes features (NBSVM), bidirectional encoder representations from transformers (BERT), and AraBERT (transformer-based model for Arabic language understanding) to identify tweets appearing to be from infected individuals. We also used named entity recognition to predict the place names mentioned in the tweets. Results: We achieved an F1 score of up to 88\% in the influenza case study and 94\% in the COVID-19 one. Adapting for nonstandard terminology and informal language helped to improve accuracy by as much as 15\%, with an average improvement of 8\%. Deep learning methods achieved an F1 score of up to 94\% during the classifying process. Our geolocation detection algorithm had an average accuracy of 54\% for predicting the location of users according to tweet content. Conclusions: This study identified two Arabic social media data sets for monitoring tweets related to influenza and COVID-19. It demonstrated the importance of including informal terms, which are regularly used by social media users, in the analysis. It also proved that BERT achieves good results when used with new terms in COVID-19 tweets. Finally, the tweet content may contain useful information to determine the location of disease spread. ", doi="10.2196/27670", url="https://medinform.jmir.org/2021/9/e27670", url="http://www.ncbi.nlm.nih.gov/pubmed/34346892" } @Article{info:doi/10.2196/30979, author="Calleja, Neville and AbdAllah, AbdelHalim and Abad, Neetu and Ahmed, Naglaa and Albarracin, Dolores and Altieri, Elena and Anoko, N. Julienne and Arcos, Ruben and Azlan, Anis Arina and Bayer, Judit and Bechmann, Anja and Bezbaruah, Supriya and Briand, C. Sylvie and Brooks, Ian and Bucci, M. Lucie and Burzo, Stefano and Czerniak, Christine and De Domenico, Manlio and Dunn, G. Adam and Ecker, H. Ullrich K. and Espinosa, Laura and Francois, Camille and Gradon, Kacper and Gruzd, Anatoliy and G{\"u}lg{\"u}n, Sultan Beste and Haydarov, Rustam and Hurley, Cherstyn and Astuti, Indra Santi and Ishizumi, Atsuyoshi and Johnson, Neil and Johnson Restrepo, Dylan and Kajimoto, Masato and Koyuncu, Ayb{\"u}ke and Kulkarni, Shibani and Lamichhane, Jaya and Lewis, Rosamund and Mahajan, Avichal and Mandil, Ahmed and McAweeney, Erin and Messer, Melanie and Moy, Wesley and Ndumbi Ngamala, Patricia and Nguyen, Tim and Nunn, Mark and Omer, B. Saad and Pagliari, Claudia and Patel, Palak and Phuong, Lynette and Prybylski, Dimitri and Rashidian, Arash and Rempel, Emily and Rubinelli, Sara and Sacco, PierLuigi and Schneider, Anton and Shu, Kai and Smith, Melanie and Sufehmi, Harry and Tangcharoensathien, Viroj and Terry, Robert and Thacker, Naveen and Trewinnard, Tom and Turner, Shannon and Tworek, Heidi and Uakkas, Saad and Vraga, Emily and Wardle, Claire and Wasserman, Herman and Wilhelm, Elisabeth and W{\"u}rz, Andrea and Yau, Brian and Zhou, Lei and Purnat, D. Tina", title="A Public Health Research Agenda for Managing Infodemics: Methods and Results of the First WHO Infodemiology Conference", journal="JMIR Infodemiology", year="2021", month="Sep", day="15", volume="1", number="1", pages="e30979", keywords="infodemic", keywords="infodemiology", keywords="infodemic management", keywords="research agenda", keywords="research policy", keywords="COVID-19", keywords="SARS-CoV-2", keywords="community resilience", keywords="knowledge translation", keywords="message amplification", keywords="misinformation", keywords="disinformation", keywords="information-seeking behavior", keywords="access to information", keywords="information literacy", keywords="communications media", keywords="internet", keywords="risk communication", keywords="evidence synthesis", keywords="attitudes", keywords="beliefs", abstract="Background: An infodemic is an overflow of information of varying quality that surges across digital and physical environments during an acute public health event. It leads to confusion, risk-taking, and behaviors that can harm health and lead to erosion of trust in health authorities and public health responses. Owing to the global scale and high stakes of the health emergency, responding to the infodemic related to the pandemic is particularly urgent. Building on diverse research disciplines and expanding the discipline of infodemiology, more evidence-based interventions are needed to design infodemic management interventions and tools and implement them by health emergency responders. Objective: The World Health Organization organized the first global infodemiology conference, entirely online, during June and July 2020, with a follow-up process from August to October 2020, to review current multidisciplinary evidence, interventions, and practices that can be applied to the COVID-19 infodemic response. This resulted in the creation of a public health research agenda for managing infodemics. Methods: As part of the conference, a structured expert judgment synthesis method was used to formulate a public health research agenda. A total of 110 participants represented diverse scientific disciplines from over 35 countries and global public health implementing partners. The conference used a laddered discussion sprint methodology by rotating participant teams, and a managed follow-up process was used to assemble a research agenda based on the discussion and structured expert feedback. This resulted in a five-workstream frame of the research agenda for infodemic management and 166 suggested research questions. The participants then ranked the questions for feasibility and expected public health impact. The expert consensus was summarized in a public health research agenda that included a list of priority research questions. Results: The public health research agenda for infodemic management has five workstreams: (1) measuring and continuously monitoring the impact of infodemics during health emergencies; (2) detecting signals and understanding the spread and risk of infodemics; (3) responding and deploying interventions that mitigate and protect against infodemics and their harmful effects; (4) evaluating infodemic interventions and strengthening the resilience of individuals and communities to infodemics; and (5) promoting the development, adaptation, and application of interventions and toolkits for infodemic management. Each workstream identifies research questions and highlights 49 high priority research questions. Conclusions: Public health authorities need to develop, validate, implement, and adapt tools and interventions for managing infodemics in acute public health events in ways that are appropriate for their countries and contexts. Infodemiology provides a scientific foundation to make this possible. This research agenda proposes a structured framework for targeted investment for the scientific community, policy makers, implementing organizations, and other stakeholders to consider. ", doi="10.2196/30979", url="https://infodemiology.jmir.org/2021/1/e30979", url="http://www.ncbi.nlm.nih.gov/pubmed/34604708" } @Article{info:doi/10.2196/27314, author="Ricard, Joseph Benjamin and Hassanpour, Saeed", title="Deep Learning for Identification of Alcohol-Related Content on Social Media (Reddit and Twitter): Exploratory Analysis of Alcohol-Related Outcomes", journal="J Med Internet Res", year="2021", month="Sep", day="15", volume="23", number="9", pages="e27314", keywords="social media", keywords="natural language processing", keywords="alcohol abuse", keywords="machine learning", abstract="Background: Many social media studies have explored the ability of thematic structures, such as hashtags and subreddits, to identify information related to a wide variety of mental health disorders. However, studies and models trained on specific themed communities are often difficult to apply to different social media platforms and related outcomes. A deep learning framework using thematic structures from Reddit and Twitter can have distinct advantages for studying alcohol abuse, particularly among the youth in the United States. Objective: This study proposes a new deep learning pipeline that uses thematic structures to identify alcohol-related content across different platforms. We apply our method on Twitter to determine the association of the prevalence of alcohol-related tweets with alcohol-related outcomes reported from the National Institute of Alcoholism and Alcohol Abuse, Centers for Disease Control Behavioral Risk Factor Surveillance System, county health rankings, and the National Industry Classification System. Methods: The Bidirectional Encoder Representations From Transformers neural network learned to classify 1,302,524 Reddit posts as either alcohol-related or control subreddits. The trained model identified 24 alcohol-related hashtags from an unlabeled data set of 843,769 random tweets. Querying alcohol-related hashtags identified 25,558,846 alcohol-related tweets, including 790,544 location-specific (geotagged) tweets. We calculated the correlation between the prevalence of alcohol-related tweets and alcohol-related outcomes, controlling for confounding effects of age, sex, income, education, and self-reported race, as recorded by the 2013-2018 American Community Survey. Results: Significant associations were observed: between alcohol-hashtagged tweets and alcohol consumption (P=.01) and heavy drinking (P=.005) but not binge drinking (P=.37), self-reported at the metropolitan-micropolitan statistical area level; between alcohol-hashtagged tweets and self-reported excessive drinking behavior (P=.03) but not motor vehicle fatalities involving alcohol (P=.21); between alcohol-hashtagged tweets and the number of breweries (P<.001), wineries (P<.001), and beer, wine, and liquor stores (P<.001) but not drinking places (P=.23), per capita at the US county and county-equivalent level; and between alcohol-hashtagged tweets and all gallons of ethanol consumed (P<.001), as well as ethanol consumed from wine (P<.001) and liquor (P=.01) sources but not beer (P=.63), at the US state level. Conclusions: Here, we present a novel natural language processing pipeline developed using Reddit's alcohol-related subreddits that identify highly specific alcohol-related Twitter hashtags. The prevalence of identified hashtags contains interpretable information about alcohol consumption at both coarse (eg, US state) and fine-grained (eg, metropolitan-micropolitan statistical area level and county) geographical designations. This approach can expand research and deep learning interventions on alcohol abuse and other behavioral health outcomes. ", doi="10.2196/27314", url="https://www.jmir.org/2021/9/e27314", url="http://www.ncbi.nlm.nih.gov/pubmed/34524095" } @Article{info:doi/10.2196/25636, author="Jiang, Crystal Li and Chu, Hang Tsz and Sun, Mengru", title="Characterization of Vaccine Tweets During the Early Stage of the COVID-19 Outbreak in the United States: Topic Modeling Analysis", journal="JMIR Infodemiology", year="2021", month="Sep", day="14", volume="1", number="1", pages="e25636", keywords="topic modeling", keywords="social media", keywords="infoveillance", keywords="vaccine", keywords="coronavirus", keywords="COVID-19", abstract="Background: During the early stages of the COVID-19 pandemic, developing safe and effective coronavirus vaccines was considered critical to arresting the spread of the disease. News and social media discussions have extensively covered the issue of coronavirus vaccines, with a mixture of vaccine advocacies, concerns, and oppositions. Objective: This study aimed to uncover the emerging themes in Twitter users' perceptions and attitudes toward vaccines during the early stages of the COVID-19 outbreak. Methods: This study employed topic modeling to analyze tweets related to coronavirus vaccines at the start of the COVID-19 outbreak in the United States (February 21 to March 20, 2020). We created a predefined query (eg, ``COVID'' AND ``vaccine'') to extract the tweet text and metadata (number of followers of the Twitter account and engagement metrics based on likes, comments, and retweeting) from the Meltwater database. After preprocessing the data, we tested Latent Dirichlet Allocation models to identify topics associated with these tweets. The model specifying 20 topics provided the best overall coherence, and each topic was interpreted based on its top associated terms. Results: In total, we analyzed 100,209 tweets containing keywords related to coronavirus and vaccines. The 20 topics were further collapsed based on shared similarities, thereby generating 7 major themes. Our analysis characterized 26.3\% (26,234/100,209) of the tweets as News Related to Coronavirus and Vaccine Development, 25.4\% (25,425/100,209) as General Discussion and Seeking of Information on Coronavirus, 12.9\% (12,882/100,209) as Financial Concerns, 12.7\% (12,696/100,209) as Venting Negative Emotions, 9.9\% (9908/100,209) as Prayers and Calls for Positivity, 8.1\% (8155/100,209) as Efficacy of Vaccine and Treatment, and 4.9\% (4909/100,209) as Conspiracies about Coronavirus and Its Vaccines. Different themes demonstrated some changes over time, mostly in close association with news or events related to vaccine developments. Twitter users who discussed conspiracy theories, the efficacy of vaccines and treatments, and financial concerns had more followers than those focused on other vaccine themes. The engagement level---the extent to which a tweet being retweeted, quoted, liked, or replied by other users---was similar among different themes, but tweets venting negative emotions yielded the lowest engagement. Conclusions: This study enriches our understanding of public concerns over new vaccines or vaccine development at early stages of the outbreak, bearing implications for influencing vaccine attitudes and guiding public health efforts to cope with infectious disease outbreaks in the future. This study concluded that public concerns centered on general policy issues related to coronavirus vaccines and that the discussions were considerably mixed with political views when vaccines were not made available. Only a small proportion of tweets focused on conspiracy theories, but these tweets demonstrated high engagement levels and were often contributed by Twitter users with more influence. ", doi="10.2196/25636", url="https://infodemiology.jmir.org/2021/1/e25636", url="http://www.ncbi.nlm.nih.gov/pubmed/34604707" } @Article{info:doi/10.2196/22446, author="Al Tamime, Reham and Weber, Ingmar", title="Tracking Exposure to Ads Amid the COVID-19 Pandemic: Development of a Public Google Ads Data Set", journal="JMIR Data", year="2021", month="Sep", day="14", volume="2", number="1", pages="e22446", keywords="COVID-19", keywords="coronavirus", keywords="SARS-CoV-2", keywords="panic buying", keywords="Google Ads", keywords="data", keywords="database", keywords="tracking", keywords="research", keywords="public availability", keywords="online behaviors", abstract="Background: The COVID-19 pandemic has had a substantial impact on economies, governments, businesses, and most importantly, people's health. To bring the spread of COVID-19 under control, strict lockdown measures have been implemented across the globe. These lockdown measures resulted in a spate of panic buying and increase in demand for hygiene products and other grocery items. Objective: In this paper, we describe a data set from Google Ads that looks at the presentation of ads to people while they browse the web during the COVID-19 pandemic. We are making the data set available to the research community. Methods: We started this ongoing data collection on March 28, 2020, leveraging Developer Tools' network requests to retrieve Google Ads data. We identified a list of items related and unrelated to panic buying. We then captured these items as targeting criteria under what people are actively researching or planning on Google Ads. Google Ads data has been filtered using additional targeting criteria such as country, gender, and parental status. Results: Since the inception of our collection, we have actively maintained and updated our repository on a monthly basis. In total, we have published over 4116 data points. This paper also presents basic statistics that reveal variations in Google Ads data across countries, gender, and parental status. Conclusions: We hope that this Google Ads data set can increase our understanding of ad exposure during the COVID-19 outbreak. In particular, this data set can lead to further studies that look at the relationship between exposure to ads, time spent web browsing, and health outcomes. ", doi="10.2196/22446", url="https://data.jmir.org/2021/1/e22446" } @Article{info:doi/10.2196/30854, author="Hu, Tao and Wang, Siqin and Luo, Wei and Zhang, Mengxi and Huang, Xiao and Yan, Yingwei and Liu, Regina and Ly, Kelly and Kacker, Viraj and She, Bing and Li, Zhenlong", title="Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective", journal="J Med Internet Res", year="2021", month="Sep", day="10", volume="23", number="9", pages="e30854", keywords="Twitter", keywords="public opinion", keywords="COVID-19 vaccines", keywords="sentiment analysis", keywords="emotion analysis", keywords="topic modeling", keywords="COVID-19", abstract="Background: The COVID-19 pandemic has imposed a large, initially uncontrollable, public health crisis both in the United States and across the world, with experts looking to vaccines as the ultimate mechanism of defense. The development and deployment of COVID-19 vaccines have been rapidly advancing via global efforts. Hence, it is crucial for governments, public health officials, and policy makers to understand public attitudes and opinions towards vaccines, such that effective interventions and educational campaigns can be designed to promote vaccine acceptance. Objective: The aim of this study was to investigate public opinion and perception on COVID-19 vaccines in the United States. We investigated the spatiotemporal trends of public sentiment and emotion towards COVID-19 vaccines and analyzed how such trends relate to popular topics found on Twitter. Methods: We collected over 300,000 geotagged tweets in the United States from March 1, 2020 to February 28, 2021. We examined the spatiotemporal patterns of public sentiment and emotion over time at both national and state scales and identified 3 phases along the pandemic timeline with sharp changes in public sentiment and emotion. Using sentiment analysis, emotion analysis (with cloud mapping of keywords), and topic modeling, we further identified 11 key events and major topics as the potential drivers to such changes. Results: An increasing trend in positive sentiment in conjunction with a decrease in negative sentiment were generally observed in most states, reflecting the rising confidence and anticipation of the public towards vaccines. The overall tendency of the 8 types of emotion implies that the public trusts and anticipates the vaccine. This is accompanied by a mixture of fear, sadness, and anger. Critical social or international events or announcements by political leaders and authorities may have potential impacts on public opinion towards vaccines. These factors help identify underlying themes and validate insights from the analysis. Conclusions: The analyses of near real-time social media big data benefit public health authorities by enabling them to monitor public attitudes and opinions towards vaccine-related information in a geo-aware manner, address the concerns of vaccine skeptics, and promote the confidence that individuals within a certain region or community have towards vaccines. ", doi="10.2196/30854", url="https://www.jmir.org/2021/9/e30854", url="http://www.ncbi.nlm.nih.gov/pubmed/34346888" } @Article{info:doi/10.2196/30800, author="Tahamtan, Iman and Potnis, Devendra and Mohammadi, Ehsan and Miller, E. Laura and Singh, Vandana", title="Framing of and Attention to COVID-19 on Twitter: Thematic Analysis of Hashtags", journal="J Med Internet Res", year="2021", month="Sep", day="10", volume="23", number="9", pages="e30800", keywords="COVID-19", keywords="framing", keywords="Twitter", keywords="social media", keywords="public opinion", keywords="engagement", keywords="public attention", keywords="thematic analysis", keywords="public health", abstract="Background: Although past research has focused on COVID-19--related frames in the news media, such research may not accurately capture and represent the perspectives of people from diverse backgrounds. Additionally, research on the public attention to COVID-19 as reflected through frames on social media is scarce. Objective: This study identified the frames about the COVID-19 pandemic in the public discourse on Twitter, which voices diverse opinions. This study also investigated the amount of public attention to those frames on Twitter. Methods: We collected 22 trending hashtags related to COVID-19 in the United States and 694,582 tweets written in English containing these hashtags in March 2020 and analyzed them via thematic analysis. Public attention to these frames was measured by evaluating the amount of public engagement with frames and public adoption of those frames. Results: We identified 9 frames including ``public health guidelines,'' ``quarantine life,'' ``solidarity,'' ``evidence and facts,'' ``call for action,'' ``politics,'' ``post-pandemic life,'' ``shortage panic,'' and ``conflict.'' Results showed that some frames such as ``call for action'' are more appealing than others during a global pandemic, receiving greater public adoption and engagement. The ``call for action'' frame had the highest engagement score, followed by ``conflict'' and ``evidence and facts.'' Additionally, ``post-pandemic life'' had the highest adoption score, followed by ``call for action'' and ``shortage panic.'' The findings indicated that the frequency of a frame on social media does not necessarily mean greater public adoption of or engagement with the frame. Conclusions: This study contributes to framing theory and research by demonstrating how trending hashtags can be used as new user-generated data to identify frames on social media. This study concludes that the identified frames such as ``quarantine life'' and ``conflict'' and themes such as ``isolation'' and ``toilet paper panic'' represent the consequences of the COVID-19 pandemic. The consequences could be (1) exclusively related to COVID-19, such as hand hygiene or isolation; (2) related to any health crisis such as social support of vulnerable groups; and (3) generic that are irrespective of COVID-19, such as homeschooling or remote working. ", doi="10.2196/30800", url="https://www.jmir.org/2021/9/e30800", url="http://www.ncbi.nlm.nih.gov/pubmed/34406961" } @Article{info:doi/10.2196/30451, author="Tomaszewski, Tre and Morales, Alex and Lourentzou, Ismini and Caskey, Rachel and Liu, Bing and Schwartz, Alan and Chin, Jessie", title="Identifying False Human Papillomavirus (HPV) Vaccine Information and Corresponding Risk Perceptions From Twitter: Advanced Predictive Models", journal="J Med Internet Res", year="2021", month="Sep", day="9", volume="23", number="9", pages="e30451", keywords="misinformation", keywords="disinformation", keywords="social media", keywords="HPV", keywords="human papillomavirus vaccination", keywords="vaccination", keywords="causality mining", keywords="cause", keywords="effect", keywords="risk perceptions", keywords="vaccine", keywords="perception", keywords="risk", keywords="Twitter", keywords="machine learning", keywords="natural language processing", keywords="cervical cancer", abstract="Background: The vaccination uptake rates of the human papillomavirus (HPV) vaccine remain low despite the fact that the effectiveness of HPV vaccines has been established for more than a decade. Vaccine hesitancy is in part due to false information about HPV vaccines on social media. Combating false HPV vaccine information is a reasonable step to addressing vaccine hesitancy. Objective: Given the substantial harm of false HPV vaccine information, there is an urgent need to identify false social media messages before it goes viral. The goal of the study is to develop a systematic and generalizable approach to identifying false HPV vaccine information on social media. Methods: This study used machine learning and natural language processing to develop a series of classification models and causality mining methods to identify and examine true and false HPV vaccine--related information on Twitter. Results: We found that the convolutional neural network model outperformed all other models in identifying tweets containing false HPV vaccine--related information (F score=91.95). We also developed completely unsupervised causality mining models to identify HPV vaccine candidate effects for capturing risk perceptions of HPV vaccines. Furthermore, we found that false information contained mostly loss-framed messages focusing on the potential risk of vaccines covering a variety of topics using more diverse vocabulary, while true information contained both gain- and loss-framed messages focusing on the effectiveness of vaccines covering fewer topics using relatively limited vocabulary. Conclusions: Our research demonstrated the feasibility and effectiveness of using predictive models to identify false HPV vaccine information and its risk perceptions on social media. ", doi="10.2196/30451", url="https://www.jmir.org/2021/9/e30451", url="http://www.ncbi.nlm.nih.gov/pubmed/34499043" } @Article{info:doi/10.2196/31409, author="Lee, Hocheol and Noh, Bi Eun and Park, Jong Sung and Nam, Kweun Hae and Lee, Ho Tae and Lee, Ram Ga and Nam, Woo Eun", title="COVID-19 Vaccine Perception in South Korea: Web Crawling Approach", journal="JMIR Public Health Surveill", year="2021", month="Sep", day="8", volume="7", number="9", pages="e31409", keywords="COVID-19 vaccine", keywords="COVID-19", keywords="instagram", keywords="social media", keywords="infodemiology", keywords="sentiment analysis", keywords="vaccine perception", keywords="South Korea", keywords="web crawling", keywords="AstraZeneca", keywords="Pfizer", abstract="Background: The US Centers for Disease Control and Prevention and the World Health Organization emphasized vaccination against COVID-19 because physical distancing proved inadequate to mitigate death, illness, and massive economic loss. Objective: This study aimed to investigate Korean citizens' perceptions of vaccines by examining their views on COVID-19 vaccines, their positive and negative perceptions of each vaccine, and ways to enhance policies to increase vaccine acceptance. Methods: This cross-sectional study analyzed posts on NAVER and Instagram to examine Korean citizens' perception of COVID-19 vaccines. The keywords searched were ``vaccine,'' ``AstraZeneca,'' and ``Pfizer.'' In total 8100 posts in NAVER and 5291 posts in Instagram were sampled through web crawling. Morphology analysis was performed, overlapping or meaningless words were removed, sentiment analysis was implemented, and 3 public health professionals reviewed the results. Results: The findings revealed a negative perception of COVID-19 vaccines; of the words crawled, the proportion of negative words for AstraZeneca was 71.0\% (476/670) and for Pfizer was 56.3\% (498/885). Among words crawled with ``vaccine,'' ``good'' ranked first, with a frequency of 13.43\% (312/2323). Meanwhile, ``side effect'' ranked highest, with a frequency of 29.2\% (163/559) for ``AstraZeneca,'' but 0.6\% (4/673) for ``Pfizer.'' With ``vaccine,'' positive words were more frequently used, whereas with ``AstraZeneca'' and ``Pfizer'' negative words were prevalent. Conclusions: There is a negative perception of AstraZeneca and Pfizer vaccines in Korea, with 1 in 4 people refusing vaccination. To address this, accurate information needs to be shared about vaccines including AstraZeneca, and the experiences of those vaccinated. Furthermore, government communication about risk management is required to increase the AstraZeneca vaccination rate for herd immunity before the vaccine expires. ", doi="10.2196/31409", url="https://publichealth.jmir.org/2021/9/e31409", url="http://www.ncbi.nlm.nih.gov/pubmed/34348890" } @Article{info:doi/10.2196/28975, author="Akpan, Justice Ikpe and Aguolu, Genevieve Obianuju and Kobara, Mamoua Yawo and Razavi, Rouzbeh and Akpan, A. Asuama and Shanker, Murali", title="Association Between What People Learned About COVID-19 Using Web Searches and Their Behavior Toward Public Health Guidelines: Empirical Infodemiology Study", journal="J Med Internet Res", year="2021", month="Sep", day="2", volume="23", number="9", pages="e28975", keywords="internet", keywords="novel coronavirus", keywords="SARS-CoV-2", keywords="COVID-19", keywords="infodemiology", keywords="misinformation", keywords="conspiracy theories", keywords="public health", abstract="Background: The use of the internet and web-based platforms to obtain public health information and manage health-related issues has become widespread in this digital age. The practice is so pervasive that the first reaction to obtaining health information is to ``Google it.'' As SARS-CoV-2 broke out in Wuhan, China, in December 2019 and quickly spread worldwide, people flocked to the internet to learn about the novel coronavirus and the disease, COVID-19. Lagging responses by governments and public health agencies to prioritize the dissemination of information about the coronavirus outbreak through the internet and the World Wide Web and to build trust gave room for others to quickly populate social media, online blogs, news outlets, and websites with misinformation and conspiracy theories about the COVID-19 pandemic, resulting in people's deviant behaviors toward public health safety measures. Objective: The goals of this study were to determine what people learned about the COVID-19 pandemic through web searches, examine any association between what people learned about COVID-19 and behavior toward public health guidelines, and analyze the impact of misinformation and conspiracy theories about the COVID-19 pandemic on people's behavior toward public health measures. Methods: This infodemiology study used Google Trends' worldwide search index, covering the first 6 months after the SARS-CoV-2 outbreak (January 1 to June 30, 2020) when the public scrambled for information about the pandemic. Data analysis employed statistical trends, correlation and regression, principal component analysis (PCA), and predictive models. Results: The PCA identified two latent variables comprising past coronavirus epidemics (pastCoVepidemics: keywords that address previous epidemics) and the ongoing COVID-19 pandemic (presCoVpandemic: keywords that explain the ongoing pandemic). Both principal components were used significantly to learn about SARS-CoV-2 and COVID-19 and explained 88.78\% of the variability. Three principal components fuelled misinformation about COVID-19: misinformation (keywords ``biological weapon,'' ``virus hoax,'' ``common cold,'' ``COVID-19 hoax,'' and ``China virus''), conspiracy theory 1 (ConspTheory1; keyword ``5G'' or ``@5G''), and conspiracy theory 2 (ConspTheory2; keyword ``ingest bleach''). These principal components explained 84.85\% of the variability. The principal components represent two measurements of public health safety guidelines---public health measures 1 (PubHealthMes1; keywords ``social distancing,'' ``wash hands,'' ``isolation,'' and ``quarantine'') and public health measures 2 (PubHealthMes2; keyword ``wear mask'')---which explained 84.7\% of the variability. Based on the PCA results and the log-linear and predictive models, ConspTheory1 (keyword ``@5G'') was identified as a predictor of people's behavior toward public health measures (PubHealthMes2). Although correlations of misinformation (keywords ``COVID-19,'' ``hoax,'' ``virus hoax,'' ``common cold,'' and more) and ConspTheory2 (keyword ``ingest bleach'') with PubHealthMes1 (keywords ``social distancing,'' ``hand wash,'' ``isolation,'' and more) were r=0.83 and r=--0.11, respectively, neither was statistically significant (P=.27 and P=.13, respectively). Conclusions: Several studies focused on the impacts of social media and related platforms on the spreading of misinformation and conspiracy theories. This study provides the first empirical evidence to the mainly anecdotal discourse on the use of web searches to learn about SARS-CoV-2 and COVID-19. ", doi="10.2196/28975", url="https://www.jmir.org/2021/9/e28975", url="http://www.ncbi.nlm.nih.gov/pubmed/34280117" } @Article{info:doi/10.2196/30409, author="Kong, Wenwen and Song, Shijie and Zhao, Chris Yuxiang and Zhu, Qinghua and Sha, Ling", title="TikTok as a Health Information Source: Assessment of the Quality of Information in Diabetes-Related Videos", journal="J Med Internet Res", year="2021", month="Sep", day="1", volume="23", number="9", pages="e30409", keywords="diabetes", keywords="information quality", keywords="infodemiology", keywords="social media", keywords="short video apps", keywords="TikTok", abstract="Background: Diabetes has become one of the most prevalent chronic diseases, and many people living with diabetes use social media to seek health information. Recently, an emerging social media app, TikTok, has received much interest owing to its popularity among general health consumers. We notice that there are many videos about diabetes on TikTok. However, it remains unclear whether the information in these videos is of satisfactory quality. Objective: This study aimed to assess the quality of the information in diabetes-related videos on TikTok. Methods: We collected a sample of 199 diabetes-related videos in Chinese. The basic information presented in the videos was coded and analyzed. First, we identified the source of each video. Next, 2 independent raters assessed each video in terms of the completeness of six types of content (the definition of the disease, symptoms, risk factors, evaluation, management, and outcomes). Then, the 2 raters independently assessed the quality of information in the videos, using the DISCERN instrument. Results: In regard to the sources of the videos, we found 6 distinct types of uploaders; these included 3 kinds of individual users (ie, health professionals, general users, and science communicators) and 3 types of organizational users (ie, news agencies, nonprofit organizations, and for-profit organizations). Regarding content, our results show that the videos were primarily about diabetes management and contained limited information on the definition of the disease, symptoms, risk factors, evaluation, and outcomes. The overall quality of the videos was acceptable, on average, although the quality of the information varied, depending on the sources. The videos created by nonprofit organizations had the highest information quality, while the videos contributed by for-profit organizations had the lowest information quality. Conclusions: Although the overall quality of the information in the diabetes videos on TikTok is acceptable, TikTok might not fully meet the health information needs of patients with diabetes, and they should exercise caution when using TikTok as a source of diabetes-related information. ", doi="10.2196/30409", url="https://www.jmir.org/2021/9/e30409", url="http://www.ncbi.nlm.nih.gov/pubmed/34468327" } @Article{info:doi/10.2196/29576, author="Park, Myung-Bae and Park, Young Eun and Lee, Sic Tae and Lee, Jinhee", title="Effect of the Period From COVID-19 Symptom Onset to Confirmation on Disease Duration: Quantitative Analysis of Publicly Available Patient Data", journal="J Med Internet Res", year="2021", month="Sep", day="1", volume="23", number="9", pages="e29576", keywords="COVID-19", keywords="SARS-CoV-2", keywords="symptoms onset", keywords="duration of prevalence", keywords="confirmation", keywords="South Korea", keywords="data crawling", keywords="social media", keywords="Internet", keywords="dataset", keywords="symptom", keywords="duration", keywords="outcome", keywords="diagnosis", keywords="prevalence", abstract="Background: In general, early intervention in disease based on early diagnosis is considered to be very important for improving health outcomes. However, there is still insufficient evidence regarding how medical care that is based on the early diagnosis of confirmed cases can affect the outcome of COVID-19 treatment. Objective: We aimed to investigate the effect of the duration from the onset of clinical symptoms to confirmation of COVID-19 on the duration from the onset of symptoms to the resolution of COVID-19 (release from quarantine). Methods: For preliminary data collection, we performed data crawling to extract data from social networks, blogs, and official websites operated by local governments. We collected data from the 4002 confirmed cases in 33 cities reported up to May 31, 2020, for whom sex and age information could be verified. Subsequently, 2494 patients with unclear symptom onset dates and 1349 patients who had not been released or had no data about their release dates were excluded. Thus, 159 patients were finally included in this study. To investigate whether rapid confirmation reduces the prevalence period, we divided the duration from symptom onset to confirmation into quartiles of ?1, ?3, ?6, and ?7 days, respectively. We investigated the duration from symptom onset to release and that from confirmation to release according to these quartiles. Furthermore, we performed multiple regression analysis to investigate the effects of rapid confirmation after symptom onset on the treatment period, duration of prevalence, and duration until release from isolation. Results: We performed multiple regression analysis to investigate the association between rapid confirmation after symptom onset and the total prevalence period (faster release from isolation). The time from symptom onset to confirmation showed a negative association with the time from confirmation to release (t1=?3.58; P<.001) and a positive association with the time from symptom onset to release (t1=5.86; P<.001); these associations were statistically significant. Conclusions: The duration from COVID-19 symptom onset to confirmation date is an important variable for predicting disease prevalence, and these results support the hypothesis that a short duration of symptom onset to confirmation can reduce the time from symptom onset to release. ", doi="10.2196/29576", url="https://www.jmir.org/2021/9/e29576", url="http://www.ncbi.nlm.nih.gov/pubmed/34280114" } @Article{info:doi/10.2196/27715, author="Bautista, Robert John and Zhang, Yan and Gwizdka, Jacek", title="US Physicians' and Nurses' Motivations, Barriers, and Recommendations for Correcting Health Misinformation on Social Media: Qualitative Interview Study", journal="JMIR Public Health Surveill", year="2021", month="Sep", day="1", volume="7", number="9", pages="e27715", keywords="correction", keywords="COVID-19", keywords="physicians", keywords="misinformation", keywords="infodemic", keywords="infodemiology", keywords="nurses", keywords="social media", abstract="Background: Health misinformation is a public health concern. Various stakeholders have called on health care professionals, such as nurses and physicians, to be more proactive in correcting health misinformation on social media. Objective: This study aims to identify US physicians' and nurses' motivations for correcting health misinformation on social media, the barriers they face in doing so, and their recommendations for overcoming such barriers. Methods: In-depth interviews were conducted with 30 participants, which comprised 15 (50\%) registered nurses and 15 (50\%) physicians. Qualitative data were analyzed by using thematic analysis. Results: Participants were personally (eg, personal choice) and professionally (eg, to fulfill the responsibility of a health care professional) motivated to correct health misinformation on social media. However, they also faced intrapersonal (eg, a lack of positive outcomes and time), interpersonal (eg, harassment and bullying), and institutional (eg, a lack of institutional support and social media training) barriers to correcting health misinformation on social media. To overcome these barriers, participants recommended that health care professionals should receive misinformation and social media training, including building their social media presence. Conclusions: US physicians and nurses are willing to correct health misinformation on social media despite several barriers. Nonetheless, this study provides recommendations that can be used to overcome such barriers. Overall, the findings can be used by health authorities and organizations to guide policies and activities aimed at encouraging more health care professionals to be present on social media to counteract health misinformation. ", doi="10.2196/27715", url="https://publichealth.jmir.org/2021/9/e27715", url="http://www.ncbi.nlm.nih.gov/pubmed/34468331" } @Article{info:doi/10.2196/21817, author="Worrall, P. Amy and Kelly, Claire and O'Neill, Aine and O'Doherty, Murray and Kelleher, Eoin and Cushen, Marie Anne and McNally, Cora and McConkey, Samuel and Glavey, Siobhan and Lavin, Michelle and de Barra, Eoghan", title="Online Search Trends Influencing Anticoagulation in Patients With COVID-19: Observational Study", journal="JMIR Form Res", year="2021", month="Aug", day="31", volume="5", number="8", pages="e21817", keywords="COVID-19", keywords="coronavirus", keywords="online search engines", keywords="anticoagulation", keywords="thrombosis", keywords="online influence", keywords="health information dissemination", abstract="Background: Early evidence of COVID-19--associated coagulopathy disseminated rapidly online during the first months of 2020, followed by clinical debate about how best to manage thrombotic risks in these patients. The rapid online spread of case reports was followed by online interim guidelines, discussions, and worldwide online searches for further information. The impact of global online search trends and online discussion on local approaches to coagulopathy in patients with COVID-19 has not been studied. Objective: The goal of this study was to investigate the relationship between online search trends using Google Trends and the rate of appropriate venous thromboembolism (VTE) prophylaxis and anticoagulation therapy in a cohort of patients with COVID-19 admitted to a tertiary hospital in Ireland. Methods: A retrospective audit of anticoagulation therapy and VTE prophylaxis among patients with COVID-19 who were admitted to a tertiary hospital was conducted between February 29 and May 31, 2020. Worldwide Google search trends of the term ``COVID-19'' and anticoagulation synonyms during this time period were determined and correlated against one another using a Spearman correlation. A P value of <.05 was considered significant, and analysis was completed using Prism, version 8 (GraphPad). Results: A statistically significant Spearman correlation (P<.001, r=0.71) was found between the two data sets, showing an increase in VTE prophylaxis in patients with COVID-19 with increasing online searches worldwide. This represents a proxy for online searches and discussion, dissemination of information, and Google search trends relating to COVID-19 and clotting risk, in particular, which correlated with an increasing trend of providing thromboprophylaxis and anticoagulation therapy to patients with COVID-19 in our tertiary center. Conclusions: We described a correlation of local change in clinical practice with worldwide online dialogue and digital search trends that influenced individual clinicians, prior to the publication of formal guidelines or a local quality-improvement intervention. ", doi="10.2196/21817", url="https://formative.jmir.org/2021/8/e21817", url="http://www.ncbi.nlm.nih.gov/pubmed/34292865" } @Article{info:doi/10.2196/29957, author="Kishore, Kamal and Jaswal, Vidushi and Verma, Madhur and Koushal, Vipin", title="Exploring the Utility of Google Mobility Data During the COVID-19 Pandemic in India: Digital Epidemiological Analysis", journal="JMIR Public Health Surveill", year="2021", month="Aug", day="30", volume="7", number="8", pages="e29957", keywords="COVID-19", keywords="lockdown", keywords="nonpharmaceutical Interventions", keywords="social distancing", keywords="digital surveillance", keywords="Google Community Mobility Reports", keywords="community mobility", abstract="Background: Association between human mobility and disease transmission has been established for COVID-19, but quantifying the levels of mobility over large geographical areas is difficult. Google has released Community Mobility Reports (CMRs) containing data about the movement of people, collated from mobile devices. Objective: The aim of this study is to explore the use of CMRs to assess the role of mobility in spreading COVID-19 infection in India. Methods: In this ecological study, we analyzed CMRs to determine human mobility between March and October 2020. The data were compared for the phases before the lockdown (between March 14 and 25, 2020), during lockdown (March 25-June 7, 2020), and after the lockdown (June 8-October 15, 2020) with the reference periods (ie, January 3-February 6, 2020). Another data set depicting the burden of COVID-19 as per various disease severity indicators was derived from a crowdsourced API. The relationship between the two data sets was investigated using the Kendall tau correlation to depict the correlation between mobility and disease severity. Results: At the national level, mobility decreased from --38\% to --77\% for all areas but residential (which showed an increase of 24.6\%) during the lockdown compared to the reference period. At the beginning of the unlock phase, the state of Sikkim (minimum cases: 7) with a --60\% reduction in mobility depicted more mobility compared to --82\% in Maharashtra (maximum cases: 1.59 million). Residential mobility was negatively correlated (--0.05 to --0.91) with all other measures of mobility. The magnitude of the correlations for intramobility indicators was comparatively low for the lockdown phase (correlation ?0.5 for 12 indicators) compared to the other phases (correlation ?0.5 for 45 and 18 indicators in the prelockdown and unlock phases, respectively). A high correlation coefficient between epidemiological and mobility indicators was observed for the lockdown and unlock phases compared to the prelockdown phase. Conclusions: Mobile-based open-source mobility data can be used to assess the effectiveness of social distancing in mitigating disease spread. CMR data depicted an association between mobility and disease severity, and we suggest using this technique to supplement future COVID-19 surveillance. ", doi="10.2196/29957", url="https://publichealth.jmir.org/2021/8/e29957", url="http://www.ncbi.nlm.nih.gov/pubmed/34174780" } @Article{info:doi/10.2196/30715, author="Luo, Chen and Ji, Kaiyuan and Tang, Yulong and Du, Zhiyuan", title="Exploring the Expression Differences Between Professionals and Laypeople Toward the COVID-19 Vaccine: Text Mining Approach", journal="J Med Internet Res", year="2021", month="Aug", day="27", volume="23", number="8", pages="e30715", keywords="COVID-19", keywords="vaccine", keywords="Zhihu", keywords="structural topic modeling", keywords="medical professional", keywords="laypeople", keywords="adverse reactions", keywords="vaccination", keywords="vaccine effectiveness", keywords="vaccine development", abstract="Background: COVID-19 is still rampant all over the world. Until now, the COVID-19 vaccine is the most promising measure to subdue contagion and achieve herd immunity. However, public vaccination intention is suboptimal. A clear division lies between medical professionals and laypeople. While most professionals eagerly promote the vaccination campaign, some laypeople exude suspicion, hesitancy, and even opposition toward COVID-19 vaccines. Objective: This study aims to employ a text mining approach to examine expression differences and thematic disparities between the professionals and laypeople within the COVID-19 vaccine context. Methods: We collected 3196 answers under 65 filtered questions concerning the COVID-19 vaccine from the China-based question and answer forum Zhihu. The questions were classified into 5 categories depending on their contents and description: adverse reactions, vaccination, vaccine effectiveness, social implications of vaccine, and vaccine development. Respondents were also manually coded into two groups: professional and laypeople. Automated text analysis was performed to calculate fundamental expression characteristics of the 2 groups, including answer length, attitude distribution, and high-frequency words. Furthermore, structural topic modeling (STM), as a cutting-edge branch in the topic modeling family, was used to extract topics under each question category, and thematic disparities were evaluated between the 2 groups. Results: Laypeople are more prevailing in the COVID-19 vaccine--related discussion. Regarding differences in expression characteristics, the professionals posted longer answers and showed a conservative stance toward vaccine effectiveness than did laypeople. Laypeople mentioned countries more frequently, while professionals were inclined to raise medical jargon. STM discloses prominent topics under each question category. Statistical analysis revealed that laypeople preferred the ``safety of Chinese-made vaccine'' topic and other vaccine-related issues in other countries. However, the professionals paid more attention to medical principles and professional standards underlying the COVID-19 vaccine. With respect to topics associated with the social implications of vaccines, the 2 groups showed no significant difference. Conclusions: Our findings indicate that laypeople and professionals share some common grounds but also hold divergent focuses toward the COVID-19 vaccine issue. These incongruities can be summarized as ``qualitatively different'' in perspective rather than ``quantitatively different'' in scientific knowledge. Among those questions closely associated with medical expertise, the ``qualitatively different'' characteristic is quite conspicuous. This study boosts the current understanding of how the public perceives the COVID-19 vaccine, in a more nuanced way. Web-based question and answer forums are a bonanza for examining perception discrepancies among various identities. STM further exhibits unique strengths over the traditional topic modeling method in statistically testing the topic preference of diverse groups. Public health practitioners should be keenly aware of the cognitive differences between professionals and laypeople, and pay special attention to the topics with significant inconsistency across groups to build consensus and promote vaccination effectively. ", doi="10.2196/30715", url="https://www.jmir.org/2021/8/e30715", url="http://www.ncbi.nlm.nih.gov/pubmed/34346885" } @Article{info:doi/10.2196/26895, author="Lin, Leesa and Song, Yi and Wang, Qian and Pu, Jialu and Sun, Yueqian Fiona and Zhang, Yixuan and Zhou, Xinyu and Larson, J. Heidi and Hou, Zhiyuan", title="Public Attitudes and Factors of COVID-19 Testing Hesitancy in the United Kingdom and China: Comparative Infodemiology Study", journal="JMIR Infodemiology", year="2021", month="Aug", day="27", volume="1", number="1", pages="e26895", keywords="COVID-19", keywords="test", keywords="public response", keywords="sentiment", keywords="social listening", keywords="United Kingdom", keywords="China", abstract="Background: Massive community-wide testing has become the cornerstone of management strategies for the COVID-19 pandemic. Objective: This study was a comparative analysis between the United Kingdom and China, which aimed to assess public attitudes and uptake regarding COVID-19 testing, with a focus on factors of COVID-19 testing hesitancy, including effectiveness, access, risk perception, and communication. Methods: We collected and manually coded 3856 UK tweets and 9299 Chinese Sina Weibo posts mentioning COVID-19 testing from June 1 to July 15, 2020. Adapted from the World Health Organization's 3C Model of Vaccine Hesitancy, we employed social listening analysis examining key factors of COVID-19 testing hesitancy (confidence, complacency, convenience, and communication). Descriptive analysis, time trends, geographical mapping, and chi-squared tests were performed to assess the temporal, spatial, and sociodemographic characteristics that determine the difference in attitudes or uptake of COVID-19 tests. Results: The UK tweets demonstrated a higher percentage of support toward COVID-19 testing than the posts from China. There were much wider reports of public uptake of COVID-19 tests in mainland China than in the United Kingdom; however, uncomfortable experiences and logistical barriers to testing were more expressed in China. The driving forces for undergoing COVID-19 testing were personal health needs, community-wide testing, and mandatory testing policies for travel, with major differences in the ranking order between the two countries. Rumors and information inquiries about COVID-19 testing were also identified. Conclusions: Public attitudes and acceptance toward COVID-19 testing constantly evolve with local epidemic situations. Policies and information campaigns that emphasize the importance of timely testing and rapid communication responses to inquiries and rumors, and provide a supportive environment for accessing tests are key to tackling COVID-19 testing hesitancy and increasing uptake. ", doi="10.2196/26895", url="https://infodemiology.jmir.org/2021/1/e26895", url="http://www.ncbi.nlm.nih.gov/pubmed/34541460" } @Article{info:doi/10.2196/30271, author="Wei, Shanzun and Ma, Ming and Wen, Xi and Wu, Changjing and Zhu, Guonian and Zhou, Xiangfu", title="Online Public Attention Toward Premature Ejaculation in Mainland China: Infodemiology Study Using the Baidu Index", journal="J Med Internet Res", year="2021", month="Aug", day="26", volume="23", number="8", pages="e30271", keywords="premature ejaculation", keywords="Baidu Index", keywords="infodemiology", keywords="public interest", keywords="patients' concern", keywords="sexuality", keywords="sexual dysfunction", abstract="Background: Premature ejaculation (PE) is one of the most described psychosocial stress and sexual complaints worldwide. Previous investigations have focused predominantly on the prospective identification of cases that meet researchers' specific criteria. The genuine demand from patients with regard to information on PE and related issues may thus be neglected. Objective: This study aims to examine the online search trend and user demand related to PE on a national and regional scale using the dominant major search engine in mainland China. Methods: The Baidu Index was queried using the PE-related terms for the period of January 2011 to December 2020. The search volume for each term was recorded to analyze the search trend and demographic distributions. For user interest, the demand and trend data were collected and analyzed. Results: Of the 36 available PE search keywords, 4 PE searching topics were identified. The Baidu Search Index for each PE topic varied from 46.30\% (86,840,487/187,558,154) to 6.40\% (12,009,307/187,558,154). The annual percent change (APC) for the complaint topic was 48.80\% (P<.001) for 2011 to 2014 and --16.82\% (P<.001) for 2014 to 2020. The APC for the inquiry topic was 16.21\% (P=.41) for 2011 to 2014 and --11.00\% (P<.001) for 2014 to 2020. For the prognosis topic, the annual APC was 11.18\% (P<.001) for 2011 to 2017 and --19.86\% (P<.001) for 2017 to 2020. For the treatment topic, the annual APC was 14.04\% (P<.001) for 2011 to 2016 and --38.83\% (P<.001) for 2016 to 2020. The age distribution of those searching for topics related to PE showed that the population aged 20 to 40 years comprised nearly 70\% of the total search inquiries (second was 17.95\% in the age group younger than 19 years). People from East China made over 50\% of the total search queries. Conclusions: The fluctuating online popularity of PE searches reflects the real-time population demands. It may help medical professionals better understand population interest, population concerns, regional variations, and gender differences on a nationwide scale and make disease-specific health care policies. The internet search data could be more reliable when the insufficient and lagging registry data are completed. ", doi="10.2196/30271", url="https://www.jmir.org/2021/8/e30271", url="http://www.ncbi.nlm.nih.gov/pubmed/34435970" } @Article{info:doi/10.2196/27681, author="Zhu, Peng Yu and Park, Woo Han", title="Development of a COVID-19 Web Information Transmission Structure Based on a Quadruple Helix Model: Webometric Network Approach Using Bing", journal="J Med Internet Res", year="2021", month="Aug", day="26", volume="23", number="8", pages="e27681", keywords="quadruple helix model", keywords="COVID-19", keywords="structural analysis", keywords="content analysis", keywords="network analysis", keywords="public health", keywords="webometrics", keywords="infodemiology", keywords="infoveillance", keywords="development", keywords="internet", keywords="online health information", keywords="structure", keywords="communication", keywords="big data", abstract="Background: Developing an understanding of the social structure and phenomenon of pandemic information sources worldwide is immensely significant. Objective: Based on the quadruple helix model, the aim of this study was to construct and analyze the structure and content of the internet information sources regarding the COVID-19 pandemic, considering time and space. The broader goal was to determine the status and limitations of web information transmission and online communication structure during public health emergencies. Methods: By sorting the second top-level domain, we divided the structure of network information sources into four levels: government, educational organizations, companies, and nonprofit organizations. We analyzed the structure of information sources and the evolution of information content at each stage using quadruple helix and network analysis methods. Results: The results of the structural analysis indicated that the online sources of information in Asia were more diverse than those in other regions in February 2020. As the pandemic spread in April, the information sources in non-Asian regions began to diversify, and the information source structure diversified further in July. With the spread of the pandemic, for an increasing number of countries, not only the government authorities of high concern but also commercial and educational organizations began to produce and provide significant amounts of information and advice. Nonprofit organizations also produced information, but to a lesser extent. The impact of the virus spread from the initial public level of the government to many levels within society. After April, the government's role in the COVID-19 network information was central. The results of the content analysis showed that there was an increased focus on discussion regarding public health--related campaign materials at all stages. The information content changed with the changing stages. In the early stages, the basic situation regarding the virus and its impact on health attracted most of the attention. Later, the content was more focused on prevention. The business and policy environment also changed from the beginning of the pandemic, and the social changes caused by the pandemic became a popular discussion topic. Conclusions: For public health emergencies, some online and offline information sources may not be sufficient. Diversified institutions must pay attention to public health emergencies and actively respond to multihelical information sources. In terms of published messages, the educational sector plays an important role in public health events. However, educational institutions release less information than governments and businesses. This study proposes that the quadruple helix not only has research significance in the field of scientific cooperation but could also be used to perform effective research regarding web information during crises. This is significant for further development of the quadruple helix model in the medical internet research area. ", doi="10.2196/27681", url="https://www.jmir.org/2021/8/e27681", url="http://www.ncbi.nlm.nih.gov/pubmed/34280119" } @Article{info:doi/10.2196/26119, author="Fu, Guanghui and Song, Changwei and Li, Jianqiang and Ma, Yue and Chen, Pan and Wang, Ruiqian and Yang, Xiang Bing and Huang, Zhisheng", title="Distant Supervision for Mental Health Management in Social Media: Suicide Risk Classification System Development Study", journal="J Med Internet Res", year="2021", month="Aug", day="26", volume="23", number="8", pages="e26119", keywords="deep learning", keywords="distant supervision", keywords="mental health", keywords="crisis prevention", abstract="Background: Web-based social media provides common people with a platform to express their emotions conveniently and anonymously. There have been nearly 2 million messages in a particular Chinese social media data source, and several thousands more are generated each day. Therefore, it has become impossible to analyze these messages manually. However, these messages have been identified as an important data source for the prevention of suicide related to depression disorder. Objective: We proposed in this paper a distant supervision approach to developing a system that can automatically identify textual comments that are indicative of a high suicide risk. Methods: To avoid expensive manual data annotations, we used a knowledge graph method to produce approximate annotations for distant supervision, which provided a basis for a deep learning architecture that was built and refined by interactions with psychology experts. There were three annotation levels, as follows: free annotations (zero cost), easy annotations (by psychology students), and hard annotations (by psychology experts). Results: Our system was evaluated accordingly and showed that its performance at each level was promising. By combining our system with several important psychology features from user blogs, we obtained a precision of 80.75\%, a recall of 75.41\%, and an F1 score of 77.98\% for the hardest test data. Conclusions: In this paper, we proposed a distant supervision approach to develop an automatic system that can classify high and low suicide risk based on social media comments. The model can therefore provide volunteers with early warnings to prevent social media users from committing suicide. ", doi="10.2196/26119", url="https://www.jmir.org/2021/8/e26119", url="http://www.ncbi.nlm.nih.gov/pubmed/34435964" } @Article{info:doi/10.2196/29387, author="Sajjadi, B. Nicholas and Feldman, Kaylea and Shepard, Samuel and Reddy, K. Arjun and Torgerson, Trevor and Hartwell, Micah and Vassar, Matt", title="Public Interest and Behavior Change in the United States Regarding Colorectal Cancer Following the Death of Chadwick Boseman: Infodemiology Investigation of Internet Search Trends Nationally and in At-Risk Areas", journal="JMIR Infodemiology", year="2021", month="Aug", day="26", volume="1", number="1", pages="e29387", keywords="Google Trends", keywords="colerectal cancer", keywords="search analytics", keywords="public health", keywords="data analytics", keywords="Chadwick Boseman", keywords="Twitter", keywords="infodemiology", abstract="Background: Colorectal cancer (CRC) has the third highest cancer mortality rate in the United States. Enhanced screening has reduced mortality rates; however, certain populations remain at high risk, notably African Americans. Raising awareness among at-risk populations may lead to improved CRC outcomes. The influence of celebrity death and illness is an important driver of public awareness. As such, the death of actor Chadwick Boseman from CRC may have influenced CRC awareness. Objective: We sought to assess the influence of Chadwick Boseman's death on public interest in CRC in the United States, evidenced by internet searches, website traffic, and donations to prominent cancer organizations. Methods: We used an auto-regressive integrated moving average model to forecast Google searching trends for the topic ``Colorectal cancer'' in the United States. We performed bivariate and multivariable regressions on state-wise CRC incidence rateand percent Black population. We obtained data from the American Cancer Society (ACS) and the Colon Cancer Foundation (CCF) for information regarding changes in website traffic and donations. Results: The expected national relative search volume (RSV) for colorectal cancer was 2.71 (95\% CI 1.76-3.66), reflecting a 3590\% (95\% CI 2632\%-5582\%) increase compared to the expected values. With multivariable regression, the statewise RSV increased for each percent Black population by 1.09 (SE 0.18, P<.001), with 42\% of the variance explained (P<.001). The American Cancer Society reported a 58,000\% increase in CRC-related website traffic the weekend following Chadwick Boseman's death compared to the weekend before. The Colon Cancer Foundation reported a 331\% increase in donations and a 144\% increase in revenue in the month following Boseman's death compared to the month prior. Conclusions: Our results suggest that Chadwick Boseman's death was associated with substantial increases in awareness of CRC. Increased awareness of CRC may support earlier detection and better prognoses. ", doi="10.2196/29387", url="https://infodemiology.jmir.org/2021/1/e29387", url="http://www.ncbi.nlm.nih.gov/pubmed/37114199" } @Article{info:doi/10.2196/28716, author="Chum, Antony and Nielsen, Andrew and Bellows, Zachary and Farrell, Eddie and Durette, Pierre-Nicolas and Banda, M. Juan and Cupchik, Gerald", title="Changes in Public Response Associated With Various COVID-19 Restrictions in Ontario, Canada: Observational Infoveillance Study Using Social Media Time Series Data", journal="J Med Internet Res", year="2021", month="Aug", day="25", volume="23", number="8", pages="e28716", keywords="COVID-19", keywords="public opinion", keywords="social media", keywords="sentiment analysis", keywords="public health restrictions", keywords="infodemiology", keywords="infoveillance", keywords="coronavirus", keywords="evaluation", abstract="Background: News media coverage of antimask protests, COVID-19 conspiracies, and pandemic politicization has overemphasized extreme views but has done little to represent views of the general public. Investigating the public's response to various pandemic restrictions can provide a more balanced assessment of current views, allowing policy makers to craft better public health messages in anticipation of poor reactions to controversial restrictions. Objective: Using data from social media, this infoveillance study aims to understand the changes in public opinion associated with the implementation of COVID-19 restrictions (eg, business and school closures, regional lockdown differences, and additional public health restrictions, such as social distancing and masking). Methods: COVID-19--related tweets in Ontario (n=1,150,362) were collected based on keywords between March 12 and October 31, 2020. Sentiment scores were calculated using the VADER (Valence Aware Dictionary and Sentiment Reasoner) algorithm for each tweet to represent its negative to positive emotion. Public health restrictions were identified using government and news media websites. Dynamic regression models with autoregressive integrated moving average errors were used to examine the association between public health restrictions and changes in public opinion over time (ie, collective attention, aggregate positive sentiment, and level of disagreement), controlling for the effects of confounders (ie, daily COVID-19 case counts, holidays, and COVID-19--related official updates). Results: In addition to expected direct effects (eg, business closures led to decreased positive sentiment and increased disagreements), the impact of restrictions on public opinion was contextually driven. For example, the negative sentiment associated with business closures was reduced with higher COVID-19 case counts. While school closures and other restrictions (eg, masking, social distancing, and travel restrictions) generated increased collective attention, they did not have an effect on aggregate sentiment or the level of disagreement (ie, sentiment polarization). Partial (ie, region-targeted) lockdowns were associated with better public response (ie, higher number of tweets with net positive sentiment and lower levels of disagreement) compared to province-wide lockdowns. Conclusions: Our study demonstrates the feasibility of a rapid and flexible method of evaluating the public response to pandemic restrictions using near real-time social media data. This information can help public health practitioners and policy makers anticipate public response to future pandemic restrictions and ensure adequate resources are dedicated to addressing increases in negative sentiment and levels of disagreement in the face of scientifically informed, but controversial, restrictions. ", doi="10.2196/28716", url="https://www.jmir.org/2021/8/e28716", url="http://www.ncbi.nlm.nih.gov/pubmed/34227996" } @Article{info:doi/10.2196/19917, author="Hornsby, B. and Ensaff, H.", title="Perspectives on Fruit and Vegetable Consumption and Government Dietary Guidelines: Content Analysis of Comments on News Websites", journal="J Med Internet Res", year="2021", month="Aug", day="19", volume="23", number="8", pages="e19917", keywords="medical news", keywords="online news", keywords="user comments", keywords="public health", keywords="population health", keywords="qualitative analysis", keywords="perspectives", keywords="dietary guidelines", keywords="diet", keywords="fruit and vegetable consumption", keywords="mobile phone", abstract="Background: News websites are an essential source of medical news for the public. Many websites offer users the opportunity to leave comments, which may provide rich insights into public perspectives on health issues. With an established role in public health, fruit and vegetable (FV) consumption is central to the government's dietary guidelines. However, FV intake continues to fall short of government recommendations. Objective: Using comments from news websites, this study aims to explore public perspectives on FV intake and related government dietary guidelines. Methods: Data comprised 2696 web user comments generated in response to substantial media coverage for a meta-analysis examining FV consumption and the risk of all-cause mortality, cardiovascular disease, and total cancer. Using an inductive thematic approach, the data were analyzed and coded in an iterative process. Results: Four overarching themes emerged: personal factors, rejection, lack of knowledge, and food landscape, each with component subthemes. The lack of clarity around government dietary health guidelines was apparent, and this, along with emergent personal factors, may hinder better consumption. Rejection was also evident, as was a quality versus quantity of life debate. Conclusions: There are gaps in the public's understanding of government guidelines, which may act as a constraint to better compliance. Further work should examine this issue and rejection and the possibility of public fatigue related to dietary health information and news. Similarly, future work should also explore targeted interventions with a specific emphasis on health literacy. ", doi="10.2196/19917", url="https://www.jmir.org/2021/8/e19917", url="http://www.ncbi.nlm.nih.gov/pubmed/34420913" } @Article{info:doi/10.2196/24523, author="Mohd Hanim, Faiz Muhammad and Md Sabri, Aslinie Budi and Yusof, Norashikin", title="Online News Coverage of the Sugar-Sweetened Beverages Tax in Malaysia: Content Analysis", journal="JMIR Public Health Surveill", year="2021", month="Aug", day="18", volume="7", number="8", pages="e24523", keywords="sugar-sweetened beverages", keywords="obesity", keywords="taxes", keywords="media content analysis", keywords="public health policy", keywords="media content", keywords="public health", keywords="netnography", keywords="malaysia", keywords="budget", abstract="Background: In Malaysia, the Sugar-Sweetened Beverages (SSBs) tax was announced during the parliament's 2019 Budget Speech. The tax was slated to be enforced by April 2019 but was later postponed to July 2019. The announcement has since generated significant media coverage and public feedback. Objective: This study presents a qualitative and quantitative cross-sectional study using netnography to examine how Malaysian online news articles responded to the SSBs tax after the announcement and postimplementation. Methods: Online news articles published on popular online news platforms from November 2018 to August 2019 were downloaded using NCapture and imported into NVivo for analysis using the inductive approach and thematic content analysis following the initial SSBs implementation announcement. Results: A total of 62 news articles were analyzed. Most of the articles positively portrayed the SSBs tax (46.8\%) and highlighted its health impacts (76\%). There were 7 key framing arguments identified in the articles. The positive arguments revolved around incentivizing manufacturers to introduce healthier products voluntarily, positive health consequences, the tax's impact on government revenue, and the use of the generated revenue toward beneficial social programs. The opposing arguments included increased operating costs to the manufacturer, the increased retail price of drinks, and how the SSBs tax is not a robust solution to obesity. The top priority sector considered in introducing the tax was the health perspective, followed by economic purposes and creating policies such as regulating the food and drinks industry. Conclusions: The majority of online news articles positively reported the implementation of the SSBs tax in Malaysia. This suggests media played a role in garnering support for the health policy. As such, relevant bodies can use negative findings to anticipate and reframe counteracting arguments opposing the SSBs tax. ", doi="10.2196/24523", url="https://publichealth.jmir.org/2021/8/e24523", url="http://www.ncbi.nlm.nih.gov/pubmed/34406125" } @Article{info:doi/10.2196/21656, author="Effenberger, Maria and Kronbichler, Andreas and Bettac, Erica and Grabherr, Felix and Grander, Christoph and Adolph, Erik Timon and Mayer, Gert and Zoller, Heinz and Perco, Paul and Tilg, Herbert", title="Using Infodemiology Metrics to Assess Public Interest in Liver Transplantation: Google Trends Analysis", journal="J Med Internet Res", year="2021", month="Aug", day="17", volume="23", number="8", pages="e21656", keywords="digital medicine", keywords="search trends", keywords="public awareness", keywords="infodemiology", keywords="eHealth", abstract="Background: Liver transplantation (LT) is the only curative treatment for end-stage liver disease. Less than 10\% of global transplantation needs are met worldwide, and the need for LT is still increasing. The death rates on the waiting list remain too high. Objective: It is, therefore, critical to raise awareness among the public and health care providers and in turn increasingly acquire donors. Methods: We performed a Google Trends search using the search terms liver transplantation and liver transplant on October 15, 2020. On the basis of the resulting monthly data, the annual average Google Trends indices were calculated for the years 2004 to 2018. We not only investigated the trend worldwide but also used data from the United Network for Organ Sharing (UNOS), Spain, and Eurotransplant. Using pairwise Spearman correlations, Google Trends indices were examined over time and compared with the total number of liver transplants retrieved from the respective official websites of UNOS, the Organizaci{\'o}n Nacional de Trasplantes, and Eurotransplant. Results: From 2004 to 2018, there was a significant decrease in the worldwide Google Trends index from 78.2 in 2004 to 20.5 in 2018 (--71.2\%). This trend was more evident in UNOS than in the Eurotransplant group. In the same period, the number of transplanted livers increased worldwide. The waiting list mortality rate was 31\% for Eurotransplant and 29\% for UNOS. However, in Spain, where there are excellent awareness programs, the Google Trends index remained stable over the years with comparable, increasing LT numbers but a significantly lower waiting list mortality (15\%). Conclusions: Public awareness in LT has decreased significantly over the past two decades. Therefore, novel awareness programs should be initialized. ", doi="10.2196/21656", url="https://www.jmir.org/2021/8/e21656", url="http://www.ncbi.nlm.nih.gov/pubmed/34402801" } @Article{info:doi/10.2196/29556, author="Tozzi, Eugenio Alberto and Gesualdo, Francesco and Urbani, Emanuele and Sbenaglia, Alessandro and Ascione, Roberto and Procopio, Nicola and Croci, Ileana and Rizzo, Caterina", title="Digital Surveillance Through an Online Decision Support Tool for COVID-19 Over One Year of the Pandemic in Italy: Observational Study", journal="J Med Internet Res", year="2021", month="Aug", day="13", volume="23", number="8", pages="e29556", keywords="COVID-19", keywords="public health", keywords="surveillance", keywords="digital surveillance", keywords="internet", keywords="online decision support system", keywords="decision support", keywords="support", keywords="online tool", keywords="Italy", keywords="observational", abstract="Background: Italy has experienced severe consequences (ie, hospitalizations and deaths) during the COVID-19 pandemic. Online decision support systems (DSS) and self-triage applications have been used in several settings to supplement health authority recommendations to prevent and manage COVID-19. A digital Italian health tech startup, Paginemediche, developed a noncommercial, online DSS with a chat user interface to assist individuals in Italy manage their potential exposure to COVID-19 and interpret their symptoms since early in the pandemic. Objective: This study aimed to compare the trend in online DSS sessions with that of COVID-19 cases reported by the national health surveillance system in Italy, from February 2020 to March 2021. Methods: We compared the number of sessions by users with a COVID-19--positive contact and users with COVID-19--compatible symptoms with the number of cases reported by the national surveillance system. To calculate the distance between the time series, we used the dynamic time warping algorithm. We applied Symbolic Aggregate approXimation (SAX) encoding to the time series in 1-week periods. We calculated the Hamming distance between the SAX strings. We shifted time series of online DSS sessions 1 week ahead. We measured the improvement in Hamming distance to verify the hypothesis that online DSS sessions anticipate the trends in cases reported to the official surveillance system. Results: We analyzed 75,557 sessions in the online DSS; 65,207 were sessions by symptomatic users, while 19,062 were by contacts of individuals with COVID-19. The highest number of online DSS sessions was recorded early in the pandemic. Second and third peaks were observed in October 2020 and March 2021, respectively, preceding the surge in notified COVID-19 cases by approximately 1 week. The distance between sessions by users with COVID-19 contacts and reported cases calculated by dynamic time warping was 61.23; the distance between sessions by symptomatic users was 93.72. The time series of users with a COVID-19 contact was more consistent with the trend in confirmed cases. With the 1-week shift, the Hamming distance between the time series of sessions by users with a COVID-19 contact and reported cases improved from 0.49 to 0.46. We repeated the analysis, restricting the time window to between July 2020 and December 2020. The corresponding Hamming distance was 0.16 before and improved to 0.08 after the time shift. Conclusions: Temporal trends in the number of online COVID-19 DSS sessions may precede the trend in reported COVID-19 cases through traditional surveillance. The trends in sessions by users with a contact with COVID-19 may better predict reported cases of COVID-19 than sessions by symptomatic users. Data from online DSS may represent a useful supplement to traditional surveillance and support the identification of early warning signals in the COVID-19 pandemic. ", doi="10.2196/29556", url="https://www.jmir.org/2021/8/e29556", url="http://www.ncbi.nlm.nih.gov/pubmed/34292866" } @Article{info:doi/10.2196/29150, author="Tan, Hao and Peng, Sheng-Lan and Zhu, Chun-Peng and You, Zuo and Miao, Ming-Cheng and Kuai, Shu-Guang", title="Long-term Effects of the COVID-19 Pandemic on Public Sentiments in Mainland China: Sentiment Analysis of Social Media Posts", journal="J Med Internet Res", year="2021", month="Aug", day="12", volume="23", number="8", pages="e29150", keywords="COVID-19", keywords="emotional trauma", keywords="public sentiment on social media", keywords="long-term effect", abstract="Background: The COVID-19 outbreak has induced negative emotions among people. These emotions are expressed by the public on social media and are rapidly spread across the internet, which could cause high levels of panic among the public. Understanding the changes in public sentiment on social media during the pandemic can provide valuable information for developing appropriate policies to reduce the negative impact of the pandemic on the public. Previous studies have consistently shown that the COVID-19 outbreak has had a devastating negative impact on public sentiment. However, it remains unclear whether there has been a variation in the public sentiment during the recovery phase of the pandemic. Objective: In this study, we aim to determine the impact of the COVID-19 pandemic in mainland China by continuously tracking public sentiment on social media throughout 2020. Methods: We collected 64,723,242 posts from Sina Weibo, China's largest social media platform, and conducted a sentiment analysis based on natural language processing to analyze the emotions reflected in these posts. Results: We found that the COVID-19 pandemic not only affected public sentiment on social media during the initial outbreak but also induced long-term negative effects even in the recovery period. These long-term negative effects were no longer correlated with the number of new confirmed COVID-19 cases both locally and nationwide during the recovery period, and they were not attributed to the postpandemic economic recession. Conclusions: The COVID-19 pandemic induced long-term negative effects on public sentiment in mainland China even as the country recovered from the pandemic. Our study findings remind public health and government administrators of the need to pay attention to public mental health even once the pandemic has concluded. ", doi="10.2196/29150", url="https://www.jmir.org/2021/8/e29150", url="http://www.ncbi.nlm.nih.gov/pubmed/34280118" } @Article{info:doi/10.2196/28800, author="Boucher, Jean-Christophe and Cornelson, Kirsten and Benham, L. Jamie and Fullerton, M. Madison and Tang, Theresa and Constantinescu, Cora and Mourali, Mehdi and Oxoby, J. Robert and Marshall, A. Deborah and Hemmati, Hadi and Badami, Abbas and Hu, Jia and Lang, Raynell", title="Analyzing Social Media to Explore the Attitudes and Behaviors Following the Announcement of Successful COVID-19 Vaccine Trials: Infodemiology Study", journal="JMIR Infodemiology", year="2021", month="Aug", day="12", volume="1", number="1", pages="e28800", keywords="coronavirus", keywords="COVID-19", keywords="public health", keywords="social media", keywords="Twitter", keywords="behavior", keywords="risk reduction", keywords="attitudes", keywords="social network analysis", keywords="machine learning", abstract="Background: The rollout of COVID-19 vaccines has brought vaccine hesitancy to the forefront in managing this pandemic. COVID-19 vaccine hesitancy is fundamentally different from that of other vaccines due to the new technologies being used, rapid development, and widespread global distribution. Attitudes on vaccines are largely driven by online information, particularly information on social media. The first step toward influencing attitudes about immunization is understanding the current patterns of communication that characterize the immunization debate on social media platforms. Objective: We aimed to evaluate societal attitudes, communication trends, and barriers to COVID-19 vaccine uptake through social media content analysis to inform communication strategies promoting vaccine acceptance. Methods: Social network analysis (SNA) and unsupervised machine learning were used to characterize COVID-19 vaccine content on Twitter globally. Tweets published in English and French were collected through the Twitter application programming interface between November 19 and 26, 2020, just following the announcement of initial COVID-19 vaccine trials. SNA was used to identify social media clusters expressing mistrustful opinions on COVID-19 vaccination. Based on the SNA results, an unsupervised machine learning approach to natural language processing using a sentence-level algorithm transfer function to detect semantic textual similarity was performed in order to identify the main themes of vaccine hesitancy. Results: The tweets (n=636,516) identified that the main themes driving the vaccine hesitancy conversation were concerns of safety, efficacy, and freedom, and mistrust in institutions (either the government or multinational corporations). A main theme was the safety and efficacy of mRNA technology and side effects. The conversation around efficacy was that vaccines were unlikely to completely rid the population of COVID-19, polymerase chain reaction testing is flawed, and there is no indication of long-term T-cell immunity for COVID-19. Nearly one-third (45,628/146,191, 31.2\%) of the conversations on COVID-19 vaccine hesitancy clusters expressed concerns for freedom or mistrust of institutions (either the government or multinational corporations) and nearly a quarter (34,756/146,191, 23.8\%) expressed criticism toward the government's handling of the pandemic. Conclusions: Social media content analysis combined with social network analysis provides insights into the themes of the vaccination conversation on Twitter. The themes of safety, efficacy, and trust in institutions will need to be considered, as targeted outreach programs and intervention strategies are deployed on Twitter to improve the uptake of COVID-19 vaccination. ", doi="10.2196/28800", url="https://infodemiology.jmir.org/2021/1/e28800", url="http://www.ncbi.nlm.nih.gov/pubmed/34447924" } @Article{info:doi/10.2196/28876, author="Rabiolo, Alessandro and Alladio, Eugenio and Morales, Esteban and McNaught, Ian Andrew and Bandello, Francesco and Afifi, A. Abdelmonem and Marchese, Alessandro", title="Forecasting the COVID-19 Epidemic by Integrating Symptom Search Behavior Into Predictive Models: Infoveillance Study", journal="J Med Internet Res", year="2021", month="Aug", day="11", volume="23", number="8", pages="e28876", keywords="Google Trends", keywords="symptoms", keywords="coronavirus", keywords="SARS-CoV-2", keywords="big data", keywords="time series", keywords="predictive models", keywords="Shiny web application", keywords="infodemiology", keywords="infoveillance", keywords="digital health", keywords="COVID-19", abstract="Background: Previous studies have suggested associations between trends of web searches and COVID-19 traditional metrics. It remains unclear whether models incorporating trends of digital searches lead to better predictions. Objective: The aim of this study is to investigate the relationship between Google Trends searches of symptoms associated with COVID-19 and confirmed COVID-19 cases and deaths. We aim to develop predictive models to forecast the COVID-19 epidemic based on a combination of Google Trends searches of symptoms and conventional COVID-19 metrics. Methods: An open-access web application was developed to evaluate Google Trends and traditional COVID-19 metrics via an interactive framework based on principal component analysis (PCA) and time series modeling. The application facilitates the analysis of symptom search behavior associated with COVID-19 disease in 188 countries. In this study, we selected the data of nine countries as case studies to represent all continents. PCA was used to perform data dimensionality reduction, and three different time series models (error, trend, seasonality; autoregressive integrated moving average; and feed-forward neural network autoregression) were used to predict COVID-19 metrics in the upcoming 14 days. The models were compared in terms of prediction ability using the root mean square error (RMSE) of the first principal component (PC1). The predictive abilities of models generated with both Google Trends data and conventional COVID-19 metrics were compared with those fitted with conventional COVID-19 metrics only. Results: The degree of correlation and the best time lag varied as a function of the selected country and topic searched; in general, the optimal time lag was within 15 days. Overall, predictions of PC1 based on both search terms and COVID-19 traditional metrics performed better than those not including Google searches (median 1.56, IQR 0.90-2.49 versus median 1.87, IQR 1.09-2.95, respectively), but the improvement in prediction varied as a function of the selected country and time frame. The best model varied as a function of country, time range, and period of time selected. Models based on a 7-day moving average led to considerably smaller RMSE values as opposed to those calculated with raw data (median 0.90, IQR 0.50-1.53 versus median 2.27, IQR 1.62-3.74, respectively). Conclusions: The inclusion of digital online searches in statistical models may improve the nowcasting and forecasting of the COVID-19 epidemic and could be used as one of the surveillance systems of COVID-19 disease. We provide a free web application operating with nearly real-time data that anyone can use to make predictions of outbreaks, improve estimates of the dynamics of ongoing epidemics, and predict future or rebound waves. ", doi="10.2196/28876", url="https://www.jmir.org/2021/8/e28876", url="http://www.ncbi.nlm.nih.gov/pubmed/34156966" } @Article{info:doi/10.2196/30251, author="Liu, Siru and Li, Jili and Liu, Jialin", title="Leveraging Transfer Learning to Analyze Opinions, Attitudes, and Behavioral Intentions Toward COVID-19 Vaccines: Social Media Content and Temporal Analysis", journal="J Med Internet Res", year="2021", month="Aug", day="10", volume="23", number="8", pages="e30251", keywords="vaccine", keywords="COVID-19", keywords="leveraging transfer learning", keywords="pandemic", keywords="infodemiology", keywords="infoveillance", keywords="public health", keywords="social media", keywords="content analysis", keywords="machine learning", keywords="online health", abstract="Background: The COVID-19 vaccine is considered to be the most promising approach to alleviate the pandemic. However, in recent surveys, acceptance of the COVID-19 vaccine has been low. To design more effective outreach interventions, there is an urgent need to understand public perceptions of COVID-19 vaccines. Objective: Our objective was to analyze the potential of leveraging transfer learning to detect tweets containing opinions, attitudes, and behavioral intentions toward COVID-19 vaccines, and to explore temporal trends as well as automatically extract topics across a large number of tweets. Methods: We developed machine learning and transfer learning models to classify tweets, followed by temporal analysis and topic modeling on a dataset of COVID-19 vaccine--related tweets posted from November 1, 2020 to January 31, 2021. We used the F1 values as the primary outcome to compare the performance of machine learning and transfer learning models. The statistical values and P values from the Augmented Dickey-Fuller test were used to assess whether users' perceptions changed over time. The main topics in tweets were extracted by latent Dirichlet allocation analysis. Results: We collected 2,678,372 tweets related to COVID-19 vaccines from 841,978 unique users and annotated 5000 tweets. The F1 values of transfer learning models were 0.792 (95\% CI 0.789-0.795), 0.578 (95\% CI 0.572-0.584), and 0.614 (95\% CI 0.606-0.622) for these three tasks, which significantly outperformed the machine learning models (logistic regression, random forest, and support vector machine). The prevalence of tweets containing attitudes and behavioral intentions varied significantly over time. Specifically, tweets containing positive behavioral intentions increased significantly in December 2020. In addition, we selected tweets in the following categories: positive attitudes, negative attitudes, positive behavioral intentions, and negative behavioral intentions. We then identified 10 main topics and relevant terms for each category. Conclusions: Overall, we provided a method to automatically analyze the public understanding of COVID-19 vaccines from real-time data in social media, which can be used to tailor educational programs and other interventions to effectively promote the public acceptance of COVID-19 vaccines. ", doi="10.2196/30251", url="https://www.jmir.org/2021/8/e30251", url="http://www.ncbi.nlm.nih.gov/pubmed/34254942" } @Article{info:doi/10.2196/28931, author="Laestadius, I. Linnea and Craig, A. Katherine and Campos-Castillo, Celeste", title="Perceptions of Alerts Issued by Social Media Platforms in Response to Self-injury Posts Among Latinx Adolescents: Qualitative Analysis", journal="J Med Internet Res", year="2021", month="Aug", day="10", volume="23", number="8", pages="e28931", keywords="adolescents", keywords="social media", keywords="mental health", keywords="NSSI", keywords="race and ethnicity", keywords="mobile phone", abstract="Background: There is growing interest in using social media data to detect and address nonsuicidal self-injury (NSSI) among adolescents. Adolescents often do not seek clinical help for NSSI and may adopt strategies to obscure detection; therefore, social media platforms may be able to facilitate early detection and treatment by using machine learning models to screen posts for harmful content and subsequently alert adults. However, such efforts have raised privacy and ethical concerns among health researchers. Little is currently known about how adolescents perceive these efforts. Objective: The aim of this study is to examine perceptions of automated alerts for NSSI posts on social media among Latinx adolescents, who are at risk for NSSI yet are underrepresented in both NSSI and health informatics research. In addition, we considered their perspectives on preferred recipients of automated alerts. Methods: We conducted semistructured, qualitative interviews with 42 Latinx adolescents between the ages of 13 and 17 years who were recruited from a nonprofit organization serving the Latinx community in Milwaukee, Wisconsin. The Latinx population in Milwaukee is largely of Mexican descent. All interviews were conducted between June and July 2019. Transcripts were analyzed using framework analysis to discern their perceptions of automated alerts sent by social media platforms and potential alert recipients. Results: Participants felt that automated alerts would make adolescents safer and expedite aid before the situation escalated. However, some worried that hyperbolic statements would generate false alerts and instigate conflicts. Interviews revealed strong opinions about ideal alert recipients. Parents were most commonly endorsed, but support was conditional on perceptions that the parent would respond appropriately. Emergency services were judged as safer but inappropriate for situations considered lower risk. Alerts sent to school staff generated the strongest privacy concerns. Altogether, the preferred alert recipients varied by individual adolescents and perceived risks in the situation. None raised ethical concerns about the collection, analysis, or storage of personal information regarding their mental health status. Conclusions: Overall, Latinx adolescents expressed broad support for automated alerts for NSSI on social media, which indicates opportunities to address NSSI. However, these efforts should be co-constructed with adolescents to ensure that preferences and needs are met, as well as embedded within broader approaches for addressing structural and cultural barriers to care. ", doi="10.2196/28931", url="https://www.jmir.org/2021/8/e28931", url="http://www.ncbi.nlm.nih.gov/pubmed/34383683" } @Article{info:doi/10.2196/28249, author="Tri Sakti, Muhammad Andi and Mohamad, Emma and Azlan, Anis Arina", title="Mining of Opinions on COVID-19 Large-Scale Social Restrictions in Indonesia: Public Sentiment and Emotion Analysis on Online Media", journal="J Med Internet Res", year="2021", month="Aug", day="9", volume="23", number="8", pages="e28249", keywords="large-scale social restrictions", keywords="social media", keywords="public sentiment", keywords="Twitter", keywords="COVID-19", keywords="infodemiology", keywords="infoveillance", abstract="Background: One of the successful measures to curb COVID-19 spread in large populations is the implementation of a movement restriction order. Globally, it was observed that countries implementing strict movement control were more successful in controlling the spread of the virus as compared with those with less stringent measures. Society's adherence to the movement control order has helped expedite the process to flatten the pandemic curve as seen in countries such as China and Malaysia. At the same time, there are countries facing challenges with society's nonconformity toward movement restriction orders due to various claims such as human rights violations as well as sociocultural and economic issues. In Indonesia, society's adherence to its large-scale social restrictions (LSSRs) order is also a challenge to achieve. Indonesia is regarded as among the worst in Southeast Asian countries in terms of managing the spread of COVID-19. It is proven by the increased number of daily confirmed cases and the total number of deaths, which was more than 6.21\% (1351/21,745) of total active cases as of May 2020. Objective: The aim of this study was to explore public sentiments and emotions toward the LSSR and identify issues, fear, and reluctance to observe this restriction among the Indonesian public. Methods: This study adopts a sentiment analysis method with a supervised machine learning approach on COVID-19-related posts on selected media platforms (Twitter, Facebook, Instagram, and YouTube). The analysis was also performed on COVID-19-related news contained in more than 500 online news platforms recognized by the Indonesian Press Council. Social media posts and news originating from Indonesian online media between March 31 and May 31, 2020, were analyzed. Emotion analysis on Twitter platform was also performed to identify collective public emotions toward the LSSR. Results: The study found that positive sentiment surpasses other sentiment categories by 51.84\% (n=1,002,947) of the total data (N=1,934,596) collected via the search engine. Negative sentiment was recorded at 35.51\% (686,892/1,934,596) and neutral sentiment at 12.65\% (244,757/1,934,596). The analysis of Twitter posts also showed that the majority of public have the emotion of ``trust'' toward the LSSR. Conclusions: Public sentiment toward the LSSR appeared to be positive despite doubts on government consistency in executing the LSSR. The emotion analysis also concluded that the majority of people believe in LSSR as the best method to break the chain of COVID-19 transmission. Overall, Indonesians showed trust and expressed hope toward the government's ability to manage this current global health crisis and win against COVID-19. ", doi="10.2196/28249", url="https://www.jmir.org/2021/8/e28249", url="http://www.ncbi.nlm.nih.gov/pubmed/34280116" } @Article{info:doi/10.2196/29929, author="Rovetta, Alessandro", title="The Impact of COVID-19 on Conspiracy Hypotheses and Risk Perception in Italy: Infodemiological Survey Study Using Google Trends", journal="JMIR Infodemiology", year="2021", month="Aug", day="6", volume="1", number="1", pages="e29929", keywords="COVID-19", keywords="fake news", keywords="Google Trends", keywords="infodemiology", keywords="Italy", keywords="risk perception", abstract="Background: COVID-19 has caused the worst international crisis since World War II. Italy was one of the countries most affected by both the pandemic and the related infodemic. The success of anti--COVID-19 strategies and future public health policies in Italy cannot separate itself from the containment of fake news and the divulgation of correct information. Objective: The aim of this paper was to analyze the impact of COVID-19 on web interest in conspiracy hypotheses and risk perception of Italian web users. Methods: Google Trends was used to monitor users' web interest in specific topics, such as conspiracy hypotheses, vaccine side effects, and pollution and climate change. The keywords adopted to represent these topics were mined from Bufale.net---an Italian website specializing in detecting online hoaxes---and Google Trends suggestions (ie, related topics and related queries). Relative search volumes (RSVs) of the time-lapse periods of 2016-2020 (pre--COVID-19) and 2020-2021 (post--COVID-19) were compared through percentage difference (?\%) and the Welch t test (t). When data series were not stationary, other ad hoc criteria were used. The trend slopes were assessed through Sen slope (SS). The significance thresholds have been indicatively set at P=.05 and t=1.9. Results: The COVID-19 pandemic drastically increased Italian netizens' interest in conspiracies (?\% ? [60, 288], t ? [6, 12]). Web interest in conspiracy-related queries across Italian regions increased and became more homogeneous compared to the pre--COVID-19 period (average RSV=80{\textpm}2.8, tmin=1.8, ?min\%=+12.4, min?SD\%=--25.8). In addition, a growing trend in web interest in the infodemic YouTube channel ByoBlu has been highlighted. Web interest in hoaxes has increased more than interest in antihoax services (t1=11.3 vs t2=4.5; $\Delta$1\%=+157.6 vs $\Delta$2\%=+84.7). Equivalently, web interest in vaccine side effects exceeded interest in pollution and climate change (SSvaccines=0.22, P<.001 vs SSpollution=0.05, P<.001; ?\%=+296.4). To date, a significant amount of fake news related to COVID-19 vaccines, unproven remedies, and origin has continued to circulate. In particular, the creation of SARS-CoV-2 in a Chinese laboratory constituted about 0.04\% of the entire web interest in the pandemic. Conclusions: COVID-19 has given a significant boost to web interest in conspiracy hypotheses and has made it more uniform across regions in Italy. The pandemic accelerated an already-growing trend in users' interest toward some fake news sources, including the 500,000-subscriber YouTube channel ByoBlu, which was removed from the platform by YouTube for disinformation in March 2021. The risk perception related to COVID-19 vaccines has been so distorted that vaccine side effect--related queries outweighed those relating to pollution and climate change, which are much more urgent issues. Moreover, a large amount of fake news has circulated about COVID-19 vaccines, remedies, and origin. Based on these findings, it is recommended that the Italian authorities implement more effective infoveillance systems, and that communication by the mass media be less sensationalistic and more consistent with the available scientific evidence. In this context, Google Trends can be used to monitor users' response to specific infodemiological countermeasures. Further research is needed to understand the psychological mechanisms that regulate risk perception. ", doi="10.2196/29929", url="https://infodemiology.jmir.org/2021/1/e29929", url="http://www.ncbi.nlm.nih.gov/pubmed/34447925" } @Article{info:doi/10.2196/26478, author="Du, Jingcheng and Preston, Sharice and Sun, Hanxiao and Shegog, Ross and Cunningham, Rachel and Boom, Julie and Savas, Lara and Amith, Muhammad and Tao, Cui", title="Using Machine Learning--Based Approaches for the Detection and Classification of Human Papillomavirus Vaccine Misinformation: Infodemiology Study of Reddit Discussions", journal="J Med Internet Res", year="2021", month="Aug", day="5", volume="23", number="8", pages="e26478", keywords="HPV vaccine", keywords="social media", keywords="misinformation", keywords="infodemiology", keywords="infoveillance", keywords="deep learning", keywords="Reddit", keywords="machine learning", abstract="Background: The rapid growth of social media as an information channel has made it possible to quickly spread inaccurate or false vaccine information, thus creating obstacles for vaccine promotion. Objective: The aim of this study is to develop and evaluate an intelligent automated protocol for identifying and classifying human papillomavirus (HPV) vaccine misinformation on social media using machine learning (ML)--based methods. Methods: Reddit posts (from 2007 to 2017, N=28,121) that contained keywords related to HPV vaccination were compiled. A random subset (2200/28,121, 7.82\%) was manually labeled for misinformation and served as the gold standard corpus for evaluation. A total of 5 ML-based algorithms, including a support vector machine, logistic regression, extremely randomized trees, a convolutional neural network, and a recurrent neural network designed to identify vaccine misinformation, were evaluated for identification performance. Topic modeling was applied to identify the major categories associated with HPV vaccine misinformation. Results: A convolutional neural network model achieved the highest area under the receiver operating characteristic curve of 0.7943. Of the 28,121 Reddit posts, 7207 (25.63\%) were classified as vaccine misinformation, with discussions about general safety issues identified as the leading type of misinformed posts (2666/7207, 36.99\%). Conclusions: ML-based approaches are effective in the identification and classification of HPV vaccine misinformation on Reddit and may be generalizable to other social media platforms. ML-based methods may provide the capacity and utility to meet the challenge involved in intelligent automated monitoring and classification of public health misinformation on social media platforms. The timely identification of vaccine misinformation on the internet is the first step in misinformation correction and vaccine promotion. ", doi="10.2196/26478", url="https://www.jmir.org/2021/8/e26478", url="http://www.ncbi.nlm.nih.gov/pubmed/34383667" } @Article{info:doi/10.2196/29570, author="Jiang, Julie and Ren, Xiang and Ferrara, Emilio", title="Social Media Polarization and Echo Chambers in the Context of COVID-19: Case Study", journal="JMIRx Med", year="2021", month="Aug", day="5", volume="2", number="3", pages="e29570", keywords="social media", keywords="opinion", keywords="infodemiology", keywords="infoveillance", keywords="COVID-19", keywords="case study", keywords="polarization", keywords="communication", keywords="Twitter", keywords="echo chamber", abstract="Background: Social media chatter in 2020 has been largely dominated by the COVID-19 pandemic. Existing research shows that COVID-19 discourse is highly politicized, with political preferences linked to beliefs and disbeliefs about the virus. As it happens with topics that become politicized, people may fall into echo chambers, which is the idea that one is only presented with information they already agree with, thereby reinforcing one's confirmation bias. Understanding the relationship between information dissemination and political preference is crucial for effective public health communication. Objective: We aimed to study the extent of polarization and examine the structure of echo chambers related to COVID-19 discourse on Twitter in the United States. Methods: First, we presented Retweet-BERT, a scalable and highly accurate model for estimating user polarity by leveraging language features and network structures. Then, by analyzing the user polarity predicted by Retweet-BERT, we provided new insights into the characterization of partisan users. Results: We observed that right-leaning users were noticeably more vocal and active in the production and consumption of COVID-19 information. We also found that most of the highly influential users were partisan, which may contribute to further polarization. Importantly, while echo chambers exist in both the right- and left-leaning communities, the right-leaning community was by far more densely connected within their echo chamber and isolated from the rest. Conclusions: We provided empirical evidence that political echo chambers are prevalent, especially in the right-leaning community, which can exacerbate the exposure to information in line with pre-existing users' views. Our findings have broader implications in developing effective public health campaigns and promoting the circulation of factual information online. ", doi="10.2196/29570", url="https://med.jmirx.org/2021/3/e29570", url="http://www.ncbi.nlm.nih.gov/pubmed/34459833" } @Article{info:doi/10.2196/28740, author="Sajjadi, B. Nicholas and Shepard, Samuel and Ottwell, Ryan and Murray, Kelly and Chronister, Justin and Hartwell, Micah and Vassar, Matt", title="Examining the Public's Most Frequently Asked Questions Regarding COVID-19 Vaccines Using Search Engine Analytics in the United States: Observational Study", journal="JMIR Infodemiology", year="2021", month="Aug", day="4", volume="1", number="1", pages="e28740", keywords="content", keywords="COVID-19", keywords="frequently asked questions", keywords="internet", keywords="machine learning", keywords="natural language processing", keywords="quality", keywords="question", keywords="SARS-CoV-2", keywords="search analytics", keywords="search engine", keywords="transparency", keywords="vaccine hesitancy", keywords="vaccine", keywords="web-based health information", abstract="Background: The emergency authorization of COVID-19 vaccines has offered the first means of long-term protection against COVID-19--related illness since the pandemic began. It is important for health care professionals to understand commonly held COVID-19 vaccine concerns and to be equipped with quality information that can be used to assist in medical decision-making. Objective: Using Google's RankBrain machine learning algorithm, we sought to characterize the content of the most frequently asked questions (FAQs) about COVID-19 vaccines evidenced by internet searches. Secondarily, we sought to examine the information transparency and quality of sources used by Google to answer FAQs on COVID-19 vaccines. Methods: We searched COVID-19 vaccine terms on Google and used the ``People also ask'' box to obtain FAQs generated by Google's machine learning algorithms. FAQs are assigned an ``answer'' source by Google. We extracted FAQs and answer sources related to COVID-19 vaccines. We used the Rothwell Classification of Questions to categorize questions on the basis of content. We classified answer sources as either academic, commercial, government, media outlet, or medical practice. We used the Journal of the American Medical Association's (JAMA's) benchmark criteria to assess information transparency and Brief DISCERN to assess information quality for answer sources. FAQ and answer source type frequencies were calculated. Chi-square tests were used to determine associations between information transparency by source type. One-way analysis of variance was used to assess differences in mean Brief DISCERN scores by source type. Results: Our search yielded 28 unique FAQs about COVID-19 vaccines. Most COVID-19 vaccine--related FAQs were seeking factual information (22/28, 78.6\%), specifically about safety and efficacy (9/22, 40.9\%). The most common source type was media outlets (12/28, 42.9\%), followed by government sources (11/28, 39.3\%). Nineteen sources met 3 or more JAMA benchmark criteria with government sources as the majority (10/19, 52.6\%). JAMA benchmark criteria performance did not significantly differ among source types ($\chi$24=7.40; P=.12). One-way analysis of variance revealed a significant difference in mean Brief DISCERN scores by source type (F4,23=10.27; P<.001). Conclusions: The most frequently asked COVID-19 vaccine--related questions pertained to vaccine safety and efficacy. We found that government sources provided the most transparent and highest-quality web-based COVID-19 vaccine--related information. Recognizing common questions and concerns about COVID-19 vaccines may assist in improving vaccination efforts. ", doi="10.2196/28740", url="https://infodemiology.jmir.org/2021/1/e28740", url="http://www.ncbi.nlm.nih.gov/pubmed/34458683" } @Article{info:doi/10.2196/28074, author="Masngut, Nasaai and Mohamad, Emma", title="Association Between Public Opinion and Malaysian Government Communication Strategies About the COVID-19 Crisis: Content Analysis of Image Repair Strategies in Social Media", journal="J Med Internet Res", year="2021", month="Aug", day="4", volume="23", number="8", pages="e28074", keywords="COVID-19", keywords="crisis", keywords="health communication", keywords="image repair", keywords="Malaysian government", keywords="sentiment", keywords="communication", keywords="content analysis", keywords="public opinion", keywords="social media", keywords="strategy", abstract="Background: The COVID-19 health crisis has posed an unprecedented challenge for governments worldwide to manage and communicate about the pandemic effectively, while maintaining public trust. Good leadership image in times of a health emergency is paramount to ensure public confidence in governments' abilities to manage the crisis. Objective: The aim of this study was to identify types of image repair strategies utilized by the Malaysian government in their communication about COVID-19 in the media and analyze public responses to these messages on social media. Methods: Content analysis was employed to analyze 120 media statements and 382 comments retrieved from Facebook pages of 2 mainstream newspapers---Berita Harian and The Star. These media statements and comments were collected within a span of 6 weeks prior to and during the first implementation of Movement Control Order by the Malaysian Government. The media statements were analyzed according to Image Repair Theory to categorize strategies employed in government communications related to COVID-19 crisis. Public opinion responses were measured using modified lexicon-based sentiment analysis to categorize positive, negative, and neutral statements. Results: The Malaysian government employed all 5 Image Repair Theory strategies in their communications in both newspapers. The strategy most utilized was reducing offensiveness (75/120, 62.5\%), followed by corrective action (30/120, 25.0\%), evading responsibilities (10/120, 8.3\%), denial (4/120, 3.3\%), and mortification (1/120, 0.8\%). This study also found multiple substrategies in government media statements including denial, shifting blame, provocation, defeasibility, accident, good intention, bolstering, minimization, differentiation, transcendence, attacking accuser, resolve problem, prevent recurrence, admit wrongdoing, and apologize. This study also found that 64.7\% of public opinion was positive in response to media statements made by the Malaysian government and also revealed a significant positive association (P=.04) between image repair strategies utilized by the Malaysian government and public opinion. Conclusions: Communication in the media may assist the government in fostering positive support from the public. Suitable image repair strategies could garner positive public responses and help build trust in times of crisis. ", doi="10.2196/28074", url="https://www.jmir.org/2021/8/e28074", url="http://www.ncbi.nlm.nih.gov/pubmed/34156967" } @Article{info:doi/10.2196/28888, author="Alshareef, Noor and Yunusa, Ismaeel and Al-Hanawi, Khaled Mohammed", title="The Influence of COVID-19 Information Sources on the Attitudes and Practices Toward COVID-19 Among the General Public of Saudi Arabia: Cross-sectional Online Survey Study", journal="JMIR Public Health Surveill", year="2021", month="Jul", day="30", volume="7", number="7", pages="e28888", keywords="attitudes", keywords="communications media", keywords="COVID-19", keywords="information-seeking behavior", keywords="pandemics", keywords="practices", keywords="Saudi Arabia", keywords="social media", keywords="sources", abstract="Background: The COVID-19 pandemic has resulted in panic among the general public, leading many people to seek out information related to COVID-19 through various sources, including social media and traditional media. Identifying public preferences for obtaining such information may help health authorities to effectively plan successful health preventive and educational intervention strategies. Objective: The aim of this study was to examine the impact of the types of sources used for obtaining COVID-19 information on the attitudes and practices of the general public in Saudi Arabia during the pandemic, and to identify the socioeconomic and demographic factors associated with the use of different sources of information. Methods: This study used data from a cross-sectional online survey conducted on residents of Saudi Arabia from March 20 to 24, 2020. Data were analyzed using descriptive, bivariate, and multivariable logistic regression analyses. Bivariate analysis of categorical variables was performed to determine the associations between information sources and socioeconomic and demographic factors. Multivariable logistic regression analyses were employed to examine whether socioeconomic and demographic variables were associated with the source of information used to obtain information about COVID-19. Moreover, univariable and multivariable logistic regression analyses were conducted to examine how sources of information influence attitudes and practices of adhering to preventive measures. Results: In this analysis of cross-sectional survey data, 3358 participants were included. Most participants reported using social media, followed by the Ministry of Health (MOH) of the Kingdom of Saudi Arabia, as their primary source of information. Seeking information via social media was significantly associated with lower odds of having an optimistic attitude (adjusted odds ratio [aOR] 0.845, 95\% CI 0.733-0.974; P=.02) and adhering to preventive measures (aOR 0.725, 95\% CI 0.630-0.835; P<.001) compared to other sources of information. Participants who obtained their COVID-19 information via the MOH had greater odds of having an optimistic attitude (aOR 1.437, 95\% CI 1.234-1.673; P<.001) and adhering to preventive measures (aOR 1.393, 95\% CI 1.201-1.615; P<.001) than those who obtained information via other sources. Conclusions: This study provides evidence that different sources of information influence attitudes and preventive actions differently within a pandemic crisis context. Health authorities in Saudi Arabia should pay attention to the use of appropriate social media channels and sources to allow for more effective dissemination of critical information to the public. ", doi="10.2196/28888", url="https://publichealth.jmir.org/2021/7/e28888", url="http://www.ncbi.nlm.nih.gov/pubmed/34081610" } @Article{info:doi/10.2196/30971, author="Purnat, D. Tina and Vacca, Paolo and Czerniak, Christine and Ball, Sarah and Burzo, Stefano and Zecchin, Tim and Wright, Amy and Bezbaruah, Supriya and Tanggol, Faizza and Dub{\'e}, {\`E}ve and Labb{\'e}, Fabienne and Dionne, Maude and Lamichhane, Jaya and Mahajan, Avichal and Briand, Sylvie and Nguyen, Tim", title="Infodemic Signal Detection During the COVID-19 Pandemic: Development of a Methodology for Identifying Potential Information Voids in Online Conversations", journal="JMIR Infodemiology", year="2021", month="Jul", day="28", volume="1", number="1", pages="e30971", keywords="infodemic", keywords="COVID-19", keywords="infodemic management", keywords="social listening", keywords="social monitoring", keywords="social media", keywords="pandemic preparedness", keywords="pandemic response", keywords="risk communication", keywords="information voids", keywords="data deficits", keywords="information overload", abstract="Background: The COVID-19 pandemic has been accompanied by an infodemic: excess information, including false or misleading information, in digital and physical environments during an acute public health event. This infodemic is leading to confusion and risk-taking behaviors that can be harmful to health, as well as to mistrust in health authorities and public health responses. The World Health Organization (WHO) is working to develop tools to provide an evidence-based response to the infodemic, enabling prioritization of health response activities. Objective: In this work, we aimed to develop a practical, structured approach to identify narratives in public online conversations on social media platforms where concerns or confusion exist or where narratives are gaining traction, thus providing actionable data to help the WHO prioritize its response efforts to address the COVID-19 infodemic. Methods: We developed a taxonomy to filter global public conversations in English and French related to COVID-19 on social media into 5 categories with 35 subcategories. The taxonomy and its implementation were validated for retrieval precision and recall, and they were reviewed and adapted as language about the pandemic in online conversations changed over time. The aggregated data for each subcategory were analyzed on a weekly basis by volume, velocity, and presence of questions to detect signals of information voids with potential for confusion or where mis- or disinformation may thrive. A human analyst reviewed and identified potential information voids and sources of confusion, and quantitative data were used to provide insights on emerging narratives, influencers, and public reactions to COVID-19--related topics. Results: A COVID-19 public health social listening taxonomy was developed, validated, and applied to filter relevant content for more focused analysis. A weekly analysis of public online conversations since March 23, 2020, enabled quantification of shifting interests in public health--related topics concerning the pandemic, and the analysis demonstrated recurring voids of verified health information. This approach therefore focuses on the detection of infodemic signals to generate actionable insights to rapidly inform decision-making for a more targeted and adaptive response, including risk communication. Conclusions: This approach has been successfully applied to identify and analyze infodemic signals, particularly information voids, to inform the COVID-19 pandemic response. More broadly, the results have demonstrated the importance of ongoing monitoring and analysis of public online conversations, as information voids frequently recur and narratives shift over time. The approach is being piloted in individual countries and WHO regions to generate localized insights and actions; meanwhile, a pilot of an artificial intelligence--based social listening platform is using this taxonomy to aggregate and compare online conversations across 20 countries. Beyond the COVID-19 pandemic, the taxonomy and methodology may be adapted for fast deployment in future public health events, and they could form the basis of a routine social listening program for health preparedness and response planning. ", doi="10.2196/30971", url="https://infodemiology.jmir.org/2021/1/e30971", url="http://www.ncbi.nlm.nih.gov/pubmed/34447926" } @Article{info:doi/10.2196/29060, author="Vandormael, Alain and Adam, Maya and Greuel, Merlin and Gates, Jennifer and Favaretti, Caterina and Hachaturyan, Violetta and B{\"a}rnighausen, Till", title="The Effect of a Wordless, Animated, Social Media Video Intervention on COVID-19 Prevention: Online Randomized Controlled Trial", journal="JMIR Public Health Surveill", year="2021", month="Jul", day="27", volume="7", number="7", pages="e29060", keywords="social media", keywords="cultural and social implications", keywords="randomized controlled trial", keywords="list experiment", keywords="information literacy", keywords="COVID-19", keywords="pandemic", keywords="digital health", keywords="infodemiology", keywords="global health", keywords="public health", abstract="Background: Innovative approaches to the dissemination of evidence-based COVID-19 health messages are urgently needed to counter social media misinformation about the pandemic. To this end, we designed a short, wordless, animated global health communication video (the CoVideo), which was rapidly distributed through social media channels to an international audience. Objective: The objectives of this study were to (1) establish the CoVideo's effectiveness in improving COVID-19 prevention knowledge, and (2) establish the CoVideo's effectiveness in increasing behavioral intent toward COVID-19 prevention. Methods: In May and June 2020, we enrolled 15,163 online participants from the United States, Mexico, the United Kingdom, Germany, and Spain. We randomized participants to (1) the CoVideo arm, (2) an attention placebo control (APC) arm, and (3) a do-nothing arm, and presented 18 knowledge questions about preventive COVID-19 behaviors, which was our first primary endpoint. To measure behavioral intent, our second primary endpoint, we randomized participants in each arm to five list experiments. Results: Globally, the video intervention was viewed 1.2 million times within the first 10 days of its release and more than 15 million times within the first 4 months. Knowledge in the CoVideo arm was significantly higher (mean 16.95, 95\% CI 16.91-16.99) than in the do-nothing (mean 16.86, 95\% CI 16.83-16.90; P<.001) arm. We observed high baseline levels of behavioral intent to perform many of the preventive behaviors featured in the video intervention. We were only able to detect a statistically significant impact of the CoVideo on one of the five preventive behaviors. Conclusions: Despite high baseline levels, the intervention was effective at boosting knowledge of COVID-19 prevention. We were only able to capture a measurable change in behavioral intent toward one of the five COVID-19 preventive behaviors examined in this study. The global reach of this health communication intervention and the high voluntary engagement of trial participants highlight several innovative features that could inform the design and dissemination of public health messages. Short, wordless, animated videos, distributed by health authorities via social media, may be an effective pathway for rapid global health communication during health crises. Trial Registration: German Clinical Trials Register DRKS00021582; https://tinyurl.com/6r4zkbbn International Registered Report Identifier (IRRID): RR2-10.1186/s13063-020-04942-7 ", doi="10.2196/29060", url="https://publichealth.jmir.org/2021/7/e29060", url="http://www.ncbi.nlm.nih.gov/pubmed/34174778" } @Article{info:doi/10.2196/23876, author="Walsh-Buhi, Eric and Houghton, Fagen Rebecca and Lange, Claire and Hockensmith, Ryli and Ferrand, John and Martinez, Lourdes", title="Pre-exposure Prophylaxis (PrEP) Information on Instagram: Content Analysis", journal="JMIR Public Health Surveill", year="2021", month="Jul", day="27", volume="7", number="7", pages="e23876", keywords="digital health", keywords="social media", keywords="HIV", keywords="pre-exposure prophylaxis", keywords="Instagram", keywords="content analysis", keywords="communication", abstract="Background: There is still an HIV epidemic in the United States, which is a substantial issue for populations bearing a disproportionate burden of HIV infections. Daily oral pre-exposure prophylaxis (PrEP) has proven to be safe and effective in reducing HIV acquisition risk. However, studies document that PrEP awareness/usage is low. There is also limited understanding of social media platforms, such as Instagram, as PrEP information sources. Objective: Given the paucity of research on PrEP-related Instagram posts and popularity of this social media platform, the purpose of this research is to describe the source characteristics, image types, and textual contents of PrEP-related posts on Instagram. Methods: Using Crowdtangle Search, a public insights tool owned/operated by Facebook, we retrieved publicly accessible and English-language-only Instagram posts for the 12-month period preceding April 22, 2020, using the following terms: Truvada or ``pre-exposure prophylaxis'' or \#truvada or \#truvadaprep or \#truvadawhore or \#truvadaforprep. We employed a qualitative coding methodology to manually extract information from posts. Using a pretested codebook, we performed content analysis on 250 posts, examining message and source characteristics (ie, organization type [eg, government, news] and individual type [eg, physician]), including information about PrEP (eg, how it works, cost), and indicated users. Frequencies and percentages were calculated for all categorical variables. A Chi-square test was conducted to determine differences between source types on a variety of message characteristics. Results: Three-quarters of the posts (193/250, 77.2\%) were posted by organizations. Of the 250 posts reviewed, approximately two-thirds (174/250, 69.6\%) included a photograph, more than half (142/250, 56.8\%) included an infographic, and approximately one-tenth (30/250, 12\%) included a video. More than half defined PrEP (137/250, 54.8\%), but fewer posts promoted PrEP use, explained how PrEP works, and included information on the effectiveness of PrEP or who can use it. The most commonly hashtagged populations among posts were men who have sex with men (MSM), but not necessarily bisexual men. Few posts contained race-/ethnicity-related hashtags (11/250, 4.4\%). Fewer posts contained transgender-associated tags (eg, \#transgirl; 5/250, 2\%). No posts contained tags related to heterosexuals or injection drug users. We found statistical differences between source types (ie, individual versus organization). Specifically, posts from organizations more frequently contained information about who can use PrEP, whereas posts from individuals more frequently contained information describing adverse effects. Conclusions: This study is among the first to review Instagram for PrEP-related content, and it answers the National AIDS Strategy's call for a clearer articulation of the science surrounding HIV risk/prevention through better understanding of the current public information environment. This study offers a snapshot of how PrEP is being discussed (and by whom) on one of the most popular social media platforms and provides a foundation for developing and implementing PrEP promotion interventions on Instagram. ", doi="10.2196/23876", url="https://publichealth.jmir.org/2021/7/e23876", url="http://www.ncbi.nlm.nih.gov/pubmed/34061759" } @Article{info:doi/10.2196/25925, author="Ram{\'i}rez-Cifuentes, Diana and Freire, Ana and Baeza-Yates, Ricardo and Sanz Lamora, Nadia and {\'A}lvarez, Aida and Gonz{\'a}lez-Rodr{\'i}guez, Alexandre and Lozano Rochel, Meritxell and Llobet Vives, Roger and Velazquez, Alejandro Diego and Gonfaus, Maria Josep and Gonz{\`a}lez, Jordi", title="Characterization of Anorexia Nervosa on Social Media: Textual, Visual, Relational, Behavioral, and Demographical Analysis", journal="J Med Internet Res", year="2021", month="Jul", day="20", volume="23", number="7", pages="e25925", keywords="social media", keywords="Twitter", keywords="Spanish", keywords="anorexia nervosa", keywords="eating disorders", keywords="user characterization", abstract="Background: Eating disorders are psychological conditions characterized by unhealthy eating habits. Anorexia nervosa (AN) is defined as the belief of being overweight despite being dangerously underweight. The psychological signs involve emotional and behavioral issues. There is evidence that signs and symptoms can manifest on social media, wherein both harmful and beneficial content is shared daily. Objective: This study aims to characterize Spanish-speaking users showing anorexia signs on Twitter through the extraction and inference of behavioral, demographical, relational, and multimodal data. By using the transtheoretical model of health behavior change, we focus on characterizing and comparing users at the different stages of the model for overcoming AN, including treatment and full recovery periods. Methods: We analyzed the writings, posting patterns, social relationships, and images shared by Twitter users who underwent different stages of anorexia nervosa and compared the differences among users going through each stage of the illness and users in the control group (ie, users without AN). We also analyzed the topics of interest of their followees (ie, users followed by study participants). We used a clustering approach to distinguish users at an early phase of the illness (precontemplation) from those that recognize that their behavior is problematic (contemplation) and generated models for the detection of tweets and images related to AN. We considered two types of control users---focused control users, which are those that use terms related to anorexia, and random control users. Results: We found significant differences between users at each stage of the recovery process (P<.001) and control groups. Users with AN tweeted more frequently at night, with a median sleep time tweets ratio (STTR) of 0.05, than random control users (STTR=0.04) and focused control users (STTR=0.03). Pictures were relevant for the characterization of users. Focused and random control users were characterized by the use of text in their profile pictures. We also found a strong polarization between focused control users and users in the first stages of the disorder. There was a strong correlation among the shared interests between users with AN and their followees ($\rho$=0.96). In addition, the interests of recovered users and users in treatment were more highly correlated to those corresponding to the focused control group ($\rho$=0.87 for both) than those of AN users ($\rho$=0.67), suggesting a shift in users' interest during the recovery process. Conclusions: We mapped the signs of AN to social media context. These results support the findings of previous studies that focused on other languages and involved a deep analysis of the topics of interest of users at each phase of the disorder. The features and patterns identified provide a basis for the development of detection tools and recommender systems. ", doi="10.2196/25925", url="https://www.jmir.org/2021/7/e25925", url="http://www.ncbi.nlm.nih.gov/pubmed/34283033" } @Article{info:doi/10.2196/29865, author="Gao, Zhiwei and Fujita, Sumio and Shimizu, Nobuyuki and Liew, Kongmeng and Murayama, Taichi and Yada, Shuntaro and Wakamiya, Shoko and Aramaki, Eiji", title="Measuring Public Concern About COVID-19 in Japanese Internet Users Through Search Queries: Infodemiological Study", journal="JMIR Public Health Surveill", year="2021", month="Jul", day="20", volume="7", number="7", pages="e29865", keywords="COVID-19", keywords="search query", keywords="infodemiology", keywords="quantitative analysis", keywords="concern", keywords="rural", keywords="urban", keywords="Internet", keywords="information-seeking behavior", keywords="attitude", keywords="Japan", abstract="Background: COVID-19 has disrupted lives and livelihoods and caused widespread panic worldwide. Emerging reports suggest that people living in rural areas in some countries are more susceptible to COVID-19. However, there is a lack of quantitative evidence that can shed light on whether residents of rural areas are more concerned about COVID-19 than residents of urban areas. Objective: This infodemiology study investigated attitudes toward COVID-19 in different Japanese prefectures by aggregating and analyzing Yahoo! JAPAN search queries. Methods: We measured COVID-19 concerns in each Japanese prefecture by aggregating search counts of COVID-19--related queries of Yahoo! JAPAN users and data related to COVID-19 cases. We then defined two indices---the localized concern index (LCI) and localized concern index by patient percentage (LCIPP)---to quantitatively represent the degree of concern. To investigate the impact of emergency declarations on people's concerns, we divided our study period into three phases according to the timing of the state of emergency in Japan: before, during, and after. In addition, we evaluated the relationship between the LCI and LCIPP in different prefectures by correlating them with prefecture-level indicators of urbanization. Results: Our results demonstrated that the concerns about COVID-19 in the prefectures changed in accordance with the declaration of the state of emergency. The correlation analyses also indicated that the differentiated types of public concern measured by the LCI and LCIPP reflect the prefectures' level of urbanization to a certain extent (ie, the LCI appears to be more suitable for quantifying COVID-19 concern in urban areas, while the LCIPP seems to be more appropriate for rural areas). Conclusions: We quantitatively defined Japanese Yahoo users' concerns about COVID-19 by using the search counts of COVID-19--related search queries. Our results also showed that the LCI and LCIPP have external validity. ", doi="10.2196/29865", url="https://publichealth.jmir.org/2021/7/e29865", url="http://www.ncbi.nlm.nih.gov/pubmed/34174781" } @Article{info:doi/10.2196/28738, author="Andy, Anietie", title="Studying How Individuals Who Express the Feeling of Loneliness in an Online Loneliness Forum Communicate in a Nonloneliness Forum: Observational Study", journal="JMIR Form Res", year="2021", month="Jul", day="20", volume="5", number="7", pages="e28738", keywords="loneliness", keywords="Reddit", keywords="nonloneliness", keywords="mental health", keywords="eHealth", keywords="forum", keywords="online forum", keywords="communication", keywords="natural language processing", keywords="language", keywords="linguistics", abstract="Background: Loneliness is a public health concern, and increasingly, individuals experiencing loneliness are seeking support on online forums, some of which focus on discussions around loneliness (loneliness forums). Some of these individuals may also seek support around loneliness on online forums not related to loneliness or well-being (nonloneliness forums). Hence, to design and implement appropriate and efficient online loneliness interventions, it is important to understand how individuals who express and seek support around loneliness on online loneliness forums communicate in nonloneliness forums; this could provide further insights into the support needs and concerns of these users. Objective: This study aims to explore how users who express the feeling of loneliness and seek support around loneliness on an online loneliness forum communicate in an online nonloneliness forum. Methods: A total of 2401 users who expressed loneliness in posts published on a loneliness forum on Reddit and had published posts in a nonloneliness forum were identified. Using latent Dirichlet allocation (a natural language processing algorithm); Linguistic Inquiry and Word Count (a psycholinguistic dictionary); and the word score--based language features valence, arousal, and dominance, the language use differences in posts published in the nonloneliness forum by these users compared to a control group of users who did not belong to any loneliness forum on Reddit were determined. Results: It was found that in posts published in the nonloneliness forum, users who expressed loneliness tend to use more words associated with the Linguistic Inquiry and Word Count categories on sadness (Cohen d=0.10) and seeking to socialize (Cohen d=0.114), and use words associated with valence (Cohen d=0.364) and dominance (Cohen d=0.117). In addition, they tend to publish posts related to latent Dirichlet allocation topics such as relationships (Cohen d=0.105) and family and friends and mental health (Cohen d=0.10). Conclusions: There are clear distinctions in language use in nonloneliness forum posts by users who express loneliness compared to a control group of users. These findings can help with the design and implementation of online interventions around loneliness. ", doi="10.2196/28738", url="https://formative.jmir.org/2021/7/e28738", url="http://www.ncbi.nlm.nih.gov/pubmed/34283026" } @Article{info:doi/10.2196/26510, author="Allem, Jon-Patrick and Dormanesh, Allison and Majmundar, Anuja and Rivera, Vanessa and Chu, Maya and Unger, B. Jennifer and Cruz, Boley Tess", title="Leading Topics in Twitter Discourse on JUUL and Puff Bar Products: Content Analysis", journal="J Med Internet Res", year="2021", month="Jul", day="19", volume="23", number="7", pages="e26510", keywords="electronic cigarettes", keywords="JUUL", keywords="public health", keywords="Puff Bar", keywords="social media", keywords="Twitter", keywords="infodemiology", abstract="Background: In response to the recent government restrictions, flavored JUUL products, which are rechargeable closed-system electronic cigarettes (e-cigarettes), are no longer available for sale. However, disposable closed-system products such as the flavored Puff Bar e-cigarette continues to be available. If e-cigarette consumers simply switch between products during the current government restrictions limited to 1 type of product over another, then such restrictions would be less effective. A step forward in this line of research is to understand how the public discusses these products by examining discourse referencing both Puff Bar and JUUL in the same conversation. Twitter data provide ample opportunity to capture such early trends that could be used to help public health researchers stay abreast of the rapidly changing e-cigarette marketplace. Objective: The goal of this study was to examine public discourse referencing both Puff Bar and JUUL products in the same conversation on Twitter. Methods: We collected data from Twitter's streaming application programming interface between July 16, 2019, and August 29, 2020, which included both ``Puff Bar'' and ``JUUL'' (n=2632). We then used an inductive approach to become familiar with the data and generate a codebook to identify common themes. Saturation was determined to be reached with 10 themes. Results: Posts often mentioned flavors, dual use, design features, youth use, health risks, switching 1 product for the other, price, confusion over the differences between products, longevity of the products, and nicotine concentration. Conclusions: On examining the public's conversations about Puff Bar and JUUL products on Twitter, having described themes in posts, this study aimed to help the tobacco control community stay informed about 2 popular e-cigarette products with different device features, which can be potentially substituted for one another. Future health communication campaigns may consider targeting the health consequences of using multiple e-cigarette products or dual use to reduce exposure to high levels of nicotine among younger populations. ", doi="10.2196/26510", url="https://www.jmir.org/2021/7/e26510", url="http://www.ncbi.nlm.nih.gov/pubmed/34279236" } @Article{info:doi/10.2196/29723, author="Fazel, S. Sajjad and Quinn, K. Emma and Ford-Sahibzada, A. Chelsea and Szarka, Steven and Peters, E. Cheryl", title="Sunscreen Posts on Twitter in the United States and Canada, 2019: Content Analysis", journal="JMIR Dermatol", year="2021", month="Jul", day="19", volume="4", number="2", pages="e29723", keywords="sunscreen", keywords="skin cancer", keywords="Twitter", keywords="misinformation", keywords="prevention", keywords="skin", keywords="social media", keywords="health promotion", keywords="melanoma", doi="10.2196/29723", url="https://derma.jmir.org/2021/2/e29723", url="http://www.ncbi.nlm.nih.gov/pubmed/37632814" } @Article{info:doi/10.2196/26769, author="Zhang, Yipeng and Lyu, Hanjia and Liu, Yubao and Zhang, Xiyang and Wang, Yu and Luo, Jiebo", title="Monitoring Depression Trends on Twitter During the COVID-19 Pandemic: Observational Study", journal="JMIR Infodemiology", year="2021", month="Jul", day="18", volume="1", number="1", pages="e26769", keywords="mental health", keywords="depression", keywords="social media", keywords="Twitter", keywords="data mining", keywords="natural language processing", keywords="transformers", keywords="COVID-19", abstract="Background: The COVID-19 pandemic has affected people's daily lives and has caused economic loss worldwide. Anecdotal evidence suggests that the pandemic has increased depression levels among the population. However, systematic studies of depression detection and monitoring during the pandemic are lacking. Objective: This study aims to develop a method to create a large-scale depression user data set in an automatic fashion so that the method is scalable and can be adapted to future events; verify the effectiveness of transformer-based deep learning language models in identifying depression users from their everyday language; examine psychological text features' importance when used in depression classification; and, finally, use the model for monitoring the fluctuation of depression levels of different groups as the disease propagates. Methods: To study this subject, we designed an effective regular expression-based search method and created the largest English Twitter depression data set containing 2575 distinct identified users with depression and their past tweets. To examine the effect of depression on people's Twitter language, we trained three transformer-based depression classification models on the data set, evaluated their performance with progressively increased training sizes, and compared the model's tweet chunk-level and user-level performances. Furthermore, inspired by psychological studies, we created a fusion classifier that combines deep learning model scores with psychological text features and users' demographic information, and investigated these features' relations to depression signals. Finally, we demonstrated our model's capability of monitoring both group-level and population-level depression trends by presenting two of its applications during the COVID-19 pandemic. Results: Our fusion model demonstrated an accuracy of 78.9\% on a test set containing 446 people, half of which were identified as having depression. Conscientiousness, neuroticism, appearance of first person pronouns, talking about biological processes such as eat and sleep, talking about power, and exhibiting sadness were shown to be important features in depression classification. Further, when used for monitoring the depression trend, our model showed that depressive users, in general, responded to the pandemic later than the control group based on their tweets (n=500). It was also shown that three US states---New York, California, and Florida---shared a similar depression trend as the whole US population (n=9050). When compared to New York and California, people in Florida demonstrated a substantially lower level of depression. Conclusions: This study proposes an efficient method that can be used to analyze the depression level of different groups of people on Twitter. We hope this study can raise awareness among researchers and the public of COVID-19's impact on people's mental health. The noninvasive monitoring system can also be readily adapted to other big events besides COVID-19 and can be useful during future outbreaks. ", doi="10.2196/26769", url="https://infodemiology.jmir.org/2021/1/e26769", url="http://www.ncbi.nlm.nih.gov/pubmed/34458682" } @Article{info:doi/10.2196/26876, author="Stevens, R. Hannah and Oh, Jung Yoo and Taylor, D. Laramie", title="Desensitization to Fear-Inducing COVID-19 Health News on Twitter: Observational Study", journal="JMIR Infodemiology", year="2021", month="Jul", day="16", volume="1", number="1", pages="e26876", keywords="desensitization", keywords="death toll", keywords="pandemic", keywords="fear-inducing", keywords="fear", keywords="health news", keywords="anxiety", keywords="COVID-19", keywords="mass media", keywords="public health", keywords="behavior change", keywords="coronavirus", abstract="Background: As of May 9, 2021, the United States had 32.7 million confirmed cases of COVID-19 (20.7\% of confirmed cases worldwide) and 580,000 deaths (17.7\% of deaths worldwide). Early on in the pandemic, widespread social, financial, and mental insecurities led to extreme and irrational coping behaviors, such as panic buying. However, despite the consistent spread of COVID-19 transmission, the public began to violate public safety measures as the pandemic got worse. Objective: In this work, we examine the effect of fear-inducing news articles on people's expression of anxiety on Twitter. Additionally, we investigate desensitization to fear-inducing health news over time, despite the steadily rising COVID-19 death toll. Methods: This study examined the anxiety levels in news articles (n=1465) and corresponding user tweets containing ``COVID,'' ``COVID-19,'' ``pandemic,'' and ``coronavirus'' over 11 months, then correlated that information with the death toll of COVID-19 in the United States. Results: Overall, tweets that shared links to anxious articles were more likely to be anxious (odds ratio [OR] 2.65, 95\% CI 1.58-4.43, P<.001). These odds decreased (OR 0.41, 95\% CI 0.2-0.83, P=.01) when the death toll reached the third quartile and fourth quartile (OR 0.42, 95\% CI 0.21-0.85, P=.01). However, user tweet anxiety rose rapidly with articles when the death toll was low and then decreased in the third quartile of deaths (OR 0.61, 95\% CI 0.37-1.01, P=.06). As predicted, in addition to the increasing death toll being matched by a lower level of article anxiety, the extent to which article anxiety elicited user tweet anxiety decreased when the death count reached the second quartile. Conclusions: The level of anxiety in users' tweets increased sharply in response to article anxiety early on in the COVID-19 pandemic, but as the casualty count climbed, news articles seemingly lost their ability to elicit anxiety among readers. Desensitization offers an explanation for why the increased threat is not eliciting widespread behavioral compliance with guidance from public health officials. This work investigated how individuals' emotional reactions to news of the COVID-19 pandemic manifest as the death toll increases. Findings suggest individuals became desensitized to the increased COVID-19 threat and their emotional responses were blunted over time. ", doi="10.2196/26876", url="https://infodemiology.jmir.org/2021/1/e26876", url="http://www.ncbi.nlm.nih.gov/pubmed/34447923" } @Article{info:doi/10.2196/28563, author="Fan, Zina and Yin, Wenqiang and Zhang, Han and Wang, Dandan and Fan, Chengxin and Chen, Zhongming and Hu, Jinwei and Ma, Dongping and Guo, Hongwei", title="COVID-19 Information Dissemination Using the WeChat Communication Index: Retrospective Analysis Study", journal="J Med Internet Res", year="2021", month="Jul", day="16", volume="23", number="7", pages="e28563", keywords="COVID-19", keywords="information dissemination", keywords="People's Daily", keywords="Chinese news", keywords="public health and communication", keywords="media salience", keywords="WeChat", abstract="Background: The COVID-19 outbreak has tremendously impacted the world. The number of confirmed cases has continued to increase, causing damage to society and the economy worldwide. The public pays close attention to information on the pandemic and learns about the disease through various media outlets. The dissemination of comprehensive and accurate COVID-19 information that the public needs helps to educate people so they can take preventive measures. Objective: This study aimed to examine the dissemination of COVID-19 information by analyzing the information released by the official WeChat account of the People's Daily during the pandemic. The most-read COVID-19 information in China was summarized, and the factors that influence information dissemination were studied to understand the characteristics that affect its dissemination. Moreover, this was conducted in order to identify how to effectively disseminate COVID-19 information and to provide suggestions on how to manage public opinion and information governance during a pandemic. Methods: This was a retrospective study based on a WeChat official account. We collected all COVID-19--related information, starting with the first report about COVID-19 from the People's Daily and ending with the last piece of information about lifting the first-level emergency response in 34 Chinese provinces. A descriptive analysis was then conducted on this information, as well as on Qingbo Big Data's dissemination index. Multiple linear regression was utilized to study the factors that affected information dissemination based on various characteristics and the dissemination index. Results: From January 19 to May 2, 2020, the People's Daily released 1984 pieces of information; 1621 were related to COVID-19, which mainly included headline news items, items with emotional content, and issues related to the pandemic's development. By analyzing the dissemination index, seven information dissemination peaks were discerned. Among the three dimensions of COVID-19 information---media salience, content, and format---eight factors affected the spread of COVID-19 information. Conclusions: Different types of pandemic-related information have varying dissemination power. To effectively disseminate information and prevent the spread of COVID-19, we should identify the factors that affect this dissemination. We should then disseminate the types of information the public is most concerned about, use information to educate people to improve their health literacy, and improve public opinion and information governance. ", doi="10.2196/28563", url="https://www.jmir.org/2021/7/e28563", url="http://www.ncbi.nlm.nih.gov/pubmed/34129515" } @Article{info:doi/10.2196/28615, author="Margus, Colton and Brown, Natasha and Hertelendy, J. Attila and Safferman, R. Michelle and Hart, Alexander and Ciottone, R. Gregory", title="Emergency Physician Twitter Use in the COVID-19 Pandemic as a Potential Predictor of Impending Surge: Retrospective Observational Study", journal="J Med Internet Res", year="2021", month="Jul", day="14", volume="23", number="7", pages="e28615", keywords="COVID-19 pandemic", keywords="emergency medicine", keywords="disaster medicine", keywords="crisis standards of care", keywords="latent Dirichlet allocation", keywords="topic modeling", keywords="Twitter", keywords="sentiment analysis", keywords="surge capacity", keywords="physician wellness", keywords="social media", keywords="internet", keywords="infodemiology", keywords="COVID-19", abstract="Background: The early conversations on social media by emergency physicians offer a window into the ongoing response to the COVID-19 pandemic. Objective: This retrospective observational study of emergency physician Twitter use details how the health care crisis has influenced emergency physician discourse online and how this discourse may have use as a harbinger of ensuing surge. Methods: Followers of the three main emergency physician professional organizations were identified using Twitter's application programming interface. They and their followers were included in the study if they identified explicitly as US-based emergency physicians. Statuses, or tweets, were obtained between January 4, 2020, when the new disease was first reported, and December 14, 2020, when vaccination first began. Original tweets underwent sentiment analysis using the previously validated Valence Aware Dictionary and Sentiment Reasoner (VADER) tool as well as topic modeling using latent Dirichlet allocation unsupervised machine learning. Sentiment and topic trends were then correlated with daily change in new COVID-19 cases and inpatient bed utilization. Results: A total of 3463 emergency physicians produced 334,747 unique English-language tweets during the study period. Out of 3463 participants, 910 (26.3\%) stated that they were in training, and 466 of 902 (51.7\%) participants who provided their gender identified as men. Overall tweet volume went from a pre-March 2020 mean of 481.9 (SD 72.7) daily tweets to a mean of 1065.5 (SD 257.3) daily tweets thereafter. Parameter and topic number tuning led to 20 tweet topics, with a topic coherence of 0.49. Except for a week in June and 4 days in November, discourse was dominated by the health care system (45,570/334,747, 13.6\%). Discussion of pandemic response, epidemiology, and clinical care were jointly found to moderately correlate with COVID-19 hospital bed utilization (Pearson r=0.41), as was the occurrence of ``covid,'' ``coronavirus,'' or ``pandemic'' in tweet texts (r=0.47). Momentum in COVID-19 tweets, as demonstrated by a sustained crossing of 7- and 28-day moving averages, was found to have occurred on an average of 45.0 (SD 12.7) days before peak COVID-19 hospital bed utilization across the country and in the four most contributory states. Conclusions: COVID-19 Twitter discussion among emergency physicians correlates with and may precede the rising of hospital burden. This study, therefore, begins to depict the extent to which the ongoing pandemic has affected the field of emergency medicine discourse online and suggests a potential avenue for understanding predictors of surge. ", doi="10.2196/28615", url="https://www.jmir.org/2021/7/e28615", url="http://www.ncbi.nlm.nih.gov/pubmed/34081612" } @Article{info:doi/10.2196/27974, author="Riem, E. Madelon M. and De Carli, Pietro and Guo, Jing and Bakermans-Kranenburg, J. Marian and van IJzendoorn, H. Marinus and Lodder, Paul", title="Internet Searches for Terms Related to Child Maltreatment During COVID-19: Infodemiology Approach", journal="JMIR Pediatr Parent", year="2021", month="Jul", day="13", volume="4", number="3", pages="e27974", keywords="child", keywords="maltreatment", keywords="COVID-19", keywords="pandemic", keywords="internet searches", keywords="information-seeking", keywords="internet", keywords="abuse", keywords="trend", keywords="Google trends", keywords="infodemiology", doi="10.2196/27974", url="https://pediatrics.jmir.org/2021/3/e27974", url="http://www.ncbi.nlm.nih.gov/pubmed/34174779" } @Article{info:doi/10.2196/25923, author="Prata, Ndola and Weidert, Karen and Zepecki, Anne and Yon, Elina and Pleasants, Elizabeth and Sams-Abiodun, Petrice and Guendelman, Sylvia", title="Using Application Programming Interfaces (APIs) to Access Google Data and Gain Insights Into Searches on Birth Control in Louisiana and Mississippi, 2014-2018: Infoveillance Study", journal="J Med Internet Res", year="2021", month="Jul", day="12", volume="23", number="7", pages="e25923", keywords="birth control", keywords="search data", keywords="Google Trends", keywords="infoveillance", keywords="infodemiology", keywords="Louisiana", keywords="Mississippi", abstract="Background: It is now common to search for health information online. A 2013 Pew Research Center survey found that 77\% of online health seekers began their query at a search engine. The widespread use of online health information seeking also applies to women's reproductive health. Despite online interest in birth control, not much is known about related interests and concerns reflected in the search terms in the United States. Objective: In this study, we identify the top search terms on Google related to birth control in Louisiana and Mississippi and compare those results to the broader United States, examining how Google searches on birth control have evolved over time and identifying regional variation within states. Methods: We accessed search data on birth control from 2014-2018 from 2 Google application programming interfaces (APIs), Google Trends and Google Health Trends. We selected Google as it is the most commonly used search engine. We focused our analysis on data from 2017 and compared with 2018 data as appropriate. To assess trends, we analyzed data from 2014 through 2018. To compare the relative search frequencies of the top queries across Louisiana, Mississippi, and the United States, we used the Google Health Trends API. Relative search volume by designated marketing area (DMA) gave us the rankings of search volume for each birth control method in each DMA as compared to one another. Results: Results showed that when people searched for ``birth control'' in Louisiana and the broader United States, they were searching for information on a diverse spectrum of methods. This differs from Mississippi, where the data indicated people were mainly searching for information related to birth control pills. Across all locations, searches for birth control pills were significantly higher than any other queries related to birth control in the United States, Louisiana, and Mississippi, and this trend remained constant from 2014 to 2018. Regional level analysis showed variations in search traffic for birth control across each state. Conclusions: The internet is a growing source of health information for many users, including information on birth control. Understanding popular Google search queries on birth control can inform in-person discussions initiated by family planning practitioners and broader birth control messaging campaigns. International Registered Report Identifier (IRRID): RR2-10.2196/16543 ", doi="10.2196/25923", url="https://www.jmir.org/2021/7/e25923", url="http://www.ncbi.nlm.nih.gov/pubmed/34255662" } @Article{info:doi/10.2196/25049, author="Sch{\"u}ck, St{\'e}phane and Roustamal, Avesta and Gedik, Ana{\"i}s and Voillot, Pam{\'e}la and Foulqui{\'e}, Pierre and Penfornis, Catherine and Job, Bernard", title="Assessing Patient Perceptions and Experiences of Paracetamol in France: Infodemiology Study Using Social Media Data Mining", journal="J Med Internet Res", year="2021", month="Jul", day="12", volume="23", number="7", pages="e25049", keywords="analgesic use", keywords="data mining", keywords="infodemiology", keywords="paracetamol", keywords="pharmacovigilance", keywords="social media", keywords="patient perception", abstract="Background: Individuals frequently turning to social media to discuss medical conditions and medication, sharing their experiences and information and asking questions among themselves. These online discussions can provide valuable insights into individual perceptions of medical treatment, and increasingly, studies are focusing on the potential use of this information to improve health care management. Objective: The objective of this infodemiology study was to identify social media posts mentioning paracetamol-containing products to develop a better understanding of patients' opinions and perceptions of the drug. Methods: Posts between January 2003 and March 2019 containing at least one mention of paracetamol were extracted from 18 French forums in May 2019 with the use of the Detec't (Kap Code) web crawler. Posts were then analyzed using the automated Detec't tool, which uses machine learning and text mining methods to inspect social media posts and extract relevant content. Posts were classified into groups: Paracetamol Only, Paracetamol and Opioids, Paracetamol and Others, and the Aggregate group. Results: Overall, 44,283 posts were analyzed from 20,883 different users. Post volume over the study period showed a peak in activity between 2009 and 2012, as well as a spike in 2017 in the Aggregate group. The number of posts tended to be higher during winter each year. Posts were made predominantly by women (14,897/20,883, 71.34\%), with 12.00\% (2507/20,883) made by men and 16.67\% (3479/20,883) by individuals of unknown gender. The mean age of web users was 39 (SD 19) years. In the Aggregate group, pain was the most common medical concept discussed (22,257/37,863, 58.78\%), and paracetamol risk was the most common discussion topic, addressed in 20.36\% (8902/43,725) of posts. Doliprane was the most common medication mentioned (14,058/44,283, 31.74\%) within the Aggregate group, and tramadol was the most commonly mentioned drug in combination with paracetamol in the Aggregate group (1038/19,587, 5.30\%). The most common unapproved indication mentioned within the Paracetamol Only group was fatigue (190/616, with 16.32\% positive for an unapproved indication), with reference to dependence made by 1.61\% (136/8470) of the web users, accounting for 1.33\% (171/12,843) of the posts in the Paracetamol Only group. Dependence mentions in the Paracetamol and Opioids group were provided by 6.94\% (248/3576) of web users, accounting for 5.44\% (342/6281) of total posts. Reference to overdose was made by 245 web users across 291 posts within the Paracetamol Only group. The most common potential adverse event detected was nausea (306/12843, 2.38\%) within the Paracetamol Only group. Conclusions: The use of social media mining with the Detec't tool provided valuable information on the perceptions and understanding of the web users, highlighting areas where providing more information for the general public on paracetamol, as well as other medications, may be of benefit. ", doi="10.2196/25049", url="https://www.jmir.org/2021/7/e25049", url="http://www.ncbi.nlm.nih.gov/pubmed/34255645" } @Article{info:doi/10.2196/27942, author="Alhassan, Mohammed Fatimah and AlDossary, Abdullah Sharifah", title="The Saudi Ministry of Health's Twitter Communication Strategies and Public Engagement During the COVID-19 Pandemic: Content Analysis Study", journal="JMIR Public Health Surveill", year="2021", month="Jul", day="12", volume="7", number="7", pages="e27942", keywords="COVID-19", keywords="Crisis and Emergency Risk Communication", keywords="effective communication", keywords="health authorities", keywords="outbreak", keywords="pandemic", keywords="public engagement", keywords="public health", keywords="social media", keywords="Twitter", abstract="Background: During a public health crisis such as the current COVID-19 pandemic, governments and health authorities need quick and accurate methods of communicating with the public. While social media can serve as a useful tool for effective communication during disease outbreaks, few studies have elucidated how these platforms are used by the Ministry of Health (MOH) during disease outbreaks in Saudi Arabia. Objective: Guided by the Crisis and Emergency Risk Communication model, this study aimed to explore the MOH's use of Twitter and the public's engagement during different stages of the COVID-19 pandemic in Saudi Arabia. Methods: Tweets and corresponding likes and retweets were extracted from the official Twitter account of the MOH in Saudi Arabia for the period of January 1 through August 31, 2020. Tweets related to COVID-19 were identified; subsequently, content analysis was performed, in which tweets were coded for the following message types: risk messages, warnings, preparations, uncertainty reduction, efficacy, reassurance, and digital health responses. Public engagement was measured by examining the numbers of likes and retweets. The association between outbreak stages and types of messages was assessed, as well as the effect of these messages on public engagement. Results: The MOH posted a total of 1393 original tweets during the study period. Of the total tweets, 1293 (92.82\%) were related to COVID-19, and 1217 were ultimately included in the analysis. The MOH posted the majority of its tweets (65.89\%) during the initial stage of the outbreak. Accordingly, the public showed the highest level of engagement (as indicated by numbers of likes and retweets) during the initial stage. The types of messages sent by the MOH significantly differed across outbreak stages, with messages related to uncertainty reduction, reassurance, and efficacy being prevalent among all stages. Tweet content, media type, and crisis stage influenced the level of public engagement. Engagement was negatively associated with the inclusion of hyperlinks and multimedia files, while higher level of public engagement was associated with the use of hashtags. Tweets related to warnings, uncertainty reduction, and reassurance received high levels of public engagement. Conclusions: This study provides insights into the Saudi MOH's communication strategy during the COVID-19 pandemic. Our results have implications for researchers, governments, health organizations, and practitioners with regard to their communication practices during outbreaks. To increase public engagement, governments and health authorities should consider the public's need for information. This, in turn, could raise public awareness regarding disease outbreaks. ", doi="10.2196/27942", url="https://publichealth.jmir.org/2021/7/e27942", url="http://www.ncbi.nlm.nih.gov/pubmed/34117860" } @Article{info:doi/10.2196/28227, author="Li, Jiacheng and Zhang, Shaowu and Zhang, Yijia and Lin, Hongfei and Wang, Jian", title="Multifeature Fusion Attention Network for Suicide Risk Assessment Based on Social Media: Algorithm Development and Validation", journal="JMIR Med Inform", year="2021", month="Jul", day="9", volume="9", number="7", pages="e28227", keywords="suicide risk assessment", keywords="social media", keywords="infodemiology", keywords="attention mechanism", keywords="neural networks", abstract="Background: Suicide has become the fifth leading cause of death worldwide. With development of the internet, social media has become an imperative source for studying psychological illnesses such as depression and suicide. Many methods have been proposed for suicide risk assessment. However, most of the existing methods cannot grasp the key information of the text. To solve this problem, we propose an efficient method to extract the core information from social media posts for suicide risk assessment. Objective: We developed a multifeature fusion recurrent attention model for suicide risk assessment. Methods: We used the bidirectional long short-term memory network to create the text representation with context information from social media posts. We further introduced a self-attention mechanism to extract the core information. We then fused linguistic features to improve our model. Results: We evaluated our model on the dataset delivered by the Computational Linguistics and Clinical Psychology 2019 shared task. The experimental results showed that our model improves the risk-F1, urgent-F1, and existence-F1 by 3.3\%, 0.9\%, and 3.7\%, respectively. Conclusions: We found that bidirectional long short-term memory performs well for long text representation, and the attention mechanism can identify the key information in the text. The external features can complete the semantic information lost by the neural network during feature extraction and further improve the performance of the model. The experimental results showed that our model performs better than the state-of-the-art method. Our work has theoretical and practical value for suicidal risk assessment. ", doi="10.2196/28227", url="https://medinform.jmir.org/2021/7/e28227", url="http://www.ncbi.nlm.nih.gov/pubmed/34255687" } @Article{info:doi/10.2196/24340, author="Sukunesan, Suku and Huynh, Minh and Sharp, Gemma", title="Examining the Pro-Eating Disorders Community on Twitter Via the Hashtag \#proana: Statistical Modeling Approach", journal="JMIR Ment Health", year="2021", month="Jul", day="9", volume="8", number="7", pages="e24340", keywords="Twitter", keywords="infodemiology", keywords="eating disorders", keywords="proana", keywords="thinspo", keywords="hashtags", keywords="transient", keywords="cybersectarianism", abstract="Background: There is increasing concern around communities that promote eating disorders (Pro-ED) on social media sites through messages and images that encourage dangerous weight control behaviors. These communities share group identity formed through interactions between members and can involve the exchange of ``tips,'' restrictive dieting plans, extreme exercise plans, and motivating imagery of thin bodies. Unlike Instagram, Facebook, or Tumblr, the absence of adequate policy to moderate Pro-ED content on Twitter presents a unique space for the Pro-ED community to freely communicate. While recent research has identified terms, themes, and common lexicon used within the Pro-ED online community, very few have been longitudinal. It is important to focus upon the engagement of Pro-ED online communities over time to further understand how members interact and stay connected, which is currently lacking. Objective: The purpose of this study was to explore beyond the common messages of Pro-ED on Twitter to understand how Pro-ED communities get traction over time by using the hashtag considered to symbolize the Pro-ED movement, \#proana. Our focus was to collect longitudinal data to gain a further understanding of the engagement of Pro-ED communities on Twitter. Methods: Descriptive statistics were used to identify the preferred tweeting style of Twitter users (either as mentioning another user in a tweet or without) as well as their most frequently used hashtag, in addition to \#proana. A series of Mann Whitney U tests were then conducted to compare preferred posting style across number of followed, followers, tweets, and favorites. This was followed by linear models using a forward step-wise approach that were applied for Pro-ED Twitter users to examine the factors associated with their number of followers. Results: This study reviewed 11,620 Pro-ED Twitter accounts that posted using the hashtag \#proana between September 2015 and July 2018. These profiles then underwent a 2-step screening of inclusion and exclusion criteria to reach the final sample of 967 profiles. Over 90\% (10,484/11,620) of the profiles were found to have less than 6 tweets within the 34-month period. Most of the users were identified as preferring a mentioning style of tweeting (718/967, 74.3\%) over not mentioning (248/967, 25.7\%). Further, \#proana and \#thinspo were used interchangeably to propagate shared themes, and there was a reciprocal effect between followers and the followed. Conclusions: Our analysis showed that the number of accounts followed and number of Pro-ED tweets posted were significant predictors for the number of followers a user has, compared to likes. Our results could potentially be useful to social media platforms to understand which features could help or otherwise curtail the spread of ED messages and activity. Our findings also show that Pro-ED communities are transient in nature, engaging in superficial discussion threads but resilient, emulating cybersectarian behavior. ", doi="10.2196/24340", url="https://mental.jmir.org/2021/7/e24340", url="http://www.ncbi.nlm.nih.gov/pubmed/34255707" } @Article{info:doi/10.2196/28346, author="Feldhege, Johannes and Moessner, Markus and Wolf, Markus and Bauer, Stephanie", title="Changes in Language Style and Topics in an Online Eating Disorder Community at the Beginning of the COVID-19 Pandemic: Observational Study", journal="J Med Internet Res", year="2021", month="Jul", day="8", volume="23", number="7", pages="e28346", keywords="COVID-19", keywords="eating disorders", keywords="online eating disorder community", keywords="language", keywords="mental health", keywords="social media", keywords="LIWC", keywords="Linguistic Inquiry and Word Count", keywords="Reddit", keywords="topic modeling", abstract="Background: COVID-19 has affected individuals with lived experience of eating disorders (EDs), with many reporting higher psychological distress, higher prevalence of ED symptoms, and compensatory behaviors. The COVID-19 pandemic and the health and safety measures taken to contain its spread also disrupted routines and reduced access to familiar coping mechanisms, social support networks, and health care services. Social media and the ED communities on social media platforms have been an important source of support for individuals with EDs in the past. So far, it is unknown how discussions in online ED communities changed as offline support networks were disrupted and people spent more time at home in the first months of the COVID-19 pandemic. Objective: The aim of this study is to identify changes in language content and style in an online ED community during the initial onset of the COVID-19 pandemic. Methods: We extracted posts and their comments from the ED community on the social media website Reddit and concatenated them to comment threads. To analyze these threads, we applied top-down and bottom-up language analysis methods based on topic modeling with latent Dirichlet allocation and 13 indicators from the Linguistic Inquiry and Word Count program, respectively. Threads were split into prepandemic (before March 11, 2020) and midpandemic (after March 11, 2020) groups. Standardized mean differences were calculated to estimate change between pre- and midpandemic threads. Results: A total of 17,715 threads (n=8772, 49.5\% prepandemic threads; n=8943, 50.5\% midpandemic threads) were extracted from the ED community and analyzed. The final topic model contained 21 topics. CIs excluding zero were found for standardized mean differences of 15 topics and 9 Linguistic Inquiry and Word Count categories covering themes such as ED symptoms, mental health, treatment for EDs, cognitive processing, social life, and emotions. Conclusions: Although we observed a reduction in discussions about ED symptoms, an increase in mental health and treatment-related topics was observed at the same time. This points to a change in the focus of the ED community from promoting potentially harmful weight loss methods to bringing attention to mental health and treatments for EDs. These results together with heightened cognitive processing, increased social references, and reduced inhibition of negative emotions detected in discussions indicate a shift in the ED community toward a pro-recovery orientation. ", doi="10.2196/28346", url="https://www.jmir.org/2021/7/e28346", url="http://www.ncbi.nlm.nih.gov/pubmed/34101612" } @Article{info:doi/10.2196/29942, author="Chan, Calvin and Sounderajah, Viknesh and Daniels, Elisabeth and Acharya, Amish and Clarke, Jonathan and Yalamanchili, Seema and Normahani, Pasha and Markar, Sheraz and Ashrafian, Hutan and Darzi, Ara", title="The Reliability and Quality of YouTube Videos as a Source of Public Health Information Regarding COVID-19 Vaccination: Cross-sectional Study", journal="JMIR Public Health Surveill", year="2021", month="Jul", day="8", volume="7", number="7", pages="e29942", keywords="COVID-19", keywords="infodemiology", keywords="public health", keywords="quality", keywords="reliability", keywords="social media", keywords="vaccination", keywords="vaccine", keywords="video", keywords="web-based health information", keywords="YouTube", abstract="Background: Recent emergency authorization and rollout of COVID-19 vaccines by regulatory bodies has generated global attention. As the most popular video-sharing platform globally, YouTube is a potent medium for the dissemination of key public health information. Understanding the nature of available content regarding COVID-19 vaccination on this widely used platform is of substantial public health interest. Objective: This study aimed to evaluate the reliability and quality of information on COVID-19 vaccination in YouTube videos. Methods: In this cross-sectional study, the phrases ``coronavirus vaccine'' and ``COVID-19 vaccine'' were searched on the UK version of YouTube on December 10, 2020. The 200 most viewed videos of each search were extracted and screened for relevance and English language. Video content and characteristics were extracted and independently rated against Health on the Net Foundation Code of Conduct and DISCERN quality criteria for consumer health information by 2 authors. Results: Forty-eight videos, with a combined total view count of 30,100,561, were included in the analysis. Topics addressed comprised the following: vaccine science (n=18, 58\%), vaccine trials (n=28, 58\%), side effects (n=23, 48\%), efficacy (n=17, 35\%), and manufacturing (n=8, 17\%). Ten (21\%) videos encouraged continued public health measures. Only 2 (4.2\%) videos made nonfactual claims. The content of 47 (98\%) videos was scored to have low (n=27, 56\%) or moderate (n=20, 42\%) adherence to Health on the Net Foundation Code of Conduct principles. Median overall DISCERN score per channel type ranged from 40.3 (IQR 34.8-47.0) to 64.3 (IQR 58.5-66.3). Educational channels produced by both medical and nonmedical professionals achieved significantly higher DISCERN scores than those of other categories. The highest median DISCERN scores were achieved by educational videos produced by medical professionals (64.3, IQR 58.5-66.3) and the lowest median scores by independent users (18, IQR 18-20). Conclusions: The overall quality and reliability of information on COVID-19 vaccines on YouTube remains poor. Videos produced by educational channels, especially by medical professionals, were higher in quality and reliability than those produced by other sources, including health-related organizations. Collaboration between health-related organizations and established medical and educational YouTube content producers provides an opportunity for the dissemination of high-quality information on COVID-19 vaccination. Such collaboration holds potential as a rapidly implementable public health intervention aiming to engage a wide audience and increase public vaccination awareness and knowledge. ", doi="10.2196/29942", url="https://publichealth.jmir.org/2021/7/e29942", url="http://www.ncbi.nlm.nih.gov/pubmed/34081599" } @Article{info:doi/10.2196/27044, author="Sousa-Pinto, Bernardo and Halonen, I. Jaana and Ant{\'o}, Aram and Jormanainen, Vesa and Czarlewski, Wienczyslawa and Bedbrook, Anna and Papadopoulos, G. Nikolaos and Freitas, Alberto and Haahtela, Tari and Ant{\'o}, M. Josep and Fonseca, Almeida Jo{\~a}o and Bousquet, Jean", title="Prediction of Asthma Hospitalizations for the Common Cold Using Google Trends: Infodemiology Study", journal="J Med Internet Res", year="2021", month="Jul", day="6", volume="23", number="7", pages="e27044", keywords="asthma", keywords="common cold", keywords="Google Trends", keywords="hospitalizations", keywords="time series analysis", keywords="mobile phone", abstract="Background: In contrast to air pollution and pollen exposure, data on the occurrence of the common cold are difficult to incorporate in models predicting asthma hospitalizations. Objective: This study aims to assess whether web-based searches on common cold would correlate with and help to predict asthma hospitalizations. Methods: We analyzed all hospitalizations with a main diagnosis of asthma occurring in 5 different countries (Portugal, Spain, Finland, Norway, and Brazil) for a period of approximately 5 years (January 1, 2012-December 17, 2016). Data on web-based searches on common cold were retrieved from Google Trends (GT) using the pseudo-influenza syndrome topic and local language search terms for common cold for the same countries and periods. We applied time series analysis methods to estimate the correlation between GT and hospitalization data. In addition, we built autoregressive models to forecast the weekly number of asthma hospitalizations for a period of 1 year (June 2015-June 2016) based on admissions and GT data from the 3 previous years. Results: In time series analyses, GT data on common cold displayed strong correlations with asthma hospitalizations occurring in Portugal (correlation coefficients ranging from 0.63 to 0.73), Spain ($\rho$=0.82-0.84), and Brazil ($\rho$=0.77-0.83) and moderate correlations with those occurring in Norway ($\rho$=0.32-0.35) and Finland ($\rho$=0.44-0.47). Similar patterns were observed in the correlation between forecasted and observed asthma hospitalizations from June 2015 to June 2016, with the number of forecasted hospitalizations differing on average between 12\% (Spain) and 33\% (Norway) from observed hospitalizations. Conclusions: Common cold--related web-based searches display moderate-to-strong correlations with asthma hospitalizations and may be useful in forecasting them. ", doi="10.2196/27044", url="https://www.jmir.org/2021/7/e27044", url="http://www.ncbi.nlm.nih.gov/pubmed/34255692" } @Article{info:doi/10.2196/27029, author="Wei, Shanzun and Ma, Ming and Wu, Changjing and Yu, Botao and Jiang, Lisha and Wen, Xi and Fu, Fudong and Shi, Ming", title="Using Search Trends to Analyze Web-Based Interest in Lower Urinary Tract Symptoms-Related Inquiries, Diagnoses, and Treatments in Mainland China: Infodemiology Study of Baidu Index Data", journal="J Med Internet Res", year="2021", month="Jul", day="6", volume="23", number="7", pages="e27029", keywords="lower urinary tract symptoms", keywords="patient education", keywords="Baidu Index", keywords="infodemiology", keywords="public interest", keywords="urinary tract disorders", keywords="infoveillance", keywords="web-based search", keywords="search engines", keywords="health care policy", keywords="digital health", abstract="Background: Lower urinary tract symptoms (LUTS) are one of the most commonly described urination disorders worldwide. Previous investigations have focused predominantly on the prospective identification of cases that meet the researchers' criteria; thus, the genuine demands regarding LUTS from patients and related issues may be neglected. Objective: We aimed to examine web-based search trends and behaviors related to LUTS on a national and regional scale by using the dominant, major search engine in mainland China. Methods: Baidu Index was queried by using LUTS-related terms for the period of January 2011 to September 2020. The search volume for each term was recorded to analyze search trends and demographic distributions. For user interest, user demand graph data and trend data were collected and analyzed. Results: Of the 13 LUTS domains, 11 domains are available in the Baidu Index database. The Baidu search index for each LUTS domain varied from 37.78\% to 1.47\%. The search trends for urinary frequency (2011-2018: annual percent change APC=7.82\%; P<.001), incomplete emptying (2011-2014: APC=17.74\%; P<.001), nocturia (2011-2018: APC=11.54\%; P<.001), dysuria (2017-2020: APC=20.77\%; P<.001), and incontinence (2011-2016: APC=13.39\%; P<.001) exhibited fluctuations over time. The search index trends for weak stream (2011-2017: APC=?4.68\%; P<.001; 2017-2020: APC=9.32\%; P=.23), split stream (2011-2013: APC=9.50\%; P=.44; 2013-2020: APC=2.05\%; P=.71), urgency (2011-2018: APC=?2.63\%; P=.03; 2018-2020: APC=8.58\%; P=.19), and nocturnal enuresis (2011-2018: APC=?3.20\%; P=.001; 2018-2020: APC=?4.21\%; P=.04) remained relatively stable and consistent. The age distribution of the population for all LUTS-related inquiries showed that individuals aged 20 to 40 years made 73.86\% (49,218,123/66,635,247) of the total search inquiries. Further, individuals aged 40 to 49 years made 12.29\% (8,193,922/66,635,247) of the total search inquiries for all LUTS-related terms. People from the east part of China made 67.79\% (45,172,031/66,635,247) of the total search queries. Additionally, most of the searches for LUTS-related terms were related to those for urinary diseases to varying degrees. Conclusions: Web-based interest in LUTS-related terms fluctuated wildly and was reflected timely by Baidu Index in mainland China. The web-based search popularity of each LUTS-related term varied significantly and differed based on personal interests, the population's concerns, regional variations, and gender. These data can be used by care providers to track the prevalence of LUTS and the population's interests, guide the establishment of disease-specific health care policies, and optimize physician-patient health care sessions. ", doi="10.2196/27029", url="https://www.jmir.org/2021/7/e27029", url="http://www.ncbi.nlm.nih.gov/pubmed/34255683" } @Article{info:doi/10.2196/27302, author="Xie, Zidian and Wang, Xueting and Gu, Yu and Li, Dongmei", title="Exploratory Analysis of Electronic Cigarette--Related Videos on YouTube: Observational Study", journal="Interact J Med Res", year="2021", month="Jul", day="6", volume="10", number="3", pages="e27302", keywords="infodemiology", keywords="infoveillance", keywords="social listening", keywords="electronic cigarettes", keywords="e-cigarette", keywords="YouTube", keywords="user engagement", keywords="provaping", keywords="vaping-warning", abstract="Background: Electronic cigarette (e-cigarette) use has become more popular than cigarette smoking, especially among youth. Social media platforms, including YouTube, are a popular means of sharing information about e-cigarette use (vaping). Objective: This study aimed to characterize the content and user engagement of e-cigarette--related YouTube videos. Methods: The top 400 YouTube search videos related to e-cigarettes were collected in January 2020. Among them, 340 valid videos were classified into provaping, vaping-warning, and neutral categories by hand coding. Additionally, the content of e-cigarette videos and their user engagement (including average views and likes) were analyzed and compared. Results: While provaping videos were dominant among e-cigarette--related YouTube videos from 2007 to 2017, vaping-warning videos started to emerge in 2013 and became dominant between 2018 and 2019. Compared to vaping-warning videos, provaping videos had higher average daily views (1077 vs 822) but lower average daily likes (12 vs 15). Among 161 provaping videos, videos on user demonstration (n=100, 62.11\%) were dominant, and videos on comparison with smoking had the highest user engagement (2522 average daily views and 28 average daily likes). Conversely, among 141 vaping-warning videos, videos on potential health risks were the most popular topic (n=57, 40.42\%) with the highest user engagement (1609 average daily views and 33 average daily likes). Conclusions: YouTube was dominated by provaping videos, with the majority of videos on user demonstrations before 2018. The vaping-warning videos became dominant between 2018 and 2019, with videos on potential health risks being the most popular topic. This study provides updated surveillance on e-cigarette--related YouTube videos and some important guidance on associated social media regulations. ", doi="10.2196/27302", url="https://www.i-jmr.org/2021/3/e27302", url="http://www.ncbi.nlm.nih.gov/pubmed/34255663" } @Article{info:doi/10.2196/25422, author="Wang, Peng and Xu, Qing and Cao, Rong-Rong and Deng, Fei-Yan and Lei, Shu-Feng", title="Global Public Interests and Dynamic Trends in Osteoporosis From 2004 to 2019: Infodemiology Study", journal="J Med Internet Res", year="2021", month="Jul", day="5", volume="23", number="7", pages="e25422", keywords="global public interest", keywords="Google trends", keywords="osteoporosis", keywords="seasonality", keywords="trends", keywords="infodemiology", keywords="information seeking", keywords="web-based information", abstract="Background: With the prolonging of human life expectancy and subsequent population aging, osteoporosis (OP) has become an important public health issue. Objective: This study aimed to understand the global public search interests and dynamic trends in ``osteoporosis'' using the data derived from Google Trends. Methods: An online search was performed using the term ``osteoporosis'' in Google Trends from January 1, 2004, to December 31, 2019, under the category ``Health.'' Cosinor analysis was used to test the seasonality of relative search volume (RSV) for ``osteoporosis.'' An analysis was conducted to investigate the public search topic rising in RSV for ``osteoporosis.'' Results: There was a descending trend of global RSV for ``osteoporosis'' from January 2004 to December 2014, and a slowly increasing trend from January 2015 to December 2019. Cosinor analysis showed significant seasonal variations in global RSV for ``osteoporosis'' (P=.01), with a peak in March and a trough in September. In addition, similar decreasing trends of RSV for ``osteoporosis'' were found in Australia, New Zealand, Ireland, and Canada from January 2004 to December 2019. Cosinor test revealed significant seasonal variations in RSV for ``osteoporosis'' in Australia, New Zealand, Canada, Ireland, UK, and USA (all P<.001). Furthermore, public search rising topics related to ``osteoporosis'' included denosumab, fracture risk assessment tool, bone density, osteopenia, osteoarthritis, and risk factor. Conclusions: Our study provided evidence about the public search interest and dynamic trends in OP using web-based data, which would be helpful for public health and policy making. ", doi="10.2196/25422", url="https://www.jmir.org/2021/7/e25422", url="http://www.ncbi.nlm.nih.gov/pubmed/36260400" } @Article{info:doi/10.2196/29238, author="Matsuda, Shinichi and Ohtomo, Takumi and Tomizawa, Shiho and Miyano, Yuki and Mogi, Miwako and Kuriki, Hiroshi and Nakayama, Terumi and Watanabe, Shinichi", title="Incorporating Unstructured Patient Narratives and Health Insurance Claims Data in Pharmacovigilance: Natural Language Processing Analysis of Patient-Generated Texts About Systemic Lupus Erythematosus", journal="JMIR Public Health Surveill", year="2021", month="Jun", day="29", volume="7", number="6", pages="e29238", keywords="social media", keywords="adverse drug reaction", keywords="pharmacovigilance", keywords="text mining", keywords="systemic lupus erythematosus", keywords="natural language processing", keywords="NLP", keywords="lupus", keywords="chronic disease", keywords="narrative", keywords="insurance", keywords="data", keywords="epidemiology", keywords="burden", keywords="Japan", keywords="patient-generated", abstract="Background: Gaining insights that cannot be obtained from health care databases from patients has become an important topic in pharmacovigilance. Objective: Our objective was to demonstrate a use case, in which patient-generated data were incorporated in pharmacovigilance, to understand the epidemiology and burden of illness in Japanese patients with systemic lupus erythematosus. Methods: We used data on systemic lupus erythematosus, an autoimmune disease that substantially impairs quality of life, from 2 independent data sets. To understand the disease's epidemiology, we analyzed a Japanese health insurance claims database. To understand the disease's burden, we analyzed text data collected from Japanese disease blogs (t?by?ki) written by patients with systemic lupus erythematosus. Natural language processing was applied to these texts to identify frequent patient-level complaints, and term frequency--inverse document frequency was used to explore patient burden during treatment. We explored health-related quality of life based on patient descriptions. Results: We analyzed data from 4694 and 635 patients with systemic lupus erythematosus in the health insurance claims database and t?by?ki blogs, respectively. Based on health insurance claims data, the prevalence of systemic lupus erythematosus is 107.70 per 100,000 persons. T?by?ki text data analysis showed that pain-related words (eg, pain, severe pain, arthralgia) became more important after starting treatment. We also found an increase in patients' references to mobility and self-care over time, which indicated increased attention to physical disability due to disease progression. Conclusions: A classical medical database represents only a part of a patient's entire treatment experience, and analysis using solely such a database cannot represent patient-level symptoms or patient concerns about treatments. This study showed that analysis of t?by?ki blogs can provide added information on patient-level details, advancing patient-centric pharmacovigilance. ", doi="10.2196/29238", url="https://publichealth.jmir.org/2021/6/e29238", url="http://www.ncbi.nlm.nih.gov/pubmed/34255719" } @Article{info:doi/10.2196/24435, author="Lyu, Chen Joanne and Han, Le Eileen and Luli, K. Garving", title="COVID-19 Vaccine--Related Discussion on Twitter: Topic Modeling and Sentiment Analysis", journal="J Med Internet Res", year="2021", month="Jun", day="29", volume="23", number="6", pages="e24435", keywords="COVID-19", keywords="vaccine", keywords="vaccination", keywords="Twitter", keywords="infodemiology", keywords="infoveillance", keywords="topic", keywords="sentiment", keywords="opinion", keywords="discussion", keywords="communication", keywords="social media", keywords="perception", keywords="concern", keywords="emotion", abstract="Background: Vaccination is a cornerstone of the prevention of communicable infectious diseases; however, vaccines have traditionally met with public fear and hesitancy, and COVID-19 vaccines are no exception. Social media use has been demonstrated to play a role in the low acceptance of vaccines. Objective: The aim of this study is to identify the topics and sentiments in the public COVID-19 vaccine--related discussion on social media and discern the salient changes in topics and sentiments over time to better understand the public perceptions, concerns, and emotions that may influence the achievement of herd immunity goals. Methods: Tweets were downloaded from a large-scale COVID-19 Twitter chatter data set from March 11, 2020, the day the World Health Organization declared COVID-19 a pandemic, to January 31, 2021. We used R software to clean the tweets and retain tweets that contained the keywords vaccination, vaccinations, vaccine, vaccines, immunization, vaccinate, and vaccinated. The final data set included in the analysis consisted of 1,499,421 unique tweets from 583,499 different users. We used R to perform latent Dirichlet allocation for topic modeling as well as sentiment and emotion analysis using the National Research Council of Canada Emotion Lexicon. Results: Topic modeling of tweets related to COVID-19 vaccines yielded 16 topics, which were grouped into 5 overarching themes. Opinions about vaccination (227,840/1,499,421 tweets, 15.2\%) was the most tweeted topic and remained a highly discussed topic during the majority of the period of our examination. Vaccine progress around the world became the most discussed topic around August 11, 2020, when Russia approved the world's first COVID-19 vaccine. With the advancement of vaccine administration, the topic of instruction on getting vaccines gradually became more salient and became the most discussed topic after the first week of January 2021. Weekly mean sentiment scores showed that despite fluctuations, the sentiment was increasingly positive in general. Emotion analysis further showed that trust was the most predominant emotion, followed by anticipation, fear, sadness, etc. The trust emotion reached its peak on November 9, 2020, when Pfizer announced that its vaccine is 90\% effective. Conclusions: Public COVID-19 vaccine--related discussion on Twitter was largely driven by major events about COVID-19 vaccines and mirrored the active news topics in mainstream media. The discussion also demonstrated a global perspective. The increasingly positive sentiment around COVID-19 vaccines and the dominant emotion of trust shown in the social media discussion may imply higher acceptance of COVID-19 vaccines compared with previous vaccines. ", doi="10.2196/24435", url="https://www.jmir.org/2021/6/e24435", url="http://www.ncbi.nlm.nih.gov/pubmed/34115608" } @Article{info:doi/10.2196/30137, author="Lee, Jae-Young and Lee, Yae-Seul and Kim, Hyun Dong and Lee, Sol Han and Yang, Ram Bo and Kim, Gyu Myeong", title="The Use of Social Media in Detecting Drug Safety--Related New Black Box Warnings, Labeling Changes, or Withdrawals: Scoping Review", journal="JMIR Public Health Surveill", year="2021", month="Jun", day="28", volume="7", number="6", pages="e30137", keywords="adverse event", keywords="black box warning", keywords="detect", keywords="pharmacovigilance", keywords="real-world data", keywords="review", keywords="safety", keywords="social media", keywords="withdrawal of approval", abstract="Background: Social media has become a new source for obtaining real-world data on adverse drug reactions. Many studies have investigated the use of social media to detect early signals of adverse drug reactions. However, the trustworthiness of signals derived from social media is questionable. To confirm this, a confirmatory study with a positive control (eg, new black box warnings, labeling changes, or withdrawals) is required. Objective: This study aimed to evaluate the use of social media in detecting new black box warnings, labeling changes, or withdrawals in advance. Methods: This scoping review adhered to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist. A researcher searched PubMed and EMBASE in January 2021. Original studies analyzing black box warnings, labeling changes, or withdrawals from social media were selected, and the results of the studies were summarized. Results: A total of 14 studies were included in this scoping review. Most studies (8/14, 57.1\%\%) collected data from a single source, and 10 (71.4\%) used specialized health care social networks and forums. The analytical methods used in these studies varied considerably. Three studies (21.4\%) manually annotated posts, while 5 (35.7\%) adopted machine learning algorithms. Nine studies (64.2\%) concluded that social media could detect signals 3 months to 9 years before action from regulatory authorities. Most of these studies (8/9, 88.9\%) were conducted on specialized health care social networks and forums. On the contrary, 5 (35.7\%) studies yielded modest or negative results. Of these, 2 (40\%) used generic social networking sites, 2 (40\%) used specialized health care networks and forums, and 1 (20\%) used both generic social networking sites and specialized health care social networks and forums. The most recently published study recommends not using social media for pharmacovigilance. Several challenges remain in using social media for pharmacovigilance regarding coverage, data quality, and analytic processing. Conclusions: Social media, along with conventional pharmacovigilance measures, can be used to detect signals associated with new black box warnings, labeling changes, or withdrawals. Several challenges remain; however, social media will be useful for signal detection of frequently mentioned drugs in specialized health care social networks and forums. Further studies are required to advance natural language processing and mine real-world data on social media. ", doi="10.2196/30137", url="https://publichealth.jmir.org/2021/6/e30137", url="http://www.ncbi.nlm.nih.gov/pubmed/34185021" } @Article{info:doi/10.2196/28328, author="Brkic, F. Faris and Besser, Gerold and Schally, Martin and Schmid, M. Elisabeth and Parzefall, Thomas and Riss, Dominik and Liu, T. David", title="Biannual Differences in Interest Peaks for Web Inquiries Into Ear Pain and Ear Drops: Infodemiology Study", journal="J Med Internet Res", year="2021", month="Jun", day="20", volume="23", number="6", pages="e28328", keywords="otitis media", keywords="otitis externa", keywords="otalgia", keywords="Google Trends", keywords="infodemiology", keywords="infoveillance", keywords="infodemic", keywords="social listening", abstract="Background: The data retrieved with the online search engine, Google Trends, can summarize internet inquiries into specified search terms. This engine may be used for analyzing inquiry peaks for different medical conditions and symptoms. Objective: The aim of this study was to analyze World Wide Web interest peaks for ``ear pain,'' ``ear infection,'' and ``ear drops.'' Methods: We used Google Trends to assess the public online interest for search terms ``ear pain,'' ``ear infection,'' and ``ear drops'' in 5 English and non--English-speaking countries from both hemispheres based on time series data. We performed our analysis for the time frame between January 1, 2004, and December 31, 2019. First, we assessed whether our search terms were most relevant to the topics of ear pain, ear infection, and ear drops. We then tested the reliability of Google Trends time series data using the intraclass correlation coefficient. In a second step, we computed univariate time series plots to depict peaks in web-based interest. In the last step, we used the cosinor analysis to test the statistical significance of seasonal interest peaks. Results: In the first part of the study, it was revealed that ``ear infection,'' ``ear pain,'' and ``ear drops'' were the most relevant search terms in the noted time frame. Next, the intraclass correlation analysis showed a moderate to excellent reliability for all 5 countries' 3 primary search terms. The subsequent analysis revealed winter interest peaks for ``ear infection'' and ``ear pain''. On the other hand, the World Wide Web search for ``ear drops'' peaked annually during the summer months. All peaks were statistically significant as revealed by the cosinor model (all P values <.001). Conclusions: It can be concluded that individuals affected by otitis media or externa, possibly the majority, look for medical information online. Therefore, there is a need for accurate and easily accessible information on these conditions in the World Wide Web, particularly on differentiating signs and therapy options. Meeting this need may facilitate timely diagnosis, proper therapy, and eventual circumvention of potentially life-threatening complications. ", doi="10.2196/28328", url="https://www.jmir.org/2021/6/e28328/", url="http://www.ncbi.nlm.nih.gov/pubmed/34185016" } @Article{info:doi/10.2196/23105, author="Argyris, Anna Young and Monu, Kafui and Tan, Pang-Ning and Aarts, Colton and Jiang, Fan and Wiseley, Anne Kaleigh", title="Using Machine Learning to Compare Provaccine and Antivaccine Discourse Among the Public on Social Media: Algorithm Development Study", journal="JMIR Public Health Surveill", year="2021", month="Jun", day="24", volume="7", number="6", pages="e23105", keywords="antivaccination movement", keywords="Twitter messaging", keywords="public health informatics", keywords="supervised machine learning algorithm", keywords="unsupervised machine learning algorithm", keywords="qualitative content analysis", keywords="data visualization", keywords="infodemiology", keywords="infodemic", keywords="health misinformation", keywords="infoveillance", keywords="social listening", abstract="Background: Despite numerous counteracting efforts, antivaccine content linked to delays and refusals to vaccinate has grown persistently on social media, while only a few provaccine campaigns have succeeded in engaging with or persuading the public to accept immunization. Many prior studies have associated the diversity of topics discussed by antivaccine advocates with the public's higher engagement with such content. Nonetheless, a comprehensive comparison of discursive topics in pro- and antivaccine content in the engagement-persuasion spectrum remains unexplored. Objective: We aimed to compare discursive topics chosen by pro- and antivaccine advocates in their attempts to influence the public to accept or reject immunization in the engagement-persuasion spectrum. Our overall objective was pursued through three specific aims as follows: (1) we classified vaccine-related tweets into provaccine, antivaccine, and neutral categories; (2) we extracted and visualized discursive topics from these tweets to explain disparities in engagement between pro- and antivaccine content; and (3) we identified how those topics frame vaccines using Entman's four framing dimensions. Methods: We adopted a multimethod approach to analyze discursive topics in the vaccine debate on public social media sites. Our approach combined (1) large-scale balanced data collection from a public social media site (ie, 39,962 tweets from Twitter); (2) the development of a supervised classification algorithm for categorizing tweets into provaccine, antivaccine, and neutral groups; (3) the application of an unsupervised clustering algorithm for identifying prominent topics discussed on both sides; and (4) a multistep qualitative content analysis for identifying the prominent discursive topics and how vaccines are framed in these topics. In so doing, we alleviated methodological challenges that have hindered previous analyses of pro- and antivaccine discursive topics. Results: Our results indicated that antivaccine topics have greater intertopic distinctiveness (ie, the degree to which discursive topics are distinct from one another) than their provaccine counterparts (t122=2.30, P=.02). In addition, while antivaccine advocates use all four message frames known to make narratives persuasive and influential, provaccine advocates have neglected having a clear problem statement. Conclusions: Based on our results, we attribute higher engagement among antivaccine advocates to the distinctiveness of the topics they discuss, and we ascribe the influence of the vaccine debate on uptake rates to the comprehensiveness of the message frames. These results show the urgency of developing clear problem statements for provaccine content to counteract the negative impact of antivaccine content on uptake rates. ", doi="10.2196/23105", url="https://publichealth.jmir.org/2021/6/e23105/", url="http://www.ncbi.nlm.nih.gov/pubmed/34185004" } @Article{info:doi/10.2196/26019, author="Zhang, Hongjie Thomas and Tham, Sern Jen", title="Calls to Action (Mobilizing Information) on Cancer in Online News: Content Analysis", journal="J Med Internet Res", year="2021", month="Jun", day="21", volume="23", number="6", pages="e26019", keywords="mobilizing information", keywords="online cancer news", keywords="quantitative content analysis", keywords="Malaysia", keywords="online news", keywords="cancer", keywords="infodemiology", keywords="media", keywords="digital media", keywords="digital health", keywords="health information", keywords="cancer health information", abstract="Background: The health belief model explains that individual intentions and motivation of health behaviors are mostly subject to externalcues to action, such as from interpersonal communications and media consumptions. The concept of mobilizing information (MI) refers to a type of mediated information that could call individuals to carry out particular health actions. Different media channels, especially digital media outlets, play an essential role as a health educator to disseminate cancer health information and persuade and mobilize cancer prevention in the community. However, little is known about calls to action (or MI) in online cancer news, especially from Asian media outlets. Objective: This study aimed at analyzing cancer news articles that contain MI and their news components on the selected Malaysian English and Chinese newspapers with online versions. Methods: The Star Online and Sin Chew Online were selected for analysis because the two newspaper websites enjoy the highest circulation and readership in the English language and the Chinese language streams, respectively. Two bilingual coders searched the cancer news articles based on sampling keywords and then read and coded each news article accordingly. Five coding variables were conceptualized from previous studies (ie, cancer type, news source, news focus, cancer risk factors, and MI), and a good consistency using Cohen kappa was built between coders. Descriptive analysis was used to examine the frequency and percentage of each coding item; chi-square test (confidence level at 95\%) was applied to analyze the differences between two newspaper websites, and the associations between variables and the presence of MI were examined through binary logistic regression. Results: Among 841 analyzed news articles, 69.6\% (585/841) presented MI. News distributions were unbalanced throughout the year in both English and Chinese newspaper websites; some months occupied peaks (ie, February and October), but cancer issues and MI for cancer prevention received minimal attention in other months. The news articles from The Star Online and Sin Chew Online were significantly different in several news components, such as the MI present rates ($\chi$2=9.25, P=.003), providing different types of MI (interactive MI: $\chi$2=12.08, P=.001), interviewing different news sources (government agency: $\chi$2=12.05, P=.001), concerning different news focus (primary cancer prevention: $\chi$2=10.98, P=.001), and mentioning different cancer risks (lifestyle risks: $\chi$2=7.43, P=.007). Binary logistic regression results reported that online cancer news articles were more likely to provide MI when interviewing nongovernmental organizations, focusing on topics related to primary cancer prevention, and highlighting lifestyle risks (odds ratio [OR] 2.77, 95\% CI 1.89-4.05; OR 97.70, 95\% CI 46.97-203.24; OR 186.28; 95\% CI 44.83-773.96; P=.001, respectively). Conclusions: This study provided new understandings regarding MI in cancer news coverage. This could wake and trigger individuals' preexisting attitudes and intentions on cancer prevention. Thus, health professionals, health journalists, and health campaign designers should concentrate on MI when distributing health information to the community. ", doi="10.2196/26019", url="https://www.jmir.org/2021/6/e26019", url="http://www.ncbi.nlm.nih.gov/pubmed/34152283" } @Article{info:doi/10.2196/26655, author="Massey, Daisy and Huang, Chenxi and Lu, Yuan and Cohen, Alina and Oren, Yahel and Moed, Tali and Matzner, Pini and Mahajan, Shiwani and Caraballo, C{\'e}sar and Kumar, Navin and Xue, Yuchen and Ding, Qinglan and Dreyer, Rachel and Roy, Brita and Krumholz, Harlan", title="Engagement With COVID-19 Public Health Measures in the United States: A Cross-sectional Social Media Analysis from June to November 2020", journal="J Med Internet Res", year="2021", month="Jun", day="21", volume="23", number="6", pages="e26655", keywords="COVID-19", keywords="public perception", keywords="social media", keywords="infodemiology", keywords="infoveillance", keywords="infodemic", keywords="social media research", keywords="social listening", keywords="social media analysis", keywords="natural language processing", keywords="Reddit data", keywords="Facebook data", keywords="COVID-19 public health measures", keywords="public health", keywords="surveillance", keywords="engagement", keywords="United States", keywords="cross-sectional", keywords="Reddit", keywords="Facebook", keywords="behavior", keywords="perception", keywords="NLP", abstract="Background: COVID-19 has continued to spread in the United States and globally. Closely monitoring public engagement and perceptions of COVID-19 and preventive measures using social media data could provide important information for understanding the progress of current interventions and planning future programs. Objective: The aim of this study is to measure the public's behaviors and perceptions regarding COVID-19 and its effects on daily life during 5 months of the pandemic. Methods: Natural language processing (NLP) algorithms were used to identify COVID-19--related and unrelated topics in over 300 million online data sources from June 15 to November 15, 2020. Posts in the sample were geotagged by NetBase, a third-party data provider, and sensitivity and positive predictive value were both calculated to validate the classification of posts. Each post may have included discussion of multiple topics. The prevalence of discussion regarding these topics was measured over this time period and compared to daily case rates in the United States. Results: The final sample size included 9,065,733 posts, 70\% of which were sourced from the United States. In October and November, discussion including mentions of COVID-19 and related health behaviors did not increase as it had from June to September, despite an increase in COVID-19 daily cases in the United States beginning in October. Additionally, discussion was more focused on daily life topics (n=6,210,255, 69\%), compared with COVID-19 in general (n=3,390,139, 37\%) and COVID-19 public health measures (n=1,836,200, 20\%). Conclusions: There was a decline in COVID-19--related social media discussion sourced mainly from the United States, even as COVID-19 cases in the United States increased to the highest rate since the beginning of the pandemic. Targeted public health messaging may be needed to ensure engagement in public health prevention measures as global vaccination efforts continue. ", doi="10.2196/26655", url="https://www.jmir.org/2021/6/e26655", url="http://www.ncbi.nlm.nih.gov/pubmed/34086593" } @Article{info:doi/10.2196/28648, author="Pollack, C. Catherine and Gilbert-Diamond, Diane and Alford-Teaster, A. Jennifer and Onega, Tracy", title="Language and Sentiment Regarding Telemedicine and COVID-19 on Twitter: Longitudinal Infodemiology Study", journal="J Med Internet Res", year="2021", month="Jun", day="21", volume="23", number="6", pages="e28648", keywords="telemedicine", keywords="telehealth", keywords="COVID-19 pandemic", keywords="social media", keywords="sentiment analysis", keywords="Twitter", keywords="COVID-19", keywords="pandemic", abstract="Background: The COVID-19 pandemic has necessitated a rapid shift in how individuals interact with and receive fundamental services, including health care. Although telemedicine is not a novel technology, previous studies have offered mixed opinions surrounding its utilization. However, there exists a dearth of research on how these opinions have evolved over the course of the current pandemic. Objective: This study aims to evaluate how the language and sentiment surrounding telemedicine has evolved throughout the COVID-19 pandemic. Methods: Tweets published between January 1, 2020, and April 24, 2021, containing at least one telemedicine-related and one COVID-19--related search term (``telemedicine-COVID'') were collected from the Twitter full archive search (N=351,718). A comparator sample containing only COVID-19 terms (``general-COVID'') was collected and sampled based on the daily distribution of telemedicine-COVID tweets. In addition to analyses of retweets and favorites, sentiment analysis was performed on both data sets in aggregate and within a subset of tweets receiving the top 100 most and least retweets. Results: Telemedicine gained prominence during the early stages of the pandemic (ie, March through May 2020) before leveling off and reaching a steady state from June 2020 onward. Telemedicine-COVID tweets had a 21\% lower average number of retweets than general-COVID tweets (incidence rate ratio 0.79, 95\% CI 0.63-0.99; P=.04), but there was no difference in favorites. A majority of telemedicine-COVID tweets (180,295/351,718, 51.3\%) were characterized as ``positive,'' compared to only 38.5\% (135,434/351,401) of general-COVID tweets (P<.001). This trend was also true on a monthly level from March 2020 through April 2021. The most retweeted posts in both telemedicine-COVID and general-COVID data sets were authored by journalists and politicians. Whereas the majority of the most retweeted posts within the telemedicine-COVID data set were positive (55/101, 54.5\%), a plurality of the most retweeted posts within the general-COVID data set were negative (44/89, 49.4\%; P=.01). Conclusions: During the COVID-19 pandemic, opinions surrounding telemedicine evolved to become more positive, especially when compared to the larger pool of COVID-19--related tweets. Decision makers should capitalize on these shifting public opinions to invest in telemedicine infrastructure and ensure its accessibility and success in a postpandemic world. ", doi="10.2196/28648", url="https://www.jmir.org/2021/6/e28648", url="http://www.ncbi.nlm.nih.gov/pubmed/34086591" } @Article{info:doi/10.2196/26368, author="Lee, Jinhee and Kwan, Yunna and Lee, Young Jun and Shin, Il Jae and Lee, Hwa Keum and Hong, Hwi Sung and Han, Joo Young and Kronbichler, Andreas and Smith, Lee and Koyanagi, Ai and Jacob, Louis and Choi, SungWon and Ghayda, Abou Ramy and Park, Myung-Bae", title="Public Interest in Immunity and the Justification for Intervention in the Early Stages of the COVID-19 Pandemic: Analysis of Google Trends Data", journal="J Med Internet Res", year="2021", month="Jun", day="18", volume="23", number="6", pages="e26368", keywords="COVID-19", keywords="social big data", keywords="infodemiology", keywords="infoveillance", keywords="social listening", keywords="immune", keywords="vitamin", keywords="big data", keywords="public interest", keywords="intervention", keywords="immune system", keywords="immunity", keywords="trends", keywords="Google Trends", keywords="internet", keywords="digital health", keywords="web-based health information", keywords="correlation", keywords="social media", keywords="infectious disease", abstract="Background: The use of social big data is an important emerging concern in public health. Internet search volumes are useful data that can sensitively detect trends of the public's attention during a pandemic outbreak situation. Objective: Our study aimed to analyze the public's interest in COVID-19 proliferation, identify the correlation between the proliferation of COVID-19 and interest in immunity and products that have been reported to confer an enhancement of immunity, and suggest measures for interventions that should be implemented from a health and medical point of view. Methods: To assess the level of public interest in infectious diseases during the initial days of the COVID-19 outbreak, we extracted Google search data from January 20, 2020, onward and compared them to data from March 15, 2020, which was approximately 2 months after the COVID-19 outbreak began. In order to determine whether the public became interested in the immune system, we selected coronavirus, immune, and vitamin as our final search terms. Results: The increase in the cumulative number of confirmed COVID-19 cases that occurred after January 20, 2020, had a strong positive correlation with the search volumes for the terms coronavirus (R=0.786; P<.001), immune (R=0.745; P<.001), and vitamin (R=0.778; P<.001), and the correlations between variables were all mutually statistically significant. Moreover, these correlations were confirmed on a country basis when we restricted our analyses to the United States, the United Kingdom, Italy, and Korea. Our findings revealed that increases in search volumes for the terms coronavirus and immune preceded the actual occurrences of confirmed cases. Conclusions: Our study shows that during the initial phase of the COVID-19 crisis, the public's desire and actions of strengthening their own immune systems were enhanced. Further, in the early stage of a pandemic, social media platforms have a high potential for informing the public about potentially helpful measures to prevent the spread of an infectious disease and provide relevant information about immunity, thereby increasing the public's knowledge. ", doi="10.2196/26368", url="https://www.jmir.org/2021/6/e26368", url="http://www.ncbi.nlm.nih.gov/pubmed/34038375" } @Article{info:doi/10.2196/27976, author="Miller, Michele and Romine, William and Oroszi, Terry", title="Public Discussion of Anthrax on Twitter: Using Machine Learning to Identify Relevant Topics and Events", journal="JMIR Public Health Surveill", year="2021", month="Jun", day="18", volume="7", number="6", pages="e27976", keywords="anthrax", keywords="big data", keywords="internet", keywords="infodemiology", keywords="infoveillance", keywords="social listening", keywords="digital health", keywords="biological weapon", keywords="terrorism", keywords="Federal Bureau of Investigation", keywords="machine learning", keywords="public health threat", keywords="Twitter", abstract="Background: Social media allows researchers to study opinions and reactions to events in real time. One area needing more study is anthrax-related events. A computational framework that utilizes machine learning techniques was created to collect tweets discussing anthrax, further categorize them as relevant by the month of data collection, and detect discussions on anthrax-related events. Objective: The objective of this study was to detect discussions on anthrax-related events and to determine the relevance of the tweets and topics of discussion over 12 months of data collection. Methods: This is an infoveillance study, using tweets in English containing the keyword ``Anthrax'' and ``Bacillus anthracis'', collected from September 25, 2017, through August 15, 2018. Machine learning techniques were used to determine what people were tweeting about anthrax. Data over time was plotted to determine whether an event was detected (a 3-fold spike in tweets). A machine learning classifier was created to categorize tweets by relevance to anthrax. Relevant tweets by month were examined using a topic modeling approach to determine the topics of discussion over time and how these events influence that discussion. Results: Over the 12 months of data collection, a total of 204,008 tweets were collected. Logistic regression analysis revealed the best performance for relevance (precision=0.81; recall=0.81; F1-score=0.80). In total, 26 topics were associated with anthrax-related events, tweets that were highly retweeted, natural outbreaks, and news stories. Conclusions: This study shows that tweets related to anthrax can be collected and analyzed over time to determine what people are discussing and to detect key anthrax-related events. Future studies are required to focus only on opinion tweets, use the methodology to study other terrorism events, or to monitor for terrorism threats. ", doi="10.2196/27976", url="https://publichealth.jmir.org/2021/6/e27976", url="http://www.ncbi.nlm.nih.gov/pubmed/34142975" } @Article{info:doi/10.2196/26956, author="Taneja, L. Sonia and Passi, Monica and Bhattacharya, Sumona and Schueler, A. Samuel and Gurram, Sandeep and Koh, Christopher", title="Social Media and Research Publication Activity During Early Stages of the COVID-19 Pandemic: Longitudinal Trend Analysis", journal="J Med Internet Res", year="2021", month="Jun", day="17", volume="23", number="6", pages="e26956", keywords="coronavirus", keywords="COVID-19", keywords="social media", keywords="gastroenterology", keywords="SARS-CoV-2", keywords="research", keywords="literature", keywords="dissemination", keywords="Twitter", keywords="preprint", abstract="Background: The COVID-19 pandemic has highlighted the importance of rapid dissemination of scientific and medical discoveries. Current platforms available for the distribution of scientific and clinical research data and information include preprint repositories and traditional peer-reviewed journals. In recent times, social media has emerged as a helpful platform to share scientific and medical discoveries. Objective: This study aimed to comparatively analyze activity on social media (specifically, Twitter) and that related to publications in the form of preprint and peer-reviewed journal articles in the context of COVID-19 and gastroenterology during the early stages of the COVID-19 pandemic. Methods: COVID-19--related data from Twitter (tweets and user data) and articles published in preprint servers (bioRxiv and medRxiv) as well as in the PubMed database were collected and analyzed during the first 6 months of the pandemic, from December 2019 through May 2020. Global and regional geographic and gastrointestinal organ--specific social media trends were compared to preprint and publication activity. Any relationship between Twitter activity and preprint articles published and that between Twitter activity and PubMed articles published overall, by organ system, and by geographic location were identified using Spearman's rank-order correlation. Results: Over the 6-month period, 73,079 tweets from 44,609 users, 7164 journal publications, and 4702 preprint publications were retrieved. Twitter activity (ie, number of tweets) peaked in March 2020, whereas preprint and publication activity (ie, number of articles published) peaked in April 2020. Overall, strong correlations were identified between trends in Twitter activity and preprint and publication activity (P<.001 for both). COVID-19 data across the three platforms mainly concentrated on pulmonology or critical care, but when analyzing the field of gastroenterology specifically, most tweets pertained to pancreatology, most publications focused on hepatology, and most preprints covered hepatology and luminal gastroenterology. Furthermore, there were significant positive associations between trends in Twitter and publication activity for all gastroenterology topics (luminal gastroenterology: P=.009; hepatology and inflammatory bowel disease: P=.006; gastrointestinal endoscopy: P=.007), except pancreatology (P=.20), suggesting that Twitter activity did not correlate with publication activity for this topic. Finally, Twitter activity was the highest in the United States (7331 tweets), whereas PubMed activity was the highest in China (1768 publications). Conclusions: The COVID-19 pandemic has highlighted the potential of social media as a vehicle for disseminating scientific information during a public health crisis. Sharing and spreading information on COVID-19 in a timely manner during the pandemic has been paramount; this was achieved at a much faster pace on social media, particularly on Twitter. Future investigation could demonstrate how social media can be used to augment and promote scholarly activity, especially as the world begins to increasingly rely on digital or virtual platforms. Scientists and clinicians should consider the use of social media in augmenting public awareness regarding their scholarly pursuits. ", doi="10.2196/26956", url="https://www.jmir.org/2021/6/e26956", url="http://www.ncbi.nlm.nih.gov/pubmed/33974550" } @Article{info:doi/10.2196/15551, author="Cheng, Qijin and Lui, Carrie and Ip, Lam Flora Wai and Yip, Fai Paul Siu", title="Typology and Impact of YouTube Videos Posted in Response to a Student Suicide Crisis: Social Media Metrics and Content Analyses", journal="JMIR Ment Health", year="2021", month="Jun", day="18", volume="8", number="6", pages="e15551", keywords="suicide", keywords="suicide prevention", keywords="social media", keywords="infodemiology", keywords="internet", keywords="digital health", keywords="YouTube", keywords="impact evaluation", keywords="network visualization", abstract="Background: Videos relating to suicide are available on YouTube, but their characteristics and impacts have seldom been examined. Objective: This study aimed to examine YouTube videos posted in response to a sudden spate of student suicides in Hong Kong during the 2015-2016 school year and evaluate the impacts of those videos. Methods: Keyword search was performed on YouTube, and relevant videos were identified. Video typology was examined through content analysis, specifically grouping the videos by who uploaded the videos, what presentation formats were used in the videos, whether the videos were originally created by the uploaders, and whether the videos disclosed the uploaders' personal experiences with suicide. Impacts of the videos were assessed in terms of reach (measured by view count), engagement (measured by comment count), and insights (measured as to what extent the comments to each video could reveal personal suicide risk and attitude toward help-seeking). Statistical analysis was conducted to compare the impacts of different types of videos. The 7 most impactful videos that were originally created by the YouTubers were selected for further analysis. They were compared with 7 videos uploaded by the same YouTubers right before the student suicide videos and 7 right after the student suicide videos. The comparison focused on their impacts and the network structure of the comments to those videos. Results: A total of 162 relevant YouTube videos were identified. They were uploaded by 7 types of stakeholders, and the most common format was one person talking to the camera. A total of 87.0\% (141/162) of the videos were originally created by the uploaders and only 8.0\% (13/162) of the videos disclosed uploader personal experiences with suicide. The uploader profiles being popular or top YouTubers and the video containing disclosure of the uploader's personal experiences were found to be significantly correlated with greater impacts (P<.001). Focusing on the 7 most impactful original videos, it is found that those videos generated more engagement, especially more interactions between the viewers, and more insights than regular videos uploaded by the same YouTubers. Conclusions: When responding to a youth suicide crisis, videos made by key opinion leaders on YouTube sharing their own experiences of overcoming suicide risks could generate significant positive impacts. These types of videos offer a precious opportunity to craft online campaigns and activities to raise suicide prevention awareness and engage vulnerable youth. ", doi="10.2196/15551", url="https://mental.jmir.org/2021/6/e15551/" } @Article{info:doi/10.2196/27052, author="Naik, Hiten and Johnson, Dimitri Maximilian Desmond and Johnson, Roger Michael", title="Internet Interest in Colon Cancer Following the Death of Chadwick Boseman: Infoveillance Study", journal="J Med Internet Res", year="2021", month="Jun", day="15", volume="23", number="6", pages="e27052", keywords="colon cancer", keywords="Google", keywords="Wikipedia", keywords="infodemiology", abstract="Background: Compared with White Americans, Black Americans have higher colon cancer mortality rates but lower up-to-date screening rates. Chadwick Boseman was a prominent Black American actor who died of colon cancer on August 28, 2020. As announcements of celebrity diagnoses often result in increased awareness, Boseman's death may have resulted in greater interest in colon cancer on the internet, particularly among Black Americans. Objective: This study aims to quantify the impact of Chadwick Boseman's death on web-based search interest in colon cancer and determine whether there was an increase in interest in regions of the United States with a greater proportion of Black Americans. Methods: We conducted an infoveillance study using Google Trends (GT) and Wikipedia pageview analysis. Using an autoregressive integrated moving average algorithm, we forecasted the weekly relative search volume (RSV) for GT search topics and terms related to colon cancer that would have been expected had his death not occurred and compared it with observed RSV data. This analysis was also conducted for the number of page views on the Wikipedia page for colorectal cancer. We then delineated GT RSV data for the term colon cancer for states and metropolitan areas in the United States and determined how the RSV values for these regions correlated with the percentage of Black Americans in that region. Differences in these correlations before and after Boseman's death were compared to determine whether there was a shift in the racial demographics of the individuals conducting the searches. Results: The observed RSVs for the topics colorectal cancer and colon cancer screening increased by 598\% and 707\%, respectively, and were on average 121\% (95\% CI 72\%-193\%) and 256\% (95\% CI 35\%-814\%) greater than expected during the first 3 months following Boseman's death. Daily Wikipedia page view volume during the 2 months following Boseman's death was on average 1979\% (95\% CI 1375\%-2894\%) greater than expected, and it was estimated that this represented 547,354 (95\% CI 497,708-585,167) excess Wikipedia page views. Before Boseman's death, there were negative correlations between the percentage of Black Americans living in a state or metropolitan area and the RSV for colon cancer in that area (r=?0.18 and r=?0.05, respectively). However, in the 2 weeks following his death, there were positive correlations between the RSV for colon cancer and the percentage of Black Americans per state and per metropolitan area (r=0.73 and r=0.33, respectively). These changes persisted for 4 months and were all statistically significant (P<.001). Conclusions: There was a significant increase in web-based activity related to colon cancer following Chadwick Boseman's death, particularly in areas with a higher proportion of Black Americans. This reflects a heightened public awareness that can be leveraged to further educate the public. ", doi="10.2196/27052", url="https://www.jmir.org/2021/6/e27052", url="http://www.ncbi.nlm.nih.gov/pubmed/34128824" } @Article{info:doi/10.2196/25010, author="Tao, Chunliang and Diaz, Destiny and Xie, Zidian and Chen, Long and Li, Dongmei and O'Connor, Richard", title="Potential Impact of a Paper About COVID-19 and Smoking on Twitter Users' Attitudes Toward Smoking: Observational Study", journal="JMIR Form Res", year="2021", month="Jun", day="15", volume="5", number="6", pages="e25010", keywords="COVID-19", keywords="smoking", keywords="Twitter", keywords="infodemiology", keywords="infodemic", keywords="infoveillance", keywords="impact", keywords="attitude", keywords="perception", keywords="observational", keywords="social media", keywords="cross-sectional", keywords="dissemination", keywords="research", abstract="Background: A cross-sectional study (Miyara et al, 2020) conducted by French researchers showed that the rate of current daily smoking was significantly lower in patients with COVID-19 than in the French general population, implying a potentially protective effect of smoking. Objective: We aimed to examine the dissemination of the Miyara et al study among Twitter users and whether a shift in their attitudes toward smoking occurred after its publication as preprint on April 21, 2020. Methods: Twitter posts were crawled between April 14 and May 4, 2020, by the Tweepy stream application programming interface, using a COVID-19--related keyword query. After filtering, the final 1929 tweets were classified into three groups: (1) tweets that were not related to the Miyara et al study before it was published, (2) tweets that were not related to Miyara et al study after it was published, and (3) tweets that were related to Miyara et al study after it was published. The attitudes toward smoking, as expressed in the tweets, were compared among the above three groups using multinomial logistic regression models in the statistical analysis software R (The R Foundation). Results: Temporal analysis showed a peak in the number of tweets discussing the results from the Miyara et al study right after its publication. Multinomial logistic regression models on sentiment scores showed that the proportion of negative attitudes toward smoking in tweets related to the Miyara et al study after it was published (17.07\%) was significantly lower than the proportion in tweets that were not related to the Miyara et al study, either before (44/126, 34.9\%; P<.001) or after the Miyara et al study was published (68/198, 34.3\%; P<.001). Conclusions: The public's attitude toward smoking shifted in a positive direction after the Miyara et al study found a lower incidence of COVID-19 cases among daily smokers. ", doi="10.2196/25010", url="https://formative.jmir.org/2021/6/e25010", url="http://www.ncbi.nlm.nih.gov/pubmed/33939624" } @Article{info:doi/10.2196/26692, author="Rao, Ashwin and Morstatter, Fred and Hu, Minda and Chen, Emily and Burghardt, Keith and Ferrara, Emilio and Lerman, Kristina", title="Political Partisanship and Antiscience Attitudes in Online Discussions About COVID-19: Twitter Content Analysis", journal="J Med Internet Res", year="2021", month="Jun", day="14", volume="23", number="6", pages="e26692", keywords="COVID-19", keywords="Twitter", keywords="infodemiology", keywords="infodemic", keywords="infoveillance", keywords="multidimensional polarization", keywords="social media", keywords="social network", abstract="Background: The novel coronavirus pandemic continues to ravage communities across the United States. Opinion surveys identified the importance of political ideology in shaping perceptions of the pandemic and compliance with preventive measures. Objective: The aim of this study was to measure political partisanship and antiscience attitudes in the discussions about the pandemic on social media, as well as their geographic and temporal distributions. Methods: We analyzed a large set of tweets from Twitter related to the pandemic, collected between January and May 2020, and developed methods to classify the ideological alignment of users along the moderacy (hardline vs moderate), political (liberal vs conservative), and science (antiscience vs proscience) dimensions. Results: We found a significant correlation in polarized views along the science and political dimensions. Moreover, politically moderate users were more aligned with proscience views, while hardline users were more aligned with antiscience views. Contrary to expectations, we did not find that polarization grew over time; instead, we saw increasing activity by moderate proscience users. We also show that antiscience conservatives in the United States tended to tweet from the southern and northwestern states, while antiscience moderates tended to tweet from the western states. The proportion of antiscience conservatives was found to correlate with COVID-19 cases. Conclusions: Our findings shed light on the multidimensional nature of polarization and the feasibility of tracking polarized opinions about the pandemic across time and space through social media data. ", doi="10.2196/26692", url="https://www.jmir.org/2021/6/e26692", url="http://www.ncbi.nlm.nih.gov/pubmed/34014831" } @Article{info:doi/10.2196/25028, author="Lee, Ji-Hyun and Park, Hyeoun-Ae and Song, Tae-Min", title="A Determinants-of-Fertility Ontology for Detecting Future Signals of Fertility Issues From Social Media Data: Development of an Ontology", journal="J Med Internet Res", year="2021", month="Jun", day="14", volume="23", number="6", pages="e25028", keywords="ontology", keywords="fertility", keywords="public policy", keywords="South Korea", keywords="social media", keywords="future", keywords="infodemiology", keywords="infoveillance", abstract="Background: South Korea has the lowest fertility rate in the world despite considerable governmental efforts to boost it. Increasing the fertility rate and achieving the desired outcomes of any implemented policies requires reliable data on the ongoing trends in fertility and preparations for the future based on these trends. Objective: The aims of this study were to (1) develop a determinants-of-fertility ontology with terminology for collecting and analyzing social media data; (2) determine the description logics, content coverage, and structural and representational layers of the ontology; and (3) use the ontology to detect future signals of fertility issues. Methods: An ontology was developed using the Ontology Development 101 methodology. The domain and scope of the ontology were defined by compiling a list of competency questions. The terms were collected from Korean government reports, Korea's Basic Plan for Low Fertility and Aging Society, a national survey about marriage and childbirth, and social media postings on fertility issues. The classes and their hierarchy were defined using a top-down approach based on an ecological model. The internal structure of classes was defined using the entity-attribute-value model. The description logics of the ontology were evaluated using Prot{\'e}g{\'e} (version 5.5.0), and the content coverage was evaluated by comparing concepts extracted from social media posts with the list of ontology classes. The structural and representational layers of the ontology were evaluated by experts. Social media data were collected from 183 online channels between January 1, 2011, and June 30, 2015. To detect future signals of fertility issues, 2 classes of the ontology, the socioeconomic and cultural environment, and public policy, were identified as keywords. A keyword issue map was constructed, and the defined keywords were mapped to identify future signals. R software (version 3.5.2) was used to mine for future signals. Results: A determinants-of-fertility ontology comprised 236 classes and terminology comprised 1464 synonyms of the 236 classes. Concept classes in the ontology were found to be coherently and consistently defined. The ontology included more than 90\% of the concepts that appeared in social media posts on fertility policies. Average scores for all of the criteria for structural and representations layers exceeded 4 on a 5-point scale. Violence and abuse (socioeconomic and cultural factor) and flexible working arrangement (fertility policy) were weak signals, suggesting that they could increase rapidly in the future. Conclusions: The determinants-of-fertility ontology developed in this study can be used as a framework for collecting and analyzing social media data on fertility issues and detecting future signals of fertility issues. The future signals identified in this study will be useful for policy makers who are developing policy responses to low fertility. ", doi="10.2196/25028", url="https://www.jmir.org/2021/6/e25028", url="http://www.ncbi.nlm.nih.gov/pubmed/34125068" } @Article{info:doi/10.2196/29802, author="Neely, Stephen and Eldredge, Christina and Sanders, Ron", title="Health Information Seeking Behaviors on Social Media During the COVID-19 Pandemic Among American Social Networking Site Users: Survey Study", journal="J Med Internet Res", year="2021", month="Jun", day="11", volume="23", number="6", pages="e29802", keywords="social media", keywords="internet", keywords="communication", keywords="public health", keywords="COVID-19", keywords="usage", keywords="United States", keywords="information seeking", keywords="web-based health information", keywords="survey", keywords="mistrust", abstract="Background: In recent years, medical journals have emphasized the increasingly critical role that social media plays in the dissemination of public health information and disease prevention guidelines. However, platforms such as Facebook and Twitter continue to pose unique challenges for clinical health care providers and public health officials alike. In order to effectively communicate during public health emergencies, such as the COVID-19 pandemic, it is increasingly critical for health care providers and public health officials to understand how patients gather health-related information on the internet and adjudicate the merits of such information. Objective: With that goal in mind, we conducted a survey of 1003 US-based adults to better understand how health consumers have used social media to learn and stay informed about the COVID-19 pandemic, the extent to which they have relied on credible scientific information sources, and how they have gone about fact-checking pandemic-related information. Methods: A web-based survey was conducted with a sample that was purchased through an industry-leading market research provider. The results were reported with a 95\% confidence level and a margin of error of 3. Participants included 1003 US-based adults (aged ?18 years). Participants were selected via a stratified quota sampling approach to ensure that the sample was representative of the US population. Balanced quotas were determined (by region of the country) for gender, age, race, and ethnicity. Results: The results showed a heavy reliance on social media during the COVID-19 pandemic; more than three-quarters of respondents (762/1003, 76\%) reported that they have relied on social media at least ``a little,'' and 59.2\% (594/1003) of respondents indicated that they read information about COVID-19 on social media at least once per week. According to the findings, most social media users (638/1003, 63.6\%) were unlikely to fact-check what they see on the internet with a health professional, despite the high levels of mistrust in the accuracy of COVID-19--related information on social media. We also found a greater likelihood of undergoing vaccination among those following more credible scientific sources on social media during the pandemic ($\chi$216=50.790; $\phi$=0.258; P<.001). Conclusions: The findings suggest that health professionals will need to be both strategic and proactive when engaging with health consumers on social media if they hope to counteract the deleterious effects of misinformation and disinformation. Effective training, institutional support, and proactive collaboration can help health professionals adapt to the evolving patterns of health information seeking. ", doi="10.2196/29802", url="https://www.jmir.org/2021/6/e29802", url="http://www.ncbi.nlm.nih.gov/pubmed/34043526" } @Article{info:doi/10.2196/27632, author="Hou, Zhiyuan and Tong, Yixin and Du, Fanxing and Lu, Linyao and Zhao, Sihong and Yu, Kexin and Piatek, J. Simon and Larson, J. Heidi and Lin, Leesa", title="Assessing COVID-19 Vaccine Hesitancy, Confidence, and Public Engagement: A Global Social Listening Study", journal="J Med Internet Res", year="2021", month="Jun", day="11", volume="23", number="6", pages="e27632", keywords="COVID-19 vaccine", keywords="hesitancy", keywords="infoveillance", keywords="infodemiology", keywords="confidence", keywords="acceptance", keywords="engagement", keywords="social media", keywords="COVID-19", abstract="Background: Monitoring public confidence and hesitancy is crucial for the COVID-19 vaccine rollout. Social media listening (infoveillance) can not only monitor public attitudes on COVID-19 vaccines but also assess the dissemination of and public engagement with these opinions. Objective: This study aims to assess global hesitancy, confidence, and public engagement toward COVID-19 vaccination. Methods: We collected posts mentioning the COVID-19 vaccine between June and July 2020 on Twitter from New York (United States), London (United Kingdom), Mumbai (India), and Sao Paulo (Brazil), and Sina Weibo posts from Beijing (China). In total, we manually coded 12,886 posts from the five global metropolises with high COVID-19 burdens, and after assessment, 7032 posts were included in the analysis. We manually double-coded these posts using a coding framework developed according to the World Health Organization's Confidence, Complacency, and Convenience model of vaccine hesitancy, and conducted engagement analysis to investigate public communication about COVID-19 vaccines on social media. Results: Among social media users, 36.4\% (571/1568) in New York, 51.3\% (738/1440) in London, 67.3\% (144/214) in Sao Paulo, 69.8\% (726/1040) in Mumbai, and 76.8\% (2128/2770) in Beijing indicated that they intended to accept a COVID-19 vaccination. With a high perceived risk of getting COVID-19, more tweeters in New York and London expressed a lack of confidence in vaccine safety, distrust in governments and experts, and widespread misinformation or rumors. Tweeters from Mumbai, Sao Paulo, and Beijing worried more about vaccine production and supply, whereas tweeters from New York and London had more concerns about vaccine distribution and inequity. Negative tweets expressing lack of vaccine confidence and misinformation or rumors had more followers and attracted more public engagement online. Conclusions: COVID-19 vaccine hesitancy is prevalent worldwide, and negative tweets attract higher engagement on social media. It is urgent to develop an effective vaccine campaign that boosts public confidence and addresses hesitancy for COVID-19 vaccine rollouts. ", doi="10.2196/27632", url="https://www.jmir.org/2021/6/e27632", url="http://www.ncbi.nlm.nih.gov/pubmed/34061757" } @Article{info:doi/10.2196/29528, author="Basch, H. Corey and Mohlman, Jan and Fera, Joseph and Tang, Hao and Pellicane, Alessia and Basch, E. Charles", title="Community Mitigation of COVID-19 and Portrayal of Testing on TikTok: Descriptive Study", journal="JMIR Public Health Surveill", year="2021", month="Jun", day="10", volume="7", number="6", pages="e29528", keywords="TikTok", keywords="social media", keywords="COVID-19", keywords="testing", keywords="disgust", keywords="anxiety", keywords="content analysis", keywords="communication", keywords="infodemiology", keywords="infoveillance", keywords="public health", keywords="digital public health", keywords="digital health", keywords="community mitigation", abstract="Background: COVID-19 testing remains an essential element of a comprehensive strategy for community mitigation. Social media is a popular source of information about health, including COVID-19 and testing information. One of the most popular communication channels used by adolescents and young adults who search for health information is TikTok---an emerging social media platform. Objective: The purpose of this study was to describe TikTok videos related to COVID-19 testing. Methods: The hashtag \#covidtesting was searched, and the first 100 videos were included in the study sample. At the time the sample was drawn, these 100 videos garnered more than 50\% of the views for all videos cataloged under the hashtag \#covidtesting. The content characteristics that were coded included mentions, displays, or suggestions of anxiety, COVID-19 symptoms, quarantine, types of tests, results of test, and disgust/unpleasantness. Additional data that were coded included the number and percentage of views, likes, and comments and the use of music, dance, and humor. Results: The 100 videos garnered more than 103 million views; 111,000 comments; and over 12.8 million likes. Even though only 44 videos mentioned or suggested disgust/unpleasantness and 44 mentioned or suggested anxiety, those that portrayed tests as disgusting/unpleasant garnered over 70\% of the total cumulative number of views (73,479,400/103,071,900, 71.29\%) and likes (9,354,691/12,872,505, 72.67\%), and those that mentioned or suggested anxiety attracted about 60\% of the total cumulative number of views (61,423,500/103,071,900, 59.59\%) and more than 8 million likes (8,339,598/12,872,505, 64.79\%). Independent one-tailed t tests ($\alpha$=.05) revealed that videos that mentioned or suggested that COVID-19 testing was disgusting/unpleasant were associated with receiving a higher number of views and likes. Conclusions: Our finding of an association between TikTok videos that mentioned or suggested that COVID-19 tests were disgusting/unpleasant and these videos' propensity to garner views and likes is of concern. There is a need for public health agencies to recognize and address connotations of COVID-19 testing on social media. ", doi="10.2196/29528", url="https://publichealth.jmir.org/2021/6/e29528", url="http://www.ncbi.nlm.nih.gov/pubmed/34081591" } @Article{info:doi/10.2196/27280, author="Guelmami, Noomen and Ben Khalifa, Maher and Chalghaf, Nasr and Kong, Dzevela Jude and Amayra, Tannoubi and Wu, Jianhong and Azaiez, Fairouz and Bragazzi, Luigi Nicola", title="Development of the 12-Item Social Media Disinformation Scale and its Association With Social Media Addiction and Mental Health Related to COVID-19 in Tunisia: Survey-Based Pilot Case Study", journal="JMIR Form Res", year="2021", month="Jun", day="9", volume="5", number="6", pages="e27280", keywords="COVID-19 pandemic", keywords="media disinformation", keywords="social media addiction", keywords="mental health", keywords="scale validation", abstract="Background: In recent years, online disinformation has increased. Fake news has been spreading about the COVID-19 pandemic. Since January 2020, the culprits and antidotes to disinformation have been digital media and social media. Objective: Our study aimed to develop and test the psychometric properties of the 12-item Social Media Disinformation Scale (SMDS-12), which assesses the consumption, confidence, and sharing of information related to COVID-19 by social media users. Methods: A total of 874 subjects were recruited over two phases: the exploratory phase group had a mean age of 28.39 years (SD 9.32) and the confirmatory phase group had a mean age of 32.84 years (SD 12.72). Participants completed the SMDS-12, the Internet Addiction Test, the COVID-19 Fear Scale, and the 10-item Perceived Stress Scale. The SMDS-12 was initially tested by exploratory factor analysis and was subsequently tested by confirmatory factor analysis. Results: The test supported the three-factor structure. In addition, no items were removed from the measurement scale, with three factors explaining up to 73.72\% of the total variance, and the items had a lambda factor loading ranging from 0.73 to 0.85. Subsequently, confirmatory factor analysis confirmed the robustness of the measure by referring to a wide range of goodness-of-fit indices that met the recommended standards. The construct validity of the scale was supported by its convergent and discriminant validity. The reliability of the instrument examined by means of three internal consistency indices, and the corrected item-total correlation, demonstrated that the three dimensions of the instrument were reliable: Cronbach $\alpha$ values were .89, .88, and .88 for the consumption, confidence, and sharing subscales, respectively. The corrected item-total correlation ranged from 0.70 to 0.78. The correlation of the instrument's dimensions with internet addiction and mental health factors showed positive associations. Conclusions: The SMDS-12 can be reliably utilized to measure the credibility of social media disinformation and can be adapted to measure the credibility of disinformation in other contexts. ", doi="10.2196/27280", url="https://formative.jmir.org/2021/6/e27280", url="http://www.ncbi.nlm.nih.gov/pubmed/34021742" } @Article{info:doi/10.2196/21709, author="Garcia-Souto, Fernando and Pereyra-Rodriguez, Juan Jose", title="Psoriasis Google Trends", journal="JMIR Dermatol", year="2021", month="Jun", day="8", volume="4", number="1", pages="e21709", keywords="Google Trends", keywords="psoriasis", keywords="treatment", doi="10.2196/21709", url="https://derma.jmir.org/2021/1/e21709", url="http://www.ncbi.nlm.nih.gov/pubmed/37625163" } @Article{info:doi/10.2196/25579, author="Allem, Jon-Patrick and Dormanesh, Allison and Majmundar, Anuja and Unger, B. Jennifer and Kirkpatrick, G. Matthew and Choube, Akshat and Aithal, Aneesh and Ferrara, Emilio and Boley Cruz, Tess", title="Topics of Nicotine-Related Discussions on Twitter: Infoveillance Study", journal="J Med Internet Res", year="2021", month="Jun", day="7", volume="23", number="6", pages="e25579", keywords="nicotine", keywords="electronic cigarettes", keywords="Twitter", keywords="social media", keywords="social bots", keywords="cessation", abstract="Background: Cultural trends in the United States, the nicotine consumer marketplace, and tobacco policies are changing. Objective: The goal of this study was to identify and describe nicotine-related topics of conversation authored by the public and social bots on Twitter, including any misinformation or misconceptions that health education campaigns could potentially correct. Methods: Twitter posts containing the term ``nicotine'' were obtained from September 30, 2018 to October 1, 2019. Methods were used to distinguish between posts from social bots and nonbots. Text classifiers were used to identify topics in posts (n=300,360). Results: Prevalent topics of posts included vaping, smoking, addiction, withdrawal, nicotine health risks, and quit nicotine, with mentions of going ``cold turkey'' and needing help in quitting. Cessation was a common topic, with mentions of quitting and stopping smoking. Social bots discussed unsubstantiated health claims including how hypnotherapy, acupuncture, magnets worn on the ears, and time spent in the sauna can help in smoking cessation. Conclusions: Health education efforts are needed to correct unsubstantiated health claims on Twitter and ultimately direct individuals who want to quit smoking to evidence-based cessation strategies. Future interventions could be designed to follow these topics of discussions on Twitter and engage with members of the public about evidence-based cessation methods in near real time when people are contemplating cessation. ", doi="10.2196/25579", url="https://www.jmir.org/2021/6/e25579", url="http://www.ncbi.nlm.nih.gov/pubmed/34096875" } @Article{info:doi/10.2196/26481, author="Cui, Limeng and Chu, Lijuan", title="YouTube Videos Related to the Fukushima Nuclear Disaster: Content Analysis", journal="JMIR Public Health Surveill", year="2021", month="Jun", day="7", volume="7", number="6", pages="e26481", keywords="YouTube", keywords="Fukushima nuclear disaster", keywords="social media", keywords="risk communication", keywords="disaster", keywords="video platform", keywords="radiation", keywords="public safety", keywords="nuclear disaster", abstract="Background: YouTube (Alphabet Incorporated) has become the most popular video-sharing platform in the world. The Fukushima Daiichi Nuclear Power Plant (FDNPP) disaster resulted in public anxiety toward nuclear power and radiation worldwide. YouTube is an important source of information about the FDNPP disaster for the world. Objective: This study's objectives were to examine the characteristics of YouTube videos related to the FDNPP disaster, analyze the content and comments of videos with a quantitative method, and determine which features contribute to making a video popular with audiences. This study is the first to examine FDNPP disaster--related videos on YouTube. Methods: We searched for the term ``Fukushima nuclear disaster'' on YouTube on November 2, 2019. The first 60 eligible videos in the relevance, upload date, view count, and rating categories were recorded. ?Videos that were irrelevant, were non-English, had inappropriate words, were machine synthesized, and were <3 minutes long were excluded. In total, 111 videos met the inclusion criteria. Parameters of the videos, including the number of subscribers, length, the number of days since the video was uploaded, region, video popularity (views, views/day, likes, likes/day, dislikes, dislikes/day, comments, comments/day), the tone of the videos, the top ten comments, affiliation, whether Japanese people participated in the video, whether the video recorder visited Fukushima, whether the video contained theoretical knowledge, and whether the video contained information about the recent situation in Fukushima, were recorded. By using criteria for content and ?technical design, two evaluators scored videos and grouped them into the useful (score: 11-14), slightly useful (score: 6-10), and useless (score: 0-5) video categories. Results: Of the 111 videos, 43 (38.7\%) videos were useful, 43 (38.7\%) were slightly useful, and 25 (22.5\%) were useless. Useful videos had good visual and aural effects, provided vivid information on the Fukushima disaster, and had a mean score of 12 (SD 0.9). Useful videos had more views per day (P<.001), likes per day (P<.001), and comments per day (P=.02) than useless and slightly useful videos. The popularity of videos had a significant correlation with clear sounds (likes/day: P=.001; comments/day: P=.02), vivid information (likes/day: P<.001; comments/day: P=.007), understanding content (likes/day: P=.001; comments/day: P=.04). There was no significant difference in likes per day (P=.72) and comments per day (P=.11) between negative and neutral- and mixed-tone videos. Videos about the recent situation in Fukushima had more likes and comments per day. Video recorders who personally visited Fukushima Prefecture had more subscribers and received more views and likes. Conclusions: The possible features that made videos popular to the public included ?video quality, videos made in Fukushima, and information on the recent situation in Fukushima. During risk communication on new forms of media, health institutes should increase publicity and be more approachable to resonate with international audiences. ", doi="10.2196/26481", url="https://publichealth.jmir.org/2021/6/e26481", url="http://www.ncbi.nlm.nih.gov/pubmed/34096880" } @Article{info:doi/10.2196/24564, author="Wawrzuta, Dominik and Jaworski, Mariusz and Gotlib, Joanna and Panczyk, Mariusz", title="Characteristics of Antivaccine Messages on Social Media: Systematic Review", journal="J Med Internet Res", year="2021", month="Jun", day="4", volume="23", number="6", pages="e24564", keywords="vaccination", keywords="social media", keywords="antivaccination movement", keywords="vaccination refusal", keywords="health communication", keywords="public health", keywords="vaccines", abstract="Background: Supporters of the antivaccination movement can easily spread information that is not scientifically proven on social media. Therefore, learning more about their posts and activities is instrumental in effectively reacting and responding to the false information they publish, which is aimed at discouraging people from taking vaccines. Objective: This study aims to gather, assess, and synthesize evidence related to the current state of knowledge about antivaccine social media users' web-based activities. Methods: We systematically reviewed English-language papers from 3 databases (Scopus, Web of Science, and PubMed). A data extraction form was established, which included authors, year of publication, specific objectives, study design, comparison, and outcomes of significance. We performed an aggregative narrative synthesis of the included studies. Results: The search strategy retrieved 731 records in total. After screening for duplicates and eligibility, 18 articles were included in the qualitative synthesis. Although most of the authors analyzed text messages, some of them studied images or videos. In addition, although most of the studies examined vaccines in general, 5 focused specifically on human papillomavirus vaccines, 2 on measles vaccines, and 1 on influenza vaccines. The synthesized studies dealt with the popularity of provaccination and antivaccination content, the style and manner in which messages about vaccines were formulated for the users, a range of topics concerning vaccines (harmful action, limited freedom of choice, and conspiracy theories), and the role and activity of bots in the dissemination of these messages in social media. Conclusions: Proponents of the antivaccine movement use a limited number of arguments in their messages; therefore, it is possible to prepare publications clarifying doubts and debunking the most common lies. Public health authorities should continuously monitor social media to quickly find new antivaccine arguments and then create information campaigns for both health professionals and other users. ", doi="10.2196/24564", url="https://www.jmir.org/2021/6/e24564", url="http://www.ncbi.nlm.nih.gov/pubmed/34085943" } @Article{info:doi/10.2196/27300, author="Guntuku, Chandra Sharath and Purtle, Jonathan and Meisel, F. Zachary and Merchant, M. Raina and Agarwal, Anish", title="Partisan Differences in Twitter Language Among US Legislators During the COVID-19 Pandemic: Cross-sectional Study", journal="J Med Internet Res", year="2021", month="Jun", day="3", volume="23", number="6", pages="e27300", keywords="Twitter", keywords="COVID-19", keywords="digital health", keywords="US legislators", keywords="natural language processing", keywords="policy makers", keywords="social media", keywords="policy", keywords="politics", keywords="language", keywords="cross-sectional", keywords="content", keywords="sentiment", keywords="infodemiology", keywords="infoveillance", abstract="Background: As policy makers continue to shape the national and local responses to the COVID-19 pandemic, the information they choose to share and how they frame their content provide key insights into the public and health care systems. Objective: We examined the language used by the members of the US House and Senate during the first 10 months of the COVID-19 pandemic and measured content and sentiment based on the tweets that they shared. Methods: We used Quorum (Quorum Analytics Inc) to access more than 300,000 tweets posted by US legislators from January 1 to October 10, 2020. We used differential language analyses to compare the content and sentiment of tweets posted by legislators based on their party affiliation. Results: We found that health care--related themes in Democratic legislators' tweets focused on racial disparities in care (odds ratio [OR] 2.24, 95\% CI 2.22-2.27; P<.001), health care and insurance (OR 1.74, 95\% CI 1.7-1.77; P<.001), COVID-19 testing (OR 1.15, 95\% CI 1.12-1.19; P<.001), and public health guidelines (OR 1.25, 95\% CI 1.22-1.29; P<.001). The dominant themes in the Republican legislators' discourse included vaccine development (OR 1.51, 95\% CI 1.47-1.55; P<.001) and hospital resources and equipment (OR 1.22, 95\% CI 1.18-1.25). Nonhealth care--related topics associated with a Democratic affiliation included protections for essential workers (OR 1.55, 95\% CI 1.52-1.59), the 2020 election and voting (OR 1.31, 95\% CI 1.27-1.35), unemployment and housing (OR 1.27, 95\% CI 1.24-1.31), crime and racism (OR 1.22, 95\% CI 1.18-1.26), public town halls (OR 1.2, 95\% CI 1.16-1.23), the Trump Administration (OR 1.22, 95\% CI 1.19-1.26), immigration (OR 1.16, 95\% CI 1.12-1.19), and the loss of life (OR 1.38, 95\% CI 1.35-1.42). The themes associated with the Republican affiliation included China (OR 1.89, 95\% CI 1.85-1.92), small business assistance (OR 1.27, 95\% CI 1.23-1.3), congressional relief bills (OR 1.23, 95\% CI 1.2-1.27), press briefings (OR 1.22, 95\% CI 1.19-1.26), and economic recovery (OR 1.2, 95\% CI 1.16-1.23). Conclusions: Divergent language use on social media corresponds to the partisan divide in the first several months of the course of the COVID-19 public health crisis. ", doi="10.2196/27300", url="https://www.jmir.org/2021/6/e27300", url="http://www.ncbi.nlm.nih.gov/pubmed/33939620" } @Article{info:doi/10.2196/28253, author="Krawczyk, Konrad and Chelkowski, Tadeusz and Laydon, J. Daniel and Mishra, Swapnil and Xifara, Denise and Gibert, Benjamin and Flaxman, Seth and Mellan, Thomas and Schw{\"a}mmle, Veit and R{\"o}ttger, Richard and Hadsund, T. Johannes and Bhatt, Samir", title="Quantifying Online News Media Coverage of the COVID-19 Pandemic: Text Mining Study and Resource", journal="J Med Internet Res", year="2021", month="Jun", day="2", volume="23", number="6", pages="e28253", keywords="text mining", keywords="COVID-19", keywords="infoveillance", keywords="sentiment analysis", keywords="public health", abstract="Background: Before the advent of an effective vaccine, nonpharmaceutical interventions, such as mask-wearing, social distancing, and lockdowns, have been the primary measures to combat the COVID-19 pandemic. Such measures are highly effective when there is high population-wide adherence, which requires information on current risks posed by the pandemic alongside a clear exposition of the rules and guidelines in place. Objective: Here we analyzed online news media coverage of COVID-19. We quantified the total volume of COVID-19 articles, their sentiment polarization, and leading subtopics to act as a reference to inform future communication strategies. Methods: We collected 26 million news articles from the front pages of 172 major online news sources in 11 countries (available online at SciRide). Using topic detection, we identified COVID-19--related content to quantify the proportion of total coverage the pandemic received in 2020. The sentiment analysis tool Vader was employed to stratify the emotional polarity of COVID-19 reporting. Further topic detection and sentiment analysis was performed on COVID-19 coverage to reveal the leading themes in pandemic reporting and their respective emotional polarizations. Results: We found that COVID-19 coverage accounted for approximately 25.3\% of all front-page online news articles between January and October 2020. Sentiment analysis of English-language sources revealed that overall COVID-19 coverage was not exclusively negatively polarized, suggesting wide heterogeneous reporting of the pandemic. Within this heterogenous coverage, 16\% of COVID-19 news articles (or 4\% of all English-language articles) can be classified as highly negatively polarized, citing issues such as death, fear, or crisis. Conclusions: The goal of COVID-19 public health communication is to increase understanding of distancing rules and to maximize the impact of governmental policy. The extent to which the quantity and quality of information from different communication channels (eg, social media, government pages, and news) influence public understanding of public health measures remains to be established. Here we conclude that a quarter of all reporting in 2020 covered COVID-19, which is indicative of information overload. In this capacity, our data and analysis form a quantitative basis for informing health communication strategies along traditional news media channels to minimize the risks of COVID-19 while vaccination is rolled out. ", doi="10.2196/28253", url="https://www.jmir.org/2021/6/e28253", url="http://www.ncbi.nlm.nih.gov/pubmed/33900934" } @Article{info:doi/10.2196/26385, author="Rotter, Dominik and Doebler, Philipp and Schmitz, Florian", title="Interests, Motives, and Psychological Burdens in Times of Crisis and Lockdown: Google Trends Analysis to Inform Policy Makers", journal="J Med Internet Res", year="2021", month="Jun", day="1", volume="23", number="6", pages="e26385", keywords="coronavirus", keywords="Google Trends", keywords="infodemiology", keywords="infoveillance", keywords="pandemic", keywords="information search", keywords="trend", keywords="COVID-19", keywords="burden", keywords="mental health", keywords="policy", keywords="online health information", abstract="Background: In the face of the COVID-19 pandemic, the German government and the 16 German federal states implemented a variety of nonpharmaceutical interventions (NPIs) to decelerate the spread of the SARS-CoV-2 virus and thus prevent a collapse of the health care system. These measures comprised, among others, social distancing, the temporary closure of shops and schools, and a ban of large public gatherings and meetings with people not living in the same household. Objective: It is fair to assume that the issued NPIs have heavily affected social life and psychological functioning. We therefore aimed to examine possible effects of this lockdown in conjunction with daily new infections and the state of the national economy on people's interests, motives, and other psychological states. Methods: We derived 249 keywords from the Google Trends database, tapping into 27 empirically and rationally selected psychological domains. To overcome issues with reliability and specificity of individual indicator variables, broad factors were derived by means of time series factor analysis. All domains were subjected to a change point analysis and time series regression analysis with infection rates, NPIs, and the state of the economy as predictors. All keywords and analyses were preregistered prior to analysis. Results: With the pandemic arriving in Germany, significant increases in people's search interests were observed in virtually all domains. Although most of the changes were short-lasting, each had a distinguishable onset during the lockdown period. Regression analysis of the Google Trends data confirmed pronounced autoregressive effects for the investigated variables, while forecasting by means of the tested predictors (ie, daily new infections, NPIs, and the state of economy) was moderate at best. Conclusions: Our findings indicate that people's interests, motives, and psychological states are heavily affected in times of crisis and lockdown. Specifically, disease- and virus-related domains (eg, pandemic disease, symptoms) peaked early, whereas personal health strategies (eg, masks, homeschooling) peaked later during the lockdown. Domains addressing social life and psychosocial functioning showed long-term increases in public interest. Renovation was the only domain to show a decrease in search interest with the onset of the lockdown. As changes in search behavior are consistent over multiple domains, a Google Trends analysis may provide information for policy makers on how to adapt and develop intervention, information, and prevention strategies, especially when NPIs are in effect. ", doi="10.2196/26385", url="https://www.jmir.org/2021/6/e26385", url="http://www.ncbi.nlm.nih.gov/pubmed/33999837" } @Article{info:doi/10.2196/24199, author="Kochan, Andrew and Ong, Shaun and Guler, Sabina and Johannson, A. Kerri and Ryerson, J. Christopher and Goobie, C. Gillian", title="Social Media Content of Idiopathic Pulmonary Fibrosis Groups and Pages on Facebook: Cross-sectional Analysis", journal="JMIR Public Health Surveill", year="2021", month="May", day="31", volume="7", number="5", pages="e24199", keywords="interstitial lung disease", keywords="idiopathic pulmonary fibrosis", keywords="patient education", keywords="social media", keywords="internet", abstract="Background: Patients use Facebook as a resource for medical information. We analyzed posts on idiopathic pulmonary fibrosis (IPF)-related Facebook groups and pages for the presence of guideline content, user engagement, and usefulness. Objective: The objective of this study was to describe and analyze posts from Facebook groups and pages that primarily focus on IPF-related content. Methods: Cross-sectional analysis was performed on a single date, identifying Facebook groups and pages resulting from separately searching ``IPF'' and ``idiopathic pulmonary fibrosis.'' For inclusion, groups and pages needed to meet either search term and be in English, publicly available, and relevant to IPF. Every 10th post was assessed for general characteristics, source, focus, and user engagement metrics. Posts were analyzed for presence of IPF guideline content, useful scientific information (eg, scientific publications), useful support information (eg, information about support groups), and potentially harmful information. Results: Eligibility criteria were met by 12 groups and 27 pages, leading to analysis of 523 posts. Of these, 42\% contained guideline content, 24\% provided useful support, 20\% provided useful scientific information, and 5\% contained potentially harmful information. The most common post source was nonmedical users (85\%). Posts most frequently focused on IPF-related news (29\%). Posts containing any guideline content had fewer likes or comments and a higher likelihood of containing potentially harmful content. Posts containing useful supportive information had more likes, shares, and comments. Conclusions: Facebook contains useful information about IPF, but posts with misinformation and less guideline content have higher user engagement, making them more visible. Identifying ways to help patients with IPF discriminate between useful and harmful information on Facebook and other social media platforms is an important task for health care professionals. ", doi="10.2196/24199", url="https://publichealth.jmir.org/2021/5/e24199", url="http://www.ncbi.nlm.nih.gov/pubmed/34057425" } @Article{info:doi/10.2196/20179, author="Moreno, A. Megan and Gaus, Quintin and Wilt, Megan and Arseniev-Koehler, Alina and Ton, Adrienne and Adrian, Molly and VanderStoep, Ann", title="Displayed Depression Symptoms on Facebook at Two Time Points: Content Analysis", journal="JMIR Form Res", year="2021", month="May", day="31", volume="5", number="5", pages="e20179", keywords="adolescents", keywords="content analysis", keywords="depression", keywords="Facebook", keywords="social media", abstract="Background: Depression is a prevalent and problematic mental disorder that often has an onset in adolescence. Previous studies have illustrated that depression disclosures on social media are common and may be linked to an individual's experiences of depression. However, most studies have examined depression displays on social media at a single time point. Objective: This study aims to investigate displayed depression symptoms on Facebook at 2 developmental time points based on symptom type and gender. Methods: Participants were recruited from an ongoing longitudinal cohort study. The content analysis of text-based Facebook data over 1 year was conducted at 2 time points: time 1 (adolescence; age 17-18 years) and time 2 (young adulthood; ages 20-22 years). Diagnostic criteria for depression were applied to each post to identify the displayed depression symptoms. Data were extracted verbatim. The analysis included nonparametric tests for comparisons. Results: A total of 78 participants' Facebook profiles were examined, of which 40 (51\%) were male. At time 1, 62\% (48/78) of the adolescents had a Facebook profile, and 54\% (26/78) displayed depression symptom references with an average of 9.4 (SD 3.1) references and 3.3 (SD 2.3) symptom types. Of the 78 participants, 15 (19\%) females and 12 (15\%) males displayed depression symptom references; these prevalence estimates were not significantly different by gender (P=.59). At time 2, 35 young adults displayed symptoms of depression with an average of 4.6 (SD 2.3) references and 2.4 (SD 1.3) symptom types. There were no differences in the prevalence of symptoms of depression displayed between males (n=19) and females (n=16; P=.63). Conclusions: This content analysis study within an ongoing cohort study illustrates the differences in depression displays on Facebook by developmental stage and symptom. This study contributes to a growing body of literature by showing that using social media to observe and understand depression during the emerging adult developmental period may be a valuable approach. ", doi="10.2196/20179", url="https://formative.jmir.org/2021/5/e20179", url="http://www.ncbi.nlm.nih.gov/pubmed/34057422" } @Article{info:doi/10.2196/27084, author="Shaklai, Sigal and Gilad-Bachrach, Ran and Yom-Tov, Elad and Stern, Naftali", title="Detecting Impending Stroke From Cognitive Traits Evident in Internet Searches: Analysis of Archival Data", journal="J Med Internet Res", year="2021", month="May", day="28", volume="23", number="5", pages="e27084", keywords="search engines", keywords="diagnosis", keywords="screening", keywords="stroke", keywords="risk", keywords="internet", keywords="trend", keywords="infodemiology", keywords="archive", keywords="prospective", keywords="algorithm", abstract="Background: Cerebrovascular disease is a leading cause of mortality and disability. Common risk assessment tools for stroke are based on the Framingham equation, which relies on traditional cardiovascular risk factors to predict an acute event in the near decade. However, no tools are currently available to predict a near/impending stroke, which might alert patients at risk to seek immediate preventive action (eg, anticoagulants for atrial fibrillation, control of hypertension). Objective: Here, we propose that an algorithm based on internet search queries can identify people at increased risk for a near stroke event. Methods: We analyzed queries submitted to the Bing search engine by 285 people who self-identified as having undergone a stroke event and 1195 controls with regard to attributes previously shown to reflect cognitive function. Controls included random people 60 years and above, or those of similar age who queried for one of nine control conditions. Results: The model performed well against all comparator groups with an area under the receiver operating characteristic curve of 0.985 or higher and a true positive rate (at a 1\% false-positive rate) above 80\% for separating patients from each of the controls. The predictive power rose as the stroke date approached and if data were acquired beginning 120 days prior to the event. Good prediction accuracy was obtained for a prospective cohort of users collected 1 year later. The most predictive attributes of the model were associated with cognitive function, including the use of common queries, repetition of queries, appearance of spelling mistakes, and number of queries per session. Conclusions: The proposed algorithm offers a screening test for a near stroke event. After clinical validation, this algorithm may enable the administration of rapid preventive intervention. Moreover, it could be applied inexpensively, continuously, and on a large scale with the aim of reducing stroke events. ", doi="10.2196/27084", url="https://www.jmir.org/2021/5/e27084", url="http://www.ncbi.nlm.nih.gov/pubmed/34047699" } @Article{info:doi/10.2196/27059, author="Daughton, R. Ashlynn and Shelley, D. Courtney and Barnard, Martha and Gerts, Dax and Watson Ross, Chrysm and Crooker, Isabel and Nadiga, Gopal and Mukundan, Nilesh and Vaquera Chavez, Yadira Nidia and Parikh, Nidhi and Pitts, Travis and Fairchild, Geoffrey", title="Mining and Validating Social Media Data for COVID-19--Related Human Behaviors Between January and July 2020: Infodemiology Study", journal="J Med Internet Res", year="2021", month="May", day="25", volume="23", number="5", pages="e27059", keywords="Twitter", keywords="social media", keywords="human behavior", keywords="infectious disease", keywords="COVID-19", keywords="coronavirus", keywords="infodemiology", keywords="infoveillance", keywords="social distancing", keywords="shelter-in-place", keywords="mobility", keywords="COVID-19 intervention", abstract="Background: Health authorities can minimize the impact of an emergent infectious disease outbreak through effective and timely risk communication, which can build trust and adherence to subsequent behavioral messaging. Monitoring the psychological impacts of an outbreak, as well as public adherence to such messaging, is also important for minimizing long-term effects of an outbreak. Objective: We used social media data from Twitter to identify human behaviors relevant to COVID-19 transmission, as well as the perceived impacts of COVID-19 on individuals, as a first step toward real-time monitoring of public perceptions to inform public health communications. Methods: We developed a coding schema for 6 categories and 11 subcategories, which included both a wide number of behaviors as well codes focused on the impacts of the pandemic (eg, economic and mental health impacts). We used this to develop training data and develop supervised learning classifiers for classes with sufficient labels. Classifiers that performed adequately were applied to our remaining corpus, and temporal and geospatial trends were assessed. We compared the classified patterns to ground truth mobility data and actual COVID-19 confirmed cases to assess the signal achieved here. Results: We applied our labeling schema to approximately 7200 tweets. The worst-performing classifiers had F1 scores of only 0.18 to 0.28 when trying to identify tweets about monitoring symptoms and testing. Classifiers about social distancing, however, were much stronger, with F1 scores of 0.64 to 0.66. We applied the social distancing classifiers to over 228 million tweets. We showed temporal patterns consistent with real-world events, and we showed correlations of up to --0.5 between social distancing signals on Twitter and ground truth mobility throughout the United States. Conclusions: Behaviors discussed on Twitter are exceptionally varied. Twitter can provide useful information for parameterizing models that incorporate human behavior, as well as for informing public health communication strategies by describing awareness of and compliance with suggested behaviors. ", doi="10.2196/27059", url="https://www.jmir.org/2021/5/e27059", url="http://www.ncbi.nlm.nih.gov/pubmed/33882015" } @Article{info:doi/10.2196/27712, author="Sivesind, Elise Torunn and Szeto, D. Mindy and Kim, William and Dellavalle, Paul Robert", title="Google Trends in Dermatology: Scoping Review of the Literature", journal="JMIR Dermatol", year="2021", month="May", day="25", volume="4", number="1", pages="e27712", keywords="Google Trends", keywords="search trends", keywords="internet", keywords="infodemiology", keywords="infoveillance", keywords="search terms", keywords="dermatology", keywords="skin cancer", keywords="databases", abstract="Background: Google Trends is a powerful online database and analytics tool of popular Google search queries over time and has the potential to inform medical practice and priorities. Objective: This review aimed to survey Google Trends literature in dermatology and elucidate its current roles and relationships with the field. Methods: A literature search was performed using PubMed to access and review relevant dermatology-related Google Trends studies published within the last 5 years. Results: Current research utilizing Google Trends data provides insight related to skin cancer, pruritus, cosmetic procedures, and COVID-19. We also found that dermatology is presently the highest-searched medical specialty---among 15 medical and surgical specialties as well as general practitioners. Google searches related to dermatology demonstrate a seasonal nature for various skin conditions and sun-related topics, depending on a region's inherent climate and hemi-sphere. In addition, celebrity social media and other viral posts have been found to potentiate Google searches about dermatology and drive public interest. Conclusions: A limited number of relevant studies may have been omitted by the simplified search strategy of this study, as well as by restriction to English language articles and articles indexed in the PubMed database. This could be expanded upon in a secondary systematic review. Future re-search is warranted to better understand how Google Trends can be utilized to improve the quality of clinic visits, drive public health campaigns, and detect disease clusters in real time. ", doi="10.2196/27712", url="https://derma.jmir.org/2021/1/e27712", url="http://www.ncbi.nlm.nih.gov/pubmed/37632813" } @Article{info:doi/10.2196/23305, author="Jang, Beakcheol and Kim, Inhwan and Kim, Wook Jong", title="Effective Training Data Extraction Method to Improve Influenza Outbreak Prediction from Online News Articles: Deep Learning Model Study", journal="JMIR Med Inform", year="2021", month="May", day="25", volume="9", number="5", pages="e23305", keywords="influenza", keywords="training data extraction", keywords="keyword", keywords="sorting", keywords="word embedding", keywords="Pearson correlation coefficient", keywords="long short-term memory", keywords="surveillance", keywords="infodemiology", keywords="infoveillance", keywords="model", abstract="Background: Each year, influenza affects 3 to 5 million people and causes 290,000 to 650,000 fatalities worldwide. To reduce the fatalities caused by influenza, several countries have established influenza surveillance systems to collect early warning data. However, proper and timely warnings are hindered by a 1- to 2-week delay between the actual disease outbreaks and the publication of surveillance data. To address the issue, novel methods for influenza surveillance and prediction using real-time internet data (such as search queries, microblogging, and news) have been proposed. Some of the currently popular approaches extract online data and use machine learning to predict influenza occurrences in a classification mode. However, many of these methods extract training data subjectively, and it is difficult to capture the latent characteristics of the data correctly. There is a critical need to devise new approaches that focus on extracting training data by reflecting the latent characteristics of the data. Objective: In this paper, we propose an effective method to extract training data in a manner that reflects the hidden features and improves the performance by filtering and selecting only the keywords related to influenza before the prediction. Methods: Although word embedding provides a distributed representation of words by encoding the hidden relationships between various tokens, we enhanced the word embeddings by selecting keywords related to the influenza outbreak and sorting the extracted keywords using the Pearson correlation coefficient in order to solely keep the tokens with high correlation with the actual influenza outbreak. The keyword extraction process was followed by a predictive model based on long short-term memory that predicts the influenza outbreak. To assess the performance of the proposed predictive model, we used and compared a variety of word embedding techniques. Results: Word embedding without our proposed sorting process showed 0.8705 prediction accuracy when 50.2 keywords were selected on average. Conversely, word embedding using our proposed sorting process showed 0.8868 prediction accuracy and an improvement in prediction accuracy of 12.6\%, although smaller amounts of training data were selected, with only 20.6 keywords on average. Conclusions: The sorting stage empowers the embedding process, which improves the feature extraction process because it acts as a knowledge base for the prediction component. The model outperformed other current approaches that use flat extraction before prediction. ", doi="10.2196/23305", url="https://medinform.jmir.org/2021/5/e23305", url="http://www.ncbi.nlm.nih.gov/pubmed/34032577" } @Article{info:doi/10.2196/29145, author="Tao, Zhuo-Ying and Su, Yu-Xiong", title="Authors' Reply to: Methodological Clarifications and Generalizing From Weibo Data. Comment on ``Nature and Diffusion of COVID-19--related Oral Health Information on Chinese Social Media: Analysis of Tweets on Weibo''", journal="J Med Internet Res", year="2021", month="May", day="21", volume="23", number="5", pages="e29145", keywords="COVID-19", keywords="dentistry", keywords="oral health", keywords="dental health", keywords="online health", keywords="social media", keywords="tweet", keywords="Weibo", keywords="China", keywords="health information", doi="10.2196/29145", url="https://www.jmir.org/2021/5/e29145", url="http://www.ncbi.nlm.nih.gov/pubmed/33989166" } @Article{info:doi/10.2196/26255, author="Yadav, Prakash Om", title="Methodological Clarifications and Generalizing From Weibo Data. Comment on ``Nature and Diffusion of COVID-19--related Oral Health Information on Chinese Social Media: Analysis of Tweets on Weibo''", journal="J Med Internet Res", year="2021", month="May", day="21", volume="23", number="5", pages="e26255", keywords="COVID-19", keywords="dentistry", keywords="oral health", keywords="dental health", keywords="online health", keywords="social media", keywords="tweet", keywords="Weibo", keywords="China", keywords="health information", doi="10.2196/26255", url="https://www.jmir.org/2021/5/e26255", url="http://www.ncbi.nlm.nih.gov/pubmed/33989161" } @Article{info:doi/10.2196/26933, author="Himelein-Wachowiak, McKenzie and Giorgi, Salvatore and Devoto, Amanda and Rahman, Muhammad and Ungar, Lyle and Schwartz, Andrew H. and Epstein, H. David and Leggio, Lorenzo and Curtis, Brenda", title="Bots and Misinformation Spread on Social Media: Implications for COVID-19", journal="J Med Internet Res", year="2021", month="May", day="20", volume="23", number="5", pages="e26933", keywords="COVID-19", keywords="coronavirus", keywords="social media", keywords="bots", keywords="infodemiology", keywords="infoveillance", keywords="social listening", keywords="infodemic", keywords="spambots", keywords="misinformation", keywords="disinformation", keywords="fake news", keywords="online communities", keywords="Twitter", keywords="public health", doi="10.2196/26933", url="https://www.jmir.org/2021/5/e26933", url="http://www.ncbi.nlm.nih.gov/pubmed/33882014" } @Article{info:doi/10.2196/26953, author="Kwok, Hang Stephen Wai and Vadde, Kumar Sai and Wang, Guanjin", title="Tweet Topics and Sentiments Relating to COVID-19 Vaccination Among Australian Twitter Users: Machine Learning Analysis", journal="J Med Internet Res", year="2021", month="May", day="19", volume="23", number="5", pages="e26953", keywords="COVID-19", keywords="vaccination", keywords="public topics", keywords="public sentiments", keywords="Twitter", keywords="infodemiology", keywords="infoveillance", keywords="social listening", keywords="infodemic", keywords="social media", keywords="natural language processing", keywords="machine learning", keywords="latent Dirichlet allocation", abstract="Background: COVID-19 is one of the greatest threats to human beings in terms of health care, economy, and society in recent history. Up to this moment, there have been no signs of remission, and there is no proven effective cure. Vaccination is the primary biomedical preventive measure against the novel coronavirus. However, public bias or sentiments, as reflected on social media, may have a significant impact on the progression toward achieving herd immunity. Objective: This study aimed to use machine learning methods to extract topics and sentiments relating to COVID-19 vaccination on Twitter. Methods: We collected 31,100 English tweets containing COVID-19 vaccine--related keywords between January and October 2020 from Australian Twitter users. Specifically, we analyzed tweets by visualizing high-frequency word clouds and correlations between word tokens. We built a latent Dirichlet allocation (LDA) topic model to identify commonly discussed topics in a large sample of tweets. We also performed sentiment analysis to understand the overall sentiments and emotions related to COVID-19 vaccination in Australia. Results: Our analysis identified 3 LDA topics: (1) attitudes toward COVID-19 and its vaccination, (2) advocating infection control measures against COVID-19, and (3) misconceptions and complaints about COVID-19 control. Nearly two-thirds of the sentiments of all tweets expressed a positive public opinion about the COVID-19 vaccine; around one-third were negative. Among the 8 basic emotions, trust and anticipation were the two prominent positive emotions observed in the tweets, while fear was the top negative emotion. Conclusions: Our findings indicate that some Twitter users in Australia supported infection control measures against COVID-19 and refuted misinformation. However, those who underestimated the risks and severity of COVID-19 may have rationalized their position on COVID-19 vaccination with conspiracy theories. We also noticed that the level of positive sentiment among the public may not be sufficient to increase vaccination coverage to a level high enough to achieve vaccination-induced herd immunity. Governments should explore public opinion and sentiments toward COVID-19 and COVID-19 vaccination, and implement an effective vaccination promotion scheme in addition to supporting the development and clinical administration of COVID-19 vaccines. ", doi="10.2196/26953", url="https://www.jmir.org/2021/5/e26953", url="http://www.ncbi.nlm.nih.gov/pubmed/33886492" } @Article{info:doi/10.2196/25661, author="Moreno, Andreas Megan and Jenkins, C. Marina and Lazovich, DeAnn", title="Tanning Misinformation Posted by Businesses on Social Media and Related Perceptions of Adolescent and Young Adult White Non-Hispanic Women: Mixed Methods Study", journal="JMIR Dermatol", year="2021", month="May", day="19", volume="4", number="1", pages="e25661", keywords="adolescent", keywords="social media", keywords="tanning", keywords="technology", keywords="media", abstract="Background: Indoor ultraviolet (UV) tanning is common and consequential, increasing the risk for cancers including melanoma and basal cell carcinoma. At-risk groups include adolescents and young adults, who often report beliefs about benefits of tanning. Adolescent and young adults are also among the most ubiquitous social media users. As previous studies support that content about tanning is common on social media, this may be a way that young women are exposed to influential content promoting tanning, including health misinformation. Objective: The purpose of this study was to evaluate health misinformation promoted by indoor tanning businesses via social media and to understand young women's perceptions of this misinformation. Methods: This mixed methods study included (1) retrospective observational content analysis of indoor tanning salons' content on Facebook over 1 year and (2) qualitative interviews with a purposeful national sample of 46 White non-Hispanic women, age 16 to 23 years, who had recently tanned indoors. We assessed experiences with tanning businesses' posted content on social media through interviews. We used the constant comparative approach for qualitative analyses. Results: Content analysis findings included data from indoor tanning businesses (n=147) across 50 states, yielding 4956 total posts. Among 9 health misinformation topics identified, the most common was the promotion of UV tanning as a safe way to get Vitamin D (n=73, 1.5\%). An example post was ``Stop by Body and Sol to get your daily dose of Vitamin D.'' Another misinformation topic was promoting tanning for health benefits (n=31, 0.62\%), an example post was ``the flu is not a season, it's an inability to adapt due to decreased sun exposure\ldots'' A total of 46 participants completed interviews (age: mean 20 years, SD 2). Almost all participants (45/46, 98\%) used Facebook, and 43.5\% (20/46) followed an indoor tanning business on social media. Approximately half of participants reported seeing social media posts from tanning salons about Vitamin D, an example of a participant comment was ``I have [seen that] a few times...'' Among the participants, approximately half believed it was safe to get Vitamin D from indoor UV tanning; a participant stated: ``I think it is a valid benefit to UV tanning.'' Conclusions: Despite the low frequency (range 0.5\%-1.5\%) of social media posts promoting health misinformation, participants commonly reported viewing these posts, and their perceptions aligned with health misinformation. Health education campaigns, possibly using social media to target at-risk populations, may be an innovative approach for tanning prevention messages. ", doi="10.2196/25661", url="https://derma.jmir.org/2021/1/e25661", url="http://www.ncbi.nlm.nih.gov/pubmed/37632797" } @Article{info:doi/10.2196/26618, author="Cresswell, Kathrin and Tahir, Ahsen and Sheikh, Zakariya and Hussain, Zain and Dom{\'i}nguez Hern{\'a}ndez, Andr{\'e}s and Harrison, Ewen and Williams, Robin and Sheikh, Aziz and Hussain, Amir", title="Understanding Public Perceptions of COVID-19 Contact Tracing Apps: Artificial Intelligence--Enabled Social Media Analysis", journal="J Med Internet Res", year="2021", month="May", day="17", volume="23", number="5", pages="e26618", keywords="artificial intelligence", keywords="sentiment analysis", keywords="COVID-19", keywords="contact tracing", keywords="social media", keywords="perception", keywords="app", keywords="exploratory", keywords="suitability", keywords="AI", keywords="Facebook", keywords="Twitter", keywords="United Kingdom", keywords="sentiment", keywords="attitude", keywords="infodemiology", keywords="infoveillance", abstract="Background: The emergence of SARS-CoV-2 in late 2019 and its subsequent spread worldwide continues to be a global health crisis. Many governments consider contact tracing of citizens through apps installed on mobile phones as a key mechanism to contain the spread of SARS-CoV-2. Objective: In this study, we sought to explore the suitability of artificial intelligence (AI)--enabled social media analyses using Facebook and Twitter to understand public perceptions of COVID-19 contact tracing apps in the United Kingdom. Methods: We extracted and analyzed over 10,000 relevant social media posts across an 8-month period, from March 1 to October 31, 2020. We used an initial filter with COVID-19--related keywords, which were predefined as part of an open Twitter-based COVID-19 dataset. We then applied a second filter using contract tracing app--related keywords and a geographical filter. We developed and utilized a hybrid, rule-based ensemble model, combining state-of-the-art lexicon rule-based and deep learning--based approaches. Results: Overall, we observed 76\% positive and 12\% negative sentiments, with the majority of negative sentiments reported in the North of England. These sentiments varied over time, likely influenced by ongoing public debates around implementing app-based contact tracing by using a centralized model where data would be shared with the health service, compared with decentralized contact-tracing technology. Conclusions: Variations in sentiments corroborate with ongoing debates surrounding the information governance of health-related information. AI-enabled social media analysis of public attitudes in health care can help facilitate the implementation of effective public health campaigns. ", doi="10.2196/26618", url="https://www.jmir.org/2021/5/e26618", url="http://www.ncbi.nlm.nih.gov/pubmed/33939622" } @Article{info:doi/10.2196/20803, author="Nguyen, Xuan-Lan Anne and Trinh, Xuan-Vi and Wang, Y. Sophia and Wu, Y. Albert", title="Determination of Patient Sentiment and Emotion in Ophthalmology: Infoveillance Tutorial on Web-Based Health Forum Discussions", journal="J Med Internet Res", year="2021", month="May", day="17", volume="23", number="5", pages="e20803", keywords="sentiment analysis", keywords="emotions analysis", keywords="natural language processing", keywords="online forums", keywords="social media", keywords="patient attitudes", keywords="medicine", keywords="infodemiology", keywords="infoveillance", keywords="digital health", abstract="Background: Clinical data in social media are an underused source of information with great potential to allow for a deeper understanding of patient values, attitudes, and preferences. Objective: This tutorial aims to describe a novel, robust, and modular method for the sentiment analysis and emotion detection of free text from web-based forums and the factors to consider during its application. Methods: We mined the discussion and user information of all posts containing search terms related to a medical subspecialty (oculoplastics) from MedHelp, the largest web-based platform for patient health forums. We used data cleaning and processing tools to define the relevant subset of results and prepare them for sentiment analysis. We executed sentiment and emotion analyses by using IBM Watson Natural Language Understanding to generate sentiment and emotion scores for the posts and their associated keywords. The keywords were aggregated using natural language processing tools. Results: Overall, 39 oculoplastic-related search terms resulted in 46,381 eligible posts within 14,329 threads. Posts were written by 18,319 users (117 doctors; 18,202 patients) and included 201,611 associated keywords. Keywords that occurred ?500 times in the corpus were used to identify the most prominent topics, including specific symptoms, medication, and complications. The sentiment and emotion scores of these keywords and eligible posts were analyzed to provide concrete examples of the potential of this methodology to allow for a better understanding of patients' attitudes. The overall sentiment score reflects a positive, neutral, or negative sentiment, whereas the emotion scores (anger, disgust, fear, joy, and sadness) represent the likelihood of the presence of the emotion. In keyword grouping analyses, medical signs, symptoms, and diseases had the lowest overall sentiment scores (?0.598). Complications were highly associated with sadness (0.485). Forum posts mentioning body parts were related to sadness (0.416) and fear (0.321). Administration was the category with the highest anger score (0.146). The top 6 forum subgroups had an overall negative sentiment score; the most negative one was the Neurology forum, with a score of ?0.438. The Undiagnosed Symptoms forum had the highest sadness score (0.448). The least likely fearful posts were those from the Eye Care forum, with a score of 0.260. The overall sentiment score was much more negative before the doctor replied. The anger, disgust, fear, and sadness emotion scores decreased in likelihood, whereas joy was slightly more likely to be expressed after doctors replied. Conclusions: This report allows physicians and researchers to efficiently mine and perform sentiment analysis on social media to better understand patients' perspectives and promote patient-centric care. Important factors to be considered during its application include evaluating the scope of the search; selecting search terms and understanding their linguistic usages; and establishing selection, filtering, and processing criteria for posts and keywords tailored to the desired results. ", doi="10.2196/20803", url="https://www.jmir.org/2021/5/e20803", url="http://www.ncbi.nlm.nih.gov/pubmed/33999001" } @Article{info:doi/10.2196/28118, author="Liu, Siru and Liu, Jialin", title="Understanding Behavioral Intentions Toward COVID-19 Vaccines: Theory-Based Content Analysis of Tweets", journal="J Med Internet Res", year="2021", month="May", day="12", volume="23", number="5", pages="e28118", keywords="vaccine", keywords="COVID-19", keywords="behavior", keywords="tweet", keywords="intention", keywords="content analysis", keywords="Twitter", keywords="social media", keywords="acceptance", keywords="threshold", keywords="willing", keywords="theory", keywords="model", keywords="infodemiology", keywords="infoveillance", abstract="Background: Acceptance rates of COVID-19 vaccines have still not reached the required threshold to achieve herd immunity. Understanding why some people are willing to be vaccinated and others are not is a critical step to develop efficient implementation strategies to promote COVID-19 vaccines. Objective: We conducted a theory-based content analysis based on the capability, opportunity, motivation--behavior (COM-B) model to characterize the factors influencing behavioral intentions toward COVID-19 vaccines mentioned on the Twitter platform. Methods: We collected tweets posted in English from November 1-22, 2020, using a combination of relevant keywords and hashtags. After excluding retweets, we randomly selected 5000 tweets for manual coding and content analysis. We performed a content analysis informed by the adapted COM-B model. Results: Of the 5000 COVID-19 vaccine--related tweets that were coded, 4796 (95.9\%) were posted by unique users. A total of 97 tweets carried positive behavioral intent, while 182 tweets contained negative behavioral intent. Of these, 28 tweets were mapped to capability factors, 155 tweets were related to motivation, 23 tweets were related to opportunities, and 74 tweets did not contain any useful information about the reasons for their behavioral intentions ($\kappa$=0.73). Some tweets mentioned two or more constructs at the same time. Tweets that were mapped to capability (P<.001), motivation (P<.001), and opportunity (P=.03) factors were more likely to indicate negative behavioral intentions. Conclusions: Most behavioral intentions regarding COVID-19 vaccines were related to the motivation construct. The themes identified in this study could be used to inform theory-based and evidence-based interventions to improve acceptance of COVID-19 vaccines. ", doi="10.2196/28118", url="https://www.jmir.org/2021/5/e28118", url="http://www.ncbi.nlm.nih.gov/pubmed/33939625" } @Article{info:doi/10.2196/18593, author="Hswen, Yulin and Zhang, Amanda and Ventelou, Bruno", title="Estimation of Asthma Symptom Onset Using Internet Search Queries: Lag-Time Series Analysis", journal="JMIR Public Health Surveill", year="2021", month="May", day="10", volume="7", number="5", pages="e18593", keywords="digital epidemiology", keywords="Google queries", keywords="asthma", keywords="symptoms", keywords="health information seeking", abstract="Background: Asthma affects over 330 million people worldwide. Timing of an asthma event is extremely important and lack of identification of asthma increases the risk of death. A major challenge for health systems is the length of time between symptom onset and care seeking, which could result in delayed treatment initiation and worsening of symptoms. Objective: This study evaluates the utility of the internet search query data for the identification of the onset of asthma symptoms. Methods: Pearson correlation coefficients between the time series of hospital admissions and Google searches were computed at lag times from 4 weeks before hospital admission to 4 weeks after hospital admission. An autoregressive integrated moving average (ARIMAX) model with an autoregressive process at lags of 1 and 2 and Google searches at weeks --1 and --2 as exogenous variables were conducted to validate our correlation results. Results: Google search volume for asthma had the highest correlation at 2 weeks before hospital admission. The ARIMAX model using an autoregressive process showed that the relative searches from Google about asthma were significant at lags 1 (P<.001) and 2 (P=.04). Conclusions: Our findings demonstrate that internet search queries may provide a real-time signal for asthma events and may be useful to measure the timing of symptom onset. ", doi="10.2196/18593", url="https://publichealth.jmir.org/2021/5/e18593", url="http://www.ncbi.nlm.nih.gov/pubmed/33970108" } @Article{info:doi/10.2196/15716, author="Bunyan, Alden and Venuturupalli, Swamy and Reuter, Katja", title="Expressed Symptoms and Attitudes Toward Using Twitter for Health Care Engagement Among Patients With Lupus on Social Media: Protocol for a Mixed Methods Study", journal="JMIR Res Protoc", year="2021", month="May", day="6", volume="10", number="5", pages="e15716", keywords="health promotion", keywords="infodemiology", keywords="infoveillance", keywords="Internet", keywords="listening", keywords="lupus", keywords="systematic lupus erythematosus", keywords="surveillance", keywords="Twitter", keywords="survey", keywords="social media", keywords="social network", abstract="Background: Lupus is a complex autoimmune disease that is difficult to diagnose and treat. It is estimated that at least 5 million Americans have lupus, with more than 16,000 new cases of lupus being reported annually in the United States. Social media provides a platform for patients to find rheumatologists and peers and build awareness of the condition. Researchers have suggested that the social network Twitter may serve as a rich avenue for exploring how patients communicate about their health issues. However, there is a lack of research about the characteristics of lupus patients on Twitter and their attitudes toward using Twitter for engaging them with their health care. Objective: This study has two objectives: (1) to conduct a content analysis of Twitter data published by users (in English) in the United States between September 1, 2017 and October 31, 2018 to identify patients who publicly discuss their lupus condition and to assess their expressed health themes and (2) to conduct a cross-sectional survey among these lupus patients on Twitter to study their attitudes toward using Twitter for engaging them with their health care. Methods: This is a mixed methods study that analyzes retrospective Twitter data and conducts a cross-sectional survey among lupus patients on Twitter. We used Symplur Signals, a health care social media analytics platform, to access the Twitter data and analyze user-generated posts that include keywords related to lupus. We will use descriptive statistics to analyze the data and identify the most prevalent topics in the Twitter content among lupus patients. We will further conduct self-report surveys via Twitter by inviting all identified lupus patients who discuss their lupus condition on Twitter. The goal of the survey is to collect data about the characteristics of lupus patients (eg, gender, race/ethnicity, educational level) and their attitudes toward using Twitter for engaging them with their health care. Results: This study has been funded by the National Center for Advancing Translational Science through a Clinical and Translational Science Award. The institutional review board at the University of Southern California (HS-19-00048) approved the study. Data extraction and cleaning are complete. We obtained 47,715 Twitter posts containing terms related to ``lupus'' from users in the United States published in English between September 1, 2017 and October 31, 2018. We included 40,885 posts in the analysis. Data analysis was completed in Fall 2020. Conclusions: The data obtained in this pilot study will shed light on whether Twitter provides a promising data source for garnering health-related attitudes among lupus patients. The data will also help to determine whether Twitter might serve as a potential outreach platform for raising awareness of lupus among patients and implementing related health education interventions. International Registered Report Identifier (IRRID): DERR1-10.2196/15716 ", doi="10.2196/15716", url="https://www.researchprotocols.org/2021/5/e15716", url="http://www.ncbi.nlm.nih.gov/pubmed/33955845" } @Article{info:doi/10.2196/28352, author="Basch, E. Charles and Basch, H. Corey and Hillyer, C. Grace and Meleo-Erwin, C. Zoe and Zagnit, A. Emily", title="YouTube Videos and Informed Decision-Making About COVID-19 Vaccination: Successive Sampling Study", journal="JMIR Public Health Surveill", year="2021", month="May", day="6", volume="7", number="5", pages="e28352", keywords="YouTube", keywords="vaccination", keywords="COVID-19", keywords="social media", keywords="communication", keywords="misinformation", keywords="disinformation", keywords="adverse reactions", abstract="Background: Social media platforms such as YouTube are used by many people to seek and share health-related information that may influence their decision-making about COVID-19 vaccination. Objective: The purpose of this study was to improve the understanding about the sources and content of widely viewed YouTube videos on COVID-19 vaccination. Methods: Using the keywords ``coronavirus vaccination,'' we searched for relevant YouTube videos, sorted them by view count, and selected two successive samples (with replacement) of the 100 most widely viewed videos in July and December 2020, respectively. Content related to COVID-19 vaccines were coded by two observers, and inter-rater reliability was demonstrated. Results: The videos observed in this study were viewed over 55 million times cumulatively. The number of videos that addressed fear increased from 6 in July to 20 in December 2020, and the cumulative views correspondingly increased from 2.6\% (1,449,915 views) to 16.6\% (9,553,368 views). There was also a large increase in the number of videos and cumulative views with respect to concerns about vaccine effectiveness, from 6 videos with approximately 6 million views in July to 25 videos with over 12 million views in December 2020. The number of videos and total cumulative views covering adverse reactions almost tripled, from 11 videos with approximately 6.5 million (11.7\% of cumulative views) in July to 31 videos with almost 15.7 million views (27.2\% of cumulative views) in December 2020. Conclusions: Our data show the potentially inaccurate and negative influence social media can have on population-wide vaccine uptake, which should be urgently addressed by agencies of the United States Public Health Service as well as its global counterparts. ", doi="10.2196/28352", url="https://publichealth.jmir.org/2021/5/e28352", url="http://www.ncbi.nlm.nih.gov/pubmed/33886487" } @Article{info:doi/10.2196/25714, author="Vaghela, Uddhav and Rabinowicz, Simon and Bratsos, Paris and Martin, Guy and Fritzilas, Epameinondas and Markar, Sheraz and Purkayastha, Sanjay and Stringer, Karl and Singh, Harshdeep and Llewellyn, Charlie and Dutta, Debabrata and Clarke, M. Jonathan and Howard, Matthew and and Serban, Ovidiu and Kinross, James", title="Using a Secure, Continually Updating, Web Source Processing Pipeline to Support the Real-Time Data Synthesis and Analysis of Scientific Literature: Development and Validation Study", journal="J Med Internet Res", year="2021", month="May", day="6", volume="23", number="5", pages="e25714", keywords="structured data synthesis", keywords="data science", keywords="critical analysis", keywords="web crawl data", keywords="pipeline", keywords="database", keywords="literature", keywords="research", keywords="COVID-19", keywords="infodemic", keywords="decision making", keywords="data", keywords="data synthesis", keywords="misinformation", keywords="infrastructure", keywords="methodology", abstract="Background: The scale and quality of the global scientific response to the COVID-19 pandemic have unquestionably saved lives. However, the COVID-19 pandemic has also triggered an unprecedented ``infodemic''; the velocity and volume of data production have overwhelmed many key stakeholders such as clinicians and policy makers, as they have been unable to process structured and unstructured data for evidence-based decision making. Solutions that aim to alleviate this data synthesis--related challenge are unable to capture heterogeneous web data in real time for the production of concomitant answers and are not based on the high-quality information in responses to a free-text query. Objective: The main objective of this project is to build a generic, real-time, continuously updating curation platform that can support the data synthesis and analysis of a scientific literature framework. Our secondary objective is to validate this platform and the curation methodology for COVID-19--related medical literature by expanding the COVID-19 Open Research Dataset via the addition of new, unstructured data. Methods: To create an infrastructure that addresses our objectives, the PanSurg Collaborative at Imperial College London has developed a unique data pipeline based on a web crawler extraction methodology. This data pipeline uses a novel curation methodology that adopts a human-in-the-loop approach for the characterization of quality, relevance, and key evidence across a range of scientific literature sources. Results: REDASA (Realtime Data Synthesis and Analysis) is now one of the world's largest and most up-to-date sources of COVID-19--related evidence; it consists of 104,000 documents. By capturing curators' critical appraisal methodologies through the discrete labeling and rating of information, REDASA rapidly developed a foundational, pooled, data science data set of over 1400 articles in under 2 weeks. These articles provide COVID-19--related information and represent around 10\% of all papers about COVID-19. Conclusions: This data set can act as ground truth for the future implementation of a live, automated systematic review. The three benefits of REDASA's design are as follows: (1) it adopts a user-friendly, human-in-the-loop methodology by embedding an efficient, user-friendly curation platform into a natural language processing search engine; (2) it provides a curated data set in the JavaScript Object Notation format for experienced academic reviewers' critical appraisal choices and decision-making methodologies; and (3) due to the wide scope and depth of its web crawling method, REDASA has already captured one of the world's largest COVID-19--related data corpora for searches and curation. ", doi="10.2196/25714", url="https://www.jmir.org/2021/5/e25714", url="http://www.ncbi.nlm.nih.gov/pubmed/33835932" } @Article{info:doi/10.2196/22933, author="Mangono, Tichakunda and Smittenaar, Peter and Caplan, Yael and Huang, S. Vincent and Sutermaster, Staci and Kemp, Hannah and Sgaier, K. Sema", title="Information-Seeking Patterns During the COVID-19 Pandemic Across the United States: Longitudinal Analysis of Google Trends Data", journal="J Med Internet Res", year="2021", month="May", day="3", volume="23", number="5", pages="e22933", keywords="Google Trends", keywords="coronavirus", keywords="COVID-19", keywords="principal component analysis", keywords="information-seeking trends", keywords="information retrieval", keywords="trend", keywords="infodemiology", keywords="infoveillance", keywords="virus", keywords="public health", keywords="information seeking", keywords="online health information", abstract="Background: The COVID-19 pandemic has impacted people's lives at unprecedented speed and scale, including how they eat and work, what they are concerned about, how much they move, and how much they can earn. Traditional surveys in the area of public health can be expensive and time-consuming, and they can rapidly become outdated. The analysis of big data sets (such as electronic patient records and surveillance systems) is very complex. Google Trends is an alternative approach that has been used in the past to analyze health behaviors; however, most existing studies on COVID-19 using these data examine a single issue or a limited geographic area. This paper explores Google Trends as a proxy for what people are thinking, needing, and planning in real time across the United States. Objective: We aimed to use Google Trends to provide both insights into and potential indicators of important changes in information-seeking patterns during pandemics such as COVID-19. We asked four questions: (1) How has information seeking changed over time? (2) How does information seeking vary between regions and states? (3) Do states have particular and distinct patterns in information seeking? (4) Do search data correlate with---or precede---real-life events? Methods: We analyzed searches on 38 terms related to COVID-19, falling into six themes: social and travel; care seeking; government programs; health programs; news and influence; and outlook and concerns. We generated data sets at the national level (covering January 1, 2016, to April 15, 2020) and state level (covering January 1 to April 15, 2020). Methods used include trend analysis of US search data; geographic analyses of the differences in search popularity across US states from March 1 to April 15, 2020; and principal component analysis to extract search patterns across states. Results: The data showed high demand for information, corresponding with increasing searches for coronavirus linked to news sources regardless of the ideological leaning of the news source. Changes in information seeking often occurred well in advance of action by the federal government. The popularity of searches for unemployment claims predicted the actual spike in weekly claims. The increase in searches for information on COVID-19 care was paralleled by a decrease in searches related to other health behaviors, such as urgent care, doctor's appointments, health insurance, Medicare, and Medicaid. Finally, concerns varied across the country; some search terms were more popular in some regions than in others. Conclusions: COVID-19 is unlikely to be the last pandemic faced by the United States. Our research holds important lessons for both state and federal governments in a fast-evolving situation that requires a finger on the pulse of public sentiment. We suggest strategic shifts for policy makers to improve the precision and effectiveness of non-pharmaceutical interventions and recommend the development of a real-time dashboard as a decision-making tool. ", doi="10.2196/22933", url="https://www.jmir.org/2021/5/e22933", url="http://www.ncbi.nlm.nih.gov/pubmed/33878015" } @Article{info:doi/10.2196/26616, author="Yang, Yuan-Chi and Al-Garadi, Ali Mohammed and Bremer, Whitney and Zhu, M. Jane and Grande, David and Sarker, Abeed", title="Developing an Automatic System for Classifying Chatter About Health Services on Twitter: Case Study for Medicaid", journal="J Med Internet Res", year="2021", month="May", day="3", volume="23", number="5", pages="e26616", keywords="natural language processing", keywords="machine learning", keywords="Twitter", keywords="infodemiology", keywords="infoveillance", keywords="social media", keywords="Medicaid", keywords="consumer feedback", abstract="Background: The wide adoption of social media in daily life renders it a rich and effective resource for conducting near real-time assessments of consumers' perceptions of health services. However, its use in these assessments can be challenging because of the vast amount of data and the diversity of content in social media chatter. Objective: This study aims to develop and evaluate an automatic system involving natural language processing and machine learning to automatically characterize user-posted Twitter data about health services using Medicaid, the single largest source of health coverage in the United States, as an example. Methods: We collected data from Twitter in two ways: via the public streaming application programming interface using Medicaid-related keywords (Corpus 1) and by using the website's search option for tweets mentioning agency-specific handles (Corpus 2). We manually labeled a sample of tweets in 5 predetermined categories or other and artificially increased the number of training posts from specific low-frequency categories. Using the manually labeled data, we trained and evaluated several supervised learning algorithms, including support vector machine, random forest (RF), na{\"i}ve Bayes, shallow neural network (NN), k-nearest neighbor, bidirectional long short-term memory, and bidirectional encoder representations from transformers (BERT). We then applied the best-performing classifier to the collected tweets for postclassification analyses to assess the utility of our methods. Results: We manually annotated 11,379 tweets (Corpus 1: 9179; Corpus 2: 2200) and used 7930 (69.7\%) for training, 1449 (12.7\%) for validation, and 2000 (17.6\%) for testing. A classifier based on BERT obtained the highest accuracies (81.7\%, Corpus 1; 80.7\%, Corpus 2) and F1 scores on consumer feedback (0.58, Corpus 1; 0.90, Corpus 2), outperforming the second best classifiers in terms of accuracy (74.6\%, RF on Corpus 1; 69.4\%, RF on Corpus 2) and F1 score on consumer feedback (0.44, NN on Corpus 1; 0.82, RF on Corpus 2). Postclassification analyses revealed differing intercorpora distributions of tweet categories, with political (400778/628411, 63.78\%) and consumer feedback (15073/27337, 55.14\%) tweets being the most frequent for Corpus 1 and Corpus 2, respectively. Conclusions: The broad and variable content of Medicaid-related tweets necessitates automatic categorization to identify topic-relevant posts. Our proposed system presents a feasible solution for automatic categorization and can be deployed and generalized for health service programs other than Medicaid. Annotated data and methods are available for future studies. ", doi="10.2196/26616", url="https://www.jmir.org/2021/5/e26616", url="http://www.ncbi.nlm.nih.gov/pubmed/33938807" } @Article{info:doi/10.2196/27341, author="Adikari, Achini and Nawaratne, Rashmika and De Silva, Daswin and Ranasinghe, Sajani and Alahakoon, Oshadi and Alahakoon, Damminda", title="Emotions of COVID-19: Content Analysis of Self-Reported Information Using Artificial Intelligence", journal="J Med Internet Res", year="2021", month="Apr", day="30", volume="23", number="4", pages="e27341", keywords="COVID-19", keywords="pandemic", keywords="lockdown", keywords="human emotions", keywords="affective computing", keywords="human-centric artificial intelligence", keywords="artificial intelligence", keywords="AI", keywords="machine learning", keywords="natural language processing", keywords="language modeling", keywords="infodemiology", keywords="infoveillance", abstract="Background: The COVID-19 pandemic has disrupted human societies around the world. This public health emergency was followed by a significant loss of human life; the ensuing social restrictions led to loss of employment, lack of interactions, and burgeoning psychological distress. As physical distancing regulations were introduced to manage outbreaks, individuals, groups, and communities engaged extensively on social media to express their thoughts and emotions. This internet-mediated communication of self-reported information encapsulates the emotional health and mental well-being of all individuals impacted by the pandemic. Objective: This research aims to investigate the human emotions related to the COVID-19 pandemic expressed on social media over time, using an artificial intelligence (AI) framework. Methods: Our study explores emotion classifications, intensities, transitions, and profiles, as well as alignment to key themes and topics, across the four stages of the pandemic: declaration of a global health crisis (ie, prepandemic), the first lockdown, easing of restrictions, and the second lockdown. This study employs an AI framework comprised of natural language processing, word embeddings, Markov models, and the growing self-organizing map algorithm, which are collectively used to investigate social media conversations. The investigation was carried out using 73,000 public Twitter conversations posted by users in Australia from January to September 2020. Results: The outcomes of this study enabled us to analyze and visualize different emotions and related concerns that were expressed and reflected on social media during the COVID-19 pandemic, which could be used to gain insights into citizens' mental health. First, the topic analysis showed the diverse as well as common concerns people had expressed during the four stages of the pandemic. It was noted that personal-level concerns expressed on social media had escalated to broader concerns over time. Second, the emotion intensity and emotion state transitions showed that fear and sadness emotions were more prominently expressed at first; however, emotions transitioned into anger and disgust over time. Negative emotions, except for sadness, were significantly higher (P<.05) in the second lockdown, showing increased frustration. Temporal emotion analysis was conducted by modeling the emotion state changes across the four stages of the pandemic, which demonstrated how different emotions emerged and shifted over time. Third, the concerns expressed by social media users were categorized into profiles, where differences could be seen between the first and second lockdown profiles. Conclusions: This study showed that the diverse emotions and concerns that were expressed and recorded on social media during the COVID-19 pandemic reflected the mental health of the general public. While this study established the use of social media to discover informed insights during a time when physical communication was impossible, the outcomes could also contribute toward postpandemic recovery and understanding psychological impact via emotion changes, and they could potentially inform health care decision making. This study exploited AI and social media to enhance our understanding of human behaviors in global emergencies, which could lead to improved planning and policy making for future crises. ", doi="10.2196/27341", url="https://www.jmir.org/2021/4/e27341", url="http://www.ncbi.nlm.nih.gov/pubmed/33819167" } @Article{info:doi/10.2196/24348, author="Oladeji, Olubusola and Zhang, Chi and Moradi, Tiam and Tarapore, Dharmesh and Stokes, C. Andrew and Marivate, Vukosi and Sengeh, D. Moinina and Nsoesie, O. Elaine", title="Monitoring Information-Seeking Patterns and Obesity Prevalence in Africa With Internet Search Data: Observational Study", journal="JMIR Public Health Surveill", year="2021", month="Apr", day="29", volume="7", number="4", pages="e24348", keywords="obesity", keywords="overweight", keywords="Africa", keywords="chronic diseases", keywords="hypertension", keywords="digital phenotype", keywords="infodemiology", keywords="infoveillance", abstract="Background: The prevalence of chronic conditions such as obesity, hypertension, and diabetes is increasing in African countries. Many chronic diseases have been linked to risk factors such as poor diet and physical inactivity. Data for these behavioral risk factors are usually obtained from surveys, which can be delayed by years. Behavioral data from digital sources, including social media and search engines, could be used for timely monitoring of behavioral risk factors. Objective: The objective of our study was to propose the use of digital data from internet sources for monitoring changes in behavioral risk factors in Africa. Methods: We obtained the adjusted volume of search queries submitted to Google for 108 terms related to diet, exercise, and disease from 2010 to 2016. We also obtained the obesity and overweight prevalence for 52 African countries from the World Health Organization (WHO) for the same period. Machine learning algorithms (ie, random forest, support vector machine, Bayes generalized linear model, gradient boosting, and an ensemble of the individual methods) were used to identify search terms and patterns that correlate with changes in obesity and overweight prevalence across Africa. Out-of-sample predictions were used to assess and validate the model performance. Results: The study included 52 African countries. In 2016, the WHO reported an overweight prevalence ranging from 20.9\% (95\% credible interval [CI] 17.1\%-25.0\%) to 66.8\% (95\% CI 62.4\%-71.0\%) and an obesity prevalence ranging from 4.5\% (95\% CI 2.9\%-6.5\%) to 32.5\% (95\% CI 27.2\%-38.1\%) in Africa. The highest obesity and overweight prevalence were noted in the northern and southern regions. Google searches for diet-, exercise-, and obesity-related terms explained 97.3\% (root-mean-square error [RMSE] 1.15) of the variation in obesity prevalence across all 52 countries. Similarly, the search data explained 96.6\% (RMSE 2.26) of the variation in the overweight prevalence. The search terms yoga, exercise, and gym were most correlated with changes in obesity and overweight prevalence in countries with the highest prevalence. Conclusions: Information-seeking patterns for diet- and exercise-related terms could indicate changes in attitudes toward and engagement in risk factors or healthy behaviors. These trends could capture population changes in risk factor prevalence, inform digital and physical interventions, and supplement official data from surveys. ", doi="10.2196/24348", url="https://publichealth.jmir.org/2021/4/e24348", url="http://www.ncbi.nlm.nih.gov/pubmed/33913815" } @Article{info:doi/10.2196/25215, author="Romer, Daniel and Jamieson, Hall Kathleen", title="Patterns of Media Use, Strength of Belief in COVID-19 Conspiracy Theories, and the Prevention of COVID-19 From March to July 2020 in the United States: Survey Study", journal="J Med Internet Res", year="2021", month="Apr", day="27", volume="23", number="4", pages="e25215", keywords="COVID-19", keywords="conspiracy beliefs", keywords="social media", keywords="print news media", keywords="broadcast news media", keywords="conservative media", keywords="vaccination", keywords="mask wearing", keywords="belief", keywords="misinformation", keywords="infodemic", keywords="United States", keywords="intention", keywords="prevention", abstract="Background: Holding conspiracy beliefs regarding the COVID-19 pandemic in the United States has been associated with reductions in both actions to prevent the spread of the infection (eg, mask wearing) and intentions to accept a vaccine when one becomes available. Patterns of media use have also been associated with acceptance of COVID-19 conspiracy beliefs. Here we ask whether the type of media on which a person relies increased, decreased, or had no additional effect on that person's COVID-19 conspiracy beliefs over a 4-month period. Objective: We used panel data to explore whether use of conservative and social media in the United States, which were previously found to be positively related to holding conspiracy beliefs about the origins and prevention of COVID-19, were associated with a net increase in the strength of those beliefs from March to July of 2020. We also asked whether mainstream news sources, which were previously found to be negatively related to belief in pandemic-related conspiracies, were associated with a net decrease in the strength of such beliefs over the study period. Additionally, we asked whether subsequent changes in pandemic conspiracy beliefs related to the use of media were also related to subsequent mask wearing and vaccination intentions. Methods: A survey that we conducted with a national US probability sample in March of 2020 and again in July with the same 840 respondents assessed belief in pandemic-related conspiracies, use of various types of media information sources, actions taken to prevent the spread of the disease and intentions to vaccinate, and various demographic characteristics. Change across the two waves was analyzed using path analytic techniques. Results: We found that conservative media use predicted an increase in conspiracy beliefs ($\beta$=.17, 99\% CI .10-.25) and that reliance on mainstream print predicted a decrease in their belief ($\beta$=--.08, 99\% CI --.14 to --.02). Although many social media platforms reported downgrading or removing false or misleading content, ongoing use of such platforms by respondents predicted growth in conspiracy beliefs as well ($\beta$=.072, 99\% CI .018-.123). Importantly, conspiracy belief changes related to media use between the two waves of the study were associated with the uptake of mask wearing and changes in vaccination intentions in July. Unlike other media, use of mainstream broadcast television predicted greater mask wearing ($\beta$=.17, 99\% CI .09-.26) and vaccination intention ($\beta$=.08, 95\% CI .02-.14), independent of conspiracy beliefs. Conclusions: The findings point to the need for greater efforts on the part of commentators, reporters, and guests on conservative media to report verifiable information about the pandemic. The results also suggest that social media platforms need to be more aggressive in downgrading, blocking, and counteracting claims about COVID-19 vaccines, claims about mask wearing, and conspiracy beliefs that have been judged problematic by public health authorities. ", doi="10.2196/25215", url="https://www.jmir.org/2021/4/e25215", url="http://www.ncbi.nlm.nih.gov/pubmed/33857008" } @Article{info:doi/10.2196/22042, author="Ovalle, Anaelia and Goldstein, Orpaz and Kachuee, Mohammad and Wu, C. Elizabeth S. and Hong, Chenglin and Holloway, W. Ian and Sarrafzadeh, Majid", title="Leveraging Social Media Activity and Machine Learning for HIV and Substance Abuse Risk Assessment: Development and Validation Study", journal="J Med Internet Res", year="2021", month="Apr", day="26", volume="23", number="4", pages="e22042", keywords="online social networks", keywords="machine learning", keywords="behavioral intervention", keywords="data mining", keywords="msm", keywords="public health", abstract="Background: Social media networks provide an abundance of diverse information that can be leveraged for data-driven applications across various social and physical sciences. One opportunity to utilize such data exists in the public health domain, where data collection is often constrained by organizational funding and limited user adoption. Furthermore, the efficacy of health interventions is often based on self-reported data, which are not always reliable. Health-promotion strategies for communities facing multiple vulnerabilities, such as men who have sex with men, can benefit from an automated system that not only determines health behavior risk but also suggests appropriate intervention targets. Objective: This study aims to determine the value of leveraging social media messages to identify health risk behavior for men who have sex with men. Methods: The Gay Social Networking Analysis Program was created as a preliminary framework for intelligent web-based health-promotion intervention. The program consisted of a data collection system that automatically gathered social media data, health questionnaires, and clinical results for sexually transmitted diseases and drug tests across 51 participants over 3 months. Machine learning techniques were utilized to assess the relationship between social media messages and participants' offline sexual health and substance use biological outcomes. The F1 score, a weighted average of precision and recall, was used to evaluate each algorithm. Natural language processing techniques were employed to create health behavior risk scores from participant messages. Results: Offline HIV, amphetamine, and methamphetamine use were correctly identified using only social media data, with machine learning models obtaining F1 scores of 82.6\%, 85.9\%, and 85.3\%, respectively. Additionally, constructed risk scores were found to be reasonably comparable to risk scores adapted from the Center for Disease Control. Conclusions: To our knowledge, our study is the first empirical evaluation of a social media--based public health intervention framework for men who have sex with men. We found that social media data were correlated with offline sexual health and substance use, verified through biological testing. The proof of concept and initial results validate that public health interventions can indeed use social media--based systems to successfully determine offline health risk behaviors. The findings demonstrate the promise of deploying a social media--based just-in-time adaptive intervention to target substance use and HIV risk behavior. ", doi="10.2196/22042", url="https://www.jmir.org/2021/4/e22042", url="http://www.ncbi.nlm.nih.gov/pubmed/33900200" } @Article{info:doi/10.2196/26720, author="Tang, Lu and Liu, Wenlin and Thomas, Benjamin and Tran, Nga Hong Thoai and Zou, Wenxue and Zhang, Xueying and Zhi, Degui", title="Texas Public Agencies' Tweets and Public Engagement During the COVID-19 Pandemic: Natural Language Processing Approach", journal="JMIR Public Health Surveill", year="2021", month="Apr", day="26", volume="7", number="4", pages="e26720", keywords="COVID-19", keywords="public health agencies", keywords="natural language processing", keywords="Twitter", keywords="health belief model", keywords="public engagement", keywords="social media", keywords="belief", keywords="public health", keywords="engagement", keywords="communication", keywords="strategy", keywords="content analysis", keywords="dissemination", abstract="Background: The ongoing COVID-19 pandemic is characterized by different morbidity and mortality rates across different states, cities, rural areas, and diverse neighborhoods. The absence of a national strategy for battling the pandemic also leaves state and local governments responsible for creating their own response strategies and policies. Objective: This study examines the content of COVID-19--related tweets posted by public health agencies in Texas and how content characteristics can predict the level of public engagement. Methods: All COVID-19--related tweets (N=7269) posted by Texas public agencies during the first 6 months of 2020 were classified in terms of each tweet's functions (whether the tweet provides information, promotes action, or builds community), the preventative measures mentioned, and the health beliefs discussed, by using natural language processing. Hierarchical linear regressions were conducted to explore how tweet content predicted public engagement. Results: The information function was the most prominent function, followed by the action or community functions. Beliefs regarding susceptibility, severity, and benefits were the most frequently covered health beliefs. Tweets that served the information or action functions were more likely to be retweeted, while tweets that served the action and community functions were more likely to be liked. Tweets that provided susceptibility information resulted in the most public engagement in terms of the number of retweets and likes. Conclusions: Public health agencies should continue to use Twitter to disseminate information, promote action, and build communities. They need to improve their strategies for designing social media messages about the benefits of disease prevention behaviors and audiences' self-efficacy. ", doi="10.2196/26720", url="https://publichealth.jmir.org/2021/4/e26720", url="http://www.ncbi.nlm.nih.gov/pubmed/33847587" } @Article{info:doi/10.2196/27214, author="Szilagyi, Istvan-Szilard and Ullrich, Torsten and Lang-Illievich, Kordula and Klivinyi, Christoph and Schittek, Alexander Gregor and Simonis, Holger and Bornemann-Cimenti, Helmar", title="Google Trends for Pain Search Terms in the World's Most Populated Regions Before and After the First Recorded COVID-19 Case: Infodemiological Study", journal="J Med Internet Res", year="2021", month="Apr", day="22", volume="23", number="4", pages="e27214", keywords="COVID-19", keywords="data mining", keywords="Google Trends", keywords="incidence", keywords="internet", keywords="interest", keywords="pain", keywords="research", keywords="trend", abstract="Background: Web-based analysis of search queries has become a very useful method in various academic fields for understanding timely and regional differences in the public interest in certain terms and concepts. Particularly in health and medical research, Google Trends has been increasingly used over the last decade. Objective: This study aimed to assess the search activity of pain-related parameters on Google Trends from among the most populated regions worldwide over a 3-year period from before the report of the first confirmed COVID-19 cases in these regions (January 2018) until December 2020. Methods: Search terms from the following regions were used for the analysis: India, China, Europe, the United States, Brazil, Pakistan, and Indonesia. In total, 24 expressions of pain location were assessed. Search terms were extracted using the local language of the respective country. Python scripts were used for data mining. All statistical calculations were performed through exploratory data analysis and nonparametric Mann--Whitney U tests. Results: Although the overall search activity for pain-related terms increased, apart from pain entities such as headache, chest pain, and sore throat, we observed discordant search activity. Among the most populous regions, pain-related search parameters for shoulder, abdominal, and chest pain, headache, and toothache differed significantly before and after the first officially confirmed COVID-19 cases (for all, P<.001). In addition, we observed a heterogenous, marked increase or reduction in pain-related search parameters among the most populated regions. Conclusions: As internet searches are a surrogate for public interest, we assume that our data are indicative of an increased incidence of pain after the onset of the COVID-19 pandemic. However, as these increased incidences vary across geographical and anatomical locations, our findings could potentially facilitate the development of specific strategies to support the most affected groups. ", doi="10.2196/27214", url="https://www.jmir.org/2021/4/e27214", url="http://www.ncbi.nlm.nih.gov/pubmed/33844638" } @Article{info:doi/10.2196/26459, author="Koyama, Sachiko and Ueha, Rumi and Kondo, Kenji", title="Loss of Smell and Taste in Patients With Suspected COVID-19: Analyses of Patients' Reports on Social Media", journal="J Med Internet Res", year="2021", month="Apr", day="22", volume="23", number="4", pages="e26459", keywords="COVID-19", keywords="anosmia", keywords="ageusia", keywords="free reports on social media", keywords="symptomatic", keywords="asymptomatic", keywords="recovery of senses", keywords="symptom", keywords="social media", keywords="smell", keywords="taste", keywords="senses", keywords="patient-reported", keywords="benefit", keywords="limit", keywords="diagnosis", abstract="Background: The year 2020 was the year of the global COVID-19 pandemic. The severity of the situation has become so substantial that many or even most of the patients with mild to moderate symptoms had to self-isolate without specific medical treatments or even without being tested for COVID-19. Many patients joined internet membership groups to exchange information and support each other. Objective: Our goal is to determine the benefits and limits of using social media to understand the symptoms of patients with suspected COVID-19 with mild to moderate symptoms and, in particular, their symptoms of anosmia (loss of the sense of smell) and ageusia (loss of the sense of taste). The voluntary reports on an internet website of a membership group will be the platform of the analyses. Methods: Posts and comments of members of an internet group known as COVID-19 Smell and Taste Loss, founded on March 24, 2020, to support patients with suspected COVID-19 were collected and analyzed daily. Demographic data were collected using the software mechanism called Group Insights on the membership group website. Results: Membership groups on social media have become rare sources of support for patients with suspected COVID-19 with mild to moderate symptoms. These groups provided mental support to their members and became resources for information on COVID-19 tests and medicines or supplements. However, the membership was voluntary, and often the members leave without notification. It is hard to be precise from the free voluntary reports. The number of women in the group (6995/9227, 75.38\% as of October 12, 2020) was about three times more than men (2272/9227, 24.62\% as of October 12, 2020), and the peak age of members was between 20-40 years in both men and women. Patients who were asymptomatic other than the senses comprised 14.93\% (53/355) of the total patients. Recovery of the senses was higher in the patients who were asymptomatic besides having anosmia and ageusia. Most (112/123, 91.06\%) patients experienced other symptoms first and then lost their senses, on average, 4.2 days later. Patients without other symptoms tended to recover earlier (P=.02). Patients with anosmia and ageusia occasionally reported distorted smell and taste (parosmia and dysgeusia) as well as experiencing or perceiving the smell and taste without the sources of the smell or taste (phantosmia and phantogeusia). Conclusions: Our analysis of the social media database of suspected COVID-19 patients' voices demonstrated that, although accurate diagnosis of patients is not always obtained with social media--based analyses, it may be a useful tool to collect a large amount of data on symptoms and the clinical course of worldwide rapidly growing infectious diseases. ", doi="10.2196/26459", url="https://www.jmir.org/2021/4/e26459", url="http://www.ncbi.nlm.nih.gov/pubmed/33788699" } @Article{info:doi/10.2196/24586, author="Furstrand, Dorthe and Pihl, Andreas and Orbe, Bayram Elif and Kingod, Natasja and S{\o}ndergaard, Jens", title="``Ask a Doctor About Coronavirus'': How Physicians on Social Media Can Provide Valid Health Information During a Pandemic", journal="J Med Internet Res", year="2021", month="Apr", day="20", volume="23", number="4", pages="e24586", keywords="COVID-19", keywords="coronavirus", keywords="digital health literacy", keywords="eHealth literacy", keywords="Facebook", keywords="framework", keywords="health information", keywords="health literacy", keywords="health promotion", keywords="infodemic", keywords="infodemiology", keywords="mental health", keywords="misinformation", keywords="pandemic", keywords="patient-physician relationship", keywords="public health", keywords="social media", keywords="trust", keywords="web-based community", doi="10.2196/24586", url="https://www.jmir.org/2021/4/e24586", url="http://www.ncbi.nlm.nih.gov/pubmed/33835935" } @Article{info:doi/10.2196/25114, author="Acquaviva, Kimberly", title="Comparison of Intercom and Megaphone Hashtags Using Four Years of Tweets From the Top 44 Schools of Nursing: Thematic Analysis", journal="JMIR Nursing", year="2021", month="Apr", day="20", volume="4", number="2", pages="e25114", keywords="Twitter", keywords="hashtag", keywords="nurses", keywords="media", keywords="intercom hashtag", keywords="megaphone hashtag", abstract="Background: When this study began in 2018, I sought to determine the extent to which the top 50 schools of nursing were using hashtags that could attract attention from journalists on Twitter. In December 2020, the timeframe was expanded to encompass 2 more years of data, and an analysis was conducted of the types of hashtags used. Objective: The study attempted to answer the following question: to what extent are top-ranked schools of nursing using hashtags that could attract attention from journalists, policy makers, and the public on Twitter? Methods: In February 2018, 47 of the top 50 schools of nursing had public Twitter accounts. The most recent 3200 tweets were extracted from each account and analyzed. There were 31,762 tweets in the time period covered (September 29, 2016, through February 22, 2018). After 13,429 retweets were excluded, 18,333 tweets remained. In December 2020, 44 of the original 47 schools of nursing still had public Twitter accounts under the same name used in the first phase of the study. Three accounts that were no longer active were removed from the 2016-2018 data set, resulting in 16,939 tweets from 44 schools of nursing. The Twitter data for the 44 schools of nursing were obtained for the time period covered in the second phase of the study (February 23, 2018, through December 13, 2020), and the most recent 3200 tweets were extracted from each of the accounts. On excluding retweets, there were 40,368 tweets in the 2018-2020 data set. The 2016-2018 data set containing 16,939 tweets was merged with the 2018-2020 data set containing 40,368 tweets, resulting in 57,307 tweets in the 2016-2020 data set. Results: Each hashtag used 100 times or more in the 2016-2020 data set was categorized as one of the following seven types: nursing, school, conference or tweet chat, health, illness/disease/condition, population, and something else. These types were then broken down into the following two categories: intercom hashtags and megaphone hashtags. Approximately 83\% of the time, schools of nursing used intercom hashtags (inward-facing hashtags focused on in-group discussion within and about the profession). Schools of nursing rarely used outward-facing megaphone hashtags. There was no discernible shift in the way that schools of nursing used hashtags after the publication of The Woodhull Study Revisited. Conclusions: Top schools of nursing use hashtags more like intercoms to communicate with other nurses rather than megaphones to invite attention from journalists, policy makers, and the public. If schools of nursing want the media to showcase their faculty members as experts, they need to increase their use of megaphone hashtags to connect the work of their faculty with topics of interest to the public. ", doi="10.2196/25114", url="https://nursing.jmir.org/2021/2/e25114", url="http://www.ncbi.nlm.nih.gov/pubmed/34345795" } @Article{info:doi/10.2196/20954, author="Liu, Sam and Perdew, Megan and Lithopoulos, Alexander and Rhodes, E. Ryan", title="The Feasibility of Using Instagram Data to Predict Exercise Identity and Physical Activity Levels: Cross-sectional Observational Study", journal="J Med Internet Res", year="2021", month="Apr", day="19", volume="23", number="4", pages="e20954", keywords="social media", keywords="exercise identity", keywords="physical activity", keywords="physical fitness", abstract="Background: Exercise identity is an important predictor for regular physical activity (PA). There is a lack of research on the potential mechanisms or antecedents of identity development. Theories of exercise identity have proposed that investment, commitment and self-referential (eg, I am an exerciser) statements, and social activation (comparison, support) may be crucial to identity development. Social media may be a potential mechanism to shape identity. Objective: The objectives of this study were to (1) explore whether participants were willing to share their Instagram data with researchers to predict their lifestyle behaviors; (2) examine whether PA-related Instagram uses (ie, the percentage of PA-related Instagram posts, fitness-related followings, and the number of likes received on PA-related posts) were positively associated with exercise identity; and (3) evaluate whether exercise identity mediates the relationship between PA-related Instagram use and weekly PA minutes. Methods: Participants (18-30 years old) were asked to complete a questionnaire to evaluate their current levels of exercise identity and PA. Participants' Instagram data for the past 12 months before the completion of the questionnaire were extracted and analyzed with their permission. Instagram posts related to PA in the 12 months before their assessment, the number of likes received for each PA-related post, and verified fitness- or PA-related followings by the participants were extracted and analyzed. Pearson correlation analyses were used to evaluate the relationship among exercise identity, PA, and Instagram uses. We conducted mediation analyses using the PROCESS macro modeling tool to examine whether exercise identity mediated the relationship between Instagram use variables and PA. Descriptive statistical analyses were used to compare the number of willing participants versus those who were not willing to share their Instagram data. Results: Of the 76 participants recruited to participate, 54\% (n=41) shared their Instagram data. The percentage of PA-related Instagram posts (r=0.38; P=.01) and fitness-related Instagram followings (r=0.39; P=.01) were significantly associated with exercise identity. The average number of ``likes'' received (r=0.05, P=.75) was not significantly associated with exercise identity. Exercise identity significantly influenced the relationship between Instagram usage metrics (ie, the percentage of PA-related Instagram posts [P=.01] and verified fitness-related Instagram accounts [P=.01]) and PA level. Exercise identity did not significantly influence the relationship between the average number of ``likes'' received for the PA-related Instagram posts and PA level. Conclusions: Our results suggest that an increase in PA-related Instagram posts and fitness-related followings were associated with a greater sense of exercise identity. Higher exercise identity led to higher PA levels. Exercise identity significantly influenced the relationship between PA-related Instagram posts (P=.01) and fitness-related followings on PA levels (P=.01). These results suggest that Instagram may influence a person's exercise identity and PA levels. Future intervention studies are warranted. ", doi="10.2196/20954", url="https://www.jmir.org/2021/4/e20954", url="http://www.ncbi.nlm.nih.gov/pubmed/33871380" } @Article{info:doi/10.2196/25323, author="Chaiken, Rebecca Sarina and Han, Lisa and Darney, G. Blair and Han, Leo", title="Factors Associated With Perceived Trust of False Abortion Websites: Cross-sectional Online Survey", journal="J Med Internet Res", year="2021", month="Apr", day="19", volume="23", number="4", pages="e25323", keywords="abortion", keywords="website trust", keywords="internet use", keywords="reproductive health", keywords="misinformation", abstract="Background: Most patients use the internet to search for health information. While there is a vast repository of searchable information online, much of the content is unregulated and therefore potentially incorrect, conflicting, or confusing. Abortion information online is particularly prone to being inaccurate as antichoice websites publish purposefully misleading information in formats that appear as neutral resources. To understand how antichoice websites appear neutral, we need to understand the specific website features of antichoice websites that impart an impression of trustworthiness. Objective: We sought to identify the characteristics of false or misleading abortion websites that make these websites appear trustworthy to the public. Methods: We conducted a cross-sectional study using Amazon's Mechanical Turk platform. We used validated questionnaires to ask participants to rate 11 antichoice websites and one neutral website identified by experts, focusing on website content, creators, and design. We collected sociodemographic data and participant views on abortion. We used a composite measure of ``mean overall trust'' as our primary outcome. Using correlation matrices, we determined which website characteristics were most associated with mean overall trust. Finally, we used linear regression to identify participant characteristics associated with overall trust. Results: Our analytic sample included 498 participants aged from 22 to 70 years, and 50.1\% (247/493) identified as female. Across 11 antichoice websites, creator confidence (``I believe that the creators of this website are honest and trustworthy'') had the highest correlation coefficient (strongest relationship) with mean overall trust (coefficient=0.70). Professional appearance (coefficient=0.59), look and feel (coefficient=0.59), perception that the information is created by experts (coefficient=0.59), association with a trustworthy organization (coefficient=0.58), valued features and functionalities (coefficient=0.54), and interactive capabilities (coefficient=0.52) all demonstrated strong relationships with mean overall trust. At the individual level, prochoice leaning was associated with higher overall trust of the neutral website (B=?0.43, 95\% CI ?0.87 to 0.01) and lower mean overall trust of the antichoice websites (B=0.52, 95\% CI 0.05 to 0.99). Conclusions: The mean overall trust of antichoice websites is most associated with design characteristics and perceived trustworthiness of website creators. Those who believe that access to abortion should be limited are more likely to have higher mean overall trust for antichoice websites. ", doi="10.2196/25323", url="https://www.jmir.org/2021/4/e25323", url="http://www.ncbi.nlm.nih.gov/pubmed/33871378" } @Article{info:doi/10.2196/26527, author="Gerts, Dax and Shelley, D. Courtney and Parikh, Nidhi and Pitts, Travis and Watson Ross, Chrysm and Fairchild, Geoffrey and Vaquera Chavez, Yadria Nidia and Daughton, R. Ashlynn", title="``Thought I'd Share First'' and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study", journal="JMIR Public Health Surveill", year="2021", month="Apr", day="14", volume="7", number="4", pages="e26527", keywords="COVID-19", keywords="coronavirus", keywords="social media", keywords="misinformation", keywords="health communication", keywords="Twitter", keywords="infodemic", keywords="infodemiology", keywords="conspiracy theories", keywords="vaccine hesitancy", keywords="5G", keywords="unsupervised learning", keywords="random forest", keywords="active learning", keywords="supervised learning", keywords="machine learning", keywords="conspiracy", keywords="communication", keywords="vaccine", keywords="public health", abstract="Background: The COVID-19 outbreak has left many people isolated within their homes; these people are turning to social media for news and social connection, which leaves them vulnerable to believing and sharing misinformation. Health-related misinformation threatens adherence to public health messaging, and monitoring its spread on social media is critical to understanding the evolution of ideas that have potentially negative public health impacts. Objective: The aim of this study is to use Twitter data to explore methods to characterize and classify four COVID-19 conspiracy theories and to provide context for each of these conspiracy theories through the first 5 months of the pandemic. Methods: We began with a corpus of COVID-19 tweets (approximately 120 million) spanning late January to early May 2020. We first filtered tweets using regular expressions (n=1.8 million) and used random forest classification models to identify tweets related to four conspiracy theories. Our classified data sets were then used in downstream sentiment analysis and dynamic topic modeling to characterize the linguistic features of COVID-19 conspiracy theories as they evolve over time. Results: Analysis using model-labeled data was beneficial for increasing the proportion of data matching misinformation indicators. Random forest classifier metrics varied across the four conspiracy theories considered (F1 scores between 0.347 and 0.857); this performance increased as the given conspiracy theory was more narrowly defined. We showed that misinformation tweets demonstrate more negative sentiment when compared to nonmisinformation tweets and that theories evolve over time, incorporating details from unrelated conspiracy theories as well as real-world events. Conclusions: Although we focus here on health-related misinformation, this combination of approaches is not specific to public health and is valuable for characterizing misinformation in general, which is an important first step in creating targeted messaging to counteract its spread. Initial messaging should aim to preempt generalized misinformation before it becomes widespread, while later messaging will need to target evolving conspiracy theories and the new facets of each as they become incorporated. ", doi="10.2196/26527", url="https://publichealth.jmir.org/2021/4/e26527", url="http://www.ncbi.nlm.nih.gov/pubmed/33764882" } @Article{info:doi/10.2196/27045, author="Zeng, Chengbo and Zhang, Jiajia and Li, Zhenlong and Sun, Xiaowen and Olatosi, Bankole and Weissman, Sharon and Li, Xiaoming", title="Spatial-Temporal Relationship Between Population Mobility and COVID-19 Outbreaks in South Carolina: Time Series Forecasting Analysis", journal="J Med Internet Res", year="2021", month="Apr", day="13", volume="23", number="4", pages="e27045", keywords="COVID-19", keywords="mobility", keywords="incidence", keywords="South Carolina", abstract="Background: Population mobility is closely associated with COVID-19 transmission, and it could be used as a proximal indicator to predict future outbreaks, which could inform proactive nonpharmaceutical interventions for disease control. South Carolina is one of the US states that reopened early, following which it experienced a sharp increase in COVID-19 cases. Objective: The aims of this study are to examine the spatial-temporal relationship between population mobility and COVID-19 outbreaks and use population mobility data to predict daily new cases at both the state and county level in South Carolina. Methods: This longitudinal study used disease surveillance data and Twitter-based population mobility data from March 6 to November 11, 2020, in South Carolina and its five counties with the largest number of cumulative confirmed COVID-19 cases. Population mobility was assessed based on the number of Twitter users with a travel distance greater than 0.5 miles. A Poisson count time series model was employed for COVID-19 forecasting. Results: Population mobility was positively associated with state-level daily COVID-19 incidence as well as incidence in the top five counties (ie, Charleston, Greenville, Horry, Spartanburg, and Richland). At the state level, the final model with a time window within the last 7 days had the smallest prediction error, and the prediction accuracy was as high as 98.7\%, 90.9\%, and 81.6\% for the next 3, 7, and 14 days, respectively. Among Charleston, Greenville, Horry, Spartanburg, and Richland counties, the best predictive models were established based on their observations in the last 9, 14, 28, 20, and 9 days, respectively. The 14-day prediction accuracy ranged from 60.3\%-74.5\%. Conclusions: Using Twitter-based population mobility data could provide acceptable predictions of COVID-19 daily new cases at both the state and county level in South Carolina. Population mobility measured via social media data could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences. ", doi="10.2196/27045", url="https://www.jmir.org/2021/4/e27045", url="http://www.ncbi.nlm.nih.gov/pubmed/33784239" } @Article{info:doi/10.2196/26874, author="Griffith, Janessa and Marani, Husayn and Monkman, Helen", title="COVID-19 Vaccine Hesitancy in Canada: Content Analysis of Tweets Using the Theoretical Domains Framework", journal="J Med Internet Res", year="2021", month="Apr", day="13", volume="23", number="4", pages="e26874", keywords="vaccine hesitancy", keywords="vaccine", keywords="COVID-19", keywords="immunization", keywords="Twitter", keywords="infodemiology", keywords="infoveillance", keywords="social media", keywords="behavioral science", keywords="behavior", keywords="Canada", keywords="content analysis", keywords="framework", keywords="hesitancy", abstract="Background: With the approval of two COVID-19 vaccines in Canada, many people feel a sense of relief, as hope is on the horizon. However, only about 75\% of people in Canada plan to receive one of the vaccines. Objective: The purpose of this study is to determine the reasons why people in Canada feel hesitant toward receiving a COVID-19 vaccine. Methods: We screened 3915 tweets from public Twitter profiles in Canada by using the search words ``vaccine'' and ``COVID.'' The tweets that met the inclusion criteria (ie, those about COVID-19 vaccine hesitancy) were coded via content analysis. Codes were then organized into themes and interpreted by using the Theoretical Domains Framework. Results: Overall, 605 tweets were identified as those about COVID-19 vaccine hesitancy. Vaccine hesitancy stemmed from the following themes: concerns over safety, suspicion about political or economic forces driving the COVID-19 pandemic or vaccine development, a lack of knowledge about the vaccine, antivaccine or confusing messages from authority figures, and a lack of legal liability from vaccine companies. This study also examined mistrust toward the medical industry not due to hesitancy, but due to the legacy of communities marginalized by health care institutions. These themes were categorized into the following five Theoretical Domains Framework constructs: knowledge, beliefs about consequences, environmental context and resources, social influence, and emotion. Conclusions: With the World Health Organization stating that one of the worst threats to global health is vaccine hesitancy, it is important to have a comprehensive understanding of the reasons behind this reluctance. By using a behavioral science framework, this study adds to the emerging knowledge about vaccine hesitancy in relation to COVID-19 vaccines by analyzing public discourse in tweets in real time. Health care leaders and clinicians may use this knowledge to develop public health interventions that are responsive to the concerns of people who are hesitant to receive vaccines. ", doi="10.2196/26874", url="https://www.jmir.org/2021/4/e26874", url="http://www.ncbi.nlm.nih.gov/pubmed/33769946" } @Article{info:doi/10.2196/26331, author="Chrzanowski, J?drzej and So?ek, Julia and Fendler, Wojciech and Jemielniak, Dariusz", title="Assessing Public Interest Based on Wikipedia's Most Visited Medical Articles During the SARS-CoV-2 Outbreak: Search Trends Analysis", journal="J Med Internet Res", year="2021", month="Apr", day="12", volume="23", number="4", pages="e26331", keywords="COVID-19", keywords="pandemic", keywords="media", keywords="Wikipedia", keywords="internet", keywords="online health information", keywords="information seeking", keywords="interest", keywords="retrospective", keywords="surveillance", keywords="infodemiology", keywords="infoveillance", abstract="Background: In the current era of widespread access to the internet, we can monitor public interest in a topic via information-targeted web browsing. We sought to provide direct proof of the global population's altered use of Wikipedia medical knowledge resulting from the new COVID-19 pandemic and related global restrictions. Objective: We aimed to identify temporal search trends and quantify changes in access to Wikipedia Medicine Project articles that were related to the COVID-19 pandemic. Methods: We performed a retrospective analysis of medical articles across nine language versions of Wikipedia and country-specific statistics for registered COVID-19 deaths. The observed patterns were compared to a forecast model of Wikipedia use, which was trained on data from 2015 to 2019. The model comprehensively analyzed specific articles and similarities between access count data from before (ie, several years prior) and during the COVID-19 pandemic. Wikipedia articles that were linked to those directly associated with the pandemic were evaluated in terms of degrees of separation and analyzed to identify similarities in access counts. We assessed the correlation between article access counts and the number of diagnosed COVID-19 cases and deaths to identify factors that drove interest in these articles and shifts in public interest during the subsequent phases of the pandemic. Results: We observed a significant (P<.001) increase in the number of entries on Wikipedia medical articles during the pandemic period. The increased interest in COVID-19--related articles temporally correlated with the number of global COVID-19 deaths and consistently correlated with the number of region-specific COVID-19 deaths. Articles with low degrees of separation were significantly similar (P<.001) in terms of access patterns that were indicative of information-seeking patterns. Conclusions: The analysis of Wikipedia medical article popularity could be a viable method for epidemiologic surveillance, as it provides important information about the reasons behind public attention and factors that sustain public interest in the long term. Moreover, Wikipedia users can potentially be directed to credible and valuable information sources that are linked with the most prominent articles. ", doi="10.2196/26331", url="https://www.jmir.org/2021/4/e26331", url="http://www.ncbi.nlm.nih.gov/pubmed/33667176" } @Article{info:doi/10.2196/26707, author="Heckman, Carolyn and Lin, Yong and Riley, Mary and Wang, Yaqun and Bhurosy, Trishnee and Mitarotondo, Anna and Xu, Baichen and Stapleton, Jerod", title="Association Between State Indoor Tanning Legislation and Google Search Trends Data in the United States From 2006 to 2019: Time-Series Analysis", journal="JMIR Dermatol", year="2021", month="Apr", day="9", volume="4", number="1", pages="e26707", keywords="adolescents", keywords="dermatology", keywords="Google Trends", keywords="indoor tanning", keywords="internet", keywords="policy", keywords="prevention", keywords="skin cancer", keywords="skin cancer prevention", keywords="tanning", keywords="trend", keywords="time series", keywords="web-based health information", keywords="young adult", keywords="youth", abstract="Background: Exposure to ultraviolet radiation from the sun or indoor tanning is the cause of most skin cancers. Although indoor tanning has decreased in recent years, it remains most common among adolescents and young adults, whose skin is particularly vulnerable to long-term damage. US states have adopted several types of legislation to attempt to minimize indoor tanning among minors: a ban on indoor tanning among all minors, a partial minor ban by age (eg, <14 years), or the requirement of parental consent or accompaniment for tanning. Currently, only 6 US states have no indoor tanning legislation for minors. Objective: This study investigated whether internet searches (as an indicator of interest) related to indoor tanning varied across US states by the type of indoor tanning legislation, using data from Google Trends from 2006 to 2019. Methods: We conducted a time-series analysis of Google Trends data on indoor tanning from 2006 to 2019 by US state. Time-series linear regression models were generated to assess the Google Trends data over time by the type of indoor tanning legislation. Results: We found that indoor tanning search rates decreased significantly for all 50 states and the District of Columbia over time (P<.01). The searches peaked in 2012 when indoor tanning received marked attention (eg, indoor tanning was banned for all minors by the first state---California). The reduction in search rates was more marked for states with a complete ban among minors compared to those with less restrictive types of legislation. Conclusions: Our findings are consistent with those of other studies on the association between indoor tanning regulations and attitudinal and behavioral trends related to indoor tanning. The main limitation of the study is that raw search data were not available for more precise analysis. With changes in interest and norms, indoor tanning and skin cancer risk among young people may change. Future studies should continue to determine the impact of such public health policies in order to inform policy efforts and minimize risks to public health. ", doi="10.2196/26707", url="https://derma.jmir.org/2021/1/e26707", url="http://www.ncbi.nlm.nih.gov/pubmed/37632845" } @Article{info:doi/10.2196/22880, author="Asgari Mehrabadi, Milad and Dutt, Nikil and Rahmani, M. Amir", title="The Causality Inference of Public Interest in Restaurants and Bars on Daily COVID-19 Cases in the United States: Google Trends Analysis", journal="JMIR Public Health Surveill", year="2021", month="Apr", day="6", volume="7", number="4", pages="e22880", keywords="bars", keywords="coronavirus", keywords="COVID-19", keywords="deep learning", keywords="infodemiology", keywords="infoveillance", keywords="Google Trends", keywords="LSTM", keywords="machine learning", keywords="restaurants", abstract="Background: The COVID-19 pandemic has affected virtually every region in the world. At the time of this study, the number of daily new cases in the United States was greater than that in any other country, and the trend was increasing in most states. Google Trends provides data regarding public interest in various topics during different periods. Analyzing these trends using data mining methods may provide useful insights and observations regarding the COVID-19 outbreak. Objective: The objective of this study is to consider the predictive ability of different search terms not directly related to COVID-19 with regard to the increase of daily cases in the United States. In particular, we are concerned with searches related to dine-in restaurants and bars. Data were obtained from the Google Trends application programming interface and the COVID-19 Tracking Project. Methods: To test the causation of one time series on another, we used the Granger causality test. We considered the causation of two different search query trends related to dine-in restaurants and bars on daily positive cases in the US states and territories with the 10 highest and 10 lowest numbers of daily new cases of COVID-19. In addition, we used Pearson correlations to measure the linear relationships between different trends. Results: Our results showed that for states and territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly occurred after reopening, significantly affected the number of daily new cases on average. California, for example, showed the most searches for restaurants on June 7, 2020; this affected the number of new cases within two weeks after the peak, with a P value of .004 for the Granger causality test. Conclusions: Although a limited number of search queries were considered, Google search trends for restaurants and bars showed a significant effect on daily new cases in US states and territories with higher numbers of daily new cases. We showed that these influential search trends can be used to provide additional information for prediction tasks regarding new cases in each region. These predictions can help health care leaders manage and control the impact of the COVID-19 outbreak on society and prepare for its outcomes. ", doi="10.2196/22880", url="https://publichealth.jmir.org/2021/4/e22880", url="http://www.ncbi.nlm.nih.gov/pubmed/33690143" } @Article{info:doi/10.2196/22734, author="Oyebode, Oladapo and Ndulue, Chinenye and Adib, Ashfaq and Mulchandani, Dinesh and Suruliraj, Banuchitra and Orji, Anulika Fidelia and Chambers, T. Christine and Meier, Sandra and Orji, Rita", title="Health, Psychosocial, and Social Issues Emanating From the COVID-19 Pandemic Based on Social Media Comments: Text Mining and Thematic Analysis Approach", journal="JMIR Med Inform", year="2021", month="Apr", day="6", volume="9", number="4", pages="e22734", keywords="social media", keywords="COVID-19", keywords="coronavirus", keywords="infodemiology", keywords="infoveillance", keywords="natural language processing", keywords="text mining", keywords="thematic analysis", keywords="interventions", keywords="health issues", keywords="psychosocial issues", keywords="social issues", abstract="Background: The COVID-19 pandemic has caused a global health crisis that affects many aspects of human lives. In the absence of vaccines and antivirals, several behavioral change and policy initiatives such as physical distancing have been implemented to control the spread of COVID-19. Social media data can reveal public perceptions toward how governments and health agencies worldwide are handling the pandemic, and the impact of the disease on people regardless of their geographic locations in line with various factors that hinder or facilitate the efforts to control the spread of the pandemic globally. Objective: This paper aims to investigate the impact of the COVID-19 pandemic on people worldwide using social media data. Methods: We applied natural language processing (NLP) and thematic analysis to understand public opinions, experiences, and issues with respect to the COVID-19 pandemic using social media data. First, we collected over 47 million COVID-19--related comments from Twitter, Facebook, YouTube, and three online discussion forums. Second, we performed data preprocessing, which involved applying NLP techniques to clean and prepare the data for automated key phrase extraction. Third, we applied the NLP approach to extract meaningful key phrases from over 1 million randomly selected comments and computed sentiment score for each key phrase and assigned sentiment polarity (ie, positive, negative, or neutral) based on the score using a lexicon-based technique. Fourth, we grouped related negative and positive key phrases into categories or broad themes. Results: A total of 34 negative themes emerged, out of which 15 were health-related issues, psychosocial issues, and social issues related to the COVID-19 pandemic from the public perspective. Some of the health-related issues were increased mortality, health concerns, struggling health systems, and fitness issues; while some of the psychosocial issues were frustrations due to life disruptions, panic shopping, and expression of fear. Social issues were harassment, domestic violence, and wrong societal attitude. In addition, 20 positive themes emerged from our results. Some of the positive themes were public awareness, encouragement, gratitude, cleaner environment, online learning, charity, spiritual support, and innovative research. Conclusions: We uncovered various negative and positive themes representing public perceptions toward the COVID-19 pandemic and recommended interventions that can help address the health, psychosocial, and social issues based on the positive themes and other research evidence. These interventions will help governments, health professionals and agencies, institutions, and individuals in their efforts to curb the spread of COVID-19 and minimize its impact, and in reacting to any future pandemics. ", doi="10.2196/22734", url="https://medinform.jmir.org/2021/4/e22734", url="http://www.ncbi.nlm.nih.gov/pubmed/33684052" } @Article{info:doi/10.2196/26627, author="Hussain, Amir and Tahir, Ahsen and Hussain, Zain and Sheikh, Zakariya and Gogate, Mandar and Dashtipour, Kia and Ali, Azhar and Sheikh, Aziz", title="Artificial Intelligence--Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study", journal="J Med Internet Res", year="2021", month="Apr", day="5", volume="23", number="4", pages="e26627", keywords="artificial intelligence", keywords="COVID-19", keywords="deep learning", keywords="Facebook", keywords="health informatics", keywords="natural language processing", keywords="public health", keywords="sentiment analysis", keywords="social media", keywords="Twitter", keywords="infodemiology", keywords="vaccination", abstract="Background: Global efforts toward the development and deployment of a vaccine for COVID-19 are rapidly advancing. To achieve herd immunity, widespread administration of vaccines is required, which necessitates significant cooperation from the general public. As such, it is crucial that governments and public health agencies understand public sentiments toward vaccines, which can help guide educational campaigns and other targeted policy interventions. Objective: The aim of this study was to develop and apply an artificial intelligence--based approach to analyze public sentiments on social media in the United Kingdom and the United States toward COVID-19 vaccines to better understand the public attitude and concerns regarding COVID-19 vaccines. Methods: Over 300,000 social media posts related to COVID-19 vaccines were extracted, including 23,571 Facebook posts from the United Kingdom and 144,864 from the United States, along with 40,268 tweets from the United Kingdom and 98,385 from the United States from March 1 to November 22, 2020. We used natural language processing and deep learning--based techniques to predict average sentiments, sentiment trends, and topics of discussion. These factors were analyzed longitudinally and geospatially, and manual reading of randomly selected posts on points of interest helped identify underlying themes and validated insights from the analysis. Results: Overall averaged positive, negative, and neutral sentiments were at 58\%, 22\%, and 17\% in the United Kingdom, compared to 56\%, 24\%, and 18\% in the United States, respectively. Public optimism over vaccine development, effectiveness, and trials as well as concerns over their safety, economic viability, and corporation control were identified. We compared our findings to those of nationwide surveys in both countries and found them to correlate broadly. Conclusions: Artificial intelligence--enabled social media analysis should be considered for adoption by institutions and governments alongside surveys and other conventional methods of assessing public attitude. Such analyses could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccines, help address the concerns of vaccine sceptics, and help develop more effective policies and communication strategies to maximize uptake. ", doi="10.2196/26627", url="https://www.jmir.org/2021/4/e26627", url="http://www.ncbi.nlm.nih.gov/pubmed/33724919" } @Article{info:doi/10.2196/26518, author="Zhou, Xinyu and Song, Yi and Jiang, Hao and Wang, Qian and Qu, Zhiqiang and Zhou, Xiaoyu and Jit, Mark and Hou, Zhiyuan and Lin, Leesa", title="Comparison of Public Responses to Containment Measures During the Initial Outbreak and Resurgence of COVID-19 in China: Infodemiology Study", journal="J Med Internet Res", year="2021", month="Apr", day="5", volume="23", number="4", pages="e26518", keywords="COVID-19", keywords="engagement", keywords="latent Dirichlet allocation", keywords="public response", keywords="sentiment", keywords="social media", keywords="topic modeling", abstract="Background: COVID-19 cases resurged worldwide in the second half of 2020. Not much is known about the changes in public responses to containment measures from the initial outbreak to resurgence. Monitoring public responses is crucial to inform policy measures to prepare for COVID-19 resurgence. Objective: This study aimed to assess and compare public responses to containment measures during the initial outbreak and resurgence of COVID-19 in China. Methods: We curated all COVID-19--related posts from Sina Weibo (China's version of Twitter) during the initial outbreak and resurgence of COVID-19 in Beijing, China. With a Python script, we constructed subsets of Weibo posts focusing on 3 containment measures: lockdown, the test-trace-isolate strategy, and suspension of gatherings. The Baidu open-source sentiment analysis model and latent Dirichlet allocation topic modeling, a widely used machine learning algorithm, were used to assess public engagement, sentiments, and frequently discussed topics on each containment measure. Results: A total of 8,985,221 Weibo posts were curated. In China, the containment measures evolved from a complete lockdown for the general population during the initial outbreak to a more targeted response strategy for high-risk populations during COVID-19 resurgence. Between the initial outbreak and resurgence, the average daily proportion of Weibo posts with negative sentiments decreased from 57\% to 47\% for the lockdown, 56\% to 51\% for the test-trace-isolate strategy, and 55\% to 48\% for the suspension of gatherings. Among the top 3 frequently discussed topics on lockdown measures, discussions on containment measures accounted for approximately 32\% in both periods, but those on the second-most frequently discussed topic shifted from the expression of negative emotions (11\%) to its impacts on daily life or work (26\%). The public expressed a high level of panic (21\%) during the initial outbreak but almost no panic (1\%) during resurgence. The more targeted test-trace-isolate measure received the most support (60\%) among all 3 containment measures in the initial outbreak, and its support rate approached 90\% during resurgence. Conclusions: Compared to the initial outbreak, the public expressed less engagement and less negative sentiments on containment measures and were more supportive toward containment measures during resurgence. Targeted test-trace-isolate strategies were more acceptable to the public. Our results indicate that when COVID-19 resurges, more targeted test-trace-isolate strategies for high-risk populations should be promoted to balance pandemic control and its impact on daily life and the economy. ", doi="10.2196/26518", url="https://www.jmir.org/2021/4/e26518", url="http://www.ncbi.nlm.nih.gov/pubmed/33750739" } @Article{info:doi/10.2196/26780, author="Al-Ramahi, Mohammad and Elnoshokaty, Ahmed and El-Gayar, Omar and Nasralah, Tareq and Wahbeh, Abdullah", title="Public Discourse Against Masks in the COVID-19 Era: Infodemiology Study of Twitter Data", journal="JMIR Public Health Surveill", year="2021", month="Apr", day="5", volume="7", number="4", pages="e26780", keywords="pandemic", keywords="coronavirus", keywords="masks", keywords="social medial, opinion analysis", keywords="COVID-19", abstract="Background: Despite scientific evidence supporting the importance of wearing masks to curtail the spread of COVID-19, wearing masks has stirred up a significant debate particularly on social media. Objective: This study aimed to investigate the topics associated with the public discourse against wearing masks in the United States. We also studied the relationship between the anti-mask discourse on social media and the number of new COVID-19 cases. Methods: We collected a total of 51,170 English tweets between January 1, 2020, and October 27, 2020, by searching for hashtags against wearing masks. We used machine learning techniques to analyze the data collected. We investigated the relationship between the volume of tweets against mask-wearing and the daily volume of new COVID-19 cases using a Pearson correlation analysis between the two-time series. Results: The results and analysis showed that social media could help identify important insights related to wearing masks. The results of topic mining identified 10 categories or themes of user concerns dominated by (1) constitutional rights and freedom of choice; (2) conspiracy theory, population control, and big pharma; and (3) fake news, fake numbers, and fake pandemic. Altogether, these three categories represent almost 65\% of the volume of tweets against wearing masks. The relationship between the volume of tweets against wearing masks and newly reported COVID-19 cases depicted a strong correlation wherein the rise in the volume of negative tweets led the rise in the number of new cases by 9 days. Conclusions: These findings demonstrated the potential of mining social media for understanding the public discourse about public health issues such as wearing masks during the COVID-19 pandemic. The results emphasized the relationship between the discourse on social media and the potential impact on real events such as changing the course of the pandemic. Policy makers are advised to proactively address public perception and work on shaping this perception through raising awareness, debunking negative sentiments, and prioritizing early policy intervention toward the most prevalent topics. ", doi="10.2196/26780", url="https://publichealth.jmir.org/2021/4/e26780", url="http://www.ncbi.nlm.nih.gov/pubmed/33720841" } @Article{info:doi/10.2196/23593, author="Sch{\"u}ck, St{\'e}phane and Foulqui{\'e}, Pierre and Mebarki, Adel and Faviez, Carole and Khadhar, Micka{\"i}l and Texier, Nathalie and Katsahian, Sandrine and Burgun, Anita and Chen, Xiaoyi", title="Concerns Discussed on Chinese and French Social Media During the COVID-19 Lockdown: Comparative Infodemiology Study Based on Topic Modeling", journal="JMIR Form Res", year="2021", month="Apr", day="5", volume="5", number="4", pages="e23593", keywords="comparative analysis", keywords="content analysis", keywords="topic model", keywords="social media", keywords="COVID-19", keywords="lockdown", keywords="China", keywords="France", keywords="impact", keywords="population", abstract="Background: During the COVID-19 pandemic, numerous countries, including China and France, have implemented lockdown measures that have been effective in controlling the epidemic. However, little is known about the impact of these measures on the population as expressed on social media from different cultural contexts. Objective: This study aims to assess and compare the evolution of the topics discussed on Chinese and French social media during the COVID-19 lockdown. Methods: We extracted posts containing COVID-19--related or lockdown-related keywords in the most commonly used microblogging social media platforms (ie, Weibo in China and Twitter in France) from 1 week before lockdown to the lifting of the lockdown. A topic model was applied independently for three periods (prelockdown, early lockdown, and mid to late lockdown) to assess the evolution of the topics discussed on Chinese and French social media. Results: A total of 6395; 23,422; and 141,643 Chinese Weibo messages, and 34,327; 119,919; and 282,965 French tweets were extracted in the prelockdown, early lockdown, and mid to late lockdown periods, respectively, in China and France. Four categories of topics were discussed in a continuously evolving way in all three periods: epidemic news and everyday life, scientific information, public measures, and solidarity and encouragement. The most represented category over all periods in both countries was epidemic news and everyday life. Scientific information was far more discussed on Weibo than in French tweets. Misinformation circulated through social media in both countries; however, it was more concerned with the virus and epidemic in China, whereas it was more concerned with the lockdown measures in France. Regarding public measures, more criticisms were identified in French tweets than on Weibo. Advantages and data privacy concerns regarding tracing apps were also addressed in French tweets. All these differences were explained by the different uses of social media, the different timelines of the epidemic, and the different cultural contexts in these two countries. Conclusions: This study is the first to compare the social media content in eastern and western countries during the unprecedented COVID-19 lockdown. Using general COVID-19--related social media data, our results describe common and different public reactions, behaviors, and concerns in China and France, even covering the topics identified in prior studies focusing on specific interests. We believe our study can help characterize country-specific public needs and appropriately address them during an outbreak. ", doi="10.2196/23593", url="https://formative.jmir.org/2021/4/e23593", url="http://www.ncbi.nlm.nih.gov/pubmed/33750736" } @Article{info:doi/10.2196/27009, author="Wang, Dandan and Qian, Yuxing", title="Echo Chamber Effect in Rumor Rebuttal Discussions About COVID-19 in China: Social Media Content and Network Analysis Study", journal="J Med Internet Res", year="2021", month="Mar", day="25", volume="23", number="3", pages="e27009", keywords="rumor rebuttal", keywords="infodemiology", keywords="infodemic", keywords="infoveillance", keywords="echo chamber effect", keywords="attitude", keywords="COVID-19", keywords="Weibo", abstract="Background: The dissemination of rumor rebuttal content on social media is vital for rumor control and disease containment during public health crises. Previous research on the effectiveness of rumor rebuttal, to a certain extent, ignored or simplified the structure of dissemination networks and users' cognition as well as decision-making and interaction behaviors. Objective: This study aimed to roughly evaluate the effectiveness of rumor rebuttal; dig deeply into the attitude-based echo chamber effect on users' responses to rumor rebuttal under multiple topics on Weibo, a Chinese social media platform, in the early stage of the COVID-19 epidemic; and evaluate the echo chamber's impact on the information characteristics of user interaction content. Methods: We used Sina Weibo's application programming interface to crawl rumor rebuttal content related to COVID-19 from 10 AM on January 23, 2020, to midnight on April 8, 2020. Using content analysis, sentiment analysis, social network analysis, and statistical analysis, we first analyzed whether and to what extent there was an echo chamber effect on the shaping of individuals' attitudes when retweeting or commenting on others' tweets. Then, we tested the heterogeneity of attitude distribution within communities and the homophily of interactions between communities. Based on the results at user and community levels, we made comprehensive judgments. Finally, we examined users' interaction content from three dimensions---sentiment expression, information seeking and sharing, and civility---to test the impact of the echo chamber effect. Results: Our results indicated that the retweeting mechanism played an essential role in promoting polarization, and the commenting mechanism played a role in consensus building. Our results showed that there might not be a significant echo chamber effect on community interactions and verified that, compared to like-minded interactions, cross-cutting interactions contained significantly more negative sentiment, information seeking and sharing, and incivility. We found that online users' information-seeking behavior was accompanied by incivility, and information-sharing behavior was accompanied by more negative sentiment, which was often accompanied by incivility. Conclusions: Our findings revealed the existence and degree of an echo chamber effect from multiple dimensions, such as topic, interaction mechanism, and interaction level, and its impact on interaction content. Based on these findings, we provide several suggestions for preventing or alleviating group polarization to achieve better rumor rebuttal. ", doi="10.2196/27009", url="https://www.jmir.org/2021/3/e27009", url="http://www.ncbi.nlm.nih.gov/pubmed/33690145" } @Article{info:doi/10.2196/26589, author="Saha, Koustuv and Torous, John and Kiciman, Emre and De Choudhury, Munmun", title="Understanding Side Effects of Antidepressants: Large-scale Longitudinal Study on Social Media Data", journal="JMIR Ment Health", year="2021", month="Mar", day="19", volume="8", number="3", pages="e26589", keywords="antidepressants", keywords="symptoms", keywords="side effects", keywords="digital pharmacovigilance", keywords="social media", keywords="mental health", keywords="linguistic markers", keywords="digital health", abstract="Background: Antidepressants are known to show heterogeneous effects across individuals and conditions, posing challenges to understanding their efficacy in mental health treatment. Social media platforms enable individuals to share their day-to-day concerns with others and thereby can function as unobtrusive, large-scale, and naturalistic data sources to study the longitudinal behavior of individuals taking antidepressants. Objective: We aim to understand the side effects of antidepressants from naturalistic expressions of individuals on social media. Methods: On a large-scale Twitter data set of individuals who self-reported using antidepressants, a quasi-experimental study using unsupervised language analysis was conducted to extract keywords that distinguish individuals who improved and who did not improve following the use of antidepressants. The net data set consists of over 8 million Twitter posts made by over 300,000 users in a 4-year period between January 1, 2014, and February 15, 2018. Results: Five major side effects of antidepressants were studied: sleep, weight, eating, pain, and sexual issues. Social media language revealed keywords related to these side effects. In particular, antidepressants were found to show a spectrum of effects from decrease to increase in each of these side effects. Conclusions: This work enhances the understanding of the side effects of antidepressants by identifying distinct linguistic markers in the longitudinal social media data of individuals showing the most and least improvement following the self-reported intake of antidepressants. One implication of this work concerns the potential of social media data as an effective means to support digital pharmacovigilance and digital therapeutics. These results can inform clinicians in tailoring their discussion and assessment of side effects and inform patients about what to potentially expect and what may or may not be within the realm of normal aftereffects of antidepressants. ", doi="10.2196/26589", url="https://mental.jmir.org/2021/3/e26589", url="http://www.ncbi.nlm.nih.gov/pubmed/33739296" } @Article{info:doi/10.2196/27078, author="Yu, Shaobin and Eisenman, David and Han, Ziqiang", title="Temporal Dynamics of Public Emotions During the COVID-19 Pandemic at the Epicenter of the Outbreak: Sentiment Analysis of Weibo Posts From Wuhan", journal="J Med Internet Res", year="2021", month="Mar", day="18", volume="23", number="3", pages="e27078", keywords="public health emergencies", keywords="emotion", keywords="infodemiology", keywords="temporal dynamics", keywords="sentiment analysis", keywords="COVID-19", abstract="Background: The ongoing COVID-19 pandemic has led to an increase in anxiety, depression, posttraumatic stress disorder, and psychological stress experienced by the general public in various degrees worldwide. However, effective, tailored mental health services and interventions cannot be achieved until we understand the patterns of mental health issues emerging after a public health crisis, especially in the context of the rapid transmission of COVID-19. Understanding the public's emotions and needs and their distribution attributes are therefore critical for creating appropriate public policies and eventually responding to the health crisis effectively, efficiently, and equitably. Objective: This study aims to detect the temporal patterns in emotional fluctuation, significant events during the COVID-19 pandemic that affected emotional changes and variations, and hourly variations of emotions within a single day by analyzing data from the Chinese social media platform Weibo. Methods: Based on a longitudinal dataset of 816,556 posts published by 27,912 Weibo users in Wuhan, China, from December 31, 2019, to April 31, 2020, we processed general sentiment inclination rating and the type of sentiments of Weibo posts by using pandas and SnowNLP Python libraries. We also grouped the publication times into 5 time groups to measure changes in netizens' sentiments during different periods in a single day. Results: Overall, negative emotions such as surprise, fear, and anger were the most salient emotions detected on Weibo. These emotions were triggered by certain milestone events such as the confirmation of human-to-human transmission of COVID-19. Emotions varied within a day. Although all emotions were more prevalent in the afternoon and night, fear and anger were more dominant in the morning and afternoon, whereas depression was more salient during the night. Conclusions: Various milestone events during the COVID-19 pandemic were the primary events that ignited netizens' emotions. In addition, Weibo users' emotions varied within a day. Our findings provide insights into providing better-tailored mental health services and interventions. ", doi="10.2196/27078", url="https://www.jmir.org/2021/3/e27078", url="http://www.ncbi.nlm.nih.gov/pubmed/33661755" } @Article{info:doi/10.2196/23970, author="Liu, Wenhui and Wei, Zhiru and Cheng, Xu and Pang, Ran and Zhang, Han and Li, Guangshuai", title="Public Interest in Cosmetic Surgical and Minimally Invasive Plastic Procedures During the COVID-19 Pandemic: Infodemiology Study of Twitter Data", journal="J Med Internet Res", year="2021", month="Mar", day="16", volume="23", number="3", pages="e23970", keywords="COVID-19", keywords="Twitter", keywords="Google Trends", keywords="plastic procedure", keywords="trend", keywords="survey", keywords="surgery", keywords="social media", abstract="Background: The unprecedented COVID-19 pandemic has brought drastic changes to the field of plastic surgery. It is critical for stakeholders in this field to identify the changes in public interest in plastic procedures to be adequately prepared to meet the challenges of the pandemic. Objective: The aim of this study is to examine tweets related to the public interest in plastic procedures during the COVID-19 pandemic and to help stakeholders in the field of plastic surgery adjust their practices and sustain their operations during the current difficult situation of the pandemic. Methods: Using a web crawler, 73,963 publicly accessible tweets about the most common cosmetic surgical and minimally invasive plastic procedures were collected. The tweets were grouped into three phases, and the tweeting frequencies and Google Trends indices were examined. Tweeting frequency, sentiment, and word frequency analyses were performed with Python modules. Results: Tweeting frequency increased by 24.0\% in phase 2 and decreased by 9.1\% in phase 3. Tweets about breast augmentation, liposuction, and abdominoplasty (``tummy tuck'') procedures consecutively increased over the three phases of the pandemic. Interest in Botox and chemical peel procedures revived first when the lockdown was lifted. The COVID-19 pandemic was associated with a negative impact on public sentiment about plastic procedures. The word frequency pattern significantly changed after phase 1 and then remained relatively stable. Conclusions: According to Twitter data, the public maintained their interest in plastic procedures during the COVID-19 pandemic. Stakeholders should consider refocusing on breast augmentation, liposuction, and abdominoplasty procedures during the current phase of the pandemic. In the case of a second wave of COVID-19, stakeholders should prepare for a temporary surge of Botox and chemical peel procedures. ", doi="10.2196/23970", url="https://www.jmir.org/2021/3/e23970", url="http://www.ncbi.nlm.nih.gov/pubmed/33608248" } @Article{info:doi/10.2196/23272, author="Park, Sungkyu and Han, Sungwon and Kim, Jeongwook and Molaie, Majid Mir and Vu, Dieu Hoang and Singh, Karandeep and Han, Jiyoung and Lee, Wonjae and Cha, Meeyoung", title="COVID-19 Discourse on Twitter in Four Asian Countries: Case Study of Risk Communication", journal="J Med Internet Res", year="2021", month="Mar", day="16", volume="23", number="3", pages="e23272", keywords="COVID-19", keywords="coronavirus", keywords="infodemic", keywords="infodemiology", keywords="infoveillance", keywords="Twitter", keywords="topic phase detection", keywords="topic modeling", keywords="latent Dirichlet allocation", keywords="risk communication", abstract="Background: COVID-19, caused by SARS-CoV-2, has led to a global pandemic. The World Health Organization has also declared an infodemic (ie, a plethora of information regarding COVID-19 containing both false and accurate information circulated on the internet). Hence, it has become critical to test the veracity of information shared online and analyze the evolution of discussed topics among citizens related to the pandemic. Objective: This research analyzes the public discourse on COVID-19. It characterizes risk communication patterns in four Asian countries with outbreaks at varying degrees of severity: South Korea, Iran, Vietnam, and India. Methods: We collected tweets on COVID-19 from four Asian countries in the early phase of the disease outbreak from January to March 2020. The data set was collected by relevant keywords in each language, as suggested by locals. We present a method to automatically extract a time--topic cohesive relationship in an unsupervised fashion based on natural language processing. The extracted topics were evaluated qualitatively based on their semantic meanings. Results: This research found that each government's official phases of the epidemic were not well aligned with the degree of public attention represented by the daily tweet counts. Inspired by the issue-attention cycle theory, the presented natural language processing model can identify meaningful transition phases in the discussed topics among citizens. The analysis revealed an inverse relationship between the tweet count and topic diversity. Conclusions: This paper compares similarities and differences of pandemic-related social media discourse in Asian countries. We observed multiple prominent peaks in the daily tweet counts across all countries, indicating multiple issue-attention cycles. Our analysis identified which topics the public concentrated on; some of these topics were related to misinformation and hate speech. These findings and the ability to quickly identify key topics can empower global efforts to fight against an infodemic during a pandemic. ", doi="10.2196/23272", url="https://www.jmir.org/2021/3/e23272", url="http://www.ncbi.nlm.nih.gov/pubmed/33684054" } @Article{info:doi/10.2196/22916, author="Gbashi, Sefater and Adebo, Ayodeji Oluwafemi and Doorsamy, Wesley and Njobeh, Berka Patrick", title="Systematic Delineation of Media Polarity on COVID-19 Vaccines in Africa: Computational Linguistic Modeling Study", journal="JMIR Med Inform", year="2021", month="Mar", day="16", volume="9", number="3", pages="e22916", keywords="COVID-19", keywords="coronavirus", keywords="vaccine", keywords="infodemiology", keywords="infoveillance", keywords="infodemic", keywords="sentiment analysis", keywords="natural language processing", keywords="media", keywords="computation", keywords="linguistic", keywords="model", keywords="communication", abstract="Background: The global onset of COVID-19 has resulted in substantial public health and socioeconomic impacts. An immediate medical breakthrough is needed. However, parallel to the emergence of the COVID-19 pandemic is the proliferation of information regarding the pandemic, which, if uncontrolled, cannot only mislead the public but also hinder the concerted efforts of relevant stakeholders in mitigating the effect of this pandemic. It is known that media communications can affect public perception and attitude toward medical treatment, vaccination, or subject matter, particularly when the population has limited knowledge on the subject. Objective: This study attempts to systematically scrutinize media communications (Google News headlines or snippets and Twitter posts) to understand the prevailing sentiments regarding COVID-19 vaccines in Africa. Methods: A total of 637 Twitter posts and 569 Google News headlines or descriptions, retrieved between February 2 and May 5, 2020, were analyzed using three standard computational linguistics models (ie, TextBlob, Valence Aware Dictionary and Sentiment Reasoner, and Word2Vec combined with a bidirectional long short-term memory neural network). Results: Our findings revealed that, contrary to general perceptions, Google News headlines or snippets and Twitter posts within the stated period were generally passive or positive toward COVID-19 vaccines in Africa. It was possible to understand these patterns in light of increasingly sustained efforts by various media and health actors in ensuring the availability of factual information about the pandemic. Conclusions: This type of analysis could contribute to understanding predominant polarities and associated potential attitudinal inclinations. Such knowledge could be critical in informing relevant public health and media engagement policies. ", doi="10.2196/22916", url="https://medinform.jmir.org/2021/3/e22916", url="http://www.ncbi.nlm.nih.gov/pubmed/33667172" } @Article{info:doi/10.2196/25807, author="Chew, Robert and Kery, Caroline and Baum, Laura and Bukowski, Thomas and Kim, Annice and Navarro, Mario", title="Predicting Age Groups of Reddit Users Based on Posting Behavior and Metadata: Classification Model Development and Validation", journal="JMIR Public Health Surveill", year="2021", month="Mar", day="16", volume="7", number="3", pages="e25807", keywords="Reddit", keywords="social media", keywords="age", keywords="machine learning", keywords="classification", abstract="Background: Social media are important for monitoring perceptions of public health issues and for educating target audiences about health; however, limited information about the demographics of social media users makes it challenging to identify conversations among target audiences and limits how well social media can be used for public health surveillance and education outreach efforts. Certain social media platforms provide demographic information on followers of a user account, if given, but they are not always disclosed, and researchers have developed machine learning algorithms to predict social media users' demographic characteristics, mainly for Twitter. To date, there has been limited research on predicting the demographic characteristics of Reddit users. Objective: We aimed to develop a machine learning algorithm that predicts the age segment of Reddit users, as either adolescents or adults, based on publicly available data. Methods: This study was conducted between January and September 2020 using publicly available Reddit posts as input data. We manually labeled Reddit users' age by identifying and reviewing public posts in which Reddit users self-reported their age. We then collected sample posts, comments, and metadata for the labeled user accounts and created variables to capture linguistic patterns, posting behavior, and account details that would distinguish the adolescent age group (aged 13 to 20 years) from the adult age group (aged 21 to 54 years). We split the data into training (n=1660) and test sets (n=415) and performed 5-fold cross validation on the training set to select hyperparameters and perform feature selection. We ran multiple classification algorithms and tested the performance of the models (precision, recall, F1 score) in predicting the age segments of the users in the labeled data. To evaluate associations between each feature and the outcome, we calculated means and confidence intervals and compared the two age groups, with 2-sample t tests, for each transformed model feature. Results: The gradient boosted trees classifier performed the best, with an F1 score of 0.78. The test set precision and recall scores were 0.79 and 0.89, respectively, for the adolescent group (n=254) and 0.78 and 0.63, respectively, for the adult group (n=161). The most important feature in the model was the number of sentences per comment (permutation score: mean 0.100, SD 0.004). Members of the adolescent age group tended to have created accounts more recently, have higher proportions of submissions and comments in the r/teenagers subreddit, and post more in subreddits with higher subscriber counts than those in the adult group. Conclusions: We created a Reddit age prediction algorithm with competitive accuracy using publicly available data, suggesting machine learning methods can help public health agencies identify age-related target audiences on Reddit. Our results also suggest that there are characteristics of Reddit users' posting behavior, linguistic patterns, and account features that distinguish adolescents from adults. ", doi="10.2196/25807", url="https://publichealth.jmir.org/2021/3/e25807", url="http://www.ncbi.nlm.nih.gov/pubmed/33724195" } @Article{info:doi/10.2196/24948, author="Gesser-Edelsburg, Anat", title="Using Narrative Evidence to Convey Health Information on Social Media: The Case of COVID-19", journal="J Med Internet Res", year="2021", month="Mar", day="15", volume="23", number="3", pages="e24948", keywords="health and risk communication", keywords="social media", keywords="narrative evidence", keywords="crisis", keywords="pandemic", keywords="misinformation", keywords="infodemic", keywords="infodemiology", keywords="COVID-19", keywords="policy", keywords="segmentation", keywords="barrier reduction", keywords="role models", keywords="empathy and support", keywords="strengthening self/community-efficacy", keywords="coping tools", keywords="preventing stigmatization", keywords="at-risk populations", keywords="communicating uncertainty", keywords="positive deviance", keywords="tailor messaging", keywords="targeted behavioral change", doi="10.2196/24948", url="https://www.jmir.org/2021/3/e24948", url="http://www.ncbi.nlm.nih.gov/pubmed/33674257" } @Article{info:doi/10.2196/25202, author="Martino, Florentine and Brooks, Ruby and Browne, Jennifer and Carah, Nicholas and Zorbas, Christina and Corben, Kirstan and Saleeba, Emma and Martin, Jane and Peeters, Anna and Backholer, Kathryn", title="The Nature and Extent of Online Marketing by Big Food and Big Alcohol During the COVID-19 Pandemic in Australia: Content Analysis Study", journal="JMIR Public Health Surveill", year="2021", month="Mar", day="12", volume="7", number="3", pages="e25202", keywords="alcohol", keywords="food and beverage", keywords="COVID-19", keywords="marketing", keywords="social media", abstract="Background: Emerging evidence demonstrates that obesity is associated with a higher risk of COVID-19 morbidity and mortality. Excessive alcohol consumption and ``comfort eating'' as coping mechanisms during times of high stress have been shown to further exacerbate mental and physical ill-health. Global examples suggest that unhealthy food and alcohol brands and companies are using the COVID-19 pandemic to further market their products. However, there has been no systematic, in-depth analysis of how ``Big Food'' and ``Big Alcohol'' are capitalizing on the COVID-19 pandemic to market their products and brands. Objective: We aimed to quantify the extent and nature of online marketing by alcohol and unhealthy food and beverage companies during the COVID-19 pandemic in Australia. Methods: We conducted a content analysis of all COVID-19-related social media posts made by leading alcohol and unhealthy food and beverage brands (n=42) and their parent companies (n=12) over a 4-month period (February to May 2020) during the COVID-19 pandemic in Australia. Results: Nearly 80\% of included brands and all parent companies posted content related to COVID-19 during the 4-month period. Quick service restaurants (QSRs), food and alcohol delivery companies, alcohol brands, and bottle shops were the most active in posting COVID-19-related content. The most common themes for COVID-19-related marketing were isolation activities and community support. Promotion of hygiene and home delivery was also common, particularly for QSRs and alcohol and food delivery companies. Parent companies were more likely to post about corporate social responsibility (CSR) initiatives, such as donations of money and products, and to offer health advice. Conclusions: This is the first study to show that Big Food and Big Alcohol are incessantly marketing their products and brands on social media platforms using themes related to COVID-19, such as isolation activities and community support. Parent companies are frequently posting about CSR initiatives, such as donations of money and products, thereby creating a fertile environment to loosen current regulation or resist further industry regulation. ``COVID-washing'' by large alcohol brands, food and beverage brands, and their parent companies is both common and concerning. The need for comprehensive regulations to restrict unhealthy food and alcohol marketing, as recommended by the World Health Organization, is particularly acute in the COVID-19 context and is urgently required to ``build back better'' in a post-COVID-19 world. ", doi="10.2196/25202", url="https://publichealth.jmir.org/2021/3/e25202", url="http://www.ncbi.nlm.nih.gov/pubmed/33709935" } @Article{info:doi/10.2196/24883, author="Slavik, E. Catherine and Buttle, Charlotte and Sturrock, L. Shelby and Darlington, Connor J. and Yiannakoulias, Niko", title="Examining Tweet Content and Engagement of Canadian Public Health Agencies and Decision Makers During COVID-19: Mixed Methods Analysis", journal="J Med Internet Res", year="2021", month="Mar", day="11", volume="23", number="3", pages="e24883", keywords="COVID-19", keywords="coronavirus", keywords="pandemic", keywords="public health", keywords="Twitter", keywords="social media", keywords="engagement", keywords="risk communication", keywords="infodemiology", keywords="content analysis", abstract="Background: Effective communication during a health crisis can ease public concerns and promote the adoption of important risk-mitigating behaviors. Public health agencies and leaders have served as the primary communicators of information related to COVID-19, and a key part of their public outreach has taken place on social media platforms. Objective: This study examined the content and engagement of COVID-19 tweets authored by Canadian public health agencies and decision makers. We propose ways for public health accounts to adjust their tweeting practices during public health crises to improve risk communication and maximize engagement. Methods: We retrieved data from tweets by Canadian public health agencies and decision makers from January 1, 2020, to June 30, 2020. The Twitter accounts were categorized as belonging to either a public health agency, regional or local health department, provincial health authority, medical health officer, or minister of health. We analyzed trends in COVID-19 tweet engagement and conducted a content analysis on a stratified random sample of 485 tweets to examine the message functions and risk communication strategies used by each account type. Results: We analyzed 32,737 tweets authored by 118 Canadian public health Twitter accounts, of which 6982 tweets were related to COVID-19. Medical health officers authored the largest percentage of COVID-19--related tweets (n=1337, 35\%) relative to their total number of tweets and averaged the highest number of retweets per COVID-19 tweet (112 retweets per tweet). Public health agencies had the highest frequency of daily tweets about COVID-19 throughout the study period. Compared to tweets containing media and user mentions, hashtags and URLs were used in tweets more frequently by all account types, appearing in 69\% (n=4798 tweets) and 68\% (n=4781 tweets) of COVID-19--related tweets, respectively. Tweets containing hashtags also received the highest average retweets (47 retweets per tweet). Our content analysis revealed that of the three tweet message functions analyzed (information, action, community), tweets providing information were the most commonly used across most account types, constituting 39\% (n=181) of all tweets; however, tweets promoting actions from users received higher than average retweets (55 retweets per tweet). When examining tweets that received one or more retweet (n=359), the difference between mean retweets across the message functions was statistically significant (P<.001). The risk communication strategies that we examined were not widely used by any account type, appearing in only 262 out of 485 tweets. However, when these strategies were used, these tweets received more retweets compared to tweets that did not use any risk communication strategies (P<.001) (61 retweets versus 13 retweets on average). Conclusions: Public health agencies and decision makers should examine what messaging best meets the needs of their Twitter audiences to maximize sharing of their communications. Public health accounts that do not currently employ risk communication strategies in their tweets may be missing an important opportunity to engage with users about the mitigation of health risks related to COVID-19. ", doi="10.2196/24883", url="https://www.jmir.org/2021/3/e24883", url="http://www.ncbi.nlm.nih.gov/pubmed/33651705" } @Article{info:doi/10.2196/23097, author="Han, Yangyang and Jiang, Binshan and Guo, Rui", title="Factors Affecting Public Adoption of COVID-19 Prevention and Treatment Information During an Infodemic: Cross-sectional Survey Study", journal="J Med Internet Res", year="2021", month="Mar", day="11", volume="23", number="3", pages="e23097", keywords="information adoption", keywords="infodemic", keywords="China", keywords="health information", keywords="infodemiology", keywords="COVID-19", keywords="public health", abstract="Background: With the spread of COVID-19, an infodemic is also emerging. In public health emergencies, the use of information to enable disease prevention and treatment is incredibly important. Although both the information adoption model (IAM) and health belief model (HBM) have their own merits, they only focus on information or public influence factors, respectively, to explain the public's intention to adopt online prevention and treatment information. Objective: The aim of this study was to fill this gap by using a combination of the IAM and the HBM as the framework for exploring the influencing factors and paths in public health events that affect the public's adoption of online health information and health behaviors, focusing on both objective and subjective factors. Methods: We carried out an online survey to collect responses from participants in China (N=501). Structural equation modeling was used to evaluate items, and confirmatory factor analysis was used to calculate construct reliability and validity. The goodness of fit of the model and mediation effects were analyzed. Results: The overall fitness indices for the model developed in this study indicated an acceptable fit. Adoption intention was predicted by information characteristics ($\beta$=.266, P<.001) and perceived usefulness ($\beta$=.565, P<.001), which jointly explained nearly 67\% of the adoption intention variance. Information characteristics ($\beta$=.244, P<.001), perceived drawbacks ($\beta$=--.097, P=.002), perceived benefits ($\beta$=.512, P<.001), and self-efficacy ($\beta$=.141, P<.001) jointly determined perceived usefulness and explained about 81\% of the variance of perceived usefulness. However, social influence did not have a statistically significant impact on perceived usefulness, and self-efficacy did not significantly influence adoption intention directly. Conclusions: By integrating IAM and HBM, this study provided the insight and understanding that perceived usefulness and adoption intention of online health information could be influenced by information characteristics, people's perceptions of information drawbacks and benefits, and self-efficacy. Moreover, people also exhibited proactive behavior rather than reactive behavior to adopt information. Thus, we should consider these factors when helping the informed public obtain useful information via two approaches: one is to improve the quality of government-based and other official information, and the other is to improve the public's capacity to obtain information, in order to promote truth and fight rumors. This will, in turn, contribute to saving lives as the pandemic continues to unfold and run its course. ", doi="10.2196/23097", url="https://www.jmir.org/2021/3/e23097", url="http://www.ncbi.nlm.nih.gov/pubmed/33600348" } @Article{info:doi/10.2196/14837, author="Ahn, Euijoon and Liu, Na and Parekh, Tej and Patel, Ronak and Baldacchino, Tanya and Mullavey, Tracy and Robinson, Amanda and Kim, Jinman", title="A Mobile App and Dashboard for Early Detection of Infectious Disease Outbreaks: Development Study", journal="JMIR Public Health Surveill", year="2021", month="Mar", day="9", volume="7", number="3", pages="e14837", keywords="public health", keywords="infectious disease reporting", keywords="mobile app", keywords="disease notification", keywords="mobile phone", abstract="Background: Outbreaks of infectious diseases pose great risks, including hospitalization and death, to public health. Therefore, improving the management of outbreaks is important for preventing widespread infection and mitigating associated risks. Mobile health technology provides new capabilities that can help better capture, monitor, and manage infectious diseases, including the ability to quickly identify potential outbreaks. Objective: This study aims to develop a new infectious disease surveillance (IDS) system comprising a mobile app for accurate data capturing and dashboard for better health care planning and decision making. Methods: We developed the IDS system using a 2-pronged approach: a literature review on available and similar disease surveillance systems to understand the fundamental requirements and face-to-face interviews to collect specific user requirements from the local public health unit team at the Nepean Hospital, Nepean Blue Mountains Local Health District, New South Wales, Australia. Results: We identified 3 fundamental requirements when designing an electronic IDS system, which are the ability to capture and report outbreak data accurately, completely, and in a timely fashion. We then developed our IDS system based on the workflow, scope, and specific requirements of the public health unit team. We also produced detailed design and requirement guidelines. In our system, the outbreak data are captured and sent from anywhere using a mobile device or a desktop PC (web interface). The data are processed using a client-server architecture and, therefore, can be analyzed in real time. Our dashboard is designed to provide a daily, weekly, monthly, and historical summary of outbreak information, which can be potentially used to develop a future intervention plan. Specific information about certain outbreaks can also be visualized interactively to understand the unique characteristics of emerging infectious diseases. Conclusions: We demonstrated the design and development of our IDS system. We suggest that the use of a mobile app and dashboard will simplify the overall data collection, reporting, and analysis processes, thereby improving the public health responses and providing accurate registration of outbreak information. Accurate data reporting and collection are a major step forward in creating a better intervention plan for future outbreaks of infectious diseases. ", doi="10.2196/14837", url="https://publichealth.jmir.org/2021/3/e14837", url="http://www.ncbi.nlm.nih.gov/pubmed/33687334" } @Article{info:doi/10.2196/26482, author="Zhang, Chunyan and Xu, Songhua and Li, Zongfang and Hu, Shunxu", title="Understanding Concerns, Sentiments, and Disparities Among Population Groups During the COVID-19 Pandemic Via Twitter Data Mining: Large-scale Cross-sectional Study", journal="J Med Internet Res", year="2021", month="Mar", day="5", volume="23", number="3", pages="e26482", keywords="COVID-19", keywords="Twitter mining", keywords="infodemiology", keywords="infoveillance", keywords="pandemic", keywords="concerns", keywords="sentiments", keywords="population groups", keywords="disparities", abstract="Background: Since the beginning of the COVID-19 pandemic in late 2019, its far-reaching impacts have been witnessed globally across all aspects of human life, such as health, economy, politics, and education. Such widely penetrating impacts cast significant and profound burdens on all population groups, incurring varied concerns and sentiments among them. Objective: This study aims to identify the concerns, sentiments, and disparities of various population groups during the COVID-19 pandemic through a cross-sectional study conducted via large-scale Twitter data mining infoveillance. Methods: This study consisted of three steps: first, tweets posted during the pandemic were collected and preprocessed on a large scale; second, the key population attributes, concerns, sentiments, and emotions were extracted via a collection of natural language processing procedures; third, multiple analyses were conducted to reveal concerns, sentiments, and disparities among population groups during the pandemic. Overall, this study implemented a quick, effective, and economical approach for analyzing population-level disparities during a public health event. The source code developed in this study was released for free public use at GitHub. Results: A total of 1,015,655 original English tweets posted from August 7 to 12, 2020, were acquired and analyzed to obtain the following results. Organizations were significantly more concerned about COVID-19 (odds ratio [OR] 3.48, 95\% CI 3.39-3.58) and expressed more fear and depression emotions than individuals. Females were less concerned about COVID-19 (OR 0.73, 95\% CI 0.71-0.75) and expressed less fear and depression emotions than males. Among all age groups (ie, ?18, 19-29, 30-39, and ?40 years of age), the attention ORs of COVID-19 fear and depression increased significantly with age. It is worth noting that not all females paid less attention to COVID-19 than males. In the age group of 40 years or older, females were more concerned than males, especially regarding the economic and education topics. In addition, males 40 years or older and 18 years or younger were the least positive. Lastly, in all sentiment analyses, the sentiment polarities regarding political topics were always the lowest among the five topics of concern across all population groups. Conclusions: Through large-scale Twitter data mining, this study revealed that meaningful differences regarding concerns and sentiments about COVID-19-related topics existed among population groups during the study period. Therefore, specialized and varied attention and support are needed for different population groups. In addition, the efficient analysis method implemented by our publicly released code can be utilized to dynamically track the evolution of each population group during the pandemic or any other major event for better informed public health research and interventions. ", doi="10.2196/26482", url="https://www.jmir.org/2021/3/e26482", url="http://www.ncbi.nlm.nih.gov/pubmed/33617460" } @Article{info:doi/10.2196/22645, author="Kammrath Betancor, Paola and Tizek, Linda and Zink, Alexander and Reinhard, Thomas and B{\"o}hringer, Daniel", title="Estimating the Incidence of Conjunctivitis by Comparing the Frequency of Google Search Terms With Clinical Data: Retrospective Study", journal="JMIR Public Health Surveill", year="2021", month="Mar", day="3", volume="7", number="3", pages="e22645", keywords="epidemic keratoconjunctivitis", keywords="big data", keywords="Google search", keywords="Freiburg clinical data", abstract="Background: Infectious conjunctivitis is contagious and may lead to an outbreak. Prevention systems can help to avoid an outbreak. Objective: We aimed to evaluate if Google search data on conjunctivitis and associated terms can be used to estimate the incidence and if the data can provide an estimation for outbreaks. Methods: We obtained Google search data over 4 years for the German term for conjunctivitis (``Bindehautentz{\"u}ndung'') and 714 associated terms in 12 selected German cities and Germany as a whole using the Google AdWords Keyword Planner. The search volume from Freiburg was correlated with clinical data from the Freiburg emergency practice (Eye Center University of Freiburg). Results: The search volume for the German term for conjunctivitis in Germany as a whole and in the 12 German cities showed a highly uniform seasonal pattern. Cross-correlation between the temporal search frequencies in Germany as a whole and the 12 selected cities was high without any lag. Cross-correlation of the search volume in Freiburg with the frequency of conjunctivitis (International Statistical Classification of Diseases and Related Health Problems [ICD] code group ``H10.-'') from the centralized ophthalmologic emergency practice in Freiburg revealed a considerable temporal association, with the emergency practice lagging behind the frequency. Additionally, Pearson correlation between the count of patients per month and the count of searches per month in Freiburg was statistically significant (P=.04). Conclusions: We observed a close correlation between the Google search volume for the signs and symptoms of conjunctivitis and the frequency of patients with a congruent diagnosis in the Freiburg region. Regional deviations from the nationwide average search volume may therefore indicate a regional outbreak of infectious conjunctivitis. ", doi="10.2196/22645", url="https://publichealth.jmir.org/2021/3/e22645", url="http://www.ncbi.nlm.nih.gov/pubmed/33656450" } @Article{info:doi/10.2196/25651, author="Huynh Dagher, Solene and Lam{\'e}, Guillaume and Hubiche, Thomas and Ezzedine, Khaled and Duong, Anh Tu", title="The Influence of Media Coverage and Governmental Policies on Google Queries Related to COVID-19 Cutaneous Symptoms: Infodemiology Study", journal="JMIR Public Health Surveill", year="2021", month="Feb", day="25", volume="7", number="2", pages="e25651", keywords="chilblains", keywords="COVID-19", keywords="dermatology", keywords="Google Trends", keywords="infodemiology", keywords="lesion", keywords="media", keywords="media coverage", keywords="online health information", keywords="skin lesions", keywords="trend", abstract="Background: During COVID-19, studies have reported the appearance of internet searches for disease symptoms before their validation by the World Health Organization. This suggested that monitoring of these searches with tools including Google Trends may help monitor the pandemic itself. In Europe and North America, dermatologists reported an unexpected outbreak of cutaneous acral lesions (eg, chilblain-like lesions) in April 2020. However, external factors such as public communications may also hinder the use of Google Trends as an infodemiology tool. Objective: The study aimed to assess the impact of media announcements and lockdown enforcement on internet searches related to cutaneous acral lesions during the COVID-19 outbreak in 2020. Methods: Two searches on Google Trends, including daily relative search volumes for (1) ``toe'' or ``chilblains'' and (2) ``coronavirus,'' were performed from January 1 to May 16, 2020, with the United States, the United Kingdom, France, Italy, Spain, and Germany as the countries of choice. The ratio of interest over time in ``chilblains'' and ``coronavirus'' was plotted. To assess the impact of lockdown enforcement and media coverage on these internet searches, we performed an interrupted time-series analysis for each country. Results: The ratio of interest over time in ``chilblains'' to ``coronavirus'' showed a constant upward trend. In France, Italy, and the United Kingdom, lockdown enforcement was associated with a significant slope change for ``chilblain'' searches with a variation coefficient of 1.06 (SE 0.42) (P=0.01), 1.04 (SE 0.28) (P<.01), and 1.21 (SE 0.44) (P=0.01), respectively. After media announcements, these ratios significantly increased in France, Spain, Italy, and the United States with variation coefficients of 18.95 (SE 5.77) (P=.001), 31.31 (SE 6.31) (P<.001), 14.57 (SE 6.33) (P=.02), and 11.24 (SE 4.93) (P=.02), respectively, followed by a significant downward trend in France (--1.82 [SE 0.45]), Spain (--1.10 [SE 0.38]), and Italy (--0.93 [SE 0.33]) (P<.001, P=0.004, and P<.001, respectively). The adjusted R2 values were 0.311, 0.351, 0.325, and 0.305 for France, Spain, Italy, and the United States, respectively, suggesting an average correlation between time and the search volume; however, this correlation was weak for Germany and the United Kingdom. Conclusions: To date, the association between chilblain-like lesions and COVID-19 remains controversial; however, our results indicate that Google queries of ``chilblain'' were highly influenced by media coverage and government policies, indicating that caution should be exercised when using Google Trends as a monitoring tool for emerging diseases. ", doi="10.2196/25651", url="https://publichealth.jmir.org/2021/2/e25651", url="http://www.ncbi.nlm.nih.gov/pubmed/33513563" } @Article{info:doi/10.2196/23279, author="Cha, Meeyoung and Cha, Chiyoung and Singh, Karandeep and Lima, Gabriel and Ahn, Yong-Yeol and Kulshrestha, Juhi and Varol, Onur", title="Prevalence of Misinformation and Factchecks on the COVID-19 Pandemic in 35 Countries: Observational Infodemiology Study", journal="JMIR Hum Factors", year="2021", month="Feb", day="13", volume="8", number="1", pages="e23279", keywords="COVID-19", keywords="coronavirus", keywords="infodemic", keywords="infodemiology", keywords="misinformation", keywords="vulnerability", keywords="LMIC countries", abstract="Background: The COVID-19 pandemic has been accompanied by an infodemic, in which a plethora of false information has been rapidly disseminated online, leading to serious harm worldwide. Objective: This study aims to analyze the prevalence of common misinformation related to the COVID-19 pandemic. Methods: We conducted an online survey via social media platforms and a survey company to determine whether respondents have been exposed to a broad set of false claims and fact-checked information on the disease. Results: We obtained more than 41,000 responses from 1257 participants in 85 countries, but for our analysis, we only included responses from 35 countries that had at least 15 respondents. We identified a strong negative correlation between a country's Gross Domestic Product per-capita and the prevalence of misinformation, with poorer countries having a higher prevalence of misinformation (Spearman $\rho$=--0.72; P<.001). We also found that fact checks spread to a lesser degree than their respective false claims, following a sublinear trend ($\beta$=.64). Conclusions: Our results imply that the potential harm of misinformation could be more substantial for low-income countries than high-income countries. Countries with poor infrastructures might have to combat not only the spreading pandemic but also the COVID-19 infodemic, which can derail efforts in saving lives. ", doi="10.2196/23279", url="https://humanfactors.jmir.org/2021/1/e23279", url="http://www.ncbi.nlm.nih.gov/pubmed/33395395" } @Article{info:doi/10.2196/23957, author="Zheng, Chengda and Xue, Jia and Sun, Yumin and Zhu, Tingshao", title="Public Opinions and Concerns Regarding the Canadian Prime Minister's Daily COVID-19 Briefing: Longitudinal Study of YouTube Comments Using Machine Learning Techniques", journal="J Med Internet Res", year="2021", month="Feb", day="23", volume="23", number="2", pages="e23957", keywords="Canada", keywords="PM Trudeau", keywords="YouTube", keywords="machine learning", keywords="big data", keywords="infodemiology", keywords="infodemic", keywords="public concerns", keywords="communication", keywords="concern", keywords="social media", keywords="video", abstract="Background: During the COVID-19 pandemic in Canada, Prime Minister Justin Trudeau provided updates on the novel coronavirus and the government's responses to the pandemic in his daily briefings from March 13 to May 22, 2020, delivered on the official Canadian Broadcasting Corporation (CBC) YouTube channel. Objective: The aim of this study was to examine comments on Canadian Prime Minister Trudeau's COVID-19 daily briefings by YouTube users and track these comments to extract the changing dynamics of the opinions and concerns of the public over time. Methods: We used machine learning techniques to longitudinally analyze a total of 46,732 English YouTube comments that were retrieved from 57 videos of Prime Minister Trudeau's COVID-19 daily briefings from March 13 to May 22, 2020. A natural language processing model, latent Dirichlet allocation, was used to choose salient topics among the sampled comments for each of the 57 videos. Thematic analysis was used to classify and summarize these salient topics into different prominent themes. Results: We found 11 prominent themes, including strict border measures, public responses to Prime Minister Trudeau's policies, essential work and frontline workers, individuals' financial challenges, rental and mortgage subsidies, quarantine, government financial aid for enterprises and individuals, personal protective equipment, Canada and China's relationship, vaccines, and reopening. Conclusions: This study is the first to longitudinally investigate public discourse and concerns related to Prime Minister Trudeau's daily COVID-19 briefings in Canada. This study contributes to establishing a real-time feedback loop between the public and public health officials on social media. Hearing and reacting to real concerns from the public can enhance trust between the government and the public to prepare for future health emergencies. ", doi="10.2196/23957", url="https://www.jmir.org/2021/2/e23957", url="http://www.ncbi.nlm.nih.gov/pubmed/33544690" } @Article{info:doi/10.2196/26302, author="Wang, Hanyin and Li, Yikuan and Hutch, Meghan and Naidech, Andrew and Luo, Yuan", title="Using Tweets to Understand How COVID-19--Related Health Beliefs Are Affected in the Age of Social Media: Twitter Data Analysis Study", journal="J Med Internet Res", year="2021", month="Feb", day="22", volume="23", number="2", pages="e26302", keywords="COVID-19", keywords="social media", keywords="health belief", keywords="Twitter", keywords="infodemic", keywords="infodemiology", keywords="machine learning", keywords="natural language processing", abstract="Background: The emergence of SARS-CoV-2 (ie, COVID-19) has given rise to a global pandemic affecting 215 countries and over 40 million people as of October 2020. Meanwhile, we are also experiencing an infodemic induced by the overabundance of information, some accurate and some inaccurate, spreading rapidly across social media platforms. Social media has arguably shifted the information acquisition and dissemination of a considerably large population of internet users toward higher interactivities. Objective: This study aimed to investigate COVID-19-related health beliefs on one of the mainstream social media platforms, Twitter, as well as potential impacting factors associated with fluctuations in health beliefs on social media. Methods: We used COVID-19-related posts from the mainstream social media platform Twitter to monitor health beliefs. A total of 92,687,660 tweets corresponding to 8,967,986 unique users from January 6 to June 21, 2020, were retrieved. To quantify health beliefs, we employed the health belief model (HBM) with four core constructs: perceived susceptibility, perceived severity, perceived benefits, and perceived barriers. We utilized natural language processing and machine learning techniques to automate the process of judging the conformity of each tweet with each of the four HBM constructs. A total of 5000 tweets were manually annotated for training the machine learning architectures. Results: The machine learning classifiers yielded areas under the receiver operating characteristic curves over 0.86 for the classification of all four HBM constructs. Our analyses revealed a basic reproduction number R0 of 7.62 for trends in the number of Twitter users posting health belief--related content over the study period. The fluctuations in the number of health belief--related tweets could reflect dynamics in case and death statistics, systematic interventions, and public events. Specifically, we observed that scientific events, such as scientific publications, and nonscientific events, such as politicians' speeches, were comparable in their ability to influence health belief trends on social media through a Kruskal-Wallis test (P=.78 and P=.92 for perceived benefits and perceived barriers, respectively). Conclusions: As an analogy of the classic epidemiology model where an infection is considered to be spreading in a population with an R0 greater than 1, we found that the number of users tweeting about COVID-19 health beliefs was amplifying in an epidemic manner and could partially intensify the infodemic. It is ``unhealthy'' that both scientific and nonscientific events constitute no disparity in impacting the health belief trends on Twitter, since nonscientific events, such as politicians' speeches, might not be endorsed by substantial evidence and could sometimes be misleading. ", doi="10.2196/26302", url="https://www.jmir.org/2021/2/e26302", url="http://www.ncbi.nlm.nih.gov/pubmed/33529155" } @Article{info:doi/10.2196/24445, author="Wong, Zheng Mark Yu and Gunasekeran, Visva Dinesh and Nusinovici, Simon and Sabanayagam, Charumathi and Yeo, Keong Khung and Cheng, Ching-Yu and Tham, Yih-Chung", title="Telehealth Demand Trends During the COVID-19 Pandemic in the Top 50 Most Affected Countries: Infodemiological Evaluation", journal="JMIR Public Health Surveill", year="2021", month="Feb", day="19", volume="7", number="2", pages="e24445", keywords="COVID-19", keywords="infodemiology", keywords="telehealth", keywords="telemedicine", keywords="internet", abstract="Background: The COVID-19 pandemic has led to urgent calls for the adoption of telehealth solutions. However, public interest and demand for telehealth during the pandemic remain unknown. Objective: We used an infodemiological approach to estimate the worldwide demand for telehealth services during COVID-19, focusing on the 50 most affected countries and comparing the demand for such services with the level of information and communications technology (ICT) infrastructure available. Methods: We used Google Trends, the Baidu Index (China), and Yandex Keyword Statistics (Russia) to extract data on worldwide and individual countries' telehealth-related internet searches from January 1 to July 7, 2020, presented as relative search volumes (RSV; range 0-100). Daily COVID-19 cases and deaths were retrieved from the World Health Organization. Individual countries' ICT infrastructure profiles were retrieved from the World Economic Forum Report. Results: Across the 50 countries, the mean RSV was 18.5 (SD 23.2), and the mean ICT index was 62.1 (SD 15.0). An overall spike in worldwide telehealth-related RSVs was observed from March 11, 2020 (RSV peaked to 76.0), which then tailed off in June-July 2020 (mean RSV for the period was 25.8), but remained higher than pre-March RSVs (mean 7.29). By country, 42 (84\%) manifested increased RSVs over the evaluation period, with the highest observed in Canada (RSV=100) and the United States (RSV=96). When evaluating associations between RSV and the ICT index, both the United States and Canada demonstrated high RSVs and ICT scores (?70.3). In contrast, European countries had relatively lower RSVs (range 3.4-19.5) despite high ICT index scores (mean 70.3). Several Latin American (Brazil, Chile, Colombia) and South Asian (India, Bangladesh, Pakistan) countries demonstrated relatively higher RSVs (range 13.8-73.3) but low ICT index scores (mean 44.6), indicating that the telehealth demand outstrips the current ICT infrastructure. Conclusions: There is generally increased interest and demand for telehealth services across the 50 countries most affected by COVID-19, highlighting the need to scale up telehealth capabilities, during and beyond the pandemic. ", doi="10.2196/24445", url="http://publichealth.jmir.org/2021/2/e24445/", url="http://www.ncbi.nlm.nih.gov/pubmed/33605883" } @Article{info:doi/10.2196/24473, author="Andy, U. Anietie and Guntuku, C. Sharath and Adusumalli, Srinath and Asch, A. David and Groeneveld, W. Peter and Ungar, H. Lyle and Merchant, M. Raina", title="Predicting Cardiovascular Risk Using Social Media Data: Performance Evaluation of Machine-Learning Models", journal="JMIR Cardio", year="2021", month="Feb", day="19", volume="5", number="1", pages="e24473", keywords="ASCVD", keywords="machine learning", keywords="natural language processing", keywords="atherosclerotic", keywords="cardiovascular disease", keywords="social media language", keywords="social media", abstract="Background: Current atherosclerotic cardiovascular disease (ASCVD) predictive models have limitations; thus, efforts are underway to improve the discriminatory power of ASCVD models. Objective: We sought to evaluate the discriminatory power of social media posts to predict the 10-year risk for ASCVD as compared to that of pooled cohort risk equations (PCEs). Methods: We consented patients receiving care in an urban academic emergency department to share access to their Facebook posts and electronic medical records (EMRs). We retrieved Facebook status updates up to 5 years prior to study enrollment for all consenting patients. We identified patients (N=181) without a prior history of coronary heart disease, an ASCVD score in their EMR, and more than 200 words in their Facebook posts. Using Facebook posts from these patients, we applied a machine-learning model to predict 10-year ASCVD risk scores. Using a machine-learning model and a psycholinguistic dictionary, Linguistic Inquiry and Word Count, we evaluated if language from posts alone could predict differences in risk scores and the association of certain words with risk categories, respectively. Results: The machine-learning model predicted the 10-year ASCVD risk scores for the categories <5\%, 5\%-7.4\%, 7.5\%-9.9\%, and ?10\% with area under the curve (AUC) values of 0.78, 0.57, 0.72, and 0.61, respectively. The machine-learning model distinguished between low risk (<10\%) and high risk (>10\%) with an AUC of 0.69. Additionally, the machine-learning model predicted the ASCVD risk score with Pearson r=0.26. Using Linguistic Inquiry and Word Count, patients with higher ASCVD scores were more likely to use words associated with sadness (r=0.32). Conclusions: Language used on social media can provide insights about an individual's ASCVD risk and inform approaches to risk modification. ", doi="10.2196/24473", url="http://cardio.jmir.org/2021/1/e24473/", url="http://www.ncbi.nlm.nih.gov/pubmed/33605888" } @Article{info:doi/10.2196/13731, author="Reuter, Katja and Lee, Delphine", title="Perspectives Toward Seeking Treatment Among Patients With Psoriasis: Protocol for a Twitter Content Analysis", journal="JMIR Res Protoc", year="2021", month="Feb", day="18", volume="10", number="2", pages="e13731", keywords="infodemiology", keywords="infoveillance", keywords="internet", keywords="surveillance", keywords="patient opinion", keywords="psoriasis, treatment", keywords="Twitter", keywords="social media", keywords="social network", abstract="Background: Psoriasis is an autoimmune disease estimated to affect more than 6 million adults in the United States. It poses a significant public health problem and contributes to rising health care costs, affecting people's quality of life and ability to work. Previous research showed that nontreatment and undertreatment of patients with psoriasis remain a significant problem. Perspectives of patients toward seeking psoriasis treatment are understudied. Social media offers a new data source of user-generated content. Researchers suggested that the social network Twitter may serve as a rich avenue for exploring how patients communicate about their health issues. Objective: The objective of this study is to conduct a content analysis of Twitter posts (in English) published by users in the United States between February 1, 2016, and October 31, 2018, to examine perspectives that potentially influence the treatment decision among patients with psoriasis. Methods: User-generated Twitter posts that include keywords related to psoriasis will be analyzed using text classifiers to identify themes related to the research questions. We will use Symplur Signals, a health care social media analytics platform, to access the Twitter data. We will use descriptive statistics to analyze the data and identify the most prevalent topics in the Twitter content among people with psoriasis. Results: This study is supported by the National Center for Advancing Translational Science through a Clinical and Translational Science Award award. Study approval was obtained from the institutional review board at the University of Southern California. Data extraction and cleaning are complete. For the time period from February 1, 2016, to October 31, 2018, we obtained 95,040 Twitter posts containing terms related to ``psoriasis'' from users in the United States published in English. After removing duplicates, retweets, and non-English tweets, we found that 75.51\% (52,301/69,264) of the psoriasis-related posts were sent by commercial or bot-like accounts, while 16,963 posts were noncommercial and will be included in the analysis to assess the patient perspective. Analysis was completed in Summer 2020. Conclusions: This protocol paper provides a detailed description of a social media research project including the process of data extraction, cleaning, and analysis. It is our goal to contribute to the development of more transparent social media research efforts. Our findings will shed light on whether Twitter provides a promising data source for garnering patient perspective data about psoriasis treatment decisions. The data will also help to determine whether Twitter might serve as a potential outreach platform for raising awareness of psoriasis and treatment options among patients and implementing related health interventions. International Registered Report Identifier (IRRID): DERR1-10.2196/13731 ", doi="10.2196/13731", url="http://www.researchprotocols.org/2021/2/e13731/", url="http://www.ncbi.nlm.nih.gov/pubmed/33599620" } @Article{info:doi/10.2196/24486, author="Li, Zhengyi and Du, Xiangyu and Liao, Xiaojing and Jiang, Xiaoqian and Champagne-Langabeer, Tiffany", title="Demystifying the Dark Web Opioid Trade: Content Analysis on Anonymous Market Listings and Forum Posts", journal="J Med Internet Res", year="2021", month="Feb", day="17", volume="23", number="2", pages="e24486", keywords="opioids", keywords="black market", keywords="anonymous markets and forums", keywords="opioid supply chain", keywords="text mining", keywords="machine learning", keywords="opioid crisis", keywords="opioid epidemic", keywords="drug abuse", abstract="Background: Opioid use disorder presents a public health issue afflicting millions across the globe. There is a pressing need to understand the opioid supply chain to gain new insights into the mitigation of opioid use and effectively combat the opioid crisis. The role of anonymous online marketplaces and forums that resemble eBay or Amazon, where anyone can post, browse, and purchase opioid commodities, has become increasingly important in opioid trading. Therefore, a greater understanding of anonymous markets and forums may enable public health officials and other stakeholders to comprehend the scope of the crisis. However, to the best of our knowledge, no large-scale study, which may cross multiple anonymous marketplaces and is cross-sectional, has been conducted to profile the opioid supply chain and unveil characteristics of opioid suppliers, commodities, and transactions. Objective: We aimed to profile the opioid supply chain in anonymous markets and forums via a large-scale, longitudinal measurement study on anonymous market listings and posts. Toward this, we propose a series of techniques to collect data; identify opioid jargon terms used in the anonymous marketplaces and forums; and profile the opioid commodities, suppliers, and transactions. Methods: We first conducted a whole-site crawl of anonymous online marketplaces and forums to solicit data. We then developed a suite of opioid domain--specific text mining techniques (eg, opioid jargon detection and opioid trading information retrieval) to recognize information relevant to opioid trading activities (eg, commodities, price, shipping information, and suppliers). Subsequently, we conducted a comprehensive, large-scale, longitudinal study to demystify opioid trading activities in anonymous markets and forums. Results: A total of 248,359 listings from 10 anonymous online marketplaces and 1,138,961 traces (ie, threads of posts) from 6 underground forums were collected. Among them, we identified 28,106 opioid product listings and 13,508 opioid-related promotional and review forum traces from 5147 unique opioid suppliers' IDs and 2778 unique opioid buyers' IDs. Our study characterized opioid suppliers (eg, activeness and cross-market activities), commodities (eg, popular items and their evolution), and transactions (eg, origins and shipping destination) in anonymous marketplaces and forums, which enabled a greater understanding of the underground trading activities involved in international opioid supply and demand. Conclusions: The results provide insight into opioid trading in the anonymous markets and forums and may prove an effective mitigation data point for illuminating the opioid supply chain. ", doi="10.2196/24486", url="http://www.jmir.org/2021/2/e24486/", url="http://www.ncbi.nlm.nih.gov/pubmed/33595442" } @Article{info:doi/10.2196/19651, author="Yang, Qian and Tai-Seale, Ming and Liu, Stephanie and Shen, Yi and Zhang, Xiaobin and Xiao, Xiaohua and Zhang, Kejun", title="Measuring Public Reaction to Violence Against Doctors in China: Interrupted Time Series Analysis of Media Reports", journal="J Med Internet Res", year="2021", month="Feb", day="16", volume="23", number="2", pages="e19651", keywords="violence against doctors", keywords="government intervention", keywords="public opinion", keywords="patient--physician relationship", abstract="Background: Violence against doctors in China is a serious problem that has attracted attention from both domestic and international media. Objective: This study investigates readers' responses to media reports on violence against doctors to identify attitudes toward perpetrators and physicians and examine if such trends are influenced by national policies. Methods: We searched 17 Chinese violence against doctors reports in international media sources from 2011 to 2020. We then tracked back the original reports and web crawled the 19,220 comments in China. To ascertain the possible turning point of public opinion, we searched violence against doctors--related policies from Tsinghua University ipolicy database from 2011 to 2020, and found 19 policies enacted by the Chinese central government aimed at alleviating the intense patient--physician relationship. We then conducted a series of interrupted time series analyses to examine the influence of these policies on public sentiment toward violence against doctors over time. Results: The interrupted time series analysis (ITSA) showed that the change in public sentiment toward violence against doctors reports was temporally associated with government interventions. The declarations of 10 of the public policies were followed by increases in the proportion of online public opinion in support of doctors (average slope changes of 0.010, P<.05). A decline in the proportion of online public opinion that blamed doctors (average level change of --0.784, P<.05) followed the declaration of 3 policies. Conclusions: The government's administrative interventions effectively shaped public opinion but only temporarily. Continued public policy interventions are needed to sustain the reduction of hostility toward medical doctors. ", doi="10.2196/19651", url="http://www.jmir.org/2021/2/e19651/", url="http://www.ncbi.nlm.nih.gov/pubmed/33591282" } @Article{info:doi/10.2196/16348, author="Hassan, Lamiece and Nenadic, Goran and Tully, Patricia Mary", title="A Social Media Campaign (\#datasaveslives) to Promote the Benefits of Using Health Data for Research Purposes: Mixed Methods Analysis", journal="J Med Internet Res", year="2021", month="Feb", day="16", volume="23", number="2", pages="e16348", keywords="social media", keywords="public engagement", keywords="social network analysis", keywords="medical research", abstract="Background: Social media provides the potential to engage a wide audience about scientific research, including the public. However, little empirical research exists to guide health scientists regarding what works and how to optimize impact. We examined the social media campaign \#datasaveslives established in 2014 to highlight positive examples of the use and reuse of health data in research. Objective: This study aims to examine how the \#datasaveslives hashtag was used on social media, how often, and by whom; thus, we aim to provide insights into the impact of a major social media campaign in the UK health informatics research community and further afield. Methods: We analyzed all publicly available posts (tweets) that included the hashtag \#datasaveslives (N=13,895) on the microblogging platform Twitter between September 1, 2016, and August 31, 2017. Using a combination of qualitative and quantitative analyses, we determined the frequency and purpose of tweets. Social network analysis was used to analyze and visualize tweet sharing (retweet) networks among hashtag users. Results: Overall, we found 4175 original posts and 9720 retweets featuring \#datasaveslives by 3649 unique Twitter users. In total, 66.01\% (2756/4175) of the original posts were retweeted at least once. Higher frequencies of tweets were observed during the weeks of prominent policy publications, popular conferences, and public engagement events. Cluster analysis based on retweet relationships revealed an interconnected series of groups of \#datasaveslives users in academia, health services and policy, and charities and patient networks. Thematic analysis of tweets showed that \#datasaveslives was used for a broader range of purposes than indexing information, including event reporting, encouraging participation and action, and showing personal support for data sharing. Conclusions: This study shows that a hashtag-based social media campaign was effective in encouraging a wide audience of stakeholders to disseminate positive examples of health research. Furthermore, the findings suggest that the campaign supported community building and bridging practices within and between the interdisciplinary sectors related to the field of health data science and encouraged individuals to demonstrate personal support for sharing health data. ", doi="10.2196/16348", url="http://www.jmir.org/2021/2/e16348/", url="http://www.ncbi.nlm.nih.gov/pubmed/33591280" } @Article{info:doi/10.2196/25734, author="Yin, Fulian and Shao, Xueying and Ji, Meiqi and Wu, Jianhong", title="Quantifying the Influence of Delay in Opinion Transmission of COVID-19 Information Propagation: Modeling Study", journal="J Med Internet Res", year="2021", month="Feb", day="12", volume="23", number="2", pages="e25734", keywords="COVID-19", keywords="delay transmission", keywords="dynamic model", keywords="Sina Microblog", keywords="social media", keywords="communication", keywords="online health information", keywords="health information", keywords="public health", keywords="opinion", keywords="strategy", keywords="model", keywords="information transmission", keywords="delay", keywords="infodemiology", keywords="infoveillance", abstract="Background: In a fast-evolving public health crisis such as the COVID-19 pandemic, multiple pieces of relevant information can be posted sequentially on a social media platform. The interval between subsequent posting times may have a different impact on the transmission and cross-propagation of the old and new information that results in a different peak value and a final size of forwarding users of the new information, depending on the content correlation and whether the new information is posted during the outbreak or quasi--steady-state phase of the old information. Objective: This study aims to help in designing effective communication strategies to ensure information is delivered to the maximal number of users. Methods: We developed and analyzed two classes of susceptible-forwarding-immune information propagation models with delay in transmission to describe the cross-propagation process of relevant information. A total of 28,661 retweets of typical information were posted frequently by each opinion leader related to COVID-19 with high influence (data acquisition up to February 19, 2020). The information was processed into discrete points with a frequency of 10 minutes, and the real data were fitted by the model numerical simulation. Furthermore, the influence of parameters on information dissemination and the design of a publishing strategy were analyzed. Results: The current epidemic outbreak situation, epidemic prevention, and other related authoritative information cannot be timely and effectively browsed by the public. The ingenious use of information release intervals can effectively enhance the interaction between information and realize the effective diffusion of information. We parameterized our models using real data from Sina Microblog and used the parameterized models to define and evaluate mutual attractiveness indexes, and we used these indexes and parameter sensitivity analyses to inform optimal strategies for new information to be effectively propagated in the microblog. The results of the parameter analysis showed that using different attractiveness indexes as the key parameters can control the information transmission with different release intervals, so it is considered as a key link in the design of an information communication strategy. At the same time, the dynamic process of information was analyzed through index evaluation. Conclusions: Our model can carry out an accurate numerical simulation of information at different release intervals and achieve a dynamic evaluation of information transmission by constructing an indicator system so as to provide theoretical support and strategic suggestions for government decision making. This study optimizes information posting strategies to maximize communication efforts for delivering key public health messages to the public for better outcomes of public health emergency management. ", doi="10.2196/25734", url="http://www.jmir.org/2021/2/e25734/", url="http://www.ncbi.nlm.nih.gov/pubmed/33529153" } @Article{info:doi/10.2196/25363, author="Mongkhon, Pajaree and Ruengorn, Chidchanok and Awiphan, Ratanaporn and Thavorn, Kednapa and Hutton, Brian and Wongpakaran, Nahathai and Wongpakaran, Tinakon and Nochaiwong, Surapon", title="Exposure to COVID-19-Related Information and its Association With Mental Health Problems in Thailand: Nationwide, Cross-sectional Survey Study", journal="J Med Internet Res", year="2021", month="Feb", day="12", volume="23", number="2", pages="e25363", keywords="coronavirus", keywords="COVID-19", keywords="insomnia", keywords="mental health", keywords="social media", keywords="depression", keywords="anxiety", keywords="stress", keywords="psychosocial problem", abstract="Background: The COVID-19 pandemic has had a negative impact on both the physical and mental health of individuals worldwide. Evidence regarding the association between mental health problems and information exposure among Thai citizens during the COVID-19 outbreak is limited. Objective: This study aimed to explore the relationship between information exposure and mental health problems during the COVID-19 pandemic in Thailand. Methods: Between April 21 and May 4, 2020, we conducted a cross-sectional, nationwide online survey of the general population in Thailand. We categorized the duration of exposure to COVID-19-related information as follows: <1 h/day (reference group), 1-2 h/day, and ?3 h/day. Mental health outcomes were assessed using the Patient Health Questionnaire-9, the Generalized Anxiety Disorder-7 scale, the Perceived Stress Scale-10, and the Insomnia Severity Index for symptoms of depression, anxiety, perceived stress, and insomnia, respectively. Multivariable logistic regression models were used to evaluate the relationship between information exposure and the risk of developing the aforementioned symptoms. An ancillary analysis using multivariable multinomial logistic regression models was also conducted to assess the possible dose-response relationship across the severity strata of mental health problems. Results: Of the 4322 eligible participants, 4004 (92.6\%) completed the online survey. Of them, 1481 (37.0\%), 1644 (41.1\%), and 879 (22.0\%) participants were exposed to COVID-19-related information for less than 1 hour per day, 1 to 2 hours per day, or 3 or more hours per day, respectively. The major source of information related to the COVID-19 pandemic was social media (95.3\%), followed by traditional media (68.7\%) and family members (34.9\%). Those exposed to information for 3 or more hours per day had a higher risk of developing symptoms of depression (adjusted odds ratio [OR] 1.35, 95\% CI 1.03-1.76; P=.03), anxiety (adjusted OR 1.88, 95\% CI 1.43-2.46; P<.001), and insomnia (adjusted OR 1.52, 95\% CI 1.17-1.97; P=.001) than people exposed to information for less than 1 hour per day. Meanwhile, people exposed to information for 1 to 2 hours per day were only at risk of developing symptoms of anxiety (adjusted OR 1.35, 95\% CI 1.08-1.69; P=.008). However, no association was found between information exposure and the risk of perceived stress. In the ancillary analysis, a dose-response relationship was observed between information exposure of 3 or more hours per day and the severity of mental health problems. Conclusions: These findings suggest that social media is the main source of COVID-19-related information. Moreover, people who are exposed to information for 3 or more hours per day are more likely to develop psychological problems, including depression, anxiety, and insomnia. Longitudinal studies investigating the long-term effects of COVID-19-related information exposure on mental health are warranted. ", doi="10.2196/25363", url="http://www.jmir.org/2021/2/e25363/", url="http://www.ncbi.nlm.nih.gov/pubmed/33523828" } @Article{info:doi/10.2196/26570, author="Wong, Chun Frankie Ho and Liu, Tianyin and Leung, Yi Dara Kiu and Zhang, Y. Anna and Au, Hong Walker Siu and Kwok, Wai Wai and Shum, Y. Angie K. and Wong, Yan Gloria Hoi and Lum, Yat-Sang Terry", title="Consuming Information Related to COVID-19 on Social Media Among Older Adults and Its Association With Anxiety, Social Trust in Information, and COVID-Safe Behaviors: Cross-sectional Telephone Survey", journal="J Med Internet Res", year="2021", month="Feb", day="11", volume="23", number="2", pages="e26570", keywords="COVID-19", keywords="anxiety", keywords="social media", keywords="infodemic", keywords="Hong Kong", abstract="Background: COVID-19-related information on social media is overabundant and sometimes questionable, resulting in an ``infodemic'' during the pandemic. While previous studies suggest social media usage increases the risk of developing anxiety symptoms, how induced anxiety affects attitudes and behaviors is less discussed, let alone during a global pandemic. Little is known about the relationship between older adults using social media during a pandemic and their anxiety, their attitudes toward social trust in information, and behaviors to avoid contracting COVID-19. Objective: The goal of this study was to investigate the associations between using social media for COVID-19-related information and anxiety symptoms as well as the mediation effect of anxiety symptoms on social trust in information and COVID-safe behaviors among older adults. Methods: A cross-sectional telephone survey was conducted in Hong Kong between May and August 2020. A rapid warm-call protocol was developed to train social workers and volunteers from participant nongovernmental organizations to conduct the telephone surveys. Questions related to COVID-safe behaviors, social trust in information, social media use, anxiety and depressive symptoms, and sociodemographic information were asked. The number of confirmed COVID-19 cases at the community level was used to account for the risk of contracting COVID-19. Ordinary least squares regressions examined the associations between social media use and anxiety symptoms, and how they were associated with social trust in information and COVID-safe behaviors. Structural equation modeling further mapped out these relationships to identify the mediation effects of anxiety symptoms. Results: This study collected information regarding 3421 adults aged 60 years and older. Use of social media for COVID-19-related information was associated with more anxiety symptoms and lower social trust in information but had no significant relationship with COVID-safe behaviors. Anxiety symptoms predicted lower social trust in information and higher COVID-safe behaviors. Lower social trust in information was predicted by using social media for COVID-19 information, mediated by anxiety symptoms, while no mediation effect was found for COVID-safe behaviors. Conclusions: Older adults who rely on social media for COVID-19-related information exhibited more anxiety symptoms, while showing mixed effects on attitudes and behaviors. Social trust in information may be challenged by unverified and contradictory information online. The negligible impact on COVID-safe behaviors suggested that social media may have caused more confusion than consolidating a consistent effort against the pandemic. Media literacy education is recommended to promote critical evaluation of COVID-19-related information and responsible sharing among older adults. ", doi="10.2196/26570", url="http://www.jmir.org/2021/2/e26570/", url="http://www.ncbi.nlm.nih.gov/pubmed/33523825" } @Article{info:doi/10.2196/25431, author="Jang, Hyeju and Rempel, Emily and Roth, David and Carenini, Giuseppe and Janjua, Zafar Naveed", title="Tracking COVID-19 Discourse on Twitter in North America: Infodemiology Study Using Topic Modeling and Aspect-Based Sentiment Analysis", journal="J Med Internet Res", year="2021", month="Feb", day="10", volume="23", number="2", pages="e25431", keywords="COVID-19", keywords="Twitter", keywords="topic modeling", keywords="aspect-based sentiment analysis", keywords="racism", keywords="anti-Asians", keywords="Canada", keywords="North America", keywords="sentiment analysis", keywords="social media", keywords="discourse", keywords="reaction", keywords="public health", abstract="Background: Social media is a rich source where we can learn about people's reactions to social issues. As COVID-19 has impacted people's lives, it is essential to capture how people react to public health interventions and understand their concerns. Objective: We aim to investigate people's reactions and concerns about COVID-19 in North America, especially in Canada. Methods: We analyzed COVID-19--related tweets using topic modeling and aspect-based sentiment analysis (ABSA), and interpreted the results with public health experts. To generate insights on the effectiveness of specific public health interventions for COVID-19, we compared timelines of topics discussed with the timing of implementation of interventions, synergistically including information on people's sentiment about COVID-19--related aspects in our analysis. In addition, to further investigate anti-Asian racism, we compared timelines of sentiments for Asians and Canadians. Results: Topic modeling identified 20 topics, and public health experts provided interpretations of the topics based on top-ranked words and representative tweets for each topic. The interpretation and timeline analysis showed that the discovered topics and their trend are highly related to public health promotions and interventions such as physical distancing, border restrictions, handwashing, staying home, and face coverings. After training the data using ABSA with human-in-the-loop, we obtained 545 aspect terms (eg, ``vaccines,'' ``economy,'' and ``masks'') and 60 opinion terms such as ``infectious'' (negative) and ``professional'' (positive), which were used for inference of sentiments of 20 key aspects selected by public health experts. The results showed negative sentiments related to the overall outbreak, misinformation and Asians, and positive sentiments related to physical distancing. Conclusions: Analyses using natural language processing techniques with domain expert involvement can produce useful information for public health. This study is the first to analyze COVID-19--related tweets in Canada in comparison with tweets in the United States by using topic modeling and human-in-the-loop domain-specific ABSA. This kind of information could help public health agencies to understand public concerns as well as what public health messages are resonating in our populations who use Twitter, which can be helpful for public health agencies when designing a policy for new interventions. ", doi="10.2196/25431", url="http://www.jmir.org/2021/2/e25431/", url="http://www.ncbi.nlm.nih.gov/pubmed/33497352" } @Article{info:doi/10.2196/24585, author="de Melo, Tiago and Figueiredo, S. Carlos M.", title="Comparing News Articles and Tweets About COVID-19 in Brazil: Sentiment Analysis and Topic Modeling Approach", journal="JMIR Public Health Surveill", year="2021", month="Feb", day="10", volume="7", number="2", pages="e24585", keywords="COVID-19", keywords="Twitter", keywords="infodemiology", keywords="news", keywords="sentiment analysis", keywords="social media", keywords="Brazil", keywords="monitoring", keywords="topic modeling", keywords="entity recognition", keywords="text analysis", abstract="Background: The COVID-19 pandemic is severely affecting people worldwide. Currently, an important approach to understand this phenomenon and its impact on the lives of people consists of monitoring social networks and news on the internet. Objective: The purpose of this study is to present a methodology to capture the main subjects and themes under discussion in news media and social media and to apply this methodology to analyze the impact of the COVID-19 pandemic in Brazil. Methods: This work proposes a methodology based on topic modeling, namely entity recognition, and sentiment analysis of texts to compare Twitter posts and news, followed by visualization of the evolution and impact of the COVID-19 pandemic. We focused our analysis on Brazil, an important epicenter of the pandemic; therefore, we faced the challenge of addressing Brazilian Portuguese texts. Results: In this work, we collected and analyzed 18,413 articles from news media and 1,597,934 tweets posted by 1,299,084 users in Brazil. The results show that the proposed methodology improved the topic sentiment analysis over time, enabling better monitoring of internet media. Additionally, with this tool, we extracted some interesting insights about the evolution of the COVID-19 pandemic in Brazil. For instance, we found that Twitter presented similar topic coverage to news media; the main entities were similar, but they differed in theme distribution and entity diversity. Moreover, some aspects represented negative sentiment toward political themes in both media, and a high incidence of mentions of a specific drug denoted high political polarization during the pandemic. Conclusions: This study identified the main themes under discussion in both news and social media and how their sentiments evolved over time. It is possible to understand the major concerns of the public during the pandemic, and all the obtained information is thus useful for decision-making by authorities. ", doi="10.2196/24585", url="http://publichealth.jmir.org/2021/2/e24585/", url="http://www.ncbi.nlm.nih.gov/pubmed/33480853" } @Article{info:doi/10.2196/22946, author="Kim, Stephanie and Mourali, Alia and Allem, Jon-Patrick and Unger, B. Jennifer and Boley Cruz, Tess and Smiley, L. Sabrina", title="Instagram Posts Related to Backwoods Cigarillo Blunts: Content Analysis", journal="JMIR Public Health Surveill", year="2021", month="Feb", day="9", volume="7", number="2", pages="e22946", keywords="Instagram", keywords="blunts", keywords="Backwoods cigarillos", keywords="smoking", abstract="Background: Instagram, one of the most popular social media platforms among youth, offers a unique opportunity to examine blunts---partially or fully hollowed-out large cigars, little cigars, and cigarillos that are filled with marijuana. Cigarillo brands like Backwoods (Imperial Tobacco Group Brands LLC) have product features that facilitate blunt making, including a variety of brand-specific flavors that enhance the smoking experience (eg, honey, dark stout). Backwoods has an active online presence with a user-friendly website. Objective: This study examined the extent to which Backwoods cigarillo--related posts on Instagram showed blunt making. Instagram offers a unique opportunity to examine blunt making as Instagram accounts will contain images reflective of behavior occurring without the prime of a researcher. Methods: Data consisted of publicly available Instagram posts with the hashtag \#backwoods collected from August 30 to September 12, 2018. Inclusion criteria for this study included an Instagram post with the hashtag ``\#backwoods''. Rules were established to content analyze posts. Categories included Type of post (ie, photo, video, or both); Blunt-related hashtags (ie, the corresponding post caption contained one or more hashtags like \#blunts, \#cannabis, and \#weed that were identified in previous social media research); Rolling blunts (ie, the post contained an image of one or more individuals rolling a Backwoods cigarillo visibly containing marijuana); and Smoking blunts (ie, the post contained an image of one or more individuals blowing smoke or holding a lit blunt). We coded images for Product flavor reference, where a code of 1 showed a Backwoods cigarillo pack with a brand-specific flavor (eg, honey, dark stout, Russian cr{\`e}me) visible in the blunt-related image, and a code of 0 indicated that it was not visible anywhere in the image. Results: Among all posts (N=1206), 871 (72.2\%) were coded as Blunt-related hashtags. A total of 125 (10.4\%) images were coded as Smoking blunts, and 25 (2.1\%) were coded as Rolling blunts (ie, Backwoods cigarillo explicitly used to roll blunts). Among blunt images, 434 of 836 (51.9\%) were coded as Product flavor (ie, a Backwoods pack with a brand-specific flavor was visible). Conclusions: Most Backwoods cigarillo--related Instagram images were blunt-related, and these blunt-related images showed Backwoods packages indicating flavor preference. Continued monitoring and surveillance of blunt-related posts on Instagram is needed to inform policies and interventions that reduce the risk that youth may experiment with blunts. Specific policies could include restrictions on product features (eg, flavors, perforated lines, attractive resealable foil pouches, sale as singles) that facilitate blunt making. ", doi="10.2196/22946", url="http://publichealth.jmir.org/2021/2/e22946/", url="http://www.ncbi.nlm.nih.gov/pubmed/33560242" } @Article{info:doi/10.2196/17149, author="Karafillakis, Emilie and Martin, Sam and Simas, Clarissa and Olsson, Kate and Takacs, Judit and Dada, Sara and Larson, Jane Heidi", title="Methods for Social Media Monitoring Related to Vaccination: Systematic Scoping Review", journal="JMIR Public Health Surveill", year="2021", month="Feb", day="8", volume="7", number="2", pages="e17149", keywords="vaccination", keywords="antivaccination movement", keywords="vaccination refusal", keywords="social media", keywords="internet", keywords="research design", keywords="review", keywords="media monitoring", keywords="social listening", keywords="infodemiology", keywords="infoveillance", abstract="Background: Social media has changed the communication landscape, exposing individuals to an ever-growing amount of information while also allowing them to create and share content. Although vaccine skepticism is not new, social media has amplified public concerns and facilitated their spread globally. Multiple studies have been conducted to monitor vaccination discussions on social media. However, there is currently insufficient evidence on the best methods to perform social media monitoring. Objective: The aim of this study was to identify the methods most commonly used for monitoring vaccination-related topics on different social media platforms, along with their effectiveness and limitations. Methods: A systematic scoping review was conducted by applying a comprehensive search strategy to multiple databases in December 2018. The articles' titles, abstracts, and full texts were screened by two reviewers using inclusion and exclusion criteria. After data extraction, a descriptive analysis was performed to summarize the methods used to monitor and analyze social media, including data extraction tools; ethical considerations; search strategies; periods monitored; geolocalization of content; and sentiments, content, and reach analyses. Results: This review identified 86 articles on social media monitoring of vaccination, most of which were published after 2015. Although 35 out of the 86 studies used manual browser search tools to collect data from social media, this was time-consuming and only allowed for the analysis of small samples compared to social media application program interfaces or automated monitoring tools. Although simple search strategies were considered less precise, only 10 out of the 86 studies used comprehensive lists of keywords (eg, with hashtags or words related to specific events or concerns). Partly due to privacy settings, geolocalization of data was extremely difficult to obtain, limiting the possibility of performing country-specific analyses. Finally, 20 out of the 86 studies performed trend or content analyses, whereas most of the studies (70\%, 60/86) analyzed sentiments toward vaccination. Automated sentiment analyses, performed using leverage, supervised machine learning, or automated software, were fast and provided strong and accurate results. Most studies focused on negative (n=33) and positive (n=31) sentiments toward vaccination, and may have failed to capture the nuances and complexity of emotions around vaccination. Finally, 49 out of the 86 studies determined the reach of social media posts by looking at numbers of followers and engagement (eg, retweets, shares, likes). Conclusions: Social media monitoring still constitutes a new means to research and understand public sentiments around vaccination. A wide range of methods are currently used by researchers. Future research should focus on evaluating these methods to offer more evidence and support the development of social media monitoring as a valuable research design. ", doi="10.2196/17149", url="http://publichealth.jmir.org/2021/2/e17149/", url="http://www.ncbi.nlm.nih.gov/pubmed/33555267" } @Article{info:doi/10.2196/26090, author="Zhang, Shuai and Pian, Wenjing and Ma, Feicheng and Ni, Zhenni and Liu, Yunmei", title="Characterizing the COVID-19 Infodemic on Chinese Social Media: Exploratory Study", journal="JMIR Public Health Surveill", year="2021", month="Feb", day="5", volume="7", number="2", pages="e26090", keywords="COVID-19", keywords="infodemic", keywords="infodemiology", keywords="epidemic", keywords="misinformation", keywords="spread characteristics", keywords="social media", keywords="China", keywords="exploratory", keywords="dissemination", abstract="Background: The COVID-19 infodemic has been disseminating rapidly on social media and posing a significant threat to people's health and governance systems. Objective: This study aimed to investigate and analyze posts related to COVID-19 misinformation on major Chinese social media platforms in order to characterize the COVID-19 infodemic. Methods: We collected posts related to COVID-19 misinformation published on major Chinese social media platforms from January 20 to May 28, 2020, by using PythonToolkit. We used content analysis to identify the quantity and source of prevalent posts and topic modeling to cluster themes related to the COVID-19 infodemic. Furthermore, we explored the quantity, sources, and theme characteristics of the COVID-19 infodemic over time. Results: The daily number of social media posts related to the COVID-19 infodemic was positively correlated with the daily number of newly confirmed (r=0.672, P<.01) and newly suspected (r=0.497, P<.01) COVID-19 cases. The COVID-19 infodemic showed a characteristic of gradual progress, which can be divided into 5 stages: incubation, outbreak, stalemate, control, and recovery. The sources of the COVID-19 infodemic can be divided into 5 types: chat platforms (1100/2745, 40.07\%), video-sharing platforms (642/2745, 23.39\%), news-sharing platforms (607/2745, 22.11\%), health care platforms (239/2745, 8.71\%), and Q\&A platforms (157/2745, 5.72\%), which slightly differed at each stage. The themes related to the COVID-19 infodemic were clustered into 8 categories: ``conspiracy theories'' (648/2745, 23.61\%), ``government response'' (544/2745, 19.82\%), ``prevention action'' (411/2745, 14.97\%), ``new cases'' (365/2745, 13.30\%), ``transmission routes'' (244/2745, 8.89\%), ``origin and nomenclature'' (228/2745, 8.30\%), ``vaccines and medicines'' (154/2745, 5.61\%), and ``symptoms and detection'' (151/2745, 5.50\%), which were prominently diverse at different stages. Additionally, the COVID-19 infodemic showed the characteristic of repeated fluctuations. Conclusions: Our study found that the COVID-19 infodemic on Chinese social media was characterized by gradual progress, videoization, and repeated fluctuations. Furthermore, our findings suggest that the COVID-19 infodemic is paralleled to the propagation of the COVID-19 epidemic. We have tracked the COVID-19 infodemic across Chinese social media, providing critical new insights into the characteristics of the infodemic and pointing out opportunities for preventing and controlling the COVID-19 infodemic. ", doi="10.2196/26090", url="http://publichealth.jmir.org/2021/2/e26090/", url="http://www.ncbi.nlm.nih.gov/pubmed/33460391" } @Article{info:doi/10.2196/26254, author="Bacsu, Juanita-Dawne and O'Connell, E. Megan and Cammer, Allison and Azizi, Mahsa and Grewal, Karl and Poole, Lisa and Green, Shoshana and Sivananthan, Saskia and Spiteri, J. Raymond", title="Using Twitter to Understand the COVID-19 Experiences of People With Dementia: Infodemiology Study", journal="J Med Internet Res", year="2021", month="Feb", day="3", volume="23", number="2", pages="e26254", keywords="Twitter", keywords="social media", keywords="dementia", keywords="COVID-19", keywords="health policy", keywords="experience", keywords="support", keywords="disorder", keywords="theme", keywords="collaborate", keywords="quality of life", abstract="Background: The COVID-19 pandemic is affecting people with dementia in numerous ways. Nevertheless, there is a paucity of research on the COVID-19 impact on people with dementia and their care partners. Objective: Using Twitter, the purpose of this study is to understand the experiences of COVID-19 for people with dementia and their care partners. Methods: We collected tweets on COVID-19 and dementia using the GetOldTweets application in Python from February 15 to September 7, 2020. Thematic analysis was used to analyze the tweets. Results: From the 5063 tweets analyzed with line-by-line coding, we identified 4 main themes including (1) separation and loss; (2) COVID-19 confusion, despair, and abandonment; (3) stress and exhaustion exacerbation; and (4) unpaid sacrifices by formal care providers. Conclusions: There is an imminent need for governments to rethink using a one-size-fits-all response to COVID-19 policy and use a collaborative approach to support people with dementia. Collaboration and more evidence-informed research are essential to reducing COVID-19 mortality and improving the quality of life for people with dementia and their care partners. ", doi="10.2196/26254", url="https://www.jmir.org/2021/2/e26254", url="http://www.ncbi.nlm.nih.gov/pubmed/33468449" } @Article{info:doi/10.2196/21156, author="Geronikolou, Styliani and Chrousos, George", title="COVID-19--Induced Fear in Infoveillance Studies: Pilot Meta-analysis Study of Preliminary Results", journal="JMIR Form Res", year="2021", month="Feb", day="3", volume="5", number="2", pages="e21156", keywords="COVID-19", keywords="social media", keywords="misinformation", keywords="infodemics", keywords="infodemiology", keywords="infoveillance", keywords="fear", keywords="meta-analysis", abstract="Background: The World Health Organization named the phenomenon of misinformation spread through social media as an ``infodemic'' and recognized the need to curb it. Misinformation infodemics undermine not only population safety but also compliance to the suggestions and prophylactic measures recommended during pandemics. Objective: The aim of this pilot study is to review the impact of social media on general population fear in ``infoveillance'' studies during the COVID-19 pandemic. Methods: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol was followed, and 6 out of 20 studies were retrieved, meta-analyzed, and had their findings presented in the form of a forest plot. Results: The summary random and significant event rate was 0.298 (95\% CI 0.213-0.400), suggesting that social media--circulated misinformation related to COVID-19 triggered public fear and other psychological manifestations. These findings merit special attention by public health authorities. Conclusions: Infodemiology and infoveillance are valid tools in the hands of epidemiologists to help prevent dissemination of false information, which has potentially damaging effects. ", doi="10.2196/21156", url="https://formative.jmir.org/2021/2/e21156", url="http://www.ncbi.nlm.nih.gov/pubmed/33400681" } @Article{info:doi/10.2196/19858, author="Bapaye, Amol Jay and Bapaye, Amol Harsh", title="Demographic Factors Influencing the Impact of Coronavirus-Related Misinformation on WhatsApp: Cross-sectional Questionnaire Study", journal="JMIR Public Health Surveill", year="2021", month="Jan", day="30", volume="7", number="1", pages="e19858", keywords="coronavirus", keywords="COVID-19", keywords="SARS--CoV--2", keywords="WhatsApp", keywords="social media", keywords="misinformation", keywords="infodemiology", keywords="infodemic", keywords="pandemic", keywords="medical informatics", abstract="Background: The risks of misinformation on social networking sites is a global issue, especially in light of the COVID-19 infodemic. WhatsApp is being used as an important source of COVID-19--related information during the current pandemic. Unlike Facebook and Twitter, limited studies have investigated the role of WhatsApp as a source of communication, information, or misinformation during crisis situations. Objective: Our study aimed to evaluate the vulnerability of demographic cohorts in a developing country toward COVID-19--related misinformation shared via WhatsApp. We also aimed to identify characteristics of WhatsApp messages associated with increased credibility of misinformation. Methods: We conducted a web-based questionnaire survey and designed a scoring system based on theories supported by the existing literature. Vulnerability (K) was measured as a ratio of the respondent's score to the maximum score. Respondents were stratified according to age and occupation, and Kmean was calculated and compared among each subgroup using single-factor analysis of variance and Hochberg GT2 tests. The questionnaire evaluated the respondents' opinion of the veracity of coronavirus-related WhatsApp messages. The responses to the false-proven messages were compared using z test between the 2 groups: coronavirus-related WhatsApp messages with an attached link and/or source and those without. Results: We analyzed 1137 responses from WhatsApp users in India. Users aged over 65 years had the highest vulnerability (Kmean=0.38, 95\% CI 0.341-0.419) to misinformation. Respondents in the age group 19-25 years had significantly lower vulnerability (Kmean=0.31, 95\% CI 0.301-0.319) than those aged over 25 years (P<.05). The vulnerability of users employed in elementary occupations was the highest (Kmean=0.38, 95\% CI 0.356-0.404), and it was significantly higher than that of professionals and students (P<.05). Interestingly, the vulnerability of healthcare workers was not significantly different from that of other occupation groups (P>.05). We found that false CRWMs with an attached link and/or source were marked true 6 times more often than false CRWMs without an attached link or source (P<.001). Conclusions: Our study demonstrates that in a developing country, WhatsApp users aged over 65 years and those involved in elementary occupations were found to be the most vulnerable to false information disseminated via WhatsApp. Health care workers, who are otherwise considered as experts with regard to this global health care crisis, also shared this vulnerability to misinformation with other occupation groups. Our findings also indicated that the presence of an attached link and/or source falsely validating an incorrect message adds significant false credibility, making it appear true. These results indicate an emergent need to address and rectify the current usage patterns of WhatsApp users. This study also provides metrics that can be used by health care organizations and government authorities of developing countries to formulate guidelines to contain the spread of WhatsApp-related misinformation. ", doi="10.2196/19858", url="http://publichealth.jmir.org/2021/1/e19858/", url="http://www.ncbi.nlm.nih.gov/pubmed/33444152" } @Article{info:doi/10.2196/26011, author="Nutley, K. Sara and Falise, M. Alyssa and Henderson, Rebecca and Apostolou, Vasiliki and Mathews, A. Carol and Striley, W. Catherine", title="Impact of the COVID-19 Pandemic on Disordered Eating Behavior: Qualitative Analysis of Social Media Posts", journal="JMIR Ment Health", year="2021", month="Jan", day="27", volume="8", number="1", pages="e26011", keywords="eating disorders", keywords="anorexia nervosa", keywords="binge eating disorder", keywords="COVID-19", keywords="coronavirus", keywords="Reddit", keywords="social media", keywords="disorder", keywords="eating", keywords="qualitative", keywords="experience", keywords="mental health", keywords="theme", abstract="Background: A growing body of evidence is suggesting a significant association between the COVID-19 pandemic and population-level mental health. Study findings suggest that individuals with a lifetime history of disordered eating behavior may be negatively affected by COVID-19--related anxiety, and prevention measures may disrupt daily functioning and limit access to treatment. However, data describing the influence of the COVID-19 pandemic on disordered eating behaviors are limited, and most findings focus on individuals in treatment settings. Objective: The aim of this study is to characterize the experiences of Reddit users worldwide who post in eating disorder (ED)--related discussion forums describing the influence of the COVID-19 pandemic on their overall mental health and disordered eating behavior. Methods: Data were collected from popular subreddits acknowledging EDs as their primary discussion topic. Unique discussion posts dated from January 1 to May 31, 2020 that referenced the COVID-19 pandemic were extracted and evaluated using inductive, thematic data analysis. Results: Six primary themes were identified: change in ED symptoms, change in exercise routine, impact of quarantine on daily life, emotional well-being, help-seeking behavior, and associated risks and health outcomes. The majority of users reported that the COVID-19 pandemic and associated public health prevention measures negatively impacted their psychiatric health and contributed to increased disordered eating behaviors. Feelings of isolation, frustration, and anxiety were common. Many individuals used Reddit forums to share personal experiences, seek advice, and offer shared accountability. Conclusions: Reddit discussion forums have provided a therapeutic community for individuals to share experiences and provide support for peers with ED during a period of increased psychiatric distress. Future research is needed to assess the impact of the COVID-19 pandemic on disordered eating behavior and to evaluate the role of social media discussion forums in mental health treatment, especially during periods of limited treatment access. ", doi="10.2196/26011", url="http://mental.jmir.org/2021/1/e26011/", url="http://www.ncbi.nlm.nih.gov/pubmed/33465035" } @Article{info:doi/10.2196/23178, author="Shimkhada, Riti and Attai, Deanna and Scheitler, AJ and Babey, Susan and Glenn, Beth and Ponce, Ninez", title="Using a Twitter Chat to Rapidly Identify Barriers and Policy Solutions for Metastatic Breast Cancer Care: Qualitative Study", journal="JMIR Public Health Surveill", year="2021", month="Jan", day="15", volume="7", number="1", pages="e23178", keywords="metastatic breast cancer", keywords="Twitter", keywords="infodemiology", keywords="infoveillance", keywords="health care barriers", keywords="health care policy", keywords="social media", keywords="policy", keywords="breast cancer", abstract="Background: Real-time, rapid assessment of barriers to care experienced by patients can be used to inform relevant health care legislation. In recent years, online communities have become a source of support for patients as well as a vehicle for discussion and collaboration among patients, clinicians, advocates, and researchers. The Breast Cancer Social Media (\#BCSM) community has hosted weekly Twitter chats since 2011. Topics vary each week, and chats draw a diverse group of participants. Partnering with the \#BCSM community, we used Twitter to gather data on barriers to care for patients with metastatic breast cancer and potential policy solutions. Metastatic breast cancer survival rates are low and in large part conditioned by time-sensitive access to care factors that might be improved through policy changes. Objective: This study was part of an assessment of the barriers to care for metastatic breast cancer with the goal of offering policy solutions for the legislative session in California. Methods: We provided 5 questions for a chat specific to metastatic breast cancer care barriers and potential policy solutions. These were discussed during the course of a \#BCSM chat on November 18, 2019. We used Symplur (Symplur LLC) analytics to generate a transcript of tweets and a profile of participants. Responses to the questions are presented in this paper. Results: There were 288 tweets from 42 users, generating 2.1 million impressions during the 1-hour chat. Participants included 23 patient advocates (most of whom were patients themselves), 7 doctors, 6 researchers or academics, 3 health care providers (2 nurses, 1 clinical psychologist), and 2 advocacy organizations. Participants noted communication gaps between patient and provider especially as related to the need for individualized medication dosing to minimize side effects and maximize quality of life. Timeliness of insurance company response, for example, to authorize treatments, was also a concern. Chat participants noted that palliative care is not well integrated into metastatic breast cancer care and that insurance company denials of coverage for these services were common. Regarding financial challenges, chat participants mentioned unexpected copays, changes in insurance drug formularies that made it difficult to anticipate drug costs, and limits on the number of physical therapy visits covered by insurance. Last, on the topic of disability benefits, participants expressed frustration about how to access disability benefits. When prompted for input regarding what health system and policy changes are necessary, participants suggested a number of ideas, including expanding the availability of nurse navigation for metastatic breast cancer, developing and offering a guide for the range of treatment and support resources patients with metastatic breast cancer, and improving access to clinical trials. Conclusions: Rapid assessments drawing from online community insights may be a critical source of data that can be used to ensure more responsive policy action to improve patient care. ", doi="10.2196/23178", url="http://publichealth.jmir.org/2021/1/e23178/", url="http://www.ncbi.nlm.nih.gov/pubmed/33315017" } @Article{info:doi/10.2196/24681, author="Golder, Su and Bach, Millie and O'Connor, Karen and Gross, Robert and Hennessy, Sean and Gonzalez Hernandez, Graciela", title="Public Perspectives on Anti-Diabetic Drugs: Exploratory Analysis of Twitter Posts", journal="JMIR Diabetes", year="2021", month="Jan", day="26", volume="6", number="1", pages="e24681", keywords="diabetes", keywords="insulin", keywords="Twitter", keywords="social media", keywords="infodemiology", keywords="infoveillance", keywords="social listening", keywords="cost", keywords="rationing", abstract="Background: Diabetes mellitus is a major global public health issue where self-management is critical to reducing disease burden. Social media has been a powerful tool to understand public perceptions. Public perception of the drugs used for the treatment of diabetes may be useful for orienting interventions to increase adherence. Objective: The aim of this study was to explore the public perceptions of anti-diabetic drugs through the analysis of health-related tweets mentioning such medications. Methods: This study uses an infoveillance social listening approach to monitor public discourse using Twitter data. We coded 4000 tweets from January 1, 2019 to October 1, 2019 containing key terms related to anti-diabetic drugs by using qualitative content analysis. Tweets were coded for whether they were truly about an anti-diabetic drug and whether they were health-related. Health-related tweets were further coded based on who was tweeting, which anti-diabetic drug was being tweeted about, and the content discussed in the tweet. The main outcome of the analysis was the themes identified by analyzing the content of health-related tweets on anti-diabetic drugs. Results: We identified 1664 health-related tweets on 33 anti-diabetic drugs. A quarter (415/1664) of the tweets were confirmed to have been from people with diabetes, 17.9\% (298/1664) from people posting about someone else, and 2.7\% (45/1664) from health care professionals. However, the role of the tweeter was unidentifiable in two-thirds of the tweets. We identified 13 themes, with the health consequences of the cost of anti-diabetic drugs being the most extensively discussed, followed by the efficacy and availability. We also identified issues that patients may conceal from health care professionals, such as purchasing medications from unofficial sources. Conclusions: This study uses an infoveillance approach using Twitter data to explore public perceptions related to anti-diabetic drugs. This analysis gives an insight into the real-life issues that an individual faces when taking anti-diabetic drugs, and such findings may be incorporated into health policies to improve compliance and efficacy. This study suggests that there is a fear of not having access to anti-diabetic drugs due to cost or physical availability and highlights the impact of the sacrifices made to access anti-diabetic drugs. Along with screening for diabetes-related health issues, health care professionals should also ask their patients about any non--health-related concerns regarding their anti-diabetic drugs. The positive tweets about dietary changes indicate that people with type 2 diabetes may be more open to self-management than what the health care professionals believe. ", doi="10.2196/24681", url="http://diabetes.jmir.org/2021/1/e24681/", url="http://www.ncbi.nlm.nih.gov/pubmed/33496671" } @Article{info:doi/10.2196/24124, author="Ottwell, Ryan and Hartwell, Micah and Beswick, Tracy and Rogers, Calli Taylor and Ivy, Heather and Goodman, Marcus and Vassar, Matt", title="Public Interest in a Potentially Harmful, Non--Evidence-Based ``Wellness'' Practice: Cross-Sectional Analysis of Perineum Sunning ", journal="JMIR Dermatol", year="2021", month="Jan", day="26", volume="4", number="1", pages="e24124", keywords="general dermatology", keywords="perineum sunning", keywords="perineum tanning", keywords="skin neoplasm", keywords="public health", keywords="social media", keywords="infodemiology", keywords="public interest", keywords="Google Trends", keywords="Twitter", abstract="Background: Perineum sunning/tanning is a potentially harmful yet popular new health trend cultivated by a viral social media post, famous public figures, and subsequent media coverage. Objective: Our primary objective is to evaluate public interest in perineum sunning/tanning. Methods: Using an observational study design, we extracted data from Google Trends for the terms ``perineum sunning,'' ``perineum tanning,'' ``Metaphysical Meagan,'' and ``Josh Brolin''; and Twitter (via SproutSocial) for ``perineum sunning'' and ``perineum tanning'' from November 1, 2019, to December 31, 2019. UberSuggest was used to investigate monthly search volumes and user engagement. We used data from Google Trends and Twitter to construct autoregressive integrated moving average (ARIMA) models to forecast public interest in perineum sunning and perineum tanning had the post on social media never occurred. Next, we performed an integral function to calculate the cumulative increase in ``perineum tanning'' from the day after the post occurred to the end of the year as the area between the forecasted values and the actual values. Using Welch t tests, we compared forecasted and actual values for ``perineum sunning'' and ``perineum tanning'' using Twitter and Google Trends data over 1-, 2-, and 4-week periods after the social media post to determine if the increased volumes were statistically significant over time. Lastly, we monitored Google Trends for ``perineum sunning'' and ``perineum tanning'' through September 30, 2020, to capture trends during the summer months. Results: Before the Instagram post went viral, there was no search interest in perineum sunning. ARIMA modeling for perineum tanning forecasted no increase in searches (0.00) if the post had not gone viral, while actual interest conveyed a relative cumulative increase of 919.00\% from the day the post went viral through December 31, 2020. The term ``perineum sunning'' was mentioned on average 804 (SD 766.1) times daily for this 7-day period, which was also significantly higher than predicted (P?.03), totaling 5628 tweets for these 7 days. The increased volume of tweets and relative search interest from Google Trends remained significantly higher for both terms over the 1-, 2-, and 4-week intervals. User engagement showed that nearly 50\% of people who searched for ``perineum sunning'' were likely to click a returned link for more information. Continued observance of search interest in perineum sunning demonstrated interest spikes in the summer months, June and July 2020. Conclusions: Google Trends and Twitter data demonstrated that one social media post claiming non--evidence-based health benefits of regular sun exposure---without the use of sunscreen---generated significant public interest. Medical journals, dermatologists, and other health care professionals are obligated to educate and correct public misperceptions about viral wellness trends such as perineum sunning. ", doi="10.2196/24124", url="http://derma.jmir.org/2021/1/e24124/", url="http://www.ncbi.nlm.nih.gov/pubmed/37632796" } @Article{info:doi/10.2196/22273, author="Erinoso, Olufemi and Wright, Ololade Kikelomo and Anya, Samuel and Kuyinu, Yetunde and Abdur-Razzaq, Hussein and Adewuya, Abiodun", title="Predictors of COVID-19 Information Sources and Their Perceived Accuracy in Nigeria: Online Cross-sectional Study", journal="JMIR Public Health Surveill", year="2021", month="Jan", day="25", volume="7", number="1", pages="e22273", keywords="COVID-19", keywords="communication", keywords="health information", keywords="public health", keywords="infodemiology", keywords="infodemic", keywords="accuracy", keywords="cross-sectional", keywords="risk", keywords="information source", keywords="predictor", keywords="Nigeria", abstract="Background: Effective communication is critical for mitigating the public health risks associated with the COVID-19 pandemic. Objective: This study assesses the source(s) of COVID-19 information among people in Nigeria, as well as the predictors and the perceived accuracy of information from these sources. Methods: We conducted an online survey of consenting adults residing in Nigeria between April and May 2020 during the lockdown and first wave of COVID-19. The major sources of information about COVID-19 were distilled from 7 potential sources (family and friends, places of worship, health care providers, internet, workplace, traditional media, and public posters/banners). An open-ended question was asked to explore how respondents determined accuracy of information. Statistical analysis was conducted using STATA 15.0 software (StataCorp Texas) with significance placed at P<.05. Approval to conduct this study was obtained from the Lagos State University Teaching Hospital Health Research Ethics Committee. Results: A total of 719 respondents completed the survey. Most respondents (n=642, 89.3\%) obtained COVID-19--related information from the internet. The majority (n=617, 85.8\%) considered their source(s) of information to be accurate, and 32.6\% (n=234) depended on only 1 out of the 7 potential sources of COVID-19 information. Respondents earning a monthly income between NGN 70,000-120,000 had lower odds of obtaining COVID-19 information from the internet compared to respondents earning less than NGN 20,000 (odds ratio [OR] 0.49, 95\% CI 0.24-0.98). In addition, a significant proportion of respondents sought accurate information from recognized health organizations, such as the Nigeria Centre for Disease Control and the World Health Organization. Conclusions: The internet was the most common source of COVID-19 information, and the population sampled had a relatively high level of perceived accuracy for the COVID-19 information received. Effective communication requires dissemination of information via credible communication channels, as identified from this study. This can be potentially beneficial for risk communication to control the pandemic. ", doi="10.2196/22273", url="http://publichealth.jmir.org/2021/1/e22273/", url="http://www.ncbi.nlm.nih.gov/pubmed/33428580" } @Article{info:doi/10.2196/25314, author="Klein, Z. Ari and Magge, Arjun and O'Connor, Karen and Flores Amaro, Ivan Jesus and Weissenbacher, Davy and Gonzalez Hernandez, Graciela", title="Toward Using Twitter for Tracking COVID-19: A Natural Language Processing Pipeline and Exploratory Data Set", journal="J Med Internet Res", year="2021", month="Jan", day="22", volume="23", number="1", pages="e25314", keywords="natural language processing", keywords="social media", keywords="data mining", keywords="COVID-19", keywords="coronavirus", keywords="pandemics", keywords="epidemiology", keywords="infodemiology", abstract="Background: In the United States, the rapidly evolving COVID-19 outbreak, the shortage of available testing, and the delay of test results present challenges for actively monitoring its spread based on testing alone. Objective: The objective of this study was to develop, evaluate, and deploy an automatic natural language processing pipeline to collect user-generated Twitter data as a complementary resource for identifying potential cases of COVID-19 in the United States that are not based on testing and, thus, may not have been reported to the Centers for Disease Control and Prevention. Methods: Beginning January 23, 2020, we collected English tweets from the Twitter Streaming application programming interface that mention keywords related to COVID-19. We applied handwritten regular expressions to identify tweets indicating that the user potentially has been exposed to COVID-19. We automatically filtered out ``reported speech'' (eg, quotations, news headlines) from the tweets that matched the regular expressions, and two annotators annotated a random sample of 8976 tweets that are geo-tagged or have profile location metadata, distinguishing tweets that self-report potential cases of COVID-19 from those that do not. We used the annotated tweets to train and evaluate deep neural network classifiers based on bidirectional encoder representations from transformers (BERT). Finally, we deployed the automatic pipeline on more than 85 million unlabeled tweets that were continuously collected between March 1 and August 21, 2020. Results: Interannotator agreement, based on dual annotations for 3644 (41\%) of the 8976 tweets, was 0.77 (Cohen $\kappa$). A deep neural network classifier, based on a BERT model that was pretrained on tweets related to COVID-19, achieved an F1-score of 0.76 (precision=0.76, recall=0.76) for detecting tweets that self-report potential cases of COVID-19. Upon deploying our automatic pipeline, we identified 13,714 tweets that self-report potential cases of COVID-19 and have US state--level geolocations. Conclusions: We have made the 13,714 tweets identified in this study, along with each tweet's time stamp and US state--level geolocation, publicly available to download. This data set presents the opportunity for future work to assess the utility of Twitter data as a complementary resource for tracking the spread of COVID-19. ", doi="10.2196/25314", url="http://www.jmir.org/2021/1/e25314/", url="http://www.ncbi.nlm.nih.gov/pubmed/33449904" } @Article{info:doi/10.2196/17187, author="Suarez-Lledo, Victor and Alvarez-Galvez, Javier", title="Prevalence of Health Misinformation on Social Media: Systematic Review", journal="J Med Internet Res", year="2021", month="Jan", day="20", volume="23", number="1", pages="e17187", keywords="social media", keywords="health misinformation", keywords="infodemiology", keywords="infodemics", keywords="social networks", keywords="poor quality information", keywords="social contagion", abstract="Background: Although at present there is broad agreement among researchers, health professionals, and policy makers on the need to control and combat health misinformation, the magnitude of this problem is still unknown. Consequently, it is fundamental to discover both the most prevalent health topics and the social media platforms from which these topics are initially framed and subsequently disseminated. Objective: This systematic review aimed to identify the main health misinformation topics and their prevalence on different social media platforms, focusing on methodological quality and the diverse solutions that are being implemented to address this public health concern. Methods: We searched PubMed, MEDLINE, Scopus, and Web of Science for articles published in English before March 2019, with a focus on the study of health misinformation in social media. We defined health misinformation as a health-related claim that is based on anecdotal evidence, false, or misleading owing to the lack of existing scientific knowledge. We included (1) articles that focused on health misinformation in social media, including those in which the authors discussed the consequences or purposes of health misinformation and (2) studies that described empirical findings regarding the measurement of health misinformation on these platforms. Results: A total of 69 studies were identified as eligible, and they covered a wide range of health topics and social media platforms. The topics were articulated around the following six principal categories: vaccines (32\%), drugs or smoking (22\%), noncommunicable diseases (19\%), pandemics (10\%), eating disorders (9\%), and medical treatments (7\%). Studies were mainly based on the following five methodological approaches: social network analysis (28\%), evaluating content (26\%), evaluating quality (24\%), content/text analysis (16\%), and sentiment analysis (6\%). Health misinformation was most prevalent in studies related to smoking products and drugs such as opioids and marijuana. Posts with misinformation reached 87\% in some studies. Health misinformation about vaccines was also very common (43\%), with the human papilloma virus vaccine being the most affected. Health misinformation related to diets or pro--eating disorder arguments were moderate in comparison to the aforementioned topics (36\%). Studies focused on diseases (ie, noncommunicable diseases and pandemics) also reported moderate misinformation rates (40\%), especially in the case of cancer. Finally, the lowest levels of health misinformation were related to medical treatments (30\%). Conclusions: The prevalence of health misinformation was the highest on Twitter and on issues related to smoking products and drugs. However, misinformation on major public health issues, such as vaccines and diseases, was also high. Our study offers a comprehensive characterization of the dominant health misinformation topics and a comprehensive description of their prevalence on different social media platforms, which can guide future studies and help in the development of evidence-based digital policy action plans. ", doi="10.2196/17187", url="http://www.jmir.org/2021/1/e17187/", url="http://www.ncbi.nlm.nih.gov/pubmed/33470931" } @Article{info:doi/10.2196/24097, author="Dadaczynski, Kevin and Okan, Orkan and Messer, Melanie and Leung, M. Angela Y. and Ros{\'a}rio, Rafaela and Darlington, Emily and Rathmann, Katharina", title="Digital Health Literacy and Web-Based Information-Seeking Behaviors of University Students in Germany During the COVID-19 Pandemic: Cross-sectional Survey Study", journal="J Med Internet Res", year="2021", month="Jan", day="15", volume="23", number="1", pages="e24097", keywords="digital health", keywords="literacy", keywords="infodemic", keywords="health information", keywords="behaviour", keywords="coronavirus", keywords="COVID-19", keywords="university student", keywords="student", keywords="infodemiology", abstract="Background: Digital communication technologies are playing an important role in the health communication strategies of governments and public health authorities during the COVID-19 pandemic. The internet and social media have become important sources of health-related information on COVID-19 and on protective behaviors. In addition, the COVID-19 infodemic is spreading faster than the coronavirus itself, which interferes with governmental health-related communication efforts. This jeopardizes national public health containment strategies. Therefore, digital health literacy is a key competence to navigate web-based COVID-19--related information and service environments. Objective: This study aimed to investigate university students' digital health literacy and web-based information-seeking behaviors during the early stages of the COVID-19 pandemic in Germany. Methods: A cross-sectional study among 14,916 university students aged ?18 years from 130 universities across all 16 federal states of Germany was conducted using a web-based survey. Along with sociodemographic characteristics (sex, age, subjective social status), the measures included five subscales from the Digital Health Literacy Instrument (DHLI), which was adapted to the specific context of the COVID-19 pandemic. Web-based information-seeking behavior was investigated by examining the web-based sources used by university students and the topics that the students searched for in connection with COVID-19. Data were analyzed using univariate and bivariate analyses. Results: Across digital health literacy dimensions, the greatest difficulties could be found for assessing the reliability of health-related information (5964/14,103, 42.3\%) and the ability to determine whether the information was written with a commercial interest (5489/14,097, 38.9\%). Moreover, the respondents indicated that they most frequently have problems finding the information they are looking for (4282/14,098, 30.4\%). When stratified according to sociodemographic characteristics, significant differences were found, with female university students reporting a lower DHLI for the dimensions of ``information searching'' and ``evaluating reliability.'' Search engines, news portals, and websites of public bodies were most often used by the respondents as sources to search for information on COVID-19 and related issues. Female students were found to use social media and health portals more frequently, while male students used Wikipedia and other web-based encyclopedias as well as YouTube more often. The use of social media was associated with a low ability to critically evaluate information, while the opposite was observed for the use of public websites. Conclusions: Although digital health literacy is well developed in university students, a significant proportion of students still face difficulties with certain abilities to evaluate information. There is a need to strengthen the digital health literacy capacities of university students using tailored interventions. Improving the quality of health-related information on the internet is also key. ", doi="10.2196/24097", url="http://www.jmir.org/2021/1/e24097/", url="http://www.ncbi.nlm.nih.gov/pubmed/33395396" } @Article{info:doi/10.2196/24220, author="Itamura, Kyohei and Wu, Arthur and Illing, Elisa and Ting, Jonathan and Higgins, Thomas", title="YouTube Videos Demonstrating the Nasopharyngeal Swab Technique for SARS-CoV-2 Specimen Collection: Content Analysis", journal="JMIR Public Health Surveill", year="2021", month="Jan", day="14", volume="7", number="1", pages="e24220", keywords="COVID-19", keywords="coronavirus", keywords="SARS-coV-2", keywords="nasopharyngeal swab", keywords="viral testing", keywords="PCR", keywords="YouTube", keywords="infodemiology", keywords="digital epidemiology", keywords="testing", keywords="diagnostic", keywords="content analysis", keywords="video", keywords="error", abstract="Background: Real-time polymerase chain reaction using nasopharyngeal swabs is currently the most widely used diagnostic test for SARS-CoV-2 detection. However, false negatives and the sensitivity of this mode of testing have posed challenges in the accurate estimation of the prevalence of SARS-CoV-2 infection rates. Objective: The purpose of this study was to evaluate whether technical and, therefore, correctable errors were being made with regard to nasopharyngeal swab procedures. Methods: We searched a web-based video database (YouTube) for videos demonstrating SARS-CoV-2 nasopharyngeal swab tests, posted from January 1 to May 15, 2020. Videos were rated by 3 blinded rhinologists for accuracy of swab angle and depth. The overall score for swab angle and swab depth for each nasopharyngeal swab demonstration video was determined based on the majority score with agreement between at least 2 of the 3 reviewers. We then comparatively evaluated video data collected from YouTube videos demonstrating the correct nasopharyngeal swab technique with data from videos demonstrating an incorrect nasopharyngeal swab technique. Multiple linear regression analysis with statistical significance set at P=.05 was performed to determine video data variables associated with the correct nasopharyngeal swab technique. Results: In all, 126 videos met the study inclusion and exclusion criteria. Of these, 52.3\% (66/126) of all videos demonstrated the correct swab angle, and 46\% (58/126) of the videos demonstrated an appropriate swab depth. Moreover, 45.2\% (57/126) of the videos demonstrated both correct nasopharyngeal swab angle and appropriate depth, whereas 46.8\% (59/126) of the videos demonstrated both incorrect nasopharyngeal swab angle and inappropriate depth. Videos with correct nasopharyngeal swab technique were associated with the swab operators identifying themselves as a medical professional or as an Ear, Nose, Throat--related medical professional. We also found an association between correct nasopharyngeal swab techniques and recency of video publication date (relative to May 15, 2020). Conclusions: Our findings show that over half of the videos documenting the nasopharyngeal swab test showed an incorrect technique, which could elevate false-negative test rates. Therefore, greater attention needs to be provided toward educating frontline health care workers who routinely perform nasopharyngeal swab procedures. ", doi="10.2196/24220", url="http://publichealth.jmir.org/2021/1/e24220/", url="http://www.ncbi.nlm.nih.gov/pubmed/33406478" } @Article{info:doi/10.2196/24069, author="van Stekelenburg, Aart and Schaap, Gabi and Veling, Harm and Buijzen, Moniek", title="Investigating and Improving the Accuracy of US Citizens' Beliefs About the COVID-19 Pandemic: Longitudinal Survey Study", journal="J Med Internet Res", year="2021", month="Jan", day="12", volume="23", number="1", pages="e24069", keywords="infodemic", keywords="infodemiology", keywords="misinformation", keywords="COVID-19 pandemic", keywords="belief accuracy", keywords="boosting", keywords="trust in scientists", keywords="political orientation", keywords="media use", abstract="Background: The COVID-19 infodemic, a surge of information and misinformation, has sparked worry about the public's perception of the coronavirus pandemic. Excessive information and misinformation can lead to belief in false information as well as reduce the accurate interpretation of true information. Such incorrect beliefs about the COVID-19 pandemic might lead to behavior that puts people at risk of both contracting and spreading the virus. Objective: The objective of this study was two-fold. First, we attempted to gain insight into public beliefs about the novel coronavirus and COVID-19 in one of the worst hit countries: the United States. Second, we aimed to test whether a short intervention could improve people's belief accuracy by empowering them to consider scientific consensus when evaluating claims related to the pandemic. Methods: We conducted a 4-week longitudinal study among US citizens, starting on April 27, 2020, just after daily COVID-19 deaths in the United States had peaked. Each week, we measured participants' belief accuracy related to the coronavirus and COVID-19 by asking them to indicate to what extent they believed a number of true and false statements (split 50/50). Furthermore, each new survey wave included both the original statements and four new statements: two false and two true statements. Half of the participants were exposed to an intervention aimed at increasing belief accuracy. The intervention consisted of a short infographic that set out three steps to verify information by searching for and verifying a scientific consensus. Results: A total of 1202 US citizens, balanced regarding age, gender, and ethnicity to approximate the US general public, completed the baseline (T0) wave survey. Retention rate for the follow-up waves--- first follow-up wave (T1), second follow-up wave (T2), and final wave (T3)---was high (?85\%). Mean scores of belief accuracy were high for all waves, with scores reflecting low belief in false statements and high belief in true statements; the belief accuracy scale ranged from --1, indicating completely inaccurate beliefs, to 1, indicating completely accurate beliefs (T0 mean 0.75, T1 mean 0.78, T2 mean 0.77, and T3 mean 0.75). Accurate beliefs were correlated with self-reported behavior aimed at preventing the coronavirus from spreading (eg, social distancing) (r at all waves was between 0.26 and 0.29 and all P values were less than .001) and were associated with trust in scientists (ie, higher trust was associated with more accurate beliefs), political orientation (ie, liberal, Democratic participants held more accurate beliefs than conservative, Republican participants), and the primary news source (ie, participants reporting CNN or Fox News as the main news source held less accurate beliefs than others). The intervention did not significantly improve belief accuracy. Conclusions: The supposed infodemic was not reflected in US citizens' beliefs about the COVID-19 pandemic. Most people were quite able to figure out the facts in these relatively early days of the crisis, calling into question the prevalence of misinformation and the public's susceptibility to misinformation. ", doi="10.2196/24069", url="http://www.jmir.org/2021/1/e24069/", url="http://www.ncbi.nlm.nih.gov/pubmed/33351776" } @Article{info:doi/10.2196/23000, author="Maytin, Lauren and Maytin, Jason and Agarwal, Priya and Krenitsky, Anna and Krenitsky, JoAnn and Epstein, S. Robert", title="Attitudes and Perceptions Toward COVID-19 Digital Surveillance: Survey of Young Adults in the United States", journal="JMIR Form Res", year="2021", month="Jan", day="8", volume="5", number="1", pages="e23000", keywords="attitude", keywords="perception", keywords="young adult", keywords="COVID-19", keywords="digital surveillance", keywords="population health technologies", keywords="surveillance", keywords="population", keywords="survey", keywords="adolescent", abstract="Background: COVID-19 is an international health crisis of particular concern in the United States, which saw surges of infections with the lifting of lockdowns and relaxed social distancing. Young adults have proven to be a critical factor for COVID-19 transmission and are an important target of the efforts to contain the pandemic. Scalable digital public health technologies could be deployed to reduce COVID-19 transmission, but their use depends on the willingness of young adults to participate in surveillance. Objective: The aim of this study is to determine the attitudes of young adults regarding COVID-19 digital surveillance, including which aspects they would accept and which they would not, as well as to determine factors that may be associated with their willingness to participate in digital surveillance. Methods: We conducted an anonymous online survey of young adults aged 18-24 years throughout the United States in June 2020. The questionnaire contained predominantly closed-ended response options with one open-ended question. Descriptive statistics were applied to the data. Results: Of 513 young adult respondents, 383 (74.7\%) agreed that COVID-19 represents a public health crisis. However, only 231 (45.1\%) agreed to actively share their COVID-19 status or symptoms for monitoring and only 171 (33.4\%) reported a willingness to allow access to their cell phone for passive location tracking or contact tracing. Conclusions: Despite largely agreeing that COVID-19 represents a serious public health risk, the majority of young adults sampled were reluctant to participate in digital monitoring to manage the pandemic. This was true for both commonly used methods of public health surveillance (such as contact tracing) and novel methods designed to facilitate a return to normal (such as frequent symptom checking through digital apps). This is a potential obstacle to ongoing containment measures (many of which rely on widespread surveillance) and may reflect a need for greater education on the benefits of public health digital surveillance for young adults. ", doi="10.2196/23000", url="http://formative.jmir.org/2021/1/e23000/", url="http://www.ncbi.nlm.nih.gov/pubmed/33347420" } @Article{info:doi/10.2196/24889, author="Chen, Shi and Zhou, Lina and Song, Yunya and Xu, Qian and Wang, Ping and Wang, Kanlun and Ge, Yaorong and Janies, Daniel", title="A Novel Machine Learning Framework for Comparison of Viral COVID-19--Related Sina Weibo and Twitter Posts: Workflow Development and Content Analysis", journal="J Med Internet Res", year="2021", month="Jan", day="6", volume="23", number="1", pages="e24889", keywords="COVID-19", keywords="Twitter", keywords="Sina Weibo", keywords="content feature extraction", keywords="cross-cultural comparison", keywords="machine learning", keywords="social media", keywords="infodemiology", keywords="infoveillance", keywords="content analysis", keywords="workflow", keywords="communication", keywords="framework", abstract="Background: Social media plays a critical role in health communications, especially during global health emergencies such as the current COVID-19 pandemic. However, there is a lack of a universal analytical framework to extract, quantify, and compare content features in public discourse of emerging health issues on different social media platforms across a broad sociocultural spectrum. Objective: We aimed to develop a novel and universal content feature extraction and analytical framework and contrast how content features differ with sociocultural background in discussions of the emerging COVID-19 global health crisis on major social media platforms. Methods: We sampled the 1000 most shared viral Twitter and Sina Weibo posts regarding COVID-19, developed a comprehensive coding scheme to identify 77 potential features across six major categories (eg, clinical and epidemiological, countermeasures, politics and policy, responses), quantified feature values (0 or 1, indicating whether or not the content feature is mentioned in the post) in each viral post across social media platforms, and performed subsequent comparative analyses. Machine learning dimension reduction and clustering analysis were then applied to harness the power of social media data and provide more unbiased characterization of web-based health communications. Results: There were substantially different distributions, prevalence, and associations of content features in public discourse about the COVID-19 pandemic on the two social media platforms. Weibo users were more likely to focus on the disease itself and health aspects, while Twitter users engaged more about policy, politics, and other societal issues. Conclusions: We extracted a rich set of content features from social media data to accurately characterize public discourse related to COVID-19 in different sociocultural backgrounds. In addition, this universal framework can be adopted to analyze social media discussions of other emerging health issues beyond the COVID-19 pandemic. ", doi="10.2196/24889", url="https://www.jmir.org/2021/1/e24889", url="http://www.ncbi.nlm.nih.gov/pubmed/33326408" } @Article{info:doi/10.2196/24562, author="Ojo, Ayotomiwa and Guntuku, Chandra Sharath and Zheng, Margaret and Beidas, S. Rinad and Ranney, L. Megan", title="How Health Care Workers Wield Influence Through Twitter Hashtags: Retrospective Cross-sectional Study of the Gun Violence and COVID-19 Public Health Crises", journal="JMIR Public Health Surveill", year="2021", month="Jan", day="6", volume="7", number="1", pages="e24562", keywords="COVID-19", keywords="firearm injury", keywords="social media", keywords="online advocacy", keywords="Twitter", keywords="infodemiology", keywords="infoveillance", keywords="tweet", keywords="campaign", keywords="health care worker", keywords="influence", keywords="public health", keywords="crisis", keywords="policy", abstract="Background: Twitter has emerged as a novel way for physicians to share ideas and advocate for policy change. \#ThisIsOurLane (firearm injury) and \#GetUsPPE (COVID-19) are examples of nationwide health care--led Twitter campaigns that went viral. Health care--initiated Twitter hashtags regarding major public health topics have gained national attention, but their content has not been systematically examined. Objective: We hypothesized that Twitter discourse on two epidemics (firearm injury and COVID-19) would differ between tweets with health care--initiated hashtags (\#ThisIsOurLane and \#GetUsPPE) versus those with non--health care--initiated hashtags (\#GunViolence and \#COVID19). Methods: Using natural language processing, we compared content, affect, and authorship of a random 1\% of tweets using \#ThisIsOurLane (Nov 2018-Oct 2019) and \#GetUsPPE (March-May 2020), compared to \#GunViolence and \#COVID19 tweets, respectively. We extracted the relative frequency of single words and phrases and created two sets of features: (1) an open-vocabulary feature set to create 50 data-driven--determined word clusters to evaluate the content of tweets; and (2) a closed-vocabulary feature for psycholinguistic categorization among case and comparator tweets. In accordance with conventional linguistic analysis, we used a P<.001, after adjusting for multiple comparisons using the Bonferroni correction, to identify potentially meaningful correlations between language features and outcomes. Results: In total, 67\% (n=4828) of?\#ThisIsOurLane tweets and 36.6\% (n=7907) of \#GetUsPPE tweets were authored by health care professionals, compared to 16\% (n=1152) of \#GunViolence and 9.8\% (n=2117) of \#COVID19 tweets. Tweets using \#ThisIsOurLane and \#GetUsPPE were more likely to contain health care--specific language; more language denoting positive emotions, affiliation, and group identity; and more action-oriented content compared to tweets with \#GunViolence or \#COVID19, respectively. Conclusions: Tweets with health care--led hashtags expressed more positivity and more action-oriented language than the comparison hashtags. As social media is increasingly used for news discourse, public education, and grassroots organizing, the public health community can take advantage of social media's broad reach to amplify truthful, actionable messages around public health issues. ", doi="10.2196/24562", url="https://publichealth.jmir.org/2021/1/e24562", url="http://www.ncbi.nlm.nih.gov/pubmed/33315578" } @Article{info:doi/10.2196/23262, author="Tang, Lu and Fujimoto, Kayo and Amith, (Tuan) Muhammad and Cunningham, Rachel and Costantini, A. Rebecca and York, Felicia and Xiong, Grace and Boom, A. Julie and Tao, Cui", title="``Down the Rabbit Hole'' of Vaccine Misinformation on YouTube: Network Exposure Study", journal="J Med Internet Res", year="2021", month="Jan", day="5", volume="23", number="1", pages="e23262", keywords="vaccine", keywords="misinformation", keywords="infodemiology", keywords="infodemic", keywords="YouTube", keywords="network analysis", abstract="Background: Social media platforms such as YouTube are hotbeds for the spread of misinformation about vaccines. Objective: The aim of this study was to explore how individuals are exposed to antivaccine misinformation on YouTube based on whether they start their viewing from a keyword-based search or from antivaccine seed videos. Methods: Four networks of videos based on YouTube recommendations were collected in November 2019. Two search networks were created from provaccine and antivaccine keywords to resemble goal-oriented browsing. Two seed networks were constructed from conspiracy and antivaccine expert seed videos to resemble direct navigation. Video contents and network structures were analyzed using the network exposure model. Results: Viewers are more likely to encounter antivaccine videos through direct navigation starting from an antivaccine video than through goal-oriented browsing. In the two seed networks, provaccine videos, antivaccine videos, and videos containing health misinformation were all found to be more likely to lead to more antivaccine videos. Conclusions: YouTube has boosted the search rankings of provaccine videos to combat the influence of antivaccine information. However, when viewers are directed to antivaccine videos on YouTube from another site, the recommendation algorithm is still likely to expose them to additional antivaccine information. ", doi="10.2196/23262", url="https://www.jmir.org/2021/1/e23262", url="http://www.ncbi.nlm.nih.gov/pubmed/33399543" } @Article{info:doi/10.2196/21212, author="Balsamo, Duilio and Bajardi, Paolo and Salomone, Alberto and Schifanella, Rossano", title="Patterns of Routes of Administration and Drug Tampering for Nonmedical Opioid Consumption: Data Mining and Content Analysis of Reddit Discussions", journal="J Med Internet Res", year="2021", month="Jan", day="4", volume="23", number="1", pages="e21212", keywords="routes of administration", keywords="drug tampering", keywords="Reddit", keywords="word embedding", keywords="social media", keywords="opioid", keywords="heroin", keywords="buprenorphine", keywords="oxycodone", keywords="fentanyl", abstract="Background: The complex unfolding of the US opioid epidemic in the last 20 years has been the subject of a large body of medical and pharmacological research, and it has sparked a multidisciplinary discussion on how to implement interventions and policies to effectively control its impact on public health. Objective: This study leverages Reddit, a social media platform, as the primary data source to investigate the opioid crisis. We aimed to find a large cohort of Reddit users interested in discussing the use of opioids, trace the temporal evolution of their interest, and extensively characterize patterns of the nonmedical consumption of opioids, with a focus on routes of administration and drug tampering. Methods: We used a semiautomatic information retrieval algorithm to identify subreddits discussing nonmedical opioid consumption and developed a methodology based on word embedding to find alternative colloquial and nonmedical terms referring to opioid substances, routes of administration, and drug-tampering methods. We modeled the preferences of adoption of substances and routes of administration, estimating their prevalence and temporal unfolding. Ultimately, through the evaluation of odds ratios based on co-mentions, we measured the strength of association between opioid substances, routes of administration, and drug tampering. Results: We identified 32 subreddits discussing nonmedical opioid usage from 2014 to 2018 and observed the evolution of interest among over 86,000 Reddit users potentially involved in firsthand opioid usage. We learned the language model of opioid consumption and provided alternative vocabularies for opioid substances, routes of administration, and drug tampering. A data-driven taxonomy of nonmedical routes of administration was proposed. We modeled the temporal evolution of interest in opioid consumption by ranking the popularity of the adoption of opioid substances and routes of administration, observing relevant trends, such as the surge in synthetic opioids like fentanyl and an increasing interest in rectal administration. In addition, we measured the strength of association between drug tampering, routes of administration, and substance consumption, finding evidence of understudied abusive behaviors, like chewing fentanyl patches and dissolving buprenorphine sublingually. Conclusions: This work investigated some important consumption-related aspects of the opioid epidemic using Reddit data. We believe that our approach may provide a novel perspective for a more comprehensive understanding of nonmedical abuse of opioids substances and inform the prevention, treatment, and control of the public health effects. ", doi="10.2196/21212", url="https://www.jmir.org/2021/1/e21212", url="http://www.ncbi.nlm.nih.gov/pubmed/33393910" } @Article{info:doi/10.2196/24694, author="Burnett, Dayle and Eapen, Valsamma and Lin, Ping-I", title="Time Trends of the Public's Attention Toward Suicide During the COVID-19 Pandemic: Retrospective, Longitudinal Time-Series Study", journal="JMIR Public Health Surveill", year="2020", month="Dec", day="30", volume="6", number="4", pages="e24694", keywords="COVID-19", keywords="suicide", keywords="infodemiology", keywords="infoveillance", keywords="Google Trends", keywords="time trend", keywords="school closure", keywords="attention", keywords="mental health", keywords="crisis", keywords="time series", abstract="Background: The COVID-19 pandemic has overwhelmed health care systems around the world. Emerging evidence has suggested that substantially few patients seek help for suicidality at clinical settings during the COVID-19 pandemic, which has elicited concerns of an imminent mental health crisis as the course of the pandemic continues to unfold. Clarifying the relationship between the public's attention to knowledge about suicide and the public's attention to knowledge about the COVID-19 pandemic may provide insight into developing prevention strategies for a putative surge of suicide in relation to the impact of the COVID-19 pandemic. Objective: The goal of this retrospective, longitudinal time-series study is to understand the relationship between temporal trends of interest for the search term ``suicide'' and those of COVID-19--related terms, such as ``social distancing,'' ``school closure,'' and ``lockdown.'' Methods: We used the Google Trends platform to collect data on daily interest levels for search terms related to suicide, several other mental health-related issues, and COVID-19 over the period between February 14, 2020 and May 13, 2020. A correlational analysis was performed to determine the association between the search term ``suicide'' and COVID-19--related search terms in 16 countries. The Mann-Kendall test was used to examine significant differences between interest levels for the search term ``suicide'' before and after school closure. Results: We found that interest levels for the search term ``suicide'' statistically significantly inversely correlated with interest levels for the search terms ``COVID-19'' or ``coronavirus'' in nearly all countries between February 14, 2020 and May 13, 2020. Additionally, search interest for the term ``suicide'' significantly and negatively correlated with that of many COVID-19--related search terms, and search interest varied between countries. The Mann-Kendall test was used to examine significant differences between search interest levels for the term ``suicide'' before and after school closure. The Netherlands (P=.19), New Zealand (P=.003), the United Kingdom (P=.006), and the United States (P=.049) showed significant negative trends in interest levels for suicide in the 2-week period preceding school closures. In contrast, interest levels for suicide had a significant positive trend in Canada (P<.001) and the United States (P=.002) after school closures. Conclusions: The public's attention to suicide might inversely correlate with the public's attention to COVID-19--related issues. Additionally, several anticontagion policies, such as school closure, might have led to a turning point for mental health crises, because the attention to suicidality increased after restrictions were implemented. Our results suggest that an increased risk of suicidal ideation may ensue due to the ongoing anticontagion policies. Timely intervention strategies for suicides should therefore be an integral part of efforts to flatten the epidemic curve. ", doi="10.2196/24694", url="http://publichealth.jmir.org/2020/4/e24694/", url="http://www.ncbi.nlm.nih.gov/pubmed/33326407" } @Article{info:doi/10.2196/23518, author="Jimenez, Jimenez Alberto and Estevez-Reboredo, M. Rosa and Santed, A. Miguel and Ramos, Victoria", title="COVID-19 Symptom-Related Google Searches and Local COVID-19 Incidence in Spain: Correlational Study", journal="J Med Internet Res", year="2020", month="Dec", day="18", volume="22", number="12", pages="e23518", keywords="behavioral epidemiology", keywords="big data", keywords="smart data", keywords="tracking", keywords="nowcasting", keywords="forecast", keywords="predict", keywords="infosurveillance", keywords="infodemiology", keywords="COVID-19", abstract="Background: COVID-19 is one of the biggest pandemics in human history, along with other disease pandemics, such as the H1N1 influenza A, bubonic plague, and smallpox pandemics. This study is a small contribution that tries to find contrasted formulas to alleviate global suffering and guarantee a more manageable future. Objective: In this study, a statistical approach was proposed to study the correlation between the incidence of COVID-19 in Spain and search data provided by Google Trends. Methods: We assessed the linear correlation between Google Trends search data and the data provided by the National Center of Epidemiology in Spain---which is dependent on the Instituto de Salud Carlos III---regarding the number of COVID-19 cases reported with a certain time lag. These data enabled the identification of anticipatory patterns. Results: In response to the ongoing outbreak, our results demonstrate that by using our correlation test, the evolution of the COVID-19 pandemic can be predicted in Spain up to 11 days in advance. Conclusions: During the epidemic, Google Trends offers the possibility to preempt health care decisions in real time by tracking people's concerns through their search patterns. This can be of great help given the critical, if not dramatic need for complementary monitoring approaches that work on a population level and inform public health decisions in real time. This study of Google search patterns, which was motivated by the fears of individuals in the face of a pandemic, can be useful in anticipating the development of the pandemic. ", doi="10.2196/23518", url="http://www.jmir.org/2020/12/e23518/", url="http://www.ncbi.nlm.nih.gov/pubmed/33156803" } @Article{info:doi/10.2196/20920, author="Leis, Angela and Ronzano, Francesco and Mayer, Angel Miguel and Furlong, I. Laura and Sanz, Ferran", title="Evaluating Behavioral and Linguistic Changes During Drug Treatment for Depression Using Tweets in Spanish: Pairwise Comparison Study", journal="J Med Internet Res", year="2020", month="Dec", day="18", volume="22", number="12", pages="e20920", keywords="depression", keywords="antidepressant drugs", keywords="serotonin uptake inhibitors", keywords="mental health", keywords="social media", keywords="infodemiology", keywords="data mining", abstract="Background: Depressive disorders are the most common mental illnesses, and they constitute the leading cause of disability worldwide. Selective serotonin reuptake inhibitors (SSRIs) are the most commonly prescribed drugs for the treatment of depressive disorders. Some people share information about their experiences with antidepressants on social media platforms such as Twitter. Analysis of the messages posted by Twitter users under SSRI treatment can yield useful information on how these antidepressants affect users' behavior. Objective: This study aims to compare the behavioral and linguistic characteristics of the tweets posted while users were likely to be under SSRI treatment, in comparison to the tweets posted by the same users when they were less likely to be taking this medication. Methods: In the first step, the timelines of Twitter users mentioning SSRI antidepressants in their tweets were selected using a list of 128 generic and brand names of SSRIs. In the second step, two datasets of tweets were created, the in-treatment dataset (made up of the tweets posted throughout the 30 days after mentioning an SSRI) and the unknown-treatment dataset (made up of tweets posted more than 90 days before or more than 90 days after any tweet mentioning an SSRI). For each user, the changes in behavioral and linguistic features between the tweets classified in these two datasets were analyzed. 186 users and their timelines with 668,842 tweets were finally included in the study. Results: The number of tweets generated per day by the users when they were in treatment was higher than it was when they were in the unknown-treatment period (P=.001). When the users were in treatment, the mean percentage of tweets posted during the daytime (from 8 AM to midnight) increased in comparison to the unknown-treatment period (P=.002). The number of characters and words per tweet was higher when the users were in treatment (P=.03 and P=.02, respectively). Regarding linguistic features, the percentage of pronouns that were first-person singular was higher when users were in treatment (P=.008). Conclusions: Behavioral and linguistic changes have been detected when users with depression are taking antidepressant medication. These features can provide interesting insights for monitoring the evolution of this disease, as well as offering additional information related to treatment adherence. This information may be especially useful in patients who are receiving long-term treatments such as people suffering from depression. ", doi="10.2196/20920", url="http://www.jmir.org/2020/12/e20920/", url="http://www.ncbi.nlm.nih.gov/pubmed/33337338" } @Article{info:doi/10.2196/24432, author="Li, Zhenlong and Li, Xiaoming and Porter, Dwayne and Zhang, Jiajia and Jiang, Yuqin and Olatosi, Bankole and Weissman, Sharon", title="Monitoring the Spatial Spread of COVID-19 and Effectiveness of Control Measures Through Human Movement Data: Proposal for a Predictive Model Using Big Data Analytics", journal="JMIR Res Protoc", year="2020", month="Dec", day="18", volume="9", number="12", pages="e24432", keywords="big data", keywords="human movement", keywords="spatial computing", keywords="COVID-19", keywords="artificial intelligence", abstract="Background: Human movement is one of the forces that drive the spatial spread of infectious diseases. To date, reducing and tracking human movement during the COVID-19 pandemic has proven effective in limiting the spread of the virus. Existing methods for monitoring and modeling the spatial spread of infectious diseases rely on various data sources as proxies of human movement, such as airline travel data, mobile phone data, and banknote tracking. However, intrinsic limitations of these data sources prevent us from systematic monitoring and analyses of human movement on different spatial scales (from local to global). Objective: Big data from social media such as geotagged tweets have been widely used in human mobility studies, yet more research is needed to validate the capabilities and limitations of using such data for studying human movement at different geographic scales (eg, from local to global) in the context of global infectious disease transmission. This study aims to develop a novel data-driven public health approach using big data from Twitter coupled with other human mobility data sources and artificial intelligence to monitor and analyze human movement at different spatial scales (from global to regional to local). Methods: We will first develop a database with optimized spatiotemporal indexing to store and manage the multisource data sets collected in this project. This database will be connected to our in-house Hadoop computing cluster for efficient big data computing and analytics. We will then develop innovative data models, predictive models, and computing algorithms to effectively extract and analyze human movement patterns using geotagged big data from Twitter and other human mobility data sources, with the goal of enhancing situational awareness and risk prediction in public health emergency response and disease surveillance systems. Results: This project was funded as of May 2020. We have started the data collection, processing, and analysis for the project. Conclusions: Research findings can help government officials, public health managers, emergency responders, and researchers answer critical questions during the pandemic regarding the current and future infectious risk of a state, county, or community and the effectiveness of social/physical distancing practices in curtailing the spread of the virus. International Registered Report Identifier (IRRID): DERR1-10.2196/24432 ", doi="10.2196/24432", url="http://www.researchprotocols.org/2020/12/e24432/", url="http://www.ncbi.nlm.nih.gov/pubmed/33301418" } @Article{info:doi/10.2196/24425, author="Nsoesie, Okanyene Elaine and Cesare, Nina and M{\"u}ller, Martin and Ozonoff, Al", title="COVID-19 Misinformation Spread in Eight Countries: Exponential Growth Modeling Study", journal="J Med Internet Res", year="2020", month="Dec", day="15", volume="22", number="12", pages="e24425", keywords="misinformation", keywords="internet", keywords="COVID-19", keywords="social media", keywords="rumors", abstract="Background: The epidemic of misinformation about COVID-19 transmission, prevention, and treatment has been going on since the start of the pandemic. However, data on the exposure and impact of misinformation is not readily available. Objective: We aim to characterize and compare the start, peak, and doubling time of COVID-19 misinformation topics across 8 countries using an exponential growth model usually employed to study infectious disease epidemics. Methods: COVID-19 misinformation topics were selected from the World Health Organization Mythbusters website. Data representing exposure was obtained from the Google Trends application programming interface for 8 English-speaking countries. Exponential growth models were used in modeling trends for each country. Results: Searches for ``coronavirus AND 5G'' started at different times but peaked in the same week for 6 countries. Searches for 5G also had the shortest doubling time across all misinformation topics, with the shortest time in Nigeria and South Africa (approximately 4-5 days). Searches for ``coronavirus AND ginger'' started at the same time (the week of January 19, 2020) for several countries, but peaks were incongruent, and searches did not always grow exponentially after the initial week. Searches for ``coronavirus AND sun'' had different start times across countries but peaked at the same time for multiple countries. Conclusions: Patterns in the start, peak, and doubling time for ``coronavirus AND 5G'' were different from the other misinformation topics and were mostly consistent across countries assessed, which might be attributable to a lack of public understanding of 5G technology. Understanding the spread of misinformation, similarities and differences across different contexts can help in the development of appropriate interventions for limiting its impact similar to how we address infectious disease epidemics. Furthermore, the rapid proliferation of misinformation that discourages adherence to public health interventions could be predictive of future increases in disease cases. ", doi="10.2196/24425", url="http://www.jmir.org/2020/12/e24425/", url="http://www.ncbi.nlm.nih.gov/pubmed/33264102" } @Article{info:doi/10.2196/21418, author="Valdez, Danny and ten Thij, Marijn and Bathina, Krishna and Rutter, A. Lauren and Bollen, Johan", title="Social Media Insights Into US Mental Health During the COVID-19 Pandemic: Longitudinal Analysis of Twitter Data", journal="J Med Internet Res", year="2020", month="Dec", day="14", volume="22", number="12", pages="e21418", keywords="social media", keywords="analytics", keywords="infodemiology", keywords="infoveillance", keywords="COVID-19", keywords="United States", keywords="mental health", keywords="informatics", keywords="sentiment analysis", keywords="Twitter", abstract="Background: The COVID-19 pandemic led to unprecedented mitigation efforts that disrupted the daily lives of millions. Beyond the general health repercussions of the pandemic itself, these measures also present a challenge to the world's mental health and health care systems. Considering that traditional survey methods are time-consuming and expensive, we need timely and proactive data sources to respond to the rapidly evolving effects of health policy on our population's mental health. Many people in the United States now use social media platforms such as Twitter to express the most minute details of their daily lives and social relations. This behavior is expected to increase during the COVID-19 pandemic, rendering social media data a rich field to understand personal well-being. Objective: This study aims to answer three research questions: (1) What themes emerge from a corpus of US tweets about COVID-19? (2) To what extent did social media use increase during the onset of the COVID-19 pandemic? and (3) Does sentiment change in response to the COVID-19 pandemic? Methods: We analyzed 86,581,237 public domain English language US tweets collected from an open-access public repository in three steps. First, we characterized the evolution of hashtags over time using latent Dirichlet allocation (LDA) topic modeling. Second, we increased the granularity of this analysis by downloading Twitter timelines of a large cohort of individuals (n=354,738) in 20 major US cities to assess changes in social media use. Finally, using this timeline data, we examined collective shifts in public mood in relation to evolving pandemic news cycles by analyzing the average daily sentiment of all timeline tweets with the Valence Aware Dictionary and Sentiment Reasoner (VADER) tool. Results: LDA topics generated in the early months of the data set corresponded to major COVID-19--specific events. However, as state and municipal governments began issuing stay-at-home orders, latent themes shifted toward US-related lifestyle changes rather than global pandemic-related events. Social media volume also increased significantly, peaking during stay-at-home mandates. Finally, VADER sentiment analysis scores of user timelines were initially high and stable but decreased significantly, and continuously, by late March. Conclusions: Our findings underscore the negative effects of the pandemic on overall population sentiment. Increased use rates suggest that, for some, social media may be a coping mechanism to combat feelings of isolation related to long-term social distancing. However, in light of the documented negative effect of heavy social media use on mental health, social media may further exacerbate negative feelings in the long-term for many individuals. Thus, considering the overburdened US mental health care structure, these findings have important implications for ongoing mitigation efforts. ", doi="10.2196/21418", url="http://www.jmir.org/2020/12/e21418/", url="http://www.ncbi.nlm.nih.gov/pubmed/33284783" } @Article{info:doi/10.2196/22609, author="Alshalan, Raghad and Al-Khalifa, Hend and Alsaeed, Duaa and Al-Baity, Heyam and Alshalan, Shahad", title="Detection of Hate Speech in COVID-19--Related Tweets in the Arab Region: Deep Learning and Topic Modeling Approach", journal="J Med Internet Res", year="2020", month="Dec", day="8", volume="22", number="12", pages="e22609", keywords="COVID-19", keywords="coronavirus", keywords="Twitter", keywords="hate speech", keywords="social network analysis", keywords="social media", keywords="public health", keywords="pandemic", keywords="deep learning", keywords="non-negative matrix factorization", keywords="NMF", keywords="convolutional neural network", keywords="CNN", abstract="Background: The massive scale of social media platforms requires an automatic solution for detecting hate speech. These automatic solutions will help reduce the need for manual analysis of content. Most previous literature has cast the hate speech detection problem as a supervised text classification task using classical machine learning methods or, more recently, deep learning methods. However, work investigating this problem in Arabic cyberspace is still limited compared to the published work on English text. Objective: This study aims to identify hate speech related to the COVID-19 pandemic posted by Twitter users in the Arab region and to discover the main issues discussed in tweets containing hate speech. Methods: We used the ArCOV-19 dataset, an ongoing collection of Arabic tweets related to COVID-19, starting from January 27, 2020. Tweets were analyzed for hate speech using a pretrained convolutional neural network (CNN) model; each tweet was given a score between 0 and 1, with 1 being the most hateful text. We also used nonnegative matrix factorization to discover the main issues and topics discussed in hate tweets. Results: The analysis of hate speech in Twitter data in the Arab region identified that the number of non--hate tweets greatly exceeded the number of hate tweets, where the percentage of hate tweets among COVID-19 related tweets was 3.2\% (11,743/547,554). The analysis also revealed that the majority of hate tweets (8385/11,743, 71.4\%) contained a low level of hate based on the score provided by the CNN. This study identified Saudi Arabia as the Arab country from which the most COVID-19 hate tweets originated during the pandemic. Furthermore, we showed that the largest number of hate tweets appeared during the time period of March 1-30, 2020, representing 51.9\% of all hate tweets (6095/11,743). Contrary to what was anticipated, in the Arab region, it was found that the spread of COVID-19--related hate speech on Twitter was weakly related with the dissemination of the pandemic based on the Pearson correlation coefficient (r=0.1982, P=.50). The study also identified the commonly discussed topics in hate tweets during the pandemic. Analysis of the 7 extracted topics showed that 6 of the 7 identified topics were related to hate speech against China and Iran. Arab users also discussed topics related to political conflicts in the Arab region during the COVID-19 pandemic. Conclusions: The COVID-19 pandemic poses serious public health challenges to nations worldwide. During the COVID-19 pandemic, frequent use of social media can contribute to the spread of hate speech. Hate speech on the web can have a negative impact on society, and hate speech may have a direct correlation with real hate crimes, which increases the threat associated with being targeted by hate speech and abusive language. This study is the first to analyze hate speech in the context of Arabic COVID-19--related tweets in the Arab region. ", doi="10.2196/22609", url="http://www.jmir.org/2020/12/e22609/", url="http://www.ncbi.nlm.nih.gov/pubmed/33207310" } @Article{info:doi/10.2196/24125, author="Xu, Qing and Shen, Ziyi and Shah, Neal and Cuomo, Raphael and Cai, Mingxiang and Brown, Matthew and Li, Jiawei and Mackey, Tim", title="Characterizing Weibo Social Media Posts From Wuhan, China During the Early Stages of the COVID-19 Pandemic: Qualitative Content Analysis", journal="JMIR Public Health Surveill", year="2020", month="Dec", day="7", volume="6", number="4", pages="e24125", keywords="COVID-19", keywords="infodemiology", keywords="infoveillance", keywords="infodemic", keywords="Weibo", keywords="social media", keywords="content analysis", keywords="China", keywords="data mining", keywords="knowledge", keywords="attitude", keywords="behavior", abstract="Background: The COVID-19 pandemic has reached 40 million confirmed cases worldwide. Given its rapid progression, it is important to examine its origins to better understand how people's knowledge, attitudes, and reactions have evolved over time. One method is to use data mining of social media conversations related to information exposure and self-reported user experiences. Objective: This study aims to characterize the knowledge, attitudes, and behaviors of social media users located at the initial epicenter of the outbreak by analyzing data from the Sina Weibo platform in Chinese. Methods: We used web scraping to collect public Weibo posts from December 31, 2019, to January 20, 2020, from users located in Wuhan City that contained COVID-19--related keywords. We then manually annotated all posts using an inductive content coding approach to identify specific information sources and key themes including news and knowledge about the outbreak, public sentiment, and public reaction to control and response measures. Results: We identified 10,159 COVID-19 posts from 8703 unique Weibo users. Among our three parent classification areas, 67.22\% (n=6829) included news and knowledge posts, 69.72\% (n=7083) included public sentiment, and 47.87\% (n=4863) included public reaction and self-reported behavior. Many of these themes were expressed concurrently in the same Weibo post. Subtopics for news and knowledge posts followed four distinct timelines and evidenced an escalation of the outbreak's seriousness as more information became available. Public sentiment primarily focused on expressions of anxiety, though some expressions of anger and even positive sentiment were also detected. Public reaction included both protective and elevated health risk behavior. Conclusions: Between the announcement of pneumonia and respiratory illness of unknown origin in late December 2019 and the discovery of human-to-human transmission on January 20, 2020, we observed a high volume of public anxiety and confusion about COVID-19, including different reactions to the news by users, negative sentiment after being exposed to information, and public reaction that translated to self-reported behavior. These findings provide early insight into changing knowledge, attitudes, and behaviors about COVID-19, and have the potential to inform future outbreak communication, response, and policy making in China and beyond. ", doi="10.2196/24125", url="http://publichealth.jmir.org/2020/4/e24125/", url="http://www.ncbi.nlm.nih.gov/pubmed/33175693" } @Article{info:doi/10.2196/20649, author="Majmundar, Anuja and Le, NamQuyen and Moran, Bridgid Meghan and Unger, B. Jennifer and Reuter, Katja", title="Public Response to a Social Media Tobacco Prevention Campaign: Content Analysis", journal="JMIR Public Health Surveill", year="2020", month="Dec", day="7", volume="6", number="4", pages="e20649", keywords="social media", keywords="health campaign", keywords="tobacco", keywords="online", keywords="health communication", keywords="internet", keywords="Twitter", keywords="Facebook", keywords="Instagram", abstract="Background: Prior research suggests that social media--based public health campaigns are often targeted by countercampaigns. Objective: Using reactance theory as the theoretical framework, this research characterizes the nature of public response to tobacco prevention messages disseminated via a social media--based campaign. We also examine whether agreement with the prevention messages is associated with comment tone and nature of the contribution to the overall discussion. Methods: User comments to tobacco prevention messages, posted between April 19, 2017 and July 12, 2017, were extracted from Twitter, Facebook, and Instagram. Two coders categorized comments in terms of tone, agreement with message, nature of contribution, mentions of government agency and regulation, promotional or spam comments, and format of comment. Chi-square analyses tested associations between agreement with the message and tone of the public response and the nature of contributions to the discussions. Results: Of the 1242 comments received (Twitter: n=1004; Facebook: n=176; Instagram: n=62), many comments used a negative tone (42.75\%) and disagreed with the health messages (39.77\%), while the majority made healthy contributions to the discussions (84.38\%). Only 0.56\% of messages mentioned government agencies, and only 0.48\% of the comments were antiregulation. Comments employing a positive tone (84.13\%) or making healthy contributions (69.11\%) were more likely to agree with the campaign messages (P=0.01). Comments employing a negative tone (71.25\%) or making toxic contributions (36.26\%) generally disagreed with the messages (P=0.01). Conclusions: The majority of user comments in response to a tobacco prevention campaign made healthy contributions. Our findings encourage the use of social media to promote dialogue about controversial health topics such as smoking. However, toxicity was characteristic of comments that disagreed with the health messages. Managing negative and toxic comments on social media is a crucial issue for social media--based tobacco prevention campaigns to consider. ", doi="10.2196/20649", url="http://publichealth.jmir.org/2020/4/e20649/", url="http://www.ncbi.nlm.nih.gov/pubmed/33284120" } @Article{info:doi/10.2196/20913, author="Saposnik, E. Florencia and Huber, F. Joelene", title="Trends in Web Searches About the Causes and Treatments of Autism Over the Past 15 Years: Exploratory Infodemiology Study", journal="JMIR Pediatr Parent", year="2020", month="Dec", day="7", volume="3", number="2", pages="e20913", keywords="autism", keywords="infodemiology", keywords="infoveillance", keywords="informatics", keywords="Google Trends", abstract="Background: Ninety percent of adults in the United States use the internet, and the majority of internet users report looking on the web for health information using search engines. The rising prevalence of autism spectrum disorder (ASD), uncertainty surrounding its etiology, and variety of intervention approaches contribute to questions about its causes and treatments. It is not known which terms people search most frequently about ASD and whether web search queries have changed over time. Infodemiology is an area of health informatics research using big data analytics to understand web search behavior. Objective: The objectives were to (1) use infodemiological data to analyze trends in web-based searches about the causes and treatments of ASD over time and (2) inform clinicians and ASD organizations about web queries regarding ASD. Methods: Google Trends was used to analyze web searches about the causes and treatments of ASD in the United States from 2004 to 2019. The search terms analyzed for queries about causes of ASD included vaccines, genetics, environmental factors, and microbiome and those for therapies included applied behavior analysis (ABA), gluten-free diet, chelation therapy, marijuana, probiotics, and stem cell therapy. Results: Google Trends results are normalized on a scale ranging from 0 to 100 to represent the frequency and relative interest of search topics. For searches about ASD causes, vaccines had the greatest frequency compared to other terms, with an initial search peak observed in 2008 (scaled score of 81), reaching the highest frequency in 2015 (scaled score of 100), and a current upward trend. In comparison, searches about genetics, environmental factors, and microbiome occurred less frequently. For web searches about ASD therapies, ABA consistently had a high frequency of search interest since 2004, reaching a maximum scaled score of 100 in 2019. The analyses of chelation therapy and gluten-free diet showed trending interest in 2005 (scaled score of 68) and 2007 (scaled score of 100), respectively, followed by a steady decline since (scaled scores of only 10 and 16, respectively, in 2019). Searches related to ASD and marijuana showed a rise in 2009 (scaled score of 35), and they continue to trend upward. Searches about probiotics and stem cell therapy have been relatively low (scaled scores of 22 and 18, respectively), but are gradually gaining interest. Web search volumes for stem cell therapy in 2019 surpassed both gluten-free diet and chelation therapy as web-searched interventions for ASD. Conclusions: Google Trends is an effective infodemiology tool to analyze large-scale web search trends about ASD. The results showed informative variation in search trends over 15 years. These data are useful to inform clinicians and organizations about web queries on topics related to ASD, identify knowledge gaps, and target web-based education and knowledge translation strategies. ", doi="10.2196/20913", url="http://pediatrics.jmir.org/2020/2/e20913/", url="http://www.ncbi.nlm.nih.gov/pubmed/33284128" } @Article{info:doi/10.2196/19504, author="Gallagher, John and Lawrence, Y. Heidi", title="Rhetorical Appeals and Tactics in New York Times Comments About Vaccines: Qualitative Analysis", journal="J Med Internet Res", year="2020", month="Dec", day="4", volume="22", number="12", pages="e19504", keywords="vaccination", keywords="qualitative research", keywords="quantitative research", keywords="rhetoric", keywords="online comments", keywords="anti-vaccination", keywords="pro-vaccination", abstract="Background: Improving persuasion in response to vaccine skepticism is a long-standing problem. Elective nonvaccination emerging from skepticism about vaccine safety and efficacy jeopardizes herd immunity, exposing those who are most vulnerable to the risk of serious diseases. Objective: This article analyzes vaccine sentiments in the New York Times as a way of improving understanding of why existing persuasive approaches may be ineffective and offers insight into how existing methods might be improved. We categorize pro-vaccine and anti-vaccine arguments, offering an in-depth analysis of pro-vaccine appeals and tactics in particular to enhance current understanding of arguments that support vaccines. Methods: Qualitative thematic analyses were used to analyze themes in rhetorical appeals across 808 vaccine-specific comments. Pro-vaccine and anti-vaccine comments were categorized to provide a broad analysis of the overall context of vaccine comments across viewpoints, with in-depth rhetorical analysis of pro-vaccine comments to address current gaps in understanding of pro-vaccine arguments in particular. Results: Appeals across 808 anti-vaccine and pro-vaccine comments were similar, though these appeals diverged in tactics and conclusions. Anti-vaccine arguments were more heterogeneous, deploying a wide range of arguments against vaccines. Additional analysis of pro-vaccine comments reveals that these comments use rhetorical strategies that could be counterproductive to producing persuasion. Pro-vaccine comments more frequently used tactics such as ad hominem arguments levied at those who refuse vaccines or used appeals to science to correct beliefs in vaccine skepticism, both of which can be ineffective when attempting to persuade a skeptical audience. Conclusions: Further study of pro-vaccine argumentation appeals and tactics could illuminate how persuasiveness could be improved in online forums. ", doi="10.2196/19504", url="http://www.jmir.org/2020/12/e19504/", url="http://www.ncbi.nlm.nih.gov/pubmed/33275110" } @Article{info:doi/10.2196/24550, author="Berkovic, Danielle and Ackerman, N. Ilana and Briggs, M. Andrew and Ayton, Darshini", title="Tweets by People With Arthritis During the COVID-19 Pandemic: Content and Sentiment Analysis", journal="J Med Internet Res", year="2020", month="Dec", day="3", volume="22", number="12", pages="e24550", keywords="COVID-19", keywords="SARS-CoV-2", keywords="novel coronavirus", keywords="social media", keywords="Twitter", keywords="content analysis", keywords="sentiment analysis", keywords="microblogging", keywords="arthritis", abstract="Background: Emerging evidence suggests that people with arthritis are reporting increased physical pain and psychological distress during the COVID-19 pandemic. At the same time, Twitter's daily usage has surged by 23\% throughout the pandemic period, presenting a unique opportunity to assess the content and sentiment of tweets. Individuals with arthritis use Twitter to communicate with peers, and to receive up-to-date information from health professionals and services about novel therapies and management techniques. Objective: The aim of this research was to identify proxy topics of importance for individuals with arthritis during the COVID-19 pandemic, and to explore the emotional context of tweets by people with arthritis during the early phase of the pandemic. Methods: From March 20 to April 20, 2020, publicly available tweets posted in English and with hashtag combinations related to arthritis and COVID-19 were extracted retrospectively from Twitter. Content analysis was used to identify common themes within tweets, and sentiment analysis was used to examine positive and negative emotions in themes to understand the COVID-19 experiences of people with arthritis. Results: In total, 149 tweets were analyzed. The majority of tweeters were female and were from the United States. Tweeters reported a range of arthritis conditions, including rheumatoid arthritis, systemic lupus erythematosus, and psoriatic arthritis. Seven themes were identified: health care experiences, personal stories, links to relevant blogs, discussion of arthritis-related symptoms, advice sharing, messages of positivity, and stay-at-home messaging. Sentiment analysis demonstrated marked anxiety around medication shortages, increased physical symptom burden, and strong desire for trustworthy information and emotional connection. Conclusions: Tweets by people with arthritis highlight the multitude of concurrent concerns during the COVID-19 pandemic. Understanding these concerns, which include heightened physical and psychological symptoms in the context of treatment misinformation, may assist clinicians to provide person-centered care during this time of great health uncertainty. ", doi="10.2196/24550", url="https://www.jmir.org/2020/12/e24550", url="http://www.ncbi.nlm.nih.gov/pubmed/33170802" } @Article{info:doi/10.2196/21451, author="Massey, M. Philip and Kearney, D. Matthew and Hauer, K. Michael and Selvan, Preethi and Koku, Emmanuel and Leader, E. Amy", title="Dimensions of Misinformation About the HPV Vaccine on Instagram: Content and Network Analysis of Social Media Characteristics", journal="J Med Internet Res", year="2020", month="Dec", day="3", volume="22", number="12", pages="e21451", keywords="social media", keywords="cancer", keywords="vaccination", keywords="health communication", keywords="public health", keywords="HPV, human papillomavirus", abstract="Background: The human papillomavirus (HPV) vaccine is a major advancement in cancer prevention and this primary prevention tool has the potential to reduce and eliminate HPV-associated cancers; however, the safety and efficacy of vaccines in general and the HPV vaccine specifically have come under attack, particularly through the spread of misinformation on social media. The popular social media platform Instagram represents a significant source of exposure to health (mis)information; 1 in 3 US adults use Instagram. Objective: The objective of this analysis was to characterize pro- and anti-HPV vaccine networks on Instagram, and to describe misinformation within the anti-HPV vaccine network. Methods: From April 2018 to December 2018, we collected publicly available English-language Instagram posts containing hashtags \#HPV, \#HPVVaccine, or \#Gardasil using Netlytic software (n=16,607). We randomly selected 10\% of the sample and content analyzed relevant posts (n=580) for text, image, and social media features as well as holistic attributes (eg, sentiments, personal stories). Among antivaccine posts, we organized elements of misinformation within four broad dimensions: 1) misinformation theoretical domains, 2) vaccine debate topics, 3) evidence base, and 4) health beliefs. We conducted univariate, bivariate, and network analyses on the subsample of posts to quantify the role and position of individual posts in the network. Results: Compared to provaccine posts (324/580, 55.9\%), antivaccine posts (256/580, 44.1\%) were more likely to originate from individuals (64.1\% antivaccine vs 25.0\% provaccine; P<.001) and include personal narratives (37.1\% vs 25.6\%; P=.003). In the antivaccine network, core misinformation characteristics included mentioning \#Gardasil, purporting to reveal a lie (ie, concealment), conspiracy theories, unsubstantiated claims, and risk of vaccine injury. Information/resource posts clustered around misinformation domains including falsification, nanopublications, and vaccine-preventable disease, whereas personal narrative posts clustered around different domains of misinformation, including concealment, injury, and conspiracy theories. The most liked post (6634 likes) in our full subsample was a positive personal narrative post, created by a non-health individual; the most liked post (5604 likes) in our antivaccine subsample was an informational post created by a health individual. Conclusions: Identifying characteristics of misinformation related to HPV vaccine on social media will inform targeted interventions (eg, network opinion leaders) and help sow corrective information and stories tailored to different falsehoods. ", doi="10.2196/21451", url="https://www.jmir.org/2020/12/e21451", url="http://www.ncbi.nlm.nih.gov/pubmed/33270038" } @Article{info:doi/10.2196/23579, author="Weiger, Caitlin and Smith, C. Katherine and Cohen, E. Joanna and Dredze, Mark and Moran, Bridgid Meghan", title="How Internet Contracts Impact Research: Content Analysis of Terms of Service on Consumer Product Websites", journal="JMIR Public Health Surveill", year="2020", month="Dec", day="2", volume="6", number="4", pages="e23579", keywords="marketing", keywords="contracts", keywords="internet", keywords="jurisprudence", keywords="ethics", abstract="Background: Companies use brand websites as a promotional tool to engage consumers on the web, which can increase product use. Given that some products are harmful to the health of consumers, it is important for marketing associated with these products to be subject to public health surveillance. However, terms of service (TOS) governing the use of brand website content may impede such important research. Objective: The aim of this study is to explore the TOS for brand websites with public health significance to assess possible legal and ethical challenges for conducting research on consumer product websites. Methods: Using Statista, we purposefully constructed a sample of 15 leading American tobacco, alcohol, psychiatric pharmaceutical, fast-food, and gun brands that have associated websites. We developed and implemented a structured coding system for the TOS on these websites and coded for the presence versus absence of different types of restriction that might impact the ability to conduct research. Results: All TOS stated that by accessing the website, users agreed to abide by the TOS (15/15, 100\%). A total of 11 out of 15 (73\%) websites had age restrictions in their TOS. All alcohol brand websites (5/15, 33\%) required users to enter their age or date of birth before viewing website content. Both websites for tobacco brands (2/15, 13\%) further required that users register and verify their age and identity to access any website content and agree that they use tobacco products. Only one website (1/15, 7\%) allowed users to display, download, copy, distribute, and translate the website content as long as it was for personal and not commercial use. A total of 33\% (5/15) of TOS unconditionally prohibited or put substantial restrictions on all of these activities and/or failed to specify if they were allowed or prohibited. Moreover, 87\% (13/15) of TOS indicated that website access could be restricted at any time. A total of 73\% (11/15) of websites specified that violating TOS could result in deleting user content from the website, revoking access by having the user's Internet Protocol address blocked, terminating log-in credentials, or enforcing legal action resulting in civil or criminal penalties. Conclusions: TOS create complications for public health surveillance related to e-marketing on brand websites. Recent court opinions have reduced the risk of federal criminal charges for violating TOS on public websites, but this risk remains unclear for private websites. The public health community needs to establish standards to guide and protect researchers from the possibility of legal repercussions related to such efforts. ", doi="10.2196/23579", url="http://publichealth.jmir.org/2020/4/e23579/", url="http://www.ncbi.nlm.nih.gov/pubmed/33263555" } @Article{info:doi/10.2196/23575, author="Zhang, Weina and Liu, Lu and Cheng, Qijin and Chen, Yan and Xu, Dong and Gong, Wenjie", title="The Relationship Between Images Posted by New Mothers on WeChat Moments and Postpartum Depression: Cohort Study", journal="J Med Internet Res", year="2020", month="Nov", day="30", volume="22", number="11", pages="e23575", keywords="social media", keywords="WeChat", keywords="WeChat Moments", keywords="postpartum depression", abstract="Background: As social media posts reflect users' emotions, WeChat Moments, the most popular social media platform in China, may offer a glimpse into postpartum depression in the population. Objective: This study aimed to investigate the features of the images that mothers posted on WeChat Moments after childbirth and to explore the correlation between these features and the mothers' risk of postpartum depression. Methods: We collected the data of 419 mothers after delivery, including their demographics, factors associated with postpartum depression, and images posted on WeChat Moments. Postpartum depression was measured using the Edinburgh Postnatal Depression Scale. Descriptive analyses were performed to assess the following: content of the images, presence of people, the people's facial expressions, and whether or not memes were posted on WeChat Moments. Logistic regression analyses were used to identify the image features associated with postpartum depression. Results: Compared with pictures of other people, we found that pictures of their children comprised the majority (3909/6887, 56.8\%) of the pictures posted by the mothers on WeChat Moments. Among the posts showing facial expressions or memes, more positive than negative emotions were expressed. Women who posted selfies during the postpartum period were more likely to have postpartum depression (P=.003; odds ratio 2.27, 95\% CI 1.33-3.87). Conclusions: The vast majority of mothers posted images conveying positive emotions during the postpartum period, but these images may have masked their depression. New mothers who have posted selfies may be at a higher risk of postpartum depression. Trial Registration: International Clinical Trials Registry Platform ChiCTR-ROC-16009255; http://www.chictr.org.cn/showproj.aspx?proj=15699 ", doi="10.2196/23575", url="http://www.jmir.org/2020/11/e23575/", url="http://www.ncbi.nlm.nih.gov/pubmed/33252343" } @Article{info:doi/10.2196/21660, author="Singh, Tavleen and Roberts, Kirk and Cohen, Trevor and Cobb, Nathan and Wang, Jing and Fujimoto, Kayo and Myneni, Sahiti", title="Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review", journal="JMIR Public Health Surveill", year="2020", month="Nov", day="30", volume="6", number="4", pages="e21660", keywords="social media", keywords="infodemiology", keywords="infoveillance", keywords="online health communities", keywords="risky health behaviors", keywords="data mining", keywords="machine learning", keywords="natural language processing", keywords="text mining", abstract="Background: Modifiable risky health behaviors, such as tobacco use, excessive alcohol use, being overweight, lack of physical activity, and unhealthy eating habits, are some of the major factors for developing chronic health conditions. Social media platforms have become indispensable means of communication in the digital era. They provide an opportunity for individuals to express themselves, as well as share their health-related concerns with peers and health care providers, with respect to risky behaviors. Such peer interactions can be utilized as valuable data sources to better understand inter-and intrapersonal psychosocial mediators and the mechanisms of social influence that drive behavior change. Objective: The objective of this review is to summarize computational and quantitative techniques facilitating the analysis of data generated through peer interactions pertaining to risky health behaviors on social media platforms. Methods: We performed a systematic review of the literature in September 2020 by searching three databases---PubMed, Web of Science, and Scopus---using relevant keywords, such as ``social media,'' ``online health communities,'' ``machine learning,'' ``data mining,'' etc. The reporting of the studies was directed by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Two reviewers independently assessed the eligibility of studies based on the inclusion and exclusion criteria. We extracted the required information from the selected studies. Results: The initial search returned a total of 1554 studies, and after careful analysis of titles, abstracts, and full texts, a total of 64 studies were included in this review. We extracted the following key characteristics from all of the studies: social media platform used for conducting the study, risky health behavior studied, the number of posts analyzed, study focus, key methodological functions and tools used for data analysis, evaluation metrics used, and summary of the key findings. The most commonly used social media platform was Twitter, followed by Facebook, QuitNet, and Reddit. The most commonly studied risky health behavior was nicotine use, followed by drug or substance abuse and alcohol use. Various supervised and unsupervised machine learning approaches were used for analyzing textual data generated from online peer interactions. Few studies utilized deep learning methods for analyzing textual data as well as image or video data. Social network analysis was also performed, as reported in some studies. Conclusions: Our review consolidates the methodological underpinnings for analyzing risky health behaviors and has enhanced our understanding of how social media can be leveraged for nuanced behavioral modeling and representation. The knowledge gained from our review can serve as a foundational component for the development of persuasive health communication and effective behavior modification technologies aimed at the individual and population levels. ", doi="10.2196/21660", url="http://publichealth.jmir.org/2020/4/e21660/", url="http://www.ncbi.nlm.nih.gov/pubmed/33252345" } @Article{info:doi/10.2196/15293, author="Yao, Hannah and Rashidian, Sina and Dong, Xinyu and Duanmu, Hongyi and Rosenthal, N. Richard and Wang, Fusheng", title="Detection of Suicidality Among Opioid Users on Reddit: Machine Learning--Based Approach", journal="J Med Internet Res", year="2020", month="Nov", day="27", volume="22", number="11", pages="e15293", keywords="opioid epidemic", keywords="opioid-related disorders", keywords="suicide", keywords="social media", keywords="machine learning", keywords="deep learning", keywords="natural language processing", abstract="Background: In recent years, both suicide and overdose rates have been increasing. Many individuals who struggle with opioid use disorder are prone to suicidal ideation; this may often result in overdose. However, these fatal overdoses are difficult to classify as intentional or unintentional. Intentional overdose is difficult to detect, partially due to the lack of predictors and social stigmas that push individuals away from seeking help. These individuals may instead use web-based means to articulate their concerns. Objective: This study aimed to extract posts of suicidality among opioid users on Reddit using machine learning methods. The performance of the models is derivative of the data purity, and the results will help us to better understand the rationale of these users, providing new insights into individuals who are part of the opioid epidemic. Methods: Reddit posts between June 2017 and June 2018 were collected from r/suicidewatch, r/depression, a set of opioid-related subreddits, and a control subreddit set. We first classified suicidal versus nonsuicidal languages and then classified users with opioid usage versus those without opioid usage. Several traditional baselines and neural network (NN) text classifiers were trained using subreddit names as the labels and combinations of semantic inputs. We then attempted to extract out-of-sample data belonging to the intersection of suicide ideation and opioid abuse. Amazon Mechanical Turk was used to provide labels for the out-of-sample data. Results: Classification results were at least 90\% across all models for at least one combination of input; the best classifier was convolutional neural network, which obtained an F1 score of 96.6\%. When predicting out-of-sample data for posts containing both suicidal ideation and signs of opioid addiction, NN classifiers produced more false positives and traditional methods produced more false negatives, which is less desirable for predicting suicidal sentiments. Conclusions: Opioid abuse is linked to the risk of unintentional overdose and suicide risk. Social media platforms such as Reddit contain metadata that can aid machine learning and provide information at a personal level that cannot be obtained elsewhere. We demonstrate that it is possible to use NNs as a tool to predict an out-of-sample target with a model built from data sets labeled by characteristics we wish to distinguish in the out-of-sample target. ", doi="10.2196/15293", url="http://www.jmir.org/2020/11/e15293/", url="http://www.ncbi.nlm.nih.gov/pubmed/33245287" } @Article{info:doi/10.2196/22152, author="Wang, Junze and Zhou, Ying and Zhang, Wei and Evans, Richard and Zhu, Chengyan", title="Concerns Expressed by Chinese Social Media Users During the COVID-19 Pandemic: Content Analysis of Sina Weibo Microblogging Data", journal="J Med Internet Res", year="2020", month="Nov", day="26", volume="22", number="11", pages="e22152", keywords="coronavirus", keywords="COVID-19", keywords="social media", keywords="public health", keywords="Sina Weibo", keywords="public opinion", keywords="citizen concerns", abstract="Background: The COVID-19 pandemic has created a global health crisis that is affecting economies and societies worldwide. During times of uncertainty and unexpected change, people have turned to social media platforms as communication tools and primary information sources. Platforms such as Twitter and Sina Weibo have allowed communities to share discussion and emotional support; they also play important roles for individuals, governments, and organizations in exchanging information and expressing opinions. However, research that studies the main concerns expressed by social media users during the pandemic is limited. Objective: The aim of this study was to examine the main concerns raised and discussed by citizens on Sina Weibo, the largest social media platform in China, during the COVID-19 pandemic. Methods: We used a web crawler tool and a set of predefined search terms (New Coronavirus Pneumonia, New Coronavirus, and COVID-19) to investigate concerns raised by Sina Weibo users. Textual information and metadata (number of likes, comments, retweets, publishing time, and publishing location) of microblog posts published between December 1, 2019, and July 32, 2020, were collected. After segmenting the words of the collected text, we used a topic modeling technique, latent Dirichlet allocation (LDA), to identify the most common topics posted by users. We analyzed the emotional tendencies of the topics, calculated the proportional distribution of the topics, performed user behavior analysis on the topics using data collected from the number of likes, comments, and retweets, and studied the changes in user concerns and differences in participation between citizens living in different regions of mainland China. Results: Based on the 203,191 eligible microblog posts collected, we identified 17 topics and grouped them into 8 themes. These topics were pandemic statistics, domestic epidemic, epidemics in other countries worldwide, COVID-19 treatments, medical resources, economic shock, quarantine and investigation, patients' outcry for help, work and production resumption, psychological influence, joint prevention and control, material donation, epidemics in neighboring countries, vaccine development, fueling and saluting antiepidemic action, detection, and study resumption. The mean sentiment was positive for 11 topics and negative for 6 topics. The topic with the highest mean of retweets was domestic epidemic, while the topic with the highest mean of likes was quarantine and investigation. Conclusions: Concerns expressed by social media users are highly correlated with the evolution of the global pandemic. During the COVID-19 pandemic, social media has provided a platform for Chinese government departments and organizations to better understand public concerns and demands. Similarly, social media has provided channels to disseminate information about epidemic prevention and has influenced public attitudes and behaviors. Government departments, especially those related to health, can create appropriate policies in a timely manner through monitoring social media platforms to guide public opinion and behavior during epidemics. ", doi="10.2196/22152", url="http://www.jmir.org/2020/11/e22152/", url="http://www.ncbi.nlm.nih.gov/pubmed/33151894" } @Article{info:doi/10.2196/21933, author="Dong, Wei and Tao, Jinhu and Xia, Xiaolin and Ye, Lin and Xu, Hanli and Jiang, Peiye and Liu, Yangyang", title="Public Emotions and Rumors Spread During the COVID-19 Epidemic in China: Web-Based Correlation Study", journal="J Med Internet Res", year="2020", month="Nov", day="25", volume="22", number="11", pages="e21933", keywords="public?emotions", keywords="rumor", keywords="infodemic", keywords="infodemiology", keywords="infoveillance", keywords="China", keywords="COVID-19", abstract="Background: Various online rumors have led to inappropriate behaviors among the public in response to the COVID-19 epidemic in China. These rumors adversely affect people's physical and mental health. Therefore, a better understanding of the relationship between public emotions and rumors during the epidemic may help generate useful strategies for guiding public emotions and dispelling rumors. Objective: This study aimed to explore whether public emotions are related to the dissemination of online rumors in the context of COVID-19. Methods: We used the web-crawling tool Scrapy to gather data published by People's Daily on Sina Weibo, a popular social media platform in China, after January 8, 2020. Netizens' comments under each Weibo post were collected. Nearly 1 million comments thus collected were divided into 5 categories: happiness, sadness, anger, fear, and neutral, based on the underlying emotional information identified and extracted from the comments by using a manual identification process. Data on rumors spread online were collected through Tencent's Jiaozhen platform. Time-lagged cross-correlation analyses were performed to examine the relationship between public emotions and rumors. Results: Our results indicated that the angrier the public felt, the more rumors there would likely be (r=0.48, P<.001). Similar results were observed for the relationship between fear and rumors (r=0.51, P<.001) and between sadness and rumors (r=0.47, P<.001). Furthermore, we found a positive correlation between happiness and rumors, with happiness lagging the emergence of rumors by 1 day (r=0.56, P<.001). In addition, our data showed a significant positive correlation between fear and fearful rumors (r=0.34, P=.02). Conclusions: Our findings confirm that public emotions are related to the rumors spread online in the context of COVID-19 in China. Moreover, these findings provide several suggestions, such as the use of web-based monitoring methods, for relevant authorities and policy makers to guide public emotions and behavior during this public health emergency. ", doi="10.2196/21933", url="http://www.jmir.org/2020/11/e21933/", url="http://www.ncbi.nlm.nih.gov/pubmed/33112757" } @Article{info:doi/10.2196/21504, author="Chang, Angela and Schulz, Johannes Peter and Tu, ShengTsung and Liu, Tingchi Matthew", title="Communicative Blame in Online Communication of the COVID-19 Pandemic: Computational Approach of Stigmatizing Cues and Negative Sentiment Gauged With Automated Analytic Techniques", journal="J Med Internet Res", year="2020", month="Nov", day="25", volume="22", number="11", pages="e21504", keywords="placing blame", keywords="culprits", keywords="sentiment analysis", keywords="infodemic analysis", keywords="political grievances", keywords="COVID-19", keywords="communication", keywords="pandemic", keywords="social media", keywords="negativity", keywords="infodemic", keywords="infodemiology", keywords="infoveillance", keywords="blame", keywords="stigma", abstract="Background: Information about a new coronavirus emerged in 2019 and rapidly spread around the world, gaining significant public attention and attracting negative bias. The use of stigmatizing language for the purpose of blaming sparked a debate. Objective: This study aims to identify social stigma and negative sentiment toward the blameworthy agents in social communities. Methods: We enabled a tailored text-mining platform to identify data in their natural settings by retrieving and filtering online sources, and constructed vocabularies and learning word representations from natural language processing for deductive analysis along with the research theme. The data sources comprised of ten news websites, eleven discussion forums, one social network, and two principal media sharing networks in Taiwan. A synthesis of news and social networking analytics was present from December 30, 2019, to March 31, 2020. Results: We collated over 1.07 million Chinese texts. Almost two-thirds of the texts on COVID-19 came from news services (n=683,887, 63.68\%), followed by Facebook (n=297,823, 27.73\%), discussion forums (n=62,119, 5.78\%), and Instagram and YouTube (n=30,154, 2.81\%). Our data showed that online news served as a hotbed for negativity and for driving emotional social posts. Online information regarding COVID-19 associated it with China---and a specific city within China through references to the ``Wuhan pneumonia''---potentially encouraging xenophobia. The adoption of this problematic moniker had a high frequency, despite the World Health Organization guideline to avoid biased perceptions and ethnic discrimination. Social stigma is disclosed through negatively valenced responses, which are associated with the most blamed targets. Conclusions: Our sample is sufficiently representative of a community because it contains a broad range of mainstream online media. Stigmatizing language linked to the COVID-19 pandemic shows a lack of civic responsibility that encourages bias, hostility, and discrimination. Frequently used stigmatizing terms were deemed offensive, and they might have contributed to recent backlashes against China by directing blame and encouraging xenophobia. The implications ranging from health risk communication to stigma mitigation and xenophobia concerns amid the COVID-19 outbreak are emphasized. Understanding the nomenclature and biased terms employed in relation to the COVID-19 outbreak is paramount. We propose solidarity with communication professionals in combating the COVID-19 outbreak and the infodemic. Finding solutions to curb the spread of virus bias, stigma, and discrimination is imperative. ", doi="10.2196/21504", url="http://www.jmir.org/2020/11/e21504/", url="http://www.ncbi.nlm.nih.gov/pubmed/33108306" } @Article{info:doi/10.2196/20550, author="Xue, Jia and Chen, Junxiang and Hu, Ran and Chen, Chen and Zheng, Chengda and Su, Yue and Zhu, Tingshao", title="Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach", journal="J Med Internet Res", year="2020", month="Nov", day="25", volume="22", number="11", pages="e20550", keywords="machine learning", keywords="Twitter data", keywords="COVID-19", keywords="infodemic", keywords="infodemiology", keywords="infoveillance", keywords="public discussion", keywords="public sentiment", keywords="Twitter", keywords="social media", keywords="virus", abstract="Background: It is important to measure the public response to the COVID-19 pandemic. Twitter is an important data source for infodemiology studies involving public response monitoring. Objective: The objective of this study is to examine COVID-19--related discussions, concerns, and sentiments using tweets posted by Twitter users. Methods: We analyzed 4 million Twitter messages related to the COVID-19 pandemic using a list of 20 hashtags (eg, ``coronavirus,'' ``COVID-19,'' ``quarantine'') from March 7 to April 21, 2020. We used a machine learning approach, Latent Dirichlet Allocation (LDA), to identify popular unigrams and bigrams, salient topics and themes, and sentiments in the collected tweets. Results: Popular unigrams included ``virus,'' ``lockdown,'' and ``quarantine.'' Popular bigrams included ``COVID-19,'' ``stay home,'' ``corona virus,'' ``social distancing,'' and ``new cases.'' We identified 13 discussion topics and categorized them into 5 different themes: (1) public health measures to slow the spread of COVID-19, (2) social stigma associated with COVID-19, (3) COVID-19 news, cases, and deaths, (4) COVID-19 in the United States, and (5) COVID-19 in the rest of the world. Across all identified topics, the dominant sentiments for the spread of COVID-19 were anticipation that measures can be taken, followed by mixed feelings of trust, anger, and fear related to different topics. The public tweets revealed a significant feeling of fear when people discussed new COVID-19 cases and deaths compared to other topics. Conclusions: This study showed that Twitter data and machine learning approaches can be leveraged for an infodemiology study, enabling research into evolving public discussions and sentiments during the COVID-19 pandemic. As the situation rapidly evolves, several topics are consistently dominant on Twitter, such as confirmed cases and death rates, preventive measures, health authorities and government policies, COVID-19 stigma, and negative psychological reactions (eg, fear). Real-time monitoring and assessment of Twitter discussions and concerns could provide useful data for public health emergency responses and planning. Pandemic-related fear, stigma, and mental health concerns are already evident and may continue to influence public trust when a second wave of COVID-19 occurs or there is a new surge of the current pandemic. ", doi="10.2196/20550", url="http://www.jmir.org/2020/11/e20550/", url="http://www.ncbi.nlm.nih.gov/pubmed/33119535" } @Article{info:doi/10.2196/22600, author="Saha, Koustuv and Torous, John and Caine, D. Eric and De Choudhury, Munmun", title="Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media", journal="J Med Internet Res", year="2020", month="Nov", day="24", volume="22", number="11", pages="e22600", keywords="social media", keywords="Twitter", keywords="language", keywords="psychosocial effects", keywords="mental health", keywords="transfer learning", keywords="depression", keywords="anxiety", keywords="stress", keywords="social support", keywords="emotions", keywords="COVID-19", keywords="coronavirus", keywords="crisis", abstract="Background: The COVID-19 pandemic has caused several disruptions in personal and collective lives worldwide. The uncertainties surrounding the pandemic have also led to multifaceted mental health concerns, which can be exacerbated with precautionary measures such as social distancing and self-quarantining, as well as societal impacts such as economic downturn and job loss. Despite noting this as a ``mental health tsunami'', the psychological effects of the COVID-19 crisis remain unexplored at scale. Consequently, public health stakeholders are currently limited in identifying ways to provide timely and tailored support during these circumstances. Objective: Our study aims to provide insights regarding people's psychosocial concerns during the COVID-19 pandemic by leveraging social media data. We aim to study the temporal and linguistic changes in symptomatic mental health and support expressions in the pandemic context. Methods: We obtained about 60 million Twitter streaming posts originating from the United States from March 24 to May 24, 2020, and compared these with about 40 million posts from a comparable period in 2019 to attribute the effect of COVID-19 on people's social media self-disclosure. Using these data sets, we studied people's self-disclosure on social media in terms of symptomatic mental health concerns and expressions of support. We employed transfer learning classifiers that identified the social media language indicative of mental health outcomes (anxiety, depression, stress, and suicidal ideation) and support (emotional and informational support). We then examined the changes in psychosocial expressions over time and language, comparing the 2020 and 2019 data sets. Results: We found that all of the examined psychosocial expressions have significantly increased during the COVID-19 crisis---mental health symptomatic expressions have increased by about 14\%, and support expressions have increased by about 5\%, both thematically related to COVID-19. We also observed a steady decline and eventual plateauing in these expressions during the COVID-19 pandemic, which may have been due to habituation or due to supportive policy measures enacted during this period. Our language analyses highlighted that people express concerns that are specific to and contextually related to the COVID-19 crisis. Conclusions: We studied the psychosocial effects of the COVID-19 crisis by using social media data from 2020, finding that people's mental health symptomatic and support expressions significantly increased during the COVID-19 period as compared to similar data from 2019. However, this effect gradually lessened over time, suggesting that people adapted to the circumstances and their ``new normal.'' Our linguistic analyses revealed that people expressed mental health concerns regarding personal and professional challenges, health care and precautionary measures, and pandemic-related awareness. This study shows the potential to provide insights to mental health care and stakeholders and policy makers in planning and implementing measures to mitigate mental health risks amid the health crisis. ", doi="10.2196/22600", url="http://www.jmir.org/2020/11/e22600/", url="http://www.ncbi.nlm.nih.gov/pubmed/33156805" } @Article{info:doi/10.2196/20044, author="Niburski, Kacper and Niburski, Oskar", title="Impact of Trump's Promotion of Unproven COVID-19 Treatments and Subsequent Internet Trends: Observational Study", journal="J Med Internet Res", year="2020", month="Nov", day="20", volume="22", number="11", pages="e20044", keywords="COVID-19", keywords="behavioral economics", keywords="public health", keywords="behavior", keywords="economics", keywords="media", keywords="influence", keywords="infodemic", keywords="infodemiology", keywords="infoveillance", keywords="Twitter", keywords="analysis", keywords="trend", abstract="Background: Individuals with large followings can influence public opinions and behaviors, especially during a pandemic. In the early days of the pandemic, US president Donald J Trump has endorsed the use of unproven therapies. Subsequently, a death attributed to the wrongful ingestion of a chloroquine-containing compound occurred. Objective: We investigated Donald J Trump's speeches and Twitter posts, as well as Google searches and Amazon purchases, and television airtime for mentions of hydroxychloroquine, chloroquine, azithromycin, and remdesivir. Methods: Twitter sourcing was catalogued with Factba.se, and analytics data, both past and present, were analyzed with Tweet Binder to assess average analytics data on key metrics. Donald J Trump's time spent discussing unverified treatments on the United States' 5 largest TV stations was catalogued with the Global Database of Events, Language, and Tone, and his speech transcripts were obtained from White House briefings. Google searches and shopping trends were analyzed with Google Trends. Amazon purchases were assessed using Helium 10 software. Results: From March 1 to April 30, 2020, Donald J Trump made 11 tweets about unproven therapies and mentioned these therapies 65 times in White House briefings, especially touting hydroxychloroquine and chloroquine. These tweets had an impression reach of 300\% above Donald J Trump's average. Following these tweets, at least 2\% of airtime on conservative networks for treatment modalities like azithromycin and continuous mentions of such treatments were observed on stations like Fox News. Google searches and purchases increased following his first press conference on March 19, 2020, and increased again following his tweets on March 21, 2020. The same is true for medications on Amazon, with purchases for medicine substitutes, such as hydroxychloroquine, increasing by 200\%. Conclusions: Individuals in positions of power can sway public purchasing, resulting in undesired effects when the individuals' claims are unverified. Public health officials must work to dissuade the use of unproven treatments for COVID-19. ", doi="10.2196/20044", url="http://www.jmir.org/2020/11/e20044/", url="http://www.ncbi.nlm.nih.gov/pubmed/33151895" } @Article{info:doi/10.2196/17903, author="Garcia-Rudolph, Alejandro and Saur{\'i}, Joan and Cegarra, Blanca and Bernabeu Guitart, Montserrat", title="Discovering the Context of People With Disabilities: Semantic Categorization Test and Environmental Factors Mapping of Word Embeddings from Reddit", journal="JMIR Med Inform", year="2020", month="Nov", day="20", volume="8", number="11", pages="e17903", keywords="disability", keywords="Reddit", keywords="social media", keywords="word2vec", keywords="semantic categorization", keywords="silhouette", keywords="activities of daily life", keywords="aspects of daily life", keywords="context", keywords="embeddings", abstract="Background: The World Health Organization's International Classification of Functioning Disability and Health (ICF) conceptualizes disability not solely as a problem that resides in the individual, but as a health experience that occurs in a context. Word embeddings build on the idea that words that occur in similar contexts tend to have similar meanings. In spite of both sharing ``context'' as a key component, word embeddings have been scarcely applied in disability. In this work, we propose social media (particularly, Reddit) to link them. Objective: The objective of our study is to train a model for generating word associations using a small dataset (a subreddit on disability) able to retrieve meaningful content. This content will be formally validated and applied to the discovery of related terms in the corpus of the disability subreddit that represent the physical, social, and attitudinal environment (as defined by a formal framework like the ICF) of people with disabilities. Methods: Reddit data were collected from pushshift.io with the pushshiftr R package as a wrapper. A word2vec model was trained with the wordVectors R package using the disability subreddit comments, and a preliminary validation was performed using a subset of Mikolov analogies. We used Van Overschelde's updated and expanded version of the Battig and Montague norms to perform a semantic categories test. Silhouette coefficients were calculated using cosine distance from the wordVectors R package. For each of the 5 ICF environmental factors (EF), we selected representative subcategories addressing different aspects of daily living (ADLs); then, for each subcategory, we identified specific terms extracted from their formal ICF definition and ran the word2vec model to generate their nearest semantic terms, validating the obtained nearest semantic terms using public evidence. Finally, we applied the model to a specific subcategory of an EF involved in a relevant use case in the field of rehabilitation. Results: We analyzed 96,314 comments posted between February 2009 and December 2019, by 10,411 Redditors. We trained word2vec and identified more than 30 analogies (eg, breakfast -- 8 am + 8 pm = dinner). The semantic categorization test showed promising results over 60 categories; for example, s(A relative)=0.562, s(A sport)=0.475 provided remarkable explanations for low s values. We mapped the representative subcategories of all EF chapters and obtained the closest terms for each, which we confirmed with publications. This allowed immediate access (? 2 seconds) to the terms related to ADLs, ranging from apps ``to know accessibility before you go'' to adapted sports (boccia). For example, for the support and relationships EF subcategory, the closest term discovered by our model was ``resilience,'' recently regarded as a key feature of rehabilitation, not yet having one unified definition. Our model discovered 10 closest terms, which we validated with publications, contributing to the ``resilience'' definition. Conclusions: This study opens up interesting opportunities for the exploration and discovery of the use of a word2vec model that has been trained with a small disability dataset, leading to immediate, accurate, and often unknown (for authors, in many cases) terms related to ADLs within the ICF framework. ", doi="10.2196/17903", url="http://medinform.jmir.org/2020/11/e17903/", url="http://www.ncbi.nlm.nih.gov/pubmed/33216006" } @Article{info:doi/10.2196/15347, author="Homan, Michael Christopher and Schrading, Nicolas J. and Ptucha, W. Raymond and Cerulli, Catherine and Ovesdotter Alm, Cecilia", title="Quantitative Methods for Analyzing Intimate Partner Violence in Microblogs: Observational Study", journal="J Med Internet Res", year="2020", month="Nov", day="19", volume="22", number="11", pages="e15347", keywords="intimate partner violence", keywords="social media", keywords="natural language processing", abstract="Background: Social media is a rich, virtually untapped source of data on the dynamics of intimate partner violence, one that is both global in scale and intimate in detail. Objective: The aim of this study is to use machine learning and other computational methods to analyze social media data for the reasons victims give for staying in or leaving abusive relationships. Methods: Human annotation, part-of-speech tagging, and machine learning predictive models, including support vector machines, were used on a Twitter data set of 8767 \#WhyIStayed and \#WhyILeft tweets each. Results: Our methods explored whether we can analyze micronarratives that include details about victims, abusers, and other stakeholders, the actions that constitute abuse, and how the stakeholders respond. Conclusions: Our findings are consistent across various machine learning methods, which correspond to observations in the clinical literature, and affirm the relevance of natural language processing and machine learning for exploring issues of societal importance in social media. ", doi="10.2196/15347", url="http://www.jmir.org/2020/11/e15347/", url="http://www.ncbi.nlm.nih.gov/pubmed/33211021" } @Article{info:doi/10.2196/21329, author="Alanazi, Eisa and Alashaikh, Abdulaziz and Alqurashi, Sarah and Alanazi, Aued", title="Identifying and Ranking Common COVID-19 Symptoms From Tweets in Arabic: Content Analysis", journal="J Med Internet Res", year="2020", month="Nov", day="18", volume="22", number="11", pages="e21329", keywords="health", keywords="informatics", keywords="social networks", keywords="Twitter", keywords="anosmia", keywords="Arabic", keywords="COVID-19", keywords="symptom", abstract="Background: A substantial amount of COVID-19--related data is generated by Twitter users every day. Self-reports of COVID-19 symptoms on Twitter can reveal a great deal about the disease and its prevalence in the community. In particular, self-reports can be used as a valuable resource to learn more about common symptoms and whether their order of appearance differs among different groups in the community. These data may be used to develop a COVID-19 risk assessment system that is tailored toward a specific group of people. Objective: The aim of this study was to identify the most common symptoms reported by patients with COVID-19, as well as the order of symptom appearance, by examining tweets in Arabic. Methods: We searched Twitter posts in Arabic for personal reports of COVID-19 symptoms from March 1 to May 27, 2020. We identified 463 Arabic users who had tweeted about testing positive for COVID-19 and extracted the symptoms they associated with the disease. Furthermore, we asked them directly via personal messaging to rank the appearance of the first 3 symptoms they had experienced immediately before (or after) their COVID-19 diagnosis. Finally, we tracked their Twitter timeline to identify additional symptoms that were mentioned within {\textpm}5 days from the day of the first tweet on their COVID-19 diagnosis. In total, 270 COVID-19 self-reports were collected, and symptoms were (at least partially) ranked. Results: The collected self-reports contained 893 symptoms from 201 (74\%) male and 69 (26\%) female Twitter users. The majority (n=270, 82\%) of the tracked users were living in Saudi Arabia (n=125, 46\%) and Kuwait (n=98, 36\%). Furthermore, 13\% (n=36) of the collected reports were from asymptomatic individuals. Of the 234 users with symptoms, 66\% (n=180) provided a chronological order of appearance for at least 3 symptoms. Fever (n=139, 59\%), headache (n=101, 43\%), and anosmia (n=91, 39\%) were the top 3 symptoms mentioned in the self-reports. Additionally, 28\% (n=65) reported that their COVID-19 experience started with a fever, 15\% (n=34) with a headache, and 12\% (n=28) with anosmia. Of the 110 symptomatic cases from Saudi Arabia, the most common 3 symptoms were fever (n=65, 59\%), anosmia (n=46, 42\%), and headache (n=42, 38\%). Conclusions: This study identified the most common symptoms of COVID-19 from tweets in Arabic. These symptoms can be further analyzed in clinical settings and may be incorporated into a real-time COVID-19 risk estimator. ", doi="10.2196/21329", url="http://www.jmir.org/2020/11/e21329/", url="http://www.ncbi.nlm.nih.gov/pubmed/33119539" } @Article{info:doi/10.2196/22639, author="Hu, Zhiwen and Yang, Zhongliang and Li, Qi and Zhang, An", title="The COVID-19 Infodemic: Infodemiology Study Analyzing Stigmatizing Search Terms", journal="J Med Internet Res", year="2020", month="Nov", day="16", volume="22", number="11", pages="e22639", keywords="infodemiology", keywords="COVID-19", keywords="infodemic", keywords="social contagion", keywords="collective perceptual biases", keywords="collective behavioral propensities", keywords="social mobilization", abstract="Background: In the context of the COVID-19 infodemic, the global profusion of monikers and hashtags for COVID-19 have found their way into daily communication and contributed to a backlash against China and the Chinese people. Objective: This study examines public engagement in crisis communication about COVID-19 during the early epidemic stage and the practical strategy of social mobilization to mitigate the infodemic. Methods: We retrieved the unbiased values of the top-ranked search phrases between December 30, 2019, and July 15, 2020, which normalized the anonymized, categorized, and aggregated samples from Google Search data. This study illustrates the most-searched terms, including the official COVID-19 terms, the stigmatized terms, and other controls, to measure the collective behavioral propensities to stigmatized terms and to explore the global reaction to the COVID-19 epidemic in the real world. We calculated the ratio of the cumulative number of COVID-19 cases to the regional population as the cumulative rate (R) of a specific country or territory and calculated the Gini coefficient (G) to measure the collective heterogeneity of crowd behavior. Results: People around the world are using stigmatizing terms on Google Search, and these terms were used earlier than the official names. Many stigmatized monikers against China (eg, ``Wuhan pneumonia,'' G=0.73; ``Wuhan coronavirus,'' G=0.60; ``China pneumonia,'' G=0.59; ``China coronavirus,'' G=0.52; ``Chinese coronavirus,'' G=0.50) had high collective heterogeneity of crowd behavior between December 30, 2019, and July 15, 2020, while the official terms ``COVID-19'' (G=0.44) and ``SARS-CoV-2'' (G=0.42) have not become de facto standard usages. Moreover, the pattern of high consistent usage was observed in 13 territories with low cumulative rates (R) between January 16 and July 15, 2020, out of 58 countries and territories that have reported confirmed cases of COVID-19. In the scientific literature, multifarious naming practices may have provoked unintended negative impacts by stigmatizing Chinese people. The World Health Organization; the United Nations Educational, Scientific and Cultural Organization; and the media initiated campaigns for fighting back against the COVID-19 infodemic with the same mission but in diverse voices. Conclusions: Infodemiological analysis can articulate the collective propensities to stigmatized monikers across search behaviors, which may reflect the collective sentiment of backlash against China and Chinese people in the real world. The full-fledged official terms are expected to fight back against the resilience of negative perceptual bias amid the COVID-19 epidemic. Such official naming efforts against the infodemic should be met with a fair share of identification in scientific conventions and sociocultural paradigms. As an integral component of preparedness, appropriate nomenclatures should be duly assigned to the newly identified coronavirus, and social mobilization in a uniform voice is a priority for combating the next infodemic. ", doi="10.2196/22639", url="http://www.jmir.org/2020/11/e22639/", url="http://www.ncbi.nlm.nih.gov/pubmed/33156807" } @Article{info:doi/10.2196/22205, author="Lee, Jae Jung and Kang, Kyung-Ah and Wang, Ping Man and Zhao, Zhi Sheng and Wong, Ha Janet Yuen and O'Connor, Siobhan and Yang, Ching Sook and Shin, Sunhwa", title="Associations Between COVID-19 Misinformation Exposure and Belief With COVID-19 Knowledge and Preventive Behaviors: Cross-Sectional Online Study", journal="J Med Internet Res", year="2020", month="Nov", day="13", volume="22", number="11", pages="e22205", keywords="COVID-19", keywords="misinformation", keywords="infodemic", keywords="infodemiology", keywords="anxiety", keywords="depression", keywords="PTSD", keywords="knowledge", keywords="preventive behaviors", keywords="prevention", keywords="behavior", abstract="Background: Online misinformation proliferation during the COVID-19 pandemic has become a major public health concern. Objective: We aimed to assess the prevalence of COVID-19 misinformation exposure and beliefs, associated factors including psychological distress with misinformation exposure, and the associations between COVID-19 knowledge and number of preventive behaviors. Methods: A cross-sectional online survey was conducted with 1049 South Korean adults in April 2020. Respondents were asked about receiving COVID-19 misinformation using 12 items identified by the World Health Organization. Logistic regression was used to compute adjusted odds ratios (aORs) for the association of receiving misinformation with sociodemographic characteristics, source of information, COVID-19 misinformation belief, and psychological distress, as well as the associations of COVID-19 misinformation belief with COVID-19 knowledge and the number of COVID-19 preventive behaviors among those who received the misinformation. All data were weighted according to the Korea census data in 2018. Results: Overall, 67.78\% (n=711) of respondents reported exposure to at least one COVID-19 misinformation item. Misinformation exposure was associated with younger age, higher education levels, and lower income. Sources of information associated with misinformation exposure were social networking services (aOR 1.67, 95\% CI 1.20-2.32) and instant messaging (aOR 1.79, 1.27-2.51). Misinformation exposure was also associated with psychological distress including anxiety (aOR 1.80, 1.24-2.61), depressive (aOR 1.47, 1.09-2.00), and posttraumatic stress disorder symptoms (aOR 1.97, 1.42-2.73), as well as misinformation belief (aOR 7.33, 5.17-10.38). Misinformation belief was associated with poorer COVID-19 knowledge (high: aOR 0.62, 0.45-0.84) and fewer preventive behaviors (?7 behaviors: aOR 0.54, 0.39-0.74). Conclusions: COVID-19 misinformation exposure was associated with misinformation belief, while misinformation belief was associated with fewer preventive behaviors. Given the potential of misinformation to undermine global efforts in COVID-19 disease control, up-to-date public health strategies are required to counter the proliferation of misinformation. ", doi="10.2196/22205", url="http://www.jmir.org/2020/11/e22205/", url="http://www.ncbi.nlm.nih.gov/pubmed/33048825" } @Article{info:doi/10.2196/18998, author="Xu, Chenjie and Cao, Zhi and Yang, Hongxi and Gao, Ying and Sun, Li and Hou, Yabing and Cao, Xinxi and Jia, Peng and Wang, Yaogang", title="Leveraging Internet Search Data to Improve the Prediction and Prevention of Noncommunicable Diseases: Retrospective Observational Study", journal="J Med Internet Res", year="2020", month="Nov", day="12", volume="22", number="11", pages="e18998", keywords="noncommunicable diseases", keywords="internet searches", keywords="Google Trends", keywords="infodemiology", keywords="infoveillance", keywords="early warning model", keywords="United States", abstract="Background: As human society enters an era of vast and easily accessible social media, a growing number of people are exploiting the internet to search and exchange medical information. Because internet search data could reflect population interest in particular health topics, they provide a new way of understanding health concerns regarding noncommunicable diseases (NCDs) and the role they play in their prevention. Objective: We aimed to explore the association of internet search data for NCDs with published disease incidence and mortality rates in the United States and to grasp the health concerns toward NCDs. Methods: We tracked NCDs by examining the correlations among the incidence rates, mortality rates, and internet searches in the United States from 2004 to 2017, and we established forecast models based on the relationship between the disease rates and internet searches. Results: Incidence and mortality rates of 29 diseases in the United States were statistically significantly correlated with the relative search volumes (RSVs) of their search terms (P<.05). From the perspective of the goodness of fit of the multiple regression prediction models, the results were closest to 1 for diabetes mellitus, stroke, atrial fibrillation and flutter, Hodgkin lymphoma, and testicular cancer; the coefficients of determination of their linear regression models for predicting incidence were 80\%, 88\%, 96\%, 80\%, and 78\%, respectively. Meanwhile, the coefficient of determination of their linear regression models for predicting mortality was 82\%, 62\%, 94\%, 78\%, and 62\%, respectively. Conclusions: An advanced understanding of search behaviors could augment traditional epidemiologic surveillance and could be used as a reference to aid in disease prediction and prevention. ", doi="10.2196/18998", url="http://www.jmir.org/2020/11/e18998/", url="http://www.ncbi.nlm.nih.gov/pubmed/33180022" } @Article{info:doi/10.2196/21978, author="Boon-Itt, Sakun and Skunkan, Yukolpat", title="Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study", journal="JMIR Public Health Surveill", year="2020", month="Nov", day="11", volume="6", number="4", pages="e21978", keywords="COVID-19", keywords="Twitter", keywords="social media", keywords="infoveillance", keywords="infodemiology", keywords="infodemic", keywords="data", keywords="health informatics", keywords="mining", keywords="perception", keywords="topic modeling", abstract="Background: COVID-19 is a scientifically and medically novel disease that is not fully understood because it has yet to be consistently and deeply studied. Among the gaps in research on the COVID-19 outbreak, there is a lack of sufficient infoveillance data. Objective: The aim of this study was to increase understanding of public awareness of COVID-19 pandemic trends and uncover meaningful themes of concern posted by Twitter users in the English language during the pandemic. Methods: Data mining was conducted on Twitter to collect a total of 107,990 tweets related to COVID-19 between December 13 and March 9, 2020. The analyses included frequency of keywords, sentiment analysis, and topic modeling to identify and explore discussion topics over time. A natural language processing approach and the latent Dirichlet allocation algorithm were used to identify the most common tweet topics as well as to categorize clusters and identify themes based on the keyword analysis. Results: The results indicate three main aspects of public awareness and concern regarding the COVID-19 pandemic. First, the trend of the spread and symptoms of COVID-19 can be divided into three stages. Second, the results of the sentiment analysis showed that people have a negative outlook toward COVID-19. Third, based on topic modeling, the themes relating to COVID-19 and the outbreak were divided into three categories: the COVID-19 pandemic emergency, how to control COVID-19, and reports on COVID-19. Conclusions: Sentiment analysis and topic modeling can produce useful information about the trends in the discussion of the COVID-19 pandemic on social media as well as alternative perspectives to investigate the COVID-19 crisis, which has created considerable public awareness. This study shows that Twitter is a good communication channel for understanding both public concern and public awareness about COVID-19. These findings can help health departments communicate information to alleviate specific public concerns about the disease. ", doi="10.2196/21978", url="http://publichealth.jmir.org/2020/4/e21978/", url="http://www.ncbi.nlm.nih.gov/pubmed/33108310" } @Article{info:doi/10.2196/24361, author="Xue, Jia and Chen, Junxiang and Chen, Chen and Hu, Ran and Zhu, Tingshao", title="The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets", journal="J Med Internet Res", year="2020", month="Nov", day="6", volume="22", number="11", pages="e24361", keywords="Twitter", keywords="family violence", keywords="COVID-19", keywords="machine learning", keywords="big data", keywords="infodemiology", keywords="infoveillance", abstract="Background: Family violence (including intimate partner violence/domestic violence, child abuse, and elder abuse) is a hidden pandemic happening alongside COVID-19. The rates of family violence are rising fast, and women and children are disproportionately affected and vulnerable during this time. Objective: This study aims to provide a large-scale analysis of public discourse on family violence and the COVID-19 pandemic on Twitter. Methods: We analyzed over 1 million tweets related to family violence and COVID-19 from April 12 to July 16, 2020. We used the machine learning approach Latent Dirichlet Allocation and identified salient themes, topics, and representative tweets. Results: We extracted 9 themes from 1,015,874 tweets on family violence and the COVID-19 pandemic: (1) increased vulnerability: COVID-19 and family violence (eg, rising rates, increases in hotline calls, homicide); (2) types of family violence (eg, child abuse, domestic violence, sexual abuse); (3) forms of family violence (eg, physical aggression, coercive control); (4) risk factors linked to family violence (eg, alcohol abuse, financial constraints, guns, quarantine); (5) victims of family violence (eg, the LGBTQ [lesbian, gay, bisexual, transgender, and queer or questioning] community, women, women of color, children); (6) social services for family violence (eg, hotlines, social workers, confidential services, shelters, funding); (7) law enforcement response (eg, 911 calls, police arrest, protective orders, abuse reports); (8) social movements and awareness (eg, support victims, raise awareness); and (9) domestic violence--related news (eg, Tara Reade, Melissa DeRosa). Conclusions: This study overcomes limitations in the existing scholarship where data on the consequences of COVID-19 on family violence are lacking. We contribute to understanding family violence during the pandemic by providing surveillance via tweets. This is essential for identifying potentially useful policy programs that can offer targeted support for victims and survivors as we prepare for future outbreaks. ", doi="10.2196/24361", url="http://www.jmir.org/2020/11/e24361/", url="http://www.ncbi.nlm.nih.gov/pubmed/33108315" } @Article{info:doi/10.2196/21646, author="Al-Rawi, Ahmed and Siddiqi, Maliha and Morgan, Rosemary and Vandan, Nimisha and Smith, Julia and Wenham, Clare", title="COVID-19 and the Gendered Use of Emojis on Twitter: Infodemiology Study", journal="J Med Internet Res", year="2020", month="Nov", day="5", volume="22", number="11", pages="e21646", keywords="emojis", keywords="social media", keywords="Twitter", keywords="gender", keywords="COVID-19", keywords="sentiment", keywords="meaning", abstract="Background: The online discussion around the COVID-19 pandemic is multifaceted, and it is important to examine the different ways by which online users express themselves. Since emojis are used as effective vehicles to convey ideas and sentiments, they can offer important insight into the public's gendered discourses about the pandemic. Objective: This study aims at exploring how people of different genders (eg, men, women, and sex and gender minorities) are discussed in relation to COVID-19 through the study of Twitter emojis. Methods: We collected over 50 million tweets referencing the hashtags \#Covid-19 and \#Covid19 for a period of more than 2 months in early 2020. Using a mixed method, we extracted three data sets containing tweets that reference men, women, and sexual and gender minorities, and we then analyzed emoji use along each gender category. We identified five major themes in our analysis including morbidity fears, health concerns, employment and financial issues, praise for frontline workers, and unique gendered emoji use. The top 600 emojis were manually classified based on their sentiment, indicating how positive, negative, or neutral each emoji is and studying their use frequencies. Results: The findings indicate that the majority of emojis are overwhelmingly positive in nature along the different genders, but sexual and gender minorities, and to a lesser extent women, are discussed more negatively than men. There were also many differences alongside discourses of men, women, and gender minorities when certain topics were discussed, such as death, financial and employment matters, gratitude, and health care, and several unique gendered emojis were used to express specific issues like community support. Conclusions: Emoji research can shed light on the gendered impacts of COVID-19, offering researchers an important source of information on health crises as they happen in real time. ", doi="10.2196/21646", url="http://www.jmir.org/2020/11/e21646/", url="http://www.ncbi.nlm.nih.gov/pubmed/33052871" } @Article{info:doi/10.2196/15577, author="McCausland, Kahlia and Maycock, Bruce and Leaver, Tama and Wolf, Katharina and Freeman, Becky and Thomson, Katie and Jancey, Jonine", title="E-Cigarette Promotion on Twitter in Australia: Content Analysis of Tweets", journal="JMIR Public Health Surveill", year="2020", month="Nov", day="5", volume="6", number="4", pages="e15577", keywords="electronic cigarette", keywords="e-cigarette", keywords="electronic nicotine delivery systems", keywords="vaping", keywords="vape", keywords="social media", keywords="twitter", keywords="content analysis", keywords="public health", keywords="public policy", abstract="Background: The sale of electronic cigarettes (e-cigarettes) containing nicotine is prohibited in all Australian states and territories; yet, the growing availability and convenience of the internet enable the promotion and exposure of e-cigarettes across countries. Social media's increasing pervasiveness has provided a powerful avenue to market products and influence social norms and risk behaviors. At present, there is no evidence of how e-cigarettes and vaping are promoted on social media in Australia. Objective: This study aimed to investigate how e-cigarettes are portrayed and promoted on Twitter through a content analysis of vaping-related tweets containing an image posted and retweeted by Australian users and how the portrayal and promotion have emerged and trended over time. Methods: In total, we analyzed 1303 tweets and accompanying images from 2012, 2014, 2016, and 2018 collected through the Tracking Infrastructure for Social Media Analysis (TrISMA), a contemporary technical and organizational infrastructure for the tracking of public communication by Australian users of social media, via a list of 15 popular e-cigarette--related terms. Results: Despite Australia's cautious approach toward e-cigarettes and the limited evidence supporting them as an efficacious smoking cessation aid, it is evident that there is a concerted effort by some Twitter users to promote these devices as a health-conducive (91/129, 70.5\%), smoking cessation product (266/1303, 20.41\%). Further, Twitter is being used in an attempt to circumvent Australian regulation and advocate for a more liberal approach to personal vaporizers (90/1303, 6.90\%). A sizeable proportion of posts was dedicated to selling or promoting vape products (347/1303, 26.63\%), and 19.95\% (260/1303) were found to be business listings. These posts used methods to try and expand their clientele further than immediate followers by touting competitions and giveaways, with those wanting to enter having to perform a sequence of steps such as liking, tagging, and reposting, ultimately exposing the post among the user's network and to others not necessarily interested in vaping. Conclusions: The borderless nature of social media presents a clear challenge for enforcing Article 13 of the World Health Organization Framework Convention on Tobacco Control, which requires all ratifying nations to implement a ban on tobacco advertising, promotion, and sponsorship. Countering the advertising and promotion of these products is a public health challenge that will require cross-border cooperation with other World Health Organization Framework Convention on Tobacco Control parties. Further research aimed at developing strategies to counter the advertising and promotion of e-cigarettes is therefore needed. ", doi="10.2196/15577", url="http://publichealth.jmir.org/2020/4/e15577/", url="http://www.ncbi.nlm.nih.gov/pubmed/33151159" } @Article{info:doi/10.2196/17247, author="Sch{\"a}fer, Florent and Faviez, Carole and Voillot, Pam{\'e}la and Foulqui{\'e}, Pierre and Najm, Matthieu and Jeanne, Jean-Fran{\c{c}}ois and Fagherazzi, Guy and Sch{\"u}ck, St{\'e}phane and Le Nev{\'e}, Boris", title="Mapping and Modeling of Discussions Related to Gastrointestinal Discomfort in French-Speaking Online Forums: Results of a 15-Year Retrospective Infodemiology Study", journal="J Med Internet Res", year="2020", month="Nov", day="3", volume="22", number="11", pages="e17247", keywords="gastrointestinal discomfort", keywords="disorders of gut-brain interactions", keywords="social media", keywords="infodemiology", keywords="topic modeling", abstract="Background: Gastrointestinal (GI) discomfort is prevalent and known to be associated with impaired quality of life. Real-world information on factors of GI discomfort and solutions used by people is, however, limited. Social media, including online forums, have been considered a new source of information to examine the health of populations in real-life settings. Objective: The aims of this retrospective infodemiology study are to identify discussion topics, characterize users, and identify perceived determinants of GI discomfort in web-based messages posted by users of French social media. Methods: Messages related to GI discomfort posted between January 2003 and August 2018 were extracted from 14 French-speaking general and specialized publicly available online forums. Extracted messages were cleaned and deidentified. Relevant medical concepts were determined on the basis of the Medical Dictionary for Regulatory Activities and vernacular terms. The identification of discussion topics was carried out by using a correlated topic model on the basis of the latent Dirichlet allocation. A nonsupervised clustering algorithm was applied to cluster forum users according to the reported symptoms of GI discomfort, discussion topics, and activity on online forums. Users' age and gender were determined by linear regression and application of a support vector machine, respectively, to characterize the identified clusters according to demographic parameters. Perceived factors of GI discomfort were classified by a combined method on the basis of syntactic analysis to identify messages with causality terms and a second topic modeling in a relevant segment of phrases. Results: A total of 198,866 messages associated with GI discomfort were included in the analysis corpus after extraction and cleaning. These messages were posted by 36,989 separate web users, most of them being women younger than 40 years. Everyday life, diet, digestion, abdominal pain, impact on the quality of life, and tips to manage stress were among the most discussed topics. Segmentation of users identified 5 clusters corresponding to chronic and acute GI concerns. Diet topic was associated with each cluster, and stress was strongly associated with abdominal pain. Psychological factors, food, and allergens were perceived as the main causes of GI discomfort by web users. Conclusions: GI discomfort is actively discussed by web users. This study reveals a complex relationship between food, stress, and GI discomfort. Our approach has shown that identifying web-based discussion topics associated with GI discomfort and its perceived factors is feasible and can serve as a complementary source of real-world evidence for caregivers. ", doi="10.2196/17247", url="https://www.jmir.org/2020/11/e17247", url="http://www.ncbi.nlm.nih.gov/pubmed/33141087" } @Article{info:doi/10.2196/17595, author="Sharma, Estelle Anjana and Mann, Ziva and Cherian, Roy and Del Rosario, Bing Jan and Yang, Janine and Sarkar, Urmimala", title="Recommendations From the Twitter Hashtag \#DoctorsAreDickheads: Qualitative Analysis", journal="J Med Internet Res", year="2020", month="Oct", day="28", volume="22", number="10", pages="e17595", keywords="social media", keywords="patient engagement", keywords="Twitter messaging", keywords="missed diagnosis", keywords="internet", keywords="physician patient relationship", abstract="Background: The social media site Twitter has 145 million daily active users worldwide and has become a popular forum for users to communicate their health care concerns and experiences as patients. In the fall of 2018, a hashtag titled \#DoctorsAreDickheads emerged, with almost 40,000 posts calling attention to health care experiences. Objective: This study aims to identify common health care conditions and conceptual themes represented within the phenomenon of this viral Twitter hashtag. Methods: We analyzed a random sample of 5.67\% (500/8818) available tweets for qualitative analysis between October 15 and December 31, 2018, when the hashtag was the most active. Team coders reviewed the same 20.0\% (100/500) tweets and the remainder individually. We abstracted the user's health care role and clinical conditions from the tweet and user profile, and used phenomenological content analysis to identify prevalent conceptual themes through sequential open coding, memoing, and discussion of concepts until an agreement was reached. Results: Our final sample comprised 491 tweets and unique Twitter users. Of this sample, 50.5\% (248/491) were from patients or patient advocates, 9.6\% (47/491) from health care professionals, 4.3\% (21/491) from caregivers, 3.7\% (18/491) from academics or researchers, 1.0\% (5/491) from journalists or media, and 31.6\% (155/491) from non--health care individuals or other. The most commonly mentioned clinical conditions were chronic pain, mental health, and musculoskeletal conditions (mainly Ehlers-Danlos syndrome). We identified 3 major themes: disbelief in patients' experience and knowledge that contributes to medical errors and harm, the power inequity between patients and providers, and metacommentary on the meaning and impact of the \#DoctorsAreDickheads hashtag. Conclusions: People publicly disclose personal and often troubling health care experiences on Twitter. This adds new accountability for the patient-provider interaction, highlights how harmful communication affects diagnostic safety, and shapes the public's viewpoint of how clinicians behave. Hashtags such as this offer valuable opportunities to learn from patient experiences. Recommendations include developing best practices for providers to improve communication, supporting patients through challenging diagnoses, and promoting patient engagement. ", doi="10.2196/17595", url="http://www.jmir.org/2020/10/e17595/", url="http://www.ncbi.nlm.nih.gov/pubmed/33112246" } @Article{info:doi/10.2196/20619, author="Han, Leo and Boniface, R. Emily and Han, Yin Lisa and Albright, Jonathan and Doty, Nora and Darney, G. Blair", title="The Abortion Web Ecosystem: Cross-Sectional Analysis of Trustworthiness and Bias", journal="J Med Internet Res", year="2020", month="Oct", day="26", volume="22", number="10", pages="e20619", keywords="internet", keywords="abortion", keywords="media", keywords="websites", keywords="infodemiology", keywords="infodemic", keywords="quality of health information", keywords="bias in patient education", abstract="Background: People use the internet as a primary source for learning about medical procedures and their associated safety profiles and risks. Although abortion is one of the most common procedures worldwide among women in their reproductive years, it is controversial and highly politicized. Substantial scientific evidence demonstrates that abortion is safe and does not increase a woman's future risk for depressive disorders or infertility. The extent to which information found on the internet reflects these medical facts in a trustworthy and unbiased manner is not known. Objective: The purpose of this study was to collate and describe the trustworthiness and political slant or bias of web-based information about abortion safety and risks of depression and infertility following abortion. Methods: We performed a cross-sectional study of internet websites using 3 search topics: (1) is abortion safe?, (2) does abortion cause depression?, and (3) does abortion cause infertility? We used the Google Adwords tool to identify the search terms most associated with those topics and Google's search engine to generate databases of websites related to each topic. We then classified and rated each website in terms of content slant (pro-choice, neutral, anti-choice), clarity of slant (obvious, in-between, or difficult/can't tell), trustworthiness (rating scale of 1-5, 5=most trustworthy), type (forum, feature, scholarly article, resource page, news article, blog, or video), and top-level domain (.com, .net, .org, .edu, .gov, or international domain). We compared website characteristics by search topic (safety, depression, or infertility) using bivariate tests. We summarized trustworthiness using the median and IQR, and we used box-and-whisker plots to visually compare trustworthiness by slant and domain type. Results: Our search methods yielded a total of 111, 120, and 85 unique sites for safety, depression, and infertility, respectively. Of all the sites (n=316), 57.3\% (181/316) were neutral, 35.4\% (112/316) were anti-choice, and 7.3\% (23/316) were pro-choice. The median trustworthiness score was 2.7 (IQR 1.7-3.7), which did not differ significantly across topics (P=.409). Anti-choice sites were less trustworthy (median score 1.3, IQR 1.0-1.7) than neutral (median score 3.3, IQR 2.7-4.0) and pro-choice (median score 3.7, IQR 3.3-4.3) sites. Anti-choice sites were also more likely to have slant clarity that was ``difficult to tell'' (41/112, 36.6\%) compared with neutral (25/181, 13.8\%) or pro-choice (4/23, 17.4\%; P<.001) sites. A negative search term used for the topic of safety (eg, ``risks'') produced sites with lower trustworthiness scores than search terms with the word ``safety'' (median score 1.7 versus 3.7, respectively; P<.001). Conclusions: People seeking information about the safety and potential risks of abortion are likely to encounter a substantial amount of untrustworthy and slanted/biased abortion information. Anti-choice sites are prevalent, often difficult to identify as anti-choice, and less trustworthy than neutral or pro-choice sites. Web searches may lead the public to believe abortion is riskier than it is. ", doi="10.2196/20619", url="http://www.jmir.org/2020/10/e20619/", url="http://www.ncbi.nlm.nih.gov/pubmed/33104002" } @Article{info:doi/10.2196/19427, author="Park, Heum Tae and Kim, Il Woo and Park, Suyeon and Ahn, Jaeouk and Cho, Kyun Moon and Kim, Sooyoung", title="Public Interest in Acne on the Internet: Comparison of Search Information From Google Trends and Naver", journal="J Med Internet Res", year="2020", month="Oct", day="26", volume="22", number="10", pages="e19427", keywords="acne vulgaris", keywords="internet", keywords="infodemiology", keywords="infoveillance", keywords="cosmetics", keywords="diet", keywords="dermatology", keywords="Google", abstract="Background: Acne vulgaris is a common skin disease primarily affecting young adults. Given that the internet has become a major source of health information, especially among the young, the internet is a powerful tool of communication and has a significant influence on patients. Objective: This study aimed to clarify the features of patients' interest in and evaluate the quality of information about acne vulgaris on the internet. Methods: We compared the search volumes on acne vulgaris with those of other dermatological diseases using Google Trends from January 2004 to August 2019. We also determined the search volumes for relevant keywords of acne vulgaris on Google and Naver and evaluated the quality of answers to the queries in KnowledgeiN. Results: The regression analysis of Google Trends data demonstrated that the patients' interest in acne vulgaris was higher than that for other dermatological diseases, such as atopic dermatitis ($\beta$=?20.33, 95\% CI --22.27 to --18.39, P<.001) and urticaria ($\beta$=?27.09, 95\% CI --29.03 to --25.15, P<.001) and has increased yearly ($\beta$=2.38, 95\% CI 2.05 to 2.71, P<.001). The search volume for acne vulgaris was significantly higher in the summer than in the spring ($\beta$=--5.04, 95\% CI --9.21 to --0.88, P=.018) and on weekends than on weekdays ($\beta$=--6.68, 95\% CI --13.18 to --0.18, P=.044). The most frequently searched relevant keywords with ``acne vulgaris'' and ``cause'' were ``stress,'' ``food,'' and ``cosmetics.'' Among food, the 2 highest acne vulgaris--related keywords were milk and wheat in Naver and coffee and ramen in Google. The queries in Naver KnowledgeiN were mostly answered by a Korean traditional medicine doctor (53.4\%) or the public (33.6\%), but only 12.0\% by dermatologists. Conclusions: Physicians should be aware of patients' interest in and beliefs about acne vulgaris to provide the best patient education and care, both online and in the clinic. ", doi="10.2196/19427", url="http://www.jmir.org/2020/10/e19427/", url="http://www.ncbi.nlm.nih.gov/pubmed/33104003" } @Article{info:doi/10.2196/22624, author="Chandrasekaran, Ranganathan and Mehta, Vikalp and Valkunde, Tejali and Moustakas, Evangelos", title="Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study", journal="J Med Internet Res", year="2020", month="Oct", day="23", volume="22", number="10", pages="e22624", keywords="coronavirus", keywords="infodemiology", keywords="infoveillance", keywords="infodemic", keywords="twitter", keywords="COVID-19", keywords="social media", keywords="sentiment analysis", keywords="trends", keywords="topic modeling", keywords="disease surveillance", abstract="Background: With restrictions on movement and stay-at-home orders in place due to the COVID-19 pandemic, social media platforms such as Twitter have become an outlet for users to express their concerns, opinions, and feelings about the pandemic. Individuals, health agencies, and governments are using Twitter to communicate about COVID-19. Objective: The aims of this study were to examine key themes and topics of English-language COVID-19--related tweets posted by individuals and to explore the trends and variations in how the COVID-19--related tweets, key topics, and associated sentiments changed over a period of time from before to after the disease was declared a pandemic. Methods: Building on the emergent stream of studies examining COVID-19--related tweets in English, we performed a temporal assessment covering the time period from January 1 to May 9, 2020, and examined variations in tweet topics and sentiment scores to uncover key trends. Combining data from two publicly available COVID-19 tweet data sets with those obtained in our own search, we compiled a data set of 13.9 million English-language COVID-19--related tweets posted by individuals. We use guided latent Dirichlet allocation (LDA) to infer themes and topics underlying the tweets, and we used VADER (Valence Aware Dictionary and sEntiment Reasoner) sentiment analysis to compute sentiment scores and examine weekly trends for 17 weeks. Results: Topic modeling yielded 26 topics, which were grouped into 10 broader themes underlying the COVID-19--related tweets. Of the 13,937,906 examined tweets, 2,858,316 (20.51\%) were about the impact of COVID-19 on the economy and markets, followed by spread and growth in cases (2,154,065, 15.45\%), treatment and recovery (1,831,339, 13.14\%), impact on the health care sector (1,588,499, 11.40\%), and governments response (1,559,591, 11.19\%). Average compound sentiment scores were found to be negative throughout the examined time period for the topics of spread and growth of cases, symptoms, racism, source of the outbreak, and political impact of COVID-19. In contrast, we saw a reversal of sentiments from negative to positive for prevention, impact on the economy and markets, government response, impact on the health care industry, and treatment and recovery. Conclusions: Identification of dominant themes, topics, sentiments, and changing trends in tweets about the COVID-19 pandemic can help governments, health care agencies, and policy makers frame appropriate responses to prevent and control the spread of the pandemic. ", doi="10.2196/22624", url="http://www.jmir.org/2020/10/e22624/", url="http://www.ncbi.nlm.nih.gov/pubmed/33006937" } @Article{info:doi/10.2196/23098, author="Gong, Xue and Han, Yangyang and Hou, Mengchi and Guo, Rui", title="Online Public Attention During the Early Days of the COVID-19 Pandemic: Infoveillance Study Based on Baidu Index", journal="JMIR Public Health Surveill", year="2020", month="Oct", day="22", volume="6", number="4", pages="e23098", keywords="Baidu Index", keywords="public attention", keywords="time lag cross-correlation analysis", keywords="COVID-19", abstract="Background: The COVID-19 pandemic has become a global public health event, attracting worldwide attention. As a tool to monitor public awareness, internet search engines have been widely used in public health emergencies. Objective: This study aims to use online search data (Baidu Index) to monitor the public's attention and verify internet search engines' function in public attention monitoring of public health emergencies. Methods: We collected the Baidu Index and the case monitoring data from January 20, 2020, to April 20, 2020. We combined the Baidu Index of keywords related to COVID-19 to describe the public attention's temporal trend and spatial distribution, and conducted the time lag cross-correlation analysis. Results: The Baidu Index temporal trend indicated that the changes of the Baidu Index had a clear correspondence with the development time node of the pandemic. The Baidu Index spatial distribution showed that in the regions of central and eastern China, with denser populations, larger internet user bases, and higher economic development levels, the public was more concerned about COVID-19. In addition, the Baidu Index was significantly correlated with six case indicators of new confirmed cases, new death cases, new cured discharge cases, cumulative confirmed cases, cumulative death cases, and cumulative cured discharge cases. Moreover, the Baidu Index was 0-4 days earlier than new confirmed and new death cases, and about 20 days earlier than new cured and discharged cases while 3-5 days later than the change of cumulative cases. Conclusions: The national public's demand for epidemic information is urgent regardless of whether it is located in the hardest hit area. The public was more sensitive to the daily new case data that represents the progress of the epidemic, but the public's attention to the epidemic situation in other areas may lag behind. We could set the Baidu Index as the sentinel and the database in the online infoveillance system for infectious disease and public health emergencies. According to the monitoring data, the government needs to prevent and control the possible outbreak in advance and communicate the risks to the public so as to ensure the physical and psychological health of the public in the epidemic. ", doi="10.2196/23098", url="http://publichealth.jmir.org/2020/4/e23098/", url="http://www.ncbi.nlm.nih.gov/pubmed/32960177" } @Article{info:doi/10.2196/22574, author="Ayers, W. John and Althouse, M. Benjamin and Poliak, Adam and Leas, C. Eric and Nobles, L. Alicia and Dredze, Mark and Smith, Davey", title="Quantifying Public Interest in Police Reforms by Mining Internet Search Data Following George Floyd's Death", journal="J Med Internet Res", year="2020", month="Oct", day="21", volume="22", number="10", pages="e22574", keywords="policing", keywords="digital health, bioinformatics", keywords="public health", keywords="public interest", keywords="data mining", keywords="internet", keywords="search", keywords="trend", keywords="Google Trends", abstract="Background: The death of George Floyd while in police custody has resurfaced serious questions about police conduct that result in the deaths of unarmed persons. Objective: Data-driven strategies that identify and prioritize the public's needs may engender a public health response to improve policing. We assessed how internet searches indicative of interest in police reform changed after Mr Floyd's death. Methods: We monitored daily Google searches (per 10 million total searches) that included the terms ``police'' and ``reform(s)'' (eg, ``reform the police,'' ``best police reforms,'' etc) originating from the United States between January 1, 2010, through July 5, 2020. We also monitored searches containing the term ``police'' with ``training,'' ``union(s),'' ``militarization,'' or ``immunity'' as markers of interest in the corresponding reform topics. Results: The 41 days following Mr Floyd's death corresponded with the greatest number of police ``reform(s)'' searches ever recorded, with 1,350,000 total searches nationally. Searches increased significantly in all 50 states and Washington DC. By reform topic, nationally there were 1,220,000 total searches for ``police'' and ``union(s)''; 820,000 for ``training''; 360,000 for ``immunity''; and 72,000 for ``militarization.'' In terms of searches for all policy topics by state, 33 states searched the most for ``training,'' 16 for ``union(s),'' and 2 for ``immunity.'' States typically in the southeast had fewer queries related to any police reform topic than other states. States that had a greater percentage of votes for President Donald Trump during the 2016 election searched more often for police ``union(s)'' while states favoring Secretary Hillary Clinton searched more for police ``training.'' Conclusions: The United States is at a historical juncture, with record interest in topics related to police reform with variability in search terms across states. Policy makers can respond to searches by considering the policies their constituencies are searching for online, notably police training and unions. Public health leaders can respond by engaging in the subject of policing and advocating for evidence-based policy reforms. ", doi="10.2196/22574", url="http://www.jmir.org/2020/10/e22574/", url="http://www.ncbi.nlm.nih.gov/pubmed/33084578" } @Article{info:doi/10.2196/18878, author="Dhaliwal, Dhamanpreet and Mannion, Cynthia", title="Antivaccine Messages on Facebook: Preliminary Audit", journal="JMIR Public Health Surveill", year="2020", month="Oct", day="20", volume="6", number="4", pages="e18878", keywords="antivaccine", keywords="vaccines", keywords="vaccination", keywords="immunization", keywords="communicable disease", abstract="Background: The World Health Organization lists vaccine hesitancy as one of 10 threats to global health. The antivaccine movement uses Facebook to promote messages on the alleged dangers and consequences of vaccinating, leading to a reluctance to immunize against preventable communicable diseases. Objective: We would like to know more about the messages these websites are sharing via social media that can influence readers and consumers. What messages is the public receiving on Facebook about immunization? What content (news articles, testimonials, videos, scientific studies) is being promoted? Methods: We proposed using a social media audit tool and 3 categorical lists to capture information on websites and posts, respectively. The keywords ``vaccine,'' ``vaccine truth,'' and ``anti-vax'' were entered in the Facebook search bar. A Facebook page was examined if it had between 2500 and 150,000 likes. Data about beliefs, calls to action, and testimonials were recorded from posts and listed under the categories Myths, Truths, and Consequences. Website data were entered in a social media audit template. Results: Users' posts reflected fear and vaccine hesitancy resulting from the alleged dangers of immunization featured on the website links. Vaccines were blamed for afflictions such as autism, cancer, and infertility. Mothers shared testimonies on alleged consequences their children suffered due to immunization, which have influenced other parents to not vaccinate their children. Users denied the current measles outbreaks in the United States to be true, retaliating against the government in protests for fabricating news. Conclusions: Some Facebook messages encourage prevailing myths about the safety and consequences of vaccines and likely contribute to parents' vaccine hesitancy. Deeply concerning is the mistrust social media has the potential to cast upon the relationship between health care providers and the public. A grasp of common misconceptions can help support health care provider practice. ", doi="10.2196/18878", url="http://publichealth.jmir.org/2020/4/e18878/", url="http://www.ncbi.nlm.nih.gov/pubmed/33079072" } @Article{info:doi/10.2196/18581, author="Scheerer, Cora and R{\"u}th, Melvin and Tizek, Linda and K{\"o}berle, Martin and Biedermann, Tilo and Zink, Alexander", title="Googling for Ticks and Borreliosis in Germany: Nationwide Google Search Analysis From 2015 to 2018", journal="J Med Internet Res", year="2020", month="Oct", day="16", volume="22", number="10", pages="e18581", keywords="Google", keywords="infodemiology", keywords="infoveillance", keywords="public health", keywords="seasonal health trend", keywords="medical internet research", keywords="tick-borne disease", keywords="tick bites, borreliosis", keywords="Lyme disease", abstract="Background: Borreliosis is the most frequently transmitted tick-borne disease in Europe. It is difficult to estimate the incidence of tick bites and associated diseases in the German population due to the lack of an obligation to register across all 16 federal states of Germany. Objective: The aim of this study is to show that Google data can be used to generate general trends of infectious diseases on the basis of borreliosis and tick bites. In addition, the possibility of using Google AdWord data to estimate incidences of infectious diseases, where there is inconsistency in the obligation to notify authorities, is investigated with the perspective to facilitate public health studies. Methods: Google AdWords Keyword Planner was used to identify search terms related to ticks and borreliosis in Germany from January 2015 to December 2018. The search volume data from the identified search terms was assessed using Excel version 15.23. In addition, SPSS version 24.0 was used to calculate the correlation between search volumes, registered cases, and temperature. Results: A total of 1999 tick-related and 542 borreliosis-related search terms were identified, with a total of 209,679,640 Google searches in all 16 German federal states in the period under review. The analysis showed a high correlation between temperature and borreliosis (r=0.88), and temperature and tick bite (r=0.83), and a very high correlation between borreliosis and tick bite (r=0.94). Furthermore, a high to very high correlation between Google searches and registered cases in each federal state was observed (Brandenburg r=0.80, Mecklenburg-West Pomerania r= 0.77, Saxony r= 0.74, and Saxony-Anhalt r=0.90; all P<.001). Conclusions: Our study provides insight into annual trends concerning interest in ticks and borreliosis that are relevant to the German population exemplary in the data of a large internet search engine. Public health studies collecting incidence data may benefit from the results indicating a significant correlation between internet search data and incidences of infectious diseases. ", doi="10.2196/18581", url="http://www.jmir.org/2020/10/e18581/", url="http://www.ncbi.nlm.nih.gov/pubmed/33064086" } @Article{info:doi/10.2196/19833, author="Dubey, Dutt Akash", title="The Resurgence of Cyber Racism During the COVID-19 Pandemic and its Aftereffects: Analysis of Sentiments and Emotions in Tweets", journal="JMIR Public Health Surveill", year="2020", month="Oct", day="15", volume="6", number="4", pages="e19833", keywords="COVID-19", keywords="pandemic", keywords="China", keywords="racism", keywords="WHO", keywords="Twitter", keywords="infodemiology", keywords="infodemic", abstract="Background: With increasing numbers of patients with COVID-19 globally, China and the World Health Organization have been blamed by some for the spread of this disease. Consequently, instances of racism and hateful acts have been reported around the world. When US President Donald Trump used the term ``Chinese Virus,'' this issue gained momentum, and ethnic Asians are now being targeted. The online situation looks similar, with increases in hateful comments and posts. Objective: The aim of this paper is to analyze the increasing instances of cyber racism during the COVID-19 pandemic, by assessing emotions and sentiments associated with tweets on Twitter. Methods: In total, 16,000 tweets from April 11-16, 2020, were analyzed to determine their associated sentiments and emotions. Statistical analysis was carried out using R. Twitter API and the sentimentr package were used to collect tweets and then evaluate their sentiments, respectively. This research analyzed the emotions and sentiments associated with terms like ``Chinese Virus,'' ``Wuhan Virus,'' and ``Chinese Corona Virus.'' Results: The results suggest that the majority of the analyzed tweets were of negative sentiment and carried emotions of fear, sadness, anger, and disgust. There was a high usage of slurs and profane words. In addition, terms like ``China Lied People Died,'' ``Wuhan Health Organization,'' ``Kung Flu,'' ``China Must Pay,'' and ``CCP is Terrorist'' were frequently used in these tweets. Conclusions: This study provides insight into the rise in cyber racism seen on Twitter. Based on the findings, it can be concluded that a substantial number of users are tweeting with mostly negative sentiments toward ethnic Asians, China, and the World Health Organization. ", doi="10.2196/19833", url="http://publichealth.jmir.org/2020/4/e19833/", url="http://www.ncbi.nlm.nih.gov/pubmed/32936772" } @Article{info:doi/10.2196/19589, author="Wang, Wenjun and Wang, Yikai and Zhang, Xin and Jia, Xiaoli and Li, Yaping and Dang, Shuangsuo", title="Using WeChat, a Chinese Social Media App, for Early Detection of the COVID-19 Outbreak in December 2019: Retrospective Study", journal="JMIR Mhealth Uhealth", year="2020", month="Oct", day="5", volume="8", number="10", pages="e19589", keywords="novel coronavirus", keywords="SARS", keywords="SARS-CoV-2", keywords="COVID-19", keywords="social media", keywords="WeChat", keywords="early detection", keywords="surveillance", keywords="infodemiology", keywords="infoveillance", abstract="Background: A novel coronavirus, SARS-CoV-2, was identified in December 2019, when the first cases were reported in Wuhan, China. The once-localized outbreak has since been declared a pandemic. As of April 24, 2020, there have been 2.7 million confirmed cases and nearly 200,000 deaths. Early warning systems using new technologies should be established to prevent or mitigate such events in the future. Objective: This study aimed to explore the possibility of detecting the SARS-CoV-2 outbreak in 2019 using social media. Methods: WeChat Index is a data service that shows how frequently a specific keyword appears in posts, subscriptions, and search over the last 90 days on WeChat, the most popular Chinese social media app. We plotted daily WeChat Index results for keywords related to SARS-CoV-2 from November 17, 2019, to February 14, 2020. Results: WeChat Index hits for ``Feidian'' (which means severe acute respiratory syndrome in Chinese) stayed at low levels until 16 days ahead of the local authority's outbreak announcement on December 31, 2019, when the index increased significantly. The WeChat Index values persisted at relatively high levels from December 15 to 29, 2019, and rose rapidly on December 30, 2019, the day before the announcement. The WeChat Index hits also spiked for the keywords ``SARS,'' ``coronavirus,'' ``novel coronavirus,'' ``shortness of breath,'' ``dyspnea,'' and ``diarrhea,'' but these terms were not as meaningful for the early detection of the outbreak as the term ``Feidian''. Conclusions: By using retrospective infoveillance data from the WeChat Index, the SARS-CoV-2 outbreak in December 2019 could have been detected about two weeks before the outbreak announcement. WeChat may offer a new approach for the early detection of disease outbreaks. ", doi="10.2196/19589", url="https://mhealth.jmir.org/2020/10/e19589", url="http://www.ncbi.nlm.nih.gov/pubmed/32931439" } @Article{info:doi/10.2196/19804, author="Wang, Di and Lyu, Chen Joanne and Zhao, Xiaoyu", title="Public Opinion About E-Cigarettes on Chinese Social Media: A Combined Study of Text Mining Analysis and Correspondence Analysis", journal="J Med Internet Res", year="2020", month="Oct", day="14", volume="22", number="10", pages="e19804", keywords="e-cigarettes", keywords="public opinion", keywords="social media", keywords="infodemiology", keywords="infoveillance", keywords="regulation", keywords="China", abstract="Background: Electronic cigarettes (e-cigarettes) have become increasingly popular. China has accelerated its legislation on e-cigarettes in recent years by issuing two policies to regulate their use: the first on August 26, 2018, and the second on November 1, 2019. Social media provide an efficient platform to access information on the public opinion of e-cigarettes. Objective: To gain insight into how policies have influenced the reaction of the Chinese public to e-cigarettes, this study aims to understand what the Chinese public say about e-cigarettes and how the focus of discussion might have changed in the context of policy implementation. Methods: This study uses a combination of text mining and correspondence analysis to content analyze 1160 e-cigarette--related questions and their corresponding answers from Zhihu, China's largest question-and-answer platform and one of the country's most trustworthy social media sources. From January 1, 2017, to December 31, 2019, Python was used to text mine the most frequently used words and phrases in public e-cigarette discussions on Zhihu. The correspondence analysis was used to examine the similarities and differences between high-frequency words and phrases across 3 periods (ie, January 1, 2017, to August 27, 2018; August 28, 2018, to October 31, 2019; and November 1, 2019, to January 1, 2020). Results: The results of the study showed that the consistent themes across time were comparisons with traditional cigarettes, health concerns, and how to choose e-cigarette products. The issuance of government policies on e-cigarettes led to a change in the focus of public discussion. The discussion of e-cigarettes in period 1 mainly focused on the use and experience of e-cigarettes. In period 2, the public's attention was not only on the substances related to e-cigarettes but also on the smoking cessation functions of e-cigarettes. In period 3, the public shifted their attention to the e-cigarette industry and government policy on the banning of e-cigarette sales to minors. Conclusions: Social media are an informative source, which can help policy makers and public health professionals understand the public's concerns over and understanding of e-cigarettes. When there was little regulation, public discussion was greatly influenced by industry claims about e-cigarettes; however, once e-cigarette policies were issued, these policies, to a large extent, set the agenda for public discussion. In addition, media reporting of these policies might have greatly influenced the way e-cigarette policies were discussed. Therefore, monitoring e-cigarette discussions on social media and responding to them in a timely manner will both help improve the public's e-cigarette literacy and facilitate the implementation of e-cigarette--related policies. ", doi="10.2196/19804", url="http://www.jmir.org/2020/10/e19804/", url="http://www.ncbi.nlm.nih.gov/pubmed/33052127" } @Article{info:doi/10.2196/17543, author="McCausland, Kahlia and Maycock, Bruce and Leaver, Tama and Wolf, Katharina and Freeman, Becky and Jancey, Jonine", title="E-Cigarette Advocates on Twitter: Content Analysis of Vaping-Related Tweets", journal="JMIR Public Health Surveill", year="2020", month="Oct", day="14", volume="6", number="4", pages="e17543", keywords="electronic nicotine delivery systems", keywords="electronic cigarettes", keywords="e-cigarette", keywords="infodemiology", keywords="infoveillance", keywords="vaping", keywords="Twitter", keywords="social media", keywords="public health", keywords="content analysis", abstract="Background: As the majority of Twitter content is publicly available, the platform has become a rich data source for public health surveillance, providing insights into emergent phenomena, such as vaping. Although there is a growing body of literature that has examined the content of vaping-related tweets, less is known about the people who generate and disseminate these messages and the role of e-cigarette advocates in the promotion of these devices. Objective: This study aimed to identify key conversation trends and patterns over time, and discern the core voices, message frames, and sentiment surrounding e-cigarette discussions on Twitter. Methods: A random sample of data were collected from Australian Twitter users who referenced at least one of 15 identified e-cigarette related keywords during 2012, 2014, 2016, or 2018. Data collection was facilitated by TrISMA (Tracking Infrastructure for Social Media Analysis) and analyzed by content analysis. Results: A sample of 4432 vaping-related tweets posted and retweeted by Australian users was analyzed. Positive sentiment (3754/4432, 84.70\%) dominated the discourse surrounding e-cigarettes, and vape retailers and manufacturers (1161/4432, 26.20\%), the general public (1079/4432, 24.35\%), and e-cigarette advocates (1038/4432, 23.42\%) were the most prominent posters. Several tactics were used by e-cigarette advocates to communicate their beliefs, including attempts to frame e-cigarettes as safer than traditional cigarettes, imply that federal government agencies lack sufficient competence or evidence for the policies they endorse about vaping, and denounce as propaganda ``gateway'' claims of youth progressing from e-cigarettes to combustible tobacco. Some of the most common themes presented in tweets were advertising or promoting e-cigarette products (2040/4432, 46.03\%), promoting e-cigarette use or intent to use (970/4432, 21.89\%), and discussing the potential of e-cigarettes to be used as a smoking cessation aid or tobacco alternative (716/4432, 16.16\%), as well as the perceived health and safety benefits and consequences of e-cigarette use (681/4432, 15.37\%). Conclusions: Australian Twitter content does not reflect the country's current regulatory approach to e-cigarettes. Rather, the conversation on Twitter generally encourages e-cigarette use, promotes vaping as a socially acceptable practice, discredits scientific evidence of health risks, and rallies around the idea that e-cigarettes should largely be outside the bounds of health policy. The one-sided nature of the discussion is concerning, as is the lack of disclosure and transparency, especially among vaping enthusiasts who dominate the majority of e-cigarette discussions on Twitter, where it is unclear if comments are endorsed, sanctioned, or even supported by the industry. ", doi="10.2196/17543", url="http://publichealth.jmir.org/2020/4/e17543/", url="http://www.ncbi.nlm.nih.gov/pubmed/33052130" } @Article{info:doi/10.2196/22635, author="Low, M. Daniel and Rumker, Laurie and Talkar, Tanya and Torous, John and Cecchi, Guillermo and Ghosh, S. Satrajit", title="Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study", journal="J Med Internet Res", year="2020", month="Oct", day="12", volume="22", number="10", pages="e22635", keywords="COVID-19", keywords="mental health", keywords="psychiatry", keywords="infodemiology", keywords="infoveillance", keywords="infodemic", keywords="social media", keywords="Reddit", keywords="natural language processing", keywords="ADHD", keywords="eating disorders", keywords="anxiety", keywords="suicidality", abstract="Background: The COVID-19 pandemic is impacting mental health, but it is not clear how people with different types of mental health problems were differentially impacted as the initial wave of cases hit. Objective: The aim of this study is to leverage natural language processing (NLP) with the goal of characterizing changes in 15 of the world's largest mental health support groups (eg, r/schizophrenia, r/SuicideWatch, r/Depression) found on the website Reddit, along with 11 non--mental health groups (eg, r/PersonalFinance, r/conspiracy) during the initial stage of the pandemic. Methods: We created and released the Reddit Mental Health Dataset including posts from 826,961 unique users from 2018 to 2020. Using regression, we analyzed trends from 90 text-derived features such as sentiment analysis, personal pronouns, and semantic categories. Using supervised machine learning, we classified posts into their respective support groups and interpreted important features to understand how different problems manifest in language. We applied unsupervised methods such as topic modeling and unsupervised clustering to uncover concerns throughout Reddit before and during the pandemic. Results: We found that the r/HealthAnxiety forum showed spikes in posts about COVID-19 early on in January, approximately 2 months before other support groups started posting about the pandemic. There were many features that significantly increased during COVID-19 for specific groups including the categories ``economic stress,'' ``isolation,'' and ``home,'' while others such as ``motion'' significantly decreased. We found that support groups related to attention-deficit/hyperactivity disorder, eating disorders, and anxiety showed the most negative semantic change during the pandemic out of all mental health groups. Health anxiety emerged as a general theme across Reddit through independent supervised and unsupervised machine learning analyses. For instance, we provide evidence that the concerns of a diverse set of individuals are converging in this unique moment of history; we discovered that the more users posted about COVID-19, the more linguistically similar (less distant) the mental health support groups became to r/HealthAnxiety ($\rho$=--0.96, P<.001). Using unsupervised clustering, we found the suicidality and loneliness clusters more than doubled in the number of posts during the pandemic. Specifically, the support groups for borderline personality disorder and posttraumatic stress disorder became significantly associated with the suicidality cluster. Furthermore, clusters surrounding self-harm and entertainment emerged. Conclusions: By using a broad set of NLP techniques and analyzing a baseline of prepandemic posts, we uncovered patterns of how specific mental health problems manifest in language, identified at-risk users, and revealed the distribution of concerns across Reddit, which could help provide better resources to its millions of users. We then demonstrated that textual analysis is sensitive to uncover mental health complaints as they appear in real time, identifying vulnerable groups and alarming themes during COVID-19, and thus may have utility during the ongoing pandemic and other world-changing events such as elections and protests. ", doi="10.2196/22635", url="http://www.jmir.org/2020/10/e22635/", url="http://www.ncbi.nlm.nih.gov/pubmed/32936777" } @Article{info:doi/10.2196/21597, author="Gozzi, Nicol{\`o} and Tizzani, Michele and Starnini, Michele and Ciulla, Fabio and Paolotti, Daniela and Panisson, Andr{\'e} and Perra, Nicola", title="Collective Response to Media Coverage of the COVID-19 Pandemic on Reddit and Wikipedia: Mixed-Methods Analysis", journal="J Med Internet Res", year="2020", month="Oct", day="12", volume="22", number="10", pages="e21597", keywords="social media", keywords="news coverage", keywords="digital epidemiology", keywords="infodemiology", keywords="infoveillance", keywords="infodemic", keywords="data science", keywords="topic modeling", keywords="pandemic", keywords="COVID-19", keywords="Reddit", keywords="Wikipedia", keywords="information", keywords="response", keywords="risk perception", keywords="behavior", abstract="Background: The exposure and consumption of information during epidemic outbreaks may alter people's risk perception and trigger behavioral changes, which can ultimately affect the evolution of the disease. It is thus of utmost importance to map the dissemination of information by mainstream media outlets and the public response to this information. However, our understanding of this exposure-response dynamic during the COVID-19 pandemic is still limited. Objective: The goal of this study is to characterize the media coverage and collective internet response to the COVID-19 pandemic in four countries: Italy, the United Kingdom, the United States, and Canada. Methods: We collected a heterogeneous data set including 227,768 web-based news articles and 13,448 YouTube videos published by mainstream media outlets, 107,898 user posts and 3,829,309 comments on the social media platform Reddit, and 278,456,892 views of COVID-19--related Wikipedia pages. To analyze the relationship between media coverage, epidemic progression, and users' collective web-based response, we considered a linear regression model that predicts the public response for each country given the amount of news exposure. We also applied topic modelling to the data set using nonnegative matrix factorization. Results: Our results show that public attention, quantified as user activity on Reddit and active searches on Wikipedia pages, is mainly driven by media coverage; meanwhile, this activity declines rapidly while news exposure and COVID-19 incidence remain high. Furthermore, using an unsupervised, dynamic topic modeling approach, we show that while the levels of attention dedicated to different topics by media outlets and internet users are in good accordance, interesting deviations emerge in their temporal patterns. Conclusions: Overall, our findings offer an additional key to interpret public perception and response to the current global health emergency and raise questions about the effects of attention saturation on people's collective awareness and risk perception and thus on their tendencies toward behavioral change. ", doi="10.2196/21597", url="http://www.jmir.org/2020/10/e21597/", url="http://www.ncbi.nlm.nih.gov/pubmed/32960775" } @Article{info:doi/10.2196/21383, author="Osadchiy, Vadim and Jiang, Tommy and Mills, Nelson Jesse and Eleswarapu, Venkata Sriram", title="Low Testosterone on Social Media: Application of Natural Language Processing to Understand Patients' Perceptions of Hypogonadism and Its Treatment", journal="J Med Internet Res", year="2020", month="Oct", day="7", volume="22", number="10", pages="e21383", keywords="hypogonadism", keywords="natural language processing", keywords="Reddit", keywords="social media", keywords="testosterone replacement therapy", keywords="Twitter", abstract="Background: Despite the results of the Testosterone Trials, physicians remain uncomfortable treating men with hypogonadism. Discouraged, men increasingly turn to social media to discuss medical concerns. Objective: The goal of the research was to apply natural language processing (NLP) techniques to social media posts for identification of themes of discussion regarding low testosterone and testosterone replacement therapy (TRT) in order to inform how physicians may better evaluate and counsel patients. Methods: We retrospectively extracted posts from the Reddit community r/Testosterone from December 2015 through May 2019. We applied an NLP technique called the meaning extraction method with principal component analysis (MEM/PCA) to computationally derive discussion themes. We then performed a prospective analysis of Twitter data (tweets) that contained the terms low testosterone, low T, and testosterone replacement from June through September 2019. Results: A total of 199,335 Reddit posts and 6659 tweets were analyzed. MEM/PCA revealed dominant themes of discussion: symptoms of hypogonadism, seeing a doctor, results of laboratory tests, derogatory comments and insults, TRT medications, and cardiovascular risk. More than 25\% of Reddit posts contained the term doctor, and more than 5\% urologist. Conclusions: This study represents the first NLP evaluation of the social media landscape surrounding hypogonadism and TRT. Although physicians traditionally limit their practices to within their clinic walls, the ubiquity of social media demands that physicians understand what patients discuss online. Physicians may do well to bring up online discussions during clinic consultations for low testosterone to pull back the curtain and dispel myths. ", doi="10.2196/21383", url="https://www.jmir.org/2020/10/e21383", url="http://www.ncbi.nlm.nih.gov/pubmed/33026354" } @Article{info:doi/10.2196/22374, author="Ahmed, Wasim and L{\'o}pez Segu{\'i}, Francesc and Vidal-Alaball, Josep and Katz, S. Matthew", title="COVID-19 and the ``Film Your Hospital'' Conspiracy Theory: Social Network Analysis of Twitter Data", journal="J Med Internet Res", year="2020", month="Oct", day="5", volume="22", number="10", pages="e22374", keywords="COVID-19", keywords="coronavirus", keywords="Twitter", keywords="misinformation", keywords="fake news", keywords="social network analysis", keywords="public health", keywords="social media", abstract="Background: During the COVID-19 pandemic, a number of conspiracy theories have emerged. A popular theory posits that the pandemic is a hoax and suggests that certain hospitals are ``empty.'' Research has shown that accepting conspiracy theories increases the likelihood that an individual may ignore government advice about social distancing and other public health interventions. Due to the possibility of a second wave and future pandemics, it is important to gain an understanding of the drivers of misinformation and strategies to mitigate it. Objective: This study set out to evaluate the \#FilmYourHospital conspiracy theory on Twitter, attempting to understand the drivers behind it. More specifically, the objectives were to determine which online sources of information were used as evidence to support the theory, the ratio of automated to organic accounts in the network, and what lessons can be learned to mitigate the spread of such a conspiracy theory in the future. Methods: Twitter data related to the \#FilmYourHospital hashtag were retrieved and analyzed using social network analysis across a 7-day period from April 13-20, 2020. The data set consisted of 22,785 tweets and 11,333 Twitter users. The Botometer tool was used to identify accounts with a higher probability of being bots. Results: The most important drivers of the conspiracy theory are ordinary citizens; one of the most influential accounts is a Brexit supporter. We found that YouTube was the information source most linked to by users. The most retweeted post belonged to a verified Twitter user, indicating that the user may have had more influence on the platform. There was a small number of automated accounts (bots) and deleted accounts within the network. Conclusions: Hashtags using and sharing conspiracy theories can be targeted in an effort to delegitimize content containing misinformation. Social media organizations need to bolster their efforts to label or remove content that contains misinformation. Public health authorities could enlist the assistance of influencers in spreading antinarrative content. ", doi="10.2196/22374", url="http://www.jmir.org/2020/10/e22374/", url="http://www.ncbi.nlm.nih.gov/pubmed/32936771" } @Article{info:doi/10.2196/19266, author="Zhou, Zeyun and Hultgren, Emerson Kyle", title="Complementing the US Food and Drug Administration Adverse Event Reporting System With Adverse Drug Reaction Reporting From Social Media: Comparative Analysis", journal="JMIR Public Health Surveill", year="2020", month="Sep", day="30", volume="6", number="3", pages="e19266", keywords="adverse drug reactions", keywords="FAERS", keywords="social media reporting", keywords="pharmacovigilance", abstract="Background: Adverse drug reactions (ADRs) can occur any time someone uses a medication. ADRs are systematically tracked and cataloged, with varying degrees of success, in order to better understand their etiology and develop methods of prevention. The US Food and Drug Administration (FDA) has developed the FDA Adverse Event Reporting System (FAERS) for this purpose. FAERS collects information from myriad sources, but the primary reporters have traditionally been medical professionals and pharmacovigilance data from manufacturers. Recent studies suggest that information shared publicly on social media platforms related to medication use could be of benefit in complementing FAERS data in order to have a richer picture of how medications are actually being used and the experiences people are having across large populations. Objective: The aim of this study is to validate the accuracy and precision of social media methodology and conduct evaluations of Twitter ADR reporting for commonly used pharmaceutical agents. Methods: ADR data from the 10 most prescribed medications according to pharmacy claims data were collected from both FAERS and Twitter. In order to obtain data from FAERS, the SafeRx database, a curated collection of FAERS data, was used to collect data from March 1, 2016, to March 31, 2017. Twitter data were manually scraped during the same time period to extract similar data using an algorithm designed to minimize noise and false signals in social media data. Results: A total of 40,539 FAERS ADR reports were obtained via SafeRx and more than 40,000 tweets containing the drug names were obtained from Twitter's Advanced Search engine. While the FAERS data were specific to ADRs, the Twitter data were more limited. Only hydrocodone/acetaminophen, prednisone, amoxicillin, gabapentin, and metformin had a sufficient volume of ADR content for review and comparison. For metformin, diarrhea was the side effect that resulted in no difference between the two platforms (P=.30). For hydrocodone/acetaminophen, ineffectiveness as an ADR that resulted in no difference (P=.60). For gabapentin, there were no differences in terms of the ADRs ineffectiveness and fatigue (P=.15 and P=.67, respectively). For amoxicillin, hypersensitivity, nausea, and rash shared similar profiles between platforms (P=.35, P=.05, and P=.31, respectively). Conclusions: FAERS and Twitter shared similarities in types of data reported and a few unique items to each data set as well. The use of Twitter as an ADR pharmacovigilance platform should continue to be studied as a unique and complementary source of information rather than a validation tool of existing ADR databases. ", doi="10.2196/19266", url="https://publichealth.jmir.org/2020/3/e19266", url="http://www.ncbi.nlm.nih.gov/pubmed/32996889" } @Article{info:doi/10.2196/19788, author="Husnayain, Atina and Shim, Eunha and Fuad, Anis and Su, Chia-Yu Emily", title="Understanding the Community Risk Perceptions of the COVID-19 Outbreak in South Korea: Infodemiology Study", journal="J Med Internet Res", year="2020", month="Sep", day="29", volume="22", number="9", pages="e19788", keywords="Google Trends", keywords="risk", keywords="perception", keywords="communication", keywords="COVID-19", keywords="South Korea", keywords="outbreak", keywords="infodemiology", abstract="Background: South Korea is among the best-performing countries in tackling the coronavirus pandemic by using mass drive-through testing, face mask use, and extensive social distancing. However, understanding the patterns of risk perception could also facilitate effective risk communication to minimize the impacts of disease spread during this crisis. Objective: We attempt to explore patterns of community health risk perceptions of COVID-19 in South Korea using internet search data. Methods: Google Trends (GT) and NAVER relative search volumes (RSVs) data were collected using COVID-19--related terms in the Korean language and were retrieved according to time, gender, age groups, types of device, and location. Online queries were compared to the number of daily new COVID-19 cases and tests reported in the Kaggle open-access data set for the time period of December 5, 2019, to May 31, 2020. Time-lag correlations calculated by Spearman rank correlation coefficients were employed to assess whether correlations between new COVID-19 cases and internet searches were affected by time. We also constructed a prediction model of new COVID-19 cases using the number of COVID-19 cases, tests, and GT and NAVER RSVs in lag periods (of 1-3 days). Single and multiple regressions were employed using backward elimination and a variance inflation factor of <5. Results: The numbers of COVID-19--related queries in South Korea increased during local events including local transmission, approval of coronavirus test kits, implementation of coronavirus drive-through tests, a face mask shortage, and a widespread campaign for social distancing as well as during international events such as the announcement of a Public Health Emergency of International Concern by the World Health Organization. Online queries were also stronger in women (r=0.763-0.823; P<.001) and age groups ?29 years (r=0.726-0.821; P<.001), 30-44 years (r=0.701-0.826; P<.001), and ?50 years (r=0.706-0.725; P<.001). In terms of spatial distribution, internet search data were higher in affected areas. Moreover, greater correlations were found in mobile searches (r=0.704-0.804; P<.001) compared to those of desktop searches (r=0.705-0.717; P<.001), indicating changing behaviors in searching for online health information during the outbreak. These varied internet searches related to COVID-19 represented community health risk perceptions. In addition, as a country with a high number of coronavirus tests, results showed that adults perceived coronavirus test--related information as being more important than disease-related knowledge. Meanwhile, younger, and older age groups had different perceptions. Moreover, NAVER RSVs can potentially be used for health risk perception assessments and disease predictions. Adding COVID-19--related searches provided by NAVER could increase the performance of the model compared to that of the COVID-19 case--based model and potentially be used to predict epidemic curves. Conclusions: The use of both GT and NAVER RSVs to explore patterns of community health risk perceptions could be beneficial for targeting risk communication from several perspectives, including time, population characteristics, and location. ", doi="10.2196/19788", url="http://www.jmir.org/2020/9/e19788/", url="http://www.ncbi.nlm.nih.gov/pubmed/32931446" } @Article{info:doi/10.2196/19668, author="Pan, Peng and Yu, Changhua and Li, Tao and Zhou, Xilei and Dai, Tingting and Tian, Hanhan and Xiong, Yaozu", title="Xigua Video as a Source of Information on Breast Cancer: Content Analysis", journal="J Med Internet Res", year="2020", month="Sep", day="29", volume="22", number="9", pages="e19668", keywords="breast cancer", keywords="internet", keywords="Xigua Video", keywords="content analysis", abstract="Background: Seeking health information on the internet is a popular trend. Xigua Video, a short video platform in China, ranks among the most accessed websites in the country and hosts an increasing number of videos with medical information. However, the nature of these videos is frequently unscientific, misleading, or even harmful. Objective: Little is known about Xigua Video as a source of information on breast cancer. Thus, the study aimed to investigate the contents, quality, and reliability of breast cancer--related content on Xigua Video. Methods: On February 4, 2020, a Xigua Video search was performed using the keyword ``breast cancer.'' Videos were categorized by 2 doctors based on whether the video content provided useful or misleading information. Furthermore, the reliability and quality of the videos were assessed using the 5-point DISCERN tool and 5-point global quality score criteria. Results: Out of the 170 videos selected for the study, 64 (37.6\%) were classified as useful, whereas 106 (62.4\%) provided misleading information. A total of 41.8\% videos (71/170) were generated by individuals compared to 19.4\% videos (33/170) contributed by health care professionals. The topics mainly covered etiology, anatomy, symptoms, preventions, treatments, and prognosis. The top topic was ``treatments'' (119/170, 70\%). The reliability scores and global quality scores of the videos in the useful information group were high (P<.001). No differences were observed between the 2 groups in terms of video length, duration in months, and comments. The number of total views was higher for the misleading information group (819,478.5 vs 647,940) but did not reach a level of statistical significance (P=.112). The uploading sources of the videos were mainly health care professionals, health information websites, medical advertisements, and individuals. Statistical differences were found between the uploading source groups in terms of reliability scores and global quality scores (P<.001). In terms of total views, video length, duration, and comments, no statistical differences were indicated among the said groups. However, a statistical difference was noted between the useful and misleading information video groups with respect to the uploading sources (P<.001). Conclusions: A large number of Xigua videos pertaining to breast cancer contain misleading information. There is a need for accurate health information to be provided on Xigua Video and other social media; health care professionals should address this challenge. ", doi="10.2196/19668", url="http://www.jmir.org/2020/9/e19668/", url="http://www.ncbi.nlm.nih.gov/pubmed/32883651" } @Article{info:doi/10.2196/22181, author="Lin, Yu-Hsuan and Chiang, Ting-Wei and Lin, Yu-Lun", title="Increased Internet Searches for Insomnia as an Indicator of Global Mental Health During the COVID-19 Pandemic: Multinational Longitudinal Study", journal="J Med Internet Res", year="2020", month="Sep", day="21", volume="22", number="9", pages="e22181", keywords="internet search", keywords="Google Trends", keywords="infodemiology", keywords="infoveillance", keywords="COVID-19", keywords="insomnia", keywords="mental health", abstract="Background: Real-time global mental health surveillance is urgently needed for tracking the long-term impact of the COVID-19 pandemic. Objective: This study aimed to use Google Trends data to investigate the impact of the pandemic on global mental health by analyzing three keywords indicative of mental distress: ``insomnia,'' ``depression,'' and ``suicide.'' Methods: We examined increases in search queries for 19 countries. Significant increases were defined as the actual daily search value (from March 20 to April 19, 2020) being higher than the 95\% CIs of the forecast from the 3-month baseline via ARIMA (autoregressive integrated moving average) modeling. We examined the correlation between increases in COVID-19--related deaths and the number of days with significant increases in search volumes for insomnia, depression, and suicide across multiple nations. Results: The countries with the greatest increases in searches for insomnia were Iran, Spain, the United States, and Italy; these countries exhibited a significant increase in insomnia searches on more than 10 of the 31 days observed. The number of COVID-19--related deaths was positively correlated to the number of days with an increase in searches for insomnia in the 19 countries ($\rho$=0.64, P=.003). By contrast, there was no significant correlation between the number of deaths and increases in searches for depression ($\rho$=--0.12, P=.63) or suicide ($\rho$=--0.07, P=.79). Conclusions: Our analysis suggests that insomnia could be a part of routine mental health screening during the COVID-19 pandemic. ", doi="10.2196/22181", url="http://www.jmir.org/2020/9/e22181/", url="http://www.ncbi.nlm.nih.gov/pubmed/32924951" } @Article{info:doi/10.2196/18306, author="Wolffsohn, S. James and Leteneux-Pantais, Claudia and Chiva-Razavi, Sima and Bentley, Sarah and Johnson, Chloe and Findley, Amy and Tolley, Chloe and Arbuckle, Rob and Kommineni, Jyothi and Tyagi, Nishith", title="Social Media Listening to Understand the Lived Experience of Presbyopia: Systematic Search and Content Analysis Study", journal="J Med Internet Res", year="2020", month="Sep", day="21", volume="22", number="9", pages="e18306", keywords="presbyopia", keywords="near vision", keywords="social media", keywords="social media listening", keywords="infodemiology", abstract="Background: Presbyopia is defined as the age-related deterioration of near vision over time which is experienced in over 80\% of people aged 40 years or older. Individuals with presbyopia have difficulty with tasks that rely on near vision. It is not currently possible to stop or reverse the aging process that causes presbyopia; generally, it is corrected with glasses, contact lenses, surgery, or the use of a magnifying glass. Objective: This study aimed to explore how individuals used social media to describe their experience of presbyopia with regard to the symptoms experienced and the impacts of presbyopia on their quality of life. Methods: Social media sources including Twitter, forums, blogs, and news outlets were searched using a predefined search string relating to symptoms and impacts of presbyopia. The data that were downloaded, based on the keywords, underwent manual review to identify relevant data points. Relevant posts were further manually analyzed through a process of data tagging, categorization, and clustering. Key themes relating to symptoms, impacts, treatment, and lived experiences were identified. Results: A total of 4456 social media posts related to presbyopia were identified between May 2017 and August 2017. Using a random sampling methodology, we selected 2229 (50.0\%) posts for manual review, with 1470 (65.9\%) of these 2229 posts identified as relevant to the study objectives. Twitter was the most commonly used channel for discussions on presbyopia compared to forums and blogs. The majority of relevant posts originated in Spain (559/1470, 38.0\%) and the United States (426/1470, 29.0\%). Of the relevant posts, 270/1470 (18.4\%) were categorized as posts written by individuals who have presbyopia, of which 37 of the 270 posts (13.7\%) discussed symptoms. On social media, individuals with presbyopia most frequently reported experiencing difficulty reading small print (24/37, 64.9\%), difficulty focusing on near objects (15/37, 40.5\%), eye strain (12/37, 32.4\%), headaches (9/37, 24.3\%), and blurred vision (8/37, 21.6\%). 81 of the 270 posts (30.0\%) discussed impacts of presbyopia---emotional burden (57/81, 70.4\%), functional or daily living impacts (46/81, 56.8\%), such as difficulty reading (46/81, 56.8\%) and using electronic devices (21/81, 25.9\%), and impacts on work (3/81, 3.7\%). Conclusions: Findings from this social media listening study provided insight into how people with presbyopia discuss their condition online and highlight the impact of presbyopia on individuals' quality of life. The social media listening methodology can be used to generate insights into the lived experience of a condition, but it is recommended that this research be combined with prospective qualitative research for added rigor and for confirmation of the relevance of the findings. ", doi="10.2196/18306", url="http://www.jmir.org/2020/9/e18306/", url="http://www.ncbi.nlm.nih.gov/pubmed/32955443" } @Article{info:doi/10.2196/18281, author="Adnan, Mehnaz and Gao, Xiaoying and Bai, Xiaohan and Newbern, Elizabeth and Sherwood, Jill and Jones, Nicholas and Baker, Michael and Wood, Tim and Gao, Wei", title="Potential Early Identification of a Large Campylobacter Outbreak Using Alternative Surveillance Data Sources: Autoregressive Modelling and Spatiotemporal Clustering", journal="JMIR Public Health Surveill", year="2020", month="Sep", day="17", volume="6", number="3", pages="e18281", keywords="Campylobacter", keywords="disease outbreaks", keywords="forecasting", keywords="spatio-temporal analysis", abstract="Background: Over one-third of the population of Havelock North, New Zealand, approximately 5500 people, were estimated to have been affected by campylobacteriosis in a large waterborne outbreak. Cases reported through the notifiable disease surveillance system (notified case reports) are inevitably delayed by several days, resulting in slowed outbreak recognition and delayed control measures. Early outbreak detection and magnitude prediction are critical to outbreak control. It is therefore important to consider alternative surveillance data sources and evaluate their potential for recognizing outbreaks at the earliest possible time. Objective: The first objective of this study is to compare and validate the selection of alternative data sources (general practice consultations, consumer helpline, Google Trends, Twitter microblogs, and school absenteeism) for their temporal predictive strength for Campylobacter cases during the Havelock North outbreak. The second objective is to examine spatiotemporal clustering of data from alternative sources to assess the size and geographic extent of the outbreak and to support efforts to attribute its source. Methods: We combined measures derived from alternative data sources during the 2016 Havelock North campylobacteriosis outbreak with notified case report counts to predict suspected daily Campylobacter case counts up to 5 days before cases reported in the disease surveillance system. Spatiotemporal clustering of the data was analyzed using Local Moran's I statistics to investigate the extent of the outbreak in both space and time within the affected area. Results: Models that combined consumer helpline data with autoregressive notified case counts had the best out-of-sample predictive accuracy for 1 and 2 days ahead of notified case reports. Models using Google Trends and Twitter typically performed the best 3 and 4 days before case notifications. Spatiotemporal clusters showed spikes in school absenteeism and consumer helpline inquiries that preceded the notified cases in the city primarily affected by the outbreak. Conclusions: Alternative data sources can provide earlier indications of a large gastroenteritis outbreak compared with conventional case notifications. Spatiotemporal analysis can assist in refining the geographical focus of an outbreak and can potentially support public health source attribution efforts. Further work is required to assess the location of such surveillance data sources and methods in routine public health practice. ", doi="10.2196/18281", url="http://publichealth.jmir.org/2020/3/e18281/", url="http://www.ncbi.nlm.nih.gov/pubmed/32940617" } @Article{info:doi/10.2196/20632, author="Hochberg, Irit and Orshalimy, Sharon and Yom-Tov, Elad", title="Real-World Evidence on the Effect of Missing an Oral Contraceptive Dose: Analysis of Internet Search Engine Queries", journal="J Med Internet Res", year="2020", month="Sep", day="15", volume="22", number="9", pages="e20632", keywords="search engines", keywords="birth control", keywords="abortion", keywords="miscarriage", abstract="Background: Oral contraceptives (OCs) are a unique chronic medication with which a memory slip may result in a threat that could change a person's life course. Subjective concerns of missed OC doses among women have been addressed infrequently. Anonymized queries to internet search engines provide unique access to concerns and information gaps faced by a large number of internet users. Objective: We aimed to quantitate the frequency of queries by women seeking information in an internet search engine, after missing one or more doses of an OC; their further queries on emergency contraception, abortion, and miscarriage; and their rate of reporting a pregnancy timed to the cycle of missing an OC. Methods: We extracted all English-language queries submitted to Bing in the United States during 2018, which mentioned a missed OC and subsequent queries of the same users on miscarriage, abortion, emergency contraceptives, and week of pregnancy. Results: We identified 26,395 Bing users in the United States who queried about missing OC pills and the fraction that further queried about miscarriage, abortion, emergency contraceptive, and week of pregnancy. Users under the age of 30 years who asked about forgetting an OC dose were more likely to ask about abortion (1.5 times) and emergency contraception (1.7 times) (P<.001 for both), while users at ages of 30-34 years were more likely to query about pregnancy (2.1 times) and miscarriage (5.4 times) (P<.001 for both). Conclusions: Our data indicate that many women missing a dose of OC might not have received sufficient information from their health care providers or chose to obtain it online. Queries about abortion and miscarriage peaking in the subsequent days indicate a common worry of possible pregnancy. These results reinforce the importance of providing comprehensive written information on missed pills when prescribing an OC. ", doi="10.2196/20632", url="http://www.jmir.org/2020/9/e20632/", url="http://www.ncbi.nlm.nih.gov/pubmed/32930672" } @Article{info:doi/10.2196/19913, author="Ali, F. Khawla and Whitebridge, Simon and Jamal, H. Mohammad and Alsafy, Mohammad and Atkin, L. Stephen", title="Perceptions, Knowledge, and Behaviors Related to COVID-19 Among Social Media Users: Cross-Sectional Study", journal="J Med Internet Res", year="2020", month="Sep", day="8", volume="22", number="9", pages="e19913", keywords="COVID-19", keywords="social media", keywords="public health", keywords="perception", keywords="knowledge", keywords="health information", keywords="health education", keywords="virus", abstract="Background: Social media is one of the most rapid and impactful ways of obtaining and delivering information in the modern era. Objective: The aim of this study was to rapidly obtain information on public perceptions, knowledge, and behaviors related to COVID-19 in order to identify deficiencies in key areas of public education. Methods: Using a cross-sectional study design, a survey web link was posted on the social media and messaging platforms Instagram, Twitter, and WhatsApp by the study investigators. Participants, aged ?18 years, filled out the survey on a voluntary basis. The main outcomes measured were knowledge of COVID-19 symptoms, protective measures against COVID-19, and source(s) of information about COVID-19. Subgroup analyses were conducted to determine the effects of age, gender, underlying illness, and working or studying in the health care industry on the perceived likelihood of acquiring COVID-19 and getting vaccinated. Results: A total of 5677 subjects completed the survey over the course of 1 week. ``Fever or chills'' (n=4973, 87.6\%) and ``shortness of breath'' (n=4695, 82.7\%) were identified as the main symptoms of COVID-19. Washing and sanitizing hands (n=4990, 87.9\%) and avoiding public places and crowds (n=4865, 85.7\%) were identified as the protective measures most frequently used against COVID-19. Social media was the most utilized source for information on the disease (n=4740, 83.5\%), followed by the World Health Organization (n=2844, 50.1\%). Subgroup analysis revealed that younger subjects (<35 years), males, and those working or studying in health care reported a higher perceived likelihood of acquiring COVID-19, whereas older subjects, females, and those working or studying in non--health care areas reported a lower perceived likelihood of acquiring COVID-19. Similar trends were observed for vaccination against COVID-19, with older subjects, females, and those working or studying in non--health care sectors reporting a lower likelihood of vaccinating against COVID-19. Conclusions: Our results are indicative of a relatively well-informed cohort implementing appropriate protective measures. However, key knowledge deficiencies exist with regards to vaccination against COVID-19, which future efforts should aim at correcting. ", doi="10.2196/19913", url="http://www.jmir.org/2020/9/e19913/", url="http://www.ncbi.nlm.nih.gov/pubmed/32841153" } @Article{info:doi/10.2196/19975, author="Benson, Ryzen and Hu, Mengke and Chen, T. Annie and Nag, Subhadeep and Zhu, Shu-Hong and Conway, Mike", title="Investigating the Attitudes of Adolescents and Young Adults Towards JUUL: Computational Study Using Twitter Data", journal="JMIR Public Health Surveill", year="2020", month="Sep", day="2", volume="6", number="3", pages="e19975", keywords="JUUL", keywords="electronic cigarettes", keywords="smoking cessation", keywords="natural language processing", keywords="NLP", keywords="Twitter", keywords="underage tobacco use", keywords="tobacco", keywords="e-cig", keywords="ENDS", keywords="electronic nicotine delivery system", keywords="machine learning", keywords="infodemiology", keywords="infoveillance", keywords="social media", keywords="public health", abstract="Background: Increases in electronic nicotine delivery system (ENDS) use among high school students from 2017 to 2019 appear to be associated with the increasing popularity of the ENDS device JUUL. Objective: We employed a content analysis approach in conjunction with natural language processing methods using Twitter data to understand salient themes regarding JUUL use on Twitter, sentiment towards JUUL, and underage JUUL use. Methods: Between July 2018 and August 2019, 11,556 unique tweets containing a JUUL-related keyword were collected. We manually annotated 4000 tweets for JUUL-related themes of use and sentiment. We used 3 machine learning algorithms to classify positive and negative JUUL sentiments as well as underage JUUL mentions. Results: Of the annotated tweets, 78.80\% (3152/4000) contained a specific mention of JUUL. Only 1.43\% (45/3152) of tweets mentioned using JUUL as a method of smoking cessation, and only 6.85\% (216/3152) of tweets mentioned the potential health effects of JUUL use. Of the machine learning methods used, the random forest classifier was the best performing algorithm among all 3 classification tasks (ie, positive sentiment, negative sentiment, and underage JUUL mentions). Conclusions: Our findings suggest that a vast majority of Twitter users are not using JUUL to aid in smoking cessation nor do they mention the potential health benefits or detriments of JUUL use. Using machine learning algorithms to identify tweets containing underage JUUL mentions can support the timely surveillance of JUUL habits and opinions, further assisting youth-targeted public health intervention strategies. ", doi="10.2196/19975", url="https://publichealth.jmir.org/2020/3/e19975", url="http://www.ncbi.nlm.nih.gov/pubmed/32876579" } @Article{info:doi/10.2196/18662, author="Hasegawa, Shin and Suzuki, Teppei and Yagahara, Ayako and Kanda, Reiko and Aono, Tatsuo and Yajima, Kazuaki and Ogasawara, Katsuhiko", title="Changing Emotions About Fukushima Related to the Fukushima Nuclear Power Station Accident---How Rumors Determined People's Attitudes: Social Media Sentiment Analysis", journal="J Med Internet Res", year="2020", month="Sep", day="2", volume="22", number="9", pages="e18662", keywords="Fukushima nuclear accident", keywords="Twitter messaging", keywords="radiation", keywords="radioactivity", keywords="radioactive hazard release", keywords="information dissemination", keywords="belief in rumors", keywords="disaster medicine", keywords="infodemiology", keywords="infoveillance", keywords="infodemic", abstract="Background: Public interest in radiation rose after the Tokyo Electric Power Company (TEPCO) Fukushima Daiichi Nuclear Power Station accident was caused by an earthquake off the Pacific coast of Tohoku on March 11, 2011. Various reports on the accident and radiation were spread by the mass media, and people displayed their emotional reactions, which were thought to be related to information about the Fukushima accident, on Twitter, Facebook, and other social networking sites. Fears about radiation were spread as well, leading to harmful rumors about Fukushima and the refusal to test children for radiation. It is believed that identifying the process by which people emotionally responded to this information, and hence became gripped by an increased aversion to Fukushima, might be useful in risk communication when similar disasters and accidents occur in the future. There are few studies surveying how people feel about radiation in Fukushima and other regions in an unbiased form. Objective: The purpose of this study is to identify how the feelings of local residents toward radiation changed according to Twitter. Methods: We used approximately 19 million tweets in Japanese containing the words ``radiation'' (???), ``radioactivity'' (???), and ``radioactive substances'' (?????) that were posted to Twitter over a 1-year period following the Fukushima nuclear accident. We used regional identifiers contained in tweets (ie, nouns, proper nouns, place names, postal codes, and telephone numbers) to categorize them according to their prefecture, and then analyzed the feelings toward those prefectures from the semantic orientation of the words contained in individual tweets (ie, positive impressions or negative impressions). Results: Tweets about radiation increased soon after the earthquake and then decreased, and feelings about radiation trended positively. We determined that, on average, tweets associating Fukushima Prefecture with radiation show more positive feelings than those about other prefectures, but have trended negatively over time. We also found that as other tweets have trended positively, only bots and retweets about Fukushima Prefecture have trended negatively. Conclusions: The number of tweets about radiation has decreased overall, and feelings about radiation have trended positively. However, the fact that tweets about Fukushima Prefecture trended negatively, despite decreasing in percentage, suggests that negative feelings toward Fukushima Prefecture have become more extreme. We found that while the bots and retweets that were not about Fukushima Prefecture gradually trended toward positive feelings, the bots and retweets about Fukushima Prefecture trended toward negative feelings. ", doi="10.2196/18662", url="https://www.jmir.org/2020/9/e18662", url="http://www.ncbi.nlm.nih.gov/pubmed/32876574" } @Article{info:doi/10.2196/17830, author="M{\"u}ller, Martin and Schneider, Manuel and Salath{\'e}, Marcel and Vayena, Effy", title="Assessing Public Opinion on CRISPR-Cas9: Combining Crowdsourcing and Deep Learning", journal="J Med Internet Res", year="2020", month="Aug", day="31", volume="22", number="8", pages="e17830", keywords="CRISPR", keywords="natural language processing", keywords="sentiment analysis", keywords="digital methods", keywords="infodemiology", keywords="infoveillace", keywords="empirical bioethics", keywords="social media", abstract="Background: The discovery of the CRISPR-Cas9--based gene editing method has opened unprecedented new potential for biological and medical engineering, sparking a growing public debate on both the potential and dangers of CRISPR applications. Given the speed of technology development and the almost instantaneous global spread of news, it is important to follow evolving debates without much delay and in sufficient detail, as certain events may have a major long-term impact on public opinion and later influence policy decisions. Objective: Social media networks such as Twitter have shown to be major drivers of news dissemination and public discourse. They provide a vast amount of semistructured data in almost real-time and give direct access to the content of the conversations. We can now mine and analyze such data quickly because of recent developments in machine learning and natural language processing. Methods: Here, we used Bidirectional Encoder Representations from Transformers (BERT), an attention-based transformer model, in combination with statistical methods to analyze the entirety of all tweets ever published on CRISPR since the publication of the first gene editing application in 2013. Results: We show that the mean sentiment of tweets was initially very positive, but began to decrease over time, and that this decline was driven by rare peaks of strong negative sentiments. Due to the high temporal resolution of the data, we were able to associate these peaks with specific events and to observe how trending topics changed over time. Conclusions: Overall, this type of analysis can provide valuable and complementary insights into ongoing public debates, extending the traditional empirical bioethics toolset. ", doi="10.2196/17830", url="http://www.jmir.org/2020/8/e17830/", url="http://www.ncbi.nlm.nih.gov/pubmed/32865499" } @Article{info:doi/10.2196/15623, author="Stens, Oleg and Weisman, H. Michael and Simard, Julia and Reuter, Katja", title="Insights From Twitter Conversations on Lupus and Reproductive Health: Protocol for a Content Analysis", journal="JMIR Res Protoc", year="2020", month="Aug", day="26", volume="9", number="8", pages="e15623", keywords="fertility", keywords="infodemiology", keywords="infoveillance", keywords="listening", keywords="lupus", keywords="monitoring", keywords="patient opinion", keywords="reproductive health", keywords="surveillance", keywords="Twitter", keywords="social media", keywords="social network", abstract="Background: Systemic lupus erythematosus (SLE) is the most common form of lupus. It is a chronic autoimmune disease that predominantly affects women of reproductive age, impacting contraception, fertility, and pregnancy. Although clinic-based studies have contributed to an increased understanding of reproductive health care needs of patients with SLE, misinformation abounds and perspectives on reproductive health issues among patients with lupus remain poorly understood. Social networks such as Twitter may serve as a data source for exploring how lupus patients communicate about their health issues, thus adding a dimension to enrich our understanding of communication regarding reproductive health in this unique patient population. Objective: The objective of this study is to conduct a content analysis of Twitter data published by users in English in the United States from September 1, 2017, to October 31, 2018, in order to examine people's perspectives on reproductive health among patients with lupus. Methods: This study will analyze user-generated posts that include keywords related to lupus and reproductive health from Twitter. To access public Twitter user data, we will use Symplur Signals, a health care social media analytics platform. Text classifiers will be used to identify topics in posts. Posts will be classified manually into the a priori and emergent categories. Based on the information available in a user's Twitter profile (ie, username, description, and profile image), we will further attempt to characterize the user who generated the post. We will use descriptive statistics to analyze the data and identify the most prevalent topics in the Twitter content among patients with lupus. Results: This study has been funded by the National Center for Advancing Translational Science (NCATS) through their Clinical and Translational Science Awards program. The Institutional Review Board at the University of Southern California approved the study (HS-18-00912). Data extraction and cleaning are complete. We obtained 47,715 Twitter posts containing terms related to ``lupus'' from users in the United States, published in English between September 1, 2017, and October 31, 2018. We will include 40,885 posts in the analysis, which will be completed in fall 2020. This study was supported by funds from the has been funded by the National Center for Advancing Translational Science (NCATS) through their Clinical and Translational Science Awards program. Conclusions: The findings from this study will provide pilot data on the use of Twitter among patients with lupus. Our findings will shed light on whether Twitter is a promising data source for learning about reproductive health issues expressed among patients with lupus. The data will also help to determine whether Twitter can serve as a potential outreach platform for raising awareness of lupus and reproductive health and for implementing relevant health interventions. International Registered Report Identifier (IRRID): DERR1-10.2196/15623 ", doi="10.2196/15623", url="http://www.researchprotocols.org/2020/8/e15623/", url="http://www.ncbi.nlm.nih.gov/pubmed/32844753" } @Article{info:doi/10.2196/20794, author="Mackey, Ken Tim and Li, Jiawei and Purushothaman, Vidya and Nali, Matthew and Shah, Neal and Bardier, Cortni and Cai, Mingxiang and Liang, Bryan", title="Big Data, Natural Language Processing, and Deep Learning to Detect and Characterize Illicit COVID-19 Product Sales: Infoveillance Study on Twitter and Instagram", journal="JMIR Public Health Surveill", year="2020", month="Aug", day="25", volume="6", number="3", pages="e20794", keywords="COVID-19", keywords="coronavirus", keywords="infectious disease", keywords="social media", keywords="surveillance", keywords="infoveillance", keywords="infodemiology", keywords="infodemic", keywords="fraud", keywords="cybercrime", abstract="Background: The coronavirus disease (COVID-19) pandemic is perhaps the greatest global health challenge of the last century. Accompanying this pandemic is a parallel ``infodemic,'' including the online marketing and sale of unapproved, illegal, and counterfeit COVID-19 health products including testing kits, treatments, and other questionable ``cures.'' Enabling the proliferation of this content is the growing ubiquity of internet-based technologies, including popular social media platforms that now have billions of global users. Objective: This study aims to collect, analyze, identify, and enable reporting of suspected fake, counterfeit, and unapproved COVID-19--related health care products from Twitter and Instagram. Methods: This study is conducted in two phases beginning with the collection of COVID-19--related Twitter and Instagram posts using a combination of web scraping on Instagram and filtering the public streaming Twitter application programming interface for keywords associated with suspect marketing and sale of COVID-19 products. The second phase involved data analysis using natural language processing (NLP) and deep learning to identify potential sellers that were then manually annotated for characteristics of interest. We also visualized illegal selling posts on a customized data dashboard to enable public health intelligence. Results: We collected a total of 6,029,323 tweets and 204,597 Instagram posts filtered for terms associated with suspect marketing and sale of COVID-19 health products from March to April for Twitter and February to May for Instagram. After applying our NLP and deep learning approaches, we identified 1271 tweets and 596 Instagram posts associated with questionable sales of COVID-19--related products. Generally, product introduction came in two waves, with the first consisting of questionable immunity-boosting treatments and a second involving suspect testing kits. We also detected a low volume of pharmaceuticals that have not been approved for COVID-19 treatment. Other major themes detected included products offered in different languages, various claims of product credibility, completely unsubstantiated products, unapproved testing modalities, and different payment and seller contact methods. Conclusions: Results from this study provide initial insight into one front of the ``infodemic'' fight against COVID-19 by characterizing what types of health products, selling claims, and types of sellers were active on two popular social media platforms at earlier stages of the pandemic. This cybercrime challenge is likely to continue as the pandemic progresses and more people seek access to COVID-19 testing and treatment. This data intelligence can help public health agencies, regulatory authorities, legitimate manufacturers, and technology platforms better remove and prevent this content from harming the public. ", doi="10.2196/20794", url="http://publichealth.jmir.org/2020/3/e20794/", url="http://www.ncbi.nlm.nih.gov/pubmed/32750006" } @Article{info:doi/10.2196/20673, author="Rovetta, Alessandro and Bhagavathula, Srikanth Akshaya", title="Global Infodemiology of COVID-19: Analysis of Google Web Searches and Instagram Hashtags", journal="J Med Internet Res", year="2020", month="Aug", day="25", volume="22", number="8", pages="e20673", keywords="COVID-19", keywords="coronavirus", keywords="Google", keywords="Instagram", keywords="infodemiology", keywords="infodemic", keywords="social media", abstract="Background: Although ``infodemiological'' methods have been used in research on coronavirus disease (COVID-19), an examination of the extent of infodemic moniker (misinformation) use on the internet remains limited. Objective: The aim of this paper is to investigate internet search behaviors related to COVID-19 and examine the circulation of infodemic monikers through two platforms---Google and Instagram---during the current global pandemic. Methods: We have defined infodemic moniker as a term, query, hashtag, or phrase that generates or feeds fake news, misinterpretations, or discriminatory phenomena. Using Google Trends and Instagram hashtags, we explored internet search activities and behaviors related to the COVID-19 pandemic from February 20, 2020, to May 6, 2020. We investigated the names used to identify the virus, health and risk perception, life during the lockdown, and information related to the adoption of COVID-19 infodemic monikers. We computed the average peak volume with a 95\% CI for the monikers. Results: The top six COVID-19--related terms searched in Google were ``coronavirus,'' ``corona,'' ``COVID,'' ``virus,'' ``corona virus,'' and ``COVID-19.'' Countries with a higher number of COVID-19 cases had a higher number of COVID-19 queries on Google. The monikers ``coronavirus ozone,'' ``coronavirus laboratory,'' ``coronavirus 5G,'' ``coronavirus conspiracy,'' and ``coronavirus bill gates'' were widely circulated on the internet. Searches on ``tips and cures'' for COVID-19 spiked in relation to the US president speculating about a ``miracle cure'' and suggesting an injection of disinfectant to treat the virus. Around two thirds (n=48,700,000, 66.1\%) of Instagram users used the hashtags ``COVID-19'' and ``coronavirus'' to disperse virus-related information. Conclusions: Globally, there is a growing interest in COVID-19, and numerous infodemic monikers continue to circulate on the internet. Based on our findings, we hope to encourage mass media regulators and health organizers to be vigilant and diminish the use and circulation of these infodemic monikers to decrease the spread of misinformation. ", doi="10.2196/20673", url="http://www.jmir.org/2020/8/e20673/", url="http://www.ncbi.nlm.nih.gov/pubmed/32748790" } @Article{info:doi/10.2196/20426, author="Senecal, Conor and Gulati, Rajiv and Lerman, Amir", title="Google Trends Insights Into Reduced Acute Coronary Syndrome Admissions During the COVID-19 Pandemic: Infodemiology Study", journal="JMIR Cardio", year="2020", month="Aug", day="24", volume="4", number="1", pages="e20426", keywords="Google Trends", keywords="acute coronary syndrome", keywords="coronary heart disease", keywords="online search", keywords="internet", keywords="trend", keywords="COVID-19", keywords="heart", keywords="cardiovascular", abstract="Background: During the coronavirus disease (COVID-19) pandemic, a reduction in the presentation of acute coronary syndrome (ACS) has been noted in several countries. However, whether these trends reflect a reduction in ACS incidence or a decrease in emergency room visits is unknown. Using Google Trends, queries for chest pain that have previously been shown to closely correlate with coronary heart disease were compared with searches for myocardial infarction and COVID-19 symptoms. Objective: The current study evaluates if search terms (or topics) pertaining to chest pain symptoms correlate with the reported decrease in presentations of ACS. Methods: Google Trends data for search terms ``chest pain,'' ``myocardial infarction,'' ``cough,'' and ``fever'' were obtained from June 1, 2019, to May 31, 2020. Related queries were evaluated for a relationship to coronary heart disease. Results: Following the onset of the COVID-19 pandemic, chest pain searches increased in all countries studied by at least 34\% (USA P=.003, Spain P=.007, UK P=.001, Italy P=.002), while searches for myocardial infarction dropped or remained unchanged. Rising searches for chest pain included ``coronavirus chest pain,'' ``home remedies for chest pain,'' and ``natural remedies for chest pain.'' Searches on COVID-19 symptoms (eg, cough, fever) rose initially but returned to baseline while chest pain--related searches remained elevated throughout May. Conclusions: Search engine queries for chest pain have risen during the pandemic as have related searches with alternative attribution for chest pain or home care for chest pain, suggesting that recent drops in ACS presentations may be due to patients avoiding the emergency room and potential treatment in the midst of the COVID-19 pandemic. ", doi="10.2196/20426", url="http://cardio.jmir.org/2020/1/e20426/", url="http://www.ncbi.nlm.nih.gov/pubmed/32831186" } @Article{info:doi/10.2196/21360, author="Ngai, Bik Cindy Sing and Singh, Gill Rita and Lu, Wenze and Koon, Chun Alex", title="Grappling With the COVID-19 Health Crisis: Content Analysis of Communication Strategies and Their Effects on Public Engagement on Social Media", journal="J Med Internet Res", year="2020", month="Aug", day="24", volume="22", number="8", pages="e21360", keywords="COVID-19", keywords="communication", keywords="public engagement", keywords="social media", keywords="infodemiology", keywords="infodemic", keywords="message style", keywords="health content frames", keywords="interactive features", keywords="framework", keywords="content analysis", abstract="Background: The coronavirus disease (COVID-19) has posed an unprecedented challenge to governments worldwide. Effective government communication of COVID-19 information with the public is of crucial importance. Objective: We investigate how the most-read state-owned newspaper in China, People's Daily, used an online social networking site, Sina Weibo, to communicate about COVID-19 and whether this could engage the public. The objective of this study is to develop an integrated framework to examine the content, message style, and interactive features of COVID-19--related posts and determine their effects on public engagement in the largest social media network in China. Methods: Content analysis was employed to scrutinize 608 COVID-19 posts, and coding was performed on three main dimensions: content, message style, and interactive features. The content dimension was coded into six subdimensions: action, new evidence, reassurance, disease prevention, health care services, and uncertainty, and the style dimension was coded into the subdimensions of narrative and nonnarrative. As for interactive features, they were coded into links to external sources, use of hashtags, use of questions to solicit feedback, and use of multimedia. Public engagement was measured in the form of the number of shares, comments, and likes on the People's Daily's Sina Weibo account from January 20, 2020, to March 11, 2020, to reveal the association between different levels of public engagement and communication strategies. A one-way analysis of variance followed by a post-hoc Tukey test and negative binomial regression analysis were employed to generate the results. Results: We found that although the content frames of action, new evidence, and reassurance delivered in a nonnarrative style were predominant in COVID-19 communication by the government, posts related to new evidence and a nonnarrative style were strong negative predictors of the number of shares. In terms of generating a high number of shares, it was found that disease prevention posts delivered in a narrative style were able to achieve this purpose. Additionally, an interaction effect was found between content and style. The use of a narrative style in disease prevention posts had a significant positive effect on generating comments and likes by the Chinese public, while links to external sources fostered sharing. Conclusions: These results have implications for governments, health organizations, medical professionals, the media, and researchers on their epidemic communication to engage the public. Selecting suitable communication strategies may foster active liking and sharing of posts on social media, which in turn, might raise the public's awareness of COVID-19 and motivate them to take preventive measures. The sharing of COVID-19 posts is particularly important because this action can reach out to a large audience, potentially helping to contain the spread of the virus. ", doi="10.2196/21360", url="http://www.jmir.org/2020/8/e21360/", url="http://www.ncbi.nlm.nih.gov/pubmed/32750013" } @Article{info:doi/10.2196/17048, author="Hswen, Yulin and Hawkins, B. Jared and Sewalk, Kara and Tuli, Gaurav and Williams, R. David and Viswanath, K. and Subramanian, V. S. and Brownstein, S. John", title="Racial and Ethnic Disparities in Patient Experiences in the United States: 4-Year Content Analysis of Twitter", journal="J Med Internet Res", year="2020", month="Aug", day="21", volume="22", number="8", pages="e17048", keywords="racial disparities", keywords="race", keywords="patient experience", keywords="policy", keywords="social media", keywords="digital epidemiology", keywords="social determinants of health", keywords="health disparities", keywords="health inequities", abstract="Background: Racial and ethnic minority groups often face worse patient experiences compared with the general population, which is directly related to poorer health outcomes within these minority populations. Evaluation of patient experience among racial and ethnic minority groups has been difficult due to lack of representation in traditional health care surveys. Objective: This study aims to assess the feasibility of Twitter for identifying racial and ethnic disparities in patient experience across the United States from 2013 to 2016. Methods: In total, 851,973 patient experience tweets with geographic location information from the United States were collected from 2013 to 2016. Patient experience tweets included discussions related to care received in a hospital, urgent care, or any other health institution. Ordinary least squares multiple regression was used to model patient experience sentiment and racial and ethnic groups over the 2013 to 2016 period and in relation to the implementation of the Patient Protection and Affordable Care Act (ACA) in 2014. Results: Racial and ethnic distribution of users on Twitter was highly correlated with population estimates from the United States Census Bureau's 5-year survey from 2016 (r2=0.99; P<.001). From 2013 to 2016, the average patient experience sentiment was highest for White patients, followed by Asian/Pacific Islander, Hispanic/Latino, and American Indian/Alaska Native patients. A reduction in negative patient experience sentiment on Twitter for all racial and ethnic groups was seen from 2013 to 2016. Twitter users who identified as Hispanic/Latino showed the greatest improvement in patient experience, with a 1.5 times greater increase (P<.001) than Twitter users who identified as White. Twitter users who identified as Black had the highest increase in patient experience postimplementation of the ACA (2014-2016) compared with preimplementation of the ACA (2013), and this change was 2.2 times (P<.001) greater than Twitter users who identified as White. Conclusions: The ACA mandated the implementation of the measurement of patient experience of care delivery. Considering that quality assessment of care is required, Twitter may offer the ability to monitor patient experiences across diverse racial and ethnic groups and inform the evaluation of health policies like the ACA. ", doi="10.2196/17048", url="http://www.jmir.org/2020/8/e17048/", url="http://www.ncbi.nlm.nih.gov/pubmed/32821062" } @Article{info:doi/10.2196/22590, author="Hung, Man and Lauren, Evelyn and Hon, S. Eric and Birmingham, C. Wendy and Xu, Julie and Su, Sharon and Hon, D. Shirley and Park, Jungweon and Dang, Peter and Lipsky, S. Martin", title="Social Network Analysis of COVID-19 Sentiments: Application of Artificial Intelligence", journal="J Med Internet Res", year="2020", month="Aug", day="18", volume="22", number="8", pages="e22590", keywords="COVID-19", keywords="coronavirus", keywords="sentiment", keywords="social network", keywords="Twitter", keywords="infodemiology", keywords="infodemic", keywords="pandemic", keywords="crisis", keywords="public health", keywords="business economy", keywords="artificial intelligence", abstract="Background: The coronavirus disease (COVID-19) pandemic led to substantial public discussion. Understanding these discussions can help institutions, governments, and individuals navigate the pandemic. Objective: The aim of this study is to analyze discussions on Twitter related to COVID-19 and to investigate the sentiments toward COVID-19. Methods: This study applied machine learning methods in the field of artificial intelligence to analyze data collected from Twitter. Using tweets originating exclusively in the United States and written in English during the 1-month period from March 20 to April 19, 2020, the study examined COVID-19--related discussions. Social network and sentiment analyses were also conducted to determine the social network of dominant topics and whether the tweets expressed positive, neutral, or negative sentiments. Geographic analysis of the tweets was also conducted. Results: There were a total of 14,180,603 likes, 863,411 replies, 3,087,812 retweets, and 641,381 mentions in tweets during the study timeframe. Out of 902,138 tweets analyzed, sentiment analysis classified 434,254 (48.2\%) tweets as having a positive sentiment, 187,042 (20.7\%) as neutral, and 280,842 (31.1\%) as negative. The study identified 5 dominant themes among COVID-19--related tweets: health care environment, emotional support, business economy, social change, and psychological stress. Alaska, Wyoming, New Mexico, Pennsylvania, and Florida were the states expressing the most negative sentiment while Vermont, North Dakota, Utah, Colorado, Tennessee, and North Carolina conveyed the most positive sentiment. Conclusions: This study identified 5 prevalent themes of COVID-19 discussion with sentiments ranging from positive to negative. These themes and sentiments can clarify the public's response to COVID-19 and help officials navigate the pandemic. ", doi="10.2196/22590", url="http://www.jmir.org/2020/8/e22590/", url="http://www.ncbi.nlm.nih.gov/pubmed/32750001" } @Article{info:doi/10.2196/17969, author="Huang, Dina and Huang, Yuru and Khanna, Sahil and Dwivedi, Pallavi and Slopen, Natalie and Green, M. Kerry and He, Xin and Puett, Robin and Nguyen, Quynh", title="Twitter-Derived Social Neighborhood Characteristics and Individual-Level Cardiometabolic Outcomes: Cross-Sectional Study in a Nationally Representative Sample", journal="JMIR Public Health Surveill", year="2020", month="Aug", day="18", volume="6", number="3", pages="e17969", keywords="neighborhood study", keywords="cardiometabolic outcomes", keywords="Twitter", abstract="Background: Social media platforms such as Twitter can serve as a potential data source for public health research to characterize the social neighborhood environment. Few studies have linked Twitter-derived characteristics to individual-level health outcomes. Objective: This study aims to assess the association between Twitter-derived social neighborhood characteristics, including happiness, food, and physical activity mentions, with individual cardiometabolic outcomes using a nationally representative sample. Methods: We collected a random 1\% of the geotagged tweets from April 2015 to March 2016 using Twitter's Streaming Application Interface (API). Twitter-derived zip code characteristics on happiness, food, and physical activity were merged to individual outcomes from restricted-use National Health and Nutrition Examination Survey (NHANES) with residential zip codes. Separate regression analyses were performed for each of the neighborhood characteristics using NHANES 2011-2016 and 2007-2016. Results: Individuals living in the zip codes with the two highest tertiles of happy tweets reported BMI of 0.65 (95\% CI --1.10 to --0.20) and 0.85 kg/m2 (95\% CI --1.48 to --0.21) lower than those living in zip codes with the lowest frequency of happy tweets. Happy tweets were also associated with a 6\%-8\% lower prevalence of hypertension. A higher prevalence of healthy food tweets was linked with an 11\% (95\% CI 2\% to 21\%) lower prevalence of obesity. Those living in areas with the highest and medium tertiles of physical activity tweets were associated with a lower prevalence of hypertension by 10\% (95\% CI 4\% to 15\%) and 8\% (95\% CI 2\% to 14\%), respectively. Conclusions: Twitter-derived social neighborhood characteristics were associated with individual-level obesity and hypertension in a nationally representative sample of US adults. Twitter data could be used for capturing neighborhood sociocultural influences on chronic conditions and may be used as a platform for chronic outcomes prevention. ", doi="10.2196/17969", url="http://publichealth.jmir.org/2020/3/e17969/", url="http://www.ncbi.nlm.nih.gov/pubmed/32808935" } @Article{info:doi/10.2196/18943, author="Struik, L. Laura and Dow-Fleisner, Sarah and Belliveau, Michelle and Thompson, Desiree and Janke, Robert", title="Tactics for Drawing Youth to Vaping: Content Analysis of Electronic Cigarette Advertisements", journal="J Med Internet Res", year="2020", month="Aug", day="14", volume="22", number="8", pages="e18943", keywords="qualitative research", keywords="electronic nicotine delivery systems", keywords="marketing", keywords="advertisement", keywords="youth", keywords="vaping", abstract="Background: The use of electronic cigarettes (e-cigarettes), also known as vaping, has risen exponentially among North American youth in recent years and has become a critical public health concern. The marketing strategies used by e-cigarette companies have been associated with the uptick in use among youth, with video advertisements on television and other electronic platforms being the most pervasive strategy. It is unknown how these advertisements may be tapping into youth needs and preferences. Objective: The aim of this 2-phase study was to examine the marketing strategies that underpin e-cigarette advertisements, specifically in the context of television. Methods: In phase 1, a scoping review was conducted to identify various influences on e-cigarette uptake among youth. Results of this scoping review informed the development of a coding framework. In phase 2, this framework was used to analyze the content of e-cigarette advertisements as seen on 2 popular television channels (Discovery and AMC). Results: In phase 1, a total of 20 articles met the inclusion criteria. The resultant framework consisted of 16 key influences on e-cigarette uptake among youth, which were categorized under 4 headings: personal, relational, environmental, and product-related. In phase 2, 38 e-cigarette advertisements were collected from iSpot.tv and represented 11 popular e-cigarette brands. All of the advertisements tapped into the cited influences of youth e-cigarette uptake, with the most commonly cited influences (product and relational) tapping into the most, at 97\% (37/38) and 53\% (20/38), respectively. Conclusions: The findings highlight the multidimensional influences on youth uptake of e-cigarettes, which has important implications for developing effective antivaping messages, and assist public health professionals in providing more comprehensive prevention and cessation support as it relates to e-cigarette use. The findings also bring forward tangible strategies employed by e-cigarette companies to recruit youth into vaping. Understanding this is vital to the development of cohesive strategies that combat these provaping messages. ", doi="10.2196/18943", url="https://www.jmir.org/2020/8/e18943", url="http://www.ncbi.nlm.nih.gov/pubmed/32663163" } @Article{info:doi/10.2196/18684, author="Yamaguchi, Yoichiro and Lee, Deokcheol and Nagai, Takuya and Funamoto, Taro and Tajima, Takuya and Chosa, Etsuo", title="Googling Musculoskeletal-Related Pain and Ranking of Medical Associations' Patient Information Pages: Google Ads Keyword Planner Analysis", journal="J Med Internet Res", year="2020", month="Aug", day="14", volume="22", number="8", pages="e18684", keywords="Google", keywords="ad words", keywords="infodemiology", keywords="musculoskeletal-related pain", keywords="patient education", keywords="medical information", abstract="Background: Most people currently use the internet to obtain information about many subjects, including health information. Thus, medical associations need to provide accurate medical information websites. Although medical associations have their own patient education pages, it is not clear if these websites actually show up in search results. Objective: The aim of this study was to evaluate how well medical associations function as online information providers by searching for information about musculoskeletal-related pain online and determining the ranking of the websites of medical associations. Methods: We conducted a Google search for frequently searched keywords. Keywords were extracted using Google Ads Keyword Planner associated with ``pain'' relevant to the musculoskeletal system from June 2016 to December 2019. The top 20 search queries were extracted and searched using the Google search engine in Japan and the United States. Results: The number of suggested queries for ``pain'' provided by Google Ads Keyword Planner was 930 in the United States and 2400 in Japan. Among the top 20 musculoskeletal-related pain queries chosen, the probability that the medical associations' websites would appear in the top 10 results was 30\% in the United States and 45\% in Japan. In five queries each, the associations' websites did not appear among the top 100 results. No significant difference was found in the rank of the associations' website search results (P=.28). Conclusions: To provide accurate medical information to patients, it is essential to undertake effective measures for search engine optimization. For orthopedic associations, it is necessary that their websites should appear among the top search results. ", doi="10.2196/18684", url="https://www.jmir.org/2020/8/e18684", url="http://www.ncbi.nlm.nih.gov/pubmed/32795991" } @Article{info:doi/10.2196/18350, author="Nasralah, Tareq and El-Gayar, Omar and Wang, Yong", title="Social Media Text Mining Framework for Drug Abuse: Development and Validation Study With an Opioid Crisis Case Analysis", journal="J Med Internet Res", year="2020", month="Aug", day="13", volume="22", number="8", pages="e18350", keywords="drug abuse", keywords="social media", keywords="infodemiology", keywords="infoveillance", keywords="text mining", keywords="opioid crisis", abstract="Background: Social media are considered promising and viable sources of data for gaining insights into various disease conditions and patients' attitudes, behaviors, and medications. They can be used to recognize communication and behavioral themes of problematic use of prescription drugs. However, mining and analyzing social media data have challenges and limitations related to topic deduction and data quality. As a result, we need a structured approach to analyze social media content related to drug abuse in a manner that can mitigate the challenges and limitations surrounding the use of such data. Objective: This study aimed to develop and evaluate a framework for mining and analyzing social media content related to drug abuse. The framework is designed to mitigate challenges and limitations related to topic deduction and data quality in social media data analytics for drug abuse. Methods: The proposed framework started with defining different terms related to the keywords, categories, and characteristics of the topic of interest. We then used the Crimson Hexagon platform to collect data based on a search query informed by a drug abuse ontology developed using the identified terms. We subsequently preprocessed the data and examined the quality using an evaluation matrix. Finally, a suitable data analysis approach could be used to analyze the collected data. Results: The framework was evaluated using the opioid epidemic as a drug abuse case analysis. We demonstrated the applicability of the proposed framework to identify public concerns toward the opioid epidemic and the most discussed topics on social media related to opioids. The results from the case analysis showed that the framework could improve the discovery and identification of topics in social media domains characterized by a plethora of highly diverse terms and lack of a commonly available dictionary or language by the community, such as in the case of opioid and drug abuse. Conclusions: The proposed framework addressed the challenges related to topic detection and data quality. We demonstrated the applicability of the proposed framework to identify the common concerns toward the opioid epidemic and the most discussed topics on social media related to opioids. ", doi="10.2196/18350", url="https://www.jmir.org/2020/8/e18350", url="http://www.ncbi.nlm.nih.gov/pubmed/32788147" } @Article{info:doi/10.2196/20775, author="Moon, Hana and Lee, Ho Geon", title="Evaluation of Korean-Language COVID-19--Related Medical Information on YouTube: Cross-Sectional Infodemiology Study", journal="J Med Internet Res", year="2020", month="Aug", day="12", volume="22", number="8", pages="e20775", keywords="COVID-19", keywords="YouTube", keywords="social media", keywords="misinformation", keywords="public health surveillance", keywords="health communication", keywords="consumer health information", keywords="health education", keywords="infectious disease outbreaks", keywords="infodemiology", keywords="infoveillance", keywords="infodemic", keywords="internet", keywords="multimedia", abstract="Background: In South Korea, the number of coronavirus disease (COVID-19) cases has declined rapidly and much sooner than in other countries. South Korea is one of the most digitalized countries in the world, and YouTube may have served as a rapid delivery mechanism for increasing public awareness of COVID-19. Thus, the platform may have helped the South Korean public fight the spread of the disease. Objective: The aim of this study is to compare the reliability, overall quality, title--content consistency, and content coverage of Korean-language YouTube videos on COVID-19, which have been uploaded by different sources. Methods: A total of 200 of the most viewed YouTube videos from January 1, 2020, to April 30, 2020, were screened, searching in Korean for the terms ``Coronavirus,'' ``COVID,'' ``Corona,'' ``Wuhan virus,'' and ``Wuhan pneumonia.'' Non-Korean videos and videos that were duplicated, irrelevant, or livestreamed were excluded. Source and video metrics were collected. The videos were scored based on the following criteria: modified DISCERN index, Journal of the American Medical Association Score (JAMAS) benchmark criteria, global quality score (GQS), title--content consistency index (TCCI), and medical information and content index (MICI). Results: Of the 105 total videos, 37.14\% (39/105) contained misleading information; independent user--generated videos showed the highest proportion of misleading information at 68.09\% (32/47), while all of the government-generated videos were useful. Government agency--generated videos achieved the highest median score of DISCERN (5.0, IQR 5.0-5.0), JAMAS (4.0, IQR 4.0-4.0), GQS (4.0, IQR 3.0-4.5), and TCCI (5.0, IQR 5.0-5.0), while independent user--generated videos achieved the lowest median score of DISCERN (2.0, IQR 1.0-3.0), JAMAS (2.0, IQR 1.5-2.0), GQS (2.0, IQR 1.5-2.0), and TCCI (3.0, IQR 3.0-4.0). However, the total MICI was not significantly different among sources. ``Transmission and precautionary measures'' were the most commonly covered content by government agencies, news agencies, and independent users. In contrast, the most mentioned content by news agencies was ``prevalence,'' followed by ``transmission and precautionary measures.'' Conclusions: Misleading videos had more likes, fewer comments, and longer running times than useful videos. Korean-language YouTube videos on COVID-19 uploaded by different sources varied significantly in terms of reliability, overall quality, and title--content consistency, but the content coverage was not significantly different. Government-generated videos had higher reliability, overall quality, and title--content consistency than independent user--generated videos. ", doi="10.2196/20775", url="http://www.jmir.org/2020/8/e20775/", url="http://www.ncbi.nlm.nih.gov/pubmed/32730221" } @Article{info:doi/10.2196/17478, author="Visweswaran, Shyam and Colditz, B. Jason and O'Halloran, Patrick and Han, Na-Rae and Taneja, B. Sanya and Welling, Joel and Chu, Kar-Hai and Sidani, E. Jaime and Primack, A. Brian", title="Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study", journal="J Med Internet Res", year="2020", month="Aug", day="12", volume="22", number="8", pages="e17478", keywords="vaping", keywords="social media", keywords="infodemiology", keywords="infoveillance", keywords="machine learning", keywords="deep learning", abstract="Background: Twitter presents a valuable and relevant social media platform to study the prevalence of information and sentiment on vaping that may be useful for public health surveillance. Machine learning classifiers that identify vaping-relevant tweets and characterize sentiments in them can underpin a Twitter-based vaping surveillance system. Compared with traditional machine learning classifiers that are reliant on annotations that are expensive to obtain, deep learning classifiers offer the advantage of requiring fewer annotated tweets by leveraging the large numbers of readily available unannotated tweets. Objective: This study aims to derive and evaluate traditional and deep learning classifiers that can identify tweets relevant to vaping, tweets of a commercial nature, and tweets with provape sentiments. Methods: We continuously collected tweets that matched vaping-related keywords over 2 months from August 2018 to October 2018. From this data set of tweets, a set of 4000 tweets was selected, and each tweet was manually annotated for relevance (vape relevant or not), commercial nature (commercial or not), and sentiment (provape or not). Using the annotated data, we derived traditional classifiers that included logistic regression, random forest, linear support vector machine, and multinomial naive Bayes. In addition, using the annotated data set and a larger unannotated data set of tweets, we derived deep learning classifiers that included a convolutional neural network (CNN), long short-term memory (LSTM) network, LSTM-CNN network, and bidirectional LSTM (BiLSTM) network. The unannotated tweet data were used to derive word vectors that deep learning classifiers can leverage to improve performance. Results: LSTM-CNN performed the best with the highest area under the receiver operating characteristic curve (AUC) of 0.96 (95\% CI 0.93-0.98) for relevance, all deep learning classifiers including LSTM-CNN performed better than the traditional classifiers with an AUC of 0.99 (95\% CI 0.98-0.99) for distinguishing commercial from noncommercial tweets, and BiLSTM performed the best with an AUC of 0.83 (95\% CI 0.78-0.89) for provape sentiment. Overall, LSTM-CNN performed the best across all 3 classification tasks. Conclusions: We derived and evaluated traditional machine learning and deep learning classifiers to identify vaping-related relevant, commercial, and provape tweets. Overall, deep learning classifiers such as LSTM-CNN had superior performance and had the added advantage of requiring no preprocessing. The performance of these classifiers supports the development of a vaping surveillance system. ", doi="10.2196/17478", url="https://www.jmir.org/2020/8/e17478", url="http://www.ncbi.nlm.nih.gov/pubmed/32784184" } @Article{info:doi/10.2196/20923, author="Pawar, S. Aditya and Nagpal, Sajan and Pawar, Neha and Lerman, O. Lilach and Eirin, Alfonso", title="General Public's Information-Seeking Patterns of Topics Related to Obesity: Google Trends Analysis", journal="JMIR Public Health Surveill", year="2020", month="Aug", day="11", volume="6", number="3", pages="e20923", keywords="obesity", keywords="normalization", keywords="public awareness", keywords="infodemiology", keywords="infoveillance", abstract="Background: Obesity is a major public health challenge, and recent literature sheds light on the concept of ``normalization'' of obesity. Objective: We aimed to study the worldwide pattern of web-based information seeking by public on obesity and on its related terms and topics using Google Trends. Methods: We compared the relative frequency of obesity-related search terms and topics between 2004 and 2019 on Google Trends. The mean relative interest scores for these terms over the 4-year quartiles were compared. Results: The mean relative interest score of the search term ``obesity'' consistently decreased with time in all four quartiles (2004-2019), whereas the relative interest scores of the search topics ``weight loss'' and ``abdominal obesity'' increased. The topic ``weight loss'' was popular during the month of January, and its median relative interest score for January was higher than that for other months for the entire study period (P<.001). The relative interest score for the search term ``obese'' decreased over time, whereas those scores for the terms ``body positivity'' and ``self-love'' increased after 2013. Conclusions: Despite a worldwide increase in the prevalence of obesity, its popularity as an internet search term diminished over time. The reason for peaks in months should be explored and applied to the awareness campaigns for better effectiveness. These patterns suggest normalization of obesity in society and a rise of public curiosity about image-related obesity rather than its medical implications and harm. ", doi="10.2196/20923", url="http://publichealth.jmir.org/2020/3/e20923/", url="http://www.ncbi.nlm.nih.gov/pubmed/32633725" } @Article{info:doi/10.2196/19611, author="Sousa-Pinto, Bernardo and Anto, Aram and Czarlewski, Wienia and Anto, M. Josep and Fonseca, Almeida Jo{\~a}o and Bousquet, Jean", title="Assessment of the Impact of Media Coverage on COVID-19--Related Google Trends Data: Infodemiology Study", journal="J Med Internet Res", year="2020", month="Aug", day="10", volume="22", number="8", pages="e19611", keywords="COVID-19", keywords="infodemiology", keywords="infodemic", keywords="Google Trends", keywords="media coverage", keywords="media", keywords="coronavirus", keywords="symptom", keywords="monitoring", keywords="trend", keywords="pandemic", abstract="Background: The influence of media coverage on web-based searches may hinder the role of Google Trends (GT) in monitoring coronavirus disease (COVID-19). Objective: The aim of this study was to assess whether COVID-19--related GT data, particularly those related to ageusia and anosmia, were primarily related to media coverage or to epidemic trends. Methods: We retrieved GT query data for searches on coronavirus, cough, anosmia, and ageusia and plotted them over a period of 5 years. In addition, we analyzed the trends of those queries for 17 countries throughout the year 2020 with a particular focus on the rises and peaks of the searches. For anosmia and ageusia, we assessed whether the respective GT data correlated with COVID-19 cases and deaths both throughout 2020 and specifically before March 16, 2020 (ie, the date when the media started reporting that these symptoms can be associated with COVID-19). Results: Over the last five years, peaks for coronavirus searches in GT were only observed during the winter of 2020. Rises and peaks in coronavirus searches appeared at similar times in the 17 different assessed countries irrespective of their epidemic situations. In 15 of these countries, rises in anosmia and ageusia searches occurred in the same week or 1 week after they were identified in the media as symptoms of COVID-19. When data prior to March 16, 2020 were analyzed, anosmia and ageusia GT data were found to have variable correlations with COVID-19 cases and deaths in the different countries. Conclusions: Our results indicate that COVID-19--related GT data are more closely related to media coverage than to epidemic trends. ", doi="10.2196/19611", url="https://www.jmir.org/2020/8/e19611", url="http://www.ncbi.nlm.nih.gov/pubmed/32530816" } @Article{info:doi/10.2196/17051, author="Kopila{\vs}, Vanja and Gajovi{\'c}, Sre{\'c}ko", title="Wildfire-Like Effect of a WhatsApp Campaign to Mobilize a Group of Predominantly Health Professionals With a University Degree on a Health Issue: Infodemiology Study", journal="J Med Internet Res", year="2020", month="Aug", day="10", volume="22", number="8", pages="e17051", keywords="instant messaging", keywords="rumor", keywords="5G mobile networks", keywords="participatory web", keywords="virality", keywords="infodemiology", keywords="infodemic", abstract="Background: Online interactions within a closed WhatsApp group can influence the attitudes and behaviors of the users in relation to health issues. Objective: This study aimed to analyze the activity of the members of a WhatsApp group initiated to raise awareness of the possible health effects of 5G mobile networks and mobilize members to sign the related petition. Methods: We retrospectively analyzed data from the WhatsApp group of 205 members that was active during 4 consecutive days in August 2019. The messages exchanged were collected, anonymized, and analyzed according to their timing and content. Results: The WhatsApp group members were invited to the group from the administrator's contacts; 91\% (187/205) had a university degree, 68\% (140/205) were medical professionals, and 24\% (50/205) held academic positions. Approximately a quarter of the members (47/205, 23\%) declared in their messages they signed the corresponding petition. The intense message exchange had wildfire-like features, and the majority of messages (126/133, 95\%) were exchanged during the first 26 hours. Despite the viral activity and high rate of members openly declaring that they signed the petition, only 8 (8/133, 6\%) messages from the group members, excluding the administrator, referred to the health issue, which was the topic of the group. No member expressed an opposite opinion to those presented by the administrator, and there was no debate in the form of exchanging opposite opinions. Conclusions: The wildfire-like activity of the WhatsApp group and open declaration of signing the petition as a result of the mobilization campaign were not accompanied by any form of a debate related to the corresponding health issue, although the group members were predominantly health professionals, with a quarter of holding academic positions. ", doi="10.2196/17051", url="https://www.jmir.org/2020/8/e17051", url="http://www.ncbi.nlm.nih.gov/pubmed/32442138" } @Article{info:doi/10.2196/16761, author="Vargas Meza, Xanat and Yamanaka, Toshimasa", title="Food Communication and its Related Sentiment in Local and Organic Food Videos on YouTube", journal="J Med Internet Res", year="2020", month="Aug", day="10", volume="22", number="8", pages="e16761", keywords="social networks", keywords="framing", keywords="semantic analysis", keywords="sentiment analysis", keywords="organic", keywords="local", keywords="food", keywords="YouTube", abstract="Background: Local and organic foods have shown increased importance and market size in recent years. However, attitudes, sentiment, and habits related to such foods in the context of video social networks have not been thoroughly researched. Given that such media have become some of the most important venues of internet traffic, it is relevant to investigate how sustainable food is communicated through such video social networks. Objective: This study aimed to explore the diffusion paths of local and organic foods on YouTube, providing a review of trends, coincidences, and differences among video discourses. Methods: A combined methodology involving webometric, framing, semantic, and sentiment analyses was employed. Results: We reported the results for the following two groups: organic and local organic videos. Although the content of 923 videos mostly included the ``Good Mother'' (organic and local organic: 282/808, 34.9\% and 311/866, 35.9\%, respectively), ``Natural Goodness'' (220/808, 27.2\% and 253/866, 29.2\%), and ``Undermining of Foundations'' (153/808, 18.9\% and 180/866, 20.7\%) frames, organic videos were more framed in terms of ``Frankenstein'' food (organic and local organic: 68/808, 8.4\% and 27/866, 3.1\%, respectively), with genetically modified organisms being a frequent topic among the comments. Organic videos (N=448) were better connected in terms of network metrics than local organic videos (N=475), which were slightly more framed regarding ``Responsibility'' (organic and local organic: 42/808, 5.1\% and 57/866, 6.5\%, respectively) and expressed more positive sentiment (M ranks for organic and local organic were 521.2 and 564.54, respectively, Z=2.15, P=.03). Conclusions: The results suggest that viewers considered sustainable food as part of a complex system and in a positive light and that food framed as artificial and dangerous sometimes functions as a counterpoint to promote organic food. ", doi="10.2196/16761", url="https://www.jmir.org/2020/8/e16761", url="http://www.ncbi.nlm.nih.gov/pubmed/32773370" } @Article{info:doi/10.2196/21143, author="Hou, Zhiyuan and Du, Fanxing and Zhou, Xinyu and Jiang, Hao and Martin, Sam and Larson, Heidi and Lin, Leesa", title="Cross-Country Comparison of Public Awareness, Rumors, and Behavioral Responses to the COVID-19 Epidemic: Infodemiology Study", journal="J Med Internet Res", year="2020", month="Aug", day="3", volume="22", number="8", pages="e21143", keywords="COVID-19", keywords="internet", keywords="surveillance", keywords="infodemic", keywords="infodemiology", keywords="infoveillance", keywords="Google Trends", keywords="public response", keywords="behavior", keywords="rumor", keywords="trend", abstract="Background: Understanding public behavioral responses to the coronavirus disease (COVID-19) epidemic and the accompanying infodemic is crucial to controlling the epidemic. Objective: The aim of this study was to assess real-time public awareness and behavioral responses to the COVID-19 epidemic across 12 selected countries. Methods: Internet surveillance was used to collect real-time data from the general public to assess public awareness and rumors (China: Baidu; worldwide: Google Trends) and behavior responses (China: Ali Index; worldwide: Google Shopping). These indices measured the daily number of searches or purchases and were compared with the numbers of daily COVID-19 cases. The trend comparisons across selected countries were observed from December 1, 2019 (prepandemic baseline) to April 11, 2020 (at least one month after the governments of selected countries took actions for the pandemic). Results: We identified missed windows of opportunity for early epidemic control in 12 countries, when public awareness was very low despite the emerging epidemic. China's epidemic and the declaration of a public health emergency of international concern did not prompt a worldwide public reaction to adopt health-protective measures; instead, most countries and regions only responded to the epidemic after their own case counts increased. Rumors and misinformation led to a surge of sales in herbal remedies in China and antimalarial drugs worldwide, and timely clarification of rumors mitigated the rush to purchase unproven remedies. Conclusions: Our comparative study highlights the urgent need for international coordination to promote mutual learning about epidemic characteristics and effective control measures as well as to trigger early and timely responses in individual countries. Early release of official guidelines and timely clarification of rumors led by governments are necessary to guide the public to take rational action. ", doi="10.2196/21143", url="https://www.jmir.org/2020/8/e21143", url="http://www.ncbi.nlm.nih.gov/pubmed/32701460" } @Article{info:doi/10.2196/19311, author="Morley, Jessica and Cowls, Josh and Taddeo, Mariarosaria and Floridi, Luciano", title="Public Health in the Information Age: Recognizing the Infosphere as a Social Determinant of Health", journal="J Med Internet Res", year="2020", month="Aug", day="3", volume="22", number="8", pages="e19311", keywords="COVID-19", keywords="public health", keywords="misinformation", keywords="disinformation", keywords="infodemic", keywords="infodemiology", keywords="infosphere", keywords="social determinants of health", keywords="information ethics", doi="10.2196/19311", url="https://www.jmir.org/2020/8/e19311", url="http://www.ncbi.nlm.nih.gov/pubmed/32648850" } @Article{info:doi/10.2196/19018, author="Ferrand, John and Hockensmith, Ryli and Houghton, Fagen Rebecca and Walsh-Buhi, R. Eric", title="Evaluating Smart Assistant Responses for Accuracy and Misinformation Regarding Human Papillomavirus Vaccination: Content Analysis Study", journal="J Med Internet Res", year="2020", month="Aug", day="3", volume="22", number="8", pages="e19018", keywords="digital health", keywords="human papillomavirus", keywords="smart assistants", keywords="chatbots", keywords="conversational agents", keywords="misinformation", keywords="infodemiology", keywords="vaccination", abstract="Background: Almost half (46\%) of Americans have used a smart assistant of some kind (eg, Apple Siri), and 25\% have used a stand-alone smart assistant (eg, Amazon Echo). This positions smart assistants as potentially useful modalities for retrieving health-related information; however, the accuracy of smart assistant responses lacks rigorous evaluation. Objective: This study aimed to evaluate the levels of accuracy, misinformation, and sentiment in smart assistant responses to human papillomavirus (HPV) vaccination--related questions. Methods: We systematically examined responses to questions about the HPV vaccine from the following four most popular smart assistants: Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana. One team member posed 10 questions to each smart assistant and recorded all queries and responses. Two raters independently coded all responses ($\kappa$=0.85). We then assessed differences among the smart assistants in terms of response accuracy, presence of misinformation, and sentiment regarding the HPV vaccine. Results: A total of 103 responses were obtained from the 10 questions posed across the smart assistants. Google Assistant data were excluded owing to nonresponse. Over half (n=63, 61\%) of the responses of the remaining three smart assistants were accurate. We found statistically significant differences across the smart assistants (N=103, $\chi$22=7.807, P=.02), with Cortana yielding the greatest proportion of misinformation. Siri yielded the greatest proportion of accurate responses (n=26, 72\%), whereas Cortana yielded the lowest proportion of accurate responses (n=33, 54\%). Most response sentiments across smart assistants were positive (n=65, 64\%) or neutral (n=18, 18\%), but Cortana's responses yielded the largest proportion of negative sentiment (n=7, 12\%). Conclusions: Smart assistants appear to be average-quality sources for HPV vaccination information, with Alexa responding most reliably. Cortana returned the largest proportion of inaccurate responses, the most misinformation, and the greatest proportion of results with negative sentiments. More collaboration between technology companies and public health entities is necessary to improve the retrieval of accurate health information via smart assistants. ", doi="10.2196/19018", url="https://www.jmir.org/2020/8/e19018", url="http://www.ncbi.nlm.nih.gov/pubmed/32744508" } @Article{info:doi/10.2196/17087, author="Hswen, Yulin and Zhang, Amanda and Sewalk, C. Kara and Tuli, Gaurav and Brownstein, S. John and Hawkins, B. Jared", title="Investigation of Geographic and Macrolevel Variations in LGBTQ Patient Experiences: Longitudinal Social Media Analysis", journal="J Med Internet Res", year="2020", month="Jul", day="31", volume="22", number="7", pages="e17087", keywords="LGBTQ", keywords="sexual and gender minorities", keywords="health care quality", keywords="health care disparities", keywords="social media", keywords="digital health", keywords="sentiment analysis", keywords="infodemiology", abstract="Background: Discrimination in the health care system contributes to worse health outcomes among lesbian, gay, bisexual, transgender, and queer (LGBTQ) patients. Objective: The aim of this study is to examine disparities in patient experience among LGBTQ persons using social media data. Methods: We collected patient experience data from Twitter from February 2013 to February 2017 in the United States. We compared the sentiment of patient experience tweets between Twitter users who self-identified as LGBTQ and non-LGBTQ. The effect of state-level partisan identity on patient experience sentiment and differences between LGBTQ users and non-LGBTQ users were analyzed. Results: We observed lower (more negative) patient experience sentiment among 13,689 LGBTQ users compared to 1,362,395 non-LGBTQ users. Increasing state-level liberal political identification was associated with higher patient experience sentiment among all users but had stronger effects for LGBTQ users. Conclusions: Our findings highlight that social media data can yield insights about patient experience for LGBTQ persons and suggest that a state-level sociopolitical environment influences patient experience for this group. Efforts are needed to reduce disparities in patient care for LGBTQ persons while taking into context the effect of the political climate on these inequities. ", doi="10.2196/17087", url="http://www.jmir.org/2020/7/e17087/", url="http://www.ncbi.nlm.nih.gov/pubmed/33137713" } @Article{info:doi/10.2196/19483, author="Cousins, C. Henry and Cousins, C. Clara and Harris, Alon and Pasquale, R. Louis", title="Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns", journal="J Med Internet Res", year="2020", month="Jul", day="30", volume="22", number="7", pages="e19483", keywords="epidemiology", keywords="infoveillance", keywords="COVID-19", keywords="internet activity", keywords="Google Trends", keywords="infectious disease", keywords="surveillance", keywords="public health", abstract="Background: Timely allocation of medical resources for coronavirus disease (COVID-19) requires early detection of regional outbreaks. Internet browsing data may predict case outbreaks in local populations that are yet to be confirmed. Objective: We investigated whether search-engine query patterns can help to predict COVID-19 case rates at the state and metropolitan area levels in the United States. Methods: We used regional confirmed case data from the New York Times and Google Trends results from 50 states and 166 county-based designated market areas (DMA). We identified search terms whose activity precedes and correlates with confirmed case rates at the national level. We used univariate regression to construct a composite explanatory variable based on best-fitting search queries offset by temporal lags. We measured the raw and z-transformed Pearson correlation and root-mean-square error (RMSE) of the explanatory variable with out-of-sample case rate data at the state and DMA levels. Results: Predictions were highly correlated with confirmed case rates at the state (mean r=0.69, 95\% CI 0.51-0.81; median RMSE 1.27, IQR 1.48) and DMA levels (mean r=0.51, 95\% CI 0.39-0.61; median RMSE 4.38, IQR 1.80), using search data available up to 10 days prior to confirmed case rates. They fit case-rate activity in 49 of 50 states and in 103 of 166 DMA at a significance level of .05. Conclusions: Identifiable patterns in search query activity may help to predict emerging regional outbreaks of COVID-19, although they remain vulnerable to stochastic changes in search intensity. ", doi="10.2196/19483", url="http://www.jmir.org/2020/7/e19483/", url="http://www.ncbi.nlm.nih.gov/pubmed/32692691" } @Article{info:doi/10.2196/13979, author="Alghamdi, Abdulrahman and Abumelha, Khalid and Allarakia, Jawad and Al-Shehri, Ahmed", title="Conversations and Misconceptions About Chemotherapy in Arabic Tweets: Content Analysis", journal="J Med Internet Res", year="2020", month="Jul", day="29", volume="22", number="7", pages="e13979", keywords="internet", keywords="chemotherapy", keywords="cancer", keywords="Twitter", keywords="social media", keywords="Arab world", keywords="misconceptions", keywords="infodemiology", keywords="infoveillance", abstract="Background: Although chemotherapy was first introduced for the treatment of cancer more than 60 years ago, the public understanding and acceptance of chemotherapy is still debatable. To the best of our knowledge, no study has assessed the conversations and misconceptions about chemotherapy as a treatment for cancer on social media platforms among the Arabic-speaking populations. Objective: The aim of this study was to assess the types of conversations and misconceptions that were shared on Twitter regarding chemotherapy as a treatment for cancer among the Arabic-speaking populations. Methods: All Arabic tweets containing any of the representative set of keywords related to chemotherapy and written between May 1, 2017 and October 31, 2017 were retrieved. A manual content analysis was performed to identify the categories of the users, general themes of the tweets, and the common misconceptions about chemotherapy. A chi-square test for independence with adjusted residuals was used to assess the significant associations between the categories of the users and the themes of the tweets. Results: A total of 402,157 tweets were retrieved, of which, we excluded 309,602 retweets and 62,651 irrelevant tweets. Therefore, 29,904 tweets were included in the final analysis. The majority of the tweets were posted by general users (25,774/29,904, 86.2\%), followed by the relatives and friends of patients with cancer (1913/29,904, 6.4\%). The tweets were classified into 9 themes; prayers and wishes for the well-being of patients undergoing chemotherapy was the most common theme (20,288/29,904, 67.8\%), followed by misconceptions about chemotherapy (2084/29,904, 7.0\%). There was a highly significant association between the category of the users and the themes of the tweets ($\chi$240= 16904.4, P<.001). Conclusions: Our findings support those of the previous infodemiology studies that Twitter is a valuable social media platform for assessing public conversations, discussions, and misconceptions about various health-related topics. The most prevalent theme of the tweets in our sample population was supportive messages for the patients undergoing chemotherapy, thereby suggesting that Twitter could play a role as a support mechanism for such patients. The second most prevalent theme of the tweets in our study was the various misconceptions about chemotherapy. The findings of our exploratory analysis can help physicians and health care organizations tailor educational efforts in the future to address different misconceptions about chemotherapy, thereby leading to increased public acceptance of chemotherapy as a suitable mode of treatment for cancer. ", doi="10.2196/13979", url="http://www.jmir.org/2020/7/e13979/", url="http://www.ncbi.nlm.nih.gov/pubmed/32723724" } @Article{info:doi/10.2196/17175, author="Safarnejad, Lida and Xu, Qian and Ge, Yaorong and Bagavathi, Arunkumar and Krishnan, Siddharth and Chen, Shi", title="Identifying Influential Factors in the Discussion Dynamics of Emerging Health Issues on Social Media: Computational Study", journal="JMIR Public Health Surveill", year="2020", month="Jul", day="28", volume="6", number="3", pages="e17175", keywords="social media", keywords="infodemiology", keywords="infoveillance", keywords="infodemic", keywords="health emergency", keywords="tweeting dynamics", keywords="events detection", keywords="online influentials", keywords="Zika", keywords="public engagement", abstract="Background: Social media has become a major resource for observing and understanding public opinions using infodemiology and infoveillance methods, especially during emergencies such as disease outbreaks. For public health agencies, understanding the driving forces of web-based discussions will help deliver more effective and efficient information to general users on social media and the web. Objective: The study aimed to identify the major contributors that drove overall Zika-related tweeting dynamics during the 2016 epidemic. In total, 3 hypothetical drivers were proposed: (1) the underlying Zika epidemic quantified as a time series of case counts; (2) sporadic but critical real-world events such as the 2016 Rio Olympics and World Health Organization's Public Health Emergency of International Concern (PHEIC) announcement, and (3) a few influential users' tweeting activities. Methods: All tweets and retweets (RTs) containing the keyword Zika posted in 2016 were collected via the Gnip application programming interface (API). We developed an analytical pipeline, EventPeriscope, to identify co-occurring trending events with Zika and quantify the strength of these events. We also retrieved Zika case data and identified the top influencers of the Zika discussion on Twitter. The influence of 3 potential drivers was examined via a multivariate time series analysis, signal processing, a content analysis, and text mining techniques. Results: Zika-related tweeting dynamics were not significantly correlated with the underlying Zika epidemic in the United States in any of the four quarters in 2016 nor in the entire year. Instead, peaks of Zika-related tweeting activity were strongly associated with a few critical real-world events, both planned, such as the Rio Olympics, and unplanned, such as the PHEIC announcement. The Rio Olympics was mentioned in >15\% of all Zika-related tweets and PHEIC occurred in 27\% of Zika-related tweets around their respective peaks. In addition, the overall tweeting dynamics of the top 100 most actively tweeting users on the Zika topic, the top 100 users receiving most RTs, and the top 100 users mentioned were the most highly correlated to and preceded the overall tweeting dynamics, making these groups of users the potential drivers of tweeting dynamics. The top 100 users who retweeted the most were not critical in driving the overall tweeting dynamics. There were very few overlaps among these different groups of potentially influential users. Conclusions: Using our proposed analytical workflow, EventPeriscope, we identified that Zika discussion dynamics on Twitter were decoupled from the actual disease epidemic in the United States but were closely related to and highly influenced by certain sporadic real-world events as well as by a few influential users. This study provided a methodology framework and insights to better understand the driving forces of web-based public discourse during health emergencies. Therefore, health agencies could deliver more effective and efficient web-based communications in emerging crises. ", doi="10.2196/17175", url="https://publichealth.jmir.org/2020/3/e17175", url="http://www.ncbi.nlm.nih.gov/pubmed/32348275" } @Article{info:doi/10.2196/17853, author="Zowalla, Richard and Wetter, Thomas and Pfeifer, Daniel", title="Crawling the German Health Web: Exploratory Study and Graph Analysis", journal="J Med Internet Res", year="2020", month="Jul", day="24", volume="22", number="7", pages="e17853", keywords="health information", keywords="internet", keywords="web crawling", keywords="distributed system", abstract="Background: The internet has become an increasingly important resource for health information. However, with a growing amount of web pages, it is nearly impossible for humans to manually keep track of evolving and continuously changing content in the health domain. To better understand the nature of all web-based health information as given in a specific language, it is important to identify (1) information hubs for the health domain, (2) content providers of high prestige, and (3) important topics and trends in the health-related web. In this context, an automatic web crawling approach can provide the necessary data for a computational and statistical analysis to answer (1) to (3). Objective: This study demonstrates the suitability of a focused crawler for the acquisition of the German Health Web (GHW) which includes all health-related web content of the three mostly German speaking countries Germany, Austria and Switzerland. Based on the gathered data, we provide a preliminary analysis of the GHW's graph structure covering its size, most important content providers and a ratio of public to private stakeholders. In addition, we provide our experiences in building and operating such a highly scalable crawler. Methods: A support vector machine classifier was trained on a large data set acquired from various German content providers to distinguish between health-related and non--health-related web pages. The classifier was evaluated using accuracy, recall and precision on an 80/20 training/test split (TD1) and against a crowd-validated data set (TD2). To implement the crawler, we extended the open-source framework StormCrawler. The actual crawl was conducted for 227 days. The crawler was evaluated by using harvest rate and its recall was estimated using a seed-target approach. Results: In total, n=22,405 seed URLs with country-code top level domains .de: 85.36\% (19,126/22,405), .at: 6.83\% (1530/22,405), .ch: 7.81\% (1749/22,405), were collected from Curlie and a previous crawl. The text classifier achieved an accuracy on TD1 of 0.937 (TD2=0.966), a precision on TD1 of 0.934 (TD2=0.954) and a recall on TD1 of 0.944 (TD2=0.989). The crawl yields 13.5 million presumably relevant and 119.5 million nonrelevant web pages. The average harvest rate was 19.76\%; recall was 0.821 (4105/5000 targets found). The resulting host-aggregated graph contains 215,372 nodes and 403,175 edges (network diameter=25; average path length=6.466; average degree=1.872; average in-degree=1.892; average out-degree=1.845; modularity=0.723). Among the 25 top-ranked pages for each country (according to PageRank), 40\% (30/75) were web sites published by public institutions. 25\% (19/75) were published by nonprofit organizations and 35\% (26/75) by private organizations or individuals. Conclusions: The results indicate, that the presented crawler is a suitable method for acquiring a large fraction of the GHW. As desired, the computed statistical data allows for determining major information hubs and important content providers on the GHW. In the future, the acquired data may be used to assess important topics and trends but also to build health-specific search engines. ", doi="10.2196/17853", url="http://www.jmir.org/2020/7/e17853/", url="http://www.ncbi.nlm.nih.gov/pubmed/32706701" } @Article{info:doi/10.2196/17633, author="Syamsuddin, Muhammad and Fakhruddin, Muhammad and Sahetapy-Engel, Marlen Jane Theresa and Soewono, Edy", title="Causality Analysis of Google Trends and Dengue Incidence in Bandung, Indonesia With Linkage of Digital Data Modeling: Longitudinal Observational Study", journal="J Med Internet Res", year="2020", month="Jul", day="24", volume="22", number="7", pages="e17633", keywords="dengue", keywords="Google Trends", keywords="infodemiology", keywords="infoveillance", keywords="vector error correction model", keywords="Granger causality", abstract="Background: The popularity of dengue can be inferred from Google Trends that summarizes Google searches of related topics. Both the disease and its Google Trends have a similar source of causation in the dengue virus, leading us to hypothesize that dengue incidence and Google Trends results have a long-run equilibrium. Objective: This research aimed to investigate the properties of this long-run equilibrium in the hope of using the information derived from Google Trends for the early detection of upcoming dengue outbreaks. Methods: This research used the cointegration method to assess a long-run equilibrium between dengue incidence and Google Trends results. The long-run equilibrium was characterized by their linear combination that generated a stationary process. The Dickey-Fuller test was adopted to check the stationarity of the processes. An error correction model (ECM) was then adopted to measure deviations from the long-run equilibrium to examine the short-term and long-term effects. The resulting models were used to determine the Granger causality between the two processes. Additional information about the two processes was obtained by examining the impulse response function and variance decomposition. Results: The Dickey-Fuller test supported an implicit null hypothesis that the dengue incidence and Google Trends results are nonstationary processes (P=.01). A further test showed that the processes were cointegrated (P=.01), indicating that their particular linear combination is a stationary process. These results permitted us to construct ECMs. The model showed the direction of causality of the two processes, indicating that Google Trends results will Granger-cause dengue incidence (not in the reverse order). Conclusions: Various hypothesis testing results in this research concluded that Google Trends results can be used as an initial indicator of upcoming dengue outbreaks. ", doi="10.2196/17633", url="http://www.jmir.org/2020/7/e17633/", url="http://www.ncbi.nlm.nih.gov/pubmed/32706682" } @Article{info:doi/10.2196/20737, author="Chen, Xi and Zhang, X. Stephen and Jahanshahi, Afshar Asghar and Alvarez-Risco, Aldo and Dai, Huiyang and Li, Jizhen and Ibarra, Garc{\'i}a Ver{\'o}nica", title="Belief in a COVID-19 Conspiracy Theory as a Predictor of Mental Health and Well-Being of Health Care Workers in Ecuador: Cross-Sectional Survey Study", journal="JMIR Public Health Surveill", year="2020", month="Jul", day="21", volume="6", number="3", pages="e20737", keywords="coronavirus", keywords="2019-nCoV", keywords="mental health", keywords="psychiatric identification", keywords="Latin America", keywords="COVID-19", keywords="conspiracy", keywords="well-being", keywords="health care worker", keywords="social media", keywords="prediction", abstract="Background: During the coronavirus disease (COVID-19) pandemic, social media platforms have become active sites for the dissemination of conspiracy theories that provide alternative explanations of the cause of the pandemic, such as secret plots by powerful and malicious groups. However, the association of individuals' beliefs in conspiracy theories about COVID-19 with mental health and well-being issues has not been investigated. This association creates an assessable channel to identify and provide assistance to people with mental health and well-being issues during the pandemic. Objective: Our aim was to provide the first evidence that belief in conspiracy theories regarding the COVID-19 pandemic is a predictor of the mental health and well-being of health care workers. Methods: We conducted a survey of 252 health care workers in Ecuador from April 10 to May 2, 2020. We analyzed the data regarding distress and anxiety caseness with logistic regression and the data regarding life and job satisfaction with linear regression. Results: Among the 252 sampled health care workers in Ecuador, 61 (24.2\%) believed that the virus was developed intentionally in a lab; 82 (32.5\%) experienced psychological distress, and 71 (28.2\%) had anxiety disorder. Compared to health care workers who were not sure where the virus originated, those who believed the virus was developed intentionally in a lab were more likely to report psychological distress and anxiety disorder and to have lower levels of job satisfaction and life satisfaction. Conclusions: This paper identifies belief in COVID-19 conspiracy theories as an important predictor of distress, anxiety, and job and life satisfaction among health care workers. This finding will enable mental health services to better target and provide help to mentally vulnerable health care workers during the ongoing COVID-19 pandemic. ", doi="10.2196/20737", url="http://publichealth.jmir.org/2020/3/e20737/", url="http://www.ncbi.nlm.nih.gov/pubmed/32658859" } @Article{info:doi/10.2196/16962, author="Liu, Hejing and Li, Qiudan and Zhan, Yongcheng and Zhang, Zhu and Zeng, D. Daniel and Leischow, J. Scott", title="Characterizing Social Media Messages Related to Underage JUUL E-Cigarette Buying and Selling: Cross-Sectional Analysis of Reddit Subreddits", journal="J Med Internet Res", year="2020", month="Jul", day="20", volume="22", number="7", pages="e16962", keywords="JUUL", keywords="e-cigarette", keywords="Reddit", keywords="cross-sectional analysis", keywords="electronic nicotine delivery system", keywords="underage JUUL use", abstract="Background: Stopping the epidemic of e-cigarette use among youth has become the common goal of both regulatory authorities and health departments. JUUL is currently the most popular e-cigarette brand on the market. Young people usually obtain and exchange information about JUUL with the help of social media platforms. Along with the rising prevalence of JUUL, posts about underage JUUL buying and selling have appeared on social media platforms such as Reddit, which sharply increase the risk of minors being exposed to JUUL. Objective: This study aims to analyze Reddit messages about JUUL buying and selling among the users of the UnderageJuul subreddit, and to further summarize the characteristics of those messages. The findings and insights can contribute to a better understanding of the patterns of underage JUUL use, and help public health officials provide timely education and guidance to minors who have intentions of accessing JUUL. Methods: We used a novel cross-subreddit method to analyze the Reddit messages on 2 subreddits. From July 9, 2017, to January 7, 2018, we collected data from the UnderageJuul subreddit, which was created for underage JUUL use discussion. The data set included 716 threads, 2935 comments, and 844 Reddit users (ie, Redditors). We collected our second data set, comprising 23,840 threads and 162,106 comments posted between July 9, 2017, and January 8, 2019, from the JUUL subreddit. We conducted analyses including the following: (1) annotation of users with buying/selling intention, (2) posting patterns discovery and topic comparison, and (3) posting activeness observation of discovered Redditors. Term frequency--inverse document frequency and regular expression-enhanced keyword search methods were applied during the content analysis to extract the posting patterns. The public posting records of the discovered users on the JUUL subreddit during the year after the UnderageJuul subreddit was shut down were analyzed to determine whether they were still active and interested in obtaining JUUL. Results: Our study revealed the following: (1) Among the 716 threads on the UnderageJuul subreddit, there were 214 threads related to JUUL sale and 168 threads related to JUUL purchase, which accounted for 53.5\% (382/714) of threads. (2) Among the 844 Redditors of the UnderageJuul subreddit, 23.82\% (201/844) of users were annotated with buying intention, and 21.10\% (178/844) of users were annotated with selling intention. There were 34 users with buying/selling intention that self-reported as being <21 years old. (3) The most common key phrases used in selling threads were ``WTS,'' ``want to sell,'' ``for sale,'' and ``selling'' (154/214, 72.0\%). The most common key phrases used in buying threads were ``look for/get JUUL/pods'' (58/168, 34.5\%) and ``WTB'' (53/168, 31.5\%). (4) The most important concern that UnderageJuul Redditors had in obtaining JUULs was the price (311/1306, 23.81\%), followed by the delivery service (68/1306, 5.21\%). (5) The most popular flavors among the users with buying/selling intention were mango, cucumber, and mint. The flavor preferences remained consistent on both subreddits. Adverse symptoms related to the mango flavor were reported by 3 users on the JUUL subreddit. (6) In total, 24.4\% (49/201) of users wanted to buy JUULs and 46.6\% (83/178) of users wanted to sell JUULs, including 11 self-reported underage users, who also participated in the discussions on the JUUL subreddit. (7) Within one year of the UnderageJuul subreddit shutting down, there were 40 users who continued to post 186 threads on the JUUL subreddit, including 10 threads indicating buying/selling willingness that were posted shortly after the UnderageJuul subreddit was closed. Conclusions: There were overlapping users active in the JUUL and UnderageJuul subreddits. The buying/selling-related content appeared in multiple venues with certain posting patterns from July 9, 2017, to January 7, 2018. Such content might lead to a high risk of health problems for minors, such as nicotine addiction. Based on these findings, this study provided some insights and suggestions that might contribute to the decision-making processes of regulators and public health officials. ", doi="10.2196/16962", url="http://www.jmir.org/2020/7/e16962/", url="http://www.ncbi.nlm.nih.gov/pubmed/32706661" } @Article{info:doi/10.2196/19969, author="Husain, Iltifat and Briggs, Blake and Lefebvre, Cedric and Cline, M. David and Stopyra, P. Jason and O'Brien, Claire Mary and Vaithi, Ramupriya and Gilmore, Scott and Countryman, Chase", title="Fluctuation of Public Interest in COVID-19 in the United States: Retrospective Analysis of Google Trends Search Data", journal="JMIR Public Health Surveill", year="2020", month="Jul", day="17", volume="6", number="3", pages="e19969", keywords="Infodemiology", keywords="COVID-19", keywords="SARS-CoV-2", keywords="digital health", keywords="Google Trends", keywords="trend", keywords="internet", keywords="public health", abstract="Background: In the absence of vaccines and established treatments, nonpharmaceutical interventions (NPIs) are fundamental tools to control coronavirus disease (COVID-19) transmission. NPIs require public interest to be successful. In the United States, there is a lack of published research on the factors that influence public interest in COVID-19. Using Google Trends, we examined the US level of public interest in COVID-19 and how it correlated to testing and with other countries. Objective: The aim of this study was to determine how public interest in COVID-19 in the United States changed over time and the key factors that drove this change, such as testing. US public interest in COVID-19 was compared to that in countries that have been more successful in their containment and mitigation strategies. Methods: In this retrospective study, Google Trends was used to analyze the volume of internet searches within the United States relating to COVID-19, focusing on dates between December 31, 2019, and March 24, 2020. The volume of internet searches related to COVID-19 was compared to that in other countries. Results: Throughout January and February 2020, there was limited search interest in COVID-19 within the United States. Interest declined for the first 21 days of February. A similar decline was seen in geographical regions that were later found to be experiencing undetected community transmission in February. Between March 9 and March 12, 2020, there was a rapid rise in search interest. This rise in search interest was positively correlated with the rise of positive tests for SARS-CoV-2 (6.3, 95\% CI ?2.9 to 9.7; P<.001). Within the United States, it took 52 days for search interest to rise substantially after the first positive case; in countries with more successful outbreak control, search interest rose in less than 15 days. Conclusions: Containment and mitigation strategies require public interest to be successful. The initial level of COVID-19 public interest in the United States was limited and even decreased during a time when containment and mitigation strategies were being established. A lack of public interest in COVID-19 existed in the United States when containment and mitigation policies were in place. Based on our analysis, it is clear that US policy makers need to develop novel methods of communicating COVID-19 public health initiatives. ", doi="10.2196/19969", url="https://publichealth.jmir.org/2020/3/e19969", url="http://www.ncbi.nlm.nih.gov/pubmed/32501806" } @Article{info:doi/10.2196/19354, author="Rajan, Anjana and Sharaf, Ravi and Brown, S. Robert and Sharaiha, Z. Reem and Lebwohl, Benjamin and Mahadev, SriHari", title="Association of Search Query Interest in Gastrointestinal Symptoms With COVID-19 Diagnosis in the United States: Infodemiology Study", journal="JMIR Public Health Surveill", year="2020", month="Jul", day="17", volume="6", number="3", pages="e19354", keywords="COVID-19", keywords="diarrhea", keywords="internet search queries", keywords="Google Trends", keywords="gastrointestinal", keywords="symptom", keywords="health information", keywords="pandemic", keywords="infectious disease", keywords="virus", abstract="Background: Coronavirus disease (COVID-19) is a novel viral illness that has rapidly spread worldwide. While the disease primarily presents as a respiratory illness, gastrointestinal symptoms such as diarrhea have been reported in up to one-third of confirmed cases, and patients may have mild symptoms that do not prompt them to seek medical attention. Internet-based infodemiology offers an approach to studying symptoms at a population level, even in individuals who do not seek medical care. Objective: This study aimed to determine if a correlation exists between internet searches for gastrointestinal symptoms and the confirmed case count of COVID-19 in the United States. Methods: The search terms chosen for analysis in this study included common gastrointestinal symptoms such as diarrhea, nausea, vomiting, and abdominal pain. Furthermore, the search terms fever and cough were used as positive controls, and constipation was used as a negative control. Daily query shares for the selected symptoms were obtained from Google Trends between October 1, 2019 and June 15, 2020 for all US states. These shares were divided into two time periods: pre--COVID-19 (prior to March 1) and post--COVID-19 (March 1-June 15). Confirmed COVID-19 case numbers were obtained from the Johns Hopkins University Center for Systems Science and Engineering data repository. Moving averages of the daily query shares (normalized to baseline pre--COVID-19) were then analyzed against the confirmed disease case count and daily new cases to establish a temporal relationship. Results: The relative search query shares of many symptoms, including nausea, vomiting, abdominal pain, and constipation, remained near or below baseline throughout the time period studied; however, there were notable increases in searches for the positive control symptoms of fever and cough as well as for diarrhea. These increases in daily search queries for fever, cough, and diarrhea preceded the rapid rise in number of cases by approximately 10 to 14 days. The search volumes for these terms began declining after mid-March despite the continued rises in cumulative cases and daily new case counts. Conclusions: Google searches for symptoms may precede the actual rises in cases and hospitalizations during pandemics. During the current COVID-19 pandemic, this study demonstrates that internet search queries for fever, cough, and diarrhea increased prior to the increased confirmed case count by available testing during the early weeks of the pandemic in the United States. While the search volumes eventually decreased significantly as the number of cases continued to rise, internet query search data may still be a useful tool at a population level to identify areas of active disease transmission at the cusp of new outbreaks. ", doi="10.2196/19354", url="http://publichealth.jmir.org/2020/3/e19354/", url="http://www.ncbi.nlm.nih.gov/pubmed/32640418" } @Article{info:doi/10.2196/13650, author="Cash, Scottye and Schwab-Reese, Marie Laura and Zipfel, Erin and Wilt, Megan and Moreno, Megan", title="What College Students Post About Depression on Facebook and the Support They Perceive: Content Analysis", journal="JMIR Form Res", year="2020", month="Jul", day="17", volume="4", number="7", pages="e13650", keywords="social media", keywords="depression", keywords="college students", keywords="qualitative", abstract="Background: College students frequently use social media sites to connect with friends. Increasingly, research suggests college students and other young adults seek mental health-related support on social media, which may present a unique venue for intervention. Objective: The purpose of this study was to examine college students' perceptions about displaying feelings of depression on Facebook and, in turn, how their social media friends responded. Methods: A primarily quantitative online survey with open response questions was distributed to students at four US universities. Qualitative responses were analyzed using content analysis. Results: A total of 34 students provided qualitative responses for analysis, these students were 85.3\% female, mean age 20.2 (SD=1.4) and 20.6\% racial/ethnic minority. Students who reported posting about depression often expressed an emotion or feeling but did not use the word ``depression'' in the post. Approximately 20\% posted language about a bad day, and 15\% posted a song or music video. Only one person reported posting a statement that directly asked for help. When friends responded to the posts, students generally perceived the responses as supportive or motivating gestures. Nearly 15\% of friends contacted the individual outside of Facebook. One individual received a negative response and no responses suggested that the individual seek help. Conclusions: This study found that college students who post about depression often do so without directly referencing depression and that friends were generally supportive. However, no participants reported their social network suggested they seek help, which may suggest increasing mental health literacy, for both support seekers and responders, would be an opportunity to improve online mental health-related support. ", doi="10.2196/13650", url="https://formative.jmir.org/2020/7/e13650", url="http://www.ncbi.nlm.nih.gov/pubmed/32706687" } @Article{info:doi/10.2196/17626, author="Viguria, Iranzu and Alvarez-Mon, Angel Miguel and Llavero-Valero, Maria and Asunsolo del Barco, Angel and Ortu{\~n}o, Felipe and Alvarez-Mon, Melchor", title="Eating Disorder Awareness Campaigns: Thematic and Quantitative Analysis Using Twitter", journal="J Med Internet Res", year="2020", month="Jul", day="14", volume="22", number="7", pages="e17626", keywords="awareness campaigns", keywords="eating disorders", keywords="Twitter", keywords="social media", abstract="Background: Health awareness initiatives are frequent but their efficacy is a matter of controversy. We have investigated the effect of the Eating Disorder Awareness Week and Wake Up Weight Watchers campaigns on Twitter. Objective: We aimed to examine whether the Eating Disorder Awareness Week and Wake Up Weight Watchers initiatives increased the volume and dissemination of Twitter conversations related to eating disorders and investigate what content generates the most interest on Twitter. Methods: Over a period of 12 consecutive days in 2018, we collected tweets containing the hashtag \#wakeupweightwatchers and hashtags related to Eating Disorder Awareness Week (\#eatingdisorderawarenessweek, \#eatingdisorderawareness, or \#EDAW), with the hashtag \#eatingdisorder as a control. The content of each tweet was rated as medical, testimony, help offer, awareness, pro-ana, or anti-ana. We analyzed the number of retweets and favorites generated, as well as the potential reach and impact of the hashtags and the characteristics of contributors. Results: The number of \#wakeupweightwatchers tweets was higher than that of Eating Disorder Awareness Week and \#eatingdisorder tweets (3900, 2056, and 1057, respectively). The content of tweets was significantly different between the hashtags analyzed (P<.001). Medical content was lower in the awareness campaigns. Awareness and help offer content were lower in \#wakeupweightwatchers tweets. Retweet and favorite ratios were highest in \#wakeupweightwatchers tweets. Eating Disorder Awareness Week achieved the highest impact, and very influential contributors participated. Conclusions: Both awareness campaigns effectively promoted tweeting about eating disorders. The majority of tweets did not promote any specific preventive or help-seeking behaviors. ", doi="10.2196/17626", url="http://www.jmir.org/2020/7/e17626/", url="http://www.ncbi.nlm.nih.gov/pubmed/32673225" } @Article{info:doi/10.2196/16649, author="Gao, Shuqing and He, Lingnan and Chen, Yue and Li, Dan and Lai, Kaisheng", title="Public Perception of Artificial Intelligence in Medical Care: Content Analysis of Social Media", journal="J Med Internet Res", year="2020", month="Jul", day="13", volume="22", number="7", pages="e16649", keywords="artificial intelligence", keywords="public perception", keywords="social media", keywords="content analysis", keywords="medical care", abstract="Background: High-quality medical resources are in high demand worldwide, and the application of artificial intelligence (AI) in medical care may help alleviate the crisis related to this shortage. The development of the medical AI industry depends to a certain extent on whether industry experts have a comprehensive understanding of the public's views on medical AI. Currently, the opinions of the general public on this matter remain unclear. Objective: The purpose of this study is to explore the public perception of AI in medical care through a content analysis of social media data, including specific topics that the public is concerned about; public attitudes toward AI in medical care and the reasons for them; and public opinion on whether AI can replace human doctors. Methods: Through an application programming interface, we collected a data set from the Sina Weibo platform comprising more than 16 million users throughout China by crawling all public posts from January to December 2017. Based on this data set, we identified 2315 posts related to AI in medical care and classified them through content analysis. Results: Among the 2315 identified posts, we found three types of AI topics discussed on the platform: (1) technology and application (n=987, 42.63\%), (2) industry development (n=706, 30.50\%), and (3) impact on society (n=622, 26.87\%). Out of 956 posts where public attitudes were expressed, 59.4\% (n=568), 34.4\% (n=329), and 6.2\% (n=59) of the posts expressed positive, neutral, and negative attitudes, respectively. The immaturity of AI technology (27/59, 46\%) and a distrust of related companies (n=15, 25\%) were the two main reasons for the negative attitudes. Across 200 posts that mentioned public attitudes toward replacing human doctors with AI, 47.5\% (n=95) and 32.5\% (n=65) of the posts expressed that AI would completely or partially replace human doctors, respectively. In comparison, 20.0\% (n=40) of the posts expressed that AI would not replace human doctors. Conclusions: Our findings indicate that people are most concerned about AI technology and applications. Generally, the majority of people held positive attitudes and believed that AI doctors would completely or partially replace human ones. Compared with previous studies on medical doctors, the general public has a more positive attitude toward medical AI. Lack of trust in AI and the absence of the humanistic care factor are essential reasons why some people still have a negative attitude toward medical AI. We suggest that practitioners may need to pay more attention to promoting the credibility of technology companies and meeting patients' emotional needs instead of focusing merely on technical issues. ", doi="10.2196/16649", url="http://www.jmir.org/2020/7/e16649/", url="http://www.ncbi.nlm.nih.gov/pubmed/32673231" } @Article{info:doi/10.2196/17693, author="Hswen, Yulin and Zhang, Amanda and Freifeld, Clark and Brownstein, S. John", title="Evaluation of Volume of News Reporting and Opioid-Related Deaths in the United States: Comparative Analysis Study of Geographic and Socioeconomic Differences", journal="J Med Internet Res", year="2020", month="Jul", day="10", volume="22", number="7", pages="e17693", keywords="opioid epidemic", keywords="news media", keywords="geographic", keywords="socioeconomic", keywords="addiction", keywords="overdose", abstract="Background: News media coverage is a powerful influence on public attitude and government action. The digitization of news media covering the current opioid epidemic has changed the landscape of coverage and may have implications for how to effectively respond to the opioid crisis. Objective: This study aims to characterize the relationship between volume of online opioid news reporting and opioid-related deaths in the United States and how these measures differ across geographic and socioeconomic county-level factors. Methods: Online news reports from February 2018 to April 2019 on opioid-related events in the United States were extracted from Google News. News data were aggregated at the county level and compared against opioid-related death counts. Ordinary least squares regression was used to model opioid-related death rate and opioid news coverage with the inclusion of socioeconomic and geographic explanatory variables. Results: A total of 35,758 relevant news reports were collected representing 1789 counties. Regression analysis revealed that opioid-related death rate was positively associated with news reporting. However, opioid-related death rate and news reporting volume showed opposite correlations with educational attainment and rurality. When controlling for variation in death rate, counties in the Northeast were overrepresented by news coverage. Conclusions: Our results suggest that regional variation in the volume of opioid-related news reporting does not reflect regional variation in opioid-related death rate. Differences in the amount of media attention may influence perceptions of the severity of opioid epidemic. Future studies should investigate the influence of media reporting on public support and action on opioid issues. ", doi="10.2196/17693", url="http://www.jmir.org/2020/7/e17693/", url="http://www.ncbi.nlm.nih.gov/pubmed/32673248" } @Article{info:doi/10.2196/13954, author="Buente, Wayne and Dalisay, Francis and Pokhrel, Pallav and Kramer, Kurihara Hanae and Pagano, Ian", title="An Instagram-Based Study to Understand Betel Nut Use Culture in Micronesia: Exploratory Content Analysis", journal="J Med Internet Res", year="2020", month="Jul", day="9", volume="22", number="7", pages="e13954", keywords="betel nut", keywords="areca catechu", keywords="areca", keywords="cancer", keywords="health", keywords="Guam", keywords="Micronesia", keywords="Instagram", keywords="mobile phone", keywords="culture", abstract="Background: A 2012 World Health Organization report recognizes betel nut use as an urgent public health threat faced by the Western Pacific region. However, compared with other addictive substances, little is known about how betel nuts are depicted on social media platforms. In particular, image-based social media platforms can be powerful tools for health communication. Studying the content of substance use on visual social media may provide valuable insights into public health interventions. Objective: This study aimed to explore and document the ways that betel nut is portrayed on the photo-sharing site Instagram. The analysis focuses on the hashtag \#pugua, which refers to the local term for betel nut in Guam and other parts of Micronesia. Methods: An exploratory content analysis of 242 Instagram posts tagged \#pugua was conducted based on previous research on substance use and Instagram and betel nut practices in Micronesia. In addition, the study examined the social engagement of betel nut content on the image-based platform. Results: The study findings revealed content themes referencing the betel nut or betel nut tree, betel nut preparation practices, and the unique social and cultural context surrounding betel nut activity in Guam and Micronesia. In addition, certain practices and cultural themes encouraged social engagement on Instagram. Conclusions: The findings from this study emphasize the cultural relevance of betel nut use in Micronesia. These findings provide a basis for empirically testing hypotheses related to the etiological roles of cultural identity and pride in shaping betel nut use behavior among Micronesians, particularly youths and young adults. Such research is likely to inform the development of culturally relevant betel nut prevention and cessation programs. ", doi="10.2196/13954", url="https://www.jmir.org/2020/7/e13954", url="http://www.ncbi.nlm.nih.gov/pubmed/32673220" } @Article{info:doi/10.2196/17103, author="Nguyen, T. Thu and Adams, Nikki and Huang, Dina and Glymour, Maria M. and Allen, M. Amani and Nguyen, C. Quynh", title="The Association Between State-Level Racial Attitudes Assessed From Twitter Data and Adverse Birth Outcomes: Observational Study", journal="JMIR Public Health Surveill", year="2020", month="Jul", day="6", volume="6", number="3", pages="e17103", keywords="social media", keywords="racial bias", keywords="birth outcomes", keywords="racial or ethnic minorities", abstract="Background: In the United States, racial disparities in birth outcomes persist and have been widening. Interpersonal and structural racism are leading explanations for the continuing racial disparities in birth outcomes, but research to confirm the role of racism and evaluate trends in the impact of racism on health outcomes has been hampered by the challenge of measuring racism. Most research on discrimination relies on self-reported experiences of discrimination, and few studies have examined racial attitudes and bias at the US national level. Objective: This study aimed to investigate the associations between state-level Twitter-derived sentiments related to racial or ethnic minorities and birth outcomes. Methods: We utilized Twitter's Streaming application programming interface to collect 26,027,740 tweets from June 2015 to December 2017, containing at least one race-related term. Sentiment analysis was performed using support vector machine, a supervised machine learning model. We constructed overall indicators of sentiment toward minorities and sentiment toward race-specific groups. For each year, state-level Twitter-derived sentiment data were merged with birth data for that year. The study participants were women who had singleton births with no congenital abnormalities from 2015 to 2017 and for whom data were available on gestational age (n=9,988,030) or birth weight (n=9,985,402). The main outcomes were low birth weight (birth weight ?2499 g) and preterm birth (gestational age <37 weeks). We estimated the incidence ratios controlling for individual-level maternal characteristics (sociodemographics, prenatal care, and health behaviors) and state-level demographics, using log binomial regression models. Results: The accuracy for identifying negative sentiments on comparing the machine learning model to manually labeled tweets was 91\%. Mothers living in states in the highest tertile for negative sentiment tweets referencing racial or ethnic minorities had greater incidences of low birth weight (8\% greater, 95\% CI 4\%-13\%) and preterm birth (8\% greater, 95\% CI 0\%-14\%) compared with mothers living in states in the lowest tertile. More negative tweets referencing minorities were associated with adverse birth outcomes in the total population, including non-Hispanic white people and racial or ethnic minorities. In stratified subgroup analyses, more negative tweets referencing specific racial or ethnic minority groups (black people, Middle Eastern people, and Muslims) were associated with poor birth outcomes for black people and minorities. Conclusions: A negative social context related to race was associated with poor birth outcomes for racial or ethnic minorities, as well as non-Hispanic white people. ", doi="10.2196/17103", url="https://publichealth.jmir.org/2020/3/e17103", url="http://www.ncbi.nlm.nih.gov/pubmed/32298232" } @Article{info:doi/10.2196/16543, author="Zepecki, Anne and Guendelman, Sylvia and DeNero, John and Prata, Ndola", title="Using Application Programming Interfaces to Access Google Data for Health Research: Protocol for a Methodological Framework", journal="JMIR Res Protoc", year="2020", month="Jul", day="6", volume="9", number="7", pages="e16543", keywords="Google", keywords="search data", keywords="infodemiology", keywords="infoveillance", keywords="infodemic", keywords="reproductive health", keywords="abortion", keywords="birth control", keywords="Google Trends", keywords="APIs", abstract="Background: Individuals are increasingly turning to search engines like Google to obtain health information and access resources. Analysis of Google search queries offers a novel approach, which is part of the methodological toolkit for infodemiology or infoveillance researchers, to understanding population health concerns and needs in real time or near-real time. While searches predominantly have been examined with the Google Trends website tool, newer application programming interfaces (APIs) are now available to academics to draw a richer landscape of searches. These APIs allow users to write code in languages like Python to retrieve sample data directly from Google servers. Objective: The purpose of this paper is to describe a novel protocol to determine the top queries, volume of queries, and the top sites reached by a population searching on the web for a specific health term. The protocol retrieves Google search data obtained from three Google APIs: Google Trends, Google Health Trends (also referred to as Flu Trends), and Google Custom Search. Methods: Our protocol consisted of four steps: (1) developing a master list of top search queries for an initial search term using Google Trends, (2) gathering information on relative search volume using Google Health Trends, (3) determining the most popular sites using Google Custom Search, and (4) calculating estimated total search volume. We tested the protocol following key procedures at each step and verified its usefulness by examining search traffic on birth control in 2017 in the United States. Two separate programmers working independently achieved similar results with insignificant variation due to sample variability. Results: We successfully tested the methodology on the initial search term birth control. We identified top search queries for birth control, of which birth control pill was the most popular and obtained the relative and estimated total search volume for the top queries: relative search volume was 0.54 for the pill, corresponding to an estimated 9.3-10.7 million searches. We used the estimates of the proportion of search activity for the top queries to arrive at a generated list of the most popular websites: for the pill, the Planned Parenthood website was the top site. Conclusions: The proposed methodological framework demonstrates how to retrieve Google query data from multiple Google APIs and provides thorough documentation required to systematically identify search queries and websites, as well as estimate relative and total search volume of queries in real time or near-real time in specific locations and time periods. Although the protocol needs further testing, it allows researchers to replicate the steps and shows promise in advancing our understanding of population-level health concerns. International Registered Report Identifier (IRRID): RR1-10.2196/16543 ", doi="10.2196/16543", url="https://www.researchprotocols.org/2020/7/e16543", url="http://www.ncbi.nlm.nih.gov/pubmed/32442159" } @Article{info:doi/10.2196/20472, author="Campos-Castillo, Celeste and Laestadius, I. Linnea", title="Racial and Ethnic Digital Divides in Posting COVID-19 Content on Social Media Among US Adults: Secondary Survey Analysis", journal="J Med Internet Res", year="2020", month="Jul", day="3", volume="22", number="7", pages="e20472", keywords="COVID-19", keywords="digital divides", keywords="user characteristics", keywords="race", keywords="ethnicity", keywords="algorithm bias", keywords="social media", keywords="bias", keywords="surveillance", keywords="public health", abstract="Background: Public health surveillance experts are leveraging user-generated content on social media to track the spread and effects of COVID-19. However, racial and ethnic digital divides, which are disparities among people who have internet access and post on social media, can bias inferences. This bias is particularly problematic in the context of the COVID-19 pandemic because due to structural inequalities, members of racial and ethnic minority groups are disproportionately vulnerable to contracting the virus and to the deleterious economic and social effects from mitigation efforts. Further, important demographic intersections with race and ethnicity, such as gender and age, are rarely investigated in work characterizing social media users; however, they reflect additional axes of inequality shaping differential exposure to COVID-19 and its effects. Objective: The aim of this study was to characterize how the race and ethnicity of US adults are associated with their odds of posting COVID-19 content on social media and how gender and age modify these odds. Methods: We performed a secondary analysis of a survey conducted by the Pew Research Center from March 19 to 24, 2020, using a national probability sample (N=10,510). Respondents were recruited from an online panel, where panelists without an internet-enabled device were given one to keep at no cost. The binary dependent variable was responses to an item asking whether respondents ``used social media to share or post information about the coronavirus.'' We used survey-weighted logistic regressions to estimate the odds of responding in the affirmative based on the race and ethnicity of respondents (white, black, Latino, other race/ethnicity), adjusted for covariates measuring sociodemographic background and COVID-19 experiences. We examined how gender (female, male) and age (18 to 30 years, 31 to 50 years, 51 to 64 years, and 65 years and older) intersected with race and ethnicity by estimating interactions. Results: Respondents who identified as black (odds ratio [OR] 1.29, 95\% CI 1.02-1.64; P=.03), Latino (OR 1.66, 95\% CI 1.36-2.04; P<.001), or other races/ethnicities (OR 1.33, 95\% CI 1.02-1.72; P=.03) had higher odds than respondents who identified as white of reporting that they posted COVID-19 content on social media. Women had higher odds of posting than men regardless of race and ethnicity (OR 1.58, 95\% CI 1.39-1.80; P<.001). Among men, respondents who identified as black, Latino, or members of other races/ethnicities were significantly more likely to post than respondents who identified as white. Older adults (65 years or older) had significantly lower odds (OR 0.73, 95\% CI 0.57-0.94; P=.01) of posting compared to younger adults (18-29 years), particularly among those identifying as other races/ethnicities. Latino respondents were the most likely to report posting across all age groups. Conclusions: In the United States, members of racial and ethnic minority groups are most likely to contribute to COVID-19 content on social media, particularly among groups traditionally less likely to use social media (older adults and men). The next step is to ensure that data collection procedures capture this diversity by encompassing a breadth of search criteria and social media platforms. ", doi="10.2196/20472", url="https://www.jmir.org/2020/7/e20472", url="http://www.ncbi.nlm.nih.gov/pubmed/32568726" } @Article{info:doi/10.2196/14337, author="Caldwell, K. Wendy and Fairchild, Geoffrey and Del Valle, Y. Sara", title="Surveilling Influenza Incidence With Centers for Disease Control and Prevention Web Traffic Data: Demonstration Using a Novel Dataset", journal="J Med Internet Res", year="2020", month="Jul", day="3", volume="22", number="7", pages="e14337", keywords="influenza", keywords="surveillance", keywords="infoveillance", keywords="infodemiology", keywords="projections and predictions", keywords="internet", keywords="data sources", abstract="Background: Influenza epidemics result in a public health and economic burden worldwide. Traditional surveillance techniques, which rely on doctor visits, provide data with a delay of 1 to 2 weeks. A means of obtaining real-time data and forecasting future outbreaks is desirable to provide more timely responses to influenza epidemics. Objective: This study aimed to present the first implementation of a novel dataset by demonstrating its ability to supplement traditional disease surveillance at multiple spatial resolutions. Methods: We used internet traffic data from the Centers for Disease Control and Prevention (CDC) website to determine the potential usability of this data source. We tested the traffic generated by 10 influenza-related pages in 8 states and 9 census divisions within the United States and compared it against clinical surveillance data. Results: Our results yielded an r2 value of 0.955 in the most successful case, promising results for some cases, and unsuccessful results for other cases. In the interest of scientific transparency to further the understanding of when internet data streams are an appropriate supplemental data source, we also included negative results (ie, unsuccessful models). Models that focused on a single influenza season were more successful than those that attempted to model multiple influenza seasons. Geographic resolution appeared to play a key role, with national and regional models being more successful, overall, than models at the state level. Conclusions: These results demonstrate that internet data may be able to complement traditional influenza surveillance in some cases but not in others. Specifically, our results show that the CDC website traffic may inform national- and division-level models but not models for each individual state. In addition, our results show better agreement when the data were broken up by seasons instead of aggregated over several years. We anticipate that this work will lead to more complex nowcasting and forecasting models using this data stream. ", doi="10.2196/14337", url="https://www.jmir.org/2020/7/e14337", url="http://www.ncbi.nlm.nih.gov/pubmed/32437327" } @Article{info:doi/10.2196/18831, author="Xu, Chenjie and Zhang, Xinyu and Wang, Yaogang", title="Mapping of Health Literacy and Social Panic Via Web Search Data During the COVID-19 Public Health Emergency: Infodemiological Study", journal="J Med Internet Res", year="2020", month="Jul", day="2", volume="22", number="7", pages="e18831", keywords="COVID-19", keywords="China", keywords="Baidu", keywords="infodemiology", keywords="web search", keywords="internet", keywords="public health", keywords="emergency", keywords="outbreak", keywords="infectious disease", keywords="pandemic", keywords="health literacy", abstract="Background: Coronavirus disease (COVID-19) is a type of pneumonia caused by a novel coronavirus that was discovered in 2019. As of May 6, 2020, 84,407 cases and 4643 deaths have been confirmed in China. The Chinese population has expressed great concern since the COVID-19 outbreak. Meanwhile, an average of 1 billion people per day are using the Baidu search engine to find COVID-19--related health information. Objective: The aim of this paper is to analyze web search data volumes related to COVID-19 in China. Methods: We conducted an infodemiological study to analyze web search data volumes related to COVID-19. Using Baidu Index data, we assessed the search frequencies of specific search terms in Baidu to describe the impact of COVID-19 on public health, psychology, behaviors, lifestyles, and social policies (from February 11, 2020, to March 17, 2020). Results: The search frequency related to COVID-19 has increased significantly since February 11th. Our heat maps demonstrate that citizens in Wuhan, Hubei Province, express more concern about COVID-19 than citizens from other cities since the outbreak first occurred in Wuhan. Wuhan citizens frequently searched for content related to ``medical help,'' ``protective materials,'' and ``pandemic progress.'' Web searches for ``return to work'' and ``go back to school'' have increased eight-fold compared to the previous month. Searches for content related to ``closed community and remote office'' have continued to rise, and searches for ``remote office demand'' have risen by 663\% from the previous quarter. Employees who have returned to work have mainly engaged in the following web searches: ``return to work and prevention measures,'' ``return to work guarantee policy,'' and ``time to return to work.'' Provinces with large, educated populations (eg, Henan, Hebei, and Shandong) have been focusing on ``online education'' whereas medium-sized cities have been paying more attention to ``online medical care.'' Conclusions: Our findings suggest that web search data may reflect changes in health literacy, social panic, and prevention and control policies in response to COVID-19. ", doi="10.2196/18831", url="https://www.jmir.org/2020/7/e18831", url="http://www.ncbi.nlm.nih.gov/pubmed/32540844" } @Article{info:doi/10.2196/17073, author="Black, C. Joshua and Margolin, R. Zachary and Olson, A. Richard and Dart, C. Richard", title="Online Conversation Monitoring to Understand the Opioid Epidemic: Epidemiological Surveillance Study", journal="JMIR Public Health Surveill", year="2020", month="Jun", day="29", volume="6", number="2", pages="e17073", keywords="epidemiological surveillance", keywords="infoveillance", keywords="infodemiology", keywords="opioids", keywords="social media", keywords="misuse", keywords="abuse", keywords="addiction", keywords="overdose", keywords="death", abstract="Background: Between 2016 and 2017, the national mortality rate involving opioids continued its escalation; opioid deaths rose from 42,249 to 47,600, bringing the public health crisis to a new height. Considering that 69\% of adults in the United States use online social media sites, a resource that builds a more complete understanding of prescription drug misuse and abuse could supplement traditional surveillance instruments. The Food and Drug Administration has identified 5 key risks and consequences of opioid drugs---misuse, abuse, addiction, overdose, and death. Identifying posts that discuss these key risks could lead to novel information that is not typically captured by traditional surveillance systems. Objective: The goal of this study was to describe the trends of online posts (frequency over time) involving abuse, misuse, addiction, overdose, and death in the United States and to describe the types of websites that host these discussions. Internet posts that mentioned fentanyl, hydrocodone, oxycodone, or oxymorphone were examined. Methods: Posts that did not refer to personal experiences were removed, after which 3.1 million posts remained. A stratified sample of 61,000 was selected. Unstructured data were classified into 5 key risks by manually coding for key outcomes of misuse, abuse, addiction, overdose, and death. Sampling probabilities of the coded posts were used to estimate the total post volume for each key risk. Results: Addiction and misuse were the two most commonly discussed key risks for hydrocodone, oxycodone, and oxymorphone. For fentanyl, overdose and death were the most discussed key risks. Fentanyl had the highest estimated number of misuse-, overdose-, and death-related mentions (41,808, 42,659, and 94,169, respectively). Oxycodone had the highest estimated number of abuse- and addiction-related mentions (3548 and 12,679, respectively). The estimated volume of online posts for fentanyl increased by more than 10-fold in late 2017 and 2018. The odds of discussing fentanyl overdose (odds ratios [OR] 4.32, 95\% CI 2.43-7.66) and death (OR 5.05, 95\% CI 3.10-8.21) were higher for social media, while the odds of discussing fentanyl abuse (OR 0.10, 95\% CI 0.04-0.22) and addiction (OR 0.24, 95\% CI 0.15-0.38) were higher for blogs and forums. Conclusions: Of the 5 FDA-defined key risks, fentanyl overdose and death has dominated discussion in recent years, while discussion of oxycodone, hydrocodone, and oxymorphone has decreased. As drug-related deaths continue to increase, an understanding of the motivations, circumstances, and consequences of drug abuse would assist in developing policy responses. Furthermore, content was notably different based on media origin, and studies that exclusively use either social media sites (such as Twitter) or blogs and forums could miss important content. This study sets out sustainable, ongoing methodology for surveilling internet postings regarding these drugs. ", doi="10.2196/17073", url="http://publichealth.jmir.org/2020/2/e17073/", url="http://www.ncbi.nlm.nih.gov/pubmed/32597786" } @Article{info:doi/10.2196/21820, author="Eysenbach, Gunther", title="How to Fight an Infodemic: The Four Pillars of Infodemic Management", journal="J Med Internet Res", year="2020", month="Jun", day="29", volume="22", number="6", pages="e21820", keywords="infodemiology", keywords="infodemic", keywords="COVID-19", keywords="infoveillance", keywords="pandemic", keywords="epidemics", keywords="emergency management", keywords="public health", doi="10.2196/21820", url="http://www.jmir.org/2020/6/e21820/", url="http://www.ncbi.nlm.nih.gov/pubmed/32589589" } @Article{info:doi/10.2196/19659, author="Tangcharoensathien, Viroj and Calleja, Neville and Nguyen, Tim and Purnat, Tina and D'Agostino, Marcelo and Garcia-Saiso, Sebastian and Landry, Mark and Rashidian, Arash and Hamilton, Clayton and AbdAllah, Abdelhalim and Ghiga, Ioana and Hill, Alexandra and Hougendobler, Daniel and van Andel, Judith and Nunn, Mark and Brooks, Ian and Sacco, Luigi Pier and De Domenico, Manlio and Mai, Philip and Gruzd, Anatoliy and Alaphilippe, Alexandre and Briand, Sylvie", title="Framework for Managing the COVID-19 Infodemic: Methods and Results of an Online, Crowdsourced WHO Technical Consultation", journal="J Med Internet Res", year="2020", month="Jun", day="26", volume="22", number="6", pages="e19659", keywords="COVID-19", keywords="infodemic", keywords="knowledge translation", keywords="message amplification", keywords="misinformation", keywords="information-seeking behavior", keywords="access to information", keywords="information literacy", keywords="communications media", keywords="internet", keywords="risk communication", keywords="evidence synthesis", abstract="Background: An infodemic is an overabundance of information---some accurate and some not---that occurs during an epidemic. In a similar manner to an epidemic, it spreads between humans via digital and physical information systems. It makes it hard for people to find trustworthy sources and reliable guidance when they need it. Objective: A World Health Organization (WHO) technical consultation on responding to the infodemic related to the coronavirus disease (COVID-19) pandemic was held, entirely online, to crowdsource suggested actions for a framework for infodemic management. Methods: A group of policy makers, public health professionals, researchers, students, and other concerned stakeholders was joined by representatives of the media, social media platforms, various private sector organizations, and civil society to suggest and discuss actions for all parts of society, and multiple related professional and scientific disciplines, methods, and technologies. A total of 594 ideas for actions were crowdsourced online during the discussions and consolidated into suggestions for an infodemic management framework. Results: The analysis team distilled the suggestions into a set of 50 proposed actions for a framework for managing infodemics in health emergencies. The consultation revealed six policy implications to consider. First, interventions and messages must be based on science and evidence, and must reach citizens and enable them to make informed decisions on how to protect themselves and their communities in a health emergency. Second, knowledge should be translated into actionable behavior-change messages, presented in ways that are understood by and accessible to all individuals in all parts of all societies. Third, governments should reach out to key communities to ensure their concerns and information needs are understood, tailoring advice and messages to address the audiences they represent. Fourth, to strengthen the analysis and amplification of information impact, strategic partnerships should be formed across all sectors, including but not limited to the social media and technology sectors, academia, and civil society. Fifth, health authorities should ensure that these actions are informed by reliable information that helps them understand the circulating narratives and changes in the flow of information, questions, and misinformation in communities. Sixth, following experiences to date in responding to the COVID-19 infodemic and the lessons from other disease outbreaks, infodemic management approaches should be further developed to support preparedness and response, and to inform risk mitigation, and be enhanced through data science and sociobehavioral and other research. Conclusions: The first version of this framework proposes five action areas in which WHO Member States and actors within society can apply, according to their mandate, an infodemic management approach adapted to national contexts and practices. Responses to the COVID-19 pandemic and the related infodemic require swift, regular, systematic, and coordinated action from multiple sectors of society and government. It remains crucial that we promote trusted information and fight misinformation, thereby helping save lives. ", doi="10.2196/19659", url="http://www.jmir.org/2020/6/e19659/", url="http://www.ncbi.nlm.nih.gov/pubmed/32558655" } @Article{info:doi/10.2196/18181, author="Lin, Ro-Ting and Cheng, Yawen and Jiang, Yan-Cheng", title="Exploring Public Awareness of Overwork Prevention With Big Data From Google Trends: Retrospective Analysis", journal="J Med Internet Res", year="2020", month="Jun", day="26", volume="22", number="6", pages="e18181", keywords="overwork", keywords="working hours", keywords="policy", keywords="big data", abstract="Background: To improve working conditions and prevent illness and deaths related to overwork, the Taiwanese government in 2015, 2016, and 2018 amended regulations regarding working time, overtime, shifts, and rest days. Such policy changes may lead to a rising public awareness of overwork-related issues, which may in turn reinforce policy development. Objective: This study aimed to investigate to what extent public awareness of overwork-related issues correlated with policy changes. Methods: Policies, laws, and regulations promulgated or amended in Taiwan between January 2004 and November 2019 were identified. We defined 3 working conditions (overwork, long working hours, and high job stress) related to overwork prevention, generated a keyword for each condition, and extracted the search volumes for each keyword on the Google search engine as proxy indicators of public awareness. We then calculated the monthly percentage change in the search volumes using the Joinpoint Regression Program. Results: Apparent peaks in search volumes were observed immediately after policy changes. Especially, policy changes in 2010 were followed by a remarkable peak in search volumes for both overwork and working hours, with the search volumes for overwork increased by 29\% per month from June 2010 to March 2011. This increase was preceded by the implementation of new overwork recognition guidelines and media reports of several suspected overwork-related events. The search volumes for working hours also steadily increased, by 2\% per month in September 2013 and afterward, reaching a peak in January 2017. The peak was likely due to the amendment to the Labor Standards Act, which called for ``1 fixed and 1 flexible day off per week,'' in 2016. The search volumes for job stress significantly increased (P=.026) but only by 0.4\% per month since March 2013. Conclusions: Over the past 15 years, Taiwanese authorities have revised and implemented several policies to prevent overwork-related health problems. Our study suggests a relationship between the implementation of policies that clearly defined the criteria for overwork and working hours and the rising public awareness of the importance of overwork prevention and shorter working hours. ", doi="10.2196/18181", url="https://www.jmir.org/2020/6/e18181", url="http://www.ncbi.nlm.nih.gov/pubmed/32589160" } @Article{info:doi/10.2196/16701, author="Mac, A. Olivia and Thayre, Amy and Tan, Shumei and Dodd, H. Rachael", title="Web-Based Health Information Following the Renewal of the Cervical Screening Program in Australia: Evaluation of Readability, Understandability, and Credibility", journal="J Med Internet Res", year="2020", month="Jun", day="26", volume="22", number="6", pages="e16701", keywords="cervical screening", keywords="internet", keywords="consumer health information", keywords="Australia", keywords="papillomavirus infections", abstract="Background: Three main changes were implemented in the Australian National Cervical Screening Program (NCSP) in December 2017: an increase in the recommended age to start screening, extended screening intervals, and change from the Papanicolaou (Pap) test to primary human papillomavirus screening (cervical screening test). The internet is a readily accessible source of information to explain the reasons for these changes to the public. It is important that web-based health information about changes to national screening programs is accessible and understandable for the general population. Objective: This study aimed to evaluate Australian web-based resources that provide information about the changes to the cervical screening program. Methods: The term cervical screening was searched in 3 search engines. The first 10 relevant results across the first 3 pages of each search engine were selected. Overall, 2 authors independently evaluated each website for readability (Flesch Reading Ease [FRE], Flesch-Kincaid Grade Level, and Simple Measure of Gobbledygook [SMOG] index), quality of information (Patient Education Materials Assessment Tool [PEMAT] for printable materials), credibility (Journal of the American Medical Association [JAMA] benchmark criteria and presence of Health on the Net Foundation code of conduct [HONcode] certification), website design, and usability with 5 simulation questions to assess the relevance of information. A descriptive analysis was conducted for the readability measures, PEMAT, and the JAMA benchmark criteria. Results: Of the 49 websites identified in the search, 15 were eligible for inclusion. The consumer-focused websites were classed as fairly difficult to read (mean FRE score 51.8, SD 13.3). The highest FRE score (easiest to read) was 70.4 (Cancer Council Australia Cervical Screening Consumer Site), and the lowest FRE score (most difficult to read) was 33.0 (NCSP Clinical Guidelines). A total of 9 consumer-focused websites and 4 health care provider--focused websites met the recommended threshold (sixth to eighth grade; SMOG index) for readability. The mean PEMAT understandability scores were 87.7\% (SD 6.0\%) for consumer-focused websites and 64.9\% (SD 13.8\%) for health care provider--focused websites. The mean actionability scores were 58.1\% (SD 19.1\%) for consumer-focused websites and 36.7\% (SD 11.0\%) for health care provider--focused websites. Moreover, 9 consumer-focused and 3 health care provider--focused websites scored above 70\% for understandability, and 2 consumer-focused websites had an actionability score above 70\%. A total of 3 websites met all 4 of the JAMA benchmark criteria, and 2 websites displayed the HONcode. Conclusions: It is important for women to have access to information that is at an appropriate reading level to better understand the implications of the changes to the cervical screening program. These findings can help health care providers direct their patients toward websites that provide information on cervical screening that is written at accessible reading levels and has high understandability. ", doi="10.2196/16701", url="http://www.jmir.org/2020/6/e16701/", url="http://www.ncbi.nlm.nih.gov/pubmed/32442134" } @Article{info:doi/10.2196/17574, author="Anwar, Mohd and Khoury, Dalia and Aldridge, P. Arnie and Parker, J. Stephanie and Conway, P. Kevin", title="Using Twitter to Surveil the Opioid Epidemic in North Carolina: An Exploratory Study", journal="JMIR Public Health Surveill", year="2020", month="Jun", day="24", volume="6", number="2", pages="e17574", keywords="opioids", keywords="surveillance", keywords="social media", abstract="Background: Over the last two decades, deaths associated with opioids have escalated in number and geographic spread, impacting more and more individuals, families, and communities. Reflecting on the shifting nature of the opioid overdose crisis, Dasgupta, Beletsky, and Ciccarone offer a triphasic framework to explain that opioid overdose deaths (OODs) shifted from prescription opioids for pain (beginning in 2000), to heroin (2010 to 2015), and then to synthetic opioids (beginning in 2013). Given the rapidly shifting nature of OODs, timelier surveillance data are critical to inform strategies that combat the opioid crisis. Using easily accessible and near real-time social media data to improve public health surveillance efforts related to the opioid crisis is a promising area of research. Objective: This study explored the potential of using Twitter data to monitor the opioid epidemic. Specifically, this study investigated the extent to which the content of opioid-related tweets corresponds with the triphasic nature of the opioid crisis and correlates with OODs in North Carolina between 2009 and 2017. Methods: Opioid-related Twitter posts were obtained using Crimson Hexagon, and were classified as relating to prescription opioids, heroin, and synthetic opioids using natural language processing. This process resulted in a corpus of 100,777 posts consisting of tweets, retweets, mentions, and replies. Using a random sample of 10,000 posts from the corpus, we identified opioid-related terms by analyzing word frequency for each year. OODs were obtained from the Multiple Cause of Death database from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC WONDER). Least squares regression and Granger tests compared patterns of opioid-related posts with OODs. Results: The pattern of tweets related to prescription opioids, heroin, and synthetic opioids resembled the triphasic nature of OODs. For prescription opioids, tweet counts and OODs were statistically unrelated. Tweets mentioning heroin and synthetic opioids were significantly associated with heroin OODs and synthetic OODs in the same year (P=.01 and P<.001, respectively), as well as in the following year (P=.03 and P=.01, respectively). Moreover, heroin tweets in a given year predicted heroin deaths better than lagged heroin OODs alone (P=.03). Conclusions: Findings support using Twitter data as a timely indicator of opioid overdose mortality, especially for heroin. ", doi="10.2196/17574", url="http://publichealth.jmir.org/2020/2/e17574/", url="http://www.ncbi.nlm.nih.gov/pubmed/32469322" } @Article{info:doi/10.2196/17280, author="Lu, Xinyi and Chen, Long and Yuan, Jianbo and Luo, Joyce and Luo, Jiebo and Xie, Zidian and Li, Dongmei", title="User Perceptions of Different Electronic Cigarette Flavors on Social Media: Observational Study", journal="J Med Internet Res", year="2020", month="Jun", day="24", volume="22", number="6", pages="e17280", keywords="e-cigarette", keywords="flavor", keywords="social media", abstract="Background: The number of electronic cigarette (e-cigarette) users has been increasing rapidly in recent years, especially among youth and young adults. More e-cigarette products have become available, including e-liquids with various brands and flavors. Various e-liquid flavors have been frequently discussed by e-cigarette users on social media. Objective: This study aimed to examine the longitudinal prevalence of mentions of electronic cigarette liquid (e-liquid) flavors and user perceptions on social media. Methods: We applied a data-driven approach to analyze the trends and macro-level user sentiments of different e-cigarette flavors on social media. With data collected from web-based stores, e-liquid flavors were classified into categories in a flavor hierarchy based on their ingredients. The e-cigarette--related posts were collected from social media platforms, including Reddit and Twitter, using e-cigarette--related keywords. The temporal trend of mentions of e-liquid flavor categories was compiled using Reddit data from January 2013 to April 2019. Twitter data were analyzed using a sentiment analysis from May to August 2019 to explore the opinions of e-cigarette users toward each flavor category. Results: More than 1000 e-liquid flavors were classified into 7 major flavor categories. The fruit and sweets categories were the 2 most frequently discussed e-liquid flavors on Reddit, contributing to approximately 58\% and 15\%, respectively, of all flavor-related posts. We showed that mentions of the fruit flavor category had a steady overall upward trend compared with other flavor categories that did not show much change over time. Results from the sentiment analysis demonstrated that most e-liquid flavor categories had significant positive sentiments, except for the beverage and tobacco categories. Conclusions: The most updated information about the popular e-liquid flavors mentioned on social media was investigated, which showed that the prevalence of mentions of e-liquid flavors and user perceptions on social media were different. Fruit was the most frequently discussed flavor category on social media. Our study provides valuable information for future regulation of flavored e-cigarettes. ", doi="10.2196/17280", url="http://www.jmir.org/2020/6/e17280/", url="http://www.ncbi.nlm.nih.gov/pubmed/32579123" } @Article{info:doi/10.2196/17196, author="Stevens, Robin and Bonett, Stephen and Bannon, Jacqueline and Chittamuru, Deepti and Slaff, Barry and Browne, K. Safa and Huang, Sarah and Bauermeister, A. Jos{\'e}", title="Association Between HIV-Related Tweets and HIV Incidence in the United States: Infodemiology Study", journal="J Med Internet Res", year="2020", month="Jun", day="24", volume="22", number="6", pages="e17196", keywords="HIV/AIDS", keywords="social media", keywords="youth", keywords="natural language processing", keywords="surveillance", abstract="Background: Adolescents and young adults in the age range of 13-24 years are at the highest risk of developing HIV infections. As social media platforms are extremely popular among youths, researchers can utilize these platforms to curb the HIV epidemic by investigating the associations between the discourses on HIV infections and the epidemiological data of HIV infections. Objective: The goal of this study was to examine how Twitter activity among young men is related to the incidence of HIV infection in the population. Methods: We used integrated human-computer techniques to characterize the HIV-related tweets by male adolescents and young male adults (age range: 13-24 years). We identified tweets related to HIV risk and prevention by using natural language processing (NLP). Our NLP algorithm identified 89.1\% (2243/2517) relevant tweets, which were manually coded by expert coders. We coded 1577 HIV-prevention tweets and 17.5\% (940/5372) of general sex-related tweets (including emojis, gifs, and images), and we achieved reliability with intraclass correlation at 0.80 or higher on key constructs. Bivariate and multivariate analyses were performed to identify the spatial patterns in posting HIV-related tweets as well as the relationships between the tweets and local HIV infection rates. Results: We analyzed 2517 tweets that were identified as relevant to HIV risk and prevention tags; these tweets were geolocated in 109 counties throughout the United States. After adjusting for region, HIV prevalence, and social disadvantage index, our findings indicated that every 100-tweet increase in HIV-specific tweets per capita from noninstitutional accounts was associated with a multiplicative effect of 0.97 (95\% CI [0.94-1.00]; P=.04) on the incidence of HIV infections in the following year in a given county. Conclusions: Twitter may serve as a proxy of public behavior related to HIV infections, and the association between the number of HIV-related tweets and HIV infection rates further supports the use of social media for HIV disease prevention. ", doi="10.2196/17196", url="https://www.jmir.org/2020/6/e17196", url="http://www.ncbi.nlm.nih.gov/pubmed/32579119" } @Article{info:doi/10.2196/17496, author="Chen, Long and Lu, Xinyi and Yuan, Jianbo and Luo, Joyce and Luo, Jiebo and Xie, Zidian and Li, Dongmei", title="A Social Media Study on the Associations of Flavored Electronic Cigarettes With Health Symptoms: Observational Study", journal="J Med Internet Res", year="2020", month="Jun", day="22", volume="22", number="6", pages="e17496", keywords="e-cigarette", keywords="social media", keywords="eHealth", abstract="Background: In recent years, flavored electronic cigarettes (e-cigarettes) have become popular among teenagers and young adults. Discussions about e-cigarettes and e-cigarette use (vaping) experiences are prevalent online, making social media an ideal resource for understanding the health risks associated with e-cigarette flavors from the users' perspective. Objective: This study aimed to investigate the potential associations between electronic cigarette liquid (e-liquid) flavors and the reporting of health symptoms using social media data. Methods: A dataset consisting of 2.8 million e-cigarette--related posts was collected using keyword filtering from Reddit, a social media platform, from January 2013 to April 2019. Temporal analysis for nine major health symptom categories was used to understand the trend of public concerns related to e-cigarettes. Sentiment analysis was conducted to obtain the proportions of positive and negative sentiment scores for all reported health symptom categories. Topic modeling was applied to reveal the topics related to e-cigarettes and health symptoms. Furthermore, generalized estimating equation (GEE) models were used to quantitatively measure potential associations between e-liquid flavors and the reporting of health symptoms. Results: Temporal analysis showed that the Respiratory category was consistently the most discussed health symptom category among all categories related to e-cigarettes on Reddit, followed by the Throat category. Sentiment analysis showed higher proportions of positive sentiment scores for all reported health symptom categories, except for the Cancer category. Topic modeling conducted on all health-related posts showed that 17 of the top 100 topics were flavor related. GEE models showed different associations between the reporting of health symptoms and e-liquid flavor categories, for example, lower association of the Beverage flavors with Respiratory compared with other flavors and higher association of the Fruit flavors with Cardiovascular than other flavors. Conclusions: This study identified different potential associations between e-liquid flavors and the reporting of health symptoms using social media data. The results of this study provide valuable information for further investigation of the health effects associated with different e-liquid flavors. ", doi="10.2196/17496", url="http://www.jmir.org/2020/6/e17496/", url="http://www.ncbi.nlm.nih.gov/pubmed/32568093" } @Article{info:doi/10.2196/19276, author="Wahbeh, Abdullah and Nasralah, Tareq and Al-Ramahi, Mohammad and El-Gayar, Omar", title="Mining Physicians' Opinions on Social Media to Obtain Insights Into COVID-19: Mixed Methods Analysis", journal="JMIR Public Health Surveill", year="2020", month="Jun", day="18", volume="6", number="2", pages="e19276", keywords="pandemic", keywords="coronavirus", keywords="COVID-19", keywords="social media", keywords="infodemiology", keywords="infoveillance", keywords="medical professionals", keywords="opinion analysis", abstract="Background: The coronavirus disease (COVID-19) pandemic is considered to be the most daunting public health challenge in decades. With no effective treatments and with time needed to develop a vaccine, alternative approaches are being used to control this pandemic. Objective: The objective of this paper was to identify topics, opinions, and recommendations about the COVID-19 pandemic discussed by medical professionals on the Twitter social medial platform. Methods: Using a mixed methods approach blending the capabilities of social media analytics and qualitative analysis, we analyzed COVID-19--related tweets posted by medical professionals and examined their content. We used qualitative analysis to explore the collected data to identify relevant tweets and uncover important concepts about the pandemic using qualitative coding. Unsupervised and supervised machine learning techniques and text analysis were used to identify topics and opinions. Results: Data were collected from 119 medical professionals on Twitter about the coronavirus pandemic. A total of 10,096 English tweets were collected from the identified medical professionals between December 1, 2019 and April 1, 2020. We identified eight topics, namely actions and recommendations, fighting misinformation, information and knowledge, the health care system, symptoms and illness, immunity, testing, and infection and transmission. The tweets mainly focused on needed actions and recommendations (2827/10,096, 28\%) to control the pandemic. Many tweets warned about misleading information (2019/10,096, 20\%) that could lead to infection of more people with the virus. Other tweets discussed general knowledge and information (911/10,096, 9\%) about the virus as well as concerns about the health care systems and workers (909/10,096, 9\%). The remaining tweets discussed information about symptoms associated with COVID-19 (810/10,096, 8\%), immunity (707/10,096, 7\%), testing (605/10,096, 6\%), and virus infection and transmission (503/10,096, 5\%). Conclusions: Our findings indicate that Twitter and social media platforms can help identify important and useful knowledge shared by medical professionals during a pandemic. ", doi="10.2196/19276", url="http://publichealth.jmir.org/2020/2/e19276/", url="http://www.ncbi.nlm.nih.gov/pubmed/32421686" } @Article{info:doi/10.2196/19981, author="Tao, Zhuo-Ying and Chu, Guang and McGrath, Colman and Hua, Fang and Leung, Yan Yiu and Yang, Wei-Fa and Su, Yu-Xiong", title="Nature and Diffusion of COVID-19--related Oral Health Information on Chinese Social Media: Analysis of Tweets on Weibo", journal="J Med Internet Res", year="2020", month="Jun", day="15", volume="22", number="6", pages="e19981", keywords="COVID-19", keywords="dentistry", keywords="oral health", keywords="online health", keywords="social media", keywords="tweet", keywords="Weibo", keywords="China", keywords="health information", abstract="Background: Social media has become increasingly important as a source of information for the public and is widely used for health-related information. The outbreak of the coronavirus disease (COVID-19) has exerted a negative impact on dental practices. Objective: The aim of this study is to analyze the nature and diffusion of COVID-19--related oral health information on the Chinese social media site Weibo. Methods: A total of 15,900 tweets related to oral health and dentistry information from Weibo during the COVID-19 outbreak in China (December 31, 2019, to March 16, 2020) were included in our study. Two researchers coded 1000 of the total tweets in advance, and two main thematic categories with eight subtypes were refined. The included tweets were analyzed over time and geographic region, and coded into eight thematic categories. Additionally, the time distributions of tweets containing information about dental services, needs of dental treatment, and home oral care during the COVID-19 epidemic were further analyzed. Results: People reacted rapidly to the emerging severe acute respiratory syndrome coronavirus 2 threat to dental services, and a large amount of COVID-19--related oral health information was tweeted on Weibo. The time and geographic distribution of tweets shared similarities with epidemiological data of the COVID-19 outbreak in China. Tweets containing home oral care and dental services content were the most frequently exchanged information (n=4803/15,900, 30.20\% and n=4478, 28.16\%, respectively). Significant differences of public attention were found between various types of bloggers in dental services--related tweets (P<.001), and the tweets from the government and media engaged the most public attention. The distributions of tweets containing information about dental services, needs of dental treatment, and home oral care information dynamically changed with time. Conclusions: Our study overviewed and analyzed social media data on the dental services and oral health information during the COVID-19 epidemic, thus, providing insights for government organizations, media, and dental professionals to better facilitate oral health communication and efficiently shape public concern through social media when routine dental services are unavailable during an unprecedented event. The study of the nature and distribution of social media can serve as a useful adjunct tool to help make public health policies. ", doi="10.2196/19981", url="http://www.jmir.org/2020/6/e19981/", url="http://www.ncbi.nlm.nih.gov/pubmed/32501808" } @Article{info:doi/10.2196/19509, author="Mackey, Tim and Purushothaman, Vidya and Li, Jiawei and Shah, Neal and Nali, Matthew and Bardier, Cortni and Liang, Bryan and Cai, Mingxiang and Cuomo, Raphael", title="Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study", journal="JMIR Public Health Surveill", year="2020", month="Jun", day="8", volume="6", number="2", pages="e19509", keywords="infoveillance", keywords="COVID-19", keywords="Twitter", keywords="machine learning", keywords="surveillance", abstract="Background: The coronavirus disease (COVID-19) pandemic is a global health emergency with over 6 million cases worldwide as of the beginning of June 2020. The pandemic is historic in scope and precedent given its emergence in an increasingly digital era. Importantly, there have been concerns about the accuracy of COVID-19 case counts due to issues such as lack of access to testing and difficulty in measuring recoveries. Objective: The aims of this study were to detect and characterize user-generated conversations that could be associated with COVID-19-related symptoms, experiences with access to testing, and mentions of disease recovery using an unsupervised machine learning approach. Methods: Tweets were collected from the Twitter public streaming application programming interface from March 3-20, 2020, filtered for general COVID-19-related keywords and then further filtered for terms that could be related to COVID-19 symptoms as self-reported by users. Tweets were analyzed using an unsupervised machine learning approach called the biterm topic model (BTM), where groups of tweets containing the same word-related themes were separated into topic clusters that included conversations about symptoms, testing, and recovery. Tweets in these clusters were then extracted and manually annotated for content analysis and assessed for their statistical and geographic characteristics. Results: A total of 4,492,954 tweets were collected that contained terms that could be related to COVID-19 symptoms. After using BTM to identify relevant topic clusters and removing duplicate tweets, we identified a total of 3465 (<1\%) tweets that included user-generated conversations about experiences that users associated with possible COVID-19 symptoms and other disease experiences. These tweets were grouped into five main categories including first- and secondhand reports of symptoms, symptom reporting concurrent with lack of testing, discussion of recovery, confirmation of negative COVID-19 diagnosis after receiving testing, and users recalling symptoms and questioning whether they might have been previously infected with COVID-19. The co-occurrence of tweets for these themes was statistically significant for users reporting symptoms with a lack of testing and with a discussion of recovery. A total of 63\% (n=1112) of the geotagged tweets were located in the United States. Conclusions: This study used unsupervised machine learning for the purposes of characterizing self-reporting of symptoms, experiences with testing, and mentions of recovery related to COVID-19. Many users reported symptoms they thought were related to COVID-19, but they were not able to get tested to confirm their concerns. In the absence of testing availability and confirmation, accurate case estimations for this period of the outbreak may never be known. Future studies should continue to explore the utility of infoveillance approaches to estimate COVID-19 disease severity. ", doi="10.2196/19509", url="http://publichealth.jmir.org/2020/2/e19509/", url="http://www.ncbi.nlm.nih.gov/pubmed/32490846" } @Article{info:doi/10.2196/19455, author="Jo, Wonkwang and Lee, Jaeho and Park, Junli and Kim, Yeol", title="Online Information Exchange and Anxiety Spread in the Early Stage of the Novel Coronavirus (COVID-19) Outbreak in South Korea: Structural Topic Model and Network Analysis", journal="J Med Internet Res", year="2020", month="Jun", day="2", volume="22", number="6", pages="e19455", keywords="coronavirus", keywords="anxiety", keywords="pandemic", keywords="online", keywords="health information exchange", keywords="topic modeling", abstract="Background: In case of a population-wide infectious disease outbreak, such as the novel coronavirus disease (COVID-19), people's online activities could significantly affect public concerns and health behaviors due to difficulty in accessing credible information from reliable sources, which in turn causes people to seek necessary information on the web. Therefore, measuring and analyzing online health communication and public sentiment is essential for establishing effective and efficient disease control policies, especially in the early stage of an outbreak. Objective: This study aimed to investigate the trends of online health communication, analyze the focus of people's anxiety in the early stages of COVID-19, and evaluate the appropriateness of online information. Methods: We collected 13,148 questions and 29,040 answers related to COVID-19 from Naver, the most popular Korean web portal (January 20, 2020, to March 2, 2020). Three main methods were used in this study: (1) the structural topic model was used to examine the topics in the online questions; (2) word network analysis was conducted to analyze the focus of people's anxiety and worry in the questions; and (3) two medical doctors assessed the appropriateness of the answers to the questions, which were primarily related to people's anxiety. Results: A total of 50 topics and 6 cohesive topic communities were identified from the questions. Among them, topic community 4 (suspecting COVID-19 infection after developing a particular symptom) accounted for the largest portion of the questions. As the number of confirmed patients increased, the proportion of topics belonging to topic community 4 also increased. Additionally, the prolonged situation led to a slight increase in the proportion of topics related to job issues. People's anxieties and worries were closely related with physical symptoms and self-protection methods. Although relatively appropriate to suspect physical symptoms, a high proportion of answers related to self-protection methods were assessed as misinformation or advertisements. Conclusions: Search activity for online information regarding the COVID-19 outbreak has been active. Many of the online questions were related to people's anxieties and worries. A considerable portion of corresponding answers had false information or were advertisements. The study results could contribute reference information to various countries that need to monitor public anxiety and provide appropriate information in the early stage of an infectious disease outbreak, including COVID-19. Our research also contributes to developing methods for measuring public opinion and sentiment in an epidemic situation based on natural language data on the internet. ", doi="10.2196/19455", url="https://www.jmir.org/2020/6/e19455", url="http://www.ncbi.nlm.nih.gov/pubmed/32463367" } @Article{info:doi/10.2196/19347, author="Jacobson, C. Nicholas and Lekkas, Damien and Price, George and Heinz, V. Michael and Song, Minkeun and O'Malley, James A. and Barr, J. Paul", title="Flattening the Mental Health Curve: COVID-19 Stay-at-Home Orders Are Associated With Alterations in Mental Health Search Behavior in the United States", journal="JMIR Ment Health", year="2020", month="Jun", day="1", volume="7", number="6", pages="e19347", keywords="COVID-19", keywords="coronavirus", keywords="stay-at-home orders", keywords="mental health", keywords="suicide", keywords="anxiety", keywords="infodemiology", keywords="infoveillance", keywords="search trends", keywords="health information needs", abstract="Background: The coronavirus disease (COVID-19) has led to dramatic changes worldwide in people's everyday lives. To combat the pandemic, many governments have implemented social distancing, quarantine, and stay-at-home orders. There is limited research on the impact of such extreme measures on mental health. Objective: The goal of this study was to examine whether stay-at-home orders produced differential changes in mental health symptoms using internet search queries on a national scale. Methods: In the United States, individual states vary in their adoption of measures to reduce the spread of COVID-19; as of March 23, 2020, 11 of the 50 states had issued stay-at-home orders. The staggered rollout of stay-at-home measures across the United States allows us to investigate whether these measures impact mental health by exploring variations in mental health search queries across the states. This paper examines the changes in mental health search queries on Google between March 16-23, 2020, across each state and Washington, DC. Specifically, this paper examines differential changes in mental health searches based on patterns of search activity following issuance of stay-at-home orders in these states compared to all other states. The participants were all the people who searched mental health terms in Google between March 16-23. Between March 16-23, 11 states underwent stay-at-home orders to prevent the transmission of COVID-19. Outcomes included search terms measuring anxiety, depression, obsessive-compulsive, negative thoughts, irritability, fatigue, anhedonia, concentration, insomnia, and suicidal ideation. Results: Analyzing over 10 million search queries using generalized additive mixed models, the results suggested that the implementation of stay-at-home orders are associated with a significant flattening of the curve for searches for suicidal ideation, anxiety, negative thoughts, and sleep disturbances, with the most prominent flattening associated with suicidal ideation and anxiety. Conclusions: These results suggest that, despite decreased social contact, mental health search queries increased rapidly prior to the issuance of stay-at-home orders, and these changes dissipated following the announcement and enactment of these orders. Although more research is needed to examine sustained effects, these results suggest mental health symptoms were associated with an immediate leveling off following the issuance of stay-at-home orders. ", doi="10.2196/19347", url="https://mental.jmir.org/2020/6/e19347", url="http://www.ncbi.nlm.nih.gov/pubmed/32459186" } @Article{info:doi/10.2196/19273, author="Chen, Emily and Lerman, Kristina and Ferrara, Emilio", title="Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set", journal="JMIR Public Health Surveill", year="2020", month="May", day="29", volume="6", number="2", pages="e19273", keywords="COVID-19", keywords="SARS-CoV-2", keywords="social media", keywords="network analysis", keywords="computational social sciences", abstract="Background: At the time of this writing, the coronavirus disease (COVID-19) pandemic outbreak has already put tremendous strain on many countries' citizens, resources, and economies around the world. Social distancing measures, travel bans, self-quarantines, and business closures are changing the very fabric of societies worldwide. With people forced out of public spaces, much of the conversation about these phenomena now occurs online on social media platforms like Twitter. Objective: In this paper, we describe a multilingual COVID-19 Twitter data set that we are making available to the research community via our COVID-19-TweetIDs GitHub repository. Methods: We started this ongoing data collection on January 28, 2020, leveraging Twitter's streaming application programming interface (API) and Tweepy to follow certain keywords and accounts that were trending at the time data collection began. We used Twitter's search API to query for past tweets, resulting in the earliest tweets in our collection dating back to January 21, 2020. Results: Since the inception of our collection, we have actively maintained and updated our GitHub repository on a weekly basis. We have published over 123 million tweets, with over 60\% of the tweets in English. This paper also presents basic statistics that show that Twitter activity responds and reacts to COVID-19-related events. Conclusions: It is our hope that our contribution will enable the study of online conversation dynamics in the context of a planetary-scale epidemic outbreak of unprecedented proportions and implications. This data set could also help track COVID-19-related misinformation and unverified rumors or enable the understanding of fear and panic---and undoubtedly more. ", doi="10.2196/19273", url="http://publichealth.jmir.org/2020/2/e19273/", url="http://www.ncbi.nlm.nih.gov/pubmed/32427106" } @Article{info:doi/10.2196/19421, author="Shen, Cuihua and Chen, Anfan and Luo, Chen and Zhang, Jingwen and Feng, Bo and Liao, Wang", title="Using Reports of Symptoms and Diagnoses on Social Media to Predict COVID-19 Case Counts in Mainland China: Observational Infoveillance Study", journal="J Med Internet Res", year="2020", month="May", day="28", volume="22", number="5", pages="e19421", keywords="COVID-19", keywords="SARS-CoV-2", keywords="novel coronavirus", keywords="infectious disease", keywords="social media", keywords="Weibo", keywords="China", keywords="disease surveillance", keywords="surveillance", keywords="infoveillance", keywords="infodemiology", abstract="Background: Coronavirus disease (COVID-19) has affected more than 200 countries and territories worldwide. This disease poses an extraordinary challenge for public health systems because screening and surveillance capacity is often severely limited, especially during the beginning of the outbreak; this can fuel the outbreak, as many patients can unknowingly infect other people. Objective: The aim of this study was to collect and analyze posts related to COVID-19 on Weibo, a popular Twitter-like social media site in China. To our knowledge, this infoveillance study employs the largest, most comprehensive, and most fine-grained social media data to date to predict COVID-19 case counts in mainland China. Methods: We built a Weibo user pool of 250 million people, approximately half the entire monthly active Weibo user population. Using a comprehensive list of 167 keywords, we retrieved and analyzed around 15 million COVID-19--related posts from our user pool from November 1, 2019 to March 31, 2020. We developed a machine learning classifier to identify ``sick posts,'' in which users report their own or other people's symptoms and diagnoses related to COVID-19. Using officially reported case counts as the outcome, we then estimated the Granger causality of sick posts and other COVID-19 posts on daily case counts. For a subset of geotagged posts (3.10\% of all retrieved posts), we also ran separate predictive models for Hubei province, the epicenter of the initial outbreak, and the rest of mainland China. Results: We found that reports of symptoms and diagnosis of COVID-19 significantly predicted daily case counts up to 14 days ahead of official statistics, whereas other COVID-19 posts did not have similar predictive power. For the subset of geotagged posts, we found that the predictive pattern held true for both Hubei province and the rest of mainland China regardless of the unequal distribution of health care resources and the outbreak timeline. Conclusions: Public social media data can be usefully harnessed to predict infection cases and inform timely responses. Researchers and disease control agencies should pay close attention to the social media infosphere regarding COVID-19. In addition to monitoring overall search and posting activities, leveraging machine learning approaches and theoretical understanding of information sharing behaviors is a promising approach to identify true disease signals and improve the effectiveness of infoveillance. ", doi="10.2196/19421", url="http://www.jmir.org/2020/5/e19421/", url="http://www.ncbi.nlm.nih.gov/pubmed/32452804" } @Article{info:doi/10.2196/13294, author="Duan, Guimin and Liao, Xin and Yu, Weiping and Li, Guihua", title="Classification and Prediction of Violence Against Chinese Medical Staff on the Sina Microblog Based on a Self-Organizing Map: Quantitative Study", journal="J Med Internet Res", year="2020", month="May", day="26", volume="22", number="5", pages="e13294", keywords="workplace violence", keywords="medical staff", keywords="social media", abstract="Background: For the last decade, doctor-patient contradiction in China has remained prominent, and workplace violence toward medical staff still occurs frequently. However, little is known about the types and laws of propagation of violence against medical staff online. Objective: By using a self-organizing map (SOM), we aimed to explore the microblog propagation law for violent incidents in China that involve medical staff, to classify the types of incidents and provide a basis for rapidly and accurately predicting trends in public opinion and developing corresponding measures to improve the relationship between doctors and patients. Methods: For this study, we selected 60 cases of violent incidents in China involving medical staff that led to heated discussions on the Sina microblog from 2011 to 2018, searched the web data of the microblog using crawler software, recorded the amount of new tweets every 2 hours, and used the SOM neural network to cluster the number of tweets. Polynomial and exponential functions in MATLAB software were applied to predict and analyze the data. Results: Trends in the propagation of online public opinion regarding the violent incidents were categorized into 8 types: bluff, waterfall, zigzag, steep, abrupt, wave, steep slope, and long slope. The communications exhibited different characteristics. The prediction effect of 4 types of incidents (ie, bluff, waterfall, zigzag, and steep slope) was good and accorded with actual spreading trends. Conclusions: Our study found that the more serious the consequences of a violent incident, such as a serious injury or death, the more attention it drew on the microblog, the faster was its propagation speed, and the longer was its duration. In these cases, the propagation types were mostly steep slope, long slope, and zigzag. In addition, the more serious the consequences of a violent incident, the higher popularity it exhibited on the microblog. The popularity within a week was significantly higher for acts resulting from patients' dissatisfaction with treatments than for acts resulting from nontherapeutic incidents. ", doi="10.2196/13294", url="http://www.jmir.org/2020/5/e13294/", url="http://www.ncbi.nlm.nih.gov/pubmed/32348253" } @Article{info:doi/10.2196/19447, author="Lwin, Oo May and Lu, Jiahui and Sheldenkar, Anita and Schulz, Johannes Peter and Shin, Wonsun and Gupta, Raj and Yang, Yinping", title="Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends", journal="JMIR Public Health Surveill", year="2020", month="May", day="22", volume="6", number="2", pages="e19447", keywords="COVID-19", keywords="Twitter", keywords="pandemic", keywords="social sentiments", keywords="emotions", keywords="infodemic", abstract="Background: With the World Health Organization's pandemic declaration and government-initiated actions against coronavirus disease (COVID-19), sentiments surrounding COVID-19 have evolved rapidly. Objective: This study aimed to examine worldwide trends of four emotions---fear, anger, sadness, and joy---and the narratives underlying those emotions during the COVID-19 pandemic. Methods: Over 20 million social media twitter posts made during the early phases of the COVID-19 outbreak from January 28 to April 9, 2020, were collected using ``wuhan,'' ``corona,'' ``nCov,'' and ``covid'' as search keywords. Results: Public emotions shifted strongly from fear to anger over the course of the pandemic, while sadness and joy also surfaced. Findings from word clouds suggest that fears around shortages of COVID-19 tests and medical supplies became increasingly widespread discussion points. Anger shifted from xenophobia at the beginning of the pandemic to discourse around the stay-at-home notices. Sadness was highlighted by the topics of losing friends and family members, while topics related to joy included words of gratitude and good health. Conclusions: Overall, global COVID-19 sentiments have shown rapid evolutions within just the span of a few weeks. Findings suggest that emotion-driven collective issues around shared public distress experiences of the COVID-19 pandemic are developing and include large-scale social isolation and the loss of human lives. The steady rise of societal concerns indicated by negative emotions needs to be monitored and controlled by complementing regular crisis communication with strategic public health communication that aims to balance public psychological wellbeing. ", doi="10.2196/19447", url="http://publichealth.jmir.org/2020/2/e19447/", url="http://www.ncbi.nlm.nih.gov/pubmed/32412418" } @Article{info:doi/10.2196/19087, author="Huang, Chunmei and Xu, Xinjie and Cai, Yuyang and Ge, Qinmin and Zeng, Guangwang and Li, Xiaopan and Zhang, Weide and Ji, Chen and Yang, Ling", title="Mining the Characteristics of COVID-19 Patients in China: Analysis of Social Media Posts", journal="J Med Internet Res", year="2020", month="May", day="17", volume="22", number="5", pages="e19087", keywords="SARS-CoV-2", keywords="COVID-19", keywords="coronavirus disease", keywords="social media", keywords="Sina Weibo", keywords="help", abstract="Background: In December 2019, pneumonia cases of unknown origin were reported in Wuhan City, Hubei Province, China. Identified as the coronavirus disease (COVID-19), the number of cases grew rapidly by human-to-human transmission in Wuhan. Social media, especially Sina Weibo (a major Chinese microblogging social media site), has become an important platform for the public to obtain information and seek help. Objective: This study aims to analyze the characteristics of suspected or laboratory-confirmed COVID-19 patients who asked for help on Sina Weibo. Methods: We conducted data mining on Sina Weibo and extracted the data of 485 patients who presented with clinical symptoms and imaging descriptions of suspected or laboratory-confirmed cases of COVID-19. In total, 9878 posts seeking help on Sina Weibo from February 3 to 20, 2020 were analyzed. We used a descriptive research methodology to describe the distribution and other epidemiological characteristics of patients with suspected or laboratory-confirmed SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infection. The distance between patients' home and the nearest designated hospital was calculated using the geographic information system ArcGIS. Results: All patients included in this study who sought help on Sina Weibo lived in Wuhan, with a median age of 63.0 years (IQR 55.0-71.0). Fever (408/485, 84.12\%) was the most common symptom. Ground-glass opacity (237/314, 75.48\%) was the most common pattern on chest computed tomography; 39.67\% (167/421) of families had suspected and/or laboratory-confirmed family members; 36.58\% (154/421) of families had 1 or 2 suspected and/or laboratory-confirmed members; and 70.52\% (232/329) of patients needed to rely on their relatives for help. The median time from illness onset to real-time reverse transcription-polymerase chain reaction (RT-PCR) testing was 8 days (IQR 5.0-10.0), and the median time from illness onset to online help was 10 days (IQR 6.0-12.0). Of 481 patients, 32.22\% (n=155) lived more than 3 kilometers away from the nearest designated hospital. Conclusions: Our findings show that patients seeking help on Sina Weibo lived in Wuhan and most were elderly. Most patients had fever symptoms, and ground-glass opacities were noted in chest computed tomography. The onset of the disease was characterized by family clustering and most families lived far from the designated hospital. Therefore, we recommend the following: (1) the most stringent centralized medical observation measures should be taken to avoid transmission in family clusters;?and (2) social media can help these patients get early attention during Wuhan's lockdown. These findings can help the government and the health department identify high-risk patients and accelerate emergency responses following public demands for help. ", doi="10.2196/19087", url="http://www.jmir.org/2020/5/e19087/", url="http://www.ncbi.nlm.nih.gov/pubmed/32401210" } @Article{info:doi/10.2196/16763, author="King, Catherine and Judge, Ciaran and Byrne, Aideen and Conlon, Niall", title="Googling Allergy in Ireland: Content Analysis", journal="J Med Internet Res", year="2020", month="May", day="13", volume="22", number="5", pages="e16763", keywords="allergy", keywords="food allergy", keywords="food intolerance", keywords="technology", keywords="Ireland", keywords="immunology", abstract="Background: Internet search engines are increasingly being utilized as the first port of call for medical information by the public. The prevalence of allergies in developed countries has risen steadily over time. There exists significant variability in the quality of health-related information available on the web. Inaccurately diagnosed and mismanaged allergic disease has major downstream effects on patients, general practitioners, and regional allergy services. Objective: This study aimed to verify whether Ireland has a relatively high rate of web-based allergy-related searches, to establish the proportion of medically accurate web pages encountered by the public, and to compare current search results localized to Dublin, Ireland with urban centers elsewhere. Methods: Google Trends was used to evaluate regional interest of allergy-related search terms over a 10-year period using terms ``allergy,'' ``allergy test,'' ``food allergy,'' and ``food intolerance.'' These terms were then inputted into Google search, localizing them to cities in Ireland, the United Kingdom, and the United States. Output for each search was reviewed by two independent clinicians and deemed rational or nonevidence based, as per current best practice guidelines. Searches localized to Dublin were initially completed in 2015 and repeated in 2019 to assess for changes in the quality of search results over time. Results: Ireland has a persistently high demand for web-based information relating to allergy and ranks first worldwide for ``allergy test,'' second for ``food allergy'' and ``food intolerance,'' and seventh for ``allergy'' over the specified 10-year timeframe. Results for each of the four subsearches in Dublin (2015) showed that over 60\% of websites promoted nonevidence-based diagnostics. A marginal improvement in scientifically robust information was seen in 2019, but results for ``allergy test'' and ``food intolerance'' continued to promote alternative testing 57\% (8/14) of the time. This strongly contrasted with results localized to Southampton and Rochester, where academic and hospital-affiliated web pages predominantly featured. Government-funded Department of Health websites did not feature in the top five results for Dublin searches ``allergy testing,'' ``food allergy,'' or ``food intolerance'' in either 2015 or 2019. Conclusions: The Irish public demonstrates a keen interest in seeking allergy-related information on the web. The proportion of evidence-based websites encountered by the Irish public is considerably lower than that encountered by patients in other urban centers. Factors contributing to this are the lack of a specialist register for allergy in Ireland, inadequate funding for allergy centers currently in operation, and insufficient promotion by the health service of their web-based health database, which contains useful patient-oriented information on allergy. Increased funding of clinical allergology services will more meaningfully impact the health of patients if there is a parallel investment by the health service in information and communication technology consultancy to amplify their presence on the web. ", doi="10.2196/16763", url="https://www.jmir.org/2020/5/e16763", url="http://www.ncbi.nlm.nih.gov/pubmed/32401220" } @Article{info:doi/10.2196/19458, author="Ahmed, Wasim and Vidal-Alaball, Josep and Downing, Joseph and L{\'o}pez Segu{\'i}, Francesc", title="COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data", journal="J Med Internet Res", year="2020", month="May", day="6", volume="22", number="5", pages="e19458", keywords="COVID-19", keywords="coronavirus", keywords="twitter", keywords="misinformation", keywords="fake news", keywords="5G", keywords="social network analysis", keywords="social media", keywords="public health", keywords="pandemic", abstract="Background: Since the beginning of December 2019, the coronavirus disease (COVID-19) has spread rapidly around the world, which has led to increased discussions across online platforms. These conversations have also included various conspiracies shared by social media users. Amongst them, a popular theory has linked 5G to the spread of COVID-19, leading to misinformation and the burning of 5G towers in the United Kingdom. The understanding of the drivers of fake news and quick policies oriented to isolate and rebate misinformation are keys to combating it. Objective: The aim of this study is to develop an understanding of the drivers of the 5G COVID-19 conspiracy theory and strategies to deal with such misinformation. Methods: This paper performs a social network analysis and content analysis of Twitter data from a 7-day period (Friday, March 27, 2020, to Saturday, April 4, 2020) in which the \#5GCoronavirus hashtag was trending on Twitter in the United Kingdom. Influential users were analyzed through social network graph clusters. The size of the nodes were ranked by their betweenness centrality score, and the graph's vertices were grouped by cluster using the Clauset-Newman-Moore algorithm. The topics and web sources used were also examined. Results: Social network analysis identified that the two largest network structures consisted of an isolates group and a broadcast group. The analysis also revealed that there was a lack of an authority figure who was actively combating such misinformation. Content analysis revealed that, of 233 sample tweets, 34.8\% (n=81) contained views that 5G and COVID-19 were linked, 32.2\% (n=75) denounced the conspiracy theory, and 33.0\% (n=77) were general tweets not expressing any personal views or opinions. Thus, 65.2\% (n=152) of tweets derived from nonconspiracy theory supporters, which suggests that, although the topic attracted high volume, only a handful of users genuinely believed the conspiracy. This paper also shows that fake news websites were the most popular web source shared by users; although, YouTube videos were also shared. The study also identified an account whose sole aim was to spread the conspiracy theory on Twitter. Conclusions: The combination of quick and targeted interventions oriented to delegitimize the sources of fake information is key to reducing their impact. Those users voicing their views against the conspiracy theory, link baiting, or sharing humorous tweets inadvertently raised the profile of the topic, suggesting that policymakers should insist in the efforts of isolating opinions that are based on fake news. Many social media platforms provide users with the ability to report inappropriate content, which should be used. This study is the first to analyze the 5G conspiracy theory in the context of COVID-19 on Twitter offering practical guidance to health authorities in how, in the context of a pandemic, rumors may be combated in the future. ", doi="10.2196/19458", url="http://www.jmir.org/2020/5/e19458/", url="http://www.ncbi.nlm.nih.gov/pubmed/32352383" } @Article{info:doi/10.2196/19301, author="Budhwani, Henna and Sun, Ruoyan", title="Creating COVID-19 Stigma by Referencing the Novel Coronavirus as the ``Chinese virus'' on Twitter: Quantitative Analysis of Social Media Data", journal="J Med Internet Res", year="2020", month="May", day="6", volume="22", number="5", pages="e19301", keywords="COVID-19", keywords="coronavirus", keywords="Twitter", keywords="stigma", keywords="social media", keywords="public health", abstract="Background: Stigma is the deleterious, structural force that devalues members of groups that hold undesirable characteristics. Since stigma is created and reinforced by society---through in-person and online social interactions---referencing the novel coronavirus as the ``Chinese virus'' or ``China virus'' has the potential to create and perpetuate stigma. Objective: The aim of this study was to assess if there was an increase in the prevalence and frequency of the phrases ``Chinese virus'' and ``China virus'' on Twitter after the March 16, 2020, US presidential reference of this term. Methods: Using the Sysomos software (Sysomos, Inc), we extracted tweets from the United States using a list of keywords that were derivatives of ``Chinese virus.'' We compared tweets at the national and state levels posted between March 9 and March 15 (preperiod) with those posted between March 19 and March 25 (postperiod). We used Stata 16 (StataCorp) for quantitative analysis, and Python (Python Software Foundation) to plot a state-level heat map. Results: A total of 16,535 ``Chinese virus'' or ``China virus'' tweets were identified in the preperiod, and 177,327 tweets were identified in the postperiod, illustrating a nearly ten-fold increase at the national level. All 50 states witnessed an increase in the number of tweets exclusively mentioning ``Chinese virus'' or ``China virus'' instead of coronavirus disease (COVID-19) or coronavirus. On average, 0.38 tweets referencing ``Chinese virus'' or ``China virus'' were posted per 10,000 people at the state level in the preperiod, and 4.08 of these stigmatizing tweets were posted in the postperiod, also indicating a ten-fold increase. The 5 states with the highest number of postperiod ``Chinese virus'' tweets were Pennsylvania (n=5249), New York (n=11,754), Florida (n=13,070), Texas (n=14,861), and California (n=19,442). Adjusting for population size, the 5 states with the highest prevalence of postperiod ``Chinese virus'' tweets were Arizona (5.85), New York (6.04), Florida (6.09), Nevada (7.72), and Wyoming (8.76). The 5 states with the largest increase in pre- to postperiod ``Chinese virus'' tweets were Kansas (n=697/58, 1202\%), South Dakota (n=185/15, 1233\%), Mississippi (n=749/54, 1387\%), New Hampshire (n=582/41, 1420\%), and Idaho (n=670/46, 1457\%). Conclusions: The rise in tweets referencing ``Chinese virus'' or ``China virus,'' along with the content of these tweets, indicate that knowledge translation may be occurring online and COVID-19 stigma is likely being perpetuated on Twitter. ", doi="10.2196/19301", url="http://www.jmir.org/2020/5/e19301/", url="http://www.ncbi.nlm.nih.gov/pubmed/32343669" } @Article{info:doi/10.2196/19374, author="Rovetta, Alessandro and Bhagavathula, Srikanth Akshaya", title="COVID-19-Related Web Search Behaviors and Infodemic Attitudes in Italy: Infodemiological Study", journal="JMIR Public Health Surveill", year="2020", month="May", day="5", volume="6", number="2", pages="e19374", keywords="novel coronavirus, COVID-19, Google search", keywords="Google Trends", keywords="infodemiology, infodemic monikers, Italy", keywords="behavior", keywords="public health", keywords="communication", keywords="digital health", keywords="online search", abstract="Background: Since the beginning of the novel coronavirus disease (COVID-19) outbreak, fake news and misleading information have circulated worldwide, which can profoundly affect public health communication. Objective: We investigated online search behavior related to the COVID-19 outbreak and the attitudes of ``infodemic monikers'' (ie, erroneous information that gives rise to interpretative mistakes, fake news, episodes of racism, etc) circulating in Italy. Methods: By using Google Trends to explore the internet search activity related to COVID-19 from January to March 2020, article titles from the most read newspapers and government websites were mined to investigate the attitudes of infodemic monikers circulating across various regions and cities in Italy. Search volume values and average peak comparison (APC) values were used to analyze the results. Results: Keywords such as ``novel coronavirus,'' ``China coronavirus,'' ``COVID-19,'' ``2019-nCOV,'' and ``SARS-COV-2'' were the top infodemic and scientific COVID-19 terms trending in Italy. The top five searches related to health were ``face masks,'' ``amuchina'' (disinfectant), ``symptoms of the novel coronavirus,'' ``health bulletin,'' and ``vaccines for coronavirus.'' The regions of Umbria and Basilicata recorded a high number of infodemic monikers (APC weighted total >140). Misinformation was widely circulated in the Campania region, and racism-related information was widespread in Umbria and Basilicata. These monikers were frequently searched (APC weighted total >100) in more than 10 major cities in Italy, including Rome. Conclusions: We identified a growing regional and population-level interest in COVID-19 in Italy. The majority of searches were related to amuchina, face masks, health bulletins, and COVID-19 symptoms. Since a large number of infodemic monikers were observed across Italy, we recommend that health agencies use Google Trends to predict human behavior as well as to manage misinformation circulation in Italy. ", doi="10.2196/19374", url="http://publichealth.jmir.org/2020/2/e19374/", url="http://www.ncbi.nlm.nih.gov/pubmed/32338613" } @Article{info:doi/10.2196/18897, author="Park, Woo Han and Park, Sejung and Chong, Miyoung", title="Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea", journal="J Med Internet Res", year="2020", month="May", day="5", volume="22", number="5", pages="e18897", keywords="infodemiology", keywords="COVID-19", keywords="SARS-CoV-2", keywords="coronavirus", keywords="Twitter", keywords="South Korea", keywords="medical news", keywords="social media", keywords="pandemic", keywords="outbreak", keywords="infectious disease", keywords="public health", abstract="Background: SARS-CoV-2 (severe acute respiratory coronavirus 2) was spreading rapidly in South Korea at the end of February 2020 following its initial outbreak in China, making Korea the new center of global attention. The role of social media amid the current coronavirus disease (COVID-19) pandemic has often been criticized, but little systematic research has been conducted on this issue. Social media functions as a convenient source of information in pandemic situations. Objective: Few infodemiology studies have applied network analysis in conjunction with content analysis. This study investigates information transmission networks and news-sharing behaviors regarding COVID-19 on Twitter in Korea. The real time aggregation of social media data can serve as a starting point for designing strategic messages for health campaigns and establishing an effective communication system during this outbreak. Methods: Korean COVID-19-related Twitter data were collected on February 29, 2020. Our final sample comprised of 43,832 users and 78,233 relationships on Twitter. We generated four networks in terms of key issues regarding COVID-19 in Korea. This study comparatively investigates how COVID-19-related issues have circulated on Twitter through network analysis. Next, we classified top news channels shared via tweets. Lastly, we conducted a content analysis of news frames used in the top-shared sources. Results: The network analysis suggests that the spread of information was faster in the Coronavirus network than in the other networks (Corona19, Shincheon, and Daegu). People who used the word ``Coronavirus'' communicated more frequently with each other. The spread of information was faster, and the diameter value was lower than for those who used other terms. Many of the news items highlighted the positive roles being played by individuals and groups, directing readers' attention to the crisis. Ethical issues such as deviant behavior among the population and an entertainment frame highlighting celebrity donations also emerged often. There was a significant difference in the use of nonportal (n=14) and portal news (n=26) sites between the four network types. The news frames used in the top sources were similar across the networks (P=.89, 95\% CI 0.004-0.006). Tweets containing medically framed news articles (mean 7.571, SD 1.988) were found to be more popular than tweets that included news articles adopting nonmedical frames (mean 5.060, SD 2.904; N=40, P=.03, 95\% CI 0.169-4.852). Conclusions: Most of the popular news on Twitter had nonmedical frames. Nevertheless, the spillover effect of the news articles that delivered medical information about COVID-19 was greater than that of news with nonmedical frames. Social media network analytics cannot replace the work of public health officials; however, monitoring public conversations and media news that propagates rapidly can assist public health professionals in their complex and fast-paced decision-making processes. ", doi="10.2196/18897", url="http://www.jmir.org/2020/5/e18897/", url="http://www.ncbi.nlm.nih.gov/pubmed/32325426" } @Article{info:doi/10.2196/19118, author="Liu, Qian and Zheng, Zequan and Zheng, Jiabin and Chen, Qiuyi and Liu, Guan and Chen, Sihan and Chu, Bojia and Zhu, Hongyu and Akinwunmi, Babatunde and Huang, Jian and Zhang, P. Casper J. and Ming, Wai-Kit", title="Health Communication Through News Media During the Early Stage of the COVID-19 Outbreak in China: Digital Topic Modeling Approach", journal="J Med Internet Res", year="2020", month="Apr", day="28", volume="22", number="4", pages="e19118", keywords="coronavirus", keywords="COVID-19", keywords="outbreak", keywords="health communication", keywords="mass media", keywords="public crisis", keywords="topic modeling", abstract="Background: In December 2019, a few coronavirus disease (COVID-19) cases were first reported in Wuhan, Hubei, China. Soon after, increasing numbers of cases were detected in other parts of China, eventually leading to a disease outbreak in China. As this dreadful disease spreads rapidly, the mass media has been active in community education on COVID-19 by delivering health information about this novel coronavirus, such as its pathogenesis, spread, prevention, and containment. Objective: The aim of this study was to collect media reports on COVID-19 and investigate the patterns of media-directed health communications as well as the role of the media in this ongoing COVID-19 crisis in China. Methods: We adopted the WiseSearch database to extract related news articles about the coronavirus from major press media between January 1, 2020, and February 20, 2020. We then sorted and analyzed the data using Python software and Python package Jieba. We sought a suitable topic number with evidence of the coherence number. We operated latent Dirichlet allocation topic modeling with a suitable topic number and generated corresponding keywords and topic names. We then divided these topics into different themes by plotting them into a 2D plane via multidimensional scaling. Results: After removing duplications and irrelevant reports, our search identified 7791 relevant news reports. We listed the number of articles published per day. According to the coherence value, we chose 20 as the number of topics and generated the topics' themes and keywords. These topics were categorized into nine main primary themes based on the topic visualization figure. The top three most popular themes were prevention and control procedures, medical treatment and research, and global or local social and economic influences, accounting for 32.57\% (n=2538), 16.08\% (n=1258), and 11.79\% (n=919) of the collected reports, respectively. Conclusions: Topic modeling of news articles can produce useful information about the significance of mass media for early health communication. Comparing the number of articles for each day and the outbreak development, we noted that mass media news reports in China lagged behind the development of COVID-19. The major themes accounted for around half the content and tended to focus on the larger society rather than on individuals. The COVID-19 crisis has become a worldwide issue, and society has become concerned about donations and support as well as mental health among others. We recommend that future work addresses the mass media's actual impact on readers during the COVID-19 crisis through sentiment analysis of news data. ", doi="10.2196/19118", url="http://www.jmir.org/2020/4/e19118/", url="http://www.ncbi.nlm.nih.gov/pubmed/32302966" } @Article{info:doi/10.2196/16206, author="Mavragani, Amaryllis", title="Infodemiology and Infoveillance: Scoping Review", journal="J Med Internet Res", year="2020", month="Apr", day="28", volume="22", number="4", pages="e16206", keywords="big data", keywords="infodemiology", keywords="infoveillance", keywords="internet", keywords="review", keywords="web-based data", abstract="Background: Web-based sources are increasingly employed in the analysis, detection, and forecasting of diseases and epidemics, and in predicting human behavior toward several health topics. This use of the internet has come to be known as infodemiology, a concept introduced by Gunther Eysenbach. Infodemiology and infoveillance studies use web-based data and have become an integral part of health informatics research over the past decade. Objective: The aim of this paper is to provide a scoping review of the state-of-the-art in infodemiology along with the background and history of the concept, to identify sources and health categories and topics, to elaborate on the validity of the employed methods, and to discuss the gaps identified in current research. Methods: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed to extract the publications that fall under the umbrella of infodemiology and infoveillance from the JMIR, PubMed, and Scopus databases. A total of 338 documents were extracted for assessment. Results: Of the 338 studies, the vast majority (n=282, 83.4\%) were published with JMIR Publications. The Journal of Medical Internet Research features almost half of the publications (n=168, 49.7\%), and JMIR Public Health and Surveillance has more than one-fifth of the examined studies (n=74, 21.9\%). The interest in the subject has been increasing every year, with 2018 featuring more than one-fourth of the total publications (n=89, 26.3\%), and the publications in 2017 and 2018 combined accounted for more than half (n=171, 50.6\%) of the total number of publications in the last decade. The most popular source was Twitter with 45.0\% (n=152), followed by Google with 24.6\% (n=83), websites and platforms with 13.9\% (n=47), blogs and forums with 10.1\% (n=34), Facebook with 8.9\% (n=30), and other search engines with 5.6\% (n=19). As for the subjects examined, conditions and diseases with 17.2\% (n=58) and epidemics and outbreaks with 15.7\% (n=53) were the most popular categories identified in this review, followed by health care (n=39, 11.5\%), drugs (n=40, 10.4\%), and smoking and alcohol (n=29, 8.6\%). Conclusions: The field of infodemiology is becoming increasingly popular, employing innovative methods and approaches for health assessment. The use of web-based sources, which provide us with information that would not be accessible otherwise and tackles the issues arising from the time-consuming traditional methods, shows that infodemiology plays an important role in health informatics research. ", doi="10.2196/16206", url="http://www.jmir.org/2020/4/e16206/", url="http://www.ncbi.nlm.nih.gov/pubmed/32310818" } @Article{info:doi/10.2196/14660, author="Wen, Wanting and Zhang, Zhu and Li, Ziqiang and Liang, Jiaqi and Zhan, Yongcheng and Zeng, D. Daniel and Leischow, J. Scott", title="Public Reactions to the Cigarette Control Regulation on a Chinese Microblogging Platform: Empirical Analysis", journal="J Med Internet Res", year="2020", month="Apr", day="27", volume="22", number="4", pages="e14660", keywords="cigarette smoking", keywords="regulations", keywords="social media", keywords="information networks", abstract="Background: On January 1, 2019, a new regulation on the control of smoking in public places was officially implemented in Hangzhou, China. On the day of the implementation, a large number of Chinese media reported the contents of the regulation on the microblog platform Weibo, causing a strong response from and heated discussion among netizens. Objective: This study aimed to conduct a content and network analysis to examine topics and patterns in the social media response to the new regulation. Methods: We analyzed all microblogs on Weibo that mentioned and explained the regulation in the first 8 days following the implementation. We conducted a content analysis on these microblogs and used social network visualization and descriptive statistics to identify key users and key microblogs. Results: Of 7924 microblogs, 12.85\% (1018/7924) were in support of the smoking control regulation, 84.12\% (6666/7924) were neutral, and 1.31\% (104/7924) were opposed to the smoking regulation control. For the negative posts, the public had doubts about the intentions of the policy, its implementation, and the regulations on electronic cigarettes. In addition, 1.72\% (136/7924) were irrelevant to the smoking regulation control. Among the 1043 users who explicitly expressed their positive or negative attitude toward the policy, a large proportion of users showed supportive attitudes (956/1043, 91.66\%). A total of 5 topics and 11 subtopics were identified. Conclusions: This study used a content and network analysis to examine topics and patterns in the social media response to the new smoking regulation. We found that the number of posts with a positive attitude toward the regulation was considerably higher than that of the posts with a negative attitude toward the regulation. Our findings may assist public health policy makers to better understand the policy's intentions, scope, and potential effects on public interest and support evidence-based public health regulations in the future. ", doi="10.2196/14660", url="http://www.jmir.org/2020/4/e14660/", url="http://www.ncbi.nlm.nih.gov/pubmed/32338615" } @Article{info:doi/10.2196/14986, author="Daughton, R. Ashlynn and Chunara, Rumi and Paul, J. Michael", title="Comparison of Social Media, Syndromic Surveillance, and Microbiologic Acute Respiratory Infection Data: Observational Study", journal="JMIR Public Health Surveill", year="2020", month="Apr", day="24", volume="6", number="2", pages="e14986", keywords="social media", keywords="infodemiology", keywords="influenza, human", keywords="selection bias", keywords="bias", keywords="logistic models", abstract="Background: Internet data can be used to improve infectious disease models. However, the representativeness and individual-level validity of internet-derived measures are largely unexplored as this requires ground truth data for study. Objective: This study sought to identify relationships between Web-based behaviors and/or conversation topics and health status using a ground truth, survey-based dataset. Methods: This study leveraged a unique dataset of self-reported surveys, microbiological laboratory tests, and social media data from the same individuals toward understanding the validity of individual-level constructs pertaining to influenza-like illness in social media data. Logistic regression models were used to identify illness in Twitter posts using user posting behaviors and topic model features extracted from users' tweets. Results: Of 396 original study participants, only 81 met the inclusion criteria for this study. Of these participants' tweets, we identified only two instances that were related to health and occurred within 2 weeks (before or after) of a survey indicating symptoms. It was not possible to predict when participants reported symptoms using features derived from topic models (area under the curve [AUC]=0.51; P=.38), though it was possible using behavior features, albeit with a very small effect size (AUC=0.53; P?.001). Individual symptoms were also generally not predictable either. The study sample and a random sample from Twitter are predictably different on held-out data (AUC=0.67; P?.001), meaning that the content posted by people who participated in this study was predictably different from that posted by random Twitter users. Individuals in the random sample and the GoViral sample used Twitter with similar frequencies (similar @ mentions, number of tweets, and number of retweets; AUC=0.50; P=.19). Conclusions: To our knowledge, this is the first instance of an attempt to use a ground truth dataset to validate infectious disease observations in social media data. The lack of signal, the lack of predictability among behaviors or topics, and the demonstrated volunteer bias in the study population are important findings for the large and growing body of disease surveillance using internet-sourced data. ", doi="10.2196/14986", url="http://publichealth.jmir.org/2020/2/e14986/", url="http://www.ncbi.nlm.nih.gov/pubmed/32329741" } @Article{info:doi/10.2196/18700, author="Li, Jiawei and Xu, Qing and Cuomo, Raphael and Purushothaman, Vidya and Mackey, Tim", title="Data Mining and Content Analysis of the Chinese Social Media Platform Weibo During the Early COVID-19 Outbreak: Retrospective Observational Infoveillance Study", journal="JMIR Public Health Surveill", year="2020", month="Apr", day="21", volume="6", number="2", pages="e18700", keywords="COVID-19", keywords="coronavirus", keywords="infectious disease", keywords="social media, surveillance", keywords="infoveillance", keywords="infodemiology", abstract="Background: The coronavirus disease (COVID-19) pandemic, which began in Wuhan, China in December 2019, is rapidly spreading worldwide with over 1.9 million cases as of mid-April 2020. Infoveillance approaches using social media can help characterize disease distribution and public knowledge, attitudes, and behaviors critical to the early stages of an outbreak. Objective: The aim of this study is to conduct a quantitative and qualitative assessment of Chinese social media posts originating in Wuhan City on the Chinese microblogging platform Weibo during the early stages of the COVID-19 outbreak. Methods: Chinese-language messages from Wuhan were collected for 39 days between December 23, 2019, and January 30, 2020, on Weibo. For quantitative analysis, the total daily cases of COVID-19 in Wuhan were obtained from the Chinese National Health Commission, and a linear regression model was used to determine if Weibo COVID-19 posts were predictive of the number of cases reported. Qualitative content analysis and an inductive manual coding approach were used to identify parent classifications of news and user-generated COVID-19 topics. Results: A total of 115,299 Weibo posts were collected during the study time frame consisting of an average of 2956 posts per day (minimum 0, maximum 13,587). Quantitative analysis found a positive correlation between the number of Weibo posts and the number of reported cases from Wuhan, with approximately 10 more COVID-19 cases per 40 social media posts (P<.001). This effect size was also larger than what was observed for the rest of China excluding Hubei Province (where Wuhan is the capital city) and held when comparing the number of Weibo posts to the incidence proportion of cases in Hubei Province. Qualitative analysis of 11,893 posts during the first 21 days of the study period with COVID-19-related posts uncovered four parent classifications including Weibo discussions about the causative agent of the disease, changing epidemiological characteristics of the outbreak, public reaction to outbreak control and response measures, and other topics. Generally, these themes also exhibited public uncertainty and changing knowledge and attitudes about COVID-19, including posts exhibiting both protective and higher-risk behaviors. Conclusions: The results of this study provide initial insight into the origins of the COVID-19 outbreak based on quantitative and qualitative analysis of Chinese social media data at the initial epicenter in Wuhan City. Future studies should continue to explore the utility of social media data to predict COVID-19 disease severity, measure public reaction and behavior, and evaluate effectiveness of outbreak communication. ", doi="10.2196/18700", url="http://publichealth.jmir.org/2020/2/e18700/", url="http://www.ncbi.nlm.nih.gov/pubmed/32293582" } @Article{info:doi/10.2196/19145, author="Basch, E. Charles and Basch, H. Corey and Hillyer, C. Grace and Jaime, Christie", title="The Role of YouTube and the Entertainment Industry in Saving Lives by Educating and Mobilizing the Public to Adopt Behaviors for Community Mitigation of COVID-19: Successive Sampling Design Study", journal="JMIR Public Health Surveill", year="2020", month="Apr", day="21", volume="6", number="2", pages="e19145", keywords="YouTube", keywords="COVID-19", keywords="social media", keywords="pandemic", keywords="outbreak", keywords="infectious disease", keywords="public health", keywords="prevention", abstract="Background: Effective community mitigation through voluntary behavior change is currently the best way to reduce mortality caused by coronavirus disease (COVID-19). This study builds on our prior study based on the scientific premise that YouTube is one of the most effective ways to communicate and mobilize the public in community mitigation to reduce exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Objective: Because of the rapidly changing nature of YouTube in the context of the COVID-19 pandemic, we conducted a follow-up study to document how coverage of preventive behaviors for effective community mitigation has changed. Methods: A successive sampling design was used to compare coverage of behaviors to mitigate community transmission of COVID-19 in the 100 most widely viewed YouTube videos in January 2020 and March 2020. Results: Videos in the January and March samples were viewed >125 million times and >355 million times, respectively. Fewer than half of the videos in either sample covered any of the prevention behaviors recommended by the US Centers for Disease Control and Prevention, but many covered key prevention behaviors and were very widely viewed. There were no videos uploaded by entertainment television in the January sample, but this source comprised the majority of videos and garnered the majority of cumulative views in the March sample. Conclusions: This study demonstrates the incredible reach of YouTube and the potential value of partnership with the entertainment industry for communicating and mobilizing the public about community mitigation to reduce mortality from the COVID-19 viral pandemic. ", doi="10.2196/19145", url="http://publichealth.jmir.org/2020/2/e19145/", url="http://www.ncbi.nlm.nih.gov/pubmed/32297593" } @Article{info:doi/10.2196/19016, author="Abd-Alrazaq, Alaa and Alhuwail, Dari and Househ, Mowafa and Hamdi, Mounir and Shah, Zubair", title="Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study", journal="J Med Internet Res", year="2020", month="Apr", day="21", volume="22", number="4", pages="e19016", keywords="coronavirus, COVID-19", keywords="SARS-CoV-2", keywords="2019-nCov", keywords="social media", keywords="public health", keywords="Twitter", keywords="infoveillance", keywords="infodemiology", keywords="health informatics", keywords="disease surveillance", abstract="Background: The recent coronavirus disease (COVID-19) pandemic is taking a toll on the world's health care infrastructure as well as the social, economic, and psychological well-being of humanity. Individuals, organizations, and governments are using social media to communicate with each other on a number of issues relating to the COVID-19 pandemic. Not much is known about the topics being shared on social media platforms relating to COVID-19. Analyzing such information can help policy makers and health care organizations assess the needs of their stakeholders and address them appropriately. Objective: This study aims to identify the main topics posted by Twitter users related to the COVID-19 pandemic. Methods: Leveraging a set of tools (Twitter's search application programming interface (API), Tweepy Python library, and PostgreSQL database) and using a set of predefined search terms (``corona,'' ``2019-nCov,'' and ``COVID-19''), we extracted the text and metadata (number of likes and retweets, and user profile information including the number of followers) of public English language tweets from February 2, 2020, to March 15, 2020. We analyzed the collected tweets using word frequencies of single (unigrams) and double words (bigrams). We leveraged latent Dirichlet allocation for topic modeling to identify topics discussed in the tweets. We also performed sentiment analysis and extracted the mean number of retweets, likes, and followers for each topic and calculated the interaction rate per topic. Results: Out of approximately 2.8 million tweets included, 167,073 unique tweets from 160,829 unique users met the inclusion criteria. Our analysis identified 12 topics, which were grouped into four main themes: origin of the virus; its sources; its impact on people, countries, and the economy; and ways of mitigating the risk of infection. The mean sentiment was positive for 10 topics and negative for 2 topics (deaths caused by COVID-19 and increased racism). The mean for tweet topics of account followers ranged from 2722 (increased racism) to 13,413 (economic losses). The highest mean of likes for the tweets was 15.4 (economic loss), while the lowest was 3.94 (travel bans and warnings). Conclusions: Public health crisis response activities on the ground and online are becoming increasingly simultaneous and intertwined. Social media provides an opportunity to directly communicate health information to the public. Health systems should work on building national and international disease detection and surveillance systems through monitoring social media. There is also a need for a more proactive and agile public health presence on social media to combat the spread of fake news. ", doi="10.2196/19016", url="http://www.jmir.org/2020/4/e19016/", url="http://www.ncbi.nlm.nih.gov/pubmed/32287039" } @Article{info:doi/10.2196/16541, author="Hoyos, R. Luis and Putra, Manesha and Armstrong, A. Abigail and Cheng, Y. Connie and Riestenberg, K. Carrie and Schooler, A. Tery and Dumesic, A. Daniel", title="Measures of Patient Dissatisfaction With Health Care in Polycystic Ovary Syndrome: Retrospective Analysis", journal="J Med Internet Res", year="2020", month="Apr", day="21", volume="22", number="4", pages="e16541", keywords="PCOS", keywords="fibroid", keywords="Google", keywords="healthcare quality", keywords="infoveillance", keywords="infodemiology", keywords="medical education", keywords="health care", keywords="internet", keywords="satisfaction", abstract="Background: Polycystic ovary syndrome (PCOS) is a common reproductive and metabolic disorder in women; however, many clinicians may not be well versed in scientific advances that aid understanding of the associated reproductive, metabolic, and psychological abnormalities. Women with PCOS are dissatisfied with health care providers, the diagnostic process, and the initial treatment of PCOS and seek information through alternative sources. This has affected the patient-physician relationship by allowing medical information acquired through the internet, whether correct or not, to become accessible to patients and reshape their health care perspective. Patient dissatisfaction with health care providers regarding PCOS raises questions about the responsibilities of academic institutions to adequately train and maintain the competence of clinicians and government agencies to sufficiently support scientific investigation in this field. Objective: The primary aim was to examine internet searching behaviors of the public regarding PCOS vs another highly prevalent gynecologic disorder. The secondary aim was to explore satisfaction with health care among patients with PCOS and their internet use. The tertiary aim was to examine medical education in reproductive endocrinology and infertility (REI) during obstetrics and gynecology (Ob/Gyn) residency as a proxy for physician knowledge in this field. Methods: Google search trends and StoryBase quantified monthly Google absolute search volumes for search terms related to PCOS and fibroids (January 2004 to December 2017; United States). The reproductive disorder, fibroids, was selected as a comparison group because of its high prevalence among women. Between female groups, monthly absolute search volumes and their trends were compared. A Web-based questionnaire (June 2015 to March 2018) explored health care experiences and the internet use of women with PCOS. REI rotation information during Ob/Gyn residency in the United States was obtained from the Association of Professors of Gynecology and Obstetrics website. Results: For PCOS (R=0.89; P<.01), but not fibroids (R=0.09; P=.25), monthly absolute search volumes increased significantly. PCOS-related monthly absolute search volumes (mean 384,423 searches, SD 88,756) were significantly greater than fibroid-related monthly absolute search volumes (mean 348,502 searches, SD 37,317; P<.05). PCOS was diagnosed by an Ob/Gyn in 60.9\% (462/759) of patients, and 57.3\% (435/759) of patients were dissatisfied with overall care. Among patients with PCOS, 98.2\% (716/729) searched for PCOS on the Web but only 18.8\% (143/729) of patients joined an online PCOS support group or forum. On average, Ob/Gyn residencies dedicated only 4\% (2/43) of total block time to REI, whereas 5.5\% (11/200) of such residencies did not offer any REI rotations. Conclusions: Over time, PCOS has been increasingly searched on the Web compared with another highly prevalent gynecologic disorder. Patients with PCOS are dissatisfied with their health care providers, who would benefit from an improved understanding of PCOS during Ob/Gyn residency training. ", doi="10.2196/16541", url="http://www.jmir.org/2020/4/e16541/", url="http://www.ncbi.nlm.nih.gov/pubmed/32314967" } @Article{info:doi/10.2196/16470, author="Li, Ang and Jiao, Dongdong and Liu, Xiaoqian and Zhu, Tingshao", title="A Comparison of the Psycholinguistic Styles of Schizophrenia-Related Stigma and Depression-Related Stigma on Social Media: Content Analysis", journal="J Med Internet Res", year="2020", month="Apr", day="21", volume="22", number="4", pages="e16470", keywords="stigma", keywords="schizophrenia", keywords="depression", keywords="psycholinguistic analysis", keywords="social media", abstract="Background: Stigma related to schizophrenia is considered to be the primary focus of antistigma campaigns. Accurate and efficient detection of stigma toward schizophrenia in mass media is essential for the development of targeted antistigma interventions at the population level. Objective: The purpose of this study was to examine the psycholinguistic characteristics of schizophrenia-related stigma on social media (ie, Sina Weibo, a Chinese microblogging website), and then to explore whether schizophrenia-related stigma can be distinguished from stigma toward other mental illnesses (ie, depression-related stigma) in terms of psycholinguistic style. Methods: A total of 19,224 schizophrenia- and 15,879 depression-related Weibo posts were collected and analyzed. First, a human-based content analysis was performed on collected posts to determine whether they reflected stigma or not. Second, by using Linguistic Inquiry and Word Count software (Simplified Chinese version), a number of psycholinguistic features were automatically extracted from each post. Third, based on selected key features, four groups of classification models were established for different purposes: (a) differentiating schizophrenia-related stigma from nonstigma, (b) differentiating a certain subcategory of schizophrenia-related stigma from other subcategories, (c) differentiating schizophrenia-related stigma from depression-related stigma, and (d) differentiating a certain subcategory of schizophrenia-related stigma from the corresponding subcategory of depression-related stigma. Results: In total, 26.22\% of schizophrenia-related posts were labeled as stigmatizing posts. The proportion of posts indicating depression-related stigma was significantly lower than that indicating schizophrenia-related stigma ($\chi$21=2484.64, P<.001). The classification performance of the models in the four groups ranged from .71 to .92 (F measure). Conclusions: The findings of this study have implications for the detection and reduction of stigma toward schizophrenia on social media. ", doi="10.2196/16470", url="http://www.jmir.org/2020/4/e16470/", url="http://www.ncbi.nlm.nih.gov/pubmed/32314969" } @Article{info:doi/10.2196/18941, author="Mavragani, Amaryllis", title="Tracking COVID-19 in Europe: Infodemiology Approach", journal="JMIR Public Health Surveill", year="2020", month="Apr", day="20", volume="6", number="2", pages="e18941", keywords="big data", keywords="coronavirus", keywords="COVID-19", keywords="infodemiology", keywords="infoveillance", keywords="Google Trends", abstract="Background: Infodemiology (ie, information epidemiology) uses web-based data to inform public health and policy. Infodemiology metrics have been widely and successfully used to assess and forecast epidemics and outbreaks. Objective: In light of the recent coronavirus disease (COVID-19) pandemic that started in Wuhan, China in 2019, online search traffic data from Google are used to track the spread of the new coronavirus disease in Europe. Methods: Time series from Google Trends from January to March 2020 on the Topic (Virus) of ``Coronavirus'' were retrieved and correlated with official data on COVID-19 cases and deaths worldwide and in the European countries that have been affected the most: Italy (at national and regional level), Spain, France, Germany, and the United Kingdom. Results: Statistically significant correlations are observed between online interest and COVID-19 cases and deaths. Furthermore, a critical point, after which the Pearson correlation coefficient starts declining (even if it is still statistically significant) was identified, indicating that this method is most efficient in regions or countries that have not yet peaked in COVID-19 cases. Conclusions: In the past, infodemiology metrics in general and data from Google Trends in particular have been shown to be useful in tracking and forecasting outbreaks, epidemics, and pandemics as, for example, in the cases of the Middle East respiratory syndrome, Ebola, measles, and Zika. With the COVID-19 pandemic still in the beginning stages, it is essential to explore and combine new methods of disease surveillance to assist with the preparedness of health care systems at the regional level. ", doi="10.2196/18941", url="http://publichealth.jmir.org/2020/2/e18941/", url="http://www.ncbi.nlm.nih.gov/pubmed/32250957" } @Article{info:doi/10.2196/17188, author="Mazuz, Keren and Yom-Tov, Elad", title="Analyzing Trends of Loneliness Through Large-Scale Analysis of Social Media Postings: Observational Study", journal="JMIR Ment Health", year="2020", month="Apr", day="20", volume="7", number="4", pages="e17188", keywords="loneliness", keywords="text postings", keywords="behavior online", keywords="social media", keywords="computer-based analysis", keywords="online self-disclosure", abstract="Background: Loneliness has become a public health problem described as an epidemic, and it has been argued that digital behavior such as social media posting affects loneliness. Objective: The aim of this study is to expand knowledge of the determinants of loneliness by investigating online postings in a social media forum devoted to loneliness. Specifically, this study aims to analyze the temporal trends in loneliness and their associations with topics of interest, especially with those related to mental health determinants. Methods: We collected a total of 19,668 postings from 11,054 users in the loneliness forum on Reddit. We asked seven crowdsourced workers to imagine themselves as writing 1 of 236 randomly chosen posts and to answer the short-form UCLA Loneliness Scale. After showing that these postings could provide an assessment of loneliness, we built a predictive model for loneliness scores based on the posts' text and applied it to all collected postings. We then analyzed trends in loneliness postings over time and their correlations with other topics of interest related to mental health determinants. Results: We found that crowdsourced workers can estimate loneliness (interclass correlation=0.19) and that predictive models are correlated with reported loneliness scores (Pearson r=0.38). Our results show that increases in loneliness are strongly associated with postings to a suicidality-related forum (hazard ratio 1.19) and to forums associated with other detrimental behaviors such as depression and illicit drug use. Clustering demonstrates that people who are lonely come from diverse demographics and from a variety of interests. Conclusions: The results demonstrate that it is possible for unrelated individuals to assess people's social media postings for loneliness. Moreover, our findings show the multidimensional nature of online loneliness and its correlated behaviors. Our study shows the advantages of studying a hard-to-reach population through social media and suggests new directions for future studies. ", doi="10.2196/17188", url="http://mental.jmir.org/2020/4/e17188/", url="http://www.ncbi.nlm.nih.gov/pubmed/32310141" } @Article{info:doi/10.2196/18828, author="Ayyoubzadeh, Mohammad Seyed and Ayyoubzadeh, Mehdi Seyed and Zahedi, Hoda and Ahmadi, Mahnaz and R Niakan Kalhori, Sharareh", title="Predicting COVID-19 Incidence Through Analysis of Google Trends Data in Iran: Data Mining and Deep Learning Pilot Study", journal="JMIR Public Health Surveill", year="2020", month="Apr", day="14", volume="6", number="2", pages="e18828", keywords="coronavirus", keywords="COVID-19", keywords="prediction", keywords="incidence", keywords="Google Trends", keywords="linear regression", keywords="LSTM", keywords="pandemic", keywords="outbreak", keywords="public health", abstract="Background: The recent global outbreak of coronavirus disease (COVID-19) is affecting many countries worldwide. Iran is one of the top 10 most affected countries. Search engines provide useful data from populations, and these data might be useful to analyze epidemics. Utilizing data mining methods on electronic resources' data might provide a better insight into the COVID-19 outbreak to manage the health crisis in each country and worldwide. Objective: This study aimed to predict the incidence of COVID-19 in Iran. Methods: Data were obtained from the Google Trends website. Linear regression and long short-term memory (LSTM) models were used to estimate the number of positive COVID-19 cases. All models were evaluated using 10-fold cross-validation, and root mean square error (RMSE) was used as the performance metric. Results: The linear regression model predicted the incidence with an RMSE of 7.562 (SD 6.492). The most effective factors besides previous day incidence included the search frequency of handwashing, hand sanitizer, and antiseptic topics. The RMSE of the LSTM model was 27.187 (SD 20.705). Conclusions: Data mining algorithms can be employed to predict trends of outbreaks. This prediction might support policymakers and health care managers to plan and allocate health care resources accordingly. ", doi="10.2196/18828", url="http://publichealth.jmir.org/2020/2/e18828/", url="http://www.ncbi.nlm.nih.gov/pubmed/32234709" } @Article{info:doi/10.2196/10919, author="Jimenez, Alberto and Santed-Germ{\'a}n, Miguel-Angel and Ramos, Victoria", title="Google Searches and Suicide Rates in Spain, 2004-2013: Correlation Study", journal="JMIR Public Health Surveill", year="2020", month="Apr", day="13", volume="6", number="2", pages="e10919", keywords="suicide", keywords="big data", keywords="infodemiology", keywords="infoveillance", keywords="incidence", keywords="help-seeking behaviors", keywords="searching behavior", keywords="early diagnosis", abstract="Background: Different studies have suggested that web search data are useful in forecasting several phenomena from the field of economics to epidemiology or health issues. Objective: This study aimed to (1) evaluate the correlation between suicide rates released by the Spanish National Statistics Institute (INE) and internet search trends in Spain reported by Google Trends (GT) for 57 suicide-related terms representing major known risks of suicide and an analysis of these results using a linear regression model and (2) study the differential association between male and female suicide rates published by the INE and internet searches of these 57 terms. Methods: The study period was from 2004 to 2013. In this study, suicide data were collected from (1) Spain's INE and (2) local internet search data from GT, both from January 2004 to December 2013. We investigated and validated 57 suicide-related terms already tested in scientific studies before 2015 that would be the best predictors of new suicide cases. We then evaluated the nowcasting effects of a GT search through a cross-correlation analysis and by linear regression of the suicide incidence data with the GT data. Results: Suicide rates in Spain in the study period were positively associated (r<-0.2) for the general population with the search volume for 7 terms and negatively for 1 from the 57 terms used in previous studies. Suicide rates for men were found to be significantly different than those of women. The search term, ``allergy,'' demonstrated a lead effect for new suicide cases (r=0.513; P=.001). The next significant correlating terms for those 57 studied were ``antidepressant,'' ``alcohol abstinence,'' ``relationship breakup'' (r=0.295, P=.001; r=0.295, P=.001; and r=0.268, P=.002, respectively). Significantly different results were obtained for men and women. Search terms that correlate with suicide rates of women are consistent with previous studies, showing that the incidence of depression is higher in women than in men, and showing different gender searching patterns. Conclusions: A better understanding of internet search behavior of both men and women in relation to suicide and related topics may help design effective suicide prevention programs based on information provided by search robots and other big data sources. ", doi="10.2196/10919", url="https://publichealth.jmir.org/2020/2/e10919", url="http://www.ncbi.nlm.nih.gov/pubmed/32281540" } @Article{info:doi/10.2196/18961, author="Hong, Young-Rock and Lawrence, John and Williams Jr, Dunc and Mainous III, Arch", title="Population-Level Interest and Telehealth Capacity of US Hospitals in Response to COVID-19: Cross-Sectional Analysis of Google Search and National Hospital Survey Data", journal="JMIR Public Health Surveill", year="2020", month="Apr", day="7", volume="6", number="2", pages="e18961", keywords="COVID-19", keywords="telehealth", keywords="telemedicine", keywords="screening", keywords="pandemic", keywords="outbreak", keywords="infectious disease", keywords="public health", abstract="Background: As the novel coronavirus disease (COVID-19) is widely spreading across the United States, there is a concern about the overloading of the nation's health care capacity. The expansion of telehealth services is expected to deliver timely care for the initial screening of symptomatic patients while minimizing exposure in health care facilities, to protect health care providers and other patients. However, it is currently unknown whether US hospitals have the telehealth capacity to meet the increasing demand and needs of patients during this pandemic. Objective: We investigated the population-level internet search volume for telehealth (as a proxy of population interest and demand) with the number of new COVID-19 cases and the proportion of hospitals that adopted a telehealth system in all US states. Methods: We used internet search volume data from Google Trends to measure population-level interest in telehealth and telemedicine between January 21, 2020 (when the first COVID-19 case was reported), and March 18, 2020. Data on COVID-19 cases in the United States were obtained from the Johns Hopkins Coronavirus Resources Center. We also used data from the 2018 American Hospital Association Annual Survey to estimate the proportion of hospitals that adopted telehealth (including telemedicine and electronic visits) and those with the capability of telemedicine intensive care unit (tele-ICU). Pearson correlation was used to examine the relations of population search volume for telehealth and telemedicine (composite score) with the cumulative numbers of COVID-19 cases in the United States during the study period and the proportion of hospitals with telehealth and tele-ICU capabilities. Results: We found that US population--level interest in telehealth increased as the number of COVID-19 cases increased, with a strong correlation (r=0.948, P<.001). We observed a higher population-level interest in telehealth in the Northeast and West census region, whereas the proportion of hospitals that adopted telehealth was higher in the Midwest region. There was no significant association between population interest and the proportion of hospitals that adopted telehealth (r=0.055, P=.70) nor hospitals having tele-ICU capability (r=--0.073, P=.61). Conclusions: As the number of COVID-19 cases increases, so does the US population's interest in telehealth. However, the level of population interest did not correlate with the proportion of hospitals providing telehealth services in the United States, suggesting that increased population demand may not be met with the current telehealth capacity. Telecommunication infrastructures in US hospitals may lack the capability to address the ongoing health care needs of patients with other health conditions. More practical investment is needed to deploy the telehealth system rapidly against the impending patient surge. ", doi="10.2196/18961", url="http://publichealth.jmir.org/2020/2/e18961/", url="http://www.ncbi.nlm.nih.gov/pubmed/32250963" } @Article{info:doi/10.2196/18790, author="Geldsetzer, Pascal", title="Use of Rapid Online Surveys to Assess People's Perceptions During Infectious Disease Outbreaks: A Cross-sectional Survey on COVID-19", journal="J Med Internet Res", year="2020", month="Apr", day="2", volume="22", number="4", pages="e18790", keywords="rapid online surveys", keywords="perceptions", keywords="knowledge", keywords="coronavirus", keywords="SARS-CoV-2", keywords="pandemic", keywords="infectious disease", keywords="outbreak", keywords="survey", keywords="COVID-19", keywords="public health", abstract="Background: Given the extensive time needed to conduct a nationally representative household survey and the commonly low response rate of phone surveys, rapid online surveys may be a promising method to assess and track knowledge and perceptions among the general public during fast-moving infectious disease outbreaks. Objective: This study aimed to apply rapid online surveying to determine knowledge and perceptions of coronavirus disease 2019 (COVID-19) among the general public in the United States and the United Kingdom. Methods: An online questionnaire was administered to 3000 adults residing in the United States and 3000 adults residing in the United Kingdom who had registered with Prolific Academic to participate in online research. Prolific Academic established strata by age (18-27, 28-37, 38-47, 48-57, or ?58 years), sex (male or female), and ethnicity (white, black or African American, Asian or Asian Indian, mixed, or ``other''), as well as all permutations of these strata. The number of participants who could enroll in each of these strata was calculated to reflect the distribution in the US and UK general population. Enrollment into the survey within each stratum was on a first-come, first-served basis. Participants completed the questionnaire between February 23 and March 2, 2020. Results: A total of 2986 and 2988 adults residing in the United States and the United Kingdom, respectively, completed the questionnaire. Of those, 64.4\% (1924/2986) of US participants and 51.5\% (1540/2988) of UK participants had a tertiary education degree, 67.5\% (2015/2986) of US participants had a total household income between US \$20,000 and US \$99,999, and 74.4\% (2223/2988) of UK participants had a total household income between {\textsterling}15,000 and {\textsterling}74,999. US and UK participants' median estimate for the probability of a fatal disease course among those infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was 5.0\% (IQR 2.0\%-15.0\%) and 3.0\% (IQR 2.0\%-10.0\%), respectively. Participants generally had good knowledge of the main mode of disease transmission and common symptoms of COVID-19. However, a substantial proportion of participants had misconceptions about how to prevent an infection and the recommended care-seeking behavior. For instance, 37.8\% (95\% CI 36.1\%-39.6\%) of US participants and 29.7\% (95\% CI 28.1\%-31.4\%) of UK participants thought that wearing a common surgical mask was ``highly effective'' in protecting them from acquiring COVID-19, and 25.6\% (95\% CI 24.1\%-27.2\%) of US participants and 29.6\% (95\% CI 28.0\%-31.3\%) of UK participants thought it was prudent to refrain from eating at Chinese restaurants. Around half (53.8\%, 95\% CI 52.1\%-55.6\%) of US participants and 39.1\% (95\% CI 37.4\%-40.9\%) of UK participants thought that children were at an especially high risk of death when infected with SARS-CoV-2. Conclusions: The distribution of participants by total household income and education followed approximately that of the US and UK general population. The findings from this online survey could guide information campaigns by public health authorities, clinicians, and the media. More broadly, rapid online surveys could be an important tool in tracking the public's knowledge and misperceptions during rapidly moving infectious disease outbreaks. ", doi="10.2196/18790", url="http://www.jmir.org/2020/4/e18790/", url="http://www.ncbi.nlm.nih.gov/pubmed/32240094" } @Article{info:doi/10.2196/18807, author="Basch, H. Corey and Hillyer, C. Grace and Meleo-Erwin, C. Zoe and Jaime, Christie and Mohlman, Jan and Basch, E. Charles", title="Preventive Behaviors Conveyed on YouTube to Mitigate Transmission of COVID-19: Cross-Sectional Study", journal="JMIR Public Health Surveill", year="2020", month="Apr", day="2", volume="6", number="2", pages="e18807", keywords="YouTube", keywords="COVID-19", keywords="social media", keywords="pandemic", keywords="outbreak", keywords="infectious disease", keywords="public health", keywords="prevention", abstract="Background: Accurate information and guidance about personal behaviors that can reduce exposure to severe acute respiratory syndrome coronavirus 2 are among the most important elements in mitigating the spread of coronavirus disease 2019 (COVID-19). With over 2 billion users, YouTube is a media channel that millions turn to when seeking information. Objective: At the time of this study, there were no published studies investigating the content of YouTube videos related to COVID-19. This study aims to address this gap in the current knowledge. Methods: The 100 most widely viewed YouTube videos uploaded throughout the month of January 2020 were reviewed and the content covered was described. Collectively, these videos were viewed over 125 million times. Results: Fewer than one-third of the videos covered any of the seven key prevention behaviors listed on the US Centers for Disease Control and Prevention website. Conclusions: These results represent an important missed opportunity for disease prevention. ", doi="10.2196/18807", url="http://publichealth.jmir.org/2020/2/e18807/", url="http://www.ncbi.nlm.nih.gov/pubmed/32240096" } @Article{info:doi/10.2196/14952, author="Rivas, Ryan and Sadah, A. Shouq and Guo, Yuhang and Hristidis, Vagelis", title="Classification of Health-Related Social Media Posts: Evaluation of Post Content--Classifier Models and Analysis of User Demographics", journal="JMIR Public Health Surveill", year="2020", month="Apr", day="1", volume="6", number="2", pages="e14952", keywords="social media", keywords="demographics", keywords="classification", abstract="Background: The increasing volume of health-related social media activity, where users connect, collaborate, and engage, has increased the significance of analyzing how people use health-related social media. Objective: The aim of this study was to classify the content (eg, posts that share experiences and seek support) of users who write health-related social media posts and study the effect of user demographics on post content. Methods: We analyzed two different types of health-related social media: (1) health-related online forums---WebMD and DailyStrength---and (2) general online social networks---Twitter and Google+. We identified several categories of post content and built classifiers to automatically detect these categories. These classifiers were used to study the distribution of categories for various demographic groups. Results: We achieved an accuracy of at least 84\% and a balanced accuracy of at least 0.81 for half of the post content categories in our experiments. In addition, 70.04\% (4741/6769) of posts by male WebMD users asked for advice, and male users' WebMD posts were more likely to ask for medical advice than female users' posts. The majority of posts on DailyStrength shared experiences, regardless of the gender, age group, or location of their authors. Furthermore, health-related posts on Twitter and Google+ were used to share experiences less frequently than posts on WebMD and DailyStrength. Conclusions: We studied and analyzed the content of health-related social media posts. Our results can guide health advocates and researchers to better target patient populations based on the application type. Given a research question or an outreach goal, our results can be used to choose the best online forums to answer the question or disseminate a message. ", doi="10.2196/14952", url="https://publichealth.jmir.org/2020/2/e14952", url="http://www.ncbi.nlm.nih.gov/pubmed/32234706" } @Article{info:doi/10.2196/18717, author="Hern{\'a}ndez-Garc{\'i}a, Ignacio and Gim{\'e}nez-J{\'u}lvez, Teresa", title="Assessment of Health Information About COVID-19 Prevention on the Internet: Infodemiological Study", journal="JMIR Public Health Surveill", year="2020", month="Apr", day="1", volume="6", number="2", pages="e18717", keywords="COVID-19", keywords="coronavirus", keywords="prevention", keywords="internet", keywords="information", keywords="evaluation", keywords="authorship", keywords="World Health Organization", keywords="official public health organizations", keywords="digital media", keywords="infodemic", keywords="infodemiology", abstract="Background: The internet is a large source of health information and has the capacity to influence its users. However, the information found on the internet often lacks scientific rigor, as anyone may upload content. This factor is a cause of great concern to scientific societies, governments, and users. Objective: The objective of our study was to investigate the information about the prevention of coronavirus disease 2019 (COVID-19) on the internet. Methods: On February 29, 2020, we performed a Google search with the terms ``Prevention coronavirus,'' ``Prevention COVID-19,'' ``Prevenci{\'o}n coronavirus,'' and ``Prevenci{\'o}n COVID-19''. A univariate analysis was performed to study the association between the type of authorship, country of publication, and recommendations to avoid COVID-19 according to the World Health Organization (WHO). Results: In total, 80 weblinks were reviewed. Most of them were produced in the United States and Spain (n=58, 73\%) by digital media sources and official public health organizations (n=60, 75\%). The most mentioned WHO preventive measure was ``wash your hands frequently'' (n=65, 81\%). A less frequent recommendation was to ``stay home if you feel unwell'' (n=26, 33\%). The analysis by type of author (official public health organizations versus digital media) revealed significant differences regarding the recommendation to wear a mask when you are healthy only if caring for a person with suspected COVID-19 (odds ratio [OR] 4.39). According to the country of publication (Spain versus the United States), significant differences were detected regarding some recommendations such as ``wash your hands frequently'' (OR 9.82), ``cover your mouth and nose with your bent elbow or tissue when you cough or sneeze'' (OR 4.59), or ``stay home if you feel unwell'' (OR 0.31). Conclusions: It is necessary to urge and promote the use of the websites of official public health organizations when seeking information on COVID-19 preventive measures on the internet. In this way, users will be able to obtain high-quality information more frequently, and such websites may improve their accessibility and positioning, given that search engines justify the positioning of links obtained in a search based on the frequency of access to them. ", doi="10.2196/18717", url="https://publichealth.jmir.org/2020/2/e18717", url="http://www.ncbi.nlm.nih.gov/pubmed/32217507" } @Article{info:doi/10.2196/15736, author="Jordan, Lisa and Kalin, James and Dabrowski, Colleen", title="Characteristics of Gun Advertisements on Social Media: Systematic Search and Content Analysis of Twitter and YouTube Posts", journal="J Med Internet Res", year="2020", month="Mar", day="27", volume="22", number="3", pages="e15736", keywords="firearms", keywords="advertising", keywords="social media", keywords="internet", keywords="gender identity", abstract="Background: Although gun violence has been identified as a major public health concern, the scope and significance of internet gun advertising is not known. Objective: This study aimed to quantify the characteristics of gun advertising on social media and to compare the reach of posts by manufacturers with those of influencers. Methods: Using a systematic search, we created a database of recent and popular Twitter and YouTube posts made public by major firearm manufacturers and influencers. From our sample of social media posts, we reviewed the content of the posts on the basis of 19 different characteristics, such as type of gun, presence of women, and military or police references. Our content analysis summarized statistical differences in the information conveyed in posts to compare advertising approaches across social media platforms. Results: Sample posts revealed that firearm manufacturers use social media to attract audiences to websites that sell firearms: 14.1\% (131/928; {\textpm}2.9) of Twitter posts, 53.6\% (228/425; {\textpm}6.2) of YouTube videos, and 89.5\% (214/239; {\textpm}5.1) of YouTube influencer videos link to websites that facilitate sales. Advertisements included women in efforts to market handguns and pistols for the purpose of protection: videos with women included protection themes 2.5 times more often than videos without women. Top manufacturers of domestic firearms received 98 million channel views, compared with 6.1 billion channel views received by the top 12 YouTube influencers. Conclusions: Firearm companies use social media as an advertising platform to connect viewers to websites that sell guns. Gun manufacturers appropriate YouTube servers, video streaming services, and the work of YouTube influencers to reach large audiences to promote the widespread sale of consumer firearms. YouTube and Twitter subsidize gun advertising by offering server and streaming services at no cost to gun manufacturers, to the commercial benefit of Google and Twitter's corporate ownership. ", doi="10.2196/15736", url="http://www.jmir.org/2020/3/e15736/", url="http://www.ncbi.nlm.nih.gov/pubmed/32217496" } @Article{info:doi/10.2196/16191, author="Stevens, C. Robin and Brawner, M. Bridgette and Kranzler, Elissa and Giorgi, Salvatore and Lazarus, Elizabeth and Abera, Maramawit and Huang, Sarah and Ungar, Lyle", title="Exploring Substance Use Tweets of Youth in the United States: Mixed Methods Study", journal="JMIR Public Health Surveill", year="2020", month="Mar", day="26", volume="6", number="1", pages="e16191", keywords="social media", keywords="illicit drug", keywords="youth", keywords="adolescent", abstract="Background: Substance use by youth remains a significant public health concern. Social media provides the opportunity to discuss and display substance use--related beliefs and behaviors, suggesting that the act of posting drug-related content, or viewing posted content, may influence substance use in youth. This aligns with empirically supported theories, which posit that behavior is influenced by perceptions of normative behavior. Nevertheless, few studies have explored the content of posts by youth related to substance use. Objective: This study aimed to identify the beliefs and behaviors of youth related to substance use by characterizing the content of youths' drug-related tweets. Using a sequential explanatory mixed methods approach, we sampled drug-relevant tweets and qualitatively examined their content. Methods: We used natural language processing to determine the frequency of drug-related words in public tweets (from 2011 to 2015) among youth Twitter users geolocated to Pennsylvania. We limited our sample by age (13-24 years), yielding approximately 23 million tweets from 20,112 users. We developed a list of drug-related keywords and phrases and selected a random sample of tweets with the most commonly used keywords to identify themes (n=249). Results: We identified two broad classes of emergent themes: functional themes and relational themes. Functional themes included posts that explicated a function of drugs in one's life, with subthemes indicative of pride, longing, coping, and reminiscing as they relate to drug use and effects. Relational themes emphasized a relational nature of substance use, capturing substance use as a part of social relationships, with subthemes indicative of drug-related identity and companionship. We also identified topical areas in tweets related to drug use, including reference to polysubstance use, pop culture, and antidrug content. Across the tweets, the themes of pride (63/249, 25.3\%) and longing (39/249, 15.7\%) were the most popular. Most tweets that expressed pride (46/63, 73\%) were explicitly related to marijuana. Nearly half of the tweets on coping (17/36, 47\%) were related to prescription drugs. Very few of the tweets contained antidrug content (9/249, 3.6\%). Conclusions: Data integration indicates that drugs are typically discussed in a positive manner, with content largely reflective of functional and relational patterns of use. The dissemination of this information, coupled with the relative absence of antidrug content, may influence youth such that they perceive drug use as normative and justified. Strategies to address the underlying causes of drug use (eg, coping with stressors) and engage antidrug messaging on social media may reduce normative perceptions and associated behaviors among youth. The findings of this study warrant research to further examine the effects of this content on beliefs and behaviors and to identify ways to leverage social media to decrease substance use in this population. ", doi="10.2196/16191", url="http://publichealth.jmir.org/2020/1/e16191/", url="http://www.ncbi.nlm.nih.gov/pubmed/32213472" } @Article{info:doi/10.2196/16038, author="Butler, F. Stephen and Oyedele, K. Natasha and Dailey Govoni, Taryn and Green, L. Jody", title="How Motivations for Using Buprenorphine Products Differ From Using Opioid Analgesics: Evidence from an Observational Study of Internet Discussions Among Recreational Users", journal="JMIR Public Health Surveill", year="2020", month="Mar", day="25", volume="6", number="1", pages="e16038", keywords="buprenorphine-naloxone combination", keywords="buprenorphine", keywords="motivation", keywords="controlled substance diversion", keywords="addiction, opioid", keywords="opioid medication-assisted treatment", abstract="Background: Opioid use disorder (OUD) poses medical and societal concerns. Although most individuals with OUD in the United States are not in drug abuse treatment, buprenorphine is considered a safe and effective OUD treatment, which reduces illicit opioid use, mortality, and other drug-related harms. However, as buprenorphine prescriptions increase, so does evidence of misused, abused, or diverted buprenorphine. Users' motivations for extratreatment use of buprenorphine (ie, misuse or abuse of one's own prescription or use of diverted medication) may be different from the motivations involved in analgesic opioid products. Previous research is based on small sample sizes and use surveys, and none directly compare the motivations for using buprenorphine products (ie, tablet or film) with other opioid products having known abuse potential. Objective: The aim of the study was to describe and compare the motivation-to-use buprenorphine products, including buprenorphine/naloxone (BNX) sublingual film and oxycodone extended-release (ER), as discussed in online forums. Methods: Web-based posts from 2012 to 2016 were collected from online forums using the Web Informed Services internet monitoring archive. A random sample of posts was coded for motivation to use. These posts were coded into the following motivation categories: (1) use to avoid withdrawal, (2) pain relief, (3) tapering from other drugs, (4) opioid addiction treatment, (5) recreational use (ie, to get high), and (6) other use. Oxycodone ER, an opioid analgesic with known abuse potential, was selected as a comparator. Results: Among all posts, 0.81\% (30,576/3,788,922) discussed motivation to use one of the target products. The examination of query-selected posts revealed significantly greater discussion of buprenorphine products than oxycodone ER (P<.001). The posts mentioning buprenorphine products were more likely than oxycodone ER to discuss treatment for OUD, tapering down use, and/or withdrawal management (P<.001). Buprenorphine-related posts discussed recreational use (375/1020, 36.76\%), although much less often than in oxycodone ER posts (425/508, 83.7\%). Despite some differences, the overall pattern of motivation to use was similar for BNX sublingual film and other buprenorphine products. Conclusions: An analysis of spontaneous, Web-based discussion among recreational substance users who post on online drug forums supports the contention that motivation-to-use patterns associated with buprenorphine products are different from those reported for oxycodone ER. Although the findings presented here are not expected to reflect the actual use of the target products, they may represent the interests and motivations of those posting on the online forums. Buprenorphine-related posts were more likely to discuss treatment for OUD, tapering, and withdrawal management than oxycodone ER. Although the findings are consistent with a purported link between the limited availability of medication-assisted therapies for substance use disorders and use of diverted buprenorphine products for self-treatment, recreational use was a motivation expressed in more than one-third of buprenorphine posts. ", doi="10.2196/16038", url="http://publichealth.jmir.org/2020/1/e16038/", url="http://www.ncbi.nlm.nih.gov/pubmed/32209533" } @Article{info:doi/10.2196/13680, author="Barros, M. Joana and Duggan, Jim and Rebholz-Schuhmann, Dietrich", title="The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review", journal="J Med Internet Res", year="2020", month="Mar", day="13", volume="22", number="3", pages="e13680", keywords="medical informatics", keywords="public health informatics", keywords="public health", keywords="infectious diseases", keywords="chronic diseases", keywords="infodemiology", keywords="infoveillance", abstract="Background: Public health surveillance is based on the continuous and systematic collection, analysis, and interpretation of data. This informs the development of early warning systems to monitor epidemics and documents the impact of intervention measures. The introduction of digital data sources, and specifically sources available on the internet, has impacted the field of public health surveillance. New opportunities enabled by the underlying availability and scale of internet-based sources (IBSs) have paved the way for novel approaches for disease surveillance, exploration of health communities, and the study of epidemic dynamics. This field and approach is also known as infodemiology or infoveillance. Objective: This review aimed to assess research findings regarding the application of IBSs for public health surveillance (infodemiology or infoveillance). To achieve this, we have presented a comprehensive systematic literature review with a focus on these sources and their limitations, the diseases targeted, and commonly applied methods. Methods: A systematic literature review was conducted targeting publications between 2012 and 2018 that leveraged IBSs for public health surveillance, outbreak forecasting, disease characterization, diagnosis prediction, content analysis, and health-topic identification. The search results were filtered according to previously defined inclusion and exclusion criteria. Results: Spanning a total of 162 publications, we determined infectious diseases to be the preferred case study (108/162, 66.7\%). Of the eight categories of IBSs (search queries, social media, news, discussion forums, websites, web encyclopedia, and online obituaries), search queries and social media were applied in 95.1\% (154/162) of the reviewed publications. We also identified limitations in representativeness and biased user age groups, as well as high susceptibility to media events by search queries, social media, and web encyclopedias. Conclusions: IBSs are a valuable proxy to study illnesses affecting the general population; however, it is important to characterize which diseases are best suited for the available sources; the literature shows that the level of engagement among online platforms can be a potential indicator. There is a necessity to understand the population's online behavior; in addition, the exploration of health information dissemination and its content is significantly unexplored. With this information, we can understand how the population communicates about illnesses online and, in the process, benefit public health. ", doi="10.2196/13680", url="http://www.jmir.org/2020/3/e13680/", url="http://www.ncbi.nlm.nih.gov/pubmed/32167477" } @Article{info:doi/10.2196/16184, author="Xu, Chenjie and Yang, Hongxi and Sun, Li and Cao, Xinxi and Hou, Yabing and Cai, Qiliang and Jia, Peng and Wang, Yaogang", title="Detecting Lung Cancer Trends by Leveraging Real-World and Internet-Based Data: Infodemiology Study", journal="J Med Internet Res", year="2020", month="Mar", day="12", volume="22", number="3", pages="e16184", keywords="lung cancer", keywords="incidence", keywords="mortality", keywords="internet searches", keywords="infodemiology", abstract="Background: Internet search data on health-related terms can reflect people's concerns about their health status in near real time, and hence serve as a supplementary metric of disease characteristics. However, studies using internet search data to monitor and predict chronic diseases at a geographically finer state-level scale are sparse. Objective: The aim of this study was to explore the associations of internet search volumes for lung cancer with published cancer incidence and mortality data in the United States. Methods: We used Google relative search volumes, which represent the search frequency of specific search terms in Google. We performed cross-sectional analyses of the original and disease metrics at both national and state levels. A smoothed time series of relative search volumes was created to eliminate the effects of irregular changes on the search frequencies and obtain the long-term trends of search volumes for lung cancer at both the national and state levels. We also performed analyses of decomposed Google relative search volume data and disease metrics at the national and state levels. Results: The monthly trends of lung cancer-related internet hits were consistent with the trends of reported lung cancer rates at the national level. Ohio had the highest frequency for lung cancer-related search terms. At the state level, the relative search volume was significantly correlated with lung cancer incidence rates in 42 states, with correlation coefficients ranging from 0.58 in Virginia to 0.94 in Oregon. Relative search volume was also significantly correlated with mortality in 47 states, with correlation coefficients ranging from 0.58 in Oklahoma to 0.94 in North Carolina. Both the incidence and mortality rates of lung cancer were correlated with decomposed relative search volumes in all states excluding Vermont. Conclusions: Internet search behaviors could reflect public awareness of lung cancer. Research on internet search behaviors could be a novel and timely approach to monitor and estimate the prevalence, incidence, and mortality rates of a broader range of cancers and even more health issues. ", doi="10.2196/16184", url="http://www.jmir.org/2020/3/e16184/", url="http://www.ncbi.nlm.nih.gov/pubmed/32163035" } @Article{info:doi/10.2196/15861, author="O'Connor, Karen and Sarker, Abeed and Perrone, Jeanmarie and Gonzalez Hernandez, Graciela", title="Promoting Reproducible Research for Characterizing Nonmedical Use of Medications Through Data Annotation: Description of a Twitter Corpus and Guidelines", journal="J Med Internet Res", year="2020", month="Feb", day="26", volume="22", number="2", pages="e15861", keywords="prescription drug misuse", keywords="social media", keywords="substance abuse detection", keywords="natural language processing", keywords="machine learning", keywords="infodemiology", keywords="infoveillance", abstract="Background: Social media data are being increasingly used for population-level health research because it provides near real-time access to large volumes of consumer-generated data. Recently, a number of studies have explored the possibility of using social media data, such as from Twitter, for monitoring prescription medication abuse. However, there is a paucity of annotated data or guidelines for data characterization that discuss how information related to abuse-prone medications is presented on Twitter. Objective: This study discusses the creation of an annotated corpus suitable for training supervised classification algorithms for the automatic classification of medication abuse--related chatter. The annotation strategies used for improving interannotator agreement (IAA), a detailed annotation guideline, and machine learning experiments that illustrate the utility of the annotated corpus are also described. Methods: We employed an iterative annotation strategy, with interannotator discussions held and updates made to the annotation guidelines at each iteration to improve IAA for the manual annotation task. Using the grounded theory approach, we first characterized tweets into fine-grained categories and then grouped them into 4 broad classes---abuse or misuse, personal consumption, mention, and unrelated. After the completion of manual annotations, we experimented with several machine learning algorithms to illustrate the utility of the corpus and generate baseline performance metrics for automatic classification on these data. Results: Our final annotated set consisted of 16,443 tweets mentioning at least 20 abuse-prone medications including opioids, benzodiazepines, atypical antipsychotics, central nervous system stimulants, and gamma-aminobutyric acid analogs. Our final overall IAA was 0.86 (Cohen kappa), which represents high agreement. The manual annotation process revealed the variety of ways in which prescription medication misuse or abuse is discussed on Twitter, including expressions indicating coingestion, nonmedical use, nonstandard route of intake, and consumption above the prescribed doses. Among machine learning classifiers, support vector machines obtained the highest automatic classification accuracy of 73.00\% (95\% CI 71.4-74.5) over the test set (n=3271). Conclusions: Our manual analysis and annotations of a large number of tweets have revealed types of information posted on Twitter about a set of abuse-prone prescription medications and their distributions. In the interests of reproducible and community-driven research, we have made our detailed annotation guidelines and the training data for the classification experiments publicly available, and the test data will be used in future shared tasks. ", doi="10.2196/15861", url="http://www.jmir.org/2020/2/e15861/", url="http://www.ncbi.nlm.nih.gov/pubmed/32130117" } @Article{info:doi/10.2196/16466, author="Kim, Gyu Myeong and Kim, Jungu and Kim, Cheol Su and Jeong, Jaegwon", title="Twitter Analysis of the Nonmedical Use and Side Effects of Methylphenidate: Machine Learning Study", journal="J Med Internet Res", year="2020", month="Feb", day="24", volume="22", number="2", pages="e16466", keywords="methylphenidate", keywords="social media", keywords="Twitter", keywords="prescription drug misuse", keywords="drug-related side effects and adverse reactions", keywords="machine learning", keywords="support vector machine", abstract="Background: Methylphenidate, a stimulant used to treat attention deficit hyperactivity disorder, has the potential to be used nonmedically, such as for studying and recreation. In an era when many people actively use social networking services, experience with the nonmedical use or side effects of methylphenidate might be shared on Twitter. Objective: The purpose of this study was to analyze tweets about the nonmedical use and side effects of methylphenidate using a machine learning approach. Methods: A total of 34,293 tweets mentioning methylphenidate from August 2018 to July 2019 were collected using searches for ``methylphenidate'' and its brand names. Tweets in a randomly selected training dataset (6860/34,293, 20.00\%) were annotated as positive or negative for two dependent variables: nonmedical use and side effects. Features such as personal noun, nonmedical use terms, medical use terms, side effect terms, sentiment scores, and the presence of a URL were generated for supervised learning. Using the labeled training dataset and features, support vector machine (SVM) classifiers were built and the performance was evaluated using F1 scores. The classifiers were applied to the test dataset to determine the number of tweets about nonmedical use and side effects. Results: Of the 6860 tweets in the training dataset, 5.19\% (356/6860) and 5.52\% (379/6860) were about nonmedical use and side effects, respectively. Performance of SVM classifiers for nonmedical use and side effects, expressed as F1 scores, were 0.547 (precision: 0.926, recall: 0.388, and accuracy: 0.967) and 0.733 (precision: 0.920, recall: 0.609, and accuracy: 0.976), respectively. In the test dataset, the SVM classifiers identified 361 tweets (1.32\%) about nonmedical use and 519 tweets (1.89\%) about side effects. The proportion of tweets about nonmedical use was highest in May 2019 (46/2624, 1.75\%) and December 2018 (36/2041, 1.76\%). Conclusions: The SVM classifiers that were built in this study were highly precise and accurate and will help to automatically identify the nonmedical use and side effects of methylphenidate using Twitter. ", doi="10.2196/16466", url="http://www.jmir.org/2020/2/e16466/", url="http://www.ncbi.nlm.nih.gov/pubmed/32130160" } @Article{info:doi/10.2196/14431, author="Griffis, Heather and Asch, A. David and Schwartz, Andrew H. and Ungar, Lyle and Buttenheim, M. Alison and Barg, K. Frances and Mitra, Nandita and Merchant, M. Raina", title="Using Social Media to Track Geographic Variability in Language About Diabetes: Infodemiology Analysis", journal="JMIR Diabetes", year="2020", month="Feb", day="11", volume="5", number="1", pages="e14431", keywords="social media", keywords="epidemiology", keywords="infodemiology", keywords="diabetes", keywords="prevalence", keywords="twitter", abstract="Background: Social media posts about diabetes could reveal patients' knowledge, attitudes, and beliefs as well as approaches for better targeting of public health messages and care management. Objective: This study aimed to characterize the language of Twitter users' posts regarding diabetes and describe the correlation of themes with the county-level prevalence of diabetes. Methods: A retrospective study of diabetes-related tweets identified from a random sample of approximately 37 billion tweets from the United States from 2009 to 2015 was conducted. We extracted diabetes-specific tweets and used machine learning to identify statistically significant topics of related terms. Topics were combined into themes and compared with the prevalence of diabetes by US counties and further compared with geography (US Census Divisions). Pearson correlation coefficients are reported for each topic and relationship with prevalence. Results: A total of 239,989 tweets from 121,494 unique users included the term diabetes. The themes emerging from the topics included unhealthy food and drink, treatment, symptoms/diagnoses, risk factors, research, recipes, news, health care, management, fundraising, diet, communication, and supplements/remedies. The theme of unhealthy foods most positively correlated with geographic areas with high prevalence of diabetes (r=0.088), whereas tweets related to research most negatively correlated (r=?0.162) with disease prevalence. Themes and topics about diabetes differed in overall frequency across the US geographical divisions, with the East South Central and South Atlantic states having a higher frequency of topics referencing unhealthy food (r range=0.073-0.146; P<.001). Conclusions: Diabetes-related tweets originating from counties with high prevalence of diabetes have different themes than tweets originating from counties with low prevalence of diabetes. Interventions could be informed from this variation to promote healthy behaviors. ", doi="10.2196/14431", url="http://diabetes.jmir.org/2020/1/e14431/" } @Article{info:doi/10.2196/13347, author="Memon, Ali Shahan and Razak, Saquib and Weber, Ingmar", title="Lifestyle Disease Surveillance Using Population Search Behavior: Feasibility Study", journal="J Med Internet Res", year="2020", month="Jan", day="27", volume="22", number="1", pages="e13347", keywords="noncommunicable diseases", keywords="lifestyle disease surveillance", keywords="infodemiology", keywords="infoveillance", keywords="Google Trends", keywords="Web search", keywords="nowcasting", keywords="public health", keywords="digital epidemiology", abstract="Background: As the process of producing official health statistics for lifestyle diseases is slow, researchers have explored using Web search data as a proxy for lifestyle disease surveillance. Existing studies, however, are prone to at least one of the following issues: ad-hoc keyword selection, overfitting, insufficient predictive evaluation, lack of generalization, and failure to compare against trivial baselines. Objective: The aims of this study were to (1) employ a corrective approach improving previous methods; (2) study the key limitations in using Google Trends for lifestyle disease surveillance; and (3) test the generalizability of our methodology to other countries beyond the United States. Methods: For each of the target variables (diabetes, obesity, and exercise), prevalence rates were collected. After a rigorous keyword selection process, data from Google Trends were collected. These data were denormalized to form spatio-temporal indices. L1-regularized regression models were trained to predict prevalence rates from denormalized Google Trends indices. Models were tested on a held-out set and compared against baselines from the literature as well as a trivial last year equals this year baseline. A similar analysis was done using a multivariate spatio-temporal model where the previous year's prevalence was included as a covariate. This model was modified to create a time-lagged regression analysis framework. Finally, a hierarchical time-lagged multivariate spatio-temporal model was created to account for subnational trends in the data. The model trained on US data was, then, applied in a transfer learning framework to Canada. Results: In the US context, our proposed models beat the performances of the prior work, as well as the trivial baselines. In terms of the mean absolute error (MAE), the best of our proposed models yields 24\% improvement (0.72-0.55; P<.001) for diabetes; 18\% improvement (1.20-0.99; P=.001) for obesity, and 34\% improvement (2.89-1.95; P<.001) for exercise. Our proposed across-country transfer learning framework also shows promising results with an average Spearman and Pearson correlation of 0.70 for diabetes and 0.90 and 0.91 for obesity, respectively. Conclusions: Although our proposed models beat the baselines, we find the modeling of lifestyle diseases to be a challenging problem, one that requires an abundance of data as well as creative modeling strategies. In doing so, this study shows a low-to-moderate validity of Google Trends in the context of lifestyle disease surveillance, even when applying novel corrective approaches, including a proposed denormalization scheme. We envision qualitative analyses to be a more practical use of Google Trends in the context of lifestyle disease surveillance. For the quantitative analyses, the highest utility of using Google Trends is in the context of transfer learning where low-resource countries could benefit from high-resource countries by using proxy models. ", doi="10.2196/13347", url="http://www.jmir.org/2020/1/e13347/", url="http://www.ncbi.nlm.nih.gov/pubmed/32012050" } @Article{info:doi/10.2196/13673, author="Kwon, Misol and Park, Eunhee", title="Perceptions and Sentiments About Electronic Cigarettes on Social Media Platforms: Systematic Review", journal="JMIR Public Health Surveill", year="2020", month="Jan", day="15", volume="6", number="1", pages="e13673", keywords="electronic cigarettes", keywords="electronic nicotine delivery systems", keywords="internet", keywords="social media", keywords="review", abstract="Background: Electronic cigarettes (e-cigarettes) have been widely promoted on the internet, and subsequently, social media has been used as an important informative platform by e-cigarette users. Beliefs and knowledge expressed on social media platforms have largely influenced e-cigarette uptake, the decision to switch from conventional smoking to e-cigarette smoking, and positive and negative connotations associated with e-cigarettes. Despite this, there is a gap in our knowledge of people's perceptions and sentiments on e-cigarettes as depicted on social media platforms. Objective: This study aimed to (1) provide an overview of studies examining the perceptions and sentiments associated with e-cigarettes on social media platforms and online discussion forums, (2) explore people's perceptions of e-cigarette therein, and (3) examine the methodological limitations and gaps of the included studies. Methods: Searches in major electronic databases, including PubMed, Cumulative Index of Nursing and Allied Health Literature, EMBASE, Web of Science, and Communication and Mass Media Complete, were conducted using the following search terms: ``electronic cigarette,'' ``electronic vaporizer,'' ``electronic nicotine,'' and ``electronic nicotine delivery systems'' combined with ``internet,'' ``social media,'' and ``internet use.'' The studies were selected if they examined participants' perceptions and sentiments of e-cigarettes on online forums or social media platforms during the 2007-2017 period. Results: A total of 21 articles were included. A total of 20 different social media platforms and online discussion forums were identified. A real-time snapshot and characteristics of sentiments, personal experience, and perceptions toward e-cigarettes on social media platforms and online forums were identified. Common topics regarding e-cigarettes included positive and negative health effects, testimony by current users, potential risks, benefits, regulations associated with e-cigarettes, and attitude toward them as smoking cessation aids. Conclusions: Although perceptions among social media users were mixed, there were more positive sentiments expressed than negative ones. This study particularly adds to our understanding of current trends in the popularity of and attitude toward e-cigarettes among social media users. In addition, this study identified conflicting perceptions about e-cigarettes among social media users. This suggests that accurate and up-to-date information on the benefits and risks of e-cigarettes needs to be disseminated to current and potential e-cigarette users via social media platforms, which can serve as important educational channels. Future research can explore the efficacy of social media--based interventions that deliver appropriate information (eg, general facts, benefits, and risks) about e-cigarettes. Trial Registration: PROSPERO CRD42019121611; https://tinyurl.com/yfr27uxs ", doi="10.2196/13673", url="http://publichealth.jmir.org/2020/1/e13673/", url="http://www.ncbi.nlm.nih.gov/pubmed/31939747" } @Article{info:doi/10.2196/14605, author="Murphy, Douglas Michael and Pinheiro, Diego and Iyengar, Rahul and Lim, Gene and Menezes, Ronaldo and Cadeiras, Martin", title="A Data-Driven Social Network Intervention for Improving Organ Donation Awareness Among Minorities: Analysis and Optimization of a Cross-Sectional Study", journal="J Med Internet Res", year="2020", month="Jan", day="14", volume="22", number="1", pages="e14605", keywords="organ donation", keywords="social media", keywords="minority health", keywords="community health education", abstract="Background: Increasing the number of organ donors may enhance organ transplantation, and past health interventions have shown the potential to generate both large-scale and sustainable changes, particularly among minorities. Objective: This study aimed to propose a conceptual data-driven framework that tracks digital markers of public organ donation awareness using Twitter and delivers an optimized social network intervention (SNI) to targeted audiences using Facebook. Methods: We monitored digital markers of organ donation awareness across the United States over a 1-year period using Twitter and examined their association with organ donation registration. We delivered this SNI on Facebook with and without optimized awareness content (ie, educational content with a weblink to an online donor registration website) to low-income Hispanics in Los Angeles over a 1-month period and measured the daily number of impressions (ie, exposure to information) and clicks (ie, engagement) among the target audience. Results: Digital markers of organ donation awareness on Twitter are associated with donation registration (beta=.0032; P<.001) such that 10 additional organ-related tweets are associated with a 3.20\% (33,933/1,060,403) increase in the number of organ donor registrations at the city level. In addition, our SNI on Facebook effectively reached 1 million users, and the use of optimization significantly increased the rate of clicks per impression (beta=.0213; P<.004). Conclusions: Our framework can provide a real-time characterization of organ donation awareness while effectively delivering tailored interventions to minority communities. It can complement past approaches to create large-scale, sustainable interventions that are capable of raising awareness and effectively mitigate disparities in organ donation. ", doi="10.2196/14605", url="https://www.jmir.org/2020/1/e14605", url="http://www.ncbi.nlm.nih.gov/pubmed/31934867" } @Article{info:doi/10.2196/15684, author="Hua, My and Sadah, Shouq and Hristidis, Vagelis and Talbot, Prue", title="Health Effects Associated With Electronic Cigarette Use: Automated Mining of Online Forums", journal="J Med Internet Res", year="2020", month="Jan", day="3", volume="22", number="1", pages="e15684", keywords="electronic cigarettes", keywords="vaping epidemic", keywords="vaping-associated pulmonary illness", keywords="e-cigarettes", keywords="electronic nicotine delivery devices", keywords="health effects", keywords="nicotine", keywords="symptoms", keywords="disorders", keywords="pulmonary disease", keywords="pneumonia", keywords="headaches", keywords="content analysis", keywords="text classification", keywords="e-cigarette, or vaping, product use associated lung injury", abstract="Background: Our previous infodemiological study was performed by manually mining health-effect data associated with electronic cigarettes (ECs) from online forums. Manual mining is time consuming and limits the number of posts that can be retrieved. Objective: Our goal in this study was to automatically extract and analyze a large number (>41,000) of online forum posts related to the health effects associated with EC use between 2008 and 2015. Methods: Data were annotated with medical concepts from the Unified Medical Language System using a modified version of the MetaMap tool. Of over 1.4 million posts, 41,216 were used to analyze symptoms (undiagnosed conditions) and disorders (physician-diagnosed terminology) associated with EC use. For each post, sentiment (positive, negative, and neutral) was also assigned. Results: Symptom and disorder data were categorized into 12 organ systems or anatomical regions. Most posts on symptoms and disorders contained negative sentiment, and affected systems were similar across all years. Health effects were reported most often in the neurological, mouth and throat, and respiratory systems. The most frequently reported symptoms and disorders were headache (n=939), coughing (n=852), malaise (n=468), asthma (n=916), dehydration (n=803), and pharyngitis (n=565). In addition, users often reported linked symptoms (eg, coughing and headache). Conclusions: Online forums are a valuable repository of data that can be used to identify positive and negative health effects associated with EC use. By automating extraction of online information, we obtained more data than in our prior study, identified new symptoms and disorders associated with EC use, determined which systems are most frequently adversely affected, identified specific symptoms and disorders most commonly reported, and tracked health effects over 7 years. ", doi="10.2196/15684", url="https://www.jmir.org/2020/1/e15684", url="http://www.ncbi.nlm.nih.gov/pubmed/31899452" } @Article{info:doi/10.2196/13076, author="Melvin, Sara and Jamal, Amanda and Hill, Kaitlyn and Wang, Wei and Young, D. Sean", title="Identifying Sleep-Deprived Authors of Tweets: Prospective Study", journal="JMIR Ment Health", year="2019", month="Dec", day="6", volume="6", number="12", pages="e13076", keywords="wearable electronic devices", keywords="safety", keywords="natural language processing", keywords="information storage and retrieval", keywords="sleep deprivation", keywords="neural networks (computer)", keywords="sleep", keywords="social media", abstract="Background: Social media data can be explored as a tool to detect sleep deprivation. First-year undergraduate students in their first quarter were invited to wear sleep-tracking devices (Basis; Intel), allow us to follow them on Twitter, and complete weekly surveys regarding their sleep. Objective: This study aimed to determine whether social media data can be used to monitor sleep deprivation. Methods: The sleep data obtained from the device were utilized to create a tiredness model that aided in labeling the tweets as sleep deprived or not at the time of posting. Labeled data were used to train and test a gated recurrent unit (GRU) neural network as to whether or not study participants were sleep deprived at the time of posting. Results: Results from the GRU neural network suggest that it is possible to classify the sleep-deprivation status of a tweet's author with an average area under the curve of 0.68. Conclusions: It is feasible to use social media to identify students' sleep deprivation. The results add to the body of research suggesting that social media data should be further explored as a potential source for monitoring health. ", doi="10.2196/13076", url="https://mental.jmir.org/2019/12/e13076", url="http://www.ncbi.nlm.nih.gov/pubmed/31808747" } @Article{info:doi/10.2196/14809, author="Timimi, Farris and Ray, Sara and Jones, Erik and Aase, Lee and Hoffman, Kathleen", title="Patient-Reported Outcomes in Online Communications on Statins, Memory, and Cognition: Qualitative Analysis Using Online Communities", journal="J Med Internet Res", year="2019", month="Nov", day="28", volume="21", number="11", pages="e14809", keywords="social media", keywords="hydroxymethylglutaryl-CoA reductase inhibitors", keywords="drug-related side effects and adverse reactions", keywords="memory loss", keywords="PROMs", keywords="pharmacovigilance", keywords="infodemiology", keywords="infoveillance", keywords="peer-support groups", abstract="Background: In drug development clinical trials, there is a need for balance between restricting variables by setting eligibility criteria and representing the broader patient population that may use a product once it is approved. Similarly, although recent policy initiatives focusing on the inclusion of historically underrepresented groups are being implemented, barriers still remain. These limitations of clinical trials may mask potential product benefits and side effects. To bridge these gaps, online communication in health communities may serve as an additional population signal for drug side effects. Objective: The aim of this study was to employ a nontraditional dataset to identify drug side-effect signals. The study was designed to apply both natural language processing (NLP) technology and hands-on linguistic analysis to a set of online posts from known statin users to (1) identify any underlying crossover between the use of statins and impairment of memory or cognition and (2) obtain patient lexicon in their descriptions of experiences with statin medications and memory changes. Methods: Researchers utilized user-generated content on Inspire, looking at over 11 million posts across Inspire. Posts were written by patients and caregivers belonging to a variety of communities on Inspire. After identifying these posts, researchers used NLP and hands-on linguistic analysis to draw and expand upon correlations among statin use, memory, and cognition. Results: NLP analysis of posts identified statistical correlations between statin users and the discussion of memory impairment, which were not observed in control groups. NLP found that, out of all members on Inspire, 3.1\% had posted about memory or cognition. In a control group of those who had posted about TNF inhibitors, 6.2\% had also posted about memory and cognition. In comparison, of all those who had posted about a statin medication, 22.6\% (P<.001) also posted about memory and cognition. Furthermore, linguistic analysis of a sample of posts provided themes and context to these statistical findings. By looking at posts from statin users about memory, four key themes were found and described in detail in the data: memory loss, aphasia, cognitive impairment, and emotional change. Conclusions: Correlations from this study point to a need for further research on the impact of statins on memory and cognition. Furthermore, when using nontraditional datasets, such as online communities, NLP and linguistic methodologies broaden the population for identifying side-effect signals. For side effects such as those on memory and cognition, where self-reporting may be unreliable, these methods can provide another avenue to inform patients, providers, and the Food and Drug Administration. ", doi="10.2196/14809", url="http://www.jmir.org/2019/11/e14809/", url="http://www.ncbi.nlm.nih.gov/pubmed/31778117" } @Article{info:doi/10.2196/13130, author="Ssendikaddiwa, Joseph and Lavergne, Ruth", title="Access to Primary Care and Internet Searches for Walk-In Clinics and Emergency Departments in Canada: Observational Study Using Google Trends and Population Health Survey Data", journal="JMIR Public Health Surveill", year="2019", month="Nov", day="18", volume="5", number="4", pages="e13130", keywords="internet", keywords="ambulatory care facilities", keywords="emergency departments", keywords="primary health care", keywords="health services accessibility", abstract="Background: Access to primary care is a challenge for many Canadians. Models of primary care vary widely among provinces, including arrangements for same-day and after-hours access. Use of walk-in clinics and emergency departments (EDs) may also vary, but data sources that allow comparison are limited. Objective: We used Google Trends to examine the relative frequency of searches for walk-in clinics and EDs across provinces and over time in Canada. We correlated provincial relative search frequencies from Google Trends with survey responses about primary care access from the Commonwealth Fund's 2016 International Health Policy Survey of Adults in 11 Countries and the 2016 Canadian Community Health Survey. Methods: We developed search strategies to capture the range of terms used for walk-in clinics (eg, urgent care clinic and after-hours clinic) and EDs (eg, emergency room) across Canadian provinces. We used Google Trends to determine the frequencies of these terms relative to total search volume within each province from January 2011 to December 2018. We calculated correlation coefficients and 95\% CIs between provincial Google Trends relative search frequencies and survey responses. Results: Relative search frequency of walk-in clinic searches increased steadily, doubling in most provinces between 2011 and 2018. Relative frequency of walk-in clinic searches was highest in the western provinces of British Columbia, Alberta, Saskatchewan, and Manitoba. At the provincial level, higher walk-in clinic relative search frequency was strongly positively correlated with the percentage of survey respondents who reported being able to get same- or next-day appointments to see a doctor or a nurse and inversely correlated with the percentage of respondents who reported going to ED for a condition that they thought could have been treated by providers at usual place of care. Relative search frequency for walk-in clinics was also inversely correlated with the percentage of respondents who reported having a regular medical provider. ED relative search frequencies were more stable over time, and we did not observe statistically significant correlation with survey data. Conclusions: Higher relative search frequency for walk-in clinics was positively correlated with the ability to get a same- or next-day appointment and inversely correlated with ED use for conditions treatable in the patient's regular place of care and also with having a regular medical provider. Findings suggest that patient use of Web-based tools to search for more convenient or accessible care through walk-in clinics is increasing over time. Further research is needed to validate Google Trends data with administrative information on service use. ", doi="10.2196/13130", url="http://publichealth.jmir.org/2019/4/e13130/", url="http://www.ncbi.nlm.nih.gov/pubmed/31738175" } @Article{info:doi/10.2196/14137, author="Nguyen, Jennifer and Gilbert, Lauren and Priede, Lianne and Heckman, Carolyn", title="The Reach of the ``Don't Fry Day'' Twitter Campaign: Content Analysis", journal="JMIR Dermatol", year="2019", month="Nov", day="13", volume="2", number="1", pages="e14137", keywords="social media", keywords="skin neoplasms", keywords="health communication", abstract="Background: Skin cancer is the most common cancer in the United States, disproportionately affecting young women. Since many young adults use Twitter, it may be an effective channel to communicate skin cancer prevention information. Objective: The study aimed to assess the reach of the National Council on Skin Cancer Prevention (NCSCP)'s 2018 Don't Fry Day Twitter campaign, categorize the types of individuals or tweeters who engaged in the campaign, and identify themes of the tweets. Methods: Descriptive statistics were used, and a content analysis of Twitter activity during the 2018 Don't Fry Day campaign was conducted. The NCSCP tweeted about Don't Fry Day and skin cancer prevention for 14 days in May 2018. Twitter contributors were categorized into groups. The number of impressions (potential views) and retweets were recorded. Content analysis was used to describe the text of the tweets. Results: A total of 1881 Twitter accounts, largely health professionals, used the Don't Fry Day hashtag, generating over 45 million impressions. These accounts were grouped into nine categories (eg, news or media and public figures). The qualitative content analysis revealed informative, minimally informative, and self-interest campaign promotion themes. Informative tweets involved individuals and organizations who would mention and give further context and information about the \#DontFryDay campaign. Subthemes of the informative theme were sun safety, contextual, and epidemiologic information. Minimally informative tweets used the hashtag (\#DontFryDay) and other types of hashtags but did not give any further context or original material in the tweets. Self-interest campaign promotion involved businesses, firms, and medical practices that would utilize and promote the campaign to boost their own ventures. Conclusions: These analyses demonstrate the large potential reach of social media public health campaigns. However, limitations of such campaigns were also identified, for example, the relatively homogeneous groups actively engaged in the campaign. This study contributes to the understanding of the types of accounts and messages engaged in social media campaigns utilizing a hashtag, providing insight into the messages and participants that are effective and those that are not to achieve campaign goals. Further research on the potential impact of social media on health behaviors and outcomes is necessary to ensure wide-reaching implications. ", doi="10.2196/14137", url="http://derma.jmir.org/2019/1/e14137/" } @Article{info:doi/10.2196/15455, author="Reuter, Katja and Zhu, Yifan and Angyan, Praveen and Le, NamQuyen and Merchant, A. Akil and Zimmer, Michael", title="Public Concern About Monitoring Twitter Users and Their Conversations to Recruit for Clinical Trials: Survey Study", journal="J Med Internet Res", year="2019", month="Oct", day="30", volume="21", number="10", pages="e15455", keywords="AIDS", keywords="cancer", keywords="clinical research", keywords="clinical trial", keywords="crowdsourcing", keywords="ethics", keywords="HIV", keywords="HPV", keywords="infoveillance", keywords="infodemiology", keywords="informed consent", keywords="Internet", keywords="research ethics", keywords="Mechanical Turk", keywords="MTurk", keywords="monitoring", keywords="obesity", keywords="privacy", keywords="public opinion", keywords="recruitment", keywords="smoking", keywords="social media", keywords="social network", keywords="surveillance", keywords="TurkPrime", keywords="Twitter", abstract="Background: Social networks such as Twitter offer the clinical research community a novel opportunity for engaging potential study participants based on user activity data. However, the availability of public social media data has led to new ethical challenges about respecting user privacy and the appropriateness of monitoring social media for clinical trial recruitment. Researchers have voiced the need for involving users' perspectives in the development of ethical norms and regulations. Objective: This study examined the attitudes and level of concern among Twitter users and nonusers about using Twitter for monitoring social media users and their conversations to recruit potential clinical trial participants. Methods: We used two online methods for recruiting study participants: the open survey was (1) advertised on Twitter between May 23 and June 8, 2017, and (2) deployed on TurkPrime, a crowdsourcing data acquisition platform, between May 23 and June 8, 2017. Eligible participants were adults, 18 years of age or older, who lived in the United States. People with and without Twitter accounts were included in the study. Results: While nearly half the respondents---on Twitter (94/603, 15.6\%) and on TurkPrime (509/603, 84.4\%)---indicated agreement that social media monitoring constitutes a form of eavesdropping that invades their privacy, over one-third disagreed and nearly 1 in 5 had no opinion. A chi-square test revealed a positive relationship between respondents' general privacy concern and their average concern about Internet research (P<.005). We found associations between respondents' Twitter literacy and their concerns about the ability for researchers to monitor their Twitter activity for clinical trial recruitment (P=.001) and whether they consider Twitter monitoring for clinical trial recruitment as eavesdropping (P<.001) and an invasion of privacy (P=.003). As Twitter literacy increased, so did people's concerns about researchers monitoring Twitter activity. Our data support the previously suggested use of the nonexceptionalist methodology for assessing social media in research, insofar as social media-based recruitment does not need to be considered exceptional and, for most, it is considered preferable to traditional in-person interventions at physical clinics. The expressed attitudes were highly contextual, depending on factors such as the type of disease or health topic (eg, HIV/AIDS vs obesity vs smoking), the entity or person monitoring users on Twitter, and the monitored information. Conclusions: The data and findings from this study contribute to the critical dialogue with the public about the use of social media in clinical research. The findings suggest that most users do not think that monitoring Twitter for clinical trial recruitment constitutes inappropriate surveillance or a violation of privacy. However, researchers should remain mindful that some participants might find social media monitoring problematic when connected with certain conditions or health topics. Further research should isolate factors that influence the level of concern among social media users across platforms and populations and inform the development of more clear and consistent guidelines. ", doi="10.2196/15455", url="http://www.jmir.org/2019/10/e15455/", url="http://www.ncbi.nlm.nih.gov/pubmed/31670698" } @Article{info:doi/10.2196/14731, author="Albalawi, Yahya and Nikolov, S. Nikola and Buckley, Jim", title="Trustworthy Health-Related Tweets on Social Media in Saudi Arabia: Tweet Metadata Analysis", journal="J Med Internet Res", year="2019", month="Oct", day="8", volume="21", number="10", pages="e14731", keywords="social media", keywords="new media", keywords="misinformation", keywords="trustworthiness", keywords="dissemination", keywords="health communication", abstract="Background: Social media platforms play a vital role in the dissemination of health information. However, evidence suggests that a high proportion of Twitter posts (ie, tweets) are not necessarily accurate, and many studies suggest that tweets do not need to be accurate, or at least evidence based, to receive traction. This is a dangerous combination in the sphere of health information. Objective: The first objective of this study is to examine health-related tweets originating from Saudi Arabia in terms of their accuracy. The second objective is to find factors that relate to the accuracy and dissemination of these tweets, thereby enabling the identification of ways to enhance the dissemination of accurate tweets. The initial findings from this study and methodological improvements will then be employed in a larger-scale study that will address these issues in more detail. Methods: A health lexicon was used to extract health-related tweets using the Twitter application programming interface and the results were further filtered manually. A total of 300 tweets were each labeled by two medical doctors; the doctors agreed that 109 tweets were either accurate or inaccurate. Other measures were taken from these tweets' metadata to see if there was any relationship between the measures and either the accuracy or the dissemination of the tweets. The entire range of this metadata was analyzed using Python, version 3.6.5 (Python Software Foundation), to answer the research questions posed. Results: A total of 34 out of 109 tweets (31.2\%) in the dataset used in this study were classified as untrustworthy health information. These came mainly from users with a non-health care background and social media accounts that had no corresponding physical (ie, organization) manifestation. Unsurprisingly, we found that traditionally trusted health sources were more likely to tweet accurate health information than other users. Likewise, these provisional results suggest that tweets posted in the morning are more trustworthy than tweets posted at night, possibly corresponding to official and casual posts, respectively. Our results also suggest that the crowd was quite good at identifying trustworthy information sources, as evidenced by the number of times a tweet's author was tagged as favorited by the community. Conclusions: The results indicate some initially surprising factors that might correlate with the accuracy of tweets and their dissemination. For example, the time a tweet was posted correlated with its accuracy, which may reflect a difference between professional (ie, morning) and hobbyist (ie, evening) tweets. More surprisingly, tweets containing a kashida---a decorative element in Arabic writing used to justify the text within lines---were more likely to be disseminated through retweets. These findings will be further assessed using data analysis techniques on a much larger dataset in future work. ", doi="10.2196/14731", url="https://www.jmir.org/2019/10/e14731", url="http://www.ncbi.nlm.nih.gov/pubmed/31596242" } @Article{info:doi/10.2196/13212, author="Mimura, Wataru and Akazawa, Manabu", title="The Association Between Internet Searches and Moisturizer Prescription in Japan: Retrospective Observational Study", journal="JMIR Public Health Surveill", year="2019", month="Oct", day="8", volume="5", number="4", pages="e13212", keywords="internet", keywords="moisturizer", keywords="heparinoid", keywords="Google Trends", keywords="time series analysis", keywords="infodemiology", abstract="Background: Heparinoid is a medication prescribed in Japan for skin diseases, such as atopic dermatitis and dry skin. Heparinoid prescription has increased with instances of internet blogs recommending its use as a cosmetic. Objective: This study aimed to examine the prescription trends in moisturizer use and analyze their association with internet searches. Methods: We used a claims database to identify pharmacy claims of heparinoid-only prescriptions in Japan. Additionally, we used Google Trends to obtain internet search data for the period between October 1, 2007, and September 31, 2017. To analyze the association between heparinoid prescriptions and internet searches, we performed an autoregressive integrated moving average approach for each time series. Results: We identified 155,733 patients who had been prescribed heparinoid. The number of prescriptions increased from 2011 onward, and related internet searches increased from 2012 onward. Internet searches were significantly correlated with total heparinoid prescription (correlation coefficient=.25, P=.005). In addition, internet searches were significantly correlated with heparinoid prescription in those aged 20-59 years at --1-month lag in Google Trends (correlation coefficient=.30, P=.001). Conclusions: Google searches related to heparinoid prescriptions showed a seasonal pattern and increased gradually over the preceding several years. Google searches were positively correlated with prescription trends. In addition, in a particular age group (20-59 years), prescriptions increased with the increase in internet searches. These results suggest that people obtained health-related information on the internet and that this affected their behavior and prescription requests. ", doi="10.2196/13212", url="https://publichealth.jmir.org/2019/4/e13212", url="http://www.ncbi.nlm.nih.gov/pubmed/31596248" } @Article{info:doi/10.2196/12878, author="Kelley, E. Dannielle and Brown, Meredith and Murray, Alice and Blake, D. Kelly", title="Prevalence and Characteristics of Twitter Posts About Court-Ordered, Tobacco-Related Corrective Statements: Descriptive Content Analysis", journal="JMIR Public Health Surveill", year="2019", month="Oct", day="8", volume="5", number="4", pages="e12878", keywords="social media", keywords="Twitter", keywords="tobacco corrective statements", keywords="tobacco industry/legislation and jurisprudence", abstract="Background: Three major US tobacco companies were recently ordered to publish corrective statements intended to prevent and restrain further fraud about the health effects of smoking. The court-ordered statements began appearing in newspapers and on television (TV) in late 2017. Objective: The objective of this study was to examine the social media dissemination of the tobacco corrective statements during the first 6 months of the implementation of the statements. Methods: We conducted a descriptive content analysis of Twitter posts using an iterative search strategy through Crimson Hexagon and randomly selected 19.74\% (456/2309) of original posts occurring between November 1, 2017, and March 27, 2018, for coding and analysis. We assessed post volume over time, source or author, valence, linked content, and reference to the industry (eg, big tobacco, tobacco industry, and Philip Morris) and media outlet (TV or newspaper). Retweeted content was coded for source/author and prevalence. Results: Most posts were published in November 2017, surrounding the initial release of the corrective statements. Content was generally neutral (58.7\%, 268/456) or positive (33.3\%, 152/456) in valence, included links to additional information about the statements (94.9\%, 433/456), referred to the industry (87.7\%, 400/456), and did not mention a specific media channel on which the statements were aired or published (15\%). The majority of original posts were created by individual users (55.2\%, 252/456), whereas the majority of retweeted posts were posted by public health organizations (51\%). Differences by source are reported, for example, organization posts are more likely to include a link to additional information compared with individual users (P=.03). Conclusions: Conversations about the court-ordered corrective statements are taking place on Twitter and are generally neutral or positive in nature. Public health organizations may be increasing the prevalence of these conversations through social media engagement. ", doi="10.2196/12878", url="https://publichealth.jmir.org/2019/4/e12878", url="http://www.ncbi.nlm.nih.gov/pubmed/31596243" } @Article{info:doi/10.2196/10728, author="Dave, Arpit and Yi, Johnny and Boothe, Andy and Brashear, Helene and Byrne, Jeffrey and Gad, Yash", title="Listening to the HysterSisters: A Retrospective Keyword Frequency Analysis of Conversations About Hysterectomy Recovery", journal="JMIR Perioper Med", year="2019", month="Sep", day="26", volume="2", number="2", pages="e10728", keywords="hysterectomy", keywords="gynecology", keywords="social media", keywords="perceived recovery", abstract="Background: In the postoperative period, individual patient experiences vary widely and are based on a diverse set of input variables influenced by all stakeholders in and throughout the surgical process. Although clinical research has primarily focused on clinical and administrative datasets to characterize the postoperative recovery experience, there is increasing interest in patient-reported outcome measures (PROMs). The growth of online communities in which patients themselves participate provides a venue to study PROMs directly. One such forum-based community is HysterSisters, dedicated to helping individuals through the experience of hysterectomy, a major surgery which removes the uterus. The surgery can be performed by a variety of methods such as minimally invasive approaches or the traditional abdominal approach using a larger incision. The community offers support for ``medical and emotional issues [...] from diagnosis, to treatment, to recovery.'' Users can specify when and what type of hysterectomy they underwent. They can discuss their shared experience of hysterectomy and provide, among other interactions, feedback, reassurance, sympathy, or advice, thus providing a unique view into conversations surrounding the hysterectomy experience. Objective: We aimed to characterize conversations about hysterectomy recovery as experienced by users of the HysterSisters online community. Methods: A retrospective keyword frequency analysis of the HysterSisters Hysterectomy Recovery forum was performed. Results: Within the Hysterectomy Recovery forum, 33,311 unique users declared their hysterectomy date and type and posted during the first 12 weeks postsurgery. A taxonomy of 8 primary symptom groups was created using a seed list of keywords generated from a term frequency analysis of these threads. Pain and bleeding were the two most mentioned symptom groups and account for almost half of all symptom mentions (19,965/40,127). For symptoms categories such as pain and hormones and emotions, there was no difference in the proportion of users mentioning related keywords, regardless of the type of hysterectomy, whereas bleeding-related or intimacy-related keywords were mentioned more frequently by users undergoing certain minimally invasive approaches when compared with those undergoing abdominal hysterectomy. Temporal patterns in symptom mentions were noted as well. The majority of all posting activity occurred in the first 3 weeks. Across all keyword groups, individuals reporting minimally invasive procedures ceased forum use of these keywords significantly earlier than those reporting abdominal hysterectomy. Peaks in conversation volume surrounding particular symptom categories were also identified at 1, 3, and 6 weeks postoperatively. Conclusions: The HysterSisters Hysterectomy Recovery forum and other such forums centered on users' health care experience can provide novel actionable insights that can improve patient-centered care during the postoperative period. This study adds another dimension to the utility of social media analytics by demonstrating that measurement of post volumes and distribution of symptom mentions over time reveal key opportunities for beneficial symptom-specific patient engagement. ", doi="10.2196/10728", url="http://periop.jmir.org/2019/2/e10728/", url="http://www.ncbi.nlm.nih.gov/pubmed/33393919" } @Article{info:doi/10.2196/13936, author="Barker, M. Kathryn and Subramanian, V. S. and Selman, Robert and Austin, Bryn S.", title="Gender Perspectives on Social Norms Surrounding Teen Pregnancy: A Thematic Analysis of Social Media Data", journal="JMIR Pediatr Parent", year="2019", month="Sep", day="17", volume="2", number="2", pages="e13936", keywords="teenage childbearing", keywords="teen pregnancy", keywords="adolescent sexual behavior", keywords="social media", keywords="social norms", keywords="gender", abstract="Background: Social concern with teen pregnancy emerged in the 1970s, and today's popular and professional health literature continues to draw on social norms that view teen pregnancy as a problem---for the teen mother, her baby, and society. It is unclear, however, how adolescents directly affected by teen pregnancy draw upon social norms against teen pregnancy in their own lives, whether the norms operate differently for girls and boys, and how these social norms affect pregnant or parenting adolescents. Objective: This research aims to examine whether and how US adolescents use, interpret, and experience social norms against teen pregnancy. Methods: Online ethnographic methods were used for the analysis of peer-to-peer exchanges from an online social network site designed for adolescents. Data were collected between March 2010 and February 2015 (n=1662). Thematic analysis was conducted using NVivo software. Results: American adolescents in this online platform draw on dominant social norms against teen pregnancy to provide rationales for why pregnancy in adolescence is wrong or should be avoided. Rationales range from potential socioeconomic harms to life-course rationales that view adolescence as a special, carefree period in life. Despite joint contributions from males and females to a pregnancy, it is primarily females who report pregnancy-related concerns, including experiences of bullying, social isolation, and fear. Conclusions: Peer exchange in this online forum indicates that American adolescents reproduce prevailing US social norms of viewing teen pregnancy as a social problem. These norms intersect with the norms of age, gender, and female sexuality. Female adolescents who transgress these norms experience bullying, shame, and stigma. Health professionals must ensure that strategies designed to prevent unintended adolescent pregnancy do not simultaneously create hardship and stigma in the lives of young women who are pregnant and parent their children. ", doi="10.2196/13936", url="http://pediatrics.jmir.org/2019/2/e13936/", url="http://www.ncbi.nlm.nih.gov/pubmed/31536963" } @Article{info:doi/10.2196/14329, author="Rieger, Agnes and Gaines, Averi and Barnett, Ian and Baldassano, Frances Claudia and Connolly Gibbons, Beth Mary and Crits-Christoph, Paul", title="Psychiatry Outpatients' Willingness to Share Social Media Posts and Smartphone Data for Research and Clinical Purposes: Survey Study", journal="JMIR Form Res", year="2019", month="Aug", day="29", volume="3", number="3", pages="e14329", keywords="social media", keywords="smartphone", keywords="outpatients", keywords="psychiatry", keywords="psychotherapy", keywords="digital health", keywords="mhealth", keywords="digital phenotyping", keywords="privacy", keywords="user preferences", abstract="Background: Psychiatry research has begun to leverage data collected from patients' social media and smartphone use. However, information regarding the feasibility of utilizing such data in an outpatient setting and the acceptability of such data in research and practice is limited. Objective: This study aimed at understanding the outpatients' willingness to have information from their social media posts and their smartphones used for clinical or research purposes. Methods: In this survey study, we surveyed patients (N=238) in an outpatient clinic waiting room. Willingness to share social media and passive smartphone data was summarized for the sample as a whole and broken down by sex, age, and race. Results: Most patients who had a social media account and who were receiving talk therapy treatment (74.4\%, 99/133) indicated that they would be willing to share their social media posts with their therapists. The percentage of patients willing to share passive smartphone data with researchers varied from 40.8\% (82/201) to 60.7\% (122/201) depending on the parameter, with sleep duration being the parameter with the highest percentage of patients willing to share. A total of 30.4\% of patients indicated that media stories of social media privacy breaches made them more hesitant about sharing passive smartphone data with researchers. Sex and race were associated with willingness to share smartphone data, with men and whites being the most willing to share. Conclusions: Our results indicate that most patients in a psychiatric outpatient setting would share social media and passive smartphone data and that further research elucidating patterns of willingness to share passive data is needed. ", doi="10.2196/14329", url="http://formative.jmir.org/2019/3/e14329/", url="http://www.ncbi.nlm.nih.gov/pubmed/31493326" } @Article{info:doi/10.2196/13837, author="Modrek, Sepideh and Chakalov, Bozhidar", title="The \#MeToo Movement in the United States: Text Analysis of Early Twitter Conversations", journal="J Med Internet Res", year="2019", month="Sep", day="03", volume="21", number="9", pages="e13837", keywords="social media", keywords="sexual abuse", keywords="sexual assault", keywords="machine learning", keywords="infodemiology", keywords="infoveillance", abstract="Background: The \#MeToo movement sparked an international debate on the sexual harassment, abuse, and assault and has taken many directions since its inception in October of 2017. Much of the early conversation took place on public social media sites such as Twitter, where the hashtag movement began. Objective: The aim of this study is to document, characterize, and quantify early public discourse and conversation of the \#MeToo movement from Twitter data in the United States. We focus on posts with public first-person revelations of sexual assault/abuse and early life experiences of such events. Methods: We purchased full tweets and associated metadata from the Twitter Premium application programming interface between October 14 and 21, 2017 (ie, the first week of the movement). We examined the content of novel English language tweets with the phrase ``MeToo'' from within the United States (N=11,935). We used machine learning methods, least absolute shrinkage and selection operator regression, and support vector machine models to summarize and classify the content of individual tweets with revelations of sexual assault and abuse and early life experiences of sexual assault and abuse. Results: We found that the most predictive words created a vivid archetype of the revelations of sexual assault and abuse. We then estimated that in the first week of the movement, 11\% of novel English language tweets with the words ``MeToo'' revealed details about the poster's experience of sexual assault or abuse and 5.8\% revealed early life experiences of such events. We examined the demographic composition of posters of sexual assault and abuse and found that white women aged 25-50 years were overrepresented in terms of their representation on Twitter. Furthermore, we found that the mass sharing of personal experiences of sexual assault and abuse had a large reach, where 6 to 34 million Twitter users may have seen such first-person revelations from someone they followed in the first week of the movement. Conclusions: These data illustrate that revelations shared went beyond acknowledgement of having experienced sexual harassment and often included vivid and traumatic descriptions of early life experiences of assault and abuse. These findings and methods underscore the value of content analysis, supported by novel machine learning methods, to improve our understanding of how widespread the revelations were, which likely amplified the spread and saliency of the \#MeToo movement. ", doi="10.2196/13837", url="https://www.jmir.org/2019/9/e13837/", url="http://www.ncbi.nlm.nih.gov/pubmed/31482849" } @Article{info:doi/10.2196/jmir.7081, author="Golder, Su and Scantlebury, Arabella and Christmas, Helen", title="Understanding Public Attitudes Toward Researchers Using Social Media for Detecting and Monitoring Adverse Events Data: Multi Methods Study", journal="J Med Internet Res", year="2019", month="Aug", day="29", volume="21", number="8", pages="e7081", keywords="adverse effects", keywords="social media", keywords="ethics", keywords="research", keywords="qualitative research", keywords="digital health", keywords="infodemiology", keywords="infoveillance", keywords="pharmacovigilance", keywords="surveillance", abstract="Background: Adverse events are underreported in research studies, particularly randomized controlled trials and pharmacovigilance studies. A method that researchers could use to identify more complete safety profiles for medications is to use social media analytics. However, patient's perspectives on the ethical issues associated with using patient reports of adverse drug events on social media are unclear. Objective: The objective of this study was to explore the ethics of using social media for detecting and monitoring adverse events for research purposes using a multi methods approach. Methods: A multi methods design comprising qualitative semistructured interviews (n=24), a focus group (n=3), and 3 Web-based discussions (n=20) with members of the public was adopted. Findings from a recent systematic review on the use of social media for monitoring adverse events provided a theoretical framework to interpret the study's findings. Results: Views were ascertained regarding the potential benefits and harms of the research, privacy expectations, informed consent, and social media platform. Although the majority of participants were supportive of social media content being used for research on adverse events, a small number of participants strongly opposed the idea. The potential benefit of the research was cited as the most influential factor to whether participants would give their consent to their data being used for research. There were also some caveats to people's support for the use of their social media data for research purposes: the type of social media platform and consideration of the vulnerability of the social media user. Informed consent was regarded as difficult to obtain and this divided the opinion on whether it should be sought. Conclusions: Social media users were generally positive about their social media data being used for research purposes; particularly for research on adverse events. However, approval was dependent on the potential benefit of the research and that individuals are protected from harm. Further study is required to establish when consent is required for an individual's social media data to be used. ", doi="10.2196/jmir.7081", url="http://www.jmir.org/2019/8/e7081/", url="http://www.ncbi.nlm.nih.gov/pubmed/31469079" } @Article{info:doi/10.2196/14077, author="Garcia-Rudolph, Alejandro and Laxe, Sara and Saur{\'i}, Joan and Bernabeu Guitart, Montserrat", title="Stroke Survivors on Twitter: Sentiment and Topic Analysis From a Gender Perspective", journal="J Med Internet Res", year="2019", month="Aug", day="26", volume="21", number="8", pages="e14077", keywords="stroke", keywords="emotions", keywords="Twitter", keywords="infodemiology", keywords="infoveillance", keywords="sentiment analysis", keywords="topic models", keywords="gender", abstract="Background: Stroke is the worldwide leading cause of long-term disabilities. Women experience more activity limitations, worse health-related quality of life, and more poststroke depression than men. Twitter is increasingly used by individuals to broadcast their day-to-day happenings, providing unobtrusive access to samples of spontaneously expressed opinions on all types of topics and emotions. Objective: This study aimed to consider the raw frequencies of words in the collection of tweets posted by a sample of stroke survivors and to compare the posts by gender of the survivor for 8 basic emotions (anger, fear, anticipation, surprise, joy, sadness, trust and disgust); determine the proportion of each emotion in the collection of tweets and statistically compare each of them by gender of the survivor; extract the main topics (represented as sets of words) that occur in the collection of tweets, relative to each gender; and assign happiness scores to tweets and topics (using a well-established tool) and compare them by gender of the survivor. Methods: We performed sentiment analysis based on a state-of-the-art lexicon (National Research Council) with syuzhet R package. The emotion scores for men and women were first subjected to an F-test and then to a Wilcoxon rank sum test. We extended the emotional analysis, assigning happiness scores with the hedonometer (a tool specifically designed considering Twitter inputs). We calculated daily happiness average scores for all tweets. We created a term map for an exploratory clustering analysis using VosViewer software. We performed structural topic modelling with stm R package, allowing us to identify main topics by gender. We assigned happiness scores to all the words defining the main identified topics and compared them by gender. Results: We analyzed 800,424 tweets posted from August 1, 2007 to December 1, 2018, by 479 stroke survivors: Women (n=244) posted 396,898 tweets, and men (n=235) posted 403,526 tweets. The stroke survivor condition and gender as well as membership in at least 3 stroke-specific Twitter lists of active users were manually verified for all 479 participants. Their total number of tweets since 2007 was 5,257,433; therefore, we analyzed the most recent 15.2\% of all their tweets. Positive emotions (anticipation, trust, and joy) were significantly higher (P<.001) in women, while negative emotions (disgust, fear, and sadness) were significantly higher (P<.001) in men in the analysis of raw frequencies and proportion of emotions. Happiness mean scores throughout the considered period show higher levels of happiness in women. We calculated the top 20 topics (with percentages and CIs) more likely addressed by gender and found that women's topics show higher levels of happiness scores. Conclusions: We applied two different approaches---the Plutchik model and hedonometer tool---to a sample of stroke survivors' tweets. We conclude that women express positive emotions and happiness much more than men. ", doi="10.2196/14077", url="http://www.jmir.org/2019/8/e14077/", url="http://www.ncbi.nlm.nih.gov/pubmed/31452514" } @Article{info:doi/10.2196/12610, author="P{\'e}rez-P{\'e}rez, Mart{\'i}n and P{\'e}rez-Rodr{\'i}guez, Gael and Fdez-Riverola, Florentino and Louren{\c{c}}o, An{\'a}lia", title="Using Twitter to Understand the Human Bowel Disease Community: Exploratory Analysis of Key Topics", journal="J Med Internet Res", year="2019", month="Aug", day="15", volume="21", number="8", pages="e12610", keywords="inflammatory bowel diseases", keywords="irritable bowel syndrome", keywords="social media", keywords="communication", keywords="data mining", keywords="natural language processing", keywords="infodemiology", abstract="Background: Nowadays, the use of social media is part of daily life, with more and more people, including governments and health organizations, using at least one platform regularly. Social media enables users to interact among large groups of people that share the same interests and suffer the same afflictions. Notably, these channels promote the ability to find and share information about health and medical conditions. Objective: This study aimed to characterize the bowel disease (BD) community on Twitter, in particular how patients understand, discuss, feel, and react to the condition. The main questions were as follows: Which are the main communities and most influential users?; Where are the main content providers from?; What are the key biomedical and scientific topics under discussion? How are topics interrelated in patient communications?; How do external events influence user activity?; What kind of external sources of information are being promoted? Methods: To answer these questions, a dataset of tweets containing terms related to BD conditions was collected from February to August 2018, accounting for a total of 24,634 tweets from 13,295 different users. Tweet preprocessing entailed the extraction of textual contents, hyperlinks, hashtags, time, location, and user information. Missing and incomplete information about the user profiles was completed using different analysis techniques. Semantic tweet topic analysis was supported by a lexicon-based entity recognizer. Furthermore, sentiment analysis enabled a closer look into the opinions expressed in the tweets, namely, gaining a deeper understanding of patients' feelings and experiences. Results: Health organizations received most of the communication, whereas BD patients and experts in bowel conditions and nutrition were among those tweeting the most. In general, the BD community was mainly discussing symptoms, BD-related diseases, and diet-based treatments. Diarrhea and constipation were the most commonly mentioned symptoms, and cancer, anxiety disorder, depression, and chronic inflammations were frequently part of BD-related tweets. Most patient tweets discussed the bad side of BD conditions and other related conditions, namely, depression, diarrhea, and fibromyalgia. In turn, gluten-free diets and probiotic supplements were often mentioned in patient tweets expressing positive emotions. However, for the most part, tweets containing mentions to foods and diets showed a similar distribution of negative and positive sentiments because the effects of certain food components (eg, fiber, iron, and magnesium) were perceived differently, depending on the state of the disease and other personal conditions of the patients. The benefits of medical cannabis for the treatment of different chronic diseases were also highlighted. Conclusions: This study evidences that Twitter is becoming an influential space for conversation about bowel conditions, namely, patient opinions about associated symptoms and treatments. So, further qualitative and quantitative content analyses hold the potential to support decision making among health-related stakeholders, including the planning of awareness campaigns. ", doi="10.2196/12610", url="http://www.jmir.org/2019/8/e12610/", url="http://www.ncbi.nlm.nih.gov/pubmed/31411142" } @Article{info:doi/10.2196/11506, author="Johnston, Jade Emily and Campbell, Katarzyna and Coleman, Tim and Lewis, Sarah and Orton, Sophie and Cooper, Sue", title="Safety of Electronic Cigarette Use During Breastfeeding: Qualitative Study Using Online Forum Discussions", journal="J Med Internet Res", year="2019", month="Aug", day="12", volume="21", number="8", pages="e11506", keywords="e-cigarette", keywords="online forum", keywords="postpartum relapse", keywords="smoking", keywords="breastfeeding", keywords="forum data", abstract="Background: Electronic cigarettes (e-cigs) are an increasingly popular alternative to smoking, helping to prevent relapse in those trying to quit and with the potential to reduce harm as they are likely to be safer than standard cigarettes. Many women return to smoking in the postpartum period having stopped during pregnancy, and while this can affect their decisions about breastfeeding, little is known about women's opinions on using e-cigs during this period. Objective: The aim of this study is to explore online forum users' current attitudes, motivations, and barriers to postpartum e-cig use, particularly as a breastfeeding mother. Methods: Data were collected via publicly accessible (identified by Google search) online forum discussions, and a priori codes identified. All transcripts were entered into NVivo for analysis, with a template approach to thematic analysis being used to code all transcripts from which themes were derived. Results: Four themes were identified: use, perceived risk, social support and evidence, with a number of subthemes identified within these. Women were using e-cigs to prevent postpartum return to smoking, but opinions on their safety were conflicting. They were concerned about possible transfer of harmful products from e-cigs via breastmilk and secondhand exposure, so they were actively seeking and sharing information on e-cigs from a variety of sources. Although some women were supportive of e-cig use, others provided harsh judgement for mothers who used them. Conclusions: E-cigs have the potential to reduce the number of women who return to smoking in the postpartum period and potentially improve breastfeeding rates, if breastfeeding mothers have access to relevant and reliable information. Health care providers should consider discussing e-cigs with mothers at risk of returning to smoking in the postpartum period. ", doi="10.2196/11506", url="https://www.jmir.org/2019/8/e11506/", url="http://www.ncbi.nlm.nih.gov/pubmed/31407672" } @Article{info:doi/10.2196/14634, author="Liu, Jing and Hou, Shengchao and Evans, Richard and Xia, Chenxi and Xia, Weidong and Ma, Jingdong", title="What Do Patients Complain About Online: A Systematic Review and Taxonomy Framework Based on Patient Centeredness", journal="J Med Internet Res", year="2019", month="Aug", day="07", volume="21", number="8", pages="e14634", keywords="patient-centered care", keywords="delivery of health care", keywords="systematic review", keywords="taxonomy", abstract="Background: Complaints made online by patients about their health care experiences are becoming prevalent because of widespread worldwide internet connectivity. An a priori framework, based on patient centeredness, may be useful in identifying the types of issues patients complain about online across multiple settings. It may also assist in examining whether the determinants of patient-centered care (PCC) mirror the determinants of patient experiences. Objective: The objective of our study was to develop a taxonomy framework for patient complaints online based on patient centeredness and to examine whether the determinants of PCC mirror the determinants of patient experiences. Methods: First, the best fit framework synthesis technique was applied to develop the proposed a priori framework. Second, electronic databases, including Web of Science, Scopus, and PubMed, were searched for articles published between 2000 and June 2018. Studies were only included if they collected primary quantitative data on patients' online complaints. Third, a deductive and inductive thematic analysis approach was adopted to code the themes of recognized complaints into the framework. Results: In total, 17 studies from 5 countries were included in this study. Patient complaint online taxonomies and theme terms varied. According to our framework, patients expressed most dissatisfaction with patient-centered processes (101,586/204,363, 49.71\%), followed by prerequisites (appropriate skills and knowledge of physicians; 50,563, 24.74\%) and the care environment (48,563/204,363, 23.76\%). The least dissatisfied theme was expected outcomes (3651/204,363, 1.79\%). People expressed little dissatisfaction with expanded PCC dimensions, such as involvement of family and friends (591/204,363, 0.29\%). Variation in the concerns across different countries' patients were also observed. Conclusions: Online complaints made by patients are of major value to health care providers, regulatory bodies, and patients themselves. Our PCC framework can be applied to analyze them under a wide range of conditions, treatments, and countries. This review has shown significant heterogeneity of patients' online complaints across different countries. ", doi="10.2196/14634", url="https://www.jmir.org/2019/8/e14634/", url="http://www.ncbi.nlm.nih.gov/pubmed/31392961" } @Article{info:doi/10.2196/13003, author="Rezaallah, Bita and Lewis, John David and Pierce, Carrie and Zeilhofer, Hans-Florian and Berg, Britt-Isabelle", title="Social Media Surveillance of Multiple Sclerosis Medications Used During Pregnancy and Breastfeeding: Content Analysis", journal="J Med Internet Res", year="2019", month="Aug", day="07", volume="21", number="8", pages="e13003", keywords="pharmacovigilance", keywords="machine learning", keywords="pregnancy outcome", keywords="postpartum", keywords="central nervous system agents", keywords="risk assessment", keywords="text mining", abstract="Background: Multiple sclerosis (MS) is a chronic neurological disease occurring mostly in women of childbearing age. Pregnant women with MS are usually excluded from clinical trials; as users of the internet, however, they are actively engaged in threads and forums on social media. Social media provides the potential to explore real-world patient experiences and concerns about the use of medicinal products during pregnancy and breastfeeding. Objective: This study aimed to analyze the content of posts concerning pregnancy and use of medicines in online forums; thus, the study aimed to gain a thorough understanding of patients' experiences with MS medication. Methods: Using the names of medicinal products as search terms, we collected posts from 21 publicly available pregnancy forums, which were accessed between March 2015 and March 2018. After the identification of relevant posts, we analyzed the content of each post using a content analysis technique and categorized the main topics that users discussed most frequently. Results: We identified 6 main topics in 70 social media posts. These topics were as follows: (1) expressing personal experiences with MS medication use during the reproductive period (55/70, 80\%), (2) seeking and sharing advice about the use of medicines (52/70, 74\%), (3) progression of MS during and after pregnancy (35/70, 50\%), (4) discussing concerns about MS medications during the reproductive period (35/70, 50\%), (5) querying the possibility of breastfeeding while taking MS medications (30/70, 42\%), and (6) commenting on communications with physicians (26/70, 37\%). Conclusions: Overall, many pregnant women or women considering pregnancy shared profound uncertainties and specific concerns about taking medicines during the reproductive period. There is a significant need to provide advice and guidance to MS patients concerning the use of medicines in pregnancy and postpartum as well as during breastfeeding. Advice must be tailored to the circumstances of each patient and, of course, to the individual medicine. Information must be provided by a trusted source with relevant expertise and made publicly available. ", doi="10.2196/13003", url="https://www.jmir.org/2019/8/e13003/", url="http://www.ncbi.nlm.nih.gov/pubmed/31392963" } @Article{info:doi/10.2196/11780, author="Moa, Aye and Muscatello, David and Chughtai, Abrar and Chen, Xin and MacIntyre, Raina C.", title="Flucast: A Real-Time Tool to Predict Severity of an Influenza Season", journal="JMIR Public Health Surveill", year="2019", month="Jul", day="23", volume="5", number="3", pages="e11780", keywords="prediction tool", keywords="influenza", keywords="risk assessment", abstract="Background: Influenza causes serious illness requiring annual health system surge capacity, yet annual seasonal variation makes it difficult to forecast and plan for the severity of an upcoming season. Research shows that hospital and health system stakeholders indicate a preference for forecasting tools that are easy to use and understand to assist with surge capacity planning for influenza. Objective: This study aimed to develop a simple risk prediction tool, Flucast, to predict the severity of an emerging influenza season. Methods: Study data were obtained from the National Notifiable Diseases Surveillance System and Australian Influenza Surveillance Reports from the Department of Health, Australia. We tested Flucast using retrospective seasonal data for 11 Australian influenza seasons. We compared five different models using parameters known early in the season that may be associated with the severity of the season. To calibrate the tool, the resulting estimates of seasonal severity were validated against independent reports of influenza-attributable morbidity and mortality. The model with the highest predictive accuracy against retrospective seasonal activity was chosen as a best-fit model to develop the Flucast tool. The tool was prospectively tested against the 2018 and the emerging 2019 influenza season. Results: The Flucast tool predicted the severity of all retrospectively studied years correctly for influenza seasonal activity in Australia. With the use of real-time data, the tool provided a reasonable early prediction of a low to moderate season for the 2018 and severe seasonal activity for the upcoming 2019 season. The tool meets stakeholder preferences for simplicity and ease of use to assist with surge capacity planning. Conclusions: The Flucast tool may be useful to inform future health system influenza preparedness planning, surge capacity, and intervention programs in real time, and can be adapted for different settings and geographic locations. ", doi="10.2196/11780", url="http://publichealth.jmir.org/2019/3/e11780/", url="http://www.ncbi.nlm.nih.gov/pubmed/31339102" } @Article{info:doi/10.2196/14398, author="Allem, Jon-Patrick and Uppu, Priyanka Sree and Boley Cruz, Tess and Unger, B. Jennifer", title="Characterizing Swisher Little Cigar--Related Posts on Twitter in 2018: Text Analysis", journal="J Med Internet Res", year="2019", month="Jul", day="19", volume="21", number="7", pages="e14398", keywords="little cigar", keywords="cigarillo", keywords="Swisher", keywords="social media", keywords="Twitter", keywords="tobacco", abstract="Background: Little cigars are growing in popularity in the United States, and Swisher is the market leader. The contexts and experiences associated with the use of Swisher-related products is understudied, but such information is available via publicly available posts on Twitter. Objective: This study aimed to analyze Twitter posts to characterize Twitter users' recent experiences with Swisher-related products. Methods: Twitter posts containing the term ``swisher'' were analyzed from January 1, 2018, to December 31, 2018. Text classifiers were used to identify topics in posts (n=81,333). Results: The most prevalent topic was Person Tagging (mentioning a Twitter account in a post; 32.77\%), followed by Flavors (eg, Grape and Strawberry; 20.96\%) and Swisher use (eg, smoke swisher; 17.44\%). Additional topics included Cannabis use (eg, blunt, roll, and gut swisher; 6.26\%), Appeal (eg, like Swisher; 5.92\%), Dislike (eg, posts that showed dissatisfaction with Swisher products; 3.53\%), Purchases (eg, buy swisher; 1.90\%), and Cigar comparison (eg, mentions of other cigar products including White-owl and Backwoods; 1.64\%). Conclusions: This paper describes common contexts and experiences associated with the use of Swisher little cigars from the population posting on Twitter in 2018. These online messages may have offline consequences for tobacco-related behaviors, indicating the need for countering from public health officials. Findings should inform us about targets for surveillance, policy, and interventions addressing Swisher little cigars as well as communication planning and tobacco product counter messaging on Twitter. ", doi="10.2196/14398", url="http://www.jmir.org/2019/7/e14398/", url="http://www.ncbi.nlm.nih.gov/pubmed/31325291" } @Article{info:doi/10.2196/13739, author="Tizek, Linda and Schielein, Maximilian and R{\"u}th, Melvin and St{\"a}nder, Sonja and Pereira, Pedro Manuel and Eberlein, Bernadette and Biedermann, Tilo and Zink, Alexander", title="Influence of Climate on Google Internet Searches for Pruritus Across 16 German Cities: Retrospective Analysis", journal="J Med Internet Res", year="2019", month="Jul", day="12", volume="21", number="7", pages="e13739", keywords="pruritus", keywords="Internet", keywords="informatics", keywords="environment", keywords="weather", keywords="retrospective studies", abstract="Background: The burden of pruritus is high, especially among patients with dermatologic diseases. Identifying trends in pruritus burden and people's medical needs is challenging, since not all affected people consult a physician. Objective: The purpose of this study was to investigate pruritus search behavior trends in Germany and identify associations with weather factors. Methods: Google AdWords Keyword Planner was used to quantify pruritus-related search queries in 16 German cities from August 2014 to July 2018. All identified keywords were qualitatively categorized and pruritus-related terms were descriptively analyzed. The number of search queries per 100,000 inhabitants of each city was compared to environmental factors such as temperature, humidity, particulate matter 10 micrometers or less in diameter (PM10), and sunshine duration to investigate potential correlations. Results: We included 1150 pruritus-related keywords, which resulted in 2,851,290 queries. ``Pruritus'' (n=115,680) and ``anal pruritus'' (n=102,390) were the most-searched-for keywords. Nearly half of all queries were related to the category localization, with Berlin and Munich having a comparatively high proportion of people that searched for pruritus in the genital and anal areas. People searched more frequently for information on chronic compared to acute pruritus. The most populated cities had the lowest number of queries per 100,000 inhabitants (Berlin, n=13,641; Hamburg, n=18,303; and Munich, n=21,363), while smaller cities (Kiel, n=35,027; and Freiburg, n=39,501) had the highest. Temperature had a greater effect on search query number (beta -7.94, 95\% CI -10.74 to -5.15) than did PM10 (beta -5.13, 95\% CI -7.04 to -3.22), humidity (beta 4.73, 95\% CI 2.70 to 6.75), or sunshine duration (beta 0.66, 95\% CI 0.36 to 0.97). The highest relative number of search queries occurred during the winter (ie, December to February). Conclusions: By taking into account the study results, Google data analysis helps to examine people's search frequency, behavior, and interest across cities and regions. The results indicated a general increase in search queries during the winter as well as differences across cities located in the same region; for example, there was a decline in search volume in Saarbrucken, while there were increases in Cologne, Frankfurt, and Dortmund. In addition, the detected correlation between search volume and weather data seems to be valuable in predicting an increase in pruritus burden, since a significant association with rising humidity and sunshine duration, as well as declining temperature and PM10, was found. Accordingly, this is an unconventional and inexpensive method to identify search behavior trends and respective inhabitants' needs. ", doi="10.2196/13739", url="http://www.jmir.org/2019/7/e13739/", url="http://www.ncbi.nlm.nih.gov/pubmed/31301128" } @Article{info:doi/10.2196/13094, author="Ning, Michael and Daniels, Jena and Schwartz, Jessey and Dunlap, Kaitlyn and Washington, Peter and Kalantarian, Haik and Du, Michael and Wall, P. Dennis", title="Identification and Quantification of Gaps in Access to Autism Resources in the United States: An Infodemiological Study", journal="J Med Internet Res", year="2019", month="Jul", day="10", volume="21", number="7", pages="e13094", keywords="autism", keywords="autism spectrum disorder", keywords="crowdsourcing", keywords="prevalence", keywords="resources", keywords="infodemiology", keywords="epidemiology", abstract="Background: Autism affects 1 in every 59 children in the United States, according to estimates from the Centers for Disease Control and Prevention's Autism and Developmental Disabilities Monitoring Network in 2018. Although similar rates of autism are reported in rural and urban areas, rural families report greater difficulty in accessing resources. An overwhelming number of families experience long waitlists for diagnostic and therapeutic services. Objective: The objective of this study was to accurately identify gaps in access to autism care using GapMap, a mobile platform that connects families with local resources while continuously collecting up-to-date autism resource epidemiological information. Methods: After being extracted from various databases, resources were deduplicated, validated, and allocated into 7 categories based on the keywords identified on the resource website. The average distance between the individuals from a simulated autism population and the nearest autism resource in our database was calculated for each US county. Resource load, an approximation of demand over supply for diagnostic resources, was calculated for each US county. Results: There are approximately 28,000 US resources validated on the GapMap database, each allocated into 1 or more of the 7 categories. States with the greatest distances to autism resources included Alaska, Nevada, Wyoming, Montana, and Arizona. Of the 7 resource categories, diagnostic resources were the most underrepresented, comprising only 8.83\% (2472/28,003) of all resources. Alarmingly, 83.86\% (2635/3142) of all US counties lacked any diagnostic resources. States with the highest diagnostic resource load included West Virginia, Kentucky, Maine, Mississippi, and New Mexico. Conclusions: Results from this study demonstrate the sparsity and uneven distribution of diagnostic resources in the United States, which may contribute to the lengthy waitlists and travel distances---barriers to be overcome to be able to receive diagnosis in specific regions. More data are needed on autism diagnosis demand to better quantify resource needs across the United States. ", doi="10.2196/13094", url="http://www.jmir.org/2019/7/e13094/", url="http://www.ncbi.nlm.nih.gov/pubmed/31293243" } @Article{info:doi/10.2196/12443, author="Chu, Kar-Hai and Colditz, Jason and Malik, Momin and Yates, Tabitha and Primack, Brian", title="Identifying Key Target Audiences for Public Health Campaigns: Leveraging Machine Learning in the Case of Hookah Tobacco Smoking", journal="J Med Internet Res", year="2019", month="Jul", day="08", volume="21", number="7", pages="e12443", keywords="smoking water pipes", keywords="waterpipe tobacco", keywords="tobacco", keywords="smoking", keywords="social media", keywords="public health", keywords="infodemiology", keywords="infoveillance", keywords="machine learning", abstract="Background: Hookah tobacco smoking (HTS) is a particularly important issue for public health professionals to address owing to its prevalence and deleterious health effects. Social media sites can be a valuable tool for public health officials to conduct informational health campaigns. Current social media platforms provide researchers with opportunities to better identify and target specific audiences and even individuals. However, we are not aware of systematic research attempting to identify audiences with mixed or ambivalent views toward HTS. Objective: The objective of this study was to (1) confirm previous research showing positively skewed HTS sentiment on Twitter using a larger dataset by leveraging machine learning techniques and (2) systematically identify individuals who exhibit mixed opinions about HTS via the Twitter platform and therefore represent key audiences for intervention. Methods: We prospectively collected tweets related to HTS from January to June 2016. We double-coded sentiment for a subset of approximately 5000 randomly sampled tweets for sentiment toward HTS and used these data to train a machine learning classifier to assess the remaining approximately 556,000 HTS-related Twitter posts. Natural language processing software was used to extract linguistic features (ie, language-based covariates). The data were processed by machine learning tools and algorithms using R. Finally, we used the results to identify individuals who, because they had consistently posted both positive and negative content, might be ambivalent toward HTS and represent an ideal audience for intervention. Results: There were 561,960 HTS-related tweets: 373,911 were classified as positive and 183,139 were classified as negative. A set of 12,861 users met a priori criteria indicating that they posted both positive and negative tweets about HTS. Conclusions: Sentiment analysis can allow researchers to identify audience segments on social media that demonstrate ambiguity toward key public health issues, such as HTS, and therefore represent ideal populations for intervention. Using large social media datasets can help public health officials to preemptively identify specific audience segments that would be most receptive to targeted campaigns. ", doi="10.2196/12443", url="http://www.jmir.org/2019/7/e12443/", url="http://www.ncbi.nlm.nih.gov/pubmed/31287063" } @Article{info:doi/10.2196/14199, author="Leis, Angela and Ronzano, Francesco and Mayer, A. Miguel and Furlong, I. Laura and Sanz, Ferran", title="Detecting Signs of Depression in Tweets in Spanish: Behavioral and Linguistic Analysis", journal="J Med Internet Res", year="2019", month="Jun", day="27", volume="21", number="6", pages="e14199", keywords="depression", keywords="social media", keywords="mental health", keywords="text mining", abstract="Background: Mental disorders have become a major concern in public health, and they are one of the main causes of the overall disease burden worldwide. Social media platforms allow us to observe the activities, thoughts, and feelings of people's daily lives, including those of patients suffering from mental disorders. There are studies that have analyzed the influence of mental disorders, including depression, in the behavior of social media users, but they have been usually focused on messages written in English. Objective: The study aimed to identify the linguistic features of tweets in Spanish and the behavioral patterns of Twitter users who generate them, which could suggest signs of depression. Methods: This study was developed in 2 steps. In the first step, the selection of users and the compilation of tweets were performed. A total of 3 datasets of tweets were created, a depressive users dataset (made up of the timeline of 90 users who explicitly mentioned that they suffer from depression), a depressive tweets dataset (a manual selection of tweets from the previous users, which included expressions indicative of depression), and a control dataset (made up of the timeline of 450 randomly selected users). In the second step, the comparison and analysis of the 3 datasets of tweets were carried out. Results: In comparison with the control dataset, the depressive users are less active in posting tweets, doing it more frequently between 23:00 and 6:00 (P<.001). The percentage of nouns used by the control dataset almost doubles that of the depressive users (P<.001). By contrast, the use of verbs is more common in the depressive users dataset (P<.001). The first-person singular pronoun was by far the most used in the depressive users dataset (80\%), and the first- and the second-person plural pronouns were the least frequent (0.4\% in both cases), this distribution being different from that of the control dataset (P<.001). Emotions related to sadness, anger, and disgust were more common in the depressive users and depressive tweets datasets, with significant differences when comparing these datasets with the control dataset (P<.001). As for negation words, they were detected in 34\% and 46\% of tweets in among depressive users and in depressive tweets, respectively, which are significantly different from the control dataset (P<.001). Negative polarity was more frequent in the depressive users (54\%) and depressive tweets (65\%) datasets than in the control dataset (43.5\%; P<.001). Conclusions: Twitter users who are potentially suffering from depression modify the general characteristics of their language and the way they interact on social media. On the basis of these changes, these users can be monitored and supported, thus introducing new opportunities for studying depression and providing additional health care services to people with this disorder. ", doi="10.2196/14199", url="http://www.jmir.org/2019/6/e14199/", url="http://www.ncbi.nlm.nih.gov/pubmed/31250832" } @Article{info:doi/10.2196/12383, author="Alessa, Ali and Faezipour, Miad", title="Preliminary Flu Outbreak Prediction Using Twitter Posts Classification and Linear Regression With Historical Centers for Disease Control and Prevention Reports: Prediction Framework Study", journal="JMIR Public Health Surveill", year="2019", month="Jun", day="23", volume="5", number="2", pages="e12383", keywords="FastText", keywords="influenza", keywords="machine learning", keywords="social networking site", keywords="text classification", abstract="Background: Social networking sites (SNSs) such as Twitter are widely used by diverse demographic populations. The amount of data within SNSs has created an efficient resource for real-time analysis. Thus, data from SNSs can be used effectively to track disease outbreaks and provide necessary warnings. Current SNS-based flu detection and prediction frameworks apply conventional machine learning approaches that require lengthy training and testing, which is not the optimal solution for new outbreaks with new signs and symptoms. Objective: The objective of this study was to propose an efficient and accurate framework that uses data from SNSs to track disease outbreaks and provide early warnings, even for newest outbreaks, accurately. Methods: We presented a framework of outbreak prediction that included 3 main modules: text classification, mapping, and linear regression for weekly flu rate predictions. The text classification module used the features of sentiment analysis and predefined keyword occurrences. Various classifiers, including FastText (FT) and 6 conventional machine learning algorithms, were evaluated to identify the most efficient and accurate one for the proposed framework. The text classifiers were trained and tested using a prelabeled dataset of flu-related and unrelated Twitter postings. The selected text classifier was then used to classify over 8,400,000 tweet documents. The flu-related documents were then mapped on a weekly basis using a mapping module. Finally, the mapped results were passed together with historical Centers for Disease Control and Prevention (CDC) data to a linear regression module for weekly flu rate predictions. Results: The evaluation of flu tweet classification showed that FT, together with the extracted features, achieved accurate results with an F-measure value of 89.9\% in addition to its efficiency. Therefore, FT was chosen to be the classification module to work together with the other modules in the proposed framework, including a regression-based estimator, for flu trend predictions. The estimator was evaluated using several regression models. Regression results show that the linear regression--based estimator achieved the highest accuracy results using the measure of Pearson correlation. Thus, the linear regression model was used for the module of weekly flu rate estimation. The prediction results were compared with the available recent data from CDC as the ground truth and showed a strong correlation of 96.29\% . Conclusions: The results demonstrated the efficiency and the accuracy of the proposed framework that can be used even for new outbreaks with new signs and symptoms. The classification results demonstrated that the FT-based framework improves the accuracy and the efficiency of flu disease surveillance systems that use unstructured data such as data from SNSs. ", doi="10.2196/12383", url="http://publichealth.jmir.org/2019/2/e12383/" } @Article{info:doi/10.2196/13456, author="On, Jeongah and Park, Hyeoun-Ae and Song, Tae-Min", title="Sentiment Analysis of Social Media on Childhood Vaccination: Development of an Ontology", journal="J Med Internet Res", year="2019", month="Jun", day="7", volume="21", number="6", pages="e13456", keywords="social media", keywords="vaccination", keywords="health information interoperability", keywords="semantics", abstract="Background: Although vaccination rates are above the threshold for herd immunity in South Korea, a growing number of parents have expressed concerns about the safety of vaccines. It is important to understand these concerns so that we can maintain high vaccination rates. Objective: The aim of this study was to develop a childhood vaccination ontology to serve as a framework for collecting and analyzing social data on childhood vaccination and to use this ontology for identifying concerns about and sentiments toward childhood vaccination from social data. Methods: The domain and scope of the ontology were determined by developing competency questions. We checked if existing ontologies and conceptual frameworks related to vaccination can be reused for the childhood vaccination ontology. Terms were collected from clinical practice guidelines, research papers, and posts on social media platforms. Class concepts were extracted from these terms. A class hierarchy was developed using a top-down approach. The ontology was evaluated in terms of description logics, face and content validity, and coverage. In total, 40,359 Korean posts on childhood vaccination were collected from 27 social media channels between January and December 2015. Vaccination issues were identified and classified using the second-level class concepts of the ontology. The sentiments were classified in 3 ways: positive, negative or neutral. Posts were analyzed using frequency, trend, logistic regression, and association rules. Results: Our childhood vaccination ontology comprised 9 superclasses with 137 subclasses and 431 synonyms for class, attribute, and value concepts. Parent's health belief appeared in 53.21\% (15,709/29,521) of posts and positive sentiments appeared in 64.08\% (17,454/27,236) of posts. Trends in sentiments toward vaccination were affected by news about vaccinations. Posts with parents' health belief, vaccination availability, and vaccination policy were associated with positive sentiments, whereas posts with experience of vaccine adverse events were associated with negative sentiments. Conclusions: The childhood vaccination ontology developed in this study was useful for collecting and analyzing social data on childhood vaccination. We expect that practitioners and researchers in the field of childhood vaccination could use our ontology to identify concerns about and sentiments toward childhood vaccination from social data. ", doi="10.2196/13456", url="http://www.jmir.org/2019/6/e13456/", url="http://www.ncbi.nlm.nih.gov/pubmed/31199290" } @Article{info:doi/10.2196/11615, author="Safarishahrbijari, Anahita and Osgood, D. Nathaniel", title="Social Media Surveillance for Outbreak Projection via Transmission Models: Longitudinal Observational Study", journal="JMIR Public Health Surveill", year="2019", month="May", day="26", volume="5", number="2", pages="e11615", keywords="machine learning", keywords="infectious disease transmission", keywords="disease models", keywords="system dynamics analysis", keywords="social media", keywords="outbreaks", keywords="infodemiology", keywords="infoveillance", abstract="Background: Although dynamic models are increasingly used by decision makers as a source of insight to guide interventions in order to control communicable disease outbreaks, such models have long suffered from a risk of rapid obsolescence due to failure to keep updated with emerging epidemiological evidence. The application of statistical filtering algorithms to high-velocity data streams has recently demonstrated effectiveness in allowing such models to be automatically regrounded by each new set of incoming observations. The attractiveness of such techniques has been enhanced by the emergence of a new generation of geospatially specific, high-velocity data sources, including daily counts of relevant searches and social media posts. The information available in such electronic data sources complements that of traditional epidemiological data sources. Objective: This study aims to evaluate the degree to which the predictive accuracy of pandemic projection models regrounded via machine learning in daily clinical data can be enhanced by extending such methods to leverage daily search counts. Methods: We combined a previously published influenza A (H1N1) pandemic projection model with the sequential Monte Carlo technique of particle filtering, to reground the model bu using confirmed incident case counts and search volumes. The effectiveness of particle filtering was evaluated using a norm discrepancy metric via predictive and dataset-specific cross-validation. Results: Our results suggested that despite the data quality limitations of daily search volume data, the predictive accuracy of dynamic models can be strongly elevated by inclusion of such data in filtering methods. Conclusions: The predictive accuracy of dynamic models can be notably enhanced by tapping a readily accessible, publicly available, high-velocity data source. This work highlights a low-cost, low-burden avenue for strengthening model-based outbreak intervention response planning using low-cost public electronic datasets. ", doi="10.2196/11615", url="http://publichealth.jmir.org/2019/2/e11615/", url="http://www.ncbi.nlm.nih.gov/pubmed/31199339" } @Article{info:doi/10.2196/14067, author="Watts, Christina and Freeman, Becky", title="``Where There's Smoke, There's Fire'': A Content Analysis of Print and Web-Based News Media Reporting of the Philip Morris--Funded Foundation for a Smoke-Free World", journal="JMIR Public Health Surveill", year="2019", month="Jun", day="06", volume="5", number="2", pages="e14067", keywords="tobacco industry", keywords="mass media", keywords="smoking, nontobacco products", abstract="Background: In September 2017, the Foundation for a Smoke-Free World (FSFW), a not-for-profit organization with a core purpose ``to accelerate global efforts to reduce deaths and harm from smoking'' was launched. However, the legitimacy of the FSFW's vision has been questioned by experts in tobacco control because of the organization's only funding partner, Philip Morris International (PMI). Objective: This study aimed to examine the response to the FSFW in Web-based and print news media to understand how the FSFW and its funding partner, PMI, were framed. Methods: News articles published within a 6-month period after the FSFW was announced were downloaded via Google News and Factiva and coded for topic, framing argument, slant, mention of tobacco control policies, and direct quotes or position statements. Results: A total of 124 news articles were analyzed. The news coverage of the FSFW was framed by 6 key arguments. Over half of the news articles presented a framing argument in opposition to the FSFW (64/124, 51.6\%). A further 20.2\% (25/124) of articles framed the FSFW positively and 28.2\% of articles (35/124) presented a neutral debate with no primary slant. The FSFW was presented as not credible because of the funding link to PMI in 29.0\% (36/124) of articles and as a tactic to mislead and undermine effective tobacco control measures in 11.3\% of articles (14/124). However, 12.9\% of articles (16/124) argued that the FSFW or PMI is part of the solution to reducing the impact of tobacco use. Evidence-based tobacco control policies were mentioned positively in 66.9\% (83/124) of news articles and 9.6\% (12/124) of articles presented tobacco control policies negatively. Conclusions: The Web-based and print news media reporting of the formation of the FSFW and its mission and vision has primarily been framed by doubt, skepticism, and disapproval. ", doi="10.2196/14067", url="http://publichealth.jmir.org/2019/2/e14067/", url="http://www.ncbi.nlm.nih.gov/pubmed/31172959" } @Article{info:doi/10.2196/11036, author="Mamidi, Ravali and Miller, Michele and Banerjee, Tanvi and Romine, William and Sheth, Amit", title="Identifying Key Topics Bearing Negative Sentiment on Twitter: Insights Concerning the 2015-2016 Zika Epidemic", journal="JMIR Public Health Surveill", year="2019", month="Jun", day="04", volume="5", number="2", pages="e11036", keywords="social media", keywords="machine learning", keywords="natural language processing", keywords="epidemiology", keywords="Zika", keywords="infodemiology", keywords="infoveillance", keywords="twitter", keywords="sentiment analysis", abstract="Background: To understand the public sentiment regarding the Zika virus, social media can be leveraged to understand how positive, negative, and neutral sentiments are expressed in society. Specifically, understanding the characteristics of negative sentiment could help inform federal disease control agencies' efforts to disseminate relevant information to the public about Zika-related issues. Objective: The purpose of this study was to analyze the public sentiment concerning Zika using posts on Twitter and determine the qualitative characteristics of positive, negative, and neutral sentiments expressed. Methods: Machine learning techniques and algorithms were used to analyze the sentiment of tweets concerning Zika. A supervised machine learning classifier was built to classify tweets into 3 sentiment categories: positive, neutral, and negative. Tweets in each category were then examined using a topic-modeling approach to determine the main topics for each category, with focus on the negative category. Results: A total of 5303 tweets were manually annotated and used to train multiple classifiers. These performed moderately well (F1 score=0.48-0.68) with text-based feature extraction. All 48,734 tweets were then categorized into the sentiment categories. Overall, 10 topics for each sentiment category were identified using topic modeling, with a focus on the negative sentiment category. Conclusions: Our study demonstrates how sentiment expressed within discussions of epidemics on Twitter can be discovered. This allows public health officials to understand public sentiment regarding an epidemic and enables them to address specific elements of negative sentiment in real time. Our negative sentiment classifier was able to identify tweets concerning Zika with 3 broad themes: neural defects,Zika abnormalities, and reports and findings. These broad themes were based on domain expertise and from topics discussed in journals such as Morbidity and Mortality Weekly Report and Vaccine. As the majority of topics in the negative sentiment category concerned symptoms, officials should focus on spreading information about prevention and treatment research. ", doi="10.2196/11036", url="http://publichealth.jmir.org/2019/2/e11036/", url="http://www.ncbi.nlm.nih.gov/pubmed/31165711" } @Article{info:doi/10.2196/13158, author="Hodgson, Saldivar Nikkie and Yom-Tov, Elad and Strong, F. William and Flores, L. Priscilla and Ricoy, N. Giselle", title="Concerns of Female Adolescents About Menarche and First Sexual Intercourse: Mixed Methods Analysis of Social Media Questions", journal="JMIR Pediatr Parent", year="2019", month="Jun", day="04", volume="2", number="1", pages="e13158", keywords="menarche", keywords="sexual intercourse", keywords="social media", keywords="infodemiology", keywords="infoveillance", abstract="Background: Adolescents use social media for information on medical and social aspects of maturation. Objective: The aim of this study was to investigate the concerns and information needs of adolescents regarding menarche and first sexual intercourse. Methods: Questions about menarche or first sexual intercourse were obtained from Yahoo Answers, a community-based social media question-and-answer website. A total of 1226 questions were analyzed. We focused on 123 question pairs made by users who asked questions on both topics and reported their ages at each. Quantitative and qualitative analyses were performed on these question pairs. Results: Qualitative analysis identified uncertainty as a significant theme for both menarche and first intercourse. Quantitative analysis showed that uncertainty was expressed in 26\% (13/50) of menarche questions and 14\% (7/50) of intercourse questions. Lack of communication was expressed in 4\% (2/50) of menarche questions, compared with 8\% (4/50) of intercourse questions. Ages at menarche and at first sexual intercourse were correlated, with women reporting menarche at the age of 13 years or younger being 2.6 times more likely to experience first sexual intercourse before the age of 16 years (P<.001, chi-square test). Older age at menarche was associated with greater lack of communication with parents (analysis of variance, P=.002). Conclusions: The questions of adolescents on the topics of menarche and first sexual intercourse express anxiety and uncertainty and are associated with a lack of information and deficient communication with parents. The more normative and expected a behavior, the less these factors appear. Therefore, parents and educators should, to the extent possible, improve communication around these topics, especially when they occur at less typical ages. ", doi="10.2196/13158", url="http://pediatrics.jmir.org/2019/1/e13158/", url="http://www.ncbi.nlm.nih.gov/pubmed/31518326" } @Article{info:doi/10.2196/12394, author="Liu, Sam and Chen, Brian and Kuo, Alex", title="Monitoring Physical Activity Levels Using Twitter Data: Infodemiology Study", journal="J Med Internet Res", year="2019", month="Jun", day="03", volume="21", number="6", pages="e12394", keywords="physical activity", keywords="social media", keywords="internet", keywords="Twitter messaging", keywords="population surveillance", keywords="public health", abstract="Background: Social media technology such as Twitter allows users to share their thoughts, feelings, and opinions online. The growing body of social media data is becoming a central part of infodemiology research as these data can be combined with other public health datasets (eg, physical activity levels) to provide real-time monitoring of psychological and behavior outcomes that inform health behaviors. Currently, it is unclear whether Twitter data can be used to monitor physical activity levels. Objective: The aim of this study was to establish the feasibility of using Twitter data to monitor physical activity levels by assessing whether the frequency and sentiment of physical activity--related tweets were associated with physical activity levels across the United States. Methods: Tweets were collected from Twitter's application programming interface (API) between January 10, 2017 and January 2, 2018. We used Twitter's garden hose method of collecting tweets, which provided a random sample of approximately 1\% of all tweets with location metadata falling within the United States. Geotagged tweets were filtered. A list of physical activity--related hashtags was collected and used to further classify these geolocated tweets. Twitter data were merged with physical activity data collected as part of the Behavioral Risk Factor Surveillance System. Multiple linear regression models were fit to assess the relationship between physical activity--related tweets and physical activity levels by county while controlling for population and socioeconomic status measures. Results: During the study period, 442,959,789 unique tweets were collected, of which 64,005,336 (14.44\%) were geotagged with latitude and longitude coordinates. Aggregated data were obtained for a total of 3138 counties in the United States. The mean county-level percentage of physically active individuals was 74.05\% (SD 5.2) and 75.30\% (SD 4.96) after adjusting for age. The model showed that the percentage of physical activity--related tweets was significantly associated with physical activity levels (beta=.11; SE 0.2; P<.001) and age-adjusted physical activity (beta=.10; SE 0.20; P<.001) on a county level while adjusting for both Gini index and education level. However, the overall explained variance of the model was low (R2=.11). The sentiment of the physical activity--related tweets was not a significant predictor of physical activity level and age-adjusted physical activity on a county level after including the Gini index and education level in the model (P>.05). Conclusions: Social media data may be a valuable tool for public health organizations to monitor physical activity levels, as it can overcome the time lag in the reporting of physical activity epidemiology data faced by traditional research methods (eg, surveys and observational studies). Consequently, this tool may have the potential to help public health organizations better mobilize and target physical activity interventions. ", doi="10.2196/12394", url="https://www.jmir.org/2019/6/e12394/", url="http://www.ncbi.nlm.nih.gov/pubmed/31162126" } @Article{info:doi/10.2196/11264, author="Nikfarjam, Azadeh and Ransohoff, D. Julia and Callahan, Alison and Jones, Erik and Loew, Brian and Kwong, Y. Bernice and Sarin, Y. Kavita and Shah, H. Nigam", title="Early Detection of Adverse Drug Reactions in Social Health Networks: A Natural Language Processing Pipeline for Signal Detection", journal="JMIR Public Health Surveill", year="2019", month="Jun", day="03", volume="5", number="2", pages="e11264", keywords="natural language processing", keywords="signal detection", keywords="adverse drug reactions", keywords="social media", keywords="drug-related side effects", keywords="medical oncology", keywords="antineoplastic agents", keywords="machine learning", abstract="Background: Adverse drug reactions (ADRs) occur in nearly all patients on chemotherapy, causing morbidity and therapy disruptions. Detection of such ADRs is limited in clinical trials, which are underpowered to detect rare events. Early recognition of ADRs in the postmarketing phase could substantially reduce morbidity and decrease societal costs. Internet community health forums provide a mechanism for individuals to discuss real-time health concerns and can enable computational detection of ADRs. Objective: The goal of this study is to identify cutaneous ADR signals in social health networks and compare the frequency and timing of these ADRs to clinical reports in the literature. Methods: We present a natural language processing-based, ADR signal-generation pipeline based on patient posts on Internet social health networks. We identified user posts from the Inspire health forums related to two chemotherapy classes: erlotinib, an epidermal growth factor receptor inhibitor, and nivolumab and pembrolizumab, immune checkpoint inhibitors. We extracted mentions of ADRs from unstructured content of patient posts. We then performed population-level association analyses and time-to-detection analyses. Results: Our system detected cutaneous ADRs from patient reports with high precision (0.90) and at frequencies comparable to those documented in the literature but an average of 7 months ahead of their literature reporting. Known ADRs were associated with higher proportional reporting ratios compared to negative controls, demonstrating the robustness of our analyses. Our named entity recognition system achieved a 0.738 microaveraged F-measure in detecting ADR entities, not limited to cutaneous ADRs, in health forum posts. Additionally, we discovered the novel ADR of hypohidrosis reported by 23 patients in erlotinib-related posts; this ADR was absent from 15 years of literature on this medication and we recently reported the finding in a clinical oncology journal. Conclusions: Several hundred million patients report health concerns in social health networks, yet this information is markedly underutilized for pharmacosurveillance. We demonstrated the ability of a natural language processing-based signal-generation pipeline to accurately detect patient reports of ADRs months in advance of literature reporting and the robustness of statistical analyses to validate system detections. Our findings suggest the important contributions that social health network data can play in contributing to more comprehensive and timely pharmacovigilance. ", doi="10.2196/11264", url="http://publichealth.jmir.org/2019/2/e11264/", url="http://www.ncbi.nlm.nih.gov/pubmed/31162134" } @Article{info:doi/10.2196/14167, author="Pereira-Sanchez, Victor and Alvarez-Mon, Angel Miguel and Asunsolo del Barco, Angel and Alvarez-Mon, Melchor and Teo, Alan", title="Exploring the Extent of the Hikikomori Phenomenon on Twitter: Mixed Methods Study of Western Language Tweets", journal="J Med Internet Res", year="2019", month="May", day="29", volume="21", number="5", pages="e14167", keywords="social isolation", keywords="loneliness", keywords="hikikomori", keywords="hidden youth", keywords="social media", keywords="Twitter", keywords="social withdrawal", abstract="Background: Hikikomori is a severe form of social withdrawal, originally described in Japan but recently reported in other countries. Debate exists as to what extent hikikomori is viewed as a problem outside of the Japanese context. Objective: We aimed to explore perceptions about hikikomori outside Japan by analyzing Western language content from the popular social media platform, Twitter. Methods: We conducted a mixed methods analysis of all publicly available tweets using the hashtag \#hikikomori between February 1 and August 16, 2018, in 5 Western languages (Catalan, English, French, Italian, and Spanish). Tweets were first classified as to whether they described hikikomori as a problem or a nonproblematic phenomenon. Tweets regarding hikikomori as a problem were then subclassified in terms of the type of problem (medical, social, or anecdotal) they referred to, and we marked if they referenced scientific publications or the presence of hikikomori in countries other than Japan. We also examined measures of interest in content related to hikikomori, including retweets, likes, and associated hashtags. Results: A total of 1042 tweets used \#hikikomori, and 656 (62.3\%) were included in the content analysis. Most of the included tweets were written in English (44.20\%) and Italian (34.16\%), and a majority (56.70\%) discussed hikikomori as a problem. Tweets referencing scientific publications (3.96\%) and hikikomori as present in countries other than Japan (13.57\%) were less common. Tweets mentioning hikikomori outside Japan were statistically more likely to be retweeted (P=.01) and liked (P=.01) than those not mentioning it, whereas tweets with explicit scientific references were statistically more retweeted (P=.01) but not liked (P=.10) than those without that reference. Retweet and like figures were not statistically significantly different among other categories and subcategories. The most associated hashtags included references to Japan, mental health, and the youth. Conclusions: Hikikomori is a repeated word in non-Japanese Western languages on Twitter, suggesting the presence of hikikomori in countries outside Japan. Most tweets treat hikikomori as a problem, but the ways they post about it are highly heterogeneous. ", doi="10.2196/14167", url="http://www.jmir.org/2019/5/e14167/", url="http://www.ncbi.nlm.nih.gov/pubmed/31144665" } @Article{info:doi/10.2196/13439, author="Mavragani, Amaryllis and Ochoa, Gabriela", title="Google Trends in Infodemiology and Infoveillance: Methodology Framework", journal="JMIR Public Health Surveill", year="2019", month="May", day="29", volume="5", number="2", pages="e13439", keywords="big data", keywords="health", keywords="infodemiology", keywords="infoveillance", keywords="internet behavior", keywords="Google Trends", doi="10.2196/13439", url="http://publichealth.jmir.org/2019/2/e13439/", url="http://www.ncbi.nlm.nih.gov/pubmed/31144671" } @Article{info:doi/10.2196/14110, author="Alvarez-Mon, Angel Miguel and Llavero-Valero, Mar{\'i}a and S{\'a}nchez-Bayona, Rodrigo and Pereira-Sanchez, Victor and Vallejo-Valdivielso, Maria and Monserrat, Jorge and Lahera, Guillermo and Asunsolo del Barco, Angel and Alvarez-Mon, Melchor", title="Areas of Interest and Stigmatic Attitudes of the General Public in Five Relevant Medical Conditions: Thematic and Quantitative Analysis Using Twitter", journal="J Med Internet Res", year="2019", month="May", day="28", volume="21", number="5", pages="e14110", keywords="social stigma", keywords="social media", keywords="psychosis", keywords="breast cancer", keywords="HIV", keywords="dementia", keywords="public opinion", keywords="diabetes", abstract="Background: Twitter is an?indicator of real-world?performance, thus, is an appropriate arena to assess the social consideration and attitudes toward psychosis. Objective: The aim of this study was to perform a mixed-methods study of the content and key metrics of tweets referring to psychosis in comparison with tweets referring to control diseases (breast cancer, diabetes, Alzheimer, and human immunodeficiency virus). Methods: Each tweet's content was rated as nonmedical (NM: testimonies, health care products, solidarity or awareness and misuse) or medical (M: included a reference to the illness's diagnosis, treatment, prognosis, or prevention). NM tweets were classified as positive or pejorative. We assessed the appropriateness of the medical content. The number of retweets generated and the potential reach and impact of the hashtags analyzed was also investigated. Results: We analyzed a total of 15,443 tweets: 8055 classified as NM and 7287 as M. Psychosis-related tweets (PRT) had a significantly higher frequency of misuse 33.3\% (212/636) vs 1.15\% (853/7419; P<.001) and pejorative content 36.2\% (231/636) vs 11.33\% (840/7419; P<.001). The medical content of the PRT showed the highest scientific appropriateness 100\% (391/391) vs 93.66\% (6030/6439; P<.001) and had a higher frequency of content about disease prevention. The potential reach and impact of the tweets related to psychosis were low, but they had a high retweet-to-tweet ratio. Conclusions: We show a reduced number and a different pattern of contents in tweets about psychosis compared with control diseases. PRT showed a predominance of nonmedical content with increased frequencies of misuse and pejorative tone. However, the medical content of PRT showed high scientific appropriateness aimed toward prevention. ", doi="10.2196/14110", url="http://www.jmir.org/2019/5/e14110/", url="http://www.ncbi.nlm.nih.gov/pubmed/31140438" } @Article{info:doi/10.2196/13302, author="Madden, Michael Kenneth and Feldman, Boris", title="Weekly, Seasonal, and Geographic Patterns in Health Contemplations About Sundown Syndrome: An Ecological Correlational Study", journal="JMIR Aging", year="2019", month="May", day="28", volume="2", number="1", pages="e13302", keywords="sundown syndrome", keywords="geriatric medicine", keywords="dementia", keywords="circadian rhythms", keywords="infodemiology", keywords="infoveillance", keywords="internet", abstract="Background: Sundown syndrome (ie, agitation later in the day) is common in older adults with dementia. The underlying etiology for these behaviors is unclear. Possibilities include increased caregiver fatigue at the end of the day and disruption of circadian rhythms by both age and neurodegenerative illness. Objective: This study sought to examine circumseptan (weekly) patterns in search volumes related to sundown syndrome, in order to determine if such searches peaked at the end of the weekend, a time when caregiver supports are least available. We also sought to examine both seasonal differences and associations of state-by-state search activity with both state latitude and yearly sun exposure. Methods: Daily Internet search query data was obtained from Google Trends (2005-2017 inclusive). Circumseptan patterns were determined by wavelet analysis, and seasonality was determined by the difference in search volumes between winter (December, January, and February) and summer (June, July, and August) months. Geographic associations between percent sunny days and latitude were done on a state-by-state basis. Results: ``Sundowning'' searches showed a significant increase at the end of the weekend with activity being 10.9\% (SD 4.0) higher on Sunday as compared to the rest of the week. Search activity showed a seasonal pattern with search activity significantly highest in the winter months (36.6 [SD 0.6] vs 13.7 [SD 0.2], P<.001). State-by-state variations in ``sundowning'' searches showed a significant negative association with increasing mean daily sunlight (R2=.16, $\beta$=-.429 [SD .149], P=.006) and showed a positive association with increasing latitude (R2=.38, $\beta$=.648 [SD .122], P<.001). Conclusions: Interest in ``sundowning'' is highest after a weekend, which is a time when external caregiver support is reduced. Searches related to sundown syndrome also were highest in winter, in states with less sun, and in states at more northerly latitudes, supporting disrupted circadian rhythms as another contributing factor to these behaviors. ", doi="10.2196/13302", url="http://aging.jmir.org/2019/1/e13302/", url="http://www.ncbi.nlm.nih.gov/pubmed/31518264" } @Article{info:doi/10.2196/11024, author="Litchman, L. Michelle and Wawrzynski, E. Sarah and Woodruff, S. Whitney and Arrington, B. Joseph and Nguyen, C. Quynh and Gee, M. Perry", title="Continuous Glucose Monitoring in the Real World Using Photosurveillance of \#Dexcom on Instagram: Exploratory Mixed Methods Study", journal="JMIR Public Health Surveill", year="2019", month="May", day="24", volume="5", number="2", pages="e11024", keywords="diabetes", keywords="continuous glucose monitoring", keywords="off-label use", keywords="social media", keywords="Instagram", keywords="photosurveillance", abstract="Background: Individuals with diabetes are using social media as a method to share and gather information about their health via the diabetes online community. Infoveillance is one methodological approach to examine health care trends. However, infoveillance, while very effective in identifying many real-world health trends, may miss opportunities that use photographs as primary sources for data. We propose a new methodology, photosurveillance, in which photographs are analyzed to examine real-world trends. Objective: The purpose of this research is to (1) assess the use of photosurveillance as a research method to examine real-world trends in diabetes and (2) report on real-world use of continuous glucose monitoring (CGM) on Instagram. Methods: This exploratory mixed methods study examined all photographs posted on Instagram that were identified with the hashtag \#dexcom over a 3-month period---December 2016 to February 2017. Photographs were coded by CGM location on the body. Original posts and corresponding comments were textually coded for length of CGM device wear and CGM failure and were analyzed for emerging themes. Results: A total of 2923 photographs were manually screened; 12.08\% (353/2923) depicted a photograph with a CGM site location. The majority (225/353, 63.7\%) of the photographs showed a CGM site in an off-label location, while 26.2\% (92/353) were in an FDA-approved location (ie, abdomen) and 10.2\% (36/353) were in an unidentifiable location. There were no significant differences in the number of likes or comments based on US Food and Drug Administration (FDA) approval. Five themes emerged from the analysis of original posts (N=353) and corresponding comments (N=2364): (1) endorsement of CGM as providing a sense of well-being; (2) reciprocating information, encouragement, and support; (3) reciprocating CGM-related frustrations; (4) life hacks to optimize CGM use; and (5) sharing and learning about off-label CGM activity. Conclusions: Our results indicate that individuals successfully used CGM in off-label locations, posting photos of these areas with greater frequency than of the abdomen, with no indication of sensor failure. While these photographs only capture a snapshot in time, these posts can be used to inform providers and industry leaders of real-world trends in CGM use. Additionally, there were instances in which sensors were worn beyond the FDA-approved 7-day period; however, they represented the minority in this study. ", doi="10.2196/11024", url="http://publichealth.jmir.org/2019/2/e11024/", url="http://www.ncbi.nlm.nih.gov/pubmed/31127724" } @Article{info:doi/10.2196/13090, author="Daughton, R. Ashlynn and Paul, J. Michael", title="Identifying Protective Health Behaviors on Twitter: Observational Study of Travel Advisories and Zika Virus", journal="J Med Internet Res", year="2019", month="May", day="13", volume="21", number="5", pages="e13090", keywords="social media", keywords="travel", keywords="behavior", keywords="communicable diseases", keywords="zika virus", keywords="public health", keywords="epidemiology", keywords="information science", keywords="travel-related illness", abstract="Background: An estimated 3.9 billion individuals live in a location endemic for common mosquito-borne diseases. The emergence of Zika virus in South America in 2015 marked the largest known Zika outbreak and caused hundreds of thousands of infections. Internet data have shown promise in identifying human behaviors relevant for tracking and understanding other diseases. Objective: Using Twitter posts regarding the 2015-16 Zika virus outbreak, we sought to identify and describe considerations and self-disclosures of a specific behavior change relevant to the spread of disease---travel cancellation. If this type of behavior is identifiable in Twitter, this approach may provide an additional source of data for disease modeling. Methods: We combined keyword filtering and machine learning classification to identify first-person reactions to Zika in 29,386 English-language tweets in the context of travel, including considerations and reports of travel cancellation. We further explored demographic, network, and linguistic characteristics of users who change their behavior compared with control groups. Results: We found differences in the demographics, social networks, and linguistic patterns of 1567 individuals identified as changing or considering changing travel behavior in response to Zika as compared with a control sample of Twitter users. We found significant differences between geographic areas in the United States, significantly more discussion by women than men, and some evidence of differences in levels of exposure to Zika-related information. Conclusions: Our findings have implications for informing the ways in which public health organizations communicate with the public on social media, and the findings contribute to our understanding of the ways in which the public perceives and acts on risks of emerging infectious diseases. ", doi="10.2196/13090", url="https://www.jmir.org/2019/5/e13090/", url="http://www.ncbi.nlm.nih.gov/pubmed/31094347" } @Article{info:doi/10.2196/12881, author="Shah, Zubair and Martin, Paige and Coiera, Enrico and Mandl, D. Kenneth and Dunn, G. Adam", title="Modeling Spatiotemporal Factors Associated With Sentiment on Twitter: Synthesis and Suggestions for Improving the Identification of Localized Deviations", journal="J Med Internet Res", year="2019", month="May", day="08", volume="21", number="5", pages="e12881", keywords="text mining", keywords="social media", keywords="public health", abstract="Background: Studies examining how sentiment on social media varies depending on timing and location appear to produce inconsistent results, making it hard to design systems that use sentiment to detect localized events for public health applications. Objective: The aim of this study was to measure how common timing and location confounders explain variation in sentiment on Twitter. Methods: Using a dataset of 16.54 million English-language tweets from 100 cities posted between July 13 and November 30, 2017, we estimated the positive and negative sentiment for each of the cities using a dictionary-based sentiment analysis and constructed models to explain the differences in sentiment using time of day, day of week, weather, city, and interaction type (conversations or broadcasting) as factors and found that all factors were independently associated with sentiment. Results: In the full multivariable model of positive (Pearson r in test data 0.236; 95\% CI 0.231-0.241) and negative (Pearson r in test data 0.306; 95\% CI 0.301-0.310) sentiment, the city and time of day explained more of the variance than weather and day of week. Models that account for these confounders produce a different distribution and ranking of important events compared with models that do not account for these confounders. Conclusions: In public health applications that aim to detect localized events by aggregating sentiment across populations of Twitter users, it is worthwhile accounting for baseline differences before looking for unexpected changes. ", doi="10.2196/12881", url="https://www.jmir.org/2019/5/e12881/", url="http://www.ncbi.nlm.nih.gov/pubmed/31344669" } @Article{info:doi/10.2196/11448, author="Arnoux-Guenegou, Armelle and Girardeau, Yannick and Chen, Xiaoyi and Deldossi, Myrtille and Aboukhamis, Rim and Faviez, Carole and Dahamna, Badisse and Karapetiantz, Pierre and Guillemin-Lanne, Sylvie and Lillo-Le Lou{\"e}t, Agn{\`e}s and Texier, Nathalie and Burgun, Anita and Katsahian, Sandrine", title="The Adverse Drug Reactions From Patient Reports in Social Media Project: Protocol for an Evaluation Against a Gold Standard", journal="JMIR Res Protoc", year="2019", month="May", day="07", volume="8", number="5", pages="e11448", keywords="social media", keywords="drug-related side effects and adverse reactions", keywords="natural language processing", keywords="data mining", keywords="MedDRA", keywords="Racine Pharma", abstract="Background: Social media is a potential source of information on postmarketing drug safety surveillance that still remains unexploited nowadays. Information technology solutions aiming at extracting adverse reactions (ADRs) from posts on health forums require a rigorous evaluation methodology if their results are to be used to make decisions. First, a gold standard, consisting of manual annotations of the ADR by human experts from the corpus extracted from social media, must be implemented and its quality must be assessed. Second, as for clinical research protocols, the sample size must rely on statistical arguments. Finally, the extraction methods must target the relation between the drug and the disease (which might be either treated or caused by the drug) rather than simple co-occurrences in the posts. Objective: We propose a standardized protocol for the evaluation of a software extracting ADRs from the messages on health forums. The study is conducted as part of the Adverse Drug Reactions from Patient Reports in Social Media project. Methods: Messages from French health forums were extracted. Entity recognition was based on Racine Pharma lexicon for drugs and Medical Dictionary for Regulatory Activities terminology for potential adverse events (AEs). Natural language processing--based techniques automated the ADR information extraction (relation between the drug and AE entities). The corpus of evaluation was a random sample of the messages containing drugs and/or AE concepts corresponding to recent pharmacovigilance alerts. A total of 2 persons experienced in medical terminology manually annotated the corpus, thus creating the gold standard, according to an annotator guideline. We will evaluate our tool against the gold standard with recall, precision, and f-measure. Interannotator agreement, reflecting gold standard quality, will be evaluated with hierarchical kappa. Granularities in the terminologies will be further explored. Results: Necessary and sufficient sample size was calculated to ensure statistical confidence in the assessed results. As we expected a global recall of 0.5, we needed at least 384 identified ADR concepts to obtain a 95\% CI with a total width of 0.10 around 0.5. The automated ADR information extraction in the corpus for evaluation is already finished. The 2 annotators already completed the annotation process. The analysis of the performance of the ADR information extraction module as compared with gold standard is ongoing. Conclusions: This protocol is based on the standardized statistical methods from clinical research to create the corpus, thus ensuring the necessary statistical power of the assessed results. Such evaluation methodology is required to make the ADR information extraction software useful for postmarketing drug safety surveillance. International Registered Report Identifier (IRRID): RR1-10.2196/11448 ", doi="10.2196/11448", url="http://www.researchprotocols.org/2019/5/e11448/", url="http://www.ncbi.nlm.nih.gov/pubmed/31066711" } @Article{info:doi/10.2196/10946, author="Nitzburg, George and Weber, Ingmar and Yom-Tov, Elad", title="Internet Searches for Medical Symptoms Before Seeking Information on 12-Step Addiction Treatment Programs: A Web-Search Log Analysis", journal="J Med Internet Res", year="2019", month="May", day="03", volume="21", number="5", pages="e10946", keywords="alcohol use disorder", keywords="substance use disorder", keywords="12-step programs", keywords="brief intervention", keywords="brief physician advice", keywords="anonymized internet search log data", abstract="Background: Brief intervention is a critical method for identifying patients with problematic substance use in primary care settings and for motivating them to consider treatment options. However, despite considerable evidence of delay discounting in patients with substance use disorders, most brief advice by physicians focuses on the long-term negative medical consequences, which may not be the best way to motivate patients to seek treatment information. Objective: Identification of the specific symptoms that most motivate individuals to seek treatment information may offer insights for further improving brief interventions. To this end, we used anonymized internet search engine data to investigate which medical conditions and symptoms preceded searches for 12-step meeting locators and general 12-step information. Methods: We extracted all queries made by people in the United States on the Bing search engine from November 2016 to July 2017. These queries were filtered for those who mentioned seeking Alcoholics Anonymous (AA) or Narcotics Anonymous (NA); in addition, queries that contained a medical symptom or condition or a synonym thereof were analyzed. We identified medical symptoms and conditions that predicted searches for seeking treatment at different time lags. Specifically, symptom queries were first determined to be significantly predictive of subsequent 12-step queries if the probability of querying a medical symptom by those who later sought information about the 12-step program exceeded the probability of that same query being made by a comparison group of all other Bing users in the United States. Second, we examined symptom queries preceding queries on the 12-step program at time lags of 0-7 days, 7-14 days, and 14-30 days, where the probability of asking about a medical symptom was greater in the 30-day time window preceding 12-step program information-seeking as compared to all previous times that the symptom was queried. Results: In our sample of 11,784 persons, we found 10 medical symptoms that predicted AA information seeking and 9 symptoms that predicted NA information seeking. Of these symptoms, a substantial number could be categorized as nonsevere in nature. Moreover, when medical symptom persistence was examined across a 1-month time period, a substantial number of nonsevere, yet persistent, symptoms were identified. Conclusions: Our results suggest that many common or nonsevere medical symptoms and conditions motivate subsequent interest in AA and NA programs. In addition to highlighting severe long-term consequences, brief interventions could be restructured to highlight how increasing substance misuse can worsen discomfort from common medical symptoms in the short term, as well as how these worsening symptoms could exacerbate social embarrassment or decrease physical attractiveness. ", doi="10.2196/10946", url="https://www.jmir.org/2019/5/e10946/", url="http://www.ncbi.nlm.nih.gov/pubmed/31066685" } @Article{info:doi/10.2196/13316, author="Huang, Ming and Zolnoori, Maryam and Balls-Berry, E. Joyce and Brockman, A. Tabetha and Patten, A. Christi and Yao, Lixia", title="Technological Innovations in Disease Management: Text Mining US Patent Data From 1995 to 2017", journal="J Med Internet Res", year="2019", month="Apr", day="30", volume="21", number="4", pages="e13316", keywords="patent", keywords="technological innovation", keywords="disease", keywords="research opportunity index", keywords="public health index", keywords="text mining", keywords="topic modeling", keywords="dynamic topic model", keywords="resource allocation", keywords="research priority", abstract="Background: Patents are important intellectual property protecting technological innovations that inspire efficient research and development in biomedicine. The number of awarded patents serves as an important indicator of economic growth and technological innovation. Researchers have mined patents to characterize the focuses and trends of technological innovations in many fields. Objective: To expand patent mining to biomedicine and facilitate future resource allocation in biomedical research for the United States, we analyzed US patent documents to determine the focuses and trends of protected technological innovations across the entire disease landscape. Methods: We analyzed more than 5 million US patent documents between 1995 and 2017, using summary statistics and dynamic topic modeling. More specifically, we investigated the disease coverage and latent topics in patent documents over time. We also incorporated the patent data into the calculation of our recently developed Research Opportunity Index (ROI) and Public Health Index (PHI), to recalibrate the resource allocation in biomedical research. Results: Our analysis showed that protected technological innovations have been primarily focused on socioeconomically critical diseases such as ``other cancers'' (malignant neoplasm of head, face, neck, abdomen, pelvis, or limb; disseminated malignant neoplasm; Merkel cell carcinoma; and malignant neoplasm, malignant carcinoid tumors, neuroendocrine tumor, and carcinoma in situ of an unspecified site), diabetes mellitus, and obesity. The United States has significantly improved resource allocation to biomedical research and development over the past 17 years, as illustrated by the decreasing PHI. Diseases with positive ROI, such as ankle and foot fracture, indicate potential research opportunities for the future. Development of novel chemical or biological drugs and electrical devices for diagnosis and disease management is the dominating topic in patented inventions. Conclusions: This multifaceted analysis of patent documents provides a deep understanding of the focuses and trends of technological innovations in disease management in patents. Our findings offer insights into future research and innovation opportunities and provide actionable information to facilitate policy makers, payers, and investors to make better evidence-based decisions regarding resource allocation in biomedicine. ", doi="10.2196/13316", url="http://www.jmir.org/2019/4/e13316/", url="http://www.ncbi.nlm.nih.gov/pubmed/31038462" } @Article{info:doi/10.2196/12974, author="Soreni, Noam and Cameron, H. Duncan and Streiner, L. David and Rowa, Karen and McCabe, E. Randi", title="Seasonality Patterns of Internet Searches on Mental Health: Exploratory Infodemiology Study", journal="JMIR Ment Health", year="2019", month="Apr", day="24", volume="6", number="4", pages="e12974", keywords="anxiety", keywords="depression", keywords="OCD", keywords="schizophrenia", keywords="autism", keywords="suicide", keywords="seasonality", keywords="Google", keywords="internet", keywords="infodemiology", keywords="infoveillance", keywords="mental health", abstract="Background: The study of seasonal patterns of public interest in psychiatric disorders has important theoretical and practical implications for service planning and delivery. The recent explosion of internet searches suggests that mining search databases yields unique information on public interest in mental health disorders, which is a significantly more affordable approach than population health studies. Objective: This study aimed to investigate seasonal patterns of internet mental health queries in Ontario, Canada. Methods: Weekly data on health queries in Ontario from Google Trends were downloaded for a 5-year period (2012-2017) for the terms ``schizophrenia,'' ``autism,'' ``bipolar,'' ``depression,'' ``anxiety,'' ``OCD'' (obsessive-compulsive disorder), and ``suicide.'' Control terms were overall search results for the terms ``health'' and ``how.'' Time-series analyses using a continuous wavelet transform were performed to isolate seasonal components in the search volume for each term. Results: All mental health queries showed significant seasonal patterns with peak periodicity occurring over the winter months and troughs occurring during summer, except for ``suicide.'' The comparison term ``health'' also exhibited seasonal periodicity, while the term ``how'' did not, indicating that general information seeking may not follow a seasonal trend in the way that mental health information seeking does. Conclusions: Seasonal patterns of internet search volume in a wide range of mental health terms were observed, with the exception of ``suicide.'' Our study demonstrates that monitoring internet search trends is an affordable, instantaneous, and naturalistic method to sample public interest in large populations and inform health policy planners. ", doi="10.2196/12974", url="https://mental.jmir.org/2019/4/e12974/", url="http://www.ncbi.nlm.nih.gov/pubmed/31017582" } @Article{info:doi/10.2196/11419, author="Blomberg, Karin and Eriksson, Mats and B{\"o}{\"o}, Rickard and Gr{\"o}nlund, {\AA}ke", title="Using a Facebook Forum to Cope With Narcolepsy After Pandemrix Vaccination: Infodemiology Study", journal="J Med Internet Res", year="2019", month="Apr", day="16", volume="21", number="4", pages="e11419", keywords="narcolepsy", keywords="mass vaccination", keywords="social media", abstract="Background: In 2010, newly diagnosed narcolepsy cases among children and adolescents were seen in several European countries as a consequence of comprehensive national vaccination campaigns with Pandemrix against H1N1 influenza. Since then, a large number of people have had to live with narcolepsy and its consequences in daily life, such as effects on school life, social relationships, and activities. Initially, the adverse effects were not well understood and there was uncertainty about whether there would be any financial compensation. The situation remained unresolved until 2016, and during these years affected people sought various ways to join forces to handle the many issues involved, including setting up a social media forum. Objective: Our aim was to examine how information was shared, and how opinions and beliefs about narcolepsy as a consequence of Pandemrix vaccination were formed through discussions on social media. Methods: We used quantitative and qualitative methods to investigate a series of messages posted in a social media forum for people affected by narcolepsy after vaccination. Results: Group activity was high throughout the years 2010 to 2016, with peaks corresponding to major narcolepsy-related events, such as the appearance of the first cases in 2010, the first payment of compensation in 2011, and passage of a law on compensation in July 2016. Unusually, most (462/774, 59.7\%) of the group took part in discussions and only 312 of 774 (40.3\%) were lurkers (compared with the usual 90\% rule of thumb for participation in an online community). The conversation in the group was largely factual and had a civil tone, even though there was a long struggle for the link between the vaccine and narcolepsy to be acknowledged and regarding the compensation issue. Radical, nonscientific views, such as those expounded by the antivaccination movement, did not shape the discussions in the group but were being actively expressed elsewhere on the internet. At the outset of the pandemic, there were 18 active Swedish discussion groups on the topic, but most dissolved quickly and only one Facebook group remained active throughout the period. Conclusions: The group studied is a good example of social media use for self-help through a difficult situation among people affected by illness and disease. This shows that social media do not by themselves induce trench warfare but, given a good group composition, can provide a necessary forum for managing an emergency situation where health care and government have failed or are mistrusted, and patients have to organize themselves so as to cope. ", doi="10.2196/11419", url="http://www.jmir.org/2019/4/e11419/", url="http://www.ncbi.nlm.nih.gov/pubmed/30990457" } @Article{info:doi/10.2196/13082, author="Lebwohl, Benjamin and Yom-Tov, Elad", title="Symptoms Prompting Interest in Celiac Disease and the Gluten-Free Diet: Analysis of Internet Search Term Data", journal="J Med Internet Res", year="2019", month="Apr", day="08", volume="21", number="4", pages="e13082", keywords="celiac disease", keywords="gluten", keywords="epidemiology", abstract="Background: Celiac disease, a common immune-based disease triggered by gluten, has diverse clinical manifestations, and the relative distribution of symptoms leading to diagnosis has not been well characterized in the population. Objective: This study aimed to use search engine data to identify a set of symptoms and conditions that would identify individuals at elevated likelihood of a subsequent celiac disease diagnosis. We also measured the relative prominence of these search terms before versus after a search related to celiac disease. Methods: We extracted English-language queries submitted to the Bing search engine in the United States and identified those who submitted a new celiac-related query during a 1-month period, without any celiac-related queries in the preceding 9 months. We compared the ratio between the number of times that each symptom or condition was asked in the 14 days preceding the first celiac-related query of each person and the number of searches for that same symptom or condition in the 14 days after the celiac-related query. Results: We identified 90,142 users who made a celiac-related query, of whom 6528 (7\%) exhibited sustained interest, defined as making a query on more than 1 day. Though a variety of symptoms and associated conditions were also queried before a celiac-related query, the maximum area under the receiver operating characteristic curve was 0.53. The symptom most likely to be queried more before than after a celiac-related query was diarrhea (query ratio [QR] 1.28). Extraintestinal symptoms queried before a celiac disease query included headache (QR 1.26), anxiety (QR 1.10), depression (QR 1.03), and attention-deficit hyperactivity disorder (QR 1.64). Conclusions: We found an increase in antecedent searches for symptoms known to be associated with celiac disease, a rise in searches for depression and anxiety, and an increase in symptoms that are associated with celiac disease but may not be reported to health care providers. The protean clinical manifestations of celiac disease are reflected in the diffuse nature of antecedent internet queries of those interested in celiac disease, underscoring the challenge of effective case-finding strategies. ", doi="10.2196/13082", url="https://www.jmir.org/2019/4/e13082/", url="http://www.ncbi.nlm.nih.gov/pubmed/30958273" } @Article{info:doi/10.2196/12214, author="Clemente, Leonardo and Lu, Fred and Santillana, Mauricio", title="Improved Real-Time Influenza Surveillance: Using Internet Search Data in Eight Latin American Countries", journal="JMIR Public Health Surveill", year="2019", month="Apr", day="04", volume="5", number="2", pages="e12214", keywords="google flu trends", keywords="influenza monitoring", keywords="real-time disease surveillance", keywords="digital epidemiology", keywords="influenza, human", keywords="developing countries", keywords="machine learning", abstract="Background: Novel influenza surveillance systems that leverage Internet-based real-time data sources including Internet search frequencies, social-network information, and crowd-sourced flu surveillance tools have shown improved accuracy over the past few years in data-rich countries like the United States. These systems not only track flu activity accurately, but they also report flu estimates a week or more ahead of the publication of reports produced by healthcare-based systems, such as those implemented and managed by the Centers for Disease Control and Prevention. Previous work has shown that the predictive capabilities of novel flu surveillance systems, like Google Flu Trends (GFT), in developing countries in Latin America have not yet delivered acceptable flu estimates. Objective: The aim of this study was to show that recent methodological improvements on the use of Internet search engine information to track diseases can lead to improved retrospective flu estimates in multiple countries in Latin America. Methods: A machine learning-based methodology that uses flu-related Internet search activity and historical information to monitor flu activity, named ARGO (AutoRegression with Google search), was extended to generate flu predictions for 8 Latin American countries (Argentina, Bolivia, Brazil, Chile, Mexico, Paraguay, Peru, and Uruguay) for the time period: January 2012 to December of 2016. These retrospective (out-of-sample) Influenza activity predictions were compared with historically observed flu suspected cases in each country, as reported by Flunet, an influenza surveillance database maintained by the World Health Organization. For a baseline comparison, retrospective (out-of-sample) flu estimates were produced for the same time period using autoregressive models that only leverage historical flu activity information. Results: Our results show that ARGO-like models' predictive power outperform autoregressive models in 6 out of 8 countries in the 2012-2016 time period. Moreover, ARGO significantly improves on historical flu estimates produced by the now discontinued GFT for the time period of 2012-2015, where GFT information is publicly available. Conclusions: We demonstrate here that a self-correcting machine learning method, leveraging Internet-based disease-related search activity and historical flu trends, has the potential to produce reliable and timely flu estimates in multiple Latin American countries. This methodology may prove helpful to local public health officials who design and implement interventions aimed at mitigating the effects of influenza outbreaks. Our methodology generally outperforms both the now-discontinued tool GFT, and autoregressive methodologies that exploit only historical flu activity to produce future disease estimates. ", doi="10.2196/12214", url="https://publichealth.jmir.org/2019/2/e12214/", url="http://www.ncbi.nlm.nih.gov/pubmed/30946017" } @Article{info:doi/10.2196/12480, author="Lama, Yuki and Hu, Dian and Jamison, Amelia and Quinn, Crouse Sandra and Broniatowski, A. David", title="Characterizing Trends in Human Papillomavirus Vaccine Discourse on Reddit (2007-2015): An Observational Study", journal="JMIR Public Health Surveill", year="2019", month="Mar", day="18", volume="5", number="1", pages="e12480", keywords="papillomavirus infections", keywords="prevention \& control", keywords="cancer prevention", keywords="cervical cancer", keywords="HPV", keywords="vaccination", keywords="papillomavirus vaccines", keywords="immunology", keywords="administration \& dosage", keywords="social media", keywords="health communication", keywords="infodemiology", abstract="Background: Despite the introduction of the human papillomavirus (HPV) vaccination as a preventive measure in 2006 for cervical and other cancers, uptake rates remain suboptimal, resulting in preventable cancer mortality. Social media, widely used for information seeking, can influence users' knowledge and attitudes regarding HPV vaccination. Little is known regarding attitudes related to HPV vaccination on Reddit (a popular news aggregation site and online community), particularly related to cancer risk and sexual activity. Examining HPV vaccine--related messages on Reddit may provide insight into how HPV discussions are characterized on forums online and influence decision making related to vaccination. Objective: We observed how the HPV vaccine is characterized on Reddit over time and by user gender. Specifically, this study aimed to determine (1) if Reddit messages are more related to cancer risks or sexual behavior and (2) what other HPV vaccine--related discussion topics appear on Reddit. Methods: We gathered all public Reddit comments from January 2007 to September 2015. We manually annotated 400 messages to generate keywords and identify salient themes. We then measured the similarity between each comment and lists of keywords associated with sexual behavior and cancer risk using Latent Semantic Analysis (LSA). Next, we used Latent Dirichlet Allocation (LDA) to characterize remaining topics within the Reddit data. Results: We analyzed 22,729 messages containing the strings hpv or human papillomavirus and vaccin. LSA findings show that HPV vaccine discussions are significantly more related to cancer compared with sexual behavior from 2008 to 2015 (P<.001). We did not find a significant difference between genders in discussions of cancer and sexual activity (P>.05). LDA analyses demonstrated that although topics related to cancer risk and sexual activity were both frequently discussed (16.1\% and 14.5\% of word tokens, respectively), the majority of online discussions featured other topics. The most frequently discussed topic was politics associated with the vaccine (17.2\%). Other topics included HPV disease and/or immunity (13.5\%), the HPV vaccine schedule (11.5\%), HPV vaccine side effects (9.7\%), hyperlinks to outside sources (9.1\%), and the risks and benefit of HPV vaccination (8.5\%). Conclusions: Reddit discourse on HPV vaccine encompasses a broad range of topics among men and women, with HPV political debates and cancer risk making up the plurality of the discussion. Our findings demonstrated that women and men both discussed HPV, highlighting that Reddit users do not perceive HPV as an issue that only pertains to women. Given the increasing use of social media as a source of health information, these results can inform the development of targeted online health communication strategies to promote HPV vaccination to young adult users of Reddit. Analyzing online discussions on Reddit can inform health communication efforts by identifying relevant, important HPV-related topics among online communities. ", doi="10.2196/12480", url="http://publichealth.jmir.org/2019/1/e12480/" } @Article{info:doi/10.2196/13142, author="Bragazzi, Luigi Nicola and Mahroum, Naim", title="Google Trends Predicts Present and Future Plague Cases During the Plague Outbreak in Madagascar: Infodemiological Study", journal="JMIR Public Health Surveill", year="2019", month="Mar", day="08", volume="5", number="1", pages="e13142", keywords="plague", keywords="infodemiology", keywords="infoveillance", keywords="infectious outbreaks", keywords="Google Trends", keywords="nowcasting and forecasting models", keywords="digital surveillance", abstract="Background: Plague is a highly infectious zoonotic disease caused by the bacillus Yersinia pestis. Three major forms of the disease are known: bubonic, septicemic, and pneumonic plague. Though highly related to the past, plague still represents a global public health concern. Cases of plague continue to be reported worldwide. In recent months, pneumonic plague cases have been reported in Madagascar. However, despite such a long-standing and rich history, it is rather difficult to get a comprehensive overview of the general situation. Within the framework of electronic health (eHealth), in which people increasingly search the internet looking for health-related material, new information and communication technologies could enable researchers to get a wealth of data, which could complement traditional surveillance of infectious diseases. Objective: In this study, we aimed to assess public reaction regarding the recent plague outbreak in Madagascar by quantitatively characterizing the public's interest. Methods: We captured public interest using Google Trends (GT) and correlated it to epidemiological real-world data in terms of incidence rate and spread pattern. Results: Statistically significant positive correlations were found between GT search data and confirmed (R2=0.549), suspected (R2=0.265), and probable (R2=0.518) cases. From a geospatial standpoint, plague-related GT queries were concentrated in Toamasina (100\%), Toliara (68\%), and Antananarivo (65\%). Concerning the forecasting models, the 1-day lag model was selected as the best regression model. Conclusions: An earlier digital Web search reaction could potentially contribute to better management of outbreaks, for example, by designing ad hoc interventions that could contain the infection both locally and at the international level, reducing its spread. ", doi="10.2196/13142", url="http://publichealth.jmir.org/2019/1/e13142/", url="http://www.ncbi.nlm.nih.gov/pubmed/30763255" } @Article{info:doi/10.2196/publichealth.9176, author="Watad, Abdulla and Watad, Samaa and Mahroum, Naim and Sharif, Kassem and Amital, Howard and Bragazzi, Luigi Nicola and Adawi, Mohammad", title="Forecasting the West Nile Virus in the United States: An Extensive Novel Data Streams--Based Time Series Analysis and Structural Equation Modeling of Related Digital Searching Behavior", journal="JMIR Public Health Surveill", year="2019", month="Feb", day="28", volume="5", number="1", pages="e9176", keywords="forecasting model", keywords="West Nile virus", keywords="Google Trends", keywords="infodemiology", keywords="infoveillance", keywords="seasonal autoregressive integrated moving average model with explicative variable (SARIMAX)", abstract="Background: West Nile virus is an arbovirus responsible for an infection that tends to peak during the late summer and early fall. Tools monitoring Web searches are emerging as powerful sources of data, especially concerning infectious diseases such as West Nile virus. Objective: This study aimed at exploring the potential predictive power of West Nile virus--related Web searches. Methods: Different novel data streams, including Google Trends, WikiTrends, YouTube, and Google News, were used to extract search trends. Data regarding West Nile virus cases were obtained from the Centers for Disease Control and Prevention. Data were analyzed using regression, times series analysis, structural equation modeling, and clustering analysis. Results: In the regression analysis, an association between Web searches and ``real-world'' epidemiological figures was found. The best seasonal autoregressive integrated moving average model with explicative variable (SARIMAX) was found to be (0,1,1)x(0,1,1)4. Using data from 2004 to 2015, we were able to predict data for 2016. From the structural equation modeling, the consumption of West Nile virus--related news fully mediated the relation between Google Trends and the consumption of YouTube videos, as well as the relation between the latter variable and the number of West Nile virus cases. Web searches fully mediated the relation between epidemiological figures and the consumption of YouTube videos, as well as the relation between epidemiological data and the number of accesses to the West Nile virus--related Wikipedia page. In the clustering analysis, the consumption of news was most similar to the Web searches pattern, which was less close to the consumption of YouTube videos and least similar to the behavior of accessing West Nile virus--related Wikipedia pages. Conclusions: Our study demonstrated an association between epidemiological data and search patterns related to the West Nile virus. Based on this correlation, further studies are needed to examine the practicality of these findings. ", doi="10.2196/publichealth.9176", url="http://publichealth.jmir.org/2019/1/e9176/", url="http://www.ncbi.nlm.nih.gov/pubmed/30601755" } @Article{info:doi/10.2196/12783, author="Wakamiya, Shoko and Morita, Mizuki and Kano, Yoshinobu and Ohkuma, Tomoko and Aramaki, Eiji", title="Tweet Classification Toward Twitter-Based Disease Surveillance: New Data, Methods, and Evaluations", journal="J Med Internet Res", year="2019", month="Feb", day="20", volume="21", number="2", pages="e12783", keywords="text mining", keywords="social media", keywords="machine learning", keywords="natural language processing", keywords="artificial intelligence", keywords="surveillance", keywords="infodemiology", keywords="infoveillance", abstract="Background: The amount of medical and clinical-related information on the Web is increasing. Among the different types of information available, social media--based data obtained directly from people are particularly valuable and are attracting significant attention. To encourage medical natural language processing (NLP) research exploiting social media data, the 13th NII Testbeds and Community for Information access Research (NTCIR-13) Medical natural language processing for Web document (MedWeb) provides pseudo-Twitter messages in a cross-language and multi-label corpus, covering 3 languages (Japanese, English, and Chinese) and annotated with 8 symptom labels (such as cold, fever, and flu). Then, participants classify each tweet into 1 of the 2 categories: those containing a patient's symptom and those that do not. Objective: This study aimed to present the results of groups participating in a Japanese subtask, English subtask, and Chinese subtask along with discussions, to clarify the issues that need to be resolved in the field of medical NLP. Methods: In summary, 8 groups (19 systems) participated in the Japanese subtask, 4 groups (12 systems) participated in the English subtask, and 2 groups (6 systems) participated in the Chinese subtask. In total, 2 baseline systems were constructed for each subtask. The performance of the participant and baseline systems was assessed using the exact match accuracy, F-measure based on precision and recall, and Hamming loss. Results: The best system achieved exactly 0.880 match accuracy, 0.920 F-measure, and 0.019 Hamming loss. The averages of match accuracy, F-measure, and Hamming loss for the Japanese subtask were 0.720, 0.820, and 0.051; those for the English subtask were 0.770, 0.850, and 0.037; and those for the Chinese subtask were 0.810, 0.880, and 0.032, respectively. Conclusions: This paper presented and discussed the performance of systems participating in the NTCIR-13 MedWeb task. As the MedWeb task settings can be formalized as the factualization of text, the achievement of this task could be directly applied to practical clinical applications. ", doi="10.2196/12783", url="http://www.jmir.org/2019/2/e12783/", url="http://www.ncbi.nlm.nih.gov/pubmed/30785407" } @Article{info:doi/10.2196/10450, author="Wakamiya, Shoko and Matsune, Shoji and Okubo, Kimihiro and Aramaki, Eiji", title="Causal Relationships Among Pollen Counts, Tweet Numbers, and Patient Numbers for Seasonal Allergic Rhinitis Surveillance: Retrospective Analysis", journal="J Med Internet Res", year="2019", month="Feb", day="20", volume="21", number="2", pages="e10450", keywords="seasonal allergic rhinitis", keywords="social media", keywords="Twitter", keywords="causal relationship", keywords="infoveillance", keywords="disease surveillance", abstract="Background: Health-related social media data are increasingly used in disease-surveillance studies, which have demonstrated moderately high correlations between the number of social media posts and the number of patients. However, there is a need to understand the causal relationship between the behavior of social media users and the actual number of patients in order to increase the credibility of disease surveillance based on social media data. Objective: This study aimed to clarify the causal relationships among pollen count, the posting behavior of social media users, and the number of patients with seasonal allergic rhinitis in the real world. Methods: This analysis was conducted using datasets of pollen counts, tweet numbers, and numbers of patients with seasonal allergic rhinitis from Kanagawa Prefecture, Japan. We examined daily pollen counts for Japanese cedar (the major cause of seasonal allergic rhinitis in Japan) and hinoki cypress (which commonly complicates seasonal allergic rhinitis) from February 1 to May 31, 2017. The daily numbers of tweets that included the keyword ``kafunsh?'' (or seasonal allergic rhinitis) were calculated between January 1 and May 31, 2017. Daily numbers of patients with seasonal allergic rhinitis from January 1 to May 31, 2017, were obtained from three healthcare institutes that participated in the study. The Granger causality test was used to examine the causal relationships among pollen count, tweet numbers, and the number of patients with seasonal allergic rhinitis from February to May 2017. To determine if time-variant factors affect these causal relationships, we analyzed the main seasonal allergic rhinitis phase (February to April) when Japanese cedar trees actively produce and release pollen. Results: Increases in pollen count were found to increase the number of tweets during the overall study period (P=.04), but not the main seasonal allergic rhinitis phase (P=.05). In contrast, increases in pollen count were found to increase patient numbers in both the study period (P=.04) and the main seasonal allergic rhinitis phase (P=.01). Increases in the number of tweets increased the patient numbers during the main seasonal allergic rhinitis phase (P=.02), but not the overall study period (P=.89). Patient numbers did not affect the number of tweets in both the overall study period (P=.24) and the main seasonal allergic rhinitis phase (P=.47). Conclusions: Understanding the causal relationships among pollen counts, tweet numbers, and numbers of patients with seasonal allergic rhinitis is an important step to increasing the credibility of surveillance systems that use social media data. Further in-depth studies are needed to identify the determinants of social media posts described in this exploratory analysis. ", doi="10.2196/10450", url="http://www.jmir.org/2019/2/e10450/", url="http://www.ncbi.nlm.nih.gov/pubmed/30785411" } @Article{info:doi/10.2196/12264, author="Hswen, Yulin and Gopaluni, Anuraag and Brownstein, S. John and Hawkins, B. Jared", title="Using Twitter to Detect Psychological Characteristics of Self-Identified Persons With Autism Spectrum Disorder: A Feasibility Study", journal="JMIR Mhealth Uhealth", year="2019", month="Feb", day="12", volume="7", number="2", pages="e12264", keywords="autism", keywords="digital data", keywords="emotion", keywords="mobile phone", keywords="obsessive-compulsive disorder", keywords="social media", keywords="textual analysis", keywords="tweets", keywords="Twitter", keywords="infodemiology", abstract="Background: More than 3.5 million Americans live with autism spectrum disorder (ASD). Major challenges persist in diagnosing ASD as no medical test exists to diagnose this disorder. Digital phenotyping holds promise to guide in the clinical diagnoses and screening of ASD. Objective: This study aims to explore the feasibility of using the Web-based social media platform Twitter to detect psychological and behavioral characteristics of self-identified persons with ASD. Methods: Data from Twitter were retrieved from 152 self-identified users with ASD and 182 randomly selected control users from March 22, 2012 to July 20, 2017. We conducted a between-group comparative textual analysis of tweets about repetitive and obsessive-compulsive behavioral characteristics typically associated with ASD. In addition, common emotional characteristics of persons with ASD, such as fear, paranoia, and anxiety, were examined between groups through textual analysis. Furthermore, we compared the timing of tweets between users with ASD and control users to identify patterns in communication. Results: Users with ASD posted a significantly higher frequency of tweets related to the specific repetitive behavior of counting compared with control users (P<.001). The textual analysis of obsessive-compulsive behavioral characteristics, such as fixate, excessive, and concern, were significantly higher among users with ASD compared with the control group (P<.001). In addition, emotional terms related to fear, paranoia, and anxiety were tweeted at a significantly higher rate among users with ASD compared with control users (P<.001). Users with ASD posted a smaller proportion of tweets during time intervals of 00:00-05:59 (P<.001), 06:00-11:59 (P<.001), and 18:00-23.59 (P<.001), as well as a greater proportion of tweets from 12:00 to 17:59 (P<.001) compared with control users. Conclusions: Social media may be a valuable resource for observing unique psychological characteristics of self-identified persons with ASD. Collecting and analyzing data from these digital platforms may afford opportunities to identify the characteristics of ASD and assist in the diagnosis or verification of ASD. This study highlights the feasibility of leveraging digital data for gaining new insights into various health conditions. ", doi="10.2196/12264", url="http://mhealth.jmir.org/2019/2/e12264/", url="http://www.ncbi.nlm.nih.gov/pubmed/30747718" } @Article{info:doi/10.2196/10432, author="Johnsen, K. Jan-Are and Eggesvik, B. Trude and R{\o}rvik, H. Thea and Hanssen, W. Miriam and Wynn, Rolf and Kummervold, Egil Per", title="Differences in Emotional and Pain-Related Language in Tweets About Dentists and Medical Doctors: Text Analysis of Twitter Content", journal="JMIR Public Health Surveill", year="2019", month="Feb", day="06", volume="5", number="1", pages="e10432", keywords="dental anxiety", keywords="dentistry", keywords="psychology", keywords="social media", keywords="internet", keywords="dental public health", keywords="Twitter", keywords="professional role", keywords="occupational stereotype", abstract="Background: Social media provides people with easy ways to communicate their attitudes and feelings to a wide audience. Many people, unfortunately, have negative associations and feelings about dental treatment due to former painful experiences. Previous research indicates that there might be a pervasive and negative occupational stereotype related to dentists and that this stereotype is expressed in many different venues, including movies and literature. Objective: This study investigates the language used in relation to dentists and medical doctors on the social media platform Twitter. The purpose is to compare the professions in terms of the use of emotional and pain-related words, which might underlie and reflect the pervasive negative stereotype identified in relation to dentists. We hypothesized that (A) tweets about dentists will have more negative emotion-related words than those about medical doctors and (B) pain-related words occur more frequently in tweets about dentists than in those about medical doctors. Methods: Twitter content (``tweets'') about dentists and medical doctors was collected using the Twitter application program interface 140Dev over a 4-week period in 2015, scanning the search terms ``dentist'' and ``doctor''. Word content of the selected tweets was analyzed using Linguistic Inquiry and Word Count software. The research hypotheses were investigated using nonparametric Wilcoxon-Mann-Whitney tests. Results: Over 2.3 million tweets were collected in total, of which about one-third contained the word ``dentist'' and about two-thirds contained the word ``doctor.'' Hypothesis A was supported since a higher proportion of negative words was used in tweets about dentists than in those about medical doctors (z=?10.47; P<.001). Similarly, tests showed a difference in the proportions of anger words (z=?12.54; P<.001), anxiety words (z=?6.96; P<.001), and sadness words (z=?9.58; P<.001), with higher proportions of these words in tweets about dentists than in those about doctors. Also, Hypothesis B was supported since a higher proportion of pain-related words was used in tweets about dentists than in those about doctors (z=?8.02; P<.001). Conclusions: The results from this study suggest that stereotypes regarding dentists and dental treatment are spread through social media such as Twitter and that social media also might represent an avenue for improving messaging and disseminating more positive attitudes toward dentists and dental treatment. ", doi="10.2196/10432", url="http://publichealth.jmir.org/2019/1/e10432/", url="http://www.ncbi.nlm.nih.gov/pubmed/30724738" } @Article{info:doi/10.2196/10677, author="Xu, Chenjie and Wang, Yi and Yang, Hongxi and Hou, Jie and Sun, Li and Zhang, Xinyu and Cao, Xinxi and Hou, Yabing and Wang, Lan and Cai, Qiliang and Wang, Yaogang", title="Association Between Cancer Incidence and Mortality in Web-Based Data in China: Infodemiology Study", journal="J Med Internet Res", year="2019", month="Jan", day="29", volume="21", number="1", pages="e10677", keywords="cancer", keywords="incidence", keywords="mortality", keywords="web-based data", keywords="internet searching", abstract="Background: Cancer poses a serious threat to the health of Chinese people, resulting in a major challenge for public health work. Today, people can obtain relevant information from not only medical workers in hospitals, but also the internet in any place in real-time. Search behaviors can reflect a population's awareness of cancer from a completely new perspective, which could be driven by the underlying cancer epidemiology. However, such Web-retrieved data are not yet well validated or understood. Objective: This study aimed to explore whether a correlation exists between the incidence and mortality of cancers and normalized internet search volumes on the big data platform, Baidu. We also assessed whether the distribution of people who searched for specific types of cancer differed by gender. Finally, we determined whether there were regional disparities among people who searched the Web for cancer-related information. Methods: Standard Boolean operators were used to choose search terms for each type of cancer. Spearman's correlation analysis was used to explore correlations among monthly search index values for each cancer type and their monthly incidence and mortality rates. We conducted cointegration analysis between search index data and incidence rates to examine whether a stable equilibrium existed between them. We also conducted cointegration analysis between search index data and mortality data. Results: The monthly Baidu index was significantly correlated with cancer incidence rates for 26 of 28 cancers in China (lung cancer: r=.80, P<.001; liver cancer: r=.28, P=.016; stomach cancer: r=.50, P<.001; esophageal cancer: r=.50, P<.001; colorectal cancer: r=.81, P<.001; pancreatic cancer: r=.86, P<.001; breast cancer: r=.56, P<.001; brain and nervous system cancer: r=.63, P<.001; leukemia: r=.75, P<.001; Non-Hodgkin lymphoma: r=.88, P<.001; Hodgkin lymphoma: r=.91, P<.001; cervical cancer: r=.64, P<.001; prostate cancer: r=.67, P<.001; bladder cancer: r=.62, P<.001; gallbladder and biliary tract cancer: r=.88, P<.001; lip and oral cavity cancer: r=.88, P<.001; ovarian cancer: r=.58, P<.001; larynx cancer: r=.82, P<.001; kidney cancer: r=.73, P<.001; squamous cell carcinoma: r=.94, P<.001; multiple myeloma: r=.84, P<.001; thyroid cancer: r=.77, P<.001; malignant skin melanoma: r=.55, P<.001; mesothelioma: r=.79, P<.001; testicular cancer: r=.57, P<.001; basal cell carcinoma: r=.83, P<.001). The monthly Baidu index was significantly correlated with cancer mortality rates for 24 of 27 cancers. In terms of the whole population, the number of women who searched for cancer-related information has slowly risen over time. People aged 30-39 years were most likely to use search engines to retrieve cancer-related knowledge. East China had the highest Web search volumes for cancer. Conclusions: Search behaviors indeed reflect public awareness of cancer from a different angle. Research on internet search behaviors could present an innovative and timely way to monitor and estimate cancer incidence and mortality rates, especially for cancers not included in national registries. ", doi="10.2196/10677", url="https://www.jmir.org/2019/1/e10677/", url="http://www.ncbi.nlm.nih.gov/pubmed/30694203" } @Article{info:doi/10.2196/12414, author="Liu, Qian and Chen, Qiuyi and Shen, Jiayi and Wu, Huailiang and Sun, Yimeng and Ming, Wai-Kit", title="Data Analysis and Visualization of Newspaper Articles on Thirdhand Smoke: A Topic Modeling Approach", journal="JMIR Med Inform", year="2019", month="Jan", day="29", volume="7", number="1", pages="e12414", keywords="media concerns", keywords="topic modeling", keywords="third-hand smoke", keywords="tobacco", keywords="indoor air quality", abstract="Background: Thirdhand smoke has been a growing topic for years in China. Thirdhand smoke (THS) consists of residual tobacco smoke pollutants that remain on surfaces and in dust. These pollutants are re-emitted as a gas or react with oxidants and other compounds in the environment to yield secondary pollutants. Objective: Collecting media reports on THS from major media outlets and analyzing this subject using topic modeling can facilitate a better understanding of the role that the media plays in communicating this health issue to the public. Methods: The data were retrieved from the Wiser and Factiva news databases. A preliminary investigation focused on articles dated between January 1, 2013, and December 31, 2017. Use of Latent Dirichlet Allocation yielded the top 10 topics about THS. The use of the modified LDAvis tool enabled an overall view of the topic model, which visualizes different topics as circles. Multidimensional scaling was used to represent the intertopic distances on a two-dimensional plane. Results: We found 745 articles dated between January 1, 2013, and December 31, 2017. The United States ranked first in terms of publications (152 articles on THS from 2013-2017). We found 279 news reports about THS from the Chinese media over the same period and 363 news reports from the United States. Given our analysis of the percentage of news related to THS in China, Topic 1 (Cancer) was the most popular among the topics and was mentioned in 31.9\% of all news stories. Topic 2 (Control of quitting smoking) was related to roughly 15\% of news items on THS. Conclusions: Data analysis and the visualization of news articles can generate useful information. Our study shows that topic modeling can offer insights into understanding news reports related to THS. This analysis of media trends indicated that related diseases, air and particulate matter (PM2.5), and control and restrictions are the major concerns of the Chinese media reporting on THS. The Chinese press still needs to consider fuller reports on THS based on scientific evidence and with less focus on sensational headlines. We recommend that additional studies be conducted related to sentiment analysis of news data to verify and measure the influence of THS-related topics. ", doi="10.2196/12414", url="http://medinform.jmir.org/2019/1/e12414/", url="http://www.ncbi.nlm.nih.gov/pubmed/30694199" } @Article{info:doi/10.2196/10179, author="Adler, Natalia and Cattuto, Ciro and Kalimeri, Kyriaki and Paolotti, Daniela and Tizzoni, Michele and Verhulst, Stefaan and Yom-Tov, Elad and Young, Andrew", title="How Search Engine Data Enhance the Understanding of Determinants of Suicide in India and Inform Prevention: Observational Study", journal="J Med Internet Res", year="2019", month="Jan", day="04", volume="21", number="1", pages="e10179", keywords="internet data", keywords="India", keywords="suicide", keywords="mobile phone", abstract="Background: India is home to 20\% of the world's suicide deaths. Although statistics regarding suicide in India are distressingly high, data and cultural issues likely contribute to a widespread underreporting of the problem. Social stigma and only recent decriminalization of suicide are among the factors hampering official agencies' collection and reporting of suicide rates. Objective: As the product of a data collaborative, this paper leverages private-sector search engine data toward gaining a fuller, more accurate picture of the suicide issue among young people in India. By combining official statistics on suicide with data generated through search queries, this paper seeks to: add an additional layer of information to more accurately represent the magnitude of the problem, determine whether search query data can serve as an effective proxy for factors contributing to suicide that are not represented in traditional datasets, and consider how data collaboratives built on search query data could inform future suicide prevention efforts in India and beyond. Methods: We combined official statistics on demographic information with data generated through search queries from Bing to gain insight into suicide rates per state in India as reported by the National Crimes Record Bureau of India. We extracted English language queries on ``suicide,'' ``depression,'' ``hanging,'' ``pesticide,'' and ``poison''. We also collected data on demographic information at the state level in India, including urbanization, growth rate, sex ratio, internet penetration, and population. We modeled the suicide rate per state as a function of the queries on each of the 5 topics considered as linear independent variables. A second model was built by integrating the demographic information as additional linear independent variables. Results: Results of the first model fit (R2) when modeling the suicide rates from the fraction of queries in each of the 5 topics, as well as the fraction of all suicide methods, show a correlation of about 0.5. This increases significantly with the removal of 3 outliers and improves slightly when 5 outliers are removed. Results for the second model fit using both query and demographic data show that for all categories, if no outliers are removed, demographic data can model suicide rates better than query data. However, when 3 outliers are removed, query data about pesticides or poisons improves the model over using demographic data. Conclusions: In this work, we used search data and demographics to model suicide rates. In this way, search data serve as a proxy for unmeasured (hidden) factors corresponding to suicide rates. Moreover, our procedure for outlier rejection serves to single out states where the suicide rates have substantially different correlations with demographic factors and query rates. ", doi="10.2196/10179", url="https://www.jmir.org/2019/1/e10179/", url="http://www.ncbi.nlm.nih.gov/pubmed/30609976" } @Article{info:doi/10.2196/11177, author="Sinnenberg, Lauren and Mancheno, Christina and Barg, K. Frances and Asch, A. David and Rivard, Lee Christy and Horst-Martz, Emma and Buttenheim, Alison and Ungar, Lyle and Merchant, Raina", title="Content Analysis of Metaphors About Hypertension and Diabetes on Twitter: Exploratory Mixed-Methods Study", journal="JMIR Diabetes", year="2018", month="Dec", day="21", volume="3", number="4", pages="e11177", keywords="cardiovascular diseases", keywords="language", keywords="metaphor", keywords="social media", keywords="hypertension", keywords="diabetes mellitus", abstract="Background: Widespread metaphors contribute to the public's understanding of health. Prior work has characterized the metaphors used to describe cancer and AIDS. Less is known about the metaphors characterizing cardiovascular disease. Objective: The objective of our study was to characterize the metaphors that Twitter users employ in discussing hypertension and diabetes. Methods: We filtered approximately 10 billion tweets for keywords related to diabetes and hypertension. We coded a random subset of 5000 tweets for the presence of metaphor and the type of metaphor employed. Results: Among the 5000 tweets, we identified 797 (15.9\%) about hypertension or diabetes that employed metaphors. When discussing the development of heart disease, Twitter users described the disease as a journey (n=202), as transmittable (n=116), as an object (n=49), or as being person-like (n=15). In discussing the experience of these diseases, some Twitter users employed war metaphors (n=101). Other users described the challenge to control their disease (n=34), the disease as an agent (n=58), or their bodies as machines (n=205). Conclusions: Metaphors are used frequently by Twitter users in their discussion of hypertension and diabetes. These metaphors can help to guide communication between patients and providers to improve public health. ", doi="10.2196/11177", url="http://diabetes.jmir.org/2018/4/e11177/", url="http://www.ncbi.nlm.nih.gov/pubmed/30578222" } @Article{info:doi/10.2196/11361, author="Poirier, Canelle and Lavenu, Audrey and Bertaud, Val{\'e}rie and Campillo-Gimenez, Boris and Chazard, Emmanuel and Cuggia, Marc and Bouzill{\'e}, Guillaume", title="Real Time Influenza Monitoring Using Hospital Big Data in Combination with Machine Learning Methods: Comparison Study", journal="JMIR Public Health Surveill", year="2018", month="Dec", day="21", volume="4", number="4", pages="e11361", keywords="electronic health records", keywords="big data", keywords="infodemiology", keywords="infoveillance", keywords="influenza", keywords="machine learning", keywords="Sentinelles network", abstract="Background: Traditional surveillance systems produce estimates of influenza-like illness (ILI) incidence rates, but with 1- to 3-week delay. Accurate real-time monitoring systems for influenza outbreaks could be useful for making public health decisions. Several studies have investigated the possibility of using internet users' activity data and different statistical models to predict influenza epidemics in near real time. However, very few studies have investigated hospital big data. Objective: Here, we compared internet and electronic health records (EHRs) data and different statistical models to identify the best approach (data type and statistical model) for ILI estimates in real time. Methods: We used Google data for internet data and the clinical data warehouse eHOP, which included all EHRs from Rennes University Hospital (France), for hospital data. We compared 3 statistical models---random forest, elastic net, and support vector machine (SVM). Results: For national ILI incidence rate, the best correlation was 0.98 and the mean squared error (MSE) was 866 obtained with hospital data and the SVM model. For the Brittany region, the best correlation was 0.923 and MSE was 2364 obtained with hospital data and the SVM model. Conclusions: We found that EHR data together with historical epidemiological information (French Sentinelles network) allowed for accurately predicting ILI incidence rates for the entire France as well as for the Brittany region and outperformed the internet data whatever was the statistical model used. Moreover, the performance of the two statistical models, elastic net and SVM, was comparable. ", doi="10.2196/11361", url="http://publichealth.jmir.org/2018/4/e11361/", url="http://www.ncbi.nlm.nih.gov/pubmed/30578212" } @Article{info:doi/10.2196/publichealth.7496, author="Aoki, Tomohiro and Suzuki, Teppei and Yagahara, Ayako and Hasegawa, Shin and Tsuji, Shintaro and Ogasawara, Katsuhiko", title="Analysis of the Regionality of the Number of Tweets Related to the 2011 Fukushima Nuclear Power Station Disaster: Content Analysis", journal="JMIR Public Health Surveill", year="2018", month="Dec", day="18", volume="4", number="4", pages="e70", keywords="Fukushima nuclear disaster", keywords="Twitter messaging", keywords="radiation", keywords="radioactivity", keywords="radioactive hazard release", keywords="geographic location", keywords="information dissemination", abstract="Background: The Great East Japan Earthquake on March 11, 2011, triggered a huge tsunami, causing the Fukushima Daiichi nuclear disaster. Radioactive substances were carried in all directions, along with the risks of radioactive contamination. Mass media companies, such as television stations and news websites, extensively reported on radiological information related to the disaster. Upon digesting the available radiological information, many citizens turned to social media, such as Twitter and Facebook, to express their opinions and feelings. Thus, the Fukushima Daiichi nuclear disaster also changed the social media landscape in Japan. However, few studies have explored how the people in Japan who received information on radiation propagated the information. Objective: This study aimed to reveal how the number of tweets by citizens containing radiological information changed regionally on Twitter. Methods: The research used about 19 million tweets that included the terms ``radiation,'' ``radioactivity,'' and ``radioactive substance'' posted for 1 year after the Fukushima Daiichi nuclear disaster. Nearly 45,000 tweets were extracted based on their inclusion of geographic information (latitude and longitude). The number of monthly tweets in 4 districts (Fukushima Prefecture, prefectures around Fukushima Prefecture, within the Tokyo Electric Power Company area, and others) were analyzed. Results: The number of tweets containing the keywords per 100,000 people at the time of the casualty outbreak was 7.05 per month in Fukushima Prefecture, 2.07 per month in prefectures around Fukushima Prefecture, 5.23 per month in the area within Tokyo Electric Power Company, and 1.35 per month in others. The number of tweets per 100,000 people more than doubled in Fukushima Prefecture 2 months after the Fukushima Daiichi nuclear disaster, whereas the number decreased to around 0.7{\textasciitilde}0.8 tweets in other districts. Conclusions: The number of tweets per 100,000 people became half of that on March 2011 3 or 4 months after the Fukushima Daiichi Nuclear Plant disaster in 3 districts except district 1 (Fukushima Prefecture); the number became a half in Fukushima Prefecture half a year later. ", doi="10.2196/publichealth.7496", url="http://publichealth.jmir.org/2018/4/e70/", url="http://www.ncbi.nlm.nih.gov/pubmed/30563815" } @Article{info:doi/10.2196/12206, author="Konheim-Kalkstein, L. Yasmine and Miron-Shatz, Talya and Israel, Jenny Leah", title="How Women Evaluate Birth Challenges: Analysis of Web-Based Birth Stories", journal="JMIR Pediatr Parent", year="2018", month="Dec", day="18", volume="1", number="2", pages="e12206", keywords="patient-centered care", keywords="decision making", keywords="parturition", keywords="women's health", abstract="Background: Birth stories provide an intimate glimpse into women's birth experiences in their own words. Understanding the emotions elicited in women by certain types of behaviors during labor and delivery could help those in the health care community provide better emotional care for women in labor. Objective: The aim of this study was to understand which supportive reactions and behaviors contributed to negative or positive emotions among women with regard to their labor and delivery experience. Methods: We sampled 10 women's stories from a popular blog that described births that strayed from the plan. Overall, 90 challenging events that occurred during labor and delivery were identified. Each challenge had an emotionally positive, negative, or neutral evaluation by the woman. We classified supportive and unsupportive behaviors in response to these challenges and examined their association with the woman's emotional appraisal of the challenges. Results: Overall, 4 types of behaviors were identified: informational inclusion, decisional inclusion (mostly by health care providers), practical support, and emotional support (mostly by partners). Supportive reactions were not associated with emotional appraisal; however, unsupportive reactions were associated with women appraising the challenge negatively (Fisher exact test, P=.02). Conclusions: Although supportive behaviors did not elicit any particular emotion, unsupportive behaviors did cause women to view challenges negatively. It is worthwhile conducting a larger scale investigation to observe what happens when patients express their needs, particularly when challenges present themselves during labor, and health care professionals strive to cater to them. ", doi="10.2196/12206", url="http://pediatrics.jmir.org/2018/2/e12206/", url="http://www.ncbi.nlm.nih.gov/pubmed/31518300" } @Article{info:doi/10.2196/11483, author="Hswen, Yulin and Naslund, A. John and Brownstein, S. John and Hawkins, B. Jared", title="Monitoring Online Discussions About Suicide Among Twitter Users With Schizophrenia: Exploratory Study", journal="JMIR Ment Health", year="2018", month="Dec", day="13", volume="5", number="4", pages="e11483", keywords="schizophrenia", keywords="social media", keywords="suicide", keywords="Twitter", keywords="digital technology", keywords="mental health", abstract="Background: People with schizophrenia experience elevated risk of suicide. Mental health symptoms, including depression and anxiety, contribute to increased risk of suicide. Digital technology could support efforts to detect suicide risk and inform suicide prevention efforts. Objective: This exploratory study examined the feasibility of monitoring online discussions about suicide among Twitter users who self-identify as having schizophrenia. Methods: Posts containing the terms suicide or suicidal were collected from a sample of Twitter users who self-identify as having schizophrenia (N=203) and a random sample of control users (N=173) over a 200-day period. Frequency and timing of posts about suicide were compared between groups. The associations between posting about suicide and common mental health symptoms were examined. Results: Twitter users who self-identify as having schizophrenia posted more tweets about suicide (mean 7.10, SD 15.98) compared to control users (mean 1.89, SD 4.79; t374=-4.13, P<.001). Twitter users who self-identify as having schizophrenia showed greater odds of tweeting about suicide compared to control users (odds ratio 2.15, 95\% CI 1.42-3.28). Among all users, tweets about suicide were associated with tweets about depression (r=0.62, P<.001) and anxiety (r=0.45, P<.001). Conclusions: Twitter users who self-identify as having schizophrenia appear to commonly discuss suicide on social media, which is associated with greater discussion about other mental health symptoms. These findings should be interpreted cautiously, as it is not possible to determine whether online discussions about suicide correlate with suicide risk. However, these patterns of online discussion may be indicative of elevated risk of suicide observed in this patient group. There may be opportunities to leverage social media for supporting suicide prevention among individuals with schizophrenia. ", doi="10.2196/11483", url="http://mental.jmir.org/2018/4/e11483/", url="http://www.ncbi.nlm.nih.gov/pubmed/30545811" } @Article{info:doi/10.2196/11542, author="Cheng, Yi-mei Tiffany and Liu, Lisa and Woo, KP Benjamin", title="Analyzing Twitter as a Platform for Alzheimer-Related Dementia Awareness: Thematic Analyses of Tweets", journal="JMIR Aging", year="2018", month="Dec", day="10", volume="1", number="2", pages="e11542", keywords="social media", keywords="Twitter", keywords="dementia", keywords="social support", abstract="Background: Dementia is a prevalent disorder among adults and often subjects an individual and his or her family. Social media websites may serve as a platform to raise awareness for dementia and allow researchers to explore health-related data. Objective: The objective of this study was to utilize Twitter, a social media website, to examine the content and location of tweets containing the keyword ``dementia'' to better understand the reasons why individuals discuss dementia. We adopted an approach that analyzed user location, user category, and tweet content subcategories to classify large publicly available datasets. Methods: A total of 398 tweets were collected using the Twitter search application programming interface with the keyword ``dementia,'' circulated between January and February 2018. Twitter users were categorized into 4 categories: general public, health care field, advocacy organization, and public broadcasting. Tweets posted by ``general public'' users were further subcategorized into 5 categories: mental health advocate, affected persons, stigmatization, marketing, and other. Placement into the categories was done through thematic analysis. Results: A total of 398 tweets were written by 359 different screen names from 28 different countries. The largest number of Twitter users were from the United States and the United Kingdom. Within the United States, the largest number of users were from California and Texas. The majority (281/398, 70.6\%) of Twitter users were categorized into the ``general public'' category. Content analysis of tweets from the ``general public'' category revealed stigmatization (113/281, 40.2\%) and mental health advocacy (102/281, 36.3\%) as the most common themes. Among tweets from California and Texas, California had more stigmatization tweets, while Texas had more mental health advocacy tweets. Conclusions: Themes from the content of tweets highlight the mixture of the political climate and the supportive network present on Twitter. The ability to use Twitter to combat stigma and raise awareness of mental health indicates the benefits that can potentially be facilitated via the platform, but negative stigmatizing tweets may interfere with the effectiveness of this social support. ", doi="10.2196/11542", url="http://aging.jmir.org/2018/2/e11542/", url="http://www.ncbi.nlm.nih.gov/pubmed/31518232" } @Article{info:doi/10.2196/11817, author="Ricard, J. Benjamin and Marsch, A. Lisa and Crosier, Benjamin and Hassanpour, Saeed", title="Exploring the Utility of Community-Generated Social Media Content for Detecting Depression: An Analytical Study on Instagram", journal="J Med Internet Res", year="2018", month="Dec", day="06", volume="20", number="12", pages="e11817", keywords="machine learning", keywords="depression", keywords="social media", keywords="mental health", abstract="Background: The content produced by individuals on various social media platforms has been successfully used to identify mental illness, including depression. However, most of the previous work in this area has focused on user-generated content, that is, content created by the individual, such as an individual's posts and pictures. In this study, we explored the predictive capability of community-generated content, that is, the data generated by a community of friends or followers, rather than by a sole individual, to identify depression among social media users. Objective: The objective of this research was to evaluate the utility of community-generated content on social media, such as comments on an individual's posts, to predict depression as defined by the clinically validated Patient Health Questionnaire-8 (PHQ-8) assessment questionnaire. We hypothesized that the results of this research may provide new insights into next generation of population-level mental illness risk assessment and intervention delivery. Methods: We created a Web-based survey on a crowdsourcing platform through which participants granted access to their Instagram profiles as well as provided their responses to PHQ-8 as a reference standard for depression status. After data quality assurance and postprocessing, the study analyzed the data of 749 participants. To build our predictive model, linguistic features were extracted from Instagram post captions and comments, including multiple sentiment scores, emoji sentiment analysis results, and meta-variables such as the number of likes and average comment length. In this study, 10.4\% (78/749) of the data were held out as a test set. The remaining 89.6\% (671/749) of the data were used to train an elastic-net regularized linear regression model to predict PHQ-8 scores. We compared different versions of this model (ie, a model trained on only user-generated data, a model trained on only community-generated data, and a model trained on the combination of both types of data) on a test set to explore the utility of community-generated data in our predictive analysis. Results: The 2 models, the first trained on only community-generated data (area under curve [AUC]=0.71) and the second trained on a combination of user-generated and community-generated data (AUC=0.72), had statistically significant performances for predicting depression based on the Mann-Whitney U test (P=.03 and P=.02, respectively). The model trained on only user-generated data (AUC=0.63; P=.11) did not achieve statistically significant results. The coefficients of the models revealed that our combined data classifier effectively amalgamated both user-generated and community-generated data and that the 2 feature sets were complementary and contained nonoverlapping information in our predictive analysis. Conclusions: The results presented in this study indicate that leveraging community-generated data from social media, in addition to user-generated data, can be informative for predicting depression among social media users. ", doi="10.2196/11817", url="http://www.jmir.org/2018/12/e11817/", url="http://www.ncbi.nlm.nih.gov/pubmed/30522991" } @Article{info:doi/10.2196/10834, author="Tufts, Christopher and Polsky, Daniel and Volpp, G. Kevin and Groeneveld, W. Peter and Ungar, Lyle and Merchant, M. Raina and Pelullo, P. Arthur", title="Characterizing Tweet Volume and Content About Common Health Conditions Across Pennsylvania: Retrospective Analysis", journal="JMIR Public Health Surveill", year="2018", month="Dec", day="06", volume="4", number="4", pages="e10834", keywords="Twitter messaging", keywords="disease", keywords="prevalence", keywords="public health surveillance", keywords="social media", abstract="Background: Tweets can provide broad, real-time perspectives about health and medical diagnoses that can inform disease surveillance in geographic regions. Less is known, however, about how much individuals post about common health conditions or what they post about. Objective: We sought to collect and analyze tweets from 1 state about high prevalence health conditions and characterize the tweet volume and content. Methods: We collected 408,296,620 tweets originating in Pennsylvania from 2012-2015 and compared the prevalence of 14 common diseases to the frequency of disease mentions on Twitter. We identified and corrected bias induced due to variance in disease term specificity and used the machine learning approach of differential language analysis to determine the content (words and themes) most highly correlated with each disease. Results: Common disease terms were included in 226,802 tweets (174,381 tweets after disease term correction). Posts about breast cancer (39,156/174,381 messages, 22.45\%; 306,127/12,702,379 prevalence, 2.41\%) and diabetes (40,217/174,381 messages, 23.06\%; 2,189,890/12,702,379 prevalence, 17.24\%) were overrepresented on Twitter relative to disease prevalence, whereas hypertension (17,245/174,381 messages, 9.89\%; 4,614,776/12,702,379 prevalence, 36.33\%), chronic obstructive pulmonary disease (1648/174,381 messages, 0.95\%; 1,083,627/12,702,379 prevalence, 8.53\%), and heart disease (13,669/174,381 messages, 7.84\%; 2,461,721/12,702,379 prevalence, 19.38\%) were underrepresented. The content of messages also varied by disease. Personal experience messages accounted for 12.88\% (578/4487) of prostate cancer tweets and 24.17\% (4046/16,742) of asthma tweets. Awareness-themed tweets were more often about breast cancer (9139/39,156 messages, 23.34\%) than asthma (1040/16,742 messages, 6.21\%). Tweets about risk factors were more often about heart disease (1375/13,669 messages, 10.06\%) than lymphoma (105/4927 messages, 2.13\%). Conclusions: Twitter provides a window into the Web-based visibility of diseases and how the volume of Web-based content about diseases varies by condition. Further, the potential value in tweets is in the rich content they provide about individuals' perspectives about diseases (eg, personal experiences, awareness, and risk factors) that are not otherwise easily captured through traditional surveys or administrative data. ", doi="10.2196/10834", url="http://publichealth.jmir.org/2018/4/e10834/", url="http://www.ncbi.nlm.nih.gov/pubmed/30522989" } @Article{info:doi/10.2196/medinform.9162, author="Jones, Josette and Pradhan, Meeta and Hosseini, Masoud and Kulanthaivel, Anand and Hosseini, Mahmood", title="Novel Approach to Cluster Patient-Generated Data Into Actionable Topics: Case Study of a Web-Based Breast Cancer Forum", journal="JMIR Med Inform", year="2018", month="Nov", day="29", volume="6", number="4", pages="e45", keywords="data interpretation", keywords="natural language processing", keywords="patient-generated information", keywords="social media", keywords="statistical analysis", keywords="infodemiology", abstract="Background: The increasing use of social media and mHealth apps has generated new opportunities for health care consumers to share information about their health and well-being. Information shared through social media contains not only medical information but also valuable information about how the survivors manage disease and recovery in the context of daily life. Objective: The objective of this study was to determine the feasibility of acquiring and modeling the topics of a major online breast cancer support forum. Breast cancer patient support forums were selected to discover the hidden, less obvious aspects of disease management and recovery. Methods: First, manual topic categorization was performed using qualitative content analysis (QCA) of each individual forum board. Second, we requested permission from the Breastcancer.org Community for a more in-depth analysis of the postings. Topic modeling was then performed using open source software Machine Learning Language Toolkit, followed by multiple linear regression (MLR) analysis to detect highly correlated topics among the different website forums. Results: QCA of the forums resulted in 20 categories of user discussion. The final topic model organized >4 million postings into 30 manageable topics. Using qualitative analysis of the topic models and statistical analysis, we grouped these 30 topics into 4 distinct clusters with similarity scores of ?0.80; these clusters were labeled Symptoms \& Diagnosis, Treatment, Financial, and Family \& Friends. A clinician review confirmed the clinical significance of the topic clusters, allowing for future detection of actionable items within social media postings. To identify the most significant topics across individual forums, MLR demonstrated that 6 topics---based on the Akaike information criterion values ranging from ?642.75 to ?412.32---were statistically significant. Conclusions: The developed method provides an insight into the areas of interest and concern, including those not ascertainable in the clinic. Such topics included support from lay and professional caregivers and late side effects of therapy that consumers discuss in social media and may be of interest to clinicians. The developed methods and results indicate the potential of social media to inform the clinical workflow with regards to the impact of recovery on daily life. ", doi="10.2196/medinform.9162", url="http://medinform.jmir.org/2018/4/e45/", url="http://www.ncbi.nlm.nih.gov/pubmed/30497991" } @Article{info:doi/10.2196/10827, author="Chen, Shi and Xu, Qian and Buchenberger, John and Bagavathi, Arunkumar and Fair, Gabriel and Shaikh, Samira and Krishnan, Siddharth", title="Dynamics of Health Agency Response and Public Engagement in Public Health Emergency: A Case Study of CDC Tweeting Patterns During the 2016 Zika Epidemic", journal="JMIR Public Health Surveill", year="2018", month="Nov", day="22", volume="4", number="4", pages="e10827", keywords="Centers for Disease Control and Prevention", keywords="public engagement", keywords="Twitter", keywords="time series analysis", keywords="Zika epidemic", keywords="social media", keywords="infodemiology", keywords="infoveillance", abstract="Background: Social media have been increasingly adopted by health agencies to disseminate information, interact with the public, and understand public opinion. Among them, the Centers for Disease Control and Prevention (CDC) is one of the first US government health agencies to adopt social media during health emergencies and crisis. It had been active on Twitter during the 2016 Zika epidemic that caused 5168 domestic noncongenital cases in the United States. Objective: The aim of this study was to quantify the temporal variabilities in CDC's tweeting activities throughout the Zika epidemic, public engagement defined as retweeting and replying, and Zika case counts. It then compares the patterns of these 3 datasets to identify possible discrepancy among domestic Zika case counts, CDC's response on Twitter, and public engagement in this topic. Methods: All of the CDC-initiated tweets published in 2016 with corresponding retweets and replies were collected from 67 CDC--associated Twitter accounts. Both univariate and multivariate time series analyses were performed in each quarter of 2016 for domestic Zika case counts, CDC tweeting activities, and public engagement in the CDC-initiated tweets. Results: CDC sent out >84.0\% (5130/6104) of its Zika tweets in the first quarter of 2016 when Zika case counts were low in the 50 US states and territories (only 560/5168, 10.8\% cases and 662/38,885, 1.70\% cases, respectively). While Zika case counts increased dramatically in the second and third quarters, CDC efforts on Twitter substantially decreased. The time series of public engagement in the CDC-initiated tweets generally differed among quarters and from that of original CDC tweets based on autoregressive integrated moving average model results. Both original CDC tweets and public engagement had the highest mutual information with Zika case counts in the second quarter. Furthermore, public engagement in the original CDC tweets was substantially correlated with and preceded actual Zika case counts. Conclusions: Considerable discrepancies existed among CDC's original tweets regarding Zika, public engagement in these tweets, and actual Zika epidemic. The patterns of these discrepancies also varied between different quarters in 2016. CDC was much more active in the early warning of Zika, especially in the first quarter of 2016. Public engagement in CDC's original tweets served as a more prominent predictor of actual Zika epidemic than the number of CDC's original tweets later in the year. ", doi="10.2196/10827", url="http://publichealth.jmir.org/2018/4/e10827/", url="http://www.ncbi.nlm.nih.gov/pubmed/30467106" } @Article{info:doi/10.2196/10262, author="Odlum, Michelle and Yoon, Sunmoo and Broadwell, Peter and Brewer, Russell and Kuang, Da", title="How Twitter Can Support the HIV/AIDS Response to Achieve the 2030 Eradication Goal: In-Depth Thematic Analysis of World AIDS Day Tweets", journal="JMIR Public Health Surveill", year="2018", month="Nov", day="22", volume="4", number="4", pages="e10262", keywords="community", keywords="human rights", keywords="social network", keywords="infodemiology", keywords="infoveillence", keywords="Twitter", abstract="Background: HIV/AIDS is a tremendous public health crisis, with a call for its eradication by 2030. A human rights response through civil society engagement is critical to support and sustain HIV eradication efforts. However, ongoing civil engagement is a challenge. Objective: This study aimed to demonstrate the use of Twitter data to assess public sentiment in support of civil society engagement. Methods: Tweets were collected during World AIDS Days 2014 and 2015. A total of 39,940 unique tweets (>10 billion users) in 2014 and 78,215 unique tweets (>33 billion users) in 2015 were analyzed. Response frequencies were aggregated using natural language processing. Hierarchical rank-2 nonnegative matrix factorization algorithm generated a hierarchy of tweets into binary trees. Tweet hierarchy clusters were thematically organized by the Joint United Nations Programme on HIV/AIDS core action principles and categorized under HIV/AIDS Prevention, Treatment or Care, or Support. Results: Topics tweeted 35 times or more were visualized. Results show a decrease in 2015 in the frequency of tweets associated with the fight to end HIV/AIDS, the recognition of women, and to achieve an AIDS-free generation. Moreover, an increase in tweets was associated with an integrative approach to the HIV/AIDS response. Hierarchical thematic differences in 2015 included no prevention discussion and the recognition of the pandemic's impact and discrimination. In addition, a decrease was observed in motivation to fast track the pandemic's end and combat HIV/AIDS. Conclusions: The human rights--based response to HIV/AIDS eradication is critical. Findings demonstrate the usefulness of Twitter as a low-cost method to assess public sentiment for enhanced knowledge, increased hope, and revitalized expectations for HIV/AIDS eradication. ", doi="10.2196/10262", url="http://publichealth.jmir.org/2018/4/e10262/", url="http://www.ncbi.nlm.nih.gov/pubmed/30467102" } @Article{info:doi/10.2196/10513, author="Zhang, Youshan and Allem, Jon-Patrick and Unger, Beth Jennifer and Boley Cruz, Tess", title="Automated Identification of Hookahs (Waterpipes) on Instagram: An Application in Feature Extraction Using Convolutional Neural Network and Support Vector Machine Classification", journal="J Med Internet Res", year="2018", month="Nov", day="21", volume="20", number="11", pages="e10513", keywords="convolutional neural network", keywords="feature extraction", keywords="image classification", keywords="Instagram", keywords="social media", keywords="support vector machine", abstract="Background: Instagram, with millions of posts per day, can be used to inform public health surveillance targets and policies. However, current research relying on image-based data often relies on hand coding of images, which is time-consuming and costly, ultimately limiting the scope of the study. Current best practices in automated image classification (eg, support vector machine (SVM), backpropagation neural network, and artificial neural network) are limited in their capacity to accurately distinguish between objects within images. Objective: This study aimed to demonstrate how a convolutional neural network (CNN) can be used to extract unique features within an image and how SVM can then be used to classify the image. Methods: Images of waterpipes or hookah (an emerging tobacco product possessing similar harms to that of cigarettes) were collected from Instagram and used in the analyses (N=840). A CNN was used to extract unique features from images identified to contain waterpipes. An SVM classifier was built to distinguish between images with and without waterpipes. Methods for image classification were then compared to show how a CNN+SVM classifier could improve accuracy. Results: As the number of validated training images increased, the total number of extracted features increased. In addition, as the number of features learned by the SVM classifier increased, the average level of accuracy increased. Overall, 99.5\% (418/420) of images classified were correctly identified as either hookah or nonhookah images. This level of accuracy was an improvement over earlier methods that used SVM, CNN, or bag-of-features alone. Conclusions: A CNN extracts more features of images, allowing an SVM classifier to be better informed, resulting in higher accuracy compared with methods that extract fewer features. Future research can use this method to grow the scope of image-based studies. The methods presented here might help detect increases in the popularity of certain tobacco products over time on social media. By taking images of waterpipes from Instagram, we place our methods in a context that can be utilized to inform health researchers analyzing social media to understand user experience with emerging tobacco products and inform public health surveillance targets and policies. ", doi="10.2196/10513", url="http://www.jmir.org/2018/11/e10513/", url="http://www.ncbi.nlm.nih.gov/pubmed/30452385" } @Article{info:doi/10.2196/10466, author="K{\"u}rzinger, Marie-Laure and Sch{\"u}ck, St{\'e}phane and Texier, Nathalie and Abdellaoui, Redhouane and Faviez, Carole and Pouget, Julie and Zhang, Ling and Tcherny-Lessenot, St{\'e}phanie and Lin, Stephen and Juhaeri, Juhaeri", title="Web-Based Signal Detection Using Medical Forums Data in France: Comparative Analysis", journal="J Med Internet Res", year="2018", month="Nov", day="20", volume="20", number="11", pages="e10466", keywords="adverse event", keywords="internet", keywords="medical forums", keywords="pharmacovigilance", keywords="signal detection", keywords="signals of disproportionate reporting", keywords="social media", abstract="Background: While traditional signal detection methods in pharmacovigilance are based on spontaneous reports, the use of social media is emerging. The potential strength of Web-based data relies on their volume and real-time availability, allowing early detection of signals of disproportionate reporting (SDRs). Objective: This study aimed (1) to assess the consistency of SDRs detected from patients' medical forums in France compared with those detected from the traditional reporting systems and (2) to assess the ability of SDRs in identifying earlier than the traditional reporting systems. Methods: Messages posted on patients' forums between 2005 and 2015 were used. We retained 8 disproportionality definitions. Comparison of SDRs from the forums with SDRs detected in VigiBase was done by describing the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, receiver operating characteristics curve, and the area under the curve (AUC). The time difference in months between the detection dates of SDRs from the forums and VigiBase was provided. Results: The comparison analysis showed that the sensitivity ranged from 29\% to 50.6\%, the specificity from 86.1\% to 95.5\%, the PPV from 51.2\% to 75.4\%, the NPV from 68.5\% to 91.6\%, and the accuracy from 68\% to 87.7\%. The AUC reached 0.85 when using the metric empirical Bayes geometric mean. Up to 38\% (12/32) of the SDRs were detected earlier in the forums than that in VigiBase. Conclusions: The specificity, PPV, and NPV were high. The overall performance was good, showing that data from medical forums may be a valuable source for signal detection. In total, up to 38\% (12/32) of the SDRs could have been detected earlier, thus, ensuring the increased safety of patients. Further enhancements are needed to investigate the reliability and validation of patients' medical forums worldwide, the extension of this analysis to all possible drugs or at least to a wider selection of drugs, as well as to further assess performance against established signals. ", doi="10.2196/10466", url="http://www.jmir.org/2018/11/e10466/", url="http://www.ncbi.nlm.nih.gov/pubmed/30459145" } @Article{info:doi/10.2196/11669, author="Allem, Jon-Patrick and Dharmapuri, Likhit and Leventhal, M. Adam and Unger, B. Jennifer and Boley Cruz, Tess", title="Hookah-Related Posts to Twitter From 2017 to 2018: Thematic Analysis", journal="J Med Internet Res", year="2018", month="Nov", day="19", volume="20", number="11", pages="e11669", keywords="hookah", keywords="waterpipe", keywords="Twitter", keywords="social media", keywords="nicotine", keywords="flavors", keywords="social smoking", keywords="infodemiology", abstract="Background: Hookah (or tobacco waterpipe) use has recently become prevalent in the United States. The contexts and experiences associated with hookah use are unclear, yet such information is abundant via publicly available hookah users' social media postings. Objective: In this study, we utilized Twitter data to characterize Twitter users' recent experiences with hookah. Methods: Twitter posts containing the term ``hookah'' were obtained from April 1, 2017 to 29 March, 2018. Text classifiers were used to identify clusters of topics that tended to co-occur in posts (n=176,706). Results: The most prevalent topic cluster was Person Tagging (use of @username to tag another Twitter account in a post) at 21.58\% (38,137/176,706) followed by Promotional or Social Events (eg, mentions of ladies' nights, parties, etc) at 20.20\% (35,701/176,706) and Appeal or Abuse Liability (eg, craving, enjoying hookah) at 18.12\% (32,013/176,706). Additional topics included Hookah Use Behavior (eg, mentions of taking a ``hit'' of hookah) at 11.67\% (20,603/176,706), Polysubstance Use (eg, hookah use along with other substances) at 10.95\% (19,353/176,706), Buying or Selling (eg, buy, order, purchase, sell) at 9.37\% (16,552/176,706), and Flavors (eg, mint, cinnamon, watermelon) at 1.66\% (2927/176,706). The topic Dislike of Hookah (eg, hate, quit, dislike) was rare at 0.59\% (1043/176,706). Conclusions: Social events, appeal or abuse liability, flavors, and polysubstance use were the common contexts and experiences associated with Twitter discussions about hookah in 2017-2018. Considered in concert with traditional data sources about hookah, these results suggest that social events, appeal or abuse liability, flavors, and polysubstance use warrant consideration as targets in future surveillance, policy making, and interventions addressing hookah. ", doi="10.2196/11669", url="http://www.jmir.org/2018/11/e11669/", url="http://www.ncbi.nlm.nih.gov/pubmed/30455162" } @Article{info:doi/10.2196/jmir.9366, author="Mavragani, Amaryllis and Ochoa, Gabriela and Tsagarakis, P. Konstantinos", title="Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review", journal="J Med Internet Res", year="2018", month="Nov", day="06", volume="20", number="11", pages="e270", keywords="big data", keywords="health assessment", keywords="infodemiology", keywords="Google Trends", keywords="medicine", keywords="review", keywords="statistical analysis", abstract="Background: In the era of information overload, are big data analytics the answer to access and better manage available knowledge? Over the last decade, the use of Web-based data in public health issues, that is, infodemiology, has been proven useful in assessing various aspects of human behavior. Google Trends is the most popular tool to gather such information, and it has been used in several topics up to this point, with health and medicine being the most focused subject. Web-based behavior is monitored and analyzed in order to examine actual human behavior so as to predict, better assess, and even prevent health-related issues that constantly arise in everyday life. Objective: This systematic review aimed at reporting and further presenting and analyzing the methods, tools, and statistical approaches for Google Trends (infodemiology) studies in health-related topics from 2006 to 2016 to provide an overview of the usefulness of said tool and be a point of reference for future research on the subject. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for selecting studies, we searched for the term ``Google Trends'' in the Scopus and PubMed databases from 2006 to 2016, applying specific criteria for types of publications and topics. A total of 109 published papers were extracted, excluding duplicates and those that did not fall inside the topics of health and medicine or the selected article types. We then further categorized the published papers according to their methodological approach, namely, visualization, seasonality, correlations, forecasting, and modeling. Results: All the examined papers comprised, by definition, time series analysis, and all but two included data visualization. A total of 23.1\% (24/104) studies used Google Trends data for examining seasonality, while 39.4\% (41/104) and 32.7\% (34/104) of the studies used correlations and modeling, respectively. Only 8.7\% (9/104) of the studies used Google Trends data for predictions and forecasting in health-related topics; therefore, it is evident that a gap exists in forecasting using Google Trends data. Conclusions: The monitoring of online queries can provide insight into human behavior, as this field is significantly and continuously growing and will be proven more than valuable in the future for assessing behavioral changes and providing ground for research using data that could not have been accessed otherwise. ", doi="10.2196/jmir.9366", url="https://www.jmir.org/2018/11/e270/", url="http://www.ncbi.nlm.nih.gov/pubmed/30401664" } @Article{info:doi/10.2196/mental.9533, author="DeJohn, D. Amber and Schulz, English Emily and Pearson, L. Amber and Lachmar, Megan E. and Wittenborn, K. Andrea", title="Identifying and Understanding Communities Using Twitter to Connect About Depression: Cross-Sectional Study", journal="JMIR Ment Health", year="2018", month="Nov", day="05", volume="5", number="4", pages="e61", keywords="depression", keywords="Web-based", keywords="social connection", keywords="Twitter", keywords="tweet", keywords="online communities", abstract="Background: Depression is the leading cause of diseases globally and is often characterized by a lack of social connection. With the rise of social media, it is seen that Twitter users are seeking Web-based connections for depression. Objective: This study aimed to identify communities where Twitter users tweeted using the hashtag \#MyDepressionLooksLike to connect about depression. Once identified, we wanted to understand which community characteristics correlated to Twitter users turning to a Web-based community to connect about depression. Methods: Tweets were collected using NCapture software from May 25 to June 1, 2016 during the Mental Health Month (n=104) in the northeastern United States and Washington DC. After mapping tweets, we used a Poisson multilevel regression model to predict tweets per community (county) offset by the population and adjusted for percent female, percent population aged 15-44 years, percent white, percent below poverty, and percent single-person households. We then compared predicted versus observed counts and calculated tweeting index values (TIVs) to represent undertweeting and overtweeting. Last, we examined trends in community characteristics by TIV using Pearson correlation. Results: We found significant associations between tweet counts and area-level proportions of females, single-person households, and population aged 15-44 years. TIVs were lower than expected (TIV 1) in eastern, seaboard areas of the study region. There were communities tweeting as expected in the western, inland areas (TIV 2). Counties tweeting more than expected were generally scattered throughout the study region with a small cluster at the base of Maine. When examining community characteristics and overtweeting and undertweeting by county, we observed a clear upward gradient in several types of nonprofits and TIV values. However, we also observed U-shaped relationships for many community factors, suggesting that the same characteristics were correlated with both overtweeting and undertweeting. Conclusions: Our findings suggest that Web-based communities, rather than replacing physical connection, may complement or serve as proxies for offline social communities, as seen through the consistent correlations between higher levels of tweeting and abundant nonprofits. Future research could expand the spatiotemporal scope to confirm these findings. ", doi="10.2196/mental.9533", url="http://mental.jmir.org/2018/4/e61/", url="http://www.ncbi.nlm.nih.gov/pubmed/30401662" } @Article{info:doi/10.2196/11085, author="Park, Hyun So and Hong, Hee Song", title="Identification of Primary Medication Concerns Regarding Thyroid Hormone Replacement Therapy From Online Patient Medication Reviews: Text Mining of Social Network Data", journal="J Med Internet Res", year="2018", month="Oct", day="24", volume="20", number="10", pages="e11085", keywords="medication counseling", keywords="social network data", keywords="primary medication concerns", keywords="satisfaction with levothyroxine treatment", abstract="Background: Patients with hypothyroidism report poor health-related quality of life despite having undergone thyroid hormone replacement therapy (THRT). Understanding patient concerns regarding levothyroxine can help improve the treatment outcomes of THRT. Objective: This study aimed to (1) identify the distinctive themes in patient concerns regarding THRT, (2) determine whether patients have unique primary medication concerns specific to their demographics, and (3) determine the predictability of primary medication concerns on patient treatment satisfaction. Methods: We collected patient reviews from WebMD in the United States (1037 reviews about generic levothyroxine and 1075 reviews about the brand version) posted between September 1, 2007, and January 30, 2017. We used natural language processing to identify the themes of medication concerns. Multiple regression analyses were conducted in order to examine the predictability of the primary medication concerns on patient treatment satisfaction. Results: Natural language processing of the patient reviews of levothyroxine posted on a social networking site produced 6 distinctive themes of patient medication concerns related to levothyroxine treatment: how to take the drug, treatment initiation, dose adjustment, symptoms of pain, generic substitutability, and appearance. Patients had different primary medication concerns unique to their gender, age, and treatment duration. Furthermore, treatment satisfaction on levothyroxine depended on what primary medication concerns the patient had. Conclusions: Natural language processing of text content available on social media could identify different themes of patient medication concerns that can be validated in future studies to inform the design of tailored medication counseling for improved patient treatment satisfaction. ", doi="10.2196/11085", url="http://www.jmir.org/2018/10/e11085/", url="http://www.ncbi.nlm.nih.gov/pubmed/30355555" } @Article{info:doi/10.2196/10043, author="Sewalk, C. Kara and Tuli, Gaurav and Hswen, Yulin and Brownstein, S. John and Hawkins, B. Jared", title="Using Twitter to Examine Web-Based Patient Experience Sentiments in the United States: Longitudinal Study", journal="J Med Internet Res", year="2018", month="Oct", day="12", volume="20", number="10", pages="e10043", keywords="health care", keywords="social media", keywords="patient experience", abstract="Background: There are documented differences in access to health care across the United States. Previous research indicates that Web-based data regarding patient experiences and opinions of health care are available from Twitter. Sentiment analyses of Twitter data can be used to examine differences in patient views of health care across the United States. Objective: The objective of our study was to provide a characterization of patient experience sentiments across the United States on Twitter over a 4-year period. Methods: Using data from Twitter, we developed a set of 4 software components to automatically label and examine a database of tweets discussing patient experience. The set includes a classifier to determine patient experience tweets, a geolocation inference engine for social data, a modified sentiment classifier, and an engine to determine if the tweet is from a metropolitan or nonmetropolitan area in the United States. Using the information retrieved, we conducted spatial and temporal examinations of tweet sentiments at national and regional levels. We examined trends in the time of the day and that of the week when tweets were posted. Statistical analyses were conducted to determine if any differences existed between the discussions of patient experience in metropolitan and nonmetropolitan areas. Results: We collected 27.3 million tweets between February 1, 2013 and February 28, 2017, using a set of patient experience-related keywords; the classifier was able to identify 2,759,257 tweets labeled as patient experience. We identified the approximate location of 31.76\% (876,384/2,759,257) patient experience tweets using a geolocation classifier to conduct spatial analyses. At the national level, we observed 27.83\% (243,903/876,384) positive patient experience tweets, 36.22\% (317,445/876,384) neutral patient experience tweets, and 35.95\% (315,036/876,384) negative patient experience tweets. There were slight differences in tweet sentiments across all regions of the United States during the 4-year study period. We found the average sentiment polarity shifted toward less negative over the study period across all the regions of the United States. We observed the sentiment of tweets to have a lower negative fraction during daytime hours, whereas the sentiment of tweets posted between 8 pm and 10 am had a higher negative fraction. Nationally, sentiment scores for tweets in metropolitan areas were found to be more extremely negative and mildly positive compared with tweets in nonmetropolitan areas. This result is statistically significant (P<.001). Tweets with extremely negative sentiments had a medium effect size (d=0.34) at the national level. Conclusions: This study presents methodologies for a deeper understanding of Web-based discussion related to patient experience across space and time and demonstrates how Twitter can provide a unique and unsolicited perspective from users on the health care they receive in the United States. ", doi="10.2196/10043", url="http://www.jmir.org/2018/10/e10043/", url="http://www.ncbi.nlm.nih.gov/pubmed/30314959" } @Article{info:doi/10.2196/jmir.9646, author="Zhao, Mengnan and Yang, C. Christopher", title="Drug Repositioning to Accelerate Drug Development Using Social Media Data: Computational Study on Parkinson Disease", journal="J Med Internet Res", year="2018", month="Oct", day="11", volume="20", number="10", pages="e271", keywords="drug repositioning", keywords="Parkinson disease", keywords="heterogeneous network", keywords="social media", abstract="Background: Due to the high cost and low success rate in new drug development, systematic drug repositioning methods are exploited to find new indications for existing drugs. Objective: We sought to propose a new computational drug repositioning method to identify repositioning drugs for Parkinson disease (PD). Methods: We developed a novel heterogeneous network mining repositioning method that constructed a 3-layer network of disease, drug, and adverse drug reaction and involved user-generated data from online health communities to identify potential candidate drugs for PD. Results: We identified 44 non-Parkinson drugs by using the proposed approach, with data collected from both pharmaceutical databases and online health communities. Based on the further literature analysis, we found literature evidence for 28 drugs. Conclusions: In summary, the proposed heterogeneous network mining repositioning approach is promising for identifying repositioning candidates for PD. It shows that adverse drug reactions are potential intermediaries to reveal relationships between disease and drug. ", doi="10.2196/jmir.9646", url="http://www.jmir.org/2018/10/e271/", url="http://www.ncbi.nlm.nih.gov/pubmed/30309833" } @Article{info:doi/10.2196/mhealth.7623, author="Leung, Ricky and Guo, Huibin and Pan, Xuan", title="Social Media Users' Perception of Telemedicine and mHealth in China: Exploratory Study", journal="JMIR Mhealth Uhealth", year="2018", month="Sep", day="25", volume="6", number="9", pages="e181", keywords="mHealth", keywords="telemedicine", keywords="China", keywords="social media", keywords="text mining", keywords="keyword analysis", keywords="mobile phone", abstract="Background: The use of telemedicine and mHealth has increased rapidly in the People's Republic of China. While telemedicine and mHealth have great potential, wide adoption of this technology depends on how patients, health care providers, and other stakeholders in the Chinese health sector perceive and accept the technology. Objective: To explore this issue, we aimed to examine a social media platform with a dedicated focus on health information technology and informatics in China. Our goal is to utilize the findings to support further research. Methods: In this exploratory study, we selected a social media platform---HC3i.cn---to examine the perception of telemedicine and mHealth in China. We performed keyword analysis and analyzed the prevalence and term frequency--inverse document frequency of keywords in the selected social media platform; furthermore, we performed qualitative analysis. Results: We organized the most prominent 16 keywords from 571 threads into 8 themes: (1) Question versus Answer; (2) Hospital versus Clinic; (3) Market versus Company; (4) Doctor versus Nurse; (5) Family versus Patient; (6) iPad versus Tablet; (7) System versus App; and (8) Security versus Caregiving. Social media participants perceived not only significant opportunities associated with telemedicine and mHealth but also barriers to overcome to realize these opportunities. Conclusions: We identified interesting issues in this paper by studying a social media platform in China. Among other things, participants in the selected platform raised concerns about quality and costs associated with the provision of telemedicine and mHealth, despite the new technology's great potential to address different issues in the Chinese health sector. The methods applied in this paper have some limitations, and the findings may not be generalizable. We have discussed directions for further research. ", doi="10.2196/mhealth.7623", url="http://mhealth.jmir.org/2018/9/e181/", url="http://www.ncbi.nlm.nih.gov/pubmed/30274969" } @Article{info:doi/10.2196/publichealth.8627, author="Wakamiya, Shoko and Kawai, Yukiko and Aramaki, Eiji", title="Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining Study", journal="JMIR Public Health Surveill", year="2018", month="Sep", day="25", volume="4", number="3", pages="e65", keywords="influenza surveillance", keywords="location mention", keywords="Twitter", keywords="social network", keywords="spatial analysis", keywords="internet", keywords="microblog", keywords="infodemiology", keywords="infoveillance", abstract="Background: The recent rise in popularity and scale of social networking services (SNSs) has resulted in an increasing need for SNS-based information extraction systems. A popular application of SNS data is health surveillance for predicting an outbreak of epidemics by detecting diseases from text messages posted on SNS platforms. Such applications share the following logic: they incorporate SNS users as social sensors. These social sensor--based approaches also share a common problem: SNS-based surveillance are much more reliable if sufficient numbers of users are active, and small or inactive populations produce inconsistent results. Objective: This study proposes a novel approach to estimate the trend of patient numbers using indirect information covering both urban areas and rural areas within the posts. Methods: We presented a TRAP model by embedding both direct information and indirect information. A collection of tweets spanning 3 years (7 million influenza-related tweets in Japanese) was used to evaluate the model. Both direct information and indirect information that mention other places were used. As indirect information is less reliable (too noisy or too old) than direct information, the indirect information data were not used directly and were considered as inhibiting direct information. For example, when indirect information appeared often, it was considered as signifying that everyone already had a known disease, leading to a small amount of direct information. Results: The estimation performance of our approach was evaluated using the correlation coefficient between the number of influenza cases as the gold standard values and the estimated values by the proposed models. The results revealed that the baseline model (BASELINE+NLP) shows .36 and that the proposed model (TRAP+NLP) improved the accuracy (.70, +.34 points). Conclusions: The proposed approach by which the indirect information inhibits direct information exhibited improved estimation performance not only in rural cities but also in urban cities, which demonstrated the effectiveness of the proposed method consisting of a TRAP model and natural language processing (NLP) classification. ", doi="10.2196/publichealth.8627", url="http://publichealth.jmir.org/2018/3/e65/", url="http://www.ncbi.nlm.nih.gov/pubmed/30274968" } @Article{info:doi/10.2196/10435, author="Pretorius, A. Kelly and Mackert, Michael and Wilcox, B. Gary", title="Sudden Infant Death Syndrome and Safe Sleep on Twitter: Analysis of Influences and Themes to Guide Health Promotion Efforts", journal="JMIR Pediatr Parent", year="2018", month="Sep", day="07", volume="1", number="2", pages="e10435", keywords="sudden infant death", keywords="sudden unexpected infant death", keywords="accidental suffocation in a sleeping environment", keywords="infant mortality", keywords="safe sleep", keywords="sleep environment", keywords="social media", keywords="Twitter", keywords="health communication", keywords="public health", abstract="Background: In the United States, sudden infant death syndrome (SIDS) is the leading cause of death in infants aged 1 month to 1 year. Approximately 3500 infants die from SIDS and sleep-related reasons on a yearly basis. Unintentional sleep-related deaths and bed sharing, a known risk factor for SIDS, are on the rise. Furthermore, ethnic disparities exist among those most affected by SIDS. Despite public health campaigns, infant mortality persists. Given the popularity of social media, understanding social media conversations around SIDS and safe sleep may assist the medical and public health communities with information needed to spread, reinforce, or counteract false information regarding SIDS and safe sleep. Objective: The objective of our study was to investigate the social media conversation around SIDS and safe sleep to understand the possible influences and guide health promotion efforts and public health research as well as enable health professionals to engage in directed communication regarding this topic. Methods: We used textual analytics to identify topics and extract meanings contained in unstructured textual data. Twitter messages were captured during September, October, and November in 2017. Tweets and retweets were collected using NUVI software in conjunction with Twitter's search API using the keywords: ``sids,'' ``infant death syndrome,'' ``sudden infant death syndrome,'' and ``safe sleep.'' This returned a total of 41,358 messages, which were analyzed using text mining and social media monitoring software. Results: Multiple themes were identified, including recommendations for safe sleep to prevent SIDS, safe sleep devices, the potential causes of SIDS, and how breastfeeding reduces SIDS. Compared with September and November, more personal and specific stories of infant loss were demonstrated in October (Pregnancy and Infant Loss Awareness Month). The top influencers were news organizations, universities, and health-related organizations. Conclusions: We identified valuable topics discussed and shared on Twitter regarding SIDS and safe sleep. The study results highlight the contradicting information a subset of the population is exposed to regarding SIDS and the continued controversy over vaccines. In addition, this analysis emphasizes the lack of public health organizations' presence on Twitter compared with the influence of universities and news media organizations. The results also demonstrate the prevalence of safe sleep products that are embedded in safe sleep messaging. These findings can assist providers in speaking about relevant topics when engaging in conversations about the prevention of SIDS and the promotion of safe sleep. Furthermore, public health agencies and advocates should utilize social media and Twitter to better communicate accurate health information as well as continue to combat the spread of false information. ", doi="10.2196/10435", url="http://pediatrics.jmir.org/2018/2/e10435/", url="http://www.ncbi.nlm.nih.gov/pubmed/31518314" } @Article{info:doi/10.2196/jmir.9373, author="Li, Qiudan and Wang, Can and Liu, Ruoran and Wang, Lei and Zeng, Dajun Daniel and Leischow, James Scott", title="Understanding Users' Vaping Experiences from Social Media: Initial Study Using Sentiment Opinion Summarization Techniques", journal="J Med Internet Res", year="2018", month="Aug", day="15", volume="20", number="8", pages="e252", keywords="electronic nicotine delivery systems", keywords="e-cigarette", keywords="e-liquid", keywords="JuiceDB", keywords="sentiment opinion summarization", keywords="social media", keywords="vaping", keywords="infodemiology", abstract="Background: E-liquid is one of the main components in electronic nicotine delivery systems (ENDS). ENDS review comments could serve as an early warning on use patterns and even function to serve as an indicator of problems or adverse events pertaining to the use of specific e-liquids---much like types of responses tracked by the Food and Drug Administration (FDA) regarding medications. Objective: This study aimed to understand users' ``vaping'' experience using sentiment opinion summarization techniques, which can help characterize how consumers think about specific e-liquids and their characteristics (eg, flavor, throat hit, and vapor production). Methods: We collected e-liquid reviews on JuiceDB from June 27, 2013 to December 31, 2017 using its public application programming interface. The dataset contains 27,070 reviews for 8058 e-liquid products. Each review is accompanied by an overall rating and a set of 4 aspect ratings of an e-liquid, each on a scale of 1-5: flavor accuracy, throat hit, value, and cloud production. An iterative dichotomiser 3 (ID3)-based influential aspect analysis model was adopted to learn the key elements that impact e-liquid use. Then, fine-grained sentiment analysis was employed to mine opinions on various aspects of vaping experience related to e-liquids. Results: We found that flavor accuracy and value were the two most important aspects that affected users' sentiments toward e-liquids. Of reviews in JuiceDB, 67.83\% (18,362/27,070) were positive, while 12.67\% (3430/27,070) were negative. This indicates that users generally hold positive attitudes toward e-liquids. Among the 9 flavors, fruity and sweet were the two most popular. Great and sweet tastes, reasonable value, and strong throat hit made users satisfied with fruity and sweet flavors, whereas ``strange'' tastes made users dislike those flavors. Meanwhile, users complained about some e-liquids' steep or expensive prices, bad quality, and harsh throat hit. There were 2342 fruity e-liquids and 2049 sweet e-liquids. There were 55.81\% (1307/2342) and 59.83\% (1226/2049) positive sentiments and 13.62\% (319/2342) and 12.88\% (264/2049) negative sentiments toward fruity e-liquids and sweet e-liquids, respectively. Great flavors and good vapors contributed to positive reviews of fruity and sweet products. However, bad tastes such as ``sour'' or ``bitter'' resulted in negative reviews. These findings can help businesses and policy makers to further improve product quality and formulate effective policy. Conclusions: This study provides an effective mechanism for analyzing users' ENDS vaping experience based on sentiment opinion summarization techniques. Sentiment opinions on aspect and products can be found using our method, which is of great importance to monitor e-liquid products and improve work efficiency. ", doi="10.2196/jmir.9373", url="http://www.jmir.org/2018/8/e252/", url="http://www.ncbi.nlm.nih.gov/pubmed/30111530" } @Article{info:doi/10.2196/jmir.9413, author="Du, Jingcheng and Tang, Lu and Xiang, Yang and Zhi, Degui and Xu, Jun and Song, Hsing-Yi and Tao, Cui", title="Public Perception Analysis of Tweets During the 2015 Measles Outbreak: Comparative Study Using Convolutional Neural Network Models", journal="J Med Internet Res", year="2018", month="Jul", day="09", volume="20", number="7", pages="e236", keywords="convolutional neural networks", keywords="social media", keywords="measles", keywords="public perception", abstract="Background: Timely understanding of public perceptions allows public health agencies to provide up-to-date responses to health crises such as infectious diseases outbreaks. Social media such as Twitter provide an unprecedented way for the prompt assessment of the large-scale public response. Objective: The aims of this study were to develop a scheme for a comprehensive public perception analysis of a measles outbreak based on Twitter data and demonstrate the superiority of the convolutional neural network (CNN) models (compared with conventional machine learning methods) on measles outbreak-related tweets classification tasks with a relatively small and highly unbalanced gold standard training set. Methods: We first designed a comprehensive scheme for the analysis of public perception of measles based on tweets, including 3 dimensions: discussion themes, emotions expressed, and attitude toward vaccination. All 1,154,156 tweets containing the word ``measles'' posted between December 1, 2014, and April 30, 2015, were purchased and downloaded from DiscoverText.com. Two expert annotators curated a gold standard of 1151 tweets (approximately 0.1\% of all tweets) based on the 3-dimensional scheme. Next, a tweet classification system based on the CNN framework was developed. We compared the performance of the CNN models to those of 4 conventional machine learning models and another neural network model. We also compared the impact of different word embeddings configurations for the CNN models: (1) Stanford GloVe embedding trained on billions of tweets in the general domain, (2) measles-specific embedding trained on our 1 million measles related tweets, and (3) a combination of the 2 embeddings. Results: Cohen kappa intercoder reliability values for the annotation were: 0.78, 0.72, and 0.80 on the 3 dimensions, respectively. Class distributions within the gold standard were highly unbalanced for all dimensions. The CNN models performed better on all classification tasks than k-nearest neighbors, na{\"i}ve Bayes, support vector machines, or random forest. Detailed comparison between support vector machines and the CNN models showed that the major contributor to the overall superiority of the CNN models is the improvement on recall, especially for classes with low occurrence. The CNN model with the 2 embedding combination led to better performance on discussion themes and emotions expressed (microaveraging F1 scores of 0.7811 and 0.8592, respectively), while the CNN model with Stanford embedding achieved best performance on attitude toward vaccination (microaveraging F1 score of 0.8642). Conclusions: The proposed scheme can successfully classify the public's opinions and emotions in multiple dimensions, which would facilitate the timely understanding of public perceptions during the outbreak of an infectious disease. Compared with conventional machine learning methods, our CNN models showed superiority on measles-related tweet classification tasks with a relatively small and highly unbalanced gold standard. With the success of these tasks, our proposed scheme and CNN-based tweets classification system is expected to be useful for the analysis of tweets about other infectious diseases such as influenza and Ebola. ", doi="10.2196/jmir.9413", url="http://www.jmir.org/2018/7/e236/", url="http://www.ncbi.nlm.nih.gov/pubmed/29986843" } @Article{info:doi/10.2196/jmir.9355, author="Hendriks, Hanneke and Van den Putte, Bas and Gebhardt, A. Winifred and Moreno, A. Megan", title="Social Drinking on Social Media: Content Analysis of the Social Aspects of Alcohol-Related Posts on Facebook and Instagram", journal="J Med Internet Res", year="2018", month="Jun", day="22", volume="20", number="6", pages="e226", keywords="social media", keywords="alcohol drinking", keywords="social interaction", abstract="Background: Alcohol is often consumed in social contexts. An emerging social context in which alcohol is becoming increasingly apparent is social media. More and more young people display alcohol-related posts on social networking sites such as Facebook and Instagram. Objective: Considering the importance of the social aspects of alcohol consumption and social media use, this study investigated the social content of alcohol posts (ie, the evaluative social context and presence of people) and social processes (ie, the posting of and reactions to posts) involved with alcohol posts on social networking sites. Methods: Participants (N=192; mean age 20.64, SD 4.68 years, 132 women and 54 men) gave researchers access to their Facebook and/or Instagram profiles, and an extensive content analysis of these profiles was conducted. Coders were trained and then coded all screenshotted timelines in terms of evaluative social context, presence of people, and reactions to post. Results: Alcohol posts of youth frequently depict alcohol in a positive social context (425/438, 97.0\%) and display people holding drinks (277/412, 67.2\%). In addition, alcohol posts were more often placed on participants' timelines by others (tagging; 238/439, 54.2\%) than posted by participants themselves (201/439, 45.8\%). Furthermore, it was revealed that such social posts received more likes (mean 35.50, SD 26.39) and comments than nonsocial posts (no people visible; mean 10.34, SD 13.19, P<.001). Conclusions: In terms of content and processes, alcohol posts on social media are social in nature and a part of young people's everyday social lives. Interventions aiming to decrease alcohol posts should therefore focus on the broad social context of individuals in which posting about alcohol takes place. Potential intervention strategies could involve making young people aware that when they post about social gatherings in which alcohol is visible and tag others, it may have unintended negative consequences and should be avoided. ", doi="10.2196/jmir.9355", url="http://www.jmir.org/2018/6/e226/", url="http://www.ncbi.nlm.nih.gov/pubmed/29934290" } @Article{info:doi/10.2196/jmir.9870, author="Keller, Sophie Michelle and Mosadeghi, Sasan and Cohen, R. Erica and Kwan, James and Spiegel, Ross Brennan Mason", title="Reproductive Health and Medication Concerns for Patients With Inflammatory Bowel Disease: Thematic and Quantitative Analysis Using Social Listening", journal="J Med Internet Res", year="2018", month="Jun", day="11", volume="20", number="6", pages="e206", keywords="pregnancy", keywords="breastfeeding", keywords="reproductive health", keywords="social media", keywords="medication adherence", keywords="infodemiology", keywords="pharmacovigilance", abstract="Background: Inflammatory bowel disease (IBD) affects many individuals of reproductive age. Most IBD medications are safe to use during pregnancy and breastfeeding; however, observational studies find that women with IBD have higher rates of voluntary childlessness due to fears about medication use during pregnancy. Understanding why and how individuals with IBD make decisions about medication adherence during important reproductive periods can help clinicians address patient fears about medication use. Objective: The objective of this study was to gain a more thorough understanding of how individuals taking IBD medications during key reproductive periods make decisions about their medication use. Methods: We collected posts from 3000 social media sites posted over a 3-year period and analyzed the posts using qualitative descriptive content analysis. The first level of analysis, open coding, identified individual concepts present in the social media posts. We subsequently created a codebook from significant or frequently occurring codes in the data. After creating the codebook, we reviewed the data and coded using our focused codes. We organized the focused codes into larger thematic categories. Results: We identified 7 main themes in 1818 social media posts. Individuals used social media to (1) seek advice about medication use related to reproductive health (13.92\%, 252/1818); (2) express beliefs about the safety of IBD therapies (7.43\%, 135/1818); (3) discuss personal experiences with medication use (16.72\%, 304/1818); (4) articulate fears and anxieties about the safety of IBD therapies (11.55\%, 210/1818); (5) discuss physician-patient relationships (3.14\%, 57/1818); (6) address concerns around conception, infertility, and IBD medications (17.38\%, 316/1818); and (7) talk about IBD symptoms during and after pregnancy and breastfeeding periods (11.33\%, 206/1818). Conclusions: Beliefs around medication safety play an important role in whether individuals with IBD decide to take medications during pregnancy and breastfeeding. Having a better understanding about why patients stop or refuse to take certain medications during key reproductive periods may allow clinicians to address specific beliefs and attitudes during office visits. ", doi="10.2196/jmir.9870", url="http://www.jmir.org/2018/6/e206/", url="http://www.ncbi.nlm.nih.gov/pubmed/29891471" } @Article{info:doi/10.2196/cancer.7952, author="Kalf, RJ Rachel and Makady, Amr and ten Ham, MT Renske and Meijboom, Kim and Goettsch, G. Wim and ", title="Use of Social Media in the Assessment of Relative Effectiveness: Explorative Review With Examples From Oncology", journal="JMIR Cancer", year="2018", month="Jun", day="08", volume="4", number="1", pages="e11", keywords="social media", keywords="relative effectiveness", keywords="real-world data", keywords="patient reported outcomes", abstract="Background: An element of health technology assessment constitutes assessing the clinical effectiveness of drugs, generally called relative effectiveness assessment. Little real-world evidence is available directly after market access, therefore randomized controlled trials are used to obtain information for relative effectiveness assessment. However, there is growing interest in using real-world data for relative effectiveness assessment. Social media may provide a source of real-world data. Objective: We assessed the extent to which social media-generated health data has provided insights for relative effectiveness assessment. Methods: An explorative literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to identify examples in oncology where health data were collected using social media. Scientific and grey literature published between January 2010 and June 2016 was identified by four reviewers, who independently screened studies for eligibility and extracted data. A descriptive qualitative analysis was performed. Results: Of 1032 articles identified, eight were included: four articles identified adverse events in response to cancer treatment, three articles disseminated quality of life surveys, and one study assessed the occurrence of disease-specific symptoms. Several strengths of social media-generated health data were highlighted in the articles, such as efficient collection of patient experiences and recruiting patients with rare diseases. Conversely, limitations included validation of authenticity and presence of information and selection bias. Conclusions: Social media may provide a potential source of real-world data for relative effectiveness assessment, particularly on aspects such as adverse events, symptom occurrence, quality of life, and adherence behavior. This potential has not yet been fully realized and the degree of usefulness for relative effectiveness assessment should be further explored. ", doi="10.2196/cancer.7952", url="http://cancer.jmir.org/2018/1/e11/", url="http://www.ncbi.nlm.nih.gov/pubmed/29884607" } @Article{info:doi/10.2196/publichealth.8218, author="Oldroyd, A. Rachel and Morris, A. Michelle and Birkin, Mark", title="Identifying Methods for Monitoring Foodborne Illness: Review of Existing Public Health Surveillance Techniques", journal="JMIR Public Health Surveill", year="2018", month="Jun", day="06", volume="4", number="2", pages="e57", keywords="disease", keywords="review", keywords="social media", keywords="foodborne diseases", keywords="public health", keywords="infodemiology", keywords="infoveillance", keywords="digital disease detection", abstract="Background: Traditional methods of monitoring foodborne illness are associated with problems of untimeliness and underreporting. In recent years, alternative data sources such as social media data have been used to monitor the incidence of disease in the population (infodemiology and infoveillance). These data sources prove timelier than traditional general practitioner data, they can help to fill the gaps in the reporting process, and they often include additional metadata that is useful for supplementary research. Objective: The aim of the study was to identify and formally analyze research papers using consumer-generated data, such as social media data or restaurant reviews, to quantify a disease or public health ailment. Studies of this nature are scarce within the food safety domain, therefore identification and understanding of transferrable methods in other health-related fields are of particular interest. Methods: Structured scoping methods were used to identify and analyze primary research papers using consumer-generated data for disease or public health surveillance. The title, abstract, and keyword fields of 5 databases were searched using predetermined search terms. A total of 5239 papers matched the search criteria, of which 145 were taken to full-text review---62 papers were deemed relevant and were subjected to data characterization and thematic analysis. Results: The majority of studies (40/62, 65\%) focused on the surveillance of influenza-like illness. Only 10 studies (16\%) used consumer-generated data to monitor outbreaks of foodborne illness. Twitter data (58/62, 94\%) and Yelp reviews (3/62, 5\%) were the most commonly used data sources. Studies reporting high correlations against baseline statistics used advanced statistical and computational approaches to calculate the incidence of disease. These include classification and regression approaches, clustering approaches, and lexicon-based approaches. Although they are computationally intensive due to the requirement of training data, studies using classification approaches reported the best performance. Conclusions: By analyzing studies in digital epidemiology, computer science, and public health, this paper has identified and analyzed methods of disease monitoring that can be transferred to foodborne disease surveillance. These methods fall into 4 main categories: basic approach, classification and regression, clustering approaches, and lexicon-based approaches. Although studies using a basic approach to calculate disease incidence generally report good performance against baseline measures, they are sensitive to chatter generated by media reports. More computationally advanced approaches are required to filter spurious messages and protect predictive systems against false alarms. Research using consumer-generated data for monitoring influenza-like illness is expansive; however, research regarding the use of restaurant reviews and social media data in the context of food safety is limited. Considering the advantages reported in this review, methods using consumer-generated data for foodborne disease surveillance warrant further investment. ", doi="10.2196/publichealth.8218", url="http://publichealth.jmir.org/2018/2/e57/" } @Article{info:doi/10.2196/publichealth.7359, author="Staal, CM Yvonne and van de Nobelen, Suzanne and Havermans, Anne and Talhout, Reinskje", title="New Tobacco and Tobacco-Related Products: Early Detection of Product Development, Marketing Strategies, and Consumer Interest", journal="JMIR Public Health Surveill", year="2018", month="May", day="28", volume="4", number="2", pages="e55", keywords="noncigarette tobacco products", keywords="electronic nicotine delivery systems", keywords="public opinion", keywords="retrospective studies", abstract="Background: A wide variety of new tobacco and tobacco-related products have emerged on the market in recent years. Objective: To understand their potential implications for public health and to guide tobacco control efforts, we have used an infoveillance approach to identify new tobacco and tobacco-related products. Methods: Our search for tobacco(-related) products consists of several tailored search profiles using combinations of keywords such as ``e-cigarette'' and ``new'' to extract information from almost 9000 preselected sources such as websites of online shops, tobacco manufacturers, and news sites. Results: Developments in e-cigarette design characteristics show a trend toward customization by possibilities to adjust temperature and airflow, and by the large variety of flavors of e-liquids. Additionally, more e-cigarettes are equipped with personalized accessories, such as mobile phones, applications, and Bluetooth. Waterpipe products follow the trend toward electronic vaping. Various heat-not-burn products were reintroduced to the market. Conclusions: Our search for tobacco(-related) products was specific and timely, though advances in product development require ongoing optimization of the search strategy. Our results show a trend toward products resembling tobacco cigarettes vaporizers that can be adapted to the consumers' needs. Our search for tobacco(-related) products could aid in the assessment of the likelihood of new products to gain market share, as a possible health risk or as an indicator for the need on independent and reliable information of the product to the general public. ", doi="10.2196/publichealth.7359", url="http://publichealth.jmir.org/2018/2/e55/" } @Article{info:doi/10.2196/jmir.9582, author="Alvarez-Mon, Angel Miguel and Asunsolo del Barco, Angel and Lahera, Guillermo and Quintero, Javier and Ferre, Francisco and Pereira-Sanchez, Victor and Ortu{\~n}o, Felipe and Alvarez-Mon, Melchor", title="Increasing Interest of Mass Communication Media and the General Public in the Distribution of Tweets About Mental Disorders: Observational Study", journal="J Med Internet Res", year="2018", month="May", day="28", volume="20", number="5", pages="e205", keywords="Twitter", keywords="social media", keywords="psychiatry", keywords="mental health", abstract="Background: The contents of traditional communication media and new internet social media reflect the interests of society. However, certain barriers and a lack of attention towards mental disorders have been previously observed. Objective: The objective of this study is to measure the relevance of influential American mainstream media outlets for the distribution of psychiatric information and the interest generated in these topics among their Twitter followers. Methods: We investigated tweets generated about mental health conditions and diseases among 15 mainstream general communication media outlets in the United States of America between January 2007 and December 2016. Our study strategy focused on identifying several psychiatric terms of primary interest. The number of retweets generated from the selected tweets was also investigated. As a control, we examined tweets generated about the main causes of death in the United States of America, the main chronic neurological degenerative diseases, and HIV. Results: In total, 13,119 tweets about mental health disorders sent by the American mainstream media outlets were analyzed. The results showed a heterogeneous distribution but preferential accumulation for a select number of conditions. Suicide and gender dysphoria accounted for half of the number of tweets sent. Variability in the number of tweets related to each control disease was also found (5998). The number of tweets sent regarding each different psychiatric or organic disease analyzed was significantly correlated with the number of retweets generated by followers (1,030,974 and 424,813 responses to mental health disorders and organic diseases, respectively). However, the probability of a tweet being retweeted differed significantly among the conditions and diseases analyzed. Furthermore, the retweeted to tweet ratio was significantly higher for psychiatric diseases than for the control diseases (odds ratio 1.11, CI 1.07-1.14; P<.001). Conclusions: American mainstream media outlets and the general public demonstrate a preferential interest for psychiatric diseases on Twitter. The heterogeneous weights given by the media outlets analyzed to the different mental health disorders and conditions are reflected in the responses of Twitter followers. ", doi="10.2196/jmir.9582", url="http://www.jmir.org/2018/5/e205/" } @Article{info:doi/10.2196/mental.9152, author="Tana, Christoffer Jonas and Kettunen, Jyrki and Eirola, Emil and Paakkonen, Heikki", title="Diurnal Variations of Depression-Related Health Information Seeking: Case Study in Finland Using Google Trends Data", journal="JMIR Ment Health", year="2018", month="May", day="23", volume="5", number="2", pages="e43", keywords="depression", keywords="consumer health information", keywords="information seeking behavior", keywords="infoveillance", keywords="infodemiology", keywords="mental health", keywords="search engine", abstract="Background: Some of the temporal variations and clock-like rhythms that govern several different health-related behaviors can be traced in near real-time with the help of search engine data. This is especially useful when studying phenomena where little or no traditional data exist. One specific area where traditional data are incomplete is the study of diurnal mood variations, or daily changes in individuals' overall mood state in relation to depression-like symptoms. Objective: The objective of this exploratory study was to analyze diurnal variations for interest in depression on the Web to discover hourly patterns of depression interest and help seeking. Methods: Hourly query volume data for 6 depression-related queries in Finland were downloaded from Google Trends in March 2017. A continuous wavelet transform (CWT) was applied to the hourly data to focus on the diurnal variation. Longer term trends and noise were also eliminated from the data to extract the diurnal variation for each query term. An analysis of variance was conducted to determine the statistical differences between the distributions of each hour. Data were also trichotomized and analyzed in 3 time blocks to make comparisons between different time periods during the day. Results: Search volumes for all depression-related query terms showed a unimodal regular pattern during the 24 hours of the day. All queries feature clear peaks during the nighttime hours around 11 PM to 4 AM and troughs between 5 AM and 10 PM. In the means of the CWT-reconstructed data, the differences in nighttime and daytime interest are evident, with a difference of 37.3 percentage points (pp) for the term ``Depression,'' 33.5 pp for ``Masennustesti,'' 30.6 pp for ``Masennus,'' 12.8 pp for ``Depression test,'' 12.0 pp for ``Masennus testi,'' and 11.8 pp for ``Masennus oireet.'' The trichotomization showed peaks in the first time block (00.00 AM-7.59 AM) for all 6 terms. The search volumes then decreased significantly during the second time block (8.00 AM-3.59 PM) for the terms ``Masennus oireet'' (P<.001), ``Masennus'' (P=.001), ``Depression'' (P=.005), and ``Depression test'' (P=.004). Higher search volumes for the terms ``Masennus'' (P=.14), ``Masennustesti'' (P=.07), and ``Depression test'' (P=.10) were present between the second and third time blocks. Conclusions: Help seeking for depression has clear diurnal patterns, with significant rise in depression-related query volumes toward the evening and night. Thus, search engine query data support the notion of the evening-worse pattern in diurnal mood variation. Information on the timely nature of depression-related interest on an hourly level could improve the chances for early intervention, which is beneficial for positive health outcomes. ", doi="10.2196/mental.9152", url="http://mental.jmir.org/2018/2/e43/", url="http://www.ncbi.nlm.nih.gov/pubmed/29792291" } @Article{info:doi/10.2196/jmir.9717, author="Cartwright, F. Alice and Karunaratne, Mihiri and Barr-Walker, Jill and Johns, E. Nicole and Upadhyay, D. Ushma", title="Identifying National Availability of Abortion Care and Distance From Major US Cities: Systematic Online Search", journal="J Med Internet Res", year="2018", month="May", day="14", volume="20", number="5", pages="e186", keywords="abortion seekers", keywords="reproductive health", keywords="internet", keywords="access to information", keywords="information seeking", keywords="abortion patients", keywords="reproductive health services", keywords="information seeking behavior", abstract="Background: Abortion is a common medical procedure, yet its availability has become more limited across the United States over the past decade. Women who do not know where to go for abortion care may use the internet to find abortion facility information, and there appears to be more online searches for abortion in states with more restrictive abortion laws. While previous studies have examined the distances women must travel to reach an abortion provider, to our knowledge no studies have used a systematic online search to document the geographic locations and services of abortion facilities. Objective: The objective of our study was to describe abortion facilities and services available in the United States from the perspective of a potential patient searching online and to identify US cities where people must travel the farthest to obtain abortion care. Methods: In early 2017, we conducted a systematic online search for abortion facilities in every state and the largest cities in each state. We recorded facility locations, types of abortion services available, and facility gestational limits. We then summarized the frequencies by region and state. If the online information was incomplete or unclear, we called the facility using a mystery shopper method, which simulates the perspective of patients calling for services. We also calculated distance to the closest abortion facility from all US cities with populations of 50,000 or more. Results: We identified 780 facilities through our online search, with the fewest in the Midwest and South. Over 30\% (236/780, 30.3\%) of all facilities advertised the provision of medication abortion services only; this proportion was close to 40\% in the Northeast (89/233, 38.2\%) and West (104/262, 39.7\%). The lowest gestational limit at which services were provided was 12 weeks in Wyoming; the highest was 28 weeks in New Mexico. People in 27 US cities must travel over 100 miles (160 km) to reach an abortion facility; the state with the largest number of such cities is Texas (n=10). Conclusions: Online searches can provide detailed information about the location of abortion facilities and the types of services they provide. However, these facilities are not evenly distributed geographically, and many large US cities do not have an abortion facility. Long distances can push women to seek abortion in later gestations when care is even more limited. ", doi="10.2196/jmir.9717", url="http://www.jmir.org/2018/5/e186/", url="http://www.ncbi.nlm.nih.gov/pubmed/29759954" } @Article{info:doi/10.2196/publichealth.8214, author="Bollegala, Danushka and Maskell, Simon and Sloane, Richard and Hajne, Joanna and Pirmohamed, Munir", title="Causality Patterns for Detecting Adverse Drug Reactions From Social Media: Text Mining Approach", journal="JMIR Public Health Surveill", year="2018", month="May", day="09", volume="4", number="2", pages="e51", keywords="machine learning", keywords="ADR detection", keywords="causality", keywords="lexical patterns", keywords="causality detection", keywords="support vector machines", abstract="Background: Detecting adverse drug reactions (ADRs) is an important task that has direct implications for the use of that drug. If we can detect previously unknown ADRs as quickly as possible, then this information can be provided to the regulators, pharmaceutical companies, and health care organizations, thereby potentially reducing drug-related morbidity and saving lives of many patients. A promising approach for detecting ADRs is to use social media platforms such as Twitter and Facebook. A high level of correlation between a drug name and an event may be an indication of a potential adverse reaction associated with that drug. Although numerous association measures have been proposed by the signal detection community for identifying ADRs, these measures are limited in that they detect correlations but often ignore causality. Objective: This study aimed to propose a causality measure that can detect an adverse reaction that is caused by a drug rather than merely being a correlated signal. Methods: To the best of our knowledge, this was the first causality-sensitive approach for detecting ADRs from social media. Specifically, the relationship between a drug and an event was represented using a set of automatically extracted lexical patterns. We then learned the weights for the extracted lexical patterns that indicate their reliability for expressing an adverse reaction of a given drug. Results: Our proposed method obtains an ADR detection accuracy of 74\% on a large-scale manually annotated dataset of tweets, covering a standard set of drugs and adverse reactions. Conclusions: By using lexical patterns, we can accurately detect the causality between drugs and adverse reaction--related events. ", doi="10.2196/publichealth.8214", url="http://publichealth.jmir.org/2018/2/e51/", url="http://www.ncbi.nlm.nih.gov/pubmed/29743155" } @Article{info:doi/10.2196/jmir.9267, author="Seabrook, M. Elizabeth and Kern, L. Margaret and Fulcher, D. Ben and Rickard, S. Nikki", title="Predicting Depression From Language-Based Emotion Dynamics: Longitudinal Analysis of Facebook and Twitter Status Updates", journal="J Med Internet Res", year="2018", month="May", day="08", volume="20", number="5", pages="e168", keywords="automated text analysis", keywords="depression", keywords="Facebook", keywords="Twitter", keywords="emotions", keywords="variability", keywords="instability", abstract="Background: Frequent expression of negative emotion words on social media has been linked to depression. However, metrics have relied on average values, not dynamic measures of emotional volatility. Objective: The aim of this study was to report on the associations between depression severity and the variability (time-unstructured) and instability (time-structured) in emotion word expression on Facebook and Twitter across status updates. Methods: Status updates and depression severity ratings of 29 Facebook users and 49 Twitter users were collected through the app MoodPrism. The average proportion of positive and negative emotion words used, within-person variability, and instability were computed. Results: Negative emotion word instability was a significant predictor of greater depression severity on Facebook (rs(29)=.44, P=.02, 95\% CI 0.09-0.69), even after controlling for the average proportion of negative emotion words used (partial rs(26)=.51, P=.006) and within-person variability (partial rs(26)=.49, P=.009). A different pattern emerged on Twitter where greater negative emotion word variability indicated lower depression severity (rs(49)=?.34, P=.01, 95\% CI ?0.58 to 0.09). Differences between Facebook and Twitter users in their emotion word patterns and psychological characteristics were also explored. Conclusions: The findings suggest that negative emotion word instability may be a simple yet sensitive measure of time-structured variability, useful when screening for depression through social media, though its usefulness may depend on the social media platform. ", doi="10.2196/jmir.9267", url="http://www.jmir.org/2018/5/e168/", url="http://www.ncbi.nlm.nih.gov/pubmed/29739736" } @Article{info:doi/10.2196/10047, author="Huang, Ming and ElTayeby, Omar and Zolnoori, Maryam and Yao, Lixia", title="Public Opinions Toward Diseases: Infodemiological Study on News Media Data", journal="J Med Internet Res", year="2018", month="May", day="08", volume="20", number="5", pages="e10047", keywords="news", keywords="Reuters", keywords="public policy", keywords="text mining", keywords="sentiment analysis", keywords="topic modeling", keywords="unmet medical need", keywords="research priority", abstract="Background: Society always has limited resources to expend on health care, or anything else. What are the unmet medical needs? How do we allocate limited resources to maximize the health and welfare of the people? These challenging questions might be re-examined systematically within an infodemiological frame on a much larger scale, leveraging the latest advancement in information technology and data science. Objective: We expanded our previous work by investigating news media data to reveal the coverage of different diseases and medical conditions, together with their sentiments and topics in news articles over three decades. We were motivated to do so since news media plays a significant role in politics and affects the public policy making. Methods: We analyzed over 3.5 million archive news articles from Reuters media during the periods of 1996/1997, 2008 and 2016, using summary statistics, sentiment analysis, and topic modeling. Summary statistics illustrated the coverage of various diseases and medical conditions during the last 3 decades. Sentiment analysis and topic modeling helped us automatically detect the sentiments of news articles (ie, positive versus negative) and topics (ie, a series of keywords) associated with each disease over time. Results: The percentages of news articles mentioning diseases and medical conditions were 0.44\%, 0.57\% and 0.81\% in the three time periods, suggesting that news media or the public has gradually increased its interests in medicine since 1996. Certain diseases such as other malignant neoplasm (34\%), other infectious diseases (20\%), and influenza (11\%) represented the most covered diseases. Two hundred and twenty-six diseases and medical conditions (97.8\%) were found to have neutral or negative sentiments in the news articles. Using topic modeling, we identified meaningful topics on these diseases and medical conditions. For instance, the smoking theme appeared in the news articles on other malignant neoplasm only during 1996/1997. The topic phrases HIV and Zika virus were linked to other infectious diseases during 1996/1997 and 2016, respectively. Conclusions: The multi-dimensional analysis of news media data allows the discovery of focus, sentiments and topics of news media in terms of diseases and medical conditions. These infodemiological discoveries could shed light on unmet medical needs and research priorities for future and provide guidance for the decision making in public policy. ", doi="10.2196/10047", url="http://www.jmir.org/2018/5/e10047/", url="http://www.ncbi.nlm.nih.gov/pubmed/29739741" } @Article{info:doi/10.2196/ijmr.9065, author="Manchaiah, Vinaya and Ratinaud, Pierre and Andersson, Gerhard", title="Representation of Tinnitus in the US Newspaper Media and in Facebook Pages: Cross-Sectional Analysis of Secondary Data", journal="Interact J Med Res", year="2018", month="May", day="08", volume="7", number="1", pages="e9", keywords="tinnitus", keywords="chronic condition", keywords="health communication", keywords="health information", keywords="media", keywords="news media", keywords="social media", abstract="Background: When people with health conditions begin to manage their health issues, one important issue that emerges is the question as to what exactly do they do with the information that they have obtained through various sources (eg, news media, social media, health professionals, friends, and family). The information they gather helps form their opinions and, to some degree, influences their attitudes toward managing their condition. Objective: This study aimed to understand how tinnitus is represented in the US newspaper media and in Facebook pages (ie, social media) using text pattern analysis. Methods: This was a cross-sectional study based upon secondary analyses of publicly available data. The 2 datasets (ie, text corpuses) analyzed in this study were generated from US newspaper media during 1980-2017 (downloaded from the database US Major Dailies by ProQuest) and Facebook pages during 2010-2016. The text corpuses were analyzed using the Iramuteq software using cluster analysis and chi-square tests. Results: The newspaper dataset had 432 articles. The cluster analysis resulted in 5 clusters, which were named as follows: (1) brain stimulation (26.2\%), (2) symptoms (13.5\%), (3) coping (19.8\%), (4) social support (24.2\%), and (5) treatment innovation (16.4\%). A time series analysis of clusters indicated a change in the pattern of information presented in newspaper media during 1980-2017 (eg, more emphasis on cluster 5, focusing on treatment inventions). The Facebook dataset had 1569 texts. The cluster analysis resulted in 7 clusters, which were named as: (1) diagnosis (21.9\%), (2) cause (4.1\%), (3) research and development (13.6\%), (4) social support (18.8\%), (5) challenges (11.1\%), (6) symptoms (21.4\%), and (7) coping (9.2\%). A time series analysis of clusters indicated no change in information presented in Facebook pages on tinnitus during 2011-2016. Conclusions: The study highlights the specific aspects about tinnitus that the US newspaper media and Facebook pages focus on, as well as how these aspects change over time. These findings can help health care providers better understand the presuppositions that tinnitus patients may have. More importantly, the findings can help public health experts and health communication experts in tailoring health information about tinnitus to promote self-management, as well as assisting in appropriate choices of treatment for those living with tinnitus. ", doi="10.2196/ijmr.9065", url="http://www.i-jmr.org/2018/1/e9/", url="http://www.ncbi.nlm.nih.gov/pubmed/29739734" } @Article{info:doi/10.2196/10180, author="Berlinberg, J. Elyse and Deiner, S. Michael and Porco, C. Travis and Acharya, R. Nisha", title="Monitoring Interest in Herpes Zoster Vaccination: Analysis of Google Search Data", journal="JMIR Public Health Surveill", year="2018", month="May", day="02", volume="4", number="2", pages="e10180", keywords="herpes zoster", keywords="vaccination", keywords="Internet", keywords="periodicity", keywords="Google Trends", keywords="infodemiology", abstract="Background: A new recombinant subunit vaccine for herpes zoster (HZ or shingles) was approved by the United States Food and Drug Administration on October 20, 2017 and is expected to replace the previous live attenuated vaccine. There have been low coverage rates with the live attenuated vaccine (Zostavax), ranging from 12-32\% of eligible patients receiving the HZ vaccine. Objective: This study aimed to provide insight into trends and potential reasons for interest in HZ vaccination. Methods: Internet search data were queried from the Google Health application programming interface from 2004-2017. Seasonality of normalized search volume was analyzed using wavelets and Fisher's g test. Results: The search terms ``shingles vaccine,'' ``zoster vaccine,'' and ``zostavax'' all exhibited significant periodicity in the fall months (P<.001), with sharp increases after recommendations for vaccination by public health-related organizations. Although the terms ``shingles blisters,'' ``shingles itch,'' ``shingles rash,'' ``skin rash,'' and ``shingles medicine'' exhibited statistically significant periodicities with a seasonal peak in the summer (P<.001), the terms ``shingles contagious,'' ``shingles pain,'' ``shingles treatment,'' and ``shingles symptoms'' did not reveal an annual trend. Conclusions: There may be increased interest in HZ vaccination during the fall and after public health organization recommendations are broadcast. This finding points to the possibility that increased awareness of the vaccine through public health announcements could be evaluated as a potential intervention for increasing vaccine coverage. ", doi="10.2196/10180", url="http://publichealth.jmir.org/2018/2/e10180/", url="http://www.ncbi.nlm.nih.gov/pubmed/29720364" } @Article{info:doi/10.2196/10327, author="Seidl, Stefanie and Schuster, Barbara and R{\"u}th, Melvin and Biedermann, Tilo and Zink, Alexander", title="What Do Germans Want to Know About Skin Cancer? A Nationwide Google Search Analysis From 2013 to 2017", journal="J Med Internet Res", year="2018", month="May", day="02", volume="20", number="5", pages="e10327", keywords="skin cancer", keywords="melanoma", keywords="nonmelanoma skin cancer (NMSC)", keywords="Google", keywords="search analysis", keywords="population", abstract="Background: Experts worldwide agree that skin cancer is a global health issue, but only a few studies have reported on world populations' interest in skin cancer. Internet search data can reflect the interest of a population in different topics and thereby identify what the population wants to know. Objective: Our aim was to assess the interest of the German population in nonmelanoma skin cancer and melanoma. Methods: Google AdWords Keyword Planner was used to identify search terms related to nonmelanoma skin cancer and melanoma in Germany from November 2013 to October 2017. The identified search terms were assessed descriptively using SPSS version 24.0. In addition, the search terms were qualitatively categorized. Results: A total of 646 skin cancer-related search terms were identified with 19,849,230 Google searches in the period under review. The search terms with the highest search volume were ``skin cancer'' (n=2,388,500, 12.03\%), ``white skin cancer'' (n=2,056,900, 10.36\%), ``basalioma'' (n=907,000, 4.57\%), and ``melanoma'' (n=717,800, 3.62\%). The most searched localizations of nonmelanoma skin cancer were ``nose'' (n=93,370, 38.99\%) and ``face'' (n=53,270, 22.24\%), and the most searched of melanoma were ``nails'' (n=46,270, 70.61\%) and ``eye'' (n=10,480, 15.99\%). The skin cancer?related category with the highest search volume was ``forms of skin cancer'' (n=10,162,540, 23.28\%) followed by ``skin alterations'' (n=4,962,020, 11.36\%). Conclusions: Our study provides insight into terms and fields of interest related to skin cancer relevant to the German population. Furthermore, temporal trends and courses are shown. This information could aid in the development and implementation of effective and sustainable awareness campaigns by developing information sources targeted to the population's broad interest or by implementing new Internet campaigns. ", doi="10.2196/10327", url="http://www.jmir.org/2018/5/e10327/", url="http://www.ncbi.nlm.nih.gov/pubmed/29698213" } @Article{info:doi/10.2196/jmir.8563, author="Young, D. Sean", title="Social Media as a New Vital Sign: Commentary", journal="J Med Internet Res", year="2018", month="Apr", day="30", volume="20", number="4", pages="e161", keywords="social media", keywords="big data", keywords="personal health records", doi="10.2196/jmir.8563", url="http://www.jmir.org/2018/4/e161/", url="http://www.ncbi.nlm.nih.gov/pubmed/29712631" } @Article{info:doi/10.2196/10029, author="Mackey, Tim and Kalyanam, Janani and Klugman, Josh and Kuzmenko, Ella and Gupta, Rashmi", title="Solution to Detect, Classify, and Report Illicit Online Marketing and Sales of Controlled Substances via Twitter: Using Machine Learning and Web Forensics to Combat Digital Opioid Access", journal="J Med Internet Res", year="2018", month="Apr", day="27", volume="20", number="4", pages="e10029", keywords="online pharmacies", keywords="drug abuse", keywords="opioid abuse", keywords="machine learning", keywords="unsupervised machine learning", keywords="prescription drug misuse", abstract="Background: On December 6 and 7, 2017, the US Department of Health and Human Services (HHS) hosted its first Code-a-Thon event aimed at leveraging technology and data-driven solutions to help combat the opioid epidemic. The authors---an interdisciplinary team from academia, the private sector, and the US Centers for Disease Control and Prevention---participated in the Code-a-Thon as part of the prevention track. Objective: The aim of this study was to develop and deploy a methodology using machine learning to accurately detect the marketing and sale of opioids by illicit online sellers via Twitter as part of participation at the HHS Opioid Code-a-Thon event. Methods: Tweets were collected from the Twitter public application programming interface stream filtered for common prescription opioid keywords in conjunction with participation in the Code-a-Thon from November 15, 2017 to December 5, 2017. An unsupervised machine learning--based approach was developed and used during the Code-a-Thon competition (24 hours) to obtain a summary of the content of the tweets to isolate those clusters associated with illegal online marketing and sale using a biterm topic model (BTM). After isolating relevant tweets, hyperlinks associated with these tweets were reviewed to assess the characteristics of illegal online sellers. Results: We collected and analyzed 213,041 tweets over the course of the Code-a-Thon containing keywords codeine, percocet, vicodin, oxycontin, oxycodone, fentanyl, and hydrocodone. Using BTM, 0.32\% (692/213,041) tweets were identified as being associated with illegal online marketing and sale of prescription opioids. After removing duplicates and dead links, we identified 34 unique ``live'' tweets, with 44\% (15/34) directing consumers to illicit online pharmacies, 32\% (11/34) linked to individual drug sellers, and 21\% (7/34) used by marketing affiliates. In addition to offering the ``no prescription'' sale of opioids, many of these vendors also sold other controlled substances and illicit drugs. Conclusions: The results of this study are in line with prior studies that have identified social media platforms, including Twitter, as a potential conduit for supply and sale of illicit opioids. To translate these results into action, authors also developed a prototype wireframe for the purposes of detecting, classifying, and reporting illicit online pharmacy tweets selling controlled substances illegally to the US Food and Drug Administration and the US Drug Enforcement Agency. Further development of solutions based on these methods has the potential to proactively alert regulators and law enforcement agencies of illegal opioid sales, while also making the online environment safer for the public. ", doi="10.2196/10029", url="http://www.jmir.org/2018/4/e10029/", url="http://www.ncbi.nlm.nih.gov/pubmed/29613851" } @Article{info:doi/10.2196/publichealth.9913, author="Wood, N. Lauren and Jamnagerwalla, Juzar and Markowitz, A. Melissa and Thum, Joseph D. and McCarty, Philip and Medendorp, R. Andrew and Raz, Shlomo and Kim, Ja-Hong", title="Public Awareness of Uterine Power Morcellation Through US Food and Drug Administration Communications: Analysis of Google Trends Search Term Patterns", journal="JMIR Public Health Surveill", year="2018", month="Apr", day="26", volume="4", number="2", pages="e47", keywords="Google", keywords="internet search activity", keywords="FDA safety communication", keywords="uterine morcellation", abstract="Background: Uterine power morcellation, where the uterus is shred into smaller pieces, is a widely used technique for removal of uterine specimens in patients undergoing minimally invasive abdominal hysterectomy or myomectomy. Complications related to power morcellation of uterine specimens led to US Food and Drug Administration (FDA) communications in 2014 ultimately recommending against the use of power morcellation for women undergoing minimally invasive hysterectomy. Subsequently, practitioners drastically decreased the use of morcellation. Objective: We aimed to determine the effect of increased patient awareness on the decrease in use of the morcellator. Google Trends is a public tool that provides data on temporal patterns of search terms, and we correlated this data with the timing of the FDA communication. Methods: Weekly relative search volume (RSV) was obtained from Google Trends using the term ``morcellation.'' Higher RSV corresponds to increases in weekly search volume. Search volumes were divided into 3 groups: the 2 years prior to the FDA communication, a 1-year period following, and thereafter, with the distribution of the weekly RSV over the 3 periods tested using 1-way analysis of variance. Additionally, we analyzed the total number of websites containing the term ``morcellation'' over this time. Results: The mean RSV prior to the FDA communication was 12.0 (SD 15.8), with the RSV being 60.3 (SD 24.7) in the 1-year after and 19.3 (SD 5.2) thereafter (P<.001). The mean number of webpages containing the term ``morcellation'' in 2011 was 10,800, rising to 18,800 during 2014 and 36,200 in 2017. Conclusions: Google search activity about morcellation of uterine specimens increased significantly after the FDA communications. This trend indicates an increased public awareness regarding morcellation and its complications. More extensive preoperative counseling and alteration of surgical technique and clinician practice may be necessary. ", doi="10.2196/publichealth.9913", url="http://publichealth.jmir.org/2018/2/e47/", url="http://www.ncbi.nlm.nih.gov/pubmed/29699965" } @Article{info:doi/10.2196/publichealth.7686, author="Clyne, Wendy and Pezaro, Sally and Deeny, Karen and Kneafsey, Rosie", title="Using Social Media to Generate and Collect Primary Data: The \#ShowsWorkplaceCompassion Twitter Research Campaign", journal="JMIR Public Health Surveill", year="2018", month="Apr", day="23", volume="4", number="2", pages="e41", keywords="work engagement", keywords="health personnel", keywords="empathy", keywords="attitude of health personnel", abstract="Background: Compassion is a core value embedded in the concept of quality in healthcare. The need for compassion toward healthcare staff in the workplace, for their own health and well-being and also to enable staff to deliver compassionate care for patients, is increasingly understood. However, we do not currently know how healthcare staff understand and characterize compassion toward themselves as opposed to patients. Objective: The aim of this study was to use social media for the generation and collection of primary data to gain understanding of the concept of workplace compassion. Methods: Tweets that contained the hashtag \#ShowsWorkplaceCompassion were collected from Twitter and analyzed. The study took place between April 21 and May 21, 2016. Participants were self-selecting users of the social media service Twitter. The study was promoted by a number of routes: the National Health Service (NHS) England website, the personal Twitter accounts of the research team, internal NHS England communications, and via social media sharing. Participants were asked to contribute their views about what activities, actions, policies, philosophies or approaches demonstrate workplace compassion in healthcare using the hashtag \#ShowsWorkplaceCompassion. All tweets including the research hashtag \#ShowsWorkplaceCompassion were extracted from Twitter and studied using content analysis. Data concerning the frequency, nature, origin, and location of Web-based engagement with the research campaign were collected using Bitly (Bitly, Inc, USA) and Symplur (Symplur LLC, USA) software. Results: A total of 260 tweets were analyzed. Of the 251 statements within the tweets that were coded, 37.8\% (95/251) of the statements concerned Leadership and Management aspects of workplace compassion, 29.5\% (74/251) were grouped under the theme related to Values and Culture, 17.5\% (44/251) of the statements related to Personalized Policies and Procedures that support workplace compassion, and 15.2\% (38/251) of the statements concerned Activities and Actions that show workplace compassion. Content analysis showed that small acts of kindness, an embedded organizational culture of caring for one another, and recognition of the emotional and physical impact of healthcare work were the most frequently mentioned characteristics of workplace compassion in healthcare. Conclusions: This study presents a new and innovative research approach using Twitter. Although previous research has analyzed the nature and pattern of tweets retrospectively, this study used Twitter to both recruit participants and collect primary data. ", doi="10.2196/publichealth.7686", url="http://publichealth.jmir.org/2018/2/e41/", url="http://www.ncbi.nlm.nih.gov/pubmed/29685866" } @Article{info:doi/10.2196/publichealth.7314, author="Wang, Ho-Wei and Chen, Duan-Rung", title="Economic Recession and Obesity-Related Internet Search Behavior in Taiwan: Analysis of Google Trends Data", journal="JMIR Public Health Surveill", year="2018", month="Apr", day="06", volume="4", number="2", pages="e37", keywords="obesity", keywords="economic recession", keywords="Google Trends", keywords="fast food", keywords="internet search", keywords="health-seeking behaviors", keywords="infodemiology", abstract="Background: Obesity is highly correlated with the development of chronic diseases and has become a critical public health issue that must be countered by aggressive action. This study determined whether data from Google Trends could provide insight into trends in obesity-related search behaviors in Taiwan. Objective: Using Google Trends, we examined how changes in economic conditions---using business cycle indicators as a proxy---were associated with people's internet search behaviors related to obesity awareness, health behaviors, and fast food restaurants. Methods: Monthly business cycle indicators were obtained from the Taiwan National Development Council. Weekly Taiwan Stock Exchange (TWSE) weighted index data were accessed and downloaded from Yahoo Finance. The weekly relative search volumes (RSV) of obesity-related terms were downloaded from Google Trends. RSVs of obesity-related terms and the TWSE from January 2007 to December 2011 (60 months) were analyzed using correlation analysis. Results: During an economic recession, the RSV of obesity awareness and health behaviors declined (r=.441, P<.001; r=.593, P<.001, respectively); however, the RSV for fast food restaurants increased (r=?.437, P<.001). Findings indicated that when the economy was faltering, people tended to be less likely to search for information related to health behaviors and obesity awareness; moreover, they were more likely to search for fast food restaurants. Conclusions: Macroeconomic conditions can have an impact on people's health-related internet searches. ", doi="10.2196/publichealth.7314", url="http://publichealth.jmir.org/2018/2/e37/", url="http://www.ncbi.nlm.nih.gov/pubmed/29625958" } @Article{info:doi/10.2196/jmir.8221, author="Chen, Tao and Dredze, Mark", title="Vaccine Images on Twitter: Analysis of What Images are Shared", journal="J Med Internet Res", year="2018", month="Apr", day="03", volume="20", number="4", pages="e130", keywords="vaccine", keywords="visual communication", keywords="image tweet", keywords="Twitter", keywords="retweet prediction", keywords="social media", abstract="Background: Visual imagery plays a key role in health communication; however, there is little understanding of what aspects of vaccine-related images make them effective communication aids. Twitter, a popular venue for discussions related to vaccination, provides numerous images that are shared with tweets. Objective: The objectives of this study were to understand how images are used in vaccine-related tweets and provide guidance with respect to the characteristics of vaccine-related images that correlate with the higher likelihood of being retweeted. Methods: We collected more than one million vaccine image messages from Twitter and characterized various properties of these images using automated image analytics. We fit a logistic regression model to predict whether or not a vaccine image tweet was retweeted, thus identifying characteristics that correlate with a higher likelihood of being shared. For comparison, we built similar models for the sharing of vaccine news on Facebook and for general image tweets. Results: Most vaccine-related images are duplicates (125,916/237,478; 53.02\%) or taken from other sources, not necessarily created by the author of the tweet. Almost half of the images contain embedded text, and many include images of people and syringes. The visual content is highly correlated with a tweet's textual topics. Vaccine image tweets are twice as likely to be shared as nonimage tweets. The sentiment of an image and the objects shown in the image were the predictive factors in determining whether an image was retweeted. Conclusions: We are the first to study vaccine images on Twitter. Our findings suggest future directions for the study and use of vaccine imagery and may inform communication strategies around vaccination. Furthermore, our study demonstrates an effective study methodology for image analysis. ", doi="10.2196/jmir.8221", url="http://www.jmir.org/2018/4/e130/", url="http://www.ncbi.nlm.nih.gov/pubmed/29615386" } @Article{info:doi/10.2196/publichealth.8198, author="Chen, Bin and Shao, Jian and Liu, Kui and Cai, Gaofeng and Jiang, Zhenggang and Huang, Yuru and Gu, Hua and Jiang, Jianmin", title="Does Eating Chicken Feet With Pickled Peppers Cause Avian Influenza? Observational Case Study on Chinese Social Media During the Avian Influenza A (H7N9) Outbreak", journal="JMIR Public Health Surveill", year="2018", month="Mar", day="29", volume="4", number="1", pages="e32", keywords="social media", keywords="misinformation", keywords="infodemiology", keywords="avian influenza A", keywords="disease outbreak", abstract="Background: A hot topic on the relationship between a popular avian-origin food and avian influenza occurred on social media during the outbreak of the emerging avian influenza A (H7N9). The misinformation generated from this topic had caused great confusion and public concern. Objective: Our goals were to analyze the trend and contents of the relevant posts during the outbreak. We also aimed to understand the characteristics of the misinformation and to provide suggestions to reduce public misconception on social media during the emerging disease outbreak. Methods: The original microblog posts were collected from China's Sina Weibo and Tencent Weibo using a combination of keywords between April 1, 2013 and June 2, 2013. We analyzed the weekly and daily trend of the relevant posts. Content analyses were applied to categorize the posts into 4 types with unified sorting criteria. The posts' characteristics and geographic locations were also analyzed in each category. We conducted further analysis on the top 5 most popular misleading posts. Results: A total of 1680 original microblog posts on the topic were retrieved and 341 (20.30\%) of these posts were categorized as misleading messages. The number of relevant posts had not increased much during the first 2 weeks but rose to a high level in the next 2 weeks after the sudden increase in number of reported cases at the beginning of week 3. The posts under ``misleading messages'' occurred and increased from the beginning of week 3, but their daily posting number decreased when the daily number of posts under ``refuting messages'' outnumbered them. The microbloggers of the misleading posts had the lowest mean rank of followers and previous posts, but their posts had a highest mean rank of posts. The proportion of ``misleading messages'' in places with no reported cases was significantly higher than that in the epidemic areas (23.6\% vs 13.8\%). The popular misleading posts appeared to be short and consisted of personal narratives, which were easily disseminated on social media. Conclusions: Our findings suggested the importance of responding to common questions and misconceptions on social media platforms from the beginning of disease outbreaks. Authorities need to release clear and reliable information related to the popular topics early on. The microbloggers posting correct information should be empowered and their posts could be promoted to clarify false information. Equal importance should be attached to clarify misinformation in both the outbreak and nonoutbreak areas. ", doi="10.2196/publichealth.8198", url="http://publichealth.jmir.org/2018/1/e32/", url="http://www.ncbi.nlm.nih.gov/pubmed/29599109" } @Article{info:doi/10.2196/publichealth.7217, author="Mejova, Yelena and Weber, Ingmar and Fernandez-Luque, Luis", title="Online Health Monitoring using Facebook Advertisement Audience Estimates in the United States: Evaluation Study", journal="JMIR Public Health Surveill", year="2018", month="Mar", day="28", volume="4", number="1", pages="e30", keywords="social media", keywords="public health", keywords="Internet", keywords="infodemiology", abstract="Background: Facebook, the most popular social network with over one billion daily users, provides rich opportunities for its use in the health domain. Though much of Facebook's data are not available to outsiders, the company provides a tool for estimating the audience of Facebook advertisements, which includes aggregated information on the demographics and interests, such as weight loss or dieting, of Facebook users. This paper explores the potential uses of Facebook ad audience estimates for eHealth by studying the following: (1) for what type of health conditions prevalence estimates can be obtained via social media and (2) what type of marker interests are useful in obtaining such estimates, which can then be used for recruitment within online health interventions. Objective: The objective of this study was to understand the limitations and capabilities of using Facebook ad audience estimates for public health monitoring and as a recruitment tool for eHealth interventions. Methods: We use the Facebook Marketing application programming interface to correlate estimated sizes of audiences having health-related interests with public health data. Using several study cases, we identify both potential benefits and challenges in using this tool. Results: We find several limitations in using Facebook ad audience estimates, for example, using placebo interest estimates to control for background level of user activity on the platform. Some Facebook interests such as plus-size clothing show encouraging levels of correlation (r=.74) across the 50 US states; however, we also sometimes find substantial correlations with the placebo interests such as r=.68 between interest in Technology and Obesity prevalence. Furthermore, we find demographic-specific peculiarities in the interests on health-related topics. Conclusions: Facebook's advertising platform provides aggregate data for more than 190 million US adults. We show how disease-specific marker interests can be used to model prevalence rates in a simple and intuitive manner. However, we also illustrate that building effective marker interests involves some trial-and-error, as many details about Facebook's black box remain opaque. ", doi="10.2196/publichealth.7217", url="http://publichealth.jmir.org/2018/1/e30/", url="http://www.ncbi.nlm.nih.gov/pubmed/29592849" } @Article{info:doi/10.2196/publichealth.7598, author="Yagahara, Ayako and Hanai, Keiri and Hasegawa, Shin and Ogasawara, Katsuhiko", title="Relationships Among Tweets Related to Radiation: Visualization Using Co-Occurring Networks", journal="JMIR Public Health Surveill", year="2018", month="Mar", day="15", volume="4", number="1", pages="e26", keywords="Twitter", keywords="social media", keywords="public concern", keywords="nuclear power plants", keywords="morphological analysis", keywords="network analysis", keywords="radiation", abstract="Background: After the Fukushima Daiichi nuclear accident on March 11, 2011, interest in, and fear of, radiation increased among citizens. When such accidents occur, appropriate risk communication must provided by the government. It is therefore necessary to understand the fears of citizens in the days after such accidents. Objective: This study aimed to identify the progression of people's concerns, specifically fear, from a study of radiation-related tweets in the days after the Fukushima Daiichi nuclear accident. Methods: From approximately 1.5 million tweets in Japanese including any of the phrases ``radiation'' (???), ``radioactivity'' (???), and ``radioactive substance'' (?????) sent March 11-17, 2011, we extracted tweets that expressed fear. We then performed a morphological analysis on the extracted tweets. Citizens' fears were visualized by creating co-occurrence networks using co-occurrence degrees showing relationship strength. Moreover, we calculated the Jaccard coefficient, which is one of the co-occurrence indices for expressing the strength of the relationship between morphemes when creating networks. Results: From the visualization of the co-occurrence networks, we found high citizen interest in ``nuclear power plant'' on March 11 and 12, ``health'' on March 12 and 13, ``medium'' on March 13 and 14, and ``economy'' on March 15. On March 16 and 17, citizens' interest changed to ``lack of goods in the afflicted area.'' In each co-occurrence network, trending topics, citizens' fears, and opinions to the government were extracted. Conclusions: This study used Twitter to understand changes in the concerns of Japanese citizens during the week after the Fukushima Daiichi nuclear accident, with a focus specifically on citizens' fears. We found that immediately after the accident, the interest in the accident itself was high, and then interest shifted to concerns affecting life, such as health and economy, as the week progressed. Clarifying citizens' fears and the dissemination of information through mass media and social media can add to improved risk communication in the future. ", doi="10.2196/publichealth.7598", url="http://publichealth.jmir.org/2018/1/e26/", url="http://www.ncbi.nlm.nih.gov/pubmed/29549069" } @Article{info:doi/10.2196/jmir.9222, author="Abdellaoui, Redhouane and Foulqui{\'e}, Pierre and Texier, Nathalie and Faviez, Carole and Burgun, Anita and Sch{\"u}ck, St{\'e}phane", title="Detection of Cases of Noncompliance to Drug Treatment in Patient Forum Posts: Topic Model Approach", journal="J Med Internet Res", year="2018", month="Mar", day="14", volume="20", number="3", pages="e85", keywords="medication adherence", keywords="compliance", keywords="infodemiology", keywords="social media", keywords="text mining", keywords="depression", keywords="psychosis", keywords="peer-to-peer support", keywords="virtual community", abstract="Background: Medication nonadherence is a major impediment to the management of many health conditions. A better understanding of the factors underlying noncompliance to treatment may help health professionals to address it. Patients use peer-to-peer virtual communities and social media to share their experiences regarding their treatments and diseases. Using topic models makes it possible to model themes present in a collection of posts, thus to identify cases of noncompliance. Objective: The aim of this study was to detect messages describing patients' noncompliant behaviors associated with a drug of interest. Thus, the objective was the clustering of posts featuring a homogeneous vocabulary related to nonadherent attitudes. Methods: We focused on escitalopram and aripiprazole used to treat depression and psychotic conditions, respectively. We implemented a probabilistic topic model to identify the topics that occurred in a corpus of messages mentioning these drugs, posted from 2004 to 2013 on three of the most popular French forums. Data were collected using a Web crawler designed by Kappa Sant{\'e} as part of the Detec't project to analyze social media for drug safety. Several topics were related to noncompliance to treatment. Results: Starting from a corpus of 3650 posts related to an antidepressant drug (escitalopram) and 2164 posts related to an antipsychotic drug (aripiprazole), the use of latent Dirichlet allocation allowed us to model several themes, including interruptions of treatment and changes in dosage. The topic model approach detected cases of noncompliance behaviors with a recall of 98.5\% (272/276) and a precision of 32.6\% (272/844). Conclusions: Topic models enabled us to explore patients' discussions on community websites and to identify posts related with noncompliant behaviors. After a manual review of the messages in the noncompliance topics, we found that noncompliance to treatment was present in 6.17\% (276/4469) of the posts. ", doi="10.2196/jmir.9222", url="http://www.jmir.org/2018/3/e85/", url="http://www.ncbi.nlm.nih.gov/pubmed/29540337" } @Article{info:doi/10.2196/publichealth.8726, author="Mavragani, Amaryllis and Sampri, Alexia and Sypsa, Karla and Tsagarakis, P. Konstantinos", title="Integrating Smart Health in the US Health Care System: Infodemiology Study of Asthma Monitoring in the Google Era", journal="JMIR Public Health Surveill", year="2018", month="Mar", day="12", volume="4", number="1", pages="e24", keywords="asthma", keywords="big data", keywords="forecasting", keywords="Google trends", keywords="health care", keywords="health informatics", keywords="internet behavior", keywords="nowcasting", keywords="online behavior", keywords="smart health", abstract="Background: With the internet's penetration and use constantly expanding, this vast amount of information can be employed in order to better assess issues in the US health care system. Google Trends, a popular tool in big data analytics, has been widely used in the past to examine interest in various medical and health-related topics and has shown great potential in forecastings, predictions, and nowcastings. As empirical relationships between online queries and human behavior have been shown to exist, a new opportunity to explore the behavior toward asthma---a common respiratory disease---is present. Objective: This study aimed at forecasting the online behavior toward asthma and examined the correlations between queries and reported cases in order to explore the possibility of nowcasting asthma prevalence in the United States using online search traffic data. Methods: Applying Holt-Winters exponential smoothing to Google Trends time series from 2004 to 2015 for the term ``asthma,'' forecasts for online queries at state and national levels are estimated from 2016 to 2020 and validated against available Google query data from January 2016 to June 2017. Correlations among yearly Google queries and between Google queries and reported asthma cases are examined. Results: Our analysis shows that search queries exhibit seasonality within each year and the relationships between each 2 years' queries are statistically significant (P<.05). Estimated forecasting models for a 5-year period (2016 through 2020) for Google queries are robust and validated against available data from January 2016 to June 2017. Significant correlations were found between (1) online queries and National Health Interview Survey lifetime asthma (r=--.82, P=.001) and current asthma (r=--.77, P=.004) rates from 2004 to 2015 and (2) between online queries and Behavioral Risk Factor Surveillance System lifetime (r=--.78, P=.003) and current asthma (r=--.79, P=.002) rates from 2004 to 2014. The correlations are negative, but lag analysis to identify the period of response cannot be employed until short-interval data on asthma prevalence are made available. Conclusions: Online behavior toward asthma can be accurately predicted, and significant correlations between online queries and reported cases exist. This method of forecasting Google queries can be used by health care officials to nowcast asthma prevalence by city, state, or nationally, subject to future availability of daily, weekly, or monthly data on reported cases. This method could therefore be used for improved monitoring and assessment of the needs surrounding the current population of patients with asthma. ", doi="10.2196/publichealth.8726", url="http://publichealth.jmir.org/2018/1/e24/", url="http://www.ncbi.nlm.nih.gov/pubmed/29530839" } @Article{info:doi/10.2196/publichealth.8186, author="Farhadloo, Mohsen and Winneg, Kenneth and Chan, Sally Man-Pui and Hall Jamieson, Kathleen and Albarracin, Dolores", title="Associations of Topics of Discussion on Twitter With Survey Measures of Attitudes, Knowledge, and Behaviors Related to Zika: Probabilistic Study in the United States", journal="JMIR Public Health Surveill", year="2018", month="Feb", day="09", volume="4", number="1", pages="e16", keywords="Zika", keywords="Twitter", keywords="topic modeling", keywords="public policy", keywords="public health", abstract="Background: Recent outbreaks of Zika virus around the world led to increased discussions about this issue on social media platforms such as Twitter. These discussions may provide useful information about attitudes, knowledge, and behaviors of the population regarding issues that are important for public policy. Objective: We sought to identify the associations of the topics of discussions on Twitter and survey measures of Zika-related attitudes, knowledge, and behaviors, not solely based upon the volume of such discussions but by analyzing the content of conversations using probabilistic techniques. Methods: Using probabilistic topic modeling with US county and week as the unit of analysis, we analyzed the content of Twitter online communications to identify topics related to the reported attitudes, knowledge, and behaviors captured in a national representative survey (N=33,193) of the US adult population over 33 weeks. Results: Our analyses revealed topics related to ``congress funding for Zika,'' ``microcephaly,'' ``Zika-related travel discussions,'' ``insect repellent,'' ``blood transfusion technology,'' and ``Zika in Miami'' were associated with our survey measures of attitudes, knowledge, and behaviors observed over the period of the study. Conclusions: Our results demonstrated that it is possible to uncover topics of discussions from Twitter communications that are associated with the Zika-related attitudes, knowledge, and behaviors of populations over time. Social media data can be used as a complementary source of information alongside traditional data sources to gauge the patterns of attitudes, knowledge, and behaviors in a population. ", doi="10.2196/publichealth.8186", url="http://publichealth.jmir.org/2018/1/e16/", url="http://www.ncbi.nlm.nih.gov/pubmed/29426815" } @Article{info:doi/10.2196/jmir.8249, author="Pan, Chih-Long and Lin, Chih-Hao and Lin, Yan-Ren and Wen, Hsin-Yu and Wen, Jet-Chau", title="The Significance of Witness Sensors for Mass Casualty Incidents and Epidemic Outbreaks", journal="J Med Internet Res", year="2018", month="Feb", day="02", volume="20", number="2", pages="e39", keywords="social media", keywords="mass casualty incident", keywords="internet", keywords="sensor", doi="10.2196/jmir.8249", url="https://www.jmir.org/2018/2/e39/", url="http://www.ncbi.nlm.nih.gov/pubmed/29396388" } @Article{info:doi/10.2196/publichealth.7444, author="Liu, Sam and Zhu, Miaoqi and Young, D. Sean", title="Monitoring Freshman College Experience Through Content Analysis of Tweets: Observational Study", journal="JMIR Public Health Surveill", year="2018", month="Jan", day="11", volume="4", number="1", pages="e5", keywords="social networking", keywords="big data", keywords="population surveillance", keywords="education", keywords="students", keywords="social media", keywords="Twitter", abstract="Background: Freshman experiences can greatly influence students' success. Traditional methods of monitoring the freshman experience, such as conducting surveys, can be resource intensive and time consuming. Social media, such as Twitter, enable users to share their daily experiences. Thus, it may be possible to use Twitter to monitor students' postsecondary experience. Objective: Our objectives were to (1) describe the proportion of content posted on Twitter by college students relating to academic studies, personal health, and social life throughout the semester; and (2) examine whether the proportion of content differed by demographics and during nonexam versus exam periods. Methods: Between October 5 and December 11, 2015, we collected tweets from 170 freshmen attending the University of California Los Angeles, California, USA, aged 18 to 20 years. We categorized the tweets into topics related to academic, personal health, and social life using keyword searches. Mann-Whitney U and Kruskal-Wallis H tests examined whether the content posted differed by sex, ethnicity, and major. The Friedman test determined whether the total number of tweets and percentage of tweets related to academic studies, personal health, and social life differed between nonexam (weeks 1-8) and final exam (weeks 9 and 10) periods. Results: Participants posted 24,421 tweets during the fall semester. Academic-related tweets (n=3433, 14.06\%) were the most prevalent during the entire semester, compared with tweets related to personal health (n=2483, 10.17\%) and social life (n=1646, 6.74\%). The proportion of academic-related tweets increased during final-exam compared with nonexam periods (mean rank 68.9, mean 18\%, standard error (SE) 0.1\% vs mean rank 80.7, mean 21\%, SE 0.2\%; Z=--2.1, P=.04). Meanwhile, the proportion of tweets related to social life decreased during final exams compared with nonexam periods (mean rank 70.2, mean 5.4\%, SE 0.01\% vs mean rank 81.8, mean 7.4\%, SE 0.01\%; Z=--4.8, P<.001). Women tweeted more often than men during both nonexam (mean rank 95.8 vs 76.8; U=2876, P=.02) and final-exam periods (mean rank 96.2 vs 76.2; U=2832, P=.01). The percentages of academic-related tweets were similar between ethnic groups during nonexam periods (P>.05). However, during the final-exam periods, the percentage of academic tweets was significantly lower among African Americans than whites ($\chi$24=15.1, P=.004). The percentages of tweets related to academic studies, personal health, and social life were not significantly different between areas of study during nonexam and exam periods (P>.05). Conclusions: The results suggest that the number of tweets related to academic studies and social life fluctuates to reflect real-time events. Student's ethnicity influenced the proportion of academic-related tweets posted. The findings from this study provide valuable information on the types of information that could be extracted from social media data. This information can be valuable for school administrators and researchers to improve students' university experience. ", doi="10.2196/publichealth.7444", url="http://publichealth.jmir.org/2018/1/e5/", url="http://www.ncbi.nlm.nih.gov/pubmed/29326096" } @Article{info:doi/10.2196/publichealth.8950, author="Lu, Sun Fred and Hou, Suqin and Baltrusaitis, Kristin and Shah, Manan and Leskovec, Jure and Sosic, Rok and Hawkins, Jared and Brownstein, John and Conidi, Giuseppe and Gunn, Julia and Gray, Josh and Zink, Anna and Santillana, Mauricio", title="Accurate Influenza Monitoring and Forecasting Using Novel Internet Data Streams: A Case Study in the Boston Metropolis", journal="JMIR Public Health Surveill", year="2018", month="Jan", day="09", volume="4", number="1", pages="e4", keywords="epidemiology", keywords="public health", keywords="machine learning", keywords="regression analysis", keywords="influenza, human", keywords="communicable diseases", keywords="statistics", keywords="patient generated data", abstract="Background: Influenza outbreaks pose major challenges to public health around the world, leading to thousands of deaths a year in the United States alone. Accurate systems that track influenza activity at the city level are necessary to provide actionable information that can be used for clinical, hospital, and community outbreak preparation. Objective: Although Internet-based real-time data sources such as Google searches and tweets have been successfully used to produce influenza activity estimates ahead of traditional health care--based systems at national and state levels, influenza tracking and forecasting at finer spatial resolutions, such as the city level, remain an open question. Our study aimed to present a precise, near real-time methodology capable of producing influenza estimates ahead of those collected and published by the Boston Public Health Commission (BPHC) for the Boston metropolitan area. This approach has great potential to be extended to other cities with access to similar data sources. Methods: We first tested the ability of Google searches, Twitter posts, electronic health records, and a crowd-sourced influenza reporting system to detect influenza activity in the Boston metropolis separately. We then adapted a multivariate dynamic regression method named ARGO (autoregression with general online information), designed for tracking influenza at the national level, and showed that it effectively uses the above data sources to monitor and forecast influenza at the city level 1 week ahead of the current date. Finally, we presented an ensemble-based approach capable of combining information from models based on multiple data sources to more robustly nowcast as well as forecast influenza activity in the Boston metropolitan area. The performances of our models were evaluated in an out-of-sample fashion over 4 influenza seasons within 2012-2016, as well as a holdout validation period from 2016 to 2017. Results: Our ensemble-based methods incorporating information from diverse models based on multiple data sources, including ARGO, produced the most robust and accurate results. The observed Pearson correlations between our out-of-sample flu activity estimates and those historically reported by the BPHC were 0.98 in nowcasting influenza and 0.94 in forecasting influenza 1 week ahead of the current date. Conclusions: We show that information from Internet-based data sources, when combined using an informed, robust methodology, can be effectively used as early indicators of influenza activity at fine geographic resolutions. ", doi="10.2196/publichealth.8950", url="http://publichealth.jmir.org/2018/1/e4/", url="http://www.ncbi.nlm.nih.gov/pubmed/29317382" } @Article{info:doi/10.2196/publichealth.7726, author="Simpson, S. Sean and Adams, Nikki and Brugman, M. Claudia and Conners, J. Thomas", title="Detecting Novel and Emerging Drug Terms Using Natural Language Processing: A Social Media Corpus Study", journal="JMIR Public Health Surveill", year="2018", month="Jan", day="08", volume="4", number="1", pages="e2", keywords="natural language processing", keywords="street drugs", keywords="social media", keywords="vocabulary", abstract="Background: With the rapid development of new psychoactive substances (NPS) and changes in the use of more traditional drugs, it is increasingly difficult for researchers and public health practitioners to keep up with emerging drugs and drug terms. Substance use surveys and diagnostic tools need to be able to ask about substances using the terms that drug users themselves are likely to be using. Analyses of social media may offer new ways for researchers to uncover and track changes in drug terms in near real time. This study describes the initial results from an innovative collaboration between substance use epidemiologists and linguistic scientists employing techniques from the field of natural language processing to examine drug-related terms in a sample of tweets from the United States. Objective: The objective of this study was to assess the feasibility of using distributed word-vector embeddings trained on social media data to uncover previously unknown (to researchers) drug terms. Methods: In this pilot study, we trained a continuous bag of words (CBOW) model of distributed word-vector embeddings on a Twitter dataset collected during July 2016 (roughly 884.2 million tokens). We queried the trained word embeddings for terms with high cosine similarity (a proxy for semantic relatedness) to well-known slang terms for marijuana to produce a list of candidate terms likely to function as slang terms for this substance. This candidate list was then compared with an expert-generated list of marijuana terms to assess the accuracy and efficacy of using word-vector embeddings to search for novel drug terminology. Results: The method described here produced a list of 200 candidate terms for the target substance (marijuana). Of these 200 candidates, 115 were determined to in fact relate to marijuana (65 terms for the substance itself, 50 terms related to paraphernalia). This included 30 terms which were used to refer to the target substance in the corpus yet did not appear on the expert-generated list and were therefore considered to be successful cases of uncovering novel drug terminology. Several of these novel terms appear to have been introduced as recently as 1 or 2 months before the corpus time slice used to train the word embeddings. Conclusions: Though the precision of the method described here is low enough as to still necessitate human review of any candidate term lists generated in such a manner, the fact that this process was able to detect 30 novel terms for the target substance based only on one month's worth of Twitter data is highly promising. We see this pilot study as an important proof of concept and a first step toward producing a fully automated drug term discovery system capable of tracking emerging NPS terms in real time. ", doi="10.2196/publichealth.7726", url="http://publichealth.jmir.org/2018/1/e2/", url="http://www.ncbi.nlm.nih.gov/pubmed/29311050" } @Article{info:doi/10.2196/jmir.8870, author="Phillips, A. Charles and Barz Leahy, Allison and Li, Yimei and Schapira, M. Marilyn and Bailey, Charles L. and Merchant, M. Raina", title="Relationship Between State-Level Google Online Search Volume and Cancer Incidence in the United States: Retrospective Study", journal="J Med Internet Res", year="2018", month="Jan", day="08", volume="20", number="1", pages="e6", keywords="Google", keywords="cancer", keywords="incidence", keywords="Internet", keywords="infodemiology", abstract="Background: In the United States, cancer is common, with high morbidity and mortality; cancer incidence varies between states. Online searches reflect public awareness, which could be driven by the underlying regional cancer epidemiology. Objective: The objective of our study was to characterize the relationship between cancer incidence and online Google search volumes in the United States for 6 common cancers. A secondary objective was to evaluate the association of search activity with cancer-related public events and celebrity news coverage. Methods: We performed a population-based, retrospective study of state-level cancer incidence from 2004 through 2013 reported by the Centers for Disease Control and Prevention for breast, prostate, colon, lung, and uterine cancers and leukemia compared to Google Trends (GT) relative search volume (RSV), a metric designed by Google to allow interest in search topics to be compared between regions. Participants included persons in the United States who searched for cancer terms on Google. The primary measures were the correlation between annual state-level cancer incidence and RSV as determined by Spearman correlation and linear regression with RSV and year as independent variables and cancer incidence as the dependent variable. Temporal associations between search activity and events raising public awareness such as cancer awareness months and cancer-related celebrity news were described. Results: At the state level, RSV was significantly correlated to incidence for breast (r=.18, P=.001), prostate (r=--.27, P<.001), lung (r=.33, P<.001), and uterine cancers (r=.39, P<.001) and leukemia (r=.13, P=.003) but not colon cancer (r=--.02, P=.66). After adjusting for time, state-level RSV was positively correlated to cancer incidence for all cancers: breast (P<.001, 95\% CI 0.06 to 0.19), prostate (P=.38, 95\% CI --0.08 to 0.22), lung (P<.001, 95\% CI 0.33 to 0.46), colon (P<.001, 95\% CI 0.11 to 0.17), and uterine cancers (P<.001, 95\% CI 0.07 to 0.12) and leukemia (P<.001, 95\% CI 0.01 to 0.03). Temporal associations in GT were noted with breast cancer awareness month but not with other cancer awareness months and celebrity events. Conclusions: Cancer incidence is correlated with online search volume at the state level. Search patterns were temporally associated with cancer awareness months and celebrity announcements. Online searches reflect public awareness. Advancing understanding of online search patterns could augment traditional epidemiologic surveillance, provide opportunities for targeted patient engagement, and allow public information campaigns to be evaluated in ways previously unable to be measured. ", doi="10.2196/jmir.8870", url="http://www.jmir.org/2018/1/e6/", url="http://www.ncbi.nlm.nih.gov/pubmed/29311051" } @Article{info:doi/10.2196/jmir.8667, author="Giat, Eitan and Yom-Tov, Elad", title="Evidence From Web-Based Dietary Search Patterns to the Role of B12 Deficiency in Non-Specific Chronic Pain: A Large-Scale Observational Study", journal="J Med Internet Res", year="2018", month="Jan", day="05", volume="20", number="1", pages="e4", keywords="B12 deficiency", keywords="diet", keywords="Internet searches", keywords="neuropsychiatric symptoms", keywords="neuropathy", abstract="Background: Profound vitamin B12 deficiency is a known cause of disease, but the role of low or intermediate levels of B12 in the development of neuropathy and other neuropsychiatric symptoms, as well as the relationship between eating meat and B12 levels, is unclear. Objective: The objective of our study was to investigate the role of low or intermediate levels of B12 in the development of neuropathy and other neuropsychiatric symptoms. Methods: We used food-related Internet search patterns from a sample of 8.5 million people based in the US as a proxy for B12 intake and correlated these searches with Internet searches related to possible effects of B12 deficiency. Results: Food-related search patterns were highly correlated with known consumption and food-related searches ($\rho$=.69). Awareness of B12 deficiency was associated with a higher consumption of B12-rich foods and with queries for B12 supplements. Searches for terms related to neurological disorders were correlated with searches for B12-poor foods, in contrast with control terms. Popular medicines, those having fewer indications, and those which are predominantly used to treat pain, were more strongly correlated with the ability to predict neuropathic pain queries using the B12 contents of food. Conclusions: Our findings show that Internet search patterns are a useful way of investigating health questions in large populations, and suggest that low B12 intake may be associated with a broader spectrum of neurological disorders than previously thought. ", doi="10.2196/jmir.8667", url="http://www.jmir.org/2018/1/e4/", url="http://www.ncbi.nlm.nih.gov/pubmed/29305340" } @Article{info:doi/10.2196/publichealth.7823, author="Sinha, S. Michael and Freifeld, C. Clark and Brownstein, S. John and Donneyong, M. Macarius and Rausch, Paula and Lappin, M. Brian and Zhou, H. Esther and Dal Pan, J. Gerald and Pawar, M. Ajinkya and Hwang, J. Thomas and Avorn, Jerry and Kesselheim, S. Aaron", title="Social Media Impact of the Food and Drug Administration's Drug Safety Communication Messaging About Zolpidem: Mixed-Methods Analysis", journal="JMIR Public Health Surveill", year="2018", month="Jan", day="05", volume="4", number="1", pages="e1", keywords="Food and Drug Administration", keywords="drug safety communications", keywords="surveillance", keywords="epidemiology", keywords="social media", keywords="Twitter", keywords="Facebook", keywords="Google Trends", abstract="Background: The Food and Drug Administration (FDA) issues drug safety communications (DSCs) to health care professionals, patients, and the public when safety issues emerge related to FDA-approved drug products. These safety messages are disseminated through social media to ensure broad uptake. Objective: The objective of this study was to assess the social media dissemination of 2 DSCs released in 2013 for the sleep aid zolpidem. Methods: We used the MedWatcher Social program and the DataSift historic query tool to aggregate Twitter and Facebook posts from October 1, 2012 through August 31, 2013, a period beginning approximately 3 months before the first DSC and ending 3 months after the second. Posts were categorized as (1) junk, (2) mention, and (3) adverse event (AE) based on a score between --0.2 (completely unrelated) to 1 (perfectly related). We also looked at Google Trends data and Wikipedia edits for the same time period. Google Trends search volume is scaled on a range of 0 to 100 and includes ``Related queries'' during the relevant time periods. An interrupted time series (ITS) analysis assessed the impact of DSCs on the counts of posts with specific mention of zolpidem-containing products. Chow tests for known structural breaks were conducted on data from Twitter, Facebook, and Google Trends. Finally, Wikipedia edits were pulled from the website's editorial history, which lists all revisions to a given page and the editor's identity. Results: In total, 174,286 Twitter posts and 59,641 Facebook posts met entry criteria. Of those, 16.63\% (28,989/174,286) of Twitter posts and 25.91\% (15,453/59,641) of Facebook posts were labeled as junk and excluded. AEs and mentions represented 9.21\% (16,051/174,286) and 74.16\% (129,246/174,286) of Twitter posts and 5.11\% (3,050/59,641) and 68.98\% (41,138/59,641) of Facebook posts, respectively. Total daily counts of posts about zolpidem-containing products increased on Twitter and Facebook on the day of the first DSC; Google searches increased on the week of the first DSC. ITS analyses demonstrated variability but pointed to an increase in interest around the first DSC. Chow tests were significant (P<.0001) for both DSCs on Facebook and Twitter, but only the first DSC on Google Trends. Wikipedia edits occurred soon after each DSC release, citing news articles rather than the DSC itself and presenting content that needed subsequent revisions for accuracy. Conclusions: Social media offers challenges and opportunities for dissemination of the DSC messages. The FDA could consider strategies for more actively disseminating DSC safety information through social media platforms, particularly when announcements require updating. The FDA may also benefit from directly contributing content to websites like Wikipedia that are frequently accessed for drug-related information. ", doi="10.2196/publichealth.7823", url="http://publichealth.jmir.org/2018/1/e1/", url="http://www.ncbi.nlm.nih.gov/pubmed/29305342" } @Article{info:doi/10.2196/jmir.8184, author="Wagner, Moritz and Lampos, Vasileios and Yom-Tov, Elad and Pebody, Richard and Cox, J. Ingemar", title="Estimating the Population Impact of a New Pediatric Influenza Vaccination Program in England Using Social Media Content", journal="J Med Internet Res", year="2017", month="Dec", day="21", volume="19", number="12", pages="e416", keywords="health intervention", keywords="influenza", keywords="vaccination", keywords="social media", keywords="Twitter", abstract="Background: The rollout of a new childhood live attenuated influenza vaccine program was launched in England in 2013, which consisted of a national campaign for all 2 and 3 year olds and several pilot locations offering the vaccine to primary school-age children (4-11 years of age) during the influenza season. The 2014/2015 influenza season saw the national program extended to include additional pilot regions, some of which offered the vaccine to secondary school children (11-13 years of age) as well. Objective: We utilized social media content to obtain a complementary assessment of the population impact of the programs that were launched in England during the 2013/2014 and 2014/2015 flu seasons. The overall community-wide impact on transmission in pilot areas was estimated for the different age groups that were targeted for vaccination. Methods: A previously developed statistical framework was applied, which consisted of a nonlinear regression model that was trained to infer influenza-like illness (ILI) rates from Twitter posts originating in pilot (school-age vaccinated) and control (unvaccinated) areas. The control areas were then used to estimate ILI rates in pilot areas, had the intervention not taken place. These predictions were compared with their corresponding Twitter-based ILI estimates. Results: Results suggest a reduction in ILI rates of 14\% (1-25\%) and 17\% (2-30\%) across all ages in only the primary school-age vaccine pilot areas during the 2013/2014 and 2014/2015 influenza seasons, respectively. No significant impact was observed in areas where two age cohorts of secondary school children were vaccinated. Conclusions: These findings corroborate independent assessments from traditional surveillance data, thereby supporting the ongoing rollout of the program to primary school-age children and providing evidence of the value of social media content as an additional syndromic surveillance tool. ", doi="10.2196/jmir.8184", url="http://www.jmir.org/2017/12/e416/", url="http://www.ncbi.nlm.nih.gov/pubmed/29269339" } @Article{info:doi/10.2196/publichealth.8641, author="Allem, Jon-Patrick and Ferrara, Emilio and Uppu, Priyanka Sree and Cruz, Boley Tess and Unger, B. Jennifer", title="E-Cigarette Surveillance With Social Media Data: Social Bots, Emerging Topics, and Trends", journal="JMIR Public Health Surveill", year="2017", month="Dec", day="20", volume="3", number="4", pages="e98", keywords="electronic cigarettes", keywords="vaping", keywords="Twitter", keywords="social media", keywords="social bots", keywords="electronic nicotine delivery system", keywords="infoveillance", abstract="Background: As e-cigarette use rapidly increases in popularity, data from online social systems (Twitter, Instagram, Google Web Search) can be used to capture and describe the social and environmental context in which individuals use, perceive, and are marketed this tobacco product. Social media data may serve as a massive focus group where people organically discuss e-cigarettes unprimed by a researcher, without instrument bias, captured in near real time and at low costs. Objective: This study documents e-cigarette--related discussions on Twitter, describing themes of conversations and locations where Twitter users often discuss e-cigarettes, to identify priority areas for e-cigarette education campaigns. Additionally, this study demonstrates the importance of distinguishing between social bots and human users when attempting to understand public health--related behaviors and attitudes. Methods: E-cigarette--related posts on Twitter (N=6,185,153) were collected from December 24, 2016, to April 21, 2017. Techniques drawn from network science were used to determine discussions of e-cigarettes by describing which hashtags co-occur (concept clusters) in a Twitter network. Posts and metadata were used to describe where geographically e-cigarette--related discussions in the United States occurred. Machine learning models were used to distinguish between Twitter posts reflecting attitudes and behaviors of genuine human users from those of social bots. Odds ratios were computed from 2x2 contingency tables to detect if hashtags varied by source (social bot vs human user) using the Fisher exact test to determine statistical significance. Results: Clusters found in the corpus of hashtags from human users included behaviors (eg, \#vaping), vaping identity (eg, \#vapelife), and vaping community (eg, \#vapenation). Additional clusters included products (eg, \#eliquids), dual tobacco use (eg, \#hookah), and polysubstance use (eg, \#marijuana). Clusters found in the corpus of hashtags from social bots included health (eg, \#health), smoking cessation (eg, \#quitsmoking), and new products (eg, \#ismog). Social bots were significantly more likely to post hashtags that referenced smoking cessation and new products compared to human users. The volume of tweets was highest in the Mid-Atlantic (eg, Pennsylvania, New Jersey, Maryland, and New York), followed by the West Coast and Southwest (eg, California, Arizona and Nevada). Conclusions: Social media data may be used to complement and extend the surveillance of health behaviors including tobacco product use. Public health researchers could harness these data and methods to identify new products or devices. Furthermore, findings from this study demonstrate the importance of distinguishing between Twitter posts from social bots and humans when attempting to understand attitudes and behaviors. Social bots may be used to perpetuate the idea that e-cigarettes are helpful in cessation and to promote new products as they enter the marketplace. ", doi="10.2196/publichealth.8641", url="http://publichealth.jmir.org/2017/4/e98/", url="http://www.ncbi.nlm.nih.gov/pubmed/29263018" } @Article{info:doi/10.2196/jmir.7467, author="Keller, Sophie Michelle and Park, J. Hannah and Cunningham, Elena Maria and Fouladian, Eleazar Joshua and Chen, Michelle and Spiegel, Ross Brennan Mason", title="Public Perceptions Regarding Use of Virtual Reality in Health Care: A Social Media Content Analysis Using Facebook", journal="J Med Internet Res", year="2017", month="Dec", day="19", volume="19", number="12", pages="e419", keywords="social media", keywords="virtual reality", keywords="qualitative research", abstract="Background: Virtual reality (VR) technology provides an immersive environment that enables users to have modified experiences of reality. VR is increasingly used to manage patients with pain, disability, obesity, neurologic dysfunction, anxiety, and depression. However, public opinion regarding the use of VR in health care has not been explored. Understanding public opinion of VR is critical to ensuring effective implementation of this emerging technology. Objective: This study aimed to examine public opinion about health care VR using social listening, a method that allows for the exploration of unfiltered views of topics discussed on social media and online forums. Methods: In March 2016, NBC News produced a video depicting the use of VR for patient care. The video was repackaged by NowThis, a social media news website, and distributed on Facebook by Upworthy, a news aggregator, yielding 4.3 million views and 2401 comments. We used Microsoft Excel Power Query and ATLAS.ti software (version 7.5, Scientific Software Development) to analyze the comments using content analysis and categorized the comments around first-, second-, and third-order concepts. We determined self-identified gender from the user's Facebook page and performed sentiment analysis of the language to analyze whether the perception of VR differed by gender using a Pearson's chi-square test. Results: Out of the 1614 analyzable comments, 1021 (63.26\%) were attributed to female Facebook users, 572 (35.44\%) to male users, and 21 (1.30\%) to users of unknown gender. There were 1197 comments coded as expressing a positive perception about VR (74.16\%), 251 coded as expressing a negative perception and/or concern (15.56\%), and 560 coded as neutral (34.70\%). Informants identified 20 use cases for VR in health care, including the use of VR for pain and stress reduction; bed-bound individuals; women during labor; and patients undergoing chemotherapy, dialysis, radiation, or imaging procedures. Negative comments expressed concerns about radiation, infection risk, motion sickness, and the ubiquity of and overall dependence on technology. There was a statistically significant association between the language valence of the Facebook post and the gender of the Facebook user; men were more likely to post negative perceptions about the use of VR for health care, whereas women were more likely to post positive perceptions (P<.001). Conclusions: Most informants expressed positive perceptions about the use of VR in a wide range of health care settings. However, many expressed concerns that should be acknowledged and addressed as health care VR continues to evolve. Our results provide guidance in determining where further research on the use of VR in patient care is needed, and offer a formal opportunity for public opinion to shape the VR research agenda. ", doi="10.2196/jmir.7467", url="http://www.jmir.org/2017/12/e419/", url="http://www.ncbi.nlm.nih.gov/pubmed/29258975" } @Article{info:doi/10.2196/publichealth.7794, author="Madden, Michael Kenneth", title="The Seasonal Periodicity of Healthy Contemplations About Exercise and Weight Loss: Ecological Correlational Study", journal="JMIR Public Health Surveill", year="2017", month="Dec", day="13", volume="3", number="4", pages="e92", keywords="healthy lifestyle", keywords="weight loss", keywords="exercise", keywords="Internet", keywords="motivation", abstract="Background: Lack of physical activity and weight gain are two of the biggest drivers of health care costs in the United States. Healthy contemplations are required before any changes in behavior, and a recent study has shown that they have underlying periodicities. Objective: The aim of this study was to examine seasonal variations in state-by-state interest in both weight loss and increasing physical activity, and how these variations were associated with geographic latitude using Google Trends search data for the United States. Methods: Internet search query data were obtained from Google Trends (2004-2016). Time series analysis (every 2 weeks) was performed to determine search volume (normalized to overall search intensity). Seasonality was determined both by the difference in search volumes between winter (December, January, and February) and summer (June, July, and August) months and by the amplitude of cosinor analysis. Results: Exercise-related searches were highest during the winter months, whereas weight loss contemplations showed a biphasic pattern (peaking in the summer and winter months). The magnitude of the seasonal difference increased with increasing latitude for both exercise (R2=.45, F1,49=40.09, beta=?.671, standard deviation [SD]=0.106, P<.001) and weight loss (R2=.24, F1,49=15.79, beta=?.494, SD=0.124, P<.001) searches. Conclusions: Healthy contemplations follow specific seasonal patterns, with the highest contemplations surrounding exercise during the winter months, and weight loss contemplations peaking during both winter and summer seasons. Knowledge of seasonal variations in passive contemplations may potentially allow for more efficient use of public health campaign resources. ", doi="10.2196/publichealth.7794", url="http://publichealth.jmir.org/2017/4/e92/", url="http://www.ncbi.nlm.nih.gov/pubmed/29237582" } @Article{info:doi/10.2196/jmir.9266, author="Bian, Jiang and Zhao, Yunpeng and Salloum, G. Ramzi and Guo, Yi and Wang, Mo and Prosperi, Mattia and Zhang, Hansi and Du, Xinsong and Ramirez-Diaz, J. Laura and He, Zhe and Sun, Yuan", title="Using Social Media Data to Understand the Impact of Promotional Information on Laypeople's Discussions: A Case Study of Lynch Syndrome", journal="J Med Internet Res", year="2017", month="Dec", day="13", volume="19", number="12", pages="e414", keywords="social media", keywords="Lynch syndrome", keywords="public health surveillance", keywords="sentiment analysis", abstract="Background: Social media is being used by various stakeholders among pharmaceutical companies, government agencies, health care organizations, professionals, and news media as a way of engaging audiences to raise disease awareness and ultimately to improve public health. Nevertheless, it is unclear what effects this health information has on laypeople. Objective: This study aimed to provide a detailed examination of how promotional health information related to Lynch syndrome impacts laypeople's discussions on a social media platform (Twitter) in terms of topic awareness and attitudes. Methods: We used topic modeling and sentiment analysis techniques on Lynch syndrome--related tweets to answer the following research questions (RQs): (1) what are the most discussed topics in Lynch syndrome--related tweets?; (2) how promotional Lynch syndrome--related information on Twitter affects laypeople's discussions?; and (3) what impact do the Lynch syndrome awareness activities in the Colon Cancer Awareness Month and Lynch Syndrome Awareness Day have on laypeople's discussions and their attitudes? In particular, we used a set of keywords to collect Lynch syndrome--related tweets from October 26, 2016 to August 11, 2017 (289 days) through the Twitter public search application programming interface (API). We experimented with two different classification methods to categorize tweets into the following three classes: (1) irrelevant, (2) promotional health information, and (3) laypeople's discussions. We applied a topic modeling method to discover the themes in these Lynch syndrome--related tweets and conducted sentiment analysis on each layperson's tweet to gauge the writer's attitude (ie, positive, negative, and neutral) toward Lynch syndrome. The topic modeling and sentiment analysis results were elaborated to answer the three RQs. Results: Of all tweets (N=16,667), 87.38\% (14,564/16,667) were related to Lynch syndrome. Of the Lynch syndrome--related tweets, 81.43\% (11,860/14,564) were classified as promotional and 18.57\% (2704/14,564) were classified as laypeople's discussions. The most discussed themes were treatment (n=4080) and genetic testing (n=3073). We found that the topic distributions in laypeople's discussions were similar to the distributions in promotional Lynch syndrome--related information. Furthermore, most people had a positive attitude when discussing Lynch syndrome. The proportion of negative tweets was 3.51\%. Within each topic, treatment (16.67\%) and genetic testing (5.60\%) had more negative tweets compared with other topics. When comparing monthly trends, laypeople's discussions had a strong correlation with promotional Lynch syndrome--related information on awareness (r=.98, P<.001), while there were moderate correlations on screening (r=.602, P=.05), genetic testing (r=.624, P=.04), treatment (r=.69, P=.02), and risk (r=.66, P=.03). We also discovered that the Colon Cancer Awareness Month (March 2017) and the Lynch Syndrome Awareness Day (March 22, 2017) had significant positive impacts on laypeople's discussions and their attitudes. Conclusions: There is evidence that participative social media platforms, namely Twitter, offer unique opportunities to inform cancer communication surveillance and to explore the mechanisms by which these new communication media affect individual health behavior and population health. ", doi="10.2196/jmir.9266", url="http://www.jmir.org/2017/12/e414/", url="http://www.ncbi.nlm.nih.gov/pubmed/29237586" } @Article{info:doi/10.2196/medinform.9170, author="P Tafti, Ahmad and Badger, Jonathan and LaRose, Eric and Shirzadi, Ehsan and Mahnke, Andrea and Mayer, John and Ye, Zhan and Page, David and Peissig, Peggy", title="Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure", journal="JMIR Med Inform", year="2017", month="Dec", day="08", volume="5", number="4", pages="e51", keywords="adverse drug event", keywords="adverse drug reaction", keywords="drug side effects", keywords="machine learning", keywords="text mining", abstract="Background: The study of adverse drug events (ADEs) is a tenured topic in medical literature. In recent years, increasing numbers of scientific articles and health-related social media posts have been generated and shared daily, albeit with very limited use for ADE study and with little known about the content with respect to ADEs. Objective: The aim of this study was to develop a big data analytics strategy that mines the content of scientific articles and health-related Web-based social media to detect and identify ADEs. Methods: We analyzed the following two data sources: (1) biomedical articles and (2) health-related social media blog posts. We developed an intelligent and scalable text mining solution on big data infrastructures composed of Apache Spark, natural language processing, and machine learning. This was combined with an Elasticsearch No-SQL distributed database to explore and visualize ADEs. Results: The accuracy, precision, recall, and area under receiver operating characteristic of the system were 92.7\%, 93.6\%, 93.0\%, and 0.905, respectively, and showed better results in comparison with traditional approaches in the literature. This work not only detected and classified ADE sentences from big data biomedical literature but also scientifically visualized ADE interactions. Conclusions: To the best of our knowledge, this work is the first to investigate a big data machine learning strategy for ADE discovery on massive datasets downloaded from PubMed Central and social media. This contribution illustrates possible capacities in big data biomedical text analysis using advanced computational methods with real-time update from new data published on a daily basis. ", doi="10.2196/medinform.9170", url="http://medinform.jmir.org/2017/4/e51/", url="http://www.ncbi.nlm.nih.gov/pubmed/29222076" } @Article{info:doi/10.2196/publichealth.9136, author="Adawi, Mohammad and Bragazzi, Luigi Nicola and Watad, Abdulla and Sharif, Kassem and Amital, Howard and Mahroum, Naim", title="Discrepancies Between Classic and Digital Epidemiology in Searching for the Mayaro Virus: Preliminary Qualitative and Quantitative Analysis of Google Trends", journal="JMIR Public Health Surveill", year="2017", month="Dec", day="01", volume="3", number="4", pages="e93", keywords="digital health", keywords="digital epidemiology", keywords="emerging viruses", keywords="Mayaro virus", keywords="arboviruses", keywords="epidemiology", keywords="epidemiological monitoring", abstract="Background: Mayaro virus (MAYV), first discovered in Trinidad in 1954, is spread by the Haemagogus mosquito. Small outbreaks have been described in the past in the Amazon jungles of Brazil and other parts of South America. Recently, a case was reported in rural Haiti. Objective: Given the emerging importance of MAYV, we aimed to explore the feasibility of exploiting a Web-based tool for monitoring and tracking MAYV cases. Methods: Google Trends is an online tracking system. A Google-based approach is particularly useful to monitor especially infectious diseases epidemics. We searched Google Trends from its inception (from January 2004 through to May 2017) for MAYV-related Web searches worldwide. Results: We noted a burst in search volumes in the period from July 2016 (relative search volume [RSV]=13\%) to December 2016 (RSV=18\%), with a peak in September 2016 (RSV=100\%). Before this burst, the average search activity related to MAYV was very low (median 1\%). MAYV-related queries were concentrated in the Caribbean. Scientific interest from the research community and media coverage affected digital seeking behavior. Conclusions: MAYV has always circulated in South America. Its recent appearance in the Caribbean has been a source of concern, which resulted in a burst of Internet queries. While Google Trends cannot be used to perform real-time epidemiological surveillance of MAYV, it can be exploited to capture the public's reaction to outbreaks. Public health workers should be aware of this, in that information and communication technologies could be used to communicate with users, reassure them about their concerns, and to empower them in making decisions affecting their health. ", doi="10.2196/publichealth.9136", url="http://publichealth.jmir.org/2017/4/e93/", url="http://www.ncbi.nlm.nih.gov/pubmed/29196278" } @Article{info:doi/10.2196/publichealth.8015, author="Samaras, Loukas and Garc{\'i}a-Barriocanal, Elena and Sicilia, Miguel-Angel", title="Syndromic Surveillance Models Using Web Data: The Case of Influenza in Greece and Italy Using Google Trends", journal="JMIR Public Health Surveill", year="2017", month="Nov", day="20", volume="3", number="4", pages="e90", keywords="Google Trends", keywords="influenza", keywords="Web, syndromic surveillance", keywords="statistical correlation", keywords="forecast", keywords="ARIMA", abstract="Background: An extended discussion and research has been performed in recent years using data collected through search queries submitted via the Internet. It has been shown that the overall activity on the Internet is related to the number of cases of an infectious disease outbreak. Objective: The aim of the study was to define a similar correlation between data from Google Trends and data collected by the official authorities of Greece and Europe by examining the development and the spread of seasonal influenza in Greece and Italy. Methods: We used multiple regressions of the terms submitted in the Google search engine related to influenza for the period from 2011 to 2012 in Greece and Italy (sample data for 104 weeks for each country). We then used the autoregressive integrated moving average statistical model to determine the correlation between the Google search data and the real influenza cases confirmed by the aforementioned authorities. Two methods were used: (1) a flu score was created for the case of Greece and (2) comparison of data from a neighboring country of Greece, which is Italy. Results: The results showed that there is a significant correlation that can help the prediction of the spread and the peak of the seasonal influenza using data from Google searches. The correlation for Greece for 2011 and 2012 was .909 and .831, respectively, and correlation for Italy for 2011 and 2012 was .979 and .933, respectively. The prediction of the peak was quite precise, providing a forecast before it arrives to population. Conclusions: We can create an Internet surveillance system based on Google searches to track influenza in Greece and Italy. ", doi="10.2196/publichealth.8015", url="http://publichealth.jmir.org/2017/4/e90/", url="http://www.ncbi.nlm.nih.gov/pubmed/29158208" } @Article{info:doi/10.2196/publichealth.8306, author="Pes{\"a}l{\"a}, Samuli and Virtanen, J. Mikko and Sane, Jussi and Mustonen, Pekka and Kaila, Minna and Helve, Otto", title="Health Information--Seeking Patterns of the General Public and Indications for Disease Surveillance: Register-Based Study Using Lyme Disease", journal="JMIR Public Health Surveill", year="2017", month="Nov", day="06", volume="3", number="4", pages="e86", keywords="search engine", keywords="evidence-based medicine", keywords="medical informatics", keywords="information systems", keywords="communications media", keywords="Lyme disease", keywords="infodemiology", keywords="infoveillance", keywords="surveillance", abstract="Background: People using the Internet to find information on health issues, such as specific diseases, usually start their search from a general search engine, for example, Google. Internet searches such as these may yield results and data of questionable quality and reliability. Health Library is a free-of-charge medical portal on the Internet providing medical information for the general public. Physician's Databases, an Internet evidence-based medicine source, provides medical information for health care professionals (HCPs) to support their clinical practice. Both databases are available throughout Finland, but the latter is used only by health professionals and pharmacies. Little is known about how the general public seeks medical information from medical sources on the Internet, how this behavior differs from HCPs' queries, and what causes possible differences in behavior. Objective: The aim of our study was to evaluate how the general public's and HCPs' information-seeking trends from Internet medical databases differ seasonally and temporally. In addition, we aimed to evaluate whether the general public's information-seeking trends could be utilized for disease surveillance and whether media coverage could affect these seeking trends. Methods: Lyme disease, serving as a well-defined disease model with distinct seasonal variation, was chosen as a case study. Two Internet medical databases, Health Library and Physician's Databases, were used. We compared the general public's article openings on Lyme disease from Health Library to HCPs' article openings on Lyme disease from Physician's Databases seasonally across Finland from 2011 to 2015. Additionally, media publications related to Lyme disease were searched from the largest and most popular media websites in Finland. Results: Both databases, Health Library and Physician's Databases, show visually similar patterns in temporal variations of article openings on Lyme disease in Finland from 2011 to 2015. However, Health Library openings show not only an increasing trend over time but also greater fluctuations, especially during peak opening seasons. Outside these seasons, publications in the media coincide with Health Library article openings only occasionally. Conclusions: Lyme disease--related information-seeking behaviors between the general public and HCPs from Internet medical portals share similar temporal variations, which is consistent with the trend seen in epidemiological data. Therefore, the general public's article openings could be used as a supplementary source of information for disease surveillance. The fluctuations in article openings appeared stronger among the general public, thus, suggesting that different factors such as media coverage, affect the information-seeking behaviors of the public versus professionals. However, media coverage may also have an influence on HCPs. Not every publication was associated with an increase in openings, but the higher the media coverage by some publications, the higher the general public's access to Health Library. ", doi="10.2196/publichealth.8306", url="http://publichealth.jmir.org/2017/4/e86/", url="http://www.ncbi.nlm.nih.gov/pubmed/29109071" } @Article{info:doi/10.2196/jmir.7486, author="Kandula, Sasikiran and Hsu, Daniel and Shaman, Jeffrey", title="Subregional Nowcasts of Seasonal Influenza Using Search Trends", journal="J Med Internet Res", year="2017", month="Nov", day="06", volume="19", number="11", pages="e370", keywords="human influenza", keywords="classification and regression trees", keywords="nowcasts", keywords="infodemiology", keywords="infoveillance", keywords="surveillance", abstract="Background: Limiting the adverse effects of seasonal influenza outbreaks at state or city level requires close monitoring of localized outbreaks and reliable forecasts of their progression. Whereas forecasting models for influenza or influenza-like illness (ILI) are becoming increasingly available, their applicability to localized outbreaks is limited by the nonavailability of real-time observations of the current outbreak state at local scales. Surveillance data collected by various health departments are widely accepted as the reference standard for estimating the state of outbreaks, and in the absence of surveillance data, nowcast proxies built using Web-based activities such as search engine queries, tweets, and access of health-related webpages can be useful. Nowcast estimates of state and municipal ILI were previously published by Google Flu Trends (GFT); however, validations of these estimates were seldom reported. Objective: The aim of this study was to develop and validate models to nowcast ILI at subregional geographic scales. Methods: We built nowcast models based on autoregressive (autoregressive integrated moving average; ARIMA) and supervised regression methods (Random forests) at the US state level using regional weighted ILI and Web-based search activity derived from Google's Extended Trends application programming interface. We validated the performance of these methods using actual surveillance data for the 50 states across six seasons. We also built state-level nowcast models using state-level estimates of ILI and compared the accuracy of these estimates with the estimates of the regional models extrapolated to the state level and with the nowcast estimates published by GFT. Results: Models built using regional ILI extrapolated to state level had a median correlation of 0.84 (interquartile range: 0.74-0.91) and a median root mean square error (RMSE) of 1.01 (IQR: 0.74-1.50), with noticeable variability across seasons and by state population size. Model forms that hypothesize the availability of timely state-level surveillance data show significantly lower errors of 0.83 (0.55-0.23). Compared with GFT, the latter model forms have lower errors but also lower correlation. Conclusions: These results suggest that the proposed methods may be an alternative to the discontinued GFT and that further improvements in the quality of subregional nowcasts may require increased access to more finely resolved surveillance data. ", doi="10.2196/jmir.7486", url="http://www.jmir.org/2017/11/e370/", url="http://www.ncbi.nlm.nih.gov/pubmed/29109069" } @Article{info:doi/10.2196/jmir.6426, author="Kim, Jung Sunny and Marsch, A. Lisa and Hancock, T. Jeffrey and Das, K. Amarendra", title="Scaling Up Research on Drug Abuse and Addiction Through Social Media Big Data", journal="J Med Internet Res", year="2017", month="Oct", day="31", volume="19", number="10", pages="e353", keywords="opioid epidemic", keywords="opioid crisis", keywords="opioid-related disorders", keywords="substance use", keywords="substance-related disorders", keywords="prescription drug misuse", keywords="addiction", keywords="Facebook", keywords="Twitter", keywords="Instagram", keywords="big data", keywords="ethics", abstract="Background: Substance use--related communication for drug use promotion and its prevention is widely prevalent on social media. Social media big data involve naturally occurring communication phenomena that are observable through social media platforms, which can be used in computational or scalable solutions to generate data-driven inferences. Despite the promising potential to utilize social media big data to monitor and treat substance use problems, the characteristics, mechanisms, and outcomes of substance use--related communications on social media are largely unknown. Understanding these aspects can help researchers effectively leverage social media big data and platforms for observation and health communication outreach for people with substance use problems. Objective: The objective of this critical review was to determine how social media big data can be used to understand communication and behavioral patterns of problematic use of prescription drugs. We elaborate on theoretical applications, ethical challenges and methodological considerations when using social media big data for research on drug abuse and addiction. Based on a critical review process, we propose a typology with key initiatives to address the knowledge gap in the use of social media for research on prescription drug abuse and addiction. Methods: First, we provided a narrative summary of the literature on drug use--related communication on social media. We also examined ethical considerations in the research processes of (1) social media big data mining, (2) subgroup or follow-up investigation, and (3) dissemination of social media data-driven findings. To develop a critical review-based typology, we searched the PubMed database and the entire e-collection theme of ``infodemiology and infoveillance'' in the Journal of Medical Internet Research / JMIR Publications. Studies that met our inclusion criteria (eg, use of social media data concerning non-medical use of prescription drugs, data informatics-driven findings) were reviewed for knowledge synthesis. User characteristics, communication characteristics, mechanisms and predictors of such communications, and the psychological and behavioral outcomes of social media use for problematic drug use--related communications are the dimensions of our typology. In addition to ethical practices and considerations, we also reviewed the methodological and computational approaches used in each study to develop our typology. Results: We developed a typology to better understand non-medical, problematic use of prescription drugs through the lens of social media big data. Highly relevant studies that met our inclusion criteria were reviewed for knowledge synthesis. The characteristics of users who shared problematic substance use--related communications on social media were reported by general group terms, such as adolescents, Twitter users, and Instagram users. All reviewed studies examined the communication characteristics, such as linguistic properties, and social networks of problematic drug use--related communications on social media. The mechanisms and predictors of such social media communications were not directly examined or empirically identified in the reviewed studies. The psychological or behavioral consequence (eg, increased behavioral intention for mimicking risky health behaviors) of engaging with and being exposed to social media communications regarding problematic drug use was another area of research that has been understudied. Conclusions: We offer theoretical applications, ethical considerations, and empirical evidence within the scope of social media communication and prescription drug abuse and addiction. Our critical review suggests that social media big data can be a tremendous resource to understand, monitor and intervene on drug abuse and addiction problems. ", doi="10.2196/jmir.6426", url="http://www.jmir.org/2017/10/e353/", url="http://www.ncbi.nlm.nih.gov/pubmed/29089287" } @Article{info:doi/10.2196/jmir.8164, author="Sarker, Abeed and Chandrashekar, Pramod and Magge, Arjun and Cai, Haitao and Klein, Ari and Gonzalez, Graciela", title="Discovering Cohorts of Pregnant Women From Social Media for Safety Surveillance and Analysis", journal="J Med Internet Res", year="2017", month="Oct", day="30", volume="19", number="10", pages="e361", keywords="natural language processing", keywords="machine learning", keywords="text mining", keywords="social media", keywords="pregnancy", keywords="cohort studies", keywords="data analysis", abstract="Background: Pregnancy exposure registries are the primary sources of information about the safety of maternal usage of medications during pregnancy. Such registries enroll pregnant women in a voluntary fashion early on in pregnancy and follow them until the end of pregnancy or longer to systematically collect information regarding specific pregnancy outcomes. Although the model of pregnancy registries has distinct advantages over other study designs, they are faced with numerous challenges and limitations such as low enrollment rate, high cost, and selection bias. Objective: The primary objectives of this study were to systematically assess whether social media (Twitter) can be used to discover cohorts of pregnant women and to develop and deploy a natural language processing and machine learning pipeline for the automatic collection of cohort information. In addition, we also attempted to ascertain, in a preliminary fashion, what types of longitudinal information may potentially be mined from the collected cohort information. Methods: Our discovery of pregnant women relies on detecting pregnancy-indicating tweets (PITs), which are statements posted by pregnant women regarding their pregnancies. We used a set of 14 patterns to first detect potential PITs. We manually annotated a sample of 14,156 of the retrieved user posts to distinguish real PITs from false positives and trained a supervised classification system to detect real PITs. We optimized the classification system via cross validation, with features and settings targeted toward optimizing precision for the positive class. For users identified to be posting real PITs via automatic classification, our pipeline collected all their available past and future posts from which other information (eg, medication usage and fetal outcomes) may be mined. Results: Our rule-based PIT detection approach retrieved over 200,000 posts over a period of 18 months. Manual annotation agreement for three annotators was very high at kappa ($\kappa$)=.79. On a blind test set, the implemented classifier obtained an overall F1 score of 0.84 (0.88 for the pregnancy class and 0.68 for the nonpregnancy class). Precision for the pregnancy class was 0.93, and recall was 0.84. Feature analysis showed that the combination of dense and sparse vectors for classification achieved optimal performance. Employing the trained classifier resulted in the identification of 71,954 users from the collected posts. Over 250 million posts were retrieved for these users, which provided a multitude of longitudinal information about them. Conclusions: Social media sources such as Twitter can be used to identify large cohorts of pregnant women and to gather longitudinal information via automated processing of their postings. Considering the many drawbacks and limitations of pregnancy registries, social media mining may provide beneficial complementary information. Although the cohort sizes identified over social media are large, future research will have to assess the completeness of the information available through them. ", doi="10.2196/jmir.8164", url="http://www.jmir.org/2017/10/e361/", url="http://www.ncbi.nlm.nih.gov/pubmed/29084707" } @Article{info:doi/10.2196/publichealth.8391, author="Yom-Tov, Elad and Lev-Ran, Shaul", title="Adverse Reactions Associated With Cannabis Consumption as Evident From Search Engine Queries", journal="JMIR Public Health Surveill", year="2017", month="Oct", day="26", volume="3", number="4", pages="e77", keywords="cannabis", keywords="search engines", keywords="pharmacovigilance", abstract="Background: Cannabis is one of the most widely used psychoactive substances worldwide, but adverse drug reactions (ADRs) associated with its use are difficult to study because of its prohibited status in many countries. Objective: Internet search engine queries have been used to investigate ADRs in pharmaceutical drugs. In this proof-of-concept study, we tested whether these queries can be used to detect the adverse reactions of cannabis use. Methods: We analyzed anonymized queries from US-based users of Bing, a widely used search engine, made over a period of 6 months and compared the results with the prevalence of cannabis use as reported in the US National Survey on Drug Use in the Household (NSDUH) and with ADRs reported in the Food and Drug Administration's Adverse Drug Reporting System. Predicted prevalence of cannabis use was estimated from the fraction of people making queries about cannabis, marijuana, and 121 additional synonyms. Predicted ADRs were estimated from queries containing layperson descriptions to 195 ICD-10 symptoms list. Results: Our results indicated that the predicted prevalence of cannabis use at the US census regional level reaches an R2 of .71 NSDUH data. Queries for ADRs made by people who also searched for cannabis reveal many of the known adverse effects of cannabis (eg, cough and psychotic symptoms), as well as plausible unknown reactions (eg, pyrexia). Conclusions: These results indicate that search engine queries can serve as an important tool for the study of adverse reactions of illicit drugs, which are difficult to study in other settings. ", doi="10.2196/publichealth.8391", url="http://publichealth.jmir.org/2017/4/e77/", url="http://www.ncbi.nlm.nih.gov/pubmed/29074469" } @Article{info:doi/10.2196/diabetes.8966, author="Oser, K. Tamara and Oser, M. Sean and McGinley, L. Erin and Stuckey, L. Heather", title="A Novel Approach to Identifying Barriers and Facilitators in Raising a Child With Type 1 Diabetes: Qualitative Analysis of Caregiver Blogs", journal="JMIR Diabetes", year="2017", month="Oct", day="26", volume="2", number="2", pages="e27", keywords="type 1 diabetes", keywords="blogs", keywords="caregiver", keywords="self-management", keywords="social media", keywords="peer support", keywords="Internet", abstract="Background: With rising incidence of type 1 diabetes (T1D) diagnoses among children and the high levels of distress experienced by the caregivers of these children, caregiver support is becoming increasingly important. Historically, relatively few support resources have existed. Increasing use of the Internet, and blogs in particular, has seen a growth of peer support between caregivers of children with T1D. However, little is known about the type and quality of information shared on T1D caregiver blogs. At the same time, the information on such blogs offers a new window into what challenges and successes caregivers experience in helping to manage their children's T1D. Objective: The purpose of this study was to (1) analyze blogs of caregivers to children with T1D to better understand the challenges and successes they face in raising a child with T1D, and (2) assess the blogs for the presence of unsafe or inaccurate clinical information or advice. Methods: An inductive thematic qualitative study was conducted of three blogs authored by caregivers of children living with T1D, which included 140 unique blog posts and 663 associated comments. Two physician investigators evaluated the blogs for presence of clinical or medical misinformation. Results: Five major themes emerged: (1) the impact of the child's diagnosis, (2) the burden of intense self-management experienced in caring for a child with T1D, (3) caregivers' use of technology to ease their fear of hypoglycemia and impacts that device alarms associated with this technology have on caregiver burden, (4) caregivers' perceptions of frequently missed or delayed diagnosis of T1D and the frustration this causes, and (5) the resilience that caregivers develop despite the burdens they experience. Misinformation was exceedingly rare and benign when it did occur. Conclusions: Blog analysis represents a novel approach to understand the T1D caregiver's experience. This qualitative study found many challenges that caregivers face in raising a child with T1D. Despite the many barriers caregivers face in managing their children's T1D, they find support through advocacy efforts and peer-to-peer blogging. Blogs provide a unique avenue for support, with only rare and benign findings of medical misinformation, and may be a resource that diabetes care providers can consider offering to families for support. ", doi="10.2196/diabetes.8966", url="http://diabetes.jmir.org/2017/2/e27/", url="http://www.ncbi.nlm.nih.gov/pubmed/30291073" } @Article{info:doi/10.2196/mental.7797, author="Abbe, Adeline and Falissard, Bruno", title="Stopping Antidepressants and Anxiolytics as Major Concerns Reported in Online Health Communities: A Text Mining Approach", journal="JMIR Ment Health", year="2017", month="Oct", day="23", volume="4", number="4", pages="e48", keywords="social media", keywords="antidepressant", keywords="anxiolytic", keywords="text mining", keywords="data mining", abstract="Background: Internet is a particularly dynamic way to quickly capture the perceptions of a population in real time. Complementary to traditional face-to-face communication, online social networks help patients to improve self-esteem and self-help. Objective: The aim of this study was to use text mining on material from an online forum exploring patients' concerns about treatment (antidepressants and anxiolytics). Methods: Concerns about treatment were collected from discussion titles in patients' online community related to antidepressants and anxiolytics. To examine the content of these titles automatically, we used text mining methods, such as word frequency in a document-term matrix and co-occurrence of words using a network analysis. It was thus possible to identify topics discussed on the forum. Results: The forum included 2415 discussions on antidepressants and anxiolytics over a period of 3 years. After a preprocessing step, the text mining algorithm identified the 99 most frequently occurring words in titles, among which were escitalopram, withdrawal, antidepressant, venlafaxine, paroxetine, and effect. Patients' concerns were related to antidepressant withdrawal, the need to share experience about symptoms, effects, and questions on weight gain with some drugs. Conclusions: Patients' expression on the Internet is a potential additional resource in addressing patients' concerns about treatment. Patient profiles are close to that of patients treated in psychiatry. ", doi="10.2196/mental.7797", url="http://mental.jmir.org/2017/4/e48/", url="http://www.ncbi.nlm.nih.gov/pubmed/29061554" } @Article{info:doi/10.2196/mental.8141, author="Lachmar, Megan E. and Wittenborn, K. Andrea and Bogen, W. Katherine and McCauley, L. Heather", title="\#MyDepressionLooksLike: Examining Public Discourse About Depression on Twitter", journal="JMIR Ment Health", year="2017", month="Oct", day="18", volume="4", number="4", pages="e43", keywords="social media", keywords="depression", keywords="community networks", keywords="social stigma", abstract="Background: Social media provides a context for billions of users to connect, express sentiments, and provide in-the-moment status updates. Because Twitter users tend to tweet emotional updates from daily life, the platform provides unique insights into experiences of mental health problems. Depression is not only one of the most prevalent health conditions but also carries a social stigma. Yet, opening up about one's depression and seeking social support may provide relief from symptoms. Objective: The aim of this study was to examine the public discourse of the trending hashtag \#MyDepressionLooksLike to look more closely at how users talk about their depressive symptoms on Twitter. Methods: We captured 3225 original content tweets for the hashtag \#MyDepressionLooksLike that circulated in May of 2016. Eliminating public service announcements, spam, and tweets with links to pictures or videos resulted in a total of 1978 tweets. Using qualitative content analysis, we coded the tweets to detect themes. Results: The content analysis revealed seven themes: dysfunctional thoughts, lifestyle challenges, social struggles, hiding behind a mask, apathy and sadness, suicidal thoughts and behaviors, and seeking relief. Conclusions: The themes revealed important information about the content of the public messages that people share about depression on Twitter. More research is needed to understand the effects of the hashtag on increasing social support for users and reducing social stigma related to depression. ", doi="10.2196/mental.8141", url="http://mental.jmir.org/2017/4/e43/", url="http://www.ncbi.nlm.nih.gov/pubmed/29046270" } @Article{info:doi/10.2196/publichealth.8133, author="Allem, Jon-Patrick and Ramanujam, Jagannathan and Lerman, Kristina and Chu, Kar-Hai and Boley Cruz, Tess and Unger, B. Jennifer", title="Identifying Sentiment of Hookah-Related Posts on Twitter", journal="JMIR Public Health Surveill", year="2017", month="Oct", day="18", volume="3", number="4", pages="e74", keywords="hookah", keywords="waterpipe", keywords="Twitter", keywords="social media", keywords="bots", keywords="big data", keywords="sentiment", abstract="Background: The increasing popularity of hookah (or waterpipe) use in the United States and elsewhere has consequences for public health because it has similar health risks to that of combustible cigarettes. While hookah use rapidly increases in popularity, social media data (Twitter, Instagram) can be used to capture and describe the social and environmental contexts in which individuals use, perceive, discuss, and are marketed this tobacco product. These data may allow people to organically report on their sentiment toward tobacco products like hookah unprimed by a researcher, without instrument bias, and at low costs. Objective: This study describes the sentiment of hookah-related posts on Twitter and describes the importance of debiasing Twitter data when attempting to understand attitudes. Methods: Hookah-related posts on Twitter (N=986,320) were collected from March 24, 2015, to December 2, 2016. Machine learning models were used to describe sentiment on 20 different emotions and to debias the data so that Twitter posts reflected sentiment of legitimate human users and not of social bots or marketing-oriented accounts that would possibly provide overly positive or overly negative sentiment of hookah. Results: From the analytical sample, 352,116 tweets (59.50\%) were classified as positive while 177,537 (30.00\%) were classified as negative, and 62,139 (10.50\%) neutral. Among all positive tweets, 218,312 (62.00\%) were classified as highly positive emotions (eg, active, alert, excited, elated, happy, and pleasant), while 133,804 (38.00\%) positive tweets were classified as passive positive emotions (eg, contented, serene, calm, relaxed, and subdued). Among all negative tweets, 95,870 (54.00\%) were classified as subdued negative emotions (eg, sad, unhappy, depressed, and bored) while the remaining 81,667 (46.00\%) negative tweets were classified as highly negative emotions (eg, tense, nervous, stressed, upset, and unpleasant). Sentiment changed drastically when comparing a corpus of tweets with social bots to one without. For example, the probability of any one tweet reflecting joy was 61.30\% from the debiased (or bot free) corpus of tweets. In contrast, the probability of any one tweet reflecting joy was 16.40\% from the biased corpus. Conclusions: Social media data provide researchers the ability to understand public sentiment and attitudes by listening to what people are saying in their own words. Tobacco control programmers in charge of risk communication may consider targeting individuals posting positive messages about hookah on Twitter or designing messages that amplify the negative sentiments. Posts on Twitter communicating positive sentiment toward hookah could add to the normalization of hookah use and is an area of future research. Findings from this study demonstrated the importance of debiasing data when attempting to understand attitudes from Twitter data. ", doi="10.2196/publichealth.8133", url="http://publichealth.jmir.org/2017/4/e74/", url="http://www.ncbi.nlm.nih.gov/pubmed/29046267" } @Article{info:doi/10.2196/jmir.8421, author="Lenoir, Philippe and Moulahi, Bilel and Az{\'e}, J{\'e}r{\^o}me and Bringay, Sandra and Mercier, Gregoire and Carbonnel, Fran{\c{c}}ois", title="Raising Awareness About Cervical Cancer Using Twitter: Content Analysis of the 2015 \#SmearForSmear Campaign", journal="J Med Internet Res", year="2017", month="Oct", day="16", volume="19", number="10", pages="e344", keywords="uterine cervical neoplasms", keywords="Papanicolaou test", keywords="social media", keywords="early detection of cancer", keywords="health promotion", keywords="Twitter", abstract="Background: Cervical cancer is the second most common cancer among women under 45 years of age. To deal with the decrease of smear test coverage in the United Kingdom, a Twitter campaign called \#SmearForSmear has been launched in 2015 for the European Cervical Cancer Prevention Week. Its aim was to encourage women to take a selfie showing their lipstick going over the edge and post it on Twitter with a raising awareness message promoting cervical cancer screening. The estimated audience was 500 million people. Other public health campaigns have been launched on social media such as Movember to encourage participation and self-engagement. Their result was unsatisfactory as their aim had been diluted to become mainly a social buzz. Objective: The objectives of this study were to identify the tweets delivering a raising awareness message promoting cervical cancer screening (sensitizing tweets) and to understand the characteristics of Twitter users posting about this campaign. Methods: We conducted a 3-step content analysis of the English tweets tagged \#SmearForSmear posted on Twitter for the 2015 European Cervical Cancer Prevention Week. Data were collected using the Twitter application programming interface. Their extraction was based on an analysis grid generated by 2 independent researchers using a thematic analysis, validated by a strong Cohen kappa coefficient. A total of 7 themes were coded for sensitizing tweets and 14 for Twitter users' status. Verbatims were thematically and then statistically analyzed. Results: A total of 3019 tweets were collected and 1881 were analyzed. Moreover, 69.96\% of tweets had been posted by people living in the United Kingdom. A total of 57.36\% of users were women, and sex was unknown in 35.99\% of cases. In addition, 54.44\% of the users had posted at least one selfie with smeared lipstick. Furthermore, 32.32\% of tweets were sensitizing. Independent factors associated with posting sensitizing tweets were women who experienced an abnormal smear test (OR [odds ratio] 13.456, 95\% CI 3.101-58.378, P<.001), female gender (OR 3.752, 95\% CI 2.133-6.598, P<.001), and people who live in the United Kingdom (OR 2.097, 95\% CI 1.447-3.038, P<.001). Nonsensitizing tweets were statistically more posted by a nonhealth or nonmedia company (OR 0.558, 95\% CI 0.383-0.814, P<.001). Conclusions: This study demonstrates that the success of a public health campaign using a social media platform depends on its ability to get its targets involved. It also suggests the need to use social marketing to help its dissemination. The clinical impact of this Twitter campaign to increase cervical cancer screening is yet to be evaluated. ", doi="10.2196/jmir.8421", url="http://www.jmir.org/2017/10/e344/", url="http://www.ncbi.nlm.nih.gov/pubmed/29038096" } @Article{info:doi/10.2196/publichealth.8078, author="Yang, Hongxi and Li, Shu and Sun, Li and Zhang, Xinyu and Hou, Jie and Wang, Yaogang", title="Effects of the Ambient Fine Particulate Matter on Public Awareness of Lung Cancer Risk in China: Evidence from the Internet-Based Big Data Platform", journal="JMIR Public Health Surveill", year="2017", month="Oct", day="03", volume="3", number="4", pages="e64", keywords="lung cancer", keywords="risk factors", keywords="particulate matter", keywords="PM2.5", keywords="Baidu Index", keywords="information seeking behavior", keywords="public awareness", keywords="China", abstract="Background: In October 2013, the International Agency for Research on Cancer classified the particulate matter from outdoor air pollution as a group 1 carcinogen and declared that particulate matter can cause lung cancer. Fine particular matter (PM2.5) pollution is becoming a serious public health concern in urban areas of China. It is essential to emphasize the importance of the public's awareness and knowledge of modifiable risk factors of lung cancer for prevention. Objective: The objective of our study was to explore the public's awareness of the association of PM2.5 with lung cancer risk in China by analyzing the relationship between the daily PM2.5 concentration and searches for the term ``lung cancer'' on an Internet big data platform, Baidu. Methods: We collected daily PM2.5 concentration data and daily Baidu Index data in 31 Chinese capital cities from January 1, 2014 to December 31, 2016. We used Spearman correlation analysis to explore correlations between the daily Baidu Index for lung cancer searches and the daily average PM2.5 concentration. Granger causality test was used to analyze the causal relationship between the 2 time-series variables. Results: In 23 of the 31 cities, the pairwise correlation coefficients (Spearman rho) between the daily Baidu Index for lung cancer searches and the daily average PM2.5 concentration were positive and statistically significant (P<.05). However, the correlation between the daily Baidu Index for lung cancer searches and the daily average PM2.5 concentration was poor (all r2s<.1). Results of Granger causality testing illustrated that there was no unidirectional causality from the daily PM2.5 concentration to the daily Baidu Index for lung cancer searches, which was statistically significant at the 5\% level for each city. Conclusions: The daily average PM2.5 concentration had a weak positive impact on the daily search interest for lung cancer on the Baidu search engine. Well-designed awareness campaigns are needed to enhance the general public's awareness of the association of PM2.5 with lung cancer risk, to lead the public to seek more information about PM2.5 and its hazards, and to cope with their environment and its risks appropriately. ", doi="10.2196/publichealth.8078", url="https://publichealth.jmir.org/2017/4/e64/", url="http://www.ncbi.nlm.nih.gov/pubmed/28974484" } @Article{info:doi/10.2196/publichealth.8060, author="Kim, Annice and Miano, Thomas and Chew, Robert and Eggers, Matthew and Nonnemaker, James", title="Classification of Twitter Users Who Tweet About E-Cigarettes", journal="JMIR Public Health Surveill", year="2017", month="Sep", day="26", volume="3", number="3", pages="e63", keywords="electronic cigarettes", keywords="social media", keywords="machine learning", abstract="Background: Despite concerns about their health risks, e?cigarettes have gained popularity in recent years. Concurrent with the recent increase in e?cigarette use, social media sites such as Twitter have become a common platform for sharing information about e-cigarettes and to promote marketing of e?cigarettes. Monitoring the trends in e?cigarette--related social media activity requires timely assessment of the content of posts and the types of users generating the content. However, little is known about the diversity of the types of users responsible for generating e?cigarette--related content on Twitter. Objective: The aim of this study was to demonstrate a novel methodology for automatically classifying Twitter users who tweet about e?cigarette--related topics into distinct categories. Methods: We collected approximately 11.5 million e?cigarette--related tweets posted between November 2014 and October 2016 and obtained a random sample of Twitter users who tweeted about e?cigarettes. Trained human coders examined the handles' profiles and manually categorized each as one of the following user types: individual (n=2168), vaper enthusiast (n=334), informed agency (n=622), marketer (n=752), and?spammer (n=1021). Next, the Twitter metadata as well as a sample of tweets for each labeled user were gathered, and features that reflect users' metadata and tweeting behavior were analyzed. Finally, multiple machine learning algorithms were tested to identify a model with the best performance in classifying user types. Results: Using a classification model that included metadata and features associated with tweeting behavior, we were able to predict with relatively high accuracy five different types of Twitter users that tweet about e?cigarettes (average F1 score=83.3\%). Accuracy varied by user type, with F1 scores of individuals, informed agencies, marketers, spammers, and vaper enthusiasts being 91.1\%, 84.4\%, 81.2\%, 79.5\%, and 47.1\%, respectively. Vaper enthusiasts were the most challenging user type to predict accurately and were commonly misclassified as marketers. The inclusion of additional tweet-derived features that capture tweeting behavior was found to significantly improve the model performance---an overall F1 score gain of 10.6\%---beyond metadata features alone. Conclusions: This study provides a method for classifying five different types of users who tweet about e?cigarettes. Our model achieved high levels of classification performance for most groups, and examining the tweeting behavior was critical in improving the model performance. Results can help identify groups engaged in conversations about e?cigarettes online to help inform public health surveillance, education, and regulatory efforts. ", doi="10.2196/publichealth.8060", url="http://publichealth.jmir.org/2017/3/e63/", url="http://www.ncbi.nlm.nih.gov/pubmed/28951381" } @Article{info:doi/10.2196/resprot.6463, author="Bousquet, Cedric and Dahamna, Badisse and Guillemin-Lanne, Sylvie and Darmoni, J. Stefan and Faviez, Carole and Huot, Charles and Katsahian, Sandrine and Leroux, Vincent and Pereira, Suzanne and Richard, Christophe and Sch{\"u}ck, St{\'e}phane and Souvignet, Julien and Lillo-Le Lou{\"e}t, Agn{\`e}s and Texier, Nathalie", title="The Adverse Drug Reactions from Patient Reports in Social Media Project: Five Major Challenges to Overcome to Operationalize Analysis and Efficiently Support Pharmacovigilance Process", journal="JMIR Res Protoc", year="2017", month="Sep", day="21", volume="6", number="9", pages="e179", keywords="pharmacovigilance", keywords="social media", keywords="big data", keywords="natural language processing", keywords="medical terminology", abstract="Background: Adverse drug reactions (ADRs) are an important cause of morbidity and mortality. Classical Pharmacovigilance process is limited by underreporting which justifies the current interest in new knowledge sources such as social media. The Adverse Drug Reactions from Patient Reports in Social Media (ADR-PRISM) project aims to extract ADRs reported by patients in these media. We identified 5 major challenges to overcome to operationalize the analysis of patient posts: (1) variable quality of information on social media, (2) guarantee of data privacy, (3) response to pharmacovigilance expert expectations, (4) identification of relevant information within Web pages, and (5) robust and evolutive architecture. Objective: This article aims to describe the current state of advancement of the ADR-PRISM project by focusing on the solutions we have chosen to address these 5 major challenges. Methods: In this article, we propose methods and describe the advancement of this project on several aspects: (1) a quality driven approach for selecting relevant social media for the extraction of knowledge on potential ADRs, (2) an assessment of ethical issues and French regulation for the analysis of data on social media, (3) an analysis of pharmacovigilance expert requirements when reviewing patient posts on the Internet, (4) an extraction method based on natural language processing, pattern based matching, and selection of relevant medical concepts in reference terminologies, and (5) specifications of a component-based architecture for the monitoring system. Results: Considering the 5 major challenges, we (1) selected a set of 21 validated criteria for selecting social media to support the extraction of potential ADRs, (2) proposed solutions to guarantee data privacy of patients posting on Internet, (3) took into account pharmacovigilance expert requirements with use case diagrams and scenarios, (4) built domain-specific knowledge resources embeding a lexicon, morphological rules, context rules, semantic rules, syntactic rules, and post-analysis processing, and (5) proposed a component-based architecture that allows storage of big data and accessibility to third-party applications through Web services. Conclusions: We demonstrated the feasibility of implementing a component-based architecture that allows collection of patient posts on the Internet, near real-time processing of those posts including annotation, and storage in big data structures. In the next steps, we will evaluate the posts identified by the system in social media to clarify the interest and relevance of such approach to improve conventional pharmacovigilance processes based on spontaneous reporting. ", doi="10.2196/resprot.6463", url="http://www.researchprotocols.org/2017/9/e179/", url="http://www.ncbi.nlm.nih.gov/pubmed/28935617" } @Article{info:doi/10.2196/jmir.7393, author="Kagashe, Ireneus and Yan, Zhijun and Suheryani, Imran", title="Enhancing Seasonal Influenza Surveillance: Topic Analysis of Widely Used Medicinal Drugs Using Twitter Data", journal="J Med Internet Res", year="2017", month="Sep", day="12", volume="19", number="9", pages="e315", keywords="machine learning", keywords="Twitter messaging", keywords="social media", keywords="disease outbreaks", keywords="influenza", keywords="public health surveillance", keywords="natural language processing", keywords="influenza vaccines", abstract="Background: Uptake of medicinal drugs (preventive or treatment) is among the approaches used to control disease outbreaks, and therefore, it is of vital importance to be aware of the counts or frequencies of most commonly used drugs and trending topics about these drugs from consumers for successful implementation of control measures. Traditional survey methods would have accomplished this study, but they are too costly in terms of resources needed, and they are subject to social desirability bias for topics discovery. Hence, there is a need to use alternative efficient means such as Twitter data and machine learning (ML) techniques. Objective: Using Twitter data, the aim of the study was to (1) provide a methodological extension for efficiently extracting widely consumed drugs during seasonal influenza and (2) extract topics from the tweets of these drugs and to infer how the insights provided by these topics can enhance seasonal influenza surveillance. Methods: From tweets collected during the 2012-13 flu season, we first identified tweets with mentions of drugs and then constructed an ML classifier using dependency words as features. The classifier was used to extract tweets that evidenced consumption of drugs, out of which we identified the mostly consumed drugs. Finally, we extracted trending topics from each of these widely used drugs' tweets using latent Dirichlet allocation (LDA). Results: Our proposed classifier obtained an F1 score of 0.82, which significantly outperformed the two benchmark classifiers (ie, P<.001 with the lexicon-based and P=.048 with the 1-gram term frequency [TF]). The classifier extracted 40,428 tweets that evidenced consumption of drugs out of 50,828 tweets with mentions of drugs. The most widely consumed drugs were influenza virus vaccines that had around 76.95\% (31,111/40,428) share of the total; other notable drugs were Theraflu, DayQuil, NyQuil, vitamins, acetaminophen, and oseltamivir. The topics of each of these drugs exhibited common themes or experiences from people who have consumed these drugs. Among these were the enabling and deterrent factors to influenza drugs uptake, which are keys to mitigating the severity of seasonal influenza outbreaks. Conclusions: The study results showed the feasibility of using tweets of widely consumed drugs to enhance seasonal influenza surveillance in lieu of the traditional or conventional surveillance approaches. Public health officials and other stakeholders can benefit from the findings of this study, especially in enhancing strategies for mitigating the severity of seasonal influenza outbreaks. The proposed methods can be extended to the outbreaks of other diseases. ", doi="10.2196/jmir.7393", url="http://www.jmir.org/2017/9/e315/", url="http://www.ncbi.nlm.nih.gov/pubmed/28899847" } @Article{info:doi/10.2196/publichealth.7714, author="Mukhija, Dhruvika and Venkatraman, Anand and Nagpal, Singh Sajan Jiv", title="Effectivity of Awareness Months in Increasing Internet Search Activity for Top Malignancies Among Women", journal="JMIR Public Health Surveill", year="2017", month="Aug", day="21", volume="3", number="3", pages="e55", keywords="colorectal cancer, lung cancer, breast cancer, cancer awareness month, infoveillance", doi="10.2196/publichealth.7714", url="http://publichealth.jmir.org/2017/3/e55/", url="http://www.ncbi.nlm.nih.gov/pubmed/28827213" } @Article{info:doi/10.2196/jmir.7956, author="Birnbaum, L. Michael and Ernala, Kiranmai Sindhu and Rizvi, F. Asra and De Choudhury, Munmun and Kane, M. John", title="A Collaborative Approach to Identifying Social Media Markers of Schizophrenia by Employing Machine Learning and Clinical Appraisals", journal="J Med Internet Res", year="2017", month="Aug", day="14", volume="19", number="8", pages="e289", keywords="schizophrenia", keywords="psychotic disorders", keywords="online social networks", keywords="machine learning", keywords="linguistic analysis", keywords="Twitter", abstract="Background: Linguistic analysis of publicly available Twitter feeds have achieved success in differentiating individuals who self-disclose online as having schizophrenia from healthy controls. To date, limited efforts have included expert input to evaluate the authenticity of diagnostic self-disclosures. Objective: This study aims to move from noisy self-reports of schizophrenia on social media to more accurate identification of diagnoses by exploring a human-machine partnered approach, wherein computational linguistic analysis of shared content is combined with clinical appraisals. Methods: Twitter timeline data, extracted from 671 users with self-disclosed diagnoses of schizophrenia, was appraised for authenticity by expert clinicians. Data from disclosures deemed true were used to build a classifier aiming to distinguish users with schizophrenia from healthy controls. Results from the classifier were compared to expert appraisals on new, unseen Twitter users. Results: Significant linguistic differences were identified in the schizophrenia group including greater use of interpersonal pronouns (P<.001), decreased emphasis on friendship (P<.001), and greater emphasis on biological processes (P<.001). The resulting classifier distinguished users with disclosures of schizophrenia deemed genuine from control users with a mean accuracy of 88\% using linguistic data alone. Compared to clinicians on new, unseen users, the classifier's precision, recall, and accuracy measures were 0.27, 0.77, and 0.59, respectively. Conclusions: These data reinforce the need for ongoing collaborations integrating expertise from multiple fields to strengthen our ability to accurately identify and effectively engage individuals with mental illness online. These collaborations are crucial to overcome some of mental illnesses' biggest challenges by using digital technology. ", doi="10.2196/jmir.7956", url="http://www.jmir.org/2017/8/e289/", url="http://www.ncbi.nlm.nih.gov/pubmed/28807891" } @Article{info:doi/10.2196/publichealth.7004, author="Roccetti, Marco and Marfia, Gustavo and Salomoni, Paola and Prandi, Catia and Zagari, Maurizio Rocco and Gningaye Kengni, Linda Faustine and Bazzoli, Franco and Montagnani, Marco", title="Attitudes of Crohn's Disease Patients: Infodemiology Case Study and Sentiment Analysis of Facebook and Twitter Posts", journal="JMIR Public Health Surveill", year="2017", month="Aug", day="09", volume="3", number="3", pages="e51", keywords="health information systems", keywords="public health informatics", keywords="consumer health information", keywords="social networking", abstract="Background: Data concerning patients originates from a variety of sources on social media. Objective: The aim of this study was to show how methodologies borrowed from different areas including computer science, econometrics, statistics, data mining, and sociology may be used to analyze Facebook data to investigate the patients' perspectives on a given medical prescription. Methods: To shed light on patients' behavior and concerns, we focused on Crohn's disease, a chronic inflammatory bowel disease, and the specific therapy with the biological drug Infliximab. To gain information from the basin of big data, we analyzed Facebook posts in the time frame from October 2011 to August 2015. We selected posts from patients affected by Crohn's disease who were experiencing or had previously been treated with the monoclonal antibody drug Infliximab. The selected posts underwent further characterization and sentiment analysis. Finally, an ethnographic review was carried out by experts from different scientific research fields (eg, computer science vs gastroenterology) and by a software system running a sentiment analysis tool. The patient feeling toward the Infliximab treatment was classified as positive, neutral, or negative, and the results from computer science, gastroenterologist, and software tool were compared using the square weighted Cohen's kappa coefficient method. Results: The first automatic selection process returned 56,000 Facebook posts, 261 of which exhibited a patient opinion concerning Infliximab. The ethnographic analysis of these 261 selected posts gave similar results, with an interrater agreement between the computer science and gastroenterology experts amounting to 87.3\% (228/261), a substantial agreement according to the square weighted Cohen's kappa coefficient method (w2K=0.6470). A positive, neutral, and negative feeling was attributed to 36\%, 27\%, and 37\% of posts by the computer science expert and 38\%, 30\%, and 32\% by the gastroenterologist, respectively. Only a slight agreement was found between the experts' opinion and the software tool. Conclusions: We show how data posted on Facebook by Crohn's disease patients are a useful dataset to understand the patient's perspective on the specific treatment with Infliximab. The genuine, nonmedically influenced patients' opinion obtained from Facebook pages can be easily reviewed by experts from different research backgrounds, with a substantial agreement on the classification of patients' sentiment. The described method allows a fast collection of big amounts of data, which can be easily analyzed to gain insight into the patients' perspective on a specific medical therapy. ", doi="10.2196/publichealth.7004", url="http://publichealth.jmir.org/2017/3/e51/", url="http://www.ncbi.nlm.nih.gov/pubmed/28793981" } @Article{info:doi/10.2196/jmir.7855, author="Liu, Kui and Huang, Sichao and Miao, Zi-Ping and Chen, Bin and Jiang, Tao and Cai, Gaofeng and Jiang, Zhenggang and Chen, Yongdi and Wang, Zhengting and Gu, Hua and Chai, Chengliang and Jiang, Jianmin", title="Identifying Potential Norovirus Epidemics in China via Internet Surveillance", journal="J Med Internet Res", year="2017", month="Aug", day="08", volume="19", number="8", pages="e282", keywords="norovirus", keywords="Internet surveillance", keywords="disease prediction", abstract="Background: Norovirus is a common virus that causes acute gastroenteritis worldwide, but a monitoring system for norovirus is unavailable in China. Objective: We aimed to identify norovirus epidemics through Internet surveillance and construct an appropriate model to predict potential norovirus infections. Methods: The norovirus-related data of a selected outbreak in Jiaxing Municipality, Zhejiang Province of China, in 2014 were collected from immediate epidemiological investigation, and the Internet search volume, as indicated by the Baidu Index, was acquired from the Baidu search engine. All correlated search keywords in relation to norovirus were captured, screened, and composited to establish the composite Baidu Index at different time lags by Spearman rank correlation. The optimal model was chosen and possibly predicted maps in Zhejiang Province were presented by ArcGIS software. Results: The combination of two vital keywords at a time lag of 1 day was ultimately identified as optimal ($\rho$=.924, P<.001). The exponential curve model was constructed to fit the trend of this epidemic, suggesting that a one-unit increase in the mean composite Baidu Index contributed to an increase of norovirus infections by 2.15 times during the outbreak. In addition to Jiaxing Municipality, Hangzhou Municipality might have had some potential epidemics in the study time from the predicted model. Conclusions: Although there are limitations with early warning and unavoidable biases, Internet surveillance may be still useful for the monitoring of norovirus epidemics when a monitoring system is unavailable. ", doi="10.2196/jmir.7855", url="http://www.jmir.org/2017/8/e282/", url="http://www.ncbi.nlm.nih.gov/pubmed/28790023" } @Article{info:doi/10.2196/medinform.7779, author="Tapi Nzali, Donald Mike and Bringay, Sandra and Lavergne, Christian and Mollevi, Caroline and Opitz, Thomas", title="What Patients Can Tell Us: Topic Analysis for Social Media on Breast Cancer", journal="JMIR Med Inform", year="2017", month="Jul", day="31", volume="5", number="3", pages="e23", keywords="breast cancer", keywords="text mining", keywords="social media", keywords="unsupervised learning", abstract="Background: Social media dedicated to health are increasingly used by patients and health professionals. They are rich textual resources with content generated through free exchange between patients. We are proposing a method to tackle the problem of retrieving clinically relevant information from such social media in order to analyze the quality of life of patients with breast cancer. Objective: Our aim was to detect the different topics discussed by patients on social media and to relate them to functional and symptomatic dimensions assessed in the internationally standardized self-administered questionnaires used in cancer clinical trials (European Organization for Research and Treatment of Cancer [EORTC] Quality of Life Questionnaire Core 30 [QLQ-C30] and breast cancer module [QLQ-BR23]). Methods: First, we applied a classic text mining technique, latent Dirichlet allocation (LDA), to detect the different topics discussed on social media dealing with breast cancer. We applied the LDA model to 2 datasets composed of messages extracted from public Facebook groups and from a public health forum (cancerdusein.org, a French breast cancer forum) with relevant preprocessing. Second, we applied a customized Jaccard coefficient to automatically compute similarity distance between the topics detected with LDA and the questions in the self-administered questionnaires used to study quality of life. Results: Among the 23 topics present in the self-administered questionnaires, 22 matched with the topics discussed by patients on social media. Interestingly, these topics corresponded to 95\% (22/23) of the forum and 86\% (20/23) of the Facebook group topics. These figures underline that topics related to quality of life are an important concern for patients. However, 5 social media topics had no corresponding topic in the questionnaires, which do not cover all of the patients' concerns. Of these 5 topics, 2 could potentially be used in the questionnaires, and these 2 topics corresponded to a total of 3.10\% (523/16,868) of topics in the cancerdusein.org corpus and 4.30\% (3014/70,092) of the Facebook corpus. Conclusions: We found a good correspondence between detected topics on social media and topics covered by the self-administered questionnaires, which substantiates the sound construction of such questionnaires. We detected new emerging topics from social media that can be used to complete current self-administered questionnaires. Moreover, we confirmed that social media mining is an important source of information for complementary analysis of quality of life. ", doi="10.2196/medinform.7779", url="http://medinform.jmir.org/2017/3/e23/", url="http://www.ncbi.nlm.nih.gov/pubmed/28760725" } @Article{info:doi/10.2196/jmir.7452, author="Jung, Hyesil and Park, Hyeoun-Ae and Song, Tae-Min", title="Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals", journal="J Med Internet Res", year="2017", month="Jul", day="24", volume="19", number="7", pages="e259", keywords="ontology", keywords="adolescent", keywords="depression", keywords="data mining", keywords="social media data", abstract="Background: Social networking services (SNSs) contain abundant information about the feelings, thoughts, interests, and patterns of behavior of adolescents that can be obtained by analyzing SNS postings. An ontology that expresses the shared concepts and their relationships in a specific field could be used as a semantic framework for social media data analytics. Objective: The aim of this study was to refine an adolescent depression ontology and terminology as a framework for analyzing social media data and to evaluate description logics between classes and the applicability of this ontology to sentiment analysis. Methods: The domain and scope of the ontology were defined using competency questions. The concepts constituting the ontology and terminology were collected from clinical practice guidelines, the literature, and social media postings on adolescent depression. Class concepts, their hierarchy, and the relationships among class concepts were defined. An internal structure of the ontology was designed using the entity-attribute-value (EAV) triplet data model, and superclasses of the ontology were aligned with the upper ontology. Description logics between classes were evaluated by mapping concepts extracted from the answers to frequently asked questions (FAQs) onto the ontology concepts derived from description logic queries. The applicability of the ontology was validated by examining the representability of 1358 sentiment phrases using the ontology EAV model and conducting sentiment analyses of social media data using ontology class concepts. Results: We developed an adolescent depression ontology that comprised 443 classes and 60 relationships among the classes; the terminology comprised 1682 synonyms of the 443 classes. In the description logics test, no error in relationships between classes was found, and about 89\% (55/62) of the concepts cited in the answers to FAQs mapped onto the ontology class. Regarding applicability, the EAV triplet models of the ontology class represented about 91.4\% of the sentiment phrases included in the sentiment dictionary. In the sentiment analyses, ``academic stresses'' and ``suicide'' contributed negatively to the sentiment of adolescent depression. Conclusions: The ontology and terminology developed in this study provide a semantic foundation for analyzing social media data on adolescent depression. To be useful in social media data analysis, the ontology, especially the terminology, needs to be updated constantly to reflect rapidly changing terms used by adolescents in social media postings. In addition, more attributes and value sets reflecting depression-related sentiments should be added to the ontology. ", doi="10.2196/jmir.7452", url="http://www.jmir.org/2017/7/e259/", url="http://www.ncbi.nlm.nih.gov/pubmed/28739560" } @Article{info:doi/10.2196/jmir.7276, author="Cheng, Qijin and Li, MH Tim and Kwok, Chi-Leung and Zhu, Tingshao and Yip, SF Paul", title="Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study", journal="J Med Internet Res", year="2017", month="Jul", day="10", volume="19", number="7", pages="e243", keywords="suicide", keywords="psychological stress", keywords="social media", keywords="Chinese", keywords="natural language", keywords="machine learning", abstract="Background: Early identification and intervention are imperative for suicide prevention. However, at-risk people often neither seek help nor take professional assessment. A tool to automatically assess their risk levels in natural settings can increase the opportunity for early intervention. Objective: The aim of this study was to explore whether computerized language analysis methods can be utilized to assess one's suicide risk and emotional distress in Chinese social media. Methods: A Web-based survey of Chinese social media (ie, Weibo) users was conducted to measure their suicide risk factors including suicide probability, Weibo suicide communication (WSC), depression, anxiety, and stress levels. Participants' Weibo posts published in the public domain were also downloaded with their consent. The Weibo posts were parsed and fitted into Simplified Chinese-Linguistic Inquiry and Word Count (SC-LIWC) categories. The associations between SC-LIWC features and the 5 suicide risk factors were examined by logistic regression. Furthermore, the support vector machine (SVM) model was applied based on the language features to automatically classify whether a Weibo user exhibited any of the 5 risk factors. Results: A total of 974 Weibo users participated in the survey. Those with high suicide probability were marked by a higher usage of pronoun (odds ratio, OR=1.18, P=.001), prepend words (OR=1.49, P=.02), multifunction words (OR=1.12, P=.04), a lower usage of verb (OR=0.78, P<.001), and a greater total word count (OR=1.007, P=.008). Second-person plural was positively associated with severe depression (OR=8.36, P=.01) and stress (OR=11, P=.005), whereas work-related words were negatively associated with WSC (OR=0.71, P=.008), severe depression (OR=0.56, P=.005), and anxiety (OR=0.77, P=.02). Inconsistently, third-person plural was found to be negatively associated with WSC (OR=0.02, P=.047) but positively with severe stress (OR=41.3, P=.04). Achievement-related words were positively associated with depression (OR=1.68, P=.003), whereas health- (OR=2.36, P=.004) and death-related (OR=2.60, P=.01) words positively associated with stress. The machine classifiers did not achieve satisfying performance in the full sample set but could classify high suicide probability (area under the curve, AUC=0.61, P=.04) and severe anxiety (AUC=0.75, P<.001) among those who have exhibited WSC. Conclusions: SC-LIWC is useful to examine language markers of suicide risk and emotional distress in Chinese social media and can identify characteristics different from previous findings in the English literature. Some findings are leading to new hypotheses for future verification. Machine classifiers based on SC-LIWC features are promising but still require further optimization for application in real life. ", doi="10.2196/jmir.7276", url="http://www.jmir.org/2017/7/e243/", url="http://www.ncbi.nlm.nih.gov/pubmed/28694239" } @Article{info:doi/10.2196/jmir.7137, author="Peiper, C. Nicholas and Baumgartner, M. Peter and Chew, F. Robert and Hsieh, P. Yuli and Bieler, S. Gayle and Bobashev, V. Georgiy and Siege, Christopher and Zarkin, A. Gary", title="Patterns of Twitter Behavior Among Networks of Cannabis Dispensaries in California", journal="J Med Internet Res", year="2017", month="Jul", day="04", volume="19", number="7", pages="e236", keywords="cannabis", keywords="marijuana", keywords="social networking", keywords="social media", keywords="Internet", abstract="Background: Twitter represents a social media platform through which medical cannabis dispensaries can rapidly promote and advertise a multitude of retail products. Yet, to date, no studies have systematically evaluated Twitter behavior among dispensaries and how these behaviors influence the formation of social networks. Objectives: This study sought to characterize common cyberbehaviors and shared follower networks among dispensaries operating in two large cannabis markets in California. Methods: From a targeted sample of 119 dispensaries in the San Francisco Bay Area and Greater Los Angeles, we collected metadata from the dispensary accounts using the Twitter API. For each city, we characterized the network structure of dispensaries based upon shared followers, then empirically derived communities with the Louvain modularity algorithm. Principal components factor analysis was employed to reduce 12 Twitter measures into a more parsimonious set of cyberbehavioral dimensions. Finally, quadratic discriminant analysis was implemented to verify the ability of the extracted dimensions to classify dispensaries into their derived communities. Results: The modularity algorithm yielded three communities in each city with distinct network structures. The principal components factor analysis reduced the 12 cyberbehaviors into five dimensions that encompassed account age, posting frequency, referencing, hyperlinks, and user engagement among the dispensary accounts. In the quadratic discriminant analysis, the dimensions correctly classified 75\% (46/61) of the communities in the San Francisco Bay Area and 71\% (41/58) in Greater Los Angeles. Conclusions: The most centralized and strongly connected dispensaries in both cities had newer accounts, higher daily activity, more frequent user engagement, and increased usage of embedded media, keywords, and hyperlinks. Measures derived from both network structure and cyberbehavioral dimensions can serve as key contextual indicators for the online surveillance of cannabis dispensaries and consumer markets over time. ", doi="10.2196/jmir.7137", url="http://www.jmir.org/2017/7/e236/", url="http://www.ncbi.nlm.nih.gov/pubmed/28676471" } @Article{info:doi/10.2196/jmir.7215, author="Wongkoblap, Akkapon and Vadillo, A. Miguel and Curcin, Vasa", title="Researching Mental Health Disorders in the Era of Social Media: Systematic Review", journal="J Med Internet Res", year="2017", month="Jun", day="29", volume="19", number="6", pages="e228", keywords="mental health", keywords="mental disorders", keywords="social networking", keywords="artificial intelligence", keywords="machine learning", keywords="public health informatics", keywords="depression", keywords="anxiety", keywords="infodemiology", abstract="Background: Mental illness is quickly becoming one of the most prevalent public health problems worldwide. Social network platforms, where users can express their emotions, feelings, and thoughts, are a valuable source of data for researching mental health, and techniques based on machine learning are increasingly used for this purpose. Objective: The objective of this review was to explore the scope and limits of cutting-edge techniques that researchers are using for predictive analytics in mental health and to review associated issues, such as ethical concerns, in this area of research. Methods: We performed a systematic literature review in March 2017, using keywords to search articles on data mining of social network data in the context of common mental health disorders, published between 2010 and March 8, 2017 in medical and computer science journals. Results: The initial search returned a total of 5386 articles. Following a careful analysis of the titles, abstracts, and main texts, we selected 48 articles for review. We coded the articles according to key characteristics, techniques used for data collection, data preprocessing, feature extraction, feature selection, model construction, and model verification. The most common analytical method was text analysis, with several studies using different flavors of image analysis and social interaction graph analysis. Conclusions: Despite an increasing number of studies investigating mental health issues using social network data, some common problems persist. Assembling large, high-quality datasets of social media users with mental disorder is problematic, not only due to biases associated with the collection methods, but also with regard to managing consent and selecting appropriate analytics techniques. ", doi="10.2196/jmir.7215", url="http://www.jmir.org/2017/6/e228/", url="http://www.ncbi.nlm.nih.gov/pubmed/28663166" } @Article{info:doi/10.2196/publichealth.6577, author="Abdellaoui, Redhouane and Sch{\"u}ck, St{\'e}phane and Texier, Nathalie and Burgun, Anita", title="Filtering Entities to Optimize Identification of Adverse Drug Reaction From Social Media: How Can the Number of Words Between Entities in the Messages Help?", journal="JMIR Public Health Surveill", year="2017", month="Jun", day="22", volume="3", number="2", pages="e36", keywords="pharmacovigilance", keywords="social media", keywords="text mining", keywords="Gaussian mixture model", keywords="EM algorithm", keywords="clustering", keywords="density estimation", abstract="Background: With the increasing popularity of Web 2.0 applications, social media has made it possible for individuals to post messages on adverse drug reactions. In such online conversations, patients discuss their symptoms, medical history, and diseases. These disorders may correspond to adverse drug reactions (ADRs) or any other medical condition. Therefore, methods must be developed to distinguish between false positives and true ADR declarations. Objective: The aim of this study was to investigate a method for filtering out disorder terms that did not correspond to adverse events by using the distance (as number of words) between the drug term and the disorder or symptom term in the post. We hypothesized that the shorter the distance between the disorder name and the drug, the higher the probability to be an ADR. Methods: We analyzed a corpus of 648 messages corresponding to a total of 1654 (drug and disorder) pairs from 5 French forums using Gaussian mixture models and an expectation-maximization (EM) algorithm . Results: The distribution of the distances between the drug term and the disorder term enabled the filtering of 50.03\% (733/1465) of the disorders that were not ADRs. Our filtering strategy achieved a precision of 95.8\% and a recall of 50.0\%. Conclusions: This study suggests that such distance between terms can be used for identifying false positives, thereby improving ADR detection in social media. ", doi="10.2196/publichealth.6577", url="http://publichealth.jmir.org/2017/2/e36/", url="http://www.ncbi.nlm.nih.gov/pubmed/28642212" } @Article{info:doi/10.2196/publichealth.7157, author="Miller, Michele and Banerjee, Tanvi and Muppalla, Roopteja and Romine, William and Sheth, Amit", title="What Are People Tweeting About Zika? An Exploratory Study Concerning Its Symptoms, Treatment, Transmission, and Prevention", journal="JMIR Public Health Surveill", year="2017", month="Jun", day="19", volume="3", number="2", pages="e38", keywords="viruses", keywords="epidemiology", keywords="social media", keywords="machine learning", abstract="Background: In order to harness what people are tweeting about Zika, there needs to be a computational framework that leverages machine learning techniques to recognize relevant Zika tweets and, further, categorize these into disease-specific categories to address specific societal concerns related to the prevention, transmission, symptoms, and treatment of Zika virus. Objective: The purpose of this study was to determine the relevancy of the tweets and what people were tweeting about the 4 disease characteristics of Zika: symptoms, transmission, prevention, and treatment. Methods: A combination of natural language processing and machine learning techniques was used to determine what people were tweeting about Zika. Specifically, a two-stage classifier system was built to find relevant tweets about Zika, and then the tweets were categorized into 4 disease categories. Tweets in each disease category were then examined using latent Dirichlet allocation (LDA) to determine the 5 main tweet topics for each disease characteristic. Results: Over 4 months, 1,234,605 tweets were collected. The number of tweets by males and females was similar (28.47\% [351,453/1,234,605] and 23.02\% [284,207/1,234,605], respectively). The classifier performed well on the training and test data for relevancy (F1 score=0.87 and 0.99, respectively) and disease characteristics (F1 score=0.79 and 0.90, respectively). Five topics for each category were found and discussed, with a focus on the symptoms category. Conclusions: We demonstrate how categories of discussion on Twitter about an epidemic can be discovered so that public health officials can understand specific societal concerns within the disease-specific categories. Our two-stage classifier was able to identify relevant tweets to enable more specific analysis, including the specific aspects of Zika that were being discussed as well as misinformation being expressed. Future studies can capture sentiments and opinions on epidemic outbreaks like Zika virus in real time, which will likely inform efforts to educate the public at large. ", doi="10.2196/publichealth.7157", url="http://publichealth.jmir.org/2017/2/e38/", url="http://www.ncbi.nlm.nih.gov/pubmed/28630032" } @Article{info:doi/10.2196/publichealth.5939, author="Doan, Son and Ritchart, Amanda and Perry, Nicholas and Chaparro, D. Juan and Conway, Mike", title="How Do You \#relax When You're \#stressed? A Content Analysis and Infodemiology Study of Stress-Related Tweets", journal="JMIR Public Health Surveill", year="2017", month="Jun", day="13", volume="3", number="2", pages="e35", keywords="social media", keywords="Twitter", keywords="stress", keywords="relaxation", keywords="natural language processing", keywords="machine learning", abstract="Background: Stress is a contributing factor to many major health problems in the United States, such as heart disease, depression, and autoimmune diseases. Relaxation is often recommended in mental health treatment as a frontline strategy to reduce stress, thereby improving health conditions. Twitter is a microblog platform that allows users to post their own personal messages (tweets), including their expressions about feelings and actions related to stress and stress management (eg, relaxing). While Twitter is increasingly used as a source of data for understanding mental health from a population perspective, the specific issue of stress---as manifested on Twitter---has not yet been the focus of any systematic study. Objective: The objective of our study was to understand how people express their feelings of stress and relaxation through Twitter messages. In addition, we aimed at investigating automated natural language processing methods to (1) classify stress versus nonstress and relaxation versus nonrelaxation tweets, and (2) identify first-hand experience---that is, who is the experiencer---in stress and relaxation tweets. Methods: We first performed a qualitative content analysis of 1326 and 781 tweets containing the keywords ``stress'' and ``relax,'' respectively. We then investigated the use of machine learning algorithms---in particular naive Bayes and support vector machines---to automatically classify tweets as stress versus nonstress and relaxation versus nonrelaxation. Finally, we applied these classifiers to sample datasets drawn from 4 cities in the United States (Los Angeles, New York, San Diego, and San Francisco) obtained from Twitter's streaming application programming interface, with the goal of evaluating the extent of any correlation between our automatic classification of tweets and results from public stress surveys. Results: Content analysis showed that the most frequent topic of stress tweets was education, followed by work and social relationships. The most frequent topic of relaxation tweets was rest \& vacation, followed by nature and water. When we applied the classifiers to the cities dataset, the proportion of stress tweets in New York and San Diego was substantially higher than that in Los Angeles and San Francisco. In addition, we found that characteristic expressions of stress and relaxation varied for each city based on its geolocation. Conclusions: This content analysis and infodemiology study revealed that Twitter, when used in conjunction with natural language processing techniques, is a useful data source for understanding stress and stress management strategies, and can potentially supplement infrequently collected survey-based stress data. ", doi="10.2196/publichealth.5939", url="http://publichealth.jmir.org/2017/2/e35/", url="http://www.ncbi.nlm.nih.gov/pubmed/28611016" } @Article{info:doi/10.2196/jmir.7219, author="van Lent, GG Liza and Sungur, Hande and Kunneman, A. Florian and van de Velde, Bob and Das, Enny", title="Too Far to Care? Measuring Public Attention and Fear for Ebola Using Twitter", journal="J Med Internet Res", year="2017", month="Jun", day="13", volume="19", number="6", pages="e193", keywords="psychological theory", keywords="epidemics", keywords="fear", keywords="distance perception", keywords="social media", abstract="Background: In 2014, the world was startled by a sudden outbreak of Ebola. Although Ebola infections and deaths occurred almost exclusively in Guinea, Sierra Leone, and Liberia, few potential Western cases, in particular, caused a great stir among the public in Western countries. Objective: This study builds on the construal level theory to examine the relationship between psychological distance to an epidemic and public attention and sentiment expressed on Twitter. Whereas previous research has shown the potential of social media to assess real-time public opinion and sentiment, generalizable insights that further the theory development lack. Methods: Epidemiological data (number of Ebola infections and fatalities) and media data (tweet volume and key events reported in the media) were collected for the 2014 Ebola outbreak, and Twitter content from the Netherlands was coded for (1) expressions of fear for self or fear for others and (2) psychological distance of the outbreak to the tweet source. Longitudinal relations were compared using vector error correction model (VECM) methodology. Results: Analyses based on 4500 tweets revealed that increases in public attention to Ebola co-occurred with severe world events related to the epidemic, but not all severe events evoked fear. As hypothesized, Web-based public attention and expressions of fear responded mainly to the psychological distance of the epidemic. A chi-square test showed a significant positive relation between proximity and fear: $\chi$22=103.2 (P<.001). Public attention and fear for self in the Netherlands showed peaks when Ebola became spatially closer by crossing the Mediterranean Sea and Atlantic Ocean. Fear for others was mostly predicted by the social distance to the affected parties. Conclusions: Spatial and social distance are important predictors of public attention to worldwide crisis such as epidemics. These factors need to be taken into account when communicating about human tragedies. ", doi="10.2196/jmir.7219", url="http://www.jmir.org/2017/6/e193/", url="http://www.ncbi.nlm.nih.gov/pubmed/28611015" } @Article{info:doi/10.2196/jmir.7508, author="Huesch, Marco and Chetlen, Alison and Segel, Joel and Schetter, Susann", title="Frequencies of Private Mentions and Sharing of Mammography and Breast Cancer Terms on Facebook: A Pilot Study", journal="J Med Internet Res", year="2017", month="Jun", day="09", volume="19", number="6", pages="e201", keywords="Facebook", keywords="online social network", keywords="social media", keywords="breast cancer screening", keywords="mammography", keywords="user comments", keywords="websites", keywords="links", abstract="Background: The most popular social networking site in the United States is Facebook, an online forum where circles of friends create, share, and interact with each other's content in a nonpublic way. Objective: Our objectives were to understand (1) the most commonly used terms and phrases relating to breast cancer screening, (2) the most commonly shared website links that other women interacted with, and (3) the most commonly shared website links, by age groups. Methods: We used a novel proprietary tool from Facebook to analyze all of the more than 1.7 million unique interactions (comments on stories, reshares, and emoji reactions) and stories associated with breast cancer screening keywords that were generated by more than 1.1 million unique female Facebook users over the 1 month between November 15 and December 15, 2016. We report frequency distributions of the most popular shared Web content by age group and keywords. Results: On average, each of 59,000 unique stories during the month was reshared 1.5 times, commented on nearly 8 times, and reacted to more than 20 times by other users. Posted stories were most often authored by women aged 45-54 years. Users shared, reshared, commented on, and reacted to website links predominantly to e-commerce sites (12,200/1.7 million, 36\% of all the most popular links), celebrity news (n=8800, 26\%), and major advocacy organizations (n=4900, 15\%; almost all accounted for by the American Cancer Society breast cancer site). Conclusions: On Facebook, women shared and reacted to links to commercial and informative websites regarding breast cancer and screening. This information could inform patient outreach regarding breast cancer screening, indirectly through better understanding of key issues, and directly through understanding avenues for paid messaging to women authoring and reacting to content in this space. ", doi="10.2196/jmir.7508", url="http://www.jmir.org/2017/6/e201/", url="http://www.ncbi.nlm.nih.gov/pubmed/28600279" } @Article{info:doi/10.2196/jmir.7485, author="Metwally, Omar and Blumberg, Seth and Ladabaum, Uri and Sinha, R. Sidhartha", title="Using Social Media to Characterize Public Sentiment Toward Medical Interventions Commonly Used for Cancer Screening: An Observational Study", journal="J Med Internet Res", year="2017", month="Jun", day="07", volume="19", number="6", pages="e200", keywords="Twitter", keywords="sentiment analysis", keywords="cancer screening", keywords="colonoscopy", keywords="mammography", keywords="Pap smear", keywords="Papanicolaou test", keywords="social media", keywords="early detection of cancer", abstract="Background: Although cancer screening reduces morbidity and mortality, millions of people worldwide remain unscreened. Social media provide a unique platform to understand public sentiment toward tools that are commonly used for cancer screening. Objective: The objective of our study was to examine public sentiment toward colonoscopy, mammography, and Pap smear and how this sentiment spreads by analyzing discourse on Twitter. Methods: In this observational study, we classified 32,847 tweets (online postings on Twitter) related to colonoscopy, mammography, or Pap smears using a naive Bayes algorithm as containing positive, negative, or neutral sentiment. Additionally, we characterized the spread of sentiment on Twitter using an established model to study contagion. Results: Colonoscopy-related tweets were more likely to express negative than positive sentiment (negative to positive ratio 1.65, 95\% CI 1.51-1.80, P<.001), in contrast to the more positive sentiment expressed regarding mammography (negative to positive ratio 0.43, 95\% CI 0.39-0.47, P<.001). The proportions of negative versus positive tweets about Pap smear were not significantly different (negative to positive ratio 0.95, 95\% CI 0.87-1.04, P=.18). Positive and negative tweets tended to share lexical features across screening modalities. Positive tweets expressed resonance with the benefits of early detection. Fear and pain were the principal lexical features seen in negative tweets. Negative sentiment for colonoscopy and mammography spread more than positive sentiment; no correlation with sentiment and spread was seen for Pap smear. Conclusions: Analysis of social media data provides a unique, quantitative framework to better understand the public's perception of medical interventions that are commonly used for cancer screening. Given the growing use of social media, public health interventions to improve cancer screening should use the health perceptions of the population as expressed in social network postings about tests that are frequently used for cancer screening, as well as other people they may influence with such postings. ", doi="10.2196/jmir.7485", url="http://www.jmir.org/2017/6/e200/", url="http://www.ncbi.nlm.nih.gov/pubmed/28592395" } @Article{info:doi/10.2196/jmir.6946, author="Davis, A. Matthew and Zheng, Kai and Liu, Yang and Levy, Helen", title="Public Response to Obamacare on Twitter", journal="J Med Internet Res", year="2017", month="May", day="26", volume="19", number="5", pages="e167", keywords="Patient Protection and Affordable Care Act", keywords="health care reform", keywords="social media", keywords="data collection", abstract="Background: The Affordable Care Act (ACA), often called ``Obamacare,'' is a controversial law that has been implemented gradually since its enactment in 2010. Polls have consistently shown that public opinion of the ACA is quite negative. Objective: The aim of our study was to examine the extent to which Twitter data can be used to measure public opinion of the ACA over time. Methods: We prospectively collected a 10\% random sample of daily tweets (approximately 52 million since July 2011) using Twitter's streaming application programming interface (API) from July 10, 2011 to July 31, 2015. Using a list of key terms and ACA-specific hashtags, we identified tweets about the ACA and examined the overall volume of tweets about the ACA in relation to key ACA events. We applied standard text sentiment analysis to assign each ACA tweet a measure of positivity or negativity and compared overall sentiment from Twitter with results from the Kaiser Family Foundation health tracking poll. Results: Public opinion on Twitter (measured via sentiment analysis) was slightly more favorable than public opinion measured by the Kaiser poll (approximately 50\% vs 40\%, respectively) but trends over time in both favorable and unfavorable views were similar in both sources. The Twitter-based measures of opinion as well as the Kaiser poll changed very little over time: correlation coefficients for favorable and unfavorable public opinion were .43 and .37, respectively. However, we found substantial spikes in the volume of ACA-related tweets in response to key events in the law's implementation, such as the first open enrollment period in October 2013 and the Supreme Court decision in June 2012. Conclusions: Twitter may be useful for tracking public opinion of health care reform as it appears to be comparable with conventional polling results. Moreover, in contrast with conventional polling, the overall amount of tweets also provides a potential indication of public interest of a particular issue at any point in time. ", doi="10.2196/jmir.6946", url="http://www.jmir.org/2017/5/e167/", url="http://www.ncbi.nlm.nih.gov/pubmed/28550002" } @Article{info:doi/10.2196/publichealth.7313, author="Leal Neto, Onicio and Dimech, Santiago George and Libel, Marlo and de Souza, Vieira Wayner and Cesse, Eduarda and Smolinski, Mark and Oliveira, Wanderson and Albuquerque, Jones", title="Sa{\'u}de na Copa: The World's First Application of Participatory Surveillance for a Mass Gathering at FIFA World Cup 2014, Brazil", journal="JMIR Public Health Surveill", year="2017", month="May", day="04", volume="3", number="2", pages="e26", keywords="mass gatherings", keywords="participatory surveillance", keywords="public health", keywords="epidemiology", abstract="Background: The 2005 International Health Regulations (IHRs) established parameters for event assessments and notifications that may constitute public health emergencies of international concern. These requirements and parameters opened up space for the use of nonofficial mechanisms (such as websites, blogs, and social networks) and technological improvements of communication that can streamline the detection, monitoring, and response to health problems, and thus reduce damage caused by these problems. Specifically, the revised IHR created space for participatory surveillance to function, in addition to the traditional surveillance mechanisms of detection, monitoring, and response. Participatory surveillance is based on crowdsourcing methods that collect information from society and then return the collective knowledge gained from that information back to society. The spread of digital social networks and wiki-style knowledge platforms has created a very favorable environment for this model of production and social control of information. Objective: The aim of this study was to describe the use of a participatory surveillance app, Healthy Cup, for the early detection of acute disease outbreaks during the F{\'e}d{\'e}ration Internationale de Football Association (FIFA) World Cup 2014. Our focus was on three specific syndromes (respiratory, diarrheal, and rash) related to six diseases that were considered important in a mass gathering context (influenza, measles, rubella, cholera, acute diarrhea, and dengue fever). Methods: From May 12 to July 13, 2014, users from anywhere in the world were able to download the Healthy Cup app and record their health condition, reporting whether they were good, very good, ill, or very ill. For users that reported being ill or very ill, a screen with a list of 10 symptoms was displayed. Participatory surveillance allows for the real-time identification of aggregates of symptoms that indicate possible cases of infectious diseases. Results: From May 12 through July 13, 2014, there were 9434 downloads of the Healthy Cup app and 7155 (75.84\%) registered users. Among the registered users, 4706 (4706/7155, 65.77\%) were active users who posted a total of 47,879 times during the study period. The maximum number of users that signed up in one day occurred on May 30, 2014, the day that the app was officially launched by the Minister of Health during a press conference. During this event, the Minister of Health announced the special government program Health in the World Cup on national television media. On that date, 3633 logins were recorded, which accounted for more than half of all sign-ups across the entire duration of the study (50.78\%, 3633/7155). Conclusions: Participatory surveillance through community engagement is an innovative way to conduct epidemiological surveillance. Compared to traditional epidemiological surveillance, advantages include lower costs of data acquisition, timeliness of information collected and shared, platform scalability, and capacity for integration between the population being served and public health services. ", doi="10.2196/publichealth.7313", url="http://publichealth.jmir.org/2017/2/e26/", url="http://www.ncbi.nlm.nih.gov/pubmed/28473308" } @Article{info:doi/10.2196/publichealth.6396, author="Alvaro, Nestor and Miyao, Yusuke and Collier, Nigel", title="TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations", journal="JMIR Public Health Surveill", year="2017", month="May", day="03", volume="3", number="2", pages="e24", keywords="Twitter", keywords="PubMed", keywords="corpus", keywords="pharmacovigilance", keywords="natural language processing", keywords="text mining", keywords="annotation", abstract="Background: Work on pharmacovigilance systems using texts from PubMed and Twitter typically target at different elements and use different annotation guidelines resulting in a scenario where there is no comparable set of documents from both Twitter and PubMed annotated in the same manner. Objective: This study aimed to provide a comparable corpus of texts from PubMed and Twitter that can be used to study drug reports from these two sources of information, allowing researchers in the area of pharmacovigilance using natural language processing (NLP) to perform experiments to better understand the similarities and differences between drug reports in Twitter and PubMed. Methods: We produced a corpus comprising 1000 tweets and 1000 PubMed sentences selected using the same strategy and annotated at entity level by the same experts (pharmacists) using the same set of guidelines. Results: The resulting corpus, annotated by two pharmacists, comprises semantically correct annotations for a set of drugs, diseases, and symptoms. This corpus contains the annotations for 3144 entities, 2749 relations, and 5003 attributes. Conclusions: We present a corpus that is unique in its characteristics as this is the first corpus for pharmacovigilance curated from Twitter messages and PubMed sentences using the same data selection and annotation strategies. We believe this corpus will be of particular interest for researchers willing to compare results from pharmacovigilance systems (eg, classifiers and named entity recognition systems) when using data from Twitter and from PubMed. We hope that given the comprehensive set of drug names and the annotated entities and relations, this corpus becomes a standard resource to compare results from different pharmacovigilance studies in the area of NLP. ", doi="10.2196/publichealth.6396", url="http://publichealth.jmir.org/2017/2/e24/", url="http://www.ncbi.nlm.nih.gov/pubmed/28468748" } @Article{info:doi/10.2196/jmir.6579, author="Rosenblum, Sara and Yom-Tov, Elad", title="Seeking Web-Based Information About Attention Deficit Hyperactivity Disorder: Where, What, and When", journal="J Med Internet Res", year="2017", month="Apr", day="21", volume="19", number="4", pages="e126", keywords="attention deficit hyperactivity disorder", keywords="Internet", keywords="search engine", keywords="coping behavior", keywords="parents", abstract="Background: Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder, prevalent among 2-10\% of the population. Objective: The objective of this study was to describe where, what, and when people search online for topics related to ADHD. Methods: Data were collected from Microsoft's Bing search engine and from the community question and answer site, Yahoo Answers. The questions were analyzed based on keywords and using further statistical methods. Results: Our results revealed that the Internet indeed constitutes a source of information for people searching the topic of ADHD, and that they search for information mostly about ADHD symptoms. Furthermore, individuals personally affected by the disorder made 2.0 more questions about ADHD compared with others. Questions begin when children reach 2 years of age, with an average age of 5.1 years. Most of the websites searched were not specifically related to ADHD and the timing of searches as well as the query content were different among those prediagnosis compared with postdiagnosis. Conclusions: The study results shed light on the features of ADHD-related searches. Thus, they may help improve the Internet as a source of reliable information, and promote improved awareness and knowledge about ADHD as well as quality of life for populations dealing with the complex phenomena of ADHD. ", doi="10.2196/jmir.6579", url="http://www.jmir.org/2017/4/e126/", url="http://www.ncbi.nlm.nih.gov/pubmed/28432038" } @Article{info:doi/10.2196/publichealth.6925, author="Stefanidis, Anthony and Vraga, Emily and Lamprianidis, Georgios and Radzikowski, Jacek and Delamater, L. Paul and Jacobsen, H. Kathryn and Pfoser, Dieter and Croitoru, Arie and Crooks, Andrew", title="Zika in Twitter: Temporal Variations of Locations, Actors, and Concepts", journal="JMIR Public Health Surveill", year="2017", month="Apr", day="20", volume="3", number="2", pages="e22", keywords="Zika virus", keywords="social media", keywords="Twitter messaging", keywords="geographic information systems", abstract="Background: The recent Zika outbreak witnessed the disease evolving from a regional health concern to a global epidemic. During this process, different communities across the globe became involved in Twitter, discussing the disease and key issues associated with it. This paper presents a study of this discussion in Twitter, at the nexus of location, actors, and concepts. Objective: Our objective in this study was to demonstrate the significance of 3 types of events: location related, actor related, and concept related, for understanding how a public health emergency of international concern plays out in social media, and Twitter in particular. Accordingly, the study contributes to research efforts toward gaining insights on the mechanisms that drive participation, contributions, and interaction in this social media platform during a disease outbreak. Methods: We collected 6,249,626 tweets referring to the Zika outbreak over a period of 12 weeks early in the outbreak (December 2015 through March 2016). We analyzed this data corpus in terms of its geographical footprint, the actors participating in the discourse, and emerging concepts associated with the issue. Data were visualized and evaluated with spatiotemporal and network analysis tools to capture the evolution of interest on the topic and to reveal connections between locations, actors, and concepts in the form of interaction networks. Results: The spatiotemporal analysis of Twitter contributions reflects the spread of interest in Zika from its original hotspot in South America to North America and then across the globe. The Centers for Disease Control and World Health Organization had a prominent presence in social media discussions. Tweets about pregnancy and abortion increased as more information about this emerging infectious disease was presented to the public and public figures became involved in this. Conclusions: The results of this study show the utility of analyzing temporal variations in the analytic triad of locations, actors, and concepts. This contributes to advancing our understanding of social media discourse during a public health emergency of international concern. ", doi="10.2196/publichealth.6925", url="http://publichealth.jmir.org/2017/2/e22/", url="http://www.ncbi.nlm.nih.gov/pubmed/28428164" } @Article{info:doi/10.2196/publichealth.5980, author="Gayle, Alberto and Shimaoka, Motomu", title="Public Response to Scientific Misconduct: Assessing Changes in Public Sentiment Toward the Stimulus-Triggered Acquisition of Pluripotency (STAP) Cell Case via Twitter", journal="JMIR Public Health Surveill", year="2017", month="Apr", day="20", volume="3", number="2", pages="e21", keywords="scientific misconduct", keywords="retraction of publication as a topic", keywords="mass media", keywords="social media", keywords="public opinion", keywords="public policy", keywords="data mining", keywords="publication", keywords="stem cells", keywords="Japan", abstract="Background: In this age of social media, any news---good or bad---has the potential to spread in unpredictable ways. Changes in public sentiment have the potential to either drive or limit investment in publicly funded activities, such as scientific research. As a result, understanding the ways in which reported cases of scientific misconduct shape public sentiment is becoming increasingly essential---for researchers and institutions, as well as for policy makers and funders. In this study, we thus set out to assess and define the patterns according to which public sentiment may change in response to reported cases of scientific misconduct. This study focuses on the public response to the events involved in a recent case of major scientific misconduct that occurred in 2014 in Japan---stimulus-triggered acquisition of pluripotency (STAP) cell case. Objectives: The aims of this study were to determine (1) the patterns according to which public sentiment changes in response to scientific misconduct; (2) whether such measures vary significantly, coincident with major timeline events; and (3) whether the changes observed mirror the response patterns reported in the literature with respect to other classes of events, such as entertainment news and disaster reports. Methods: The recent STAP cell scandal is used as a test case. Changes in the volume and polarity of discussion were assessed using a sampling of case-related Twitter data, published between January 28, 2014 and March 15, 2015. Rapidminer was used for text processing and the popular bag-of-words algorithm, SentiWordNet, was used in Rapidminer to calculate sentiment for each sample Tweet. Relative volume and sentiment was then assessed overall, month-to-month, and with respect to individual entities. Results: Despite the ostensibly negative subject, average sentiment over the observed period tended to be neutral (?0.04); however, a notable downward trend (y=?0.01 x +0.09; R {\texttwosuperior}=.45) was observed month-to-month. Notably polarized tweets accounted for less than one-third of sampled discussion: 17.49\% (1656/9467) negative and 12.59\% positive (1192/9467). Significant polarization was found in only 4 out of the 15 months covered, with significant variation month-to-month (P<.001). Significant increases in polarization tended to coincide with increased discussion volume surrounding major events (P<.001). Conclusions: These results suggest that public opinion toward scientific research may be subject to the same sensationalist dynamics driving public opinion in other, consumer-oriented topics. The patterns in public response observed here, with respect to the STAP cell case, were found to be consistent with those observed in the literature with respect to other classes of news-worthy events on Twitter. Discussion was found to become strongly polarized only during times of increased public attention, and such increases tended to be driven primarily by negative reporting and reactionary commentary. ", doi="10.2196/publichealth.5980", url="http://publichealth.jmir.org/2017/2/e21/", url="http://www.ncbi.nlm.nih.gov/pubmed/28428163" } @Article{info:doi/10.2196/publichealth.6764, author="Pes{\"a}l{\"a}, Samuli and Virtanen, J. Mikko and Sane, Jussi and Jousimaa, Jukkapekka and Lyytik{\"a}inen, Outi and Murtopuro, Satu and Mustonen, Pekka and Kaila, Minna and Helve, Otto", title="Health Care Professionals' Evidence-Based Medicine Internet Searches Closely Mimic the Known Seasonal Variation of Lyme Borreliosis: A Register-Based Study", journal="JMIR Public Health Surveill", year="2017", month="Apr", day="11", volume="3", number="2", pages="e19", keywords="search engine", keywords="evidence-based medicine", keywords="information systems", keywords="public health surveillance", keywords="Lyme borreliosis", abstract="Background: Both health care professionals and nonprofessionals seek medical information on the Internet. Using Web-based search engine searches to detect epidemic diseases has, however, been problematic. Physician's databases (PD) is a chargeable evidence-based medicine (EBM) portal on the Internet for health care professionals and is available throughout the entire health care system in Finland. Lyme borreliosis (LB), a well-defined disease model, shows temporal and regional variation in Finland. Little data exist on health care professionals' searches from Internet-based EBM databases in public health surveillance. Objective: The aim of this study was to assess whether health care professionals' use of Internet EBM databases could describe seasonal increases of the disease and supplement routine public health surveillance. Methods: Two registers, PD and the register of primary health care diagnoses (Avohilmo), were used to compare health care professionals' Internet searches on LB from EBM databases and national register-based LB diagnoses in order to evaluate annual and regional variations of LB in the whole country and in three selected high-incidence LB regions in Finland during 2011-2015. Results: Both registers, PD and Avohilmo, show visually similar patterns in annual and regional variation of LB in Finland and in the three high-incidence LB regions during 2011-2015. Conclusions: Health care professionals' Internet searches from EBM databases coincide with national register diagnoses of LB. PD searches showed a clear seasonal variation. In addition, notable regional differences were present in both registers. However, physicians' Internet medical searches should be considered as a supplementary source of information for disease surveillance. ", doi="10.2196/publichealth.6764", url="http://publichealth.jmir.org/2017/2/e19/", url="http://www.ncbi.nlm.nih.gov/pubmed/28400357" } @Article{info:doi/10.2196/publichealth.7304, author="Baltrusaitis, Kristin and Santillana, Mauricio and Crawley, W. Adam and Chunara, Rumi and Smolinski, Mark and Brownstein, S. John", title="Determinants of Participants' Follow-Up and Characterization of Representativeness in Flu Near You, A Participatory Disease Surveillance System", journal="JMIR Public Health Surveill", year="2017", month="Apr", day="07", volume="3", number="2", pages="e18", keywords="public health surveillance", keywords="influenza, human", keywords="community-based participatory research", keywords="crowdsourcing", keywords="public health informatics", keywords="digital disease detection", abstract="Background: Flu Near You (FNY) is an Internet-based participatory surveillance system in the United States and Canada that allows volunteers to report influenza-like symptoms using a brief weekly symptom report. Objective: Our objective was to evaluate the representativeness of the FNY population compared with the general population of the United States, explore the demographic and behavioral characteristics associated with FNY's high-participation users, and summarize results from a user survey of a cohort of FNY participants. Methods: We compared (1) the representativeness of sex and age groups of FNY participants during the 2014-2015 flu season versus the general US population and (2) the distribution of Human Development Index (HDI) scores of FNY participants versus that of the general US population. We analyzed associations between demographic and behavioral factors and the level of participant follow-up (ie, high vs low). Finally, descriptive statistics of responses from FNY's 2015 and 2016 end-of-season user surveys were calculated. Results: During the 2014-2015 influenza season, 47,234 unique participants had at least one FNY symptom report that was either self-reported (users) or submitted on their behalf (household members). The proportion of female FNY participants was significantly higher than that of the general US population (n=28,906, 61.2\% vs 51.1\%, P<.001). Although each age group was represented in the FNY population, the age distribution was significantly different from that of the US population (P<.001). Compared with the US population, FNY had a greater proportion of individuals with HDI >5.0, signaling that the FNY user distribution was more affluent and educated than the US population baseline. We found that high-participation use (ie, higher participation in follow-up symptom reports) was associated with sex (females were 25\% less likely than men to be high-participation users), higher HDI, not reporting an influenza-like illness at the first symptom report, older age, and reporting for household members (all differences between high- and low-participation users P<.001). Approximately 10\% of FNY users completed an additional survey at the end of the flu season that assessed detailed user characteristics (3217/33,324 in 2015; 4850/44,313 in 2016). Of these users, most identified as being either retired or employed in the health, education, and social services sectors and indicated that they achieved a bachelor's degree or higher. Conclusions: The representativeness of the FNY population and characteristics of its high-participation users are consistent with what has been observed in other Internet-based influenza surveillance systems. With targeted recruitment of underrepresented populations, FNY may improve as a complementary system to timely tracking of flu activity, especially in populations that do not seek medical attention and in areas with poor official surveillance data. ", doi="10.2196/publichealth.7304", url="http://publichealth.jmir.org/2017/2/e18/", url="http://www.ncbi.nlm.nih.gov/pubmed/28389417" } @Article{info:doi/10.2196/publichealth.7015, author="Vasconcellos-Silva, Roberto Paulo and Carvalho, Feres D{\'a}rlinton Barbosa and Trajano, Val{\'e}ria and de La Rocque, Rodriguez Lucia and Sawada, Braz Anunciata Cristina Marins and Juvanhol, Lopes Leidjaira", title="Using Google Trends Data to Study Public Interest in Breast Cancer Screening in Brazil: Why Not a Pink February?", journal="JMIR Public Health Surveill", year="2017", month="Apr", day="06", volume="3", number="2", pages="e17", keywords="Internet", keywords="cancer information seeking", keywords="breast cancer", keywords="mass screening", keywords="health communication", keywords="early detection of cancer", keywords="infoveillance", keywords="infodemiology", abstract="Background: One of the major challenges of the Brazilian Ministry of Health is to foster interest in breast cancer screening (BCS), especially among women at high risk. Strategies have been developed to promote the early identification of breast cancer mainly by Pink October campaigns. The massive number of queries conducted through Google creates traffic data that can be analyzed to show unrevealed interest cycles and their seasonalities. Objectives: Using Google Trends, we studied cycles of public interest in queries toward mammography and breast cancer along the last 5 years. We hypothesize that these data may be correlated with collective interest cycles leveraged by national BCS campaigns such as Pink October. Methods: Google Trends was employed to normalize traffic data on a scale from 0 (<1\% of the peak volume) to 100 (peak of traffic) presented as weekly relative search volume (RSV) concerning mammography and breast cancer as search terms. A time series covered the last 261 weeks (November 2011 to October 2016), and RSV of both terms were compared with their respective annual means. Polynomial trendlines (second order) were employed to estimate overall trends. Results: We found an upward trend for both terms over the 5 years, with almost parallel trendlines. Remarkable peaks were found along Pink October months--- mammography and breast cancer searches were leveraged up reaching, respectively, 119.1\% (2016) and 196.8\% (2015) above annual means. Short downward RSVs along December-January months were also noteworthy along all the studied period. These trends traced an N-shaped pattern with higher peaks in Pink October months and sharp falls along subsequent December and January. Conclusions: Considering these findings, it would be reasonable to bring Pink October to the beginning of each year, thereby extending the beneficial effect of the campaigns. It would be more appropriate to start screening campaigns at the beginning of the year, when new resolutions are taken and new projects are added to everyday routines. Our work raises attention to the study of traffic data to encourage health campaign analysts to undertake better analysis based on marketing practices. ", doi="10.2196/publichealth.7015", url="http://publichealth.jmir.org/2017/2/e17/", url="http://www.ncbi.nlm.nih.gov/pubmed/28385679" } @Article{info:doi/10.2196/jmir.7022, author="Lienemann, A. Brianna and Unger, B. Jennifer and Cruz, Boley Tess and Chu, Kar-Hai", title="Methods for Coding Tobacco-Related Twitter Data: A Systematic Review", journal="J Med Internet Res", year="2017", month="Mar", day="31", volume="19", number="3", pages="e91", keywords="tobacco", keywords="Internet", keywords="social marketing", keywords="review", abstract="Background: As Twitter has grown in popularity to 313 million monthly active users, researchers have increasingly been using it as a data source for tobacco-related research. Objective: The objective of this systematic review was to assess the methodological approaches of categorically coded tobacco Twitter data and make recommendations for future studies. Methods: Data sources included PsycINFO, Web of Science, PubMed, ABI/INFORM, Communication Source, and Tobacco Regulatory Science. Searches were limited to peer-reviewed journals and conference proceedings in English from January 2006 to July 2016. The initial search identified 274 articles using a Twitter keyword and a tobacco keyword. One coder reviewed all abstracts and identified 27 articles that met the following inclusion criteria: (1) original research, (2) focused on tobacco or a tobacco product, (3) analyzed Twitter data, and (4) coded Twitter data categorically. One coder extracted data collection and coding methods. Results: E-cigarettes were the most common type of Twitter data analyzed, followed by specific tobacco campaigns. The most prevalent data sources were Gnip and Twitter's Streaming application programming interface (API). The primary methods of coding were hand-coding and machine learning. The studies predominantly coded for relevance, sentiment, theme, user or account, and location of user. Conclusions: Standards for data collection and coding should be developed to be able to more easily compare and replicate tobacco-related Twitter results. Additional recommendations include the following: sample Twitter's databases multiple times, make a distinction between message attitude and emotional tone for sentiment, code images and URLs, and analyze user profiles. Being relatively novel and widely used among adolescents and black and Hispanic individuals, Twitter could provide a rich source of tobacco surveillance data among vulnerable populations. ", doi="10.2196/jmir.7022", url="http://www.jmir.org/2017/3/e91/", url="http://www.ncbi.nlm.nih.gov/pubmed/28363883" } @Article{info:doi/10.2196/publichealth.7054, author="Noll-Hussong, Michael", title="Whiplash Syndrome Reloaded: Digital Echoes of Whiplash Syndrome in the European Internet Search Engine Context", journal="JMIR Public Health Surveill", year="2017", month="Mar", day="27", volume="3", number="1", pages="e15", keywords="search engine", keywords="whiplash injuries", keywords="legislation \& jurisprudence", keywords="medicolegal aspects", keywords="compensation and redress", keywords="compensation", keywords="accidents, traffic", keywords="adult", keywords="female", keywords="humans", keywords="incidence", keywords="insurance claim reporting", keywords="male", keywords="neck pain", keywords="prognosis", keywords="search engine analytics", keywords="whiplash syndrome", keywords="Google Trends", abstract="Background: In many Western countries, after a motor vehicle collision, those involved seek health care for the assessment of injuries and for insurance documentation purposes. In contrast, in many less wealthy countries, there may be limited access to care and no insurance or compensation system. Objective: The purpose of this infodemiology study was to investigate the global pattern of evolving Internet usage in countries with and without insurance and the corresponding compensation systems for whiplash injury. Methods: We used the Internet search engine analytics via Google Trends to study the health information-seeking behavior concerning whiplash injury at national population levels in Europe. Results: We found that the search for ``whiplash'' is strikingly and consistently often associated with the search for ``compensation'' in countries or cultures with a tort system. Frequent or traumatic painful injuries; diseases or disorders such as arthritis, headache, radius, and hip fracture; depressive disorders; and fibromyalgia were not associated similarly with searches on ``compensation.'' Conclusions: In this study, we present evidence from the evolving viewpoint of naturalistic Internet search engine analytics that the expectations for receiving compensation may influence Internet search behavior in relation to whiplash injury. ", doi="10.2196/publichealth.7054", url="http://publichealth.jmir.org/2017/1/e15/", url="http://www.ncbi.nlm.nih.gov/pubmed/28347974" } @Article{info:doi/10.2196/publichealth.6033, author="Menachemi, Nir and Rahurkar, Saurabh and Rahurkar, Mandar", title="Using Web-Based Search Data to Study the Public's Reactions to Societal Events: The Case of the Sandy Hook Shooting", journal="JMIR Public Health Surveill", year="2017", month="Mar", day="23", volume="3", number="1", pages="e12", keywords="Internet", keywords="search engine", keywords="firearms", keywords="health policy", keywords="information seeking behavior", keywords="public health informatics", keywords="gun control debate", abstract="Background: Internet search is the most common activity on the World Wide Web and generates a vast amount of user-reported data regarding their information-seeking preferences and behavior. Although this data has been successfully used to examine outbreaks, health care utilization, and outcomes related to quality of care, its value in informing public health policy remains unclear. Objective: The aim of this study was to evaluate the role of Internet search query data in health policy development. To do so, we studied the public's reaction to a major societal event in the context of the 2012 Sandy Hook School shooting incident. Methods: Query data from the Yahoo! search engine regarding firearm-related searches was analyzed to examine changes in user-selected search terms and subsequent websites visited for a period of 14 days before and after the shooting incident. Results: A total of 5,653,588 firearm-related search queries were analyzed. In the after period, queries increased for search terms related to ``guns'' (+50.06\%), ``shooting incident'' (+333.71\%), ``ammunition'' (+155.14\%), and ``gun-related laws'' (+535.47\%). The highest increase (+1054.37\%) in Web traffic was seen by news websites following ``shooting incident'' queries whereas searches for ``guns'' (+61.02\%) and ``ammunition'' (+173.15\%) resulted in notable increases in visits to retail websites. Firearm-related queries generally returned to baseline levels after approximately 10 days. Conclusions: Search engine queries present a viable infodemiology metric on public reactions and subsequent behaviors to major societal events and could be used by policymakers to inform policy development. ", doi="10.2196/publichealth.6033", url="http://publichealth.jmir.org/2017/1/e12/", url="http://www.ncbi.nlm.nih.gov/pubmed/28336508" } @Article{info:doi/10.2196/jmir.6895, author="Mowery, Danielle and Smith, Hilary and Cheney, Tyler and Stoddard, Greg and Coppersmith, Glen and Bryan, Craig and Conway, Mike", title="Understanding Depressive Symptoms and Psychosocial Stressors on Twitter: A Corpus-Based Study", journal="J Med Internet Res", year="2017", month="Feb", day="28", volume="19", number="2", pages="e48", keywords="social media", keywords="Twitter messaging", keywords="natural language processing", keywords="major depressive disorder", keywords="data annotation", keywords="machine learning", abstract="Background: With a lifetime prevalence of 16.2\%, major depressive disorder is the fifth biggest contributor to the disease burden in the United States. Objective: The aim of this study, building on previous work qualitatively analyzing depression-related Twitter data, was to describe the development of a comprehensive annotation scheme (ie, coding scheme) for manually annotating Twitter data with Diagnostic and Statistical Manual of Mental Disorders, Edition 5 (DSM 5) major depressive symptoms (eg, depressed mood, weight change, psychomotor agitation, or retardation) and Diagnostic and Statistical Manual of Mental Disorders, Edition IV (DSM-IV) psychosocial stressors (eg, educational problems, problems with primary support group, housing problems). Methods: Using this annotation scheme, we developed an annotated corpus, Depressive Symptom and Psychosocial Stressors Acquired Depression, the SAD corpus, consisting of 9300 tweets randomly sampled from the Twitter application programming interface (API) using depression-related keywords (eg, depressed, gloomy, grief). An analysis of our annotated corpus yielded several key results. Results: First, 72.09\% (6829/9473) of tweets containing relevant keywords were nonindicative of depressive symptoms (eg, ``we're in for a new economic depression''). Second, the most prevalent symptoms in our dataset were depressed mood and fatigue or loss of energy. Third, less than 2\% of tweets contained more than one depression related category (eg, diminished ability to think or concentrate, depressed mood). Finally, we found very high positive correlations between some depression-related symptoms in our annotated dataset (eg, fatigue or loss of energy and educational problems; educational problems and diminished ability to think). Conclusions: We successfully developed an annotation scheme and an annotated corpus, the SAD corpus, consisting of 9300 tweets randomly-selected from the Twitter application programming interface using depression-related keywords. Our analyses suggest that keyword queries alone might not be suitable for public health monitoring because context can change the meaning of keyword in a statement. However, postprocessing approaches could be useful for reducing the noise and improving the signal needed to detect depression symptoms using social media. ", doi="10.2196/jmir.6895", url="http://www.jmir.org/2017/2/e48/", url="http://www.ncbi.nlm.nih.gov/pubmed/28246066" } @Article{info:doi/10.2196/jmir.5694, author="Rose, W. Shyanika and Jo, L. Catherine and Binns, Steven and Buenger, Melissa and Emery, Sherry and Ribisl, M. Kurt", title="Perceptions of Menthol Cigarettes Among Twitter Users: Content and Sentiment Analysis", journal="J Med Internet Res", year="2017", month="Feb", day="27", volume="19", number="2", pages="e56", keywords="tobacco products", keywords="menthol", keywords="smoking", keywords="social media", keywords="Twitter messaging", keywords="policy", keywords="public opinion", abstract="Background: Menthol cigarettes are used disproportionately by African American, female, and adolescent smokers. Twitter is also used disproportionately by minority and younger populations, providing a unique window into conversations reflecting social norms, behavioral intentions, and sentiment toward menthol cigarettes. Objective: Our purpose was to identify the content and frequency of conversations about menthol cigarettes, including themes, populations, user smoking status, other tobacco or substances, tweet characteristics, and sentiment. We also examined differences in menthol cigarette sentiment by prevalent categories, which allowed us to assess potential perceptions, misperceptions, and social norms about menthol cigarettes on Twitter. This approach can inform communication about these products, particularly to subgroups who are at risk for menthol cigarette use. Methods: Through a combination of human and machine classification, we identified 94,627 menthol cigarette-relevant tweets from February 1, 2012 to January 31, 2013 (1 year) from over 47 million tobacco-related messages gathered prospectively from the Twitter Firehose of all public tweets and metadata. Then, 4 human coders evaluated a random sample of 7000 tweets for categories, including sentiment toward menthol cigarettes. Results: We found that 47.98\% (3194/6657) of tweets expressed positive sentiment, while 40.26\% (2680/6657) were negative toward menthol cigarettes. The majority of tweets by likely smokers (2653/4038, 65.70\%) expressed positive sentiment, while 91.2\% (320/351) of nonsmokers and 71.7\% (91/127) of former smokers indicated negative views. Positive views toward menthol cigarettes were predominant in tweets that discussed addiction or craving, marijuana, smoking, taste or sensation, song lyrics, and tobacco industry or marketing or tweets that were commercial in nature. Negative views toward menthol were more common in tweets about smoking cessation, health, African Americans, women, and children and adolescents---largely due to expression of negative stereotypes associated with these groups' use of menthol cigarettes. Conclusions: Examinations of public opinions toward menthol cigarettes through social media can help to inform the framing of public communication about menthol cigarettes, particularly in light of potential regulation by the European Union, US Food and Drug Administration, other jurisdictions, and localities. ", doi="10.2196/jmir.5694", url="http://www.jmir.org/2017/2/e56/", url="http://www.ncbi.nlm.nih.gov/pubmed/28242592" } @Article{info:doi/10.2196/publichealth.6872, author="Matsuda, Shinichi and Aoki, Kotonari and Tomizawa, Shiho and Sone, Masayoshi and Tanaka, Riwa and Kuriki, Hiroshi and Takahashi, Yoichiro", title="Analysis of Patient Narratives in Disease Blogs on the Internet: An Exploratory Study of Social Pharmacovigilance", journal="JMIR Public Health Surveill", year="2017", month="Feb", day="24", volume="3", number="1", pages="e10", keywords="Internet", keywords="social media", keywords="adverse drug reaction", keywords="pharmacovigilance", keywords="text mining", abstract="Background: Although several reports have suggested that patient-generated data from Internet sources could be used to improve drug safety and pharmacovigilance, few studies have identified such data sources in Japan. We introduce a unique Japanese data source: t?by?ki, which translates literally as ``an account of a struggle with disease.'' Objective: The objective of this study was to evaluate the basic characteristics of the TOBYO database, a collection of t?by?ki blogs on the Internet, and discuss potential applications for pharmacovigilance. Methods: We analyzed the overall gender and age distribution of the patient-generated TOBYO database and compared this with other external databases generated by health care professionals. For detailed analysis, we prepared separate datasets for blogs written by patients with depression and blogs written by patients with rheumatoid arthritis (RA), because these conditions were expected to entail subjective patient symptoms such as discomfort, insomnia, and pain. Frequently appearing medical terms were counted, and their variations were compared with those in an external adverse drug reaction (ADR) reporting database. Frequently appearing words regarding patients with depression and patients with RA were visualized using word clouds and word cooccurrence networks. Results: As of June 4, 2016, the TOBYO database comprised 54,010 blogs representing 1405 disorders. Overall, more entries were written by female bloggers (68.8\%) than by male bloggers (30.8\%). The most frequently observed disorders were breast cancer (4983 blogs), depression (3556), infertility (2430), RA (1118), and panic disorder (1090). Comparison of medical terms observed in t?by?ki blogs with those in an external ADR reporting database showed that subjective and symptomatic events and general terms tended to be frequently observed in t?by?ki blogs (eg, anxiety, headache, and pain), whereas events using more technical medical terms (eg, syndrome and abnormal laboratory test result) tended to be observed frequently in the ADR database. We also confirmed the feasibility of using visualization techniques to obtain insights from unstructured text-based t?by?ki blog data. Word clouds described the characteristics of each disorder, such as ``sleeping'' and ``anxiety'' in depression and ``pain'' and ``painful'' in RA. Conclusions: Pharmacovigilance should maintain a strong focus on patients' actual experiences, concerns, and outcomes, and this approach can be expected to uncover hidden adverse event signals earlier and to help us understand adverse events in a patient-centered way. Patient-generated t?by?ki blogs in the TOBYO database showed unique characteristics that were different from the data in existing sources generated by health care professionals. Analysis of t?by?ki blogs would add value to the assessment of disorders with a high prevalence in women, psychiatric disorders in which subjective symptoms have important clinical meaning, refractory disorders, and other chronic disorders. ", doi="10.2196/publichealth.6872", url="http://publichealth.jmir.org/2017/1/e10/", url="http://www.ncbi.nlm.nih.gov/pubmed/28235749" } @Article{info:doi/10.2196/publichealth.6174, author="Anderson, S. Laurie and Bell, G. Heidi and Gilbert, Michael and Davidson, E. Julie and Winter, Christina and Barratt, J. Monica and Win, Beta and Painter, L. Jeffery and Menone, Christopher and Sayegh, Jonathan and Dasgupta, Nabarun", title="Using Social Listening Data to Monitor Misuse and Nonmedical Use of Bupropion: A Content Analysis", journal="JMIR Public Health Surveill", year="2017", month="Feb", day="1", volume="3", number="1", pages="e6", keywords="social media", keywords="Internet", keywords="prescription drug misuse", keywords="substance-related disorders", keywords="pharmacovigilance", keywords="harm reduction", keywords="community-based participatory research", keywords="bupropion", keywords="amitriptyline", keywords="venlafaxine hydrochloride", abstract="Background: The nonmedical use of pharmaceutical products has become a significant public health concern. Traditionally, the evaluation of nonmedical use has focused on controlled substances with addiction risk. Currently, there is no effective means of evaluating the nonmedical use of noncontrolled antidepressants. Objective: Social listening, in the context of public health sometimes called infodemiology or infoveillance, is the process of identifying and assessing what is being said about a company, product, brand, or individual, within forms of electronic interactive media. The objectives of this study were (1) to determine whether content analysis of social listening data could be utilized to identify posts discussing potential misuse or nonmedical use of bupropion and two comparators, amitriptyline and venlafaxine, and (2) to describe and characterize these posts. Methods: Social listening was performed on all publicly available posts cumulative through July 29, 2015, from two harm-reduction Web forums, Bluelight and Opiophile, which mentioned the study drugs. The acquired data were stripped of personally identifiable identification (PII). A set of generic, brand, and vernacular product names was used to identify product references in posts. Posts were obtained using natural language processing tools to identify vernacular references to drug misuse-related Preferred Terms from the English Medical Dictionary for Regulatory Activities (MedDRA) version 18 terminology. Posts were reviewed manually by coders, who extracted relevant details. Results: A total of 7756 references to at least one of the study antidepressants were identified within posts gathered for this study. Of these posts, 668 (8.61\%, 668/7756) referenced misuse or nonmedical use of the drug, with bupropion accounting for 438 (65.6\%, 438/668). Of the 668 posts, nonmedical use was discouraged by 40.6\% (178/438), 22\% (22/100), and 18.5\% (24/130) and encouraged by 12.3\% (54/438), 10\% (10/100), and 10.8\% (14/130) for bupropion, amitriptyline, and venlafaxine, respectively. The most commonly reported desired effects were similar to stimulants with bupropion, sedatives with amitriptyline, and dissociatives with venlafaxine. The nasal route of administration was most frequently reported for bupropion, whereas the oral route was most frequently reported for amitriptyline and venlafaxine. Bupropion and venlafaxine were most commonly procured from health care providers, whereas amitriptyline was most commonly obtained or stolen from a third party. The Fleiss kappa for interrater agreement among 20 items with 7 categorical response options evaluated by all 11 raters was 0.448 (95\% CI 0.421-0.457). Conclusions: Social listening, conducted in collaboration with harm-reduction Web forums, offers a valuable new data source that can be used for monitoring nonmedical use of antidepressants. Additional work on the capabilities of social listening will help further delineate the benefits and limitations of this rapidly evolving data source. ", doi="10.2196/publichealth.6174", url="http://publichealth.jmir.org/2017/1/e6/", url="http://www.ncbi.nlm.nih.gov/pubmed/28148472" } @Article{info:doi/10.2196/jmir.5780, author="Zhan, Yongcheng and Liu, Ruoran and Li, Qiudan and Leischow, James Scott and Zeng, Dajun Daniel", title="Identifying Topics for E-Cigarette User-Generated Contents: A Case Study From Multiple Social Media Platforms", journal="J Med Internet Res", year="2017", month="Jan", day="20", volume="19", number="1", pages="e24", keywords="electronic cigarettes", keywords="topic modeling", keywords="Latent Dirichlet Allocation", keywords="social media", keywords="infodemiology", abstract="Background: Electronic cigarette (e-cigarette) is an emerging product with a rapid-growth market in recent years. Social media has become an important platform for information seeking and sharing. We aim to mine hidden topics from e-cigarette datasets collected from different social media platforms. Objective: This paper aims to gain a systematic understanding of the characteristics of various types of social media, which will provide deep insights into how consumers and policy makers effectively use social media to track e-cigarette-related content and adjust their decisions and policies. Methods: We collected data from Reddit (27,638 e-cigarette flavor-related posts from January 1, 2011, to June 30, 2015), JuiceDB (14,433 e-juice reviews from June 26, 2013 to November 12, 2015), and Twitter (13,356 ``e-cig ban''-related tweets from January, 1, 2010 to June 30, 2015). Latent Dirichlet Allocation, a generative model for topic modeling, was used to analyze the topics from these data. Results: We found four types of topics across the platforms: (1) promotions, (2) flavor discussions, (3) experience sharing, and (4) regulation debates. Promotions included sales from vendors to users, as well as trades among users. A total of 10.72\% (2,962/27,638) of the posts from Reddit were related to trading. Promotion links were found between social media platforms. Most of the links (87.30\%) in JuiceDB were related to Reddit posts. JuiceDB and Reddit identified consistent flavor categories. E-cigarette vaping methods and features such as steeping, throat hit, and vapor production were broadly discussed both on Reddit and on JuiceDB. Reddit provided space for policy discussions and majority of the posts (60.7\%) holding a negative attitude toward regulations, whereas Twitter was used to launch campaigns using certain hashtags. Our findings are based on data across different platforms. The topic distribution between Reddit and JuiceDB was significantly different (P<.001), which indicated that the user discussions focused on different perspectives across the platforms. Conclusions: This study examined Reddit, JuiceDB, and Twitter as social media data sources for e-cigarette research. These mined findings could be further used by other researchers and policy makers. By utilizing the automatic topic-modeling method, the proposed unified feedback model could be a useful tool for policy makers to comprehensively consider how to collect valuable feedback from social media. ", doi="10.2196/jmir.5780", url="http://www.jmir.org/2017/1/e24/", url="http://www.ncbi.nlm.nih.gov/pubmed/28108428" } @Article{info:doi/10.2196/publichealth.6344, author="Delir Haghighi, Pari and Kang, Yong-Bin and Buchbinder, Rachelle and Burstein, Frada and Whittle, Samuel", title="Investigating Subjective Experience and the Influence of Weather Among Individuals With Fibromyalgia: A Content Analysis of Twitter", journal="JMIR Public Health Surveill", year="2017", month="Jan", day="19", volume="3", number="1", pages="e4", keywords="fibromyalgia", keywords="Twitter messaging", keywords="social networks", keywords="pain", keywords="weather", keywords="sentiment analysis", keywords="infodemiology", abstract="Background: Little is understood about the determinants of symptom expression in individuals with fibromyalgia syndrome (FMS). While individuals with FMS often report environmental influences, including weather events, on their symptom severity, a consistent effect of specific weather conditions on FMS symptoms has yet to be demonstrated. Content analysis of a large number of messages by individuals with FMS on Twitter can provide valuable insights into variation in the fibromyalgia experience from a first-person perspective. Objective: The objective of our study was to use content analysis of tweets to investigate the association between weather conditions and fibromyalgia symptoms among individuals who tweet about fibromyalgia. Our second objective was to gain insight into how Twitter is used as a form of communication and expression by individuals with fibromyalgia and to explore and uncover thematic clusters and communities related to weather. Methods: Computerized sentiment analysis was performed to measure the association between negative sentiment scores (indicative of severe symptoms such as pain) and coincident environmental variables. Date, time, and location data for each individual tweet were used to identify corresponding climate data (such as temperature). We used graph analysis to investigate the frequency and distribution of domain-related terms exchanged in Twitter and their association strengths. A community detection algorithm was applied to partition the graph and detect different communities. Results: We analyzed 140,432 tweets related to fibromyalgia from 2008 to 2014. There was a very weak positive correlation between humidity and negative sentiment scores (r=.009, P=.001). There was no significant correlation between other environmental variables and negative sentiment scores. The graph analysis showed that ``pain'' and ``chronicpain'' were the most frequently used terms. The Louvain method identified 6 communities. Community 1 was related to feelings and symptoms at the time (subjective experience). It also included a list of weather-related terms such as ``weather,'' ``cold,'' and ``rain.'' Conclusions: According to our results, a uniform causal effect of weather variation on fibromyalgia symptoms at the group level remains unlikely. Any impact of weather on fibromyalgia symptoms may vary geographically or at an individual level. Future work will further explore geographic variation and interactions focusing on individual pain trajectories over time. ", doi="10.2196/publichealth.6344", url="http://publichealth.jmir.org/2017/1/e4/", url="http://www.ncbi.nlm.nih.gov/pubmed/28104577" } @Article{info:doi/10.2196/mental.5626, author="Liu, Sam and Zhu, Miaoqi and Yu, Jin Dong and Rasin, Alexander and Young, D. Sean", title="Using Real-Time Social Media Technologies to Monitor Levels of Perceived Stress and Emotional State in College Students: A Web-Based Questionnaire Study", journal="JMIR Ment Health", year="2017", month="Jan", day="10", volume="4", number="1", pages="e2", keywords="social media", keywords="twitter messaging, stress", keywords="monitoring", abstract="Background: College can be stressful for many freshmen as they cope with a variety of stressors. Excess stress can negatively affect both psychological and physical health. Thus, there is a need to find innovative and cost-effective strategies to help identify students experiencing high levels of stress to receive appropriate treatment. Social media use has been rapidly growing, and recent studies have reported that data from these technologies can be used for public health surveillance. Currently, no studies have examined whether Twitter data can be used to monitor stress level and emotional state among college students. Objective: The primary objective of our study was to investigate whether students' perceived levels of stress were associated with the sentiment and emotions of their tweets. The secondary objective was to explore whether students' emotional state was associated with the sentiment and emotions of their tweets. Methods: We recruited 181 first-year freshman students aged 18-20 years at University of California, Los Angeles. All participants were asked to complete a questionnaire that assessed their demographic characteristics, levels of stress, and emotional state for the last 7 days. All questionnaires were completed within a 48-hour period. All tweets posted by the participants from that week (November 2 to 8, 2015) were mined and manually categorized based on their sentiment (positive, negative, neutral) and emotion (anger, fear, love, happiness) expressed. Ordinal regressions were used to assess whether weekly levels of stress and emotional states were associated with the percentage of positive, neutral, negative, anger, fear, love, or happiness tweets. Results: A total of 121 participants completed the survey and were included in our analysis. A total of 1879 tweets were analyzed. A higher level of weekly stress was significantly associated with a greater percentage of negative sentiment tweets (beta=1.7, SE 0.7; P=.02) and tweets containing emotions of fear (beta=2.4, SE 0.9; P=.01) and love (beta=3.6, SE 1.4; P=.01). A greater level of anger was negatively associated with the percentage of positive sentiment (beta=--1.6, SE 0.8; P=.05) and tweets related to the emotions of happiness (beta=--2.2, SE 0.9; P=.02). A greater level of fear was positively associated with the percentage of negative sentiment (beta=1.67, SE 0.7; P=.01), particularly a greater proportion of tweets related to the emotion of fear (beta=2.4, SE 0.8; P=.01). Participants who reported a greater level of love showed a smaller percentage of negative sentiment tweets (beta=--1.3, SE 0.7; P=0.05). Emotions of happiness were positively associated with the percentage of tweets related to the emotion of happiness (beta=--1.8, SE 0.8; P=.02) and negatively associated with percentage of negative sentiment tweets (beta=--1.7, SE 0.7; P=.02) and tweets related to the emotion of fear (beta=--2.8, SE 0.8; P=.01). Conclusions: Sentiment and emotions expressed in the tweets have the potential to provide real-time monitoring of stress level and emotional well-being in college students. ", doi="10.2196/mental.5626", url="http://mental.jmir.org/2017/1/e2/", url="http://www.ncbi.nlm.nih.gov/pubmed/28073737" } @Article{info:doi/10.2196/jmir.6486, author="Smith, J. Robert and Crutchley, Patrick and Schwartz, Andrew H. and Ungar, Lyle and Shofer, Frances and Padrez, A. Kevin and Merchant, M. Raina", title="Variations in Facebook Posting Patterns Across Validated Patient Health Conditions: A Prospective Cohort Study", journal="J Med Internet Res", year="2017", month="Jan", day="6", volume="19", number="1", pages="e7", keywords="Facebook", keywords="depression", keywords="natural language processing", keywords="social media", abstract="Background: Social media is emerging as an insightful platform for studying health. To develop targeted health interventions involving social media, we sought to identify the patient demographic and disease predictors of frequency of posting on Facebook. Objective: The aims were to explore the language topics correlated with frequency of social media use across a cohort of social media users within a health care setting, evaluate the differences in the quantity of social media postings across individuals with different disease diagnoses, and determine if patients could accurately predict their own levels of social media engagement. Methods: Patients seeking care at a single, academic, urban, tertiary care emergency department from March to October 2014 were queried on their willingness to share data from their Facebook accounts and electronic medical records (EMRs). For each participant, the total content of Facebook posts was extracted. Using the latent Dirichlet allocation natural language processing technique, Facebook language topics were correlated with frequency of Facebook use. The mean number of Facebook posts over 6 months prior to enrollment was then compared across validated health outcomes in the sample. Results: A total of 695 patients consented to provide access to their EMR and social media data. Significantly correlated language topics among participants with the highest quartile of posts contained health terms, such as ``cough,'' ``headaches,'' and ``insomnia.'' When adjusted for demographics, individuals with a history of depression had significantly higher posts (mean 38, 95\% CI 28-50) than individuals without a history of depression (mean 22, 95\% CI 19-26, P=.001). Except for depression, across prevalent health outcomes in the sample (hypertension, diabetes, asthma), there were no significant posting differences between individuals with or without each condition. Conclusions: High-frequency posters in our sample were more likely to post about health and to have a diagnosis of depression. The direction of causality between depression and social media use requires further evaluation. Our findings suggest that patients with depression may be appropriate targets for health-related interventions on social media. ", doi="10.2196/jmir.6486", url="http://www.jmir.org/2017/1/e7/", url="http://www.ncbi.nlm.nih.gov/pubmed/28062392" } @Article{info:doi/10.2196/publichealth.6551, author="Lazard, J. Allison and Saffer, J. Adam and Wilcox, B. Gary and Chung, DongWoo Arnold and Mackert, S. Michael and Bernhardt, M. Jay", title="E-Cigarette Social Media Messages: A Text Mining Analysis of Marketing and Consumer Conversations on Twitter", journal="JMIR Public Health Surveill", year="2016", month="Dec", day="12", volume="2", number="2", pages="e171", keywords="e-cigarettes", keywords="social media", keywords="tweet", keywords="Internet", abstract="Background: As the use of electronic cigarettes (e-cigarettes) rises, social media likely influences public awareness and perception of this emerging tobacco product. Objective: This study examined the public conversation on Twitter to determine overarching themes and insights for trending topics from commercial and consumer users. Methods: Text mining uncovered key patterns and important topics for e-cigarettes on Twitter. SAS Text Miner 12.1 software (SAS Institute Inc) was used for descriptive text mining to reveal the primary topics from tweets collected from March 24, 2015, to July 3, 2015, using a Python script in conjunction with Twitter's streaming application programming interface. A total of 18 keywords related to e-cigarettes were used and resulted in a total of 872,544 tweets that were sorted into overarching themes through a text topic node for tweets (126,127) and retweets (114,451) that represented more than 1\% of the conversation. Results: While some of the final themes were marketing-focused, many topics represented diverse proponent and user conversations that included discussion of policies, personal experiences, and the differentiation of e-cigarettes from traditional tobacco, often by pointing to the lack of evidence for the harm or risks of e-cigarettes or taking the position that e-cigarettes should be promoted as smoking cessation devices. Conclusions: These findings reveal that unique, large-scale public conversations are occurring on Twitter alongside e-cigarette advertising and promotion. Proponents and users are turning to social media to share knowledge, experience, and questions about e-cigarette use. Future research should focus on these unique conversations to understand how they influence attitudes towards and use of e-cigarettes. ", doi="10.2196/publichealth.6551", url="http://publichealth.jmir.org/2016/2/e171/", url="http://www.ncbi.nlm.nih.gov/pubmed/27956376" } @Article{info:doi/10.2196/jmir.6670, author="Massey, M. Philip and Leader, Amy and Yom-Tov, Elad and Budenz, Alexandra and Fisher, Kara and Klassen, C. Ann", title="Applying Multiple Data Collection Tools to Quantify Human Papillomavirus Vaccine Communication on Twitter", journal="J Med Internet Res", year="2016", month="Dec", day="05", volume="18", number="12", pages="e318", keywords="HPV vaccine", keywords="Twitter", keywords="communication methods", keywords="content analysis", keywords="data mining", abstract="Background: Human papillomavirus (HPV) is the most common sexually transmitted infection in the United States. There are several vaccines that protect against strains of HPV most associated with cervical and other cancers. Thus, HPV vaccination has become an important component of adolescent preventive health care. As media evolves, more information about HPV vaccination is shifting to social media platforms such as Twitter. Health information consumed on social media may be especially influential for segments of society such as younger populations, as well as ethnic and racial minorities. Objective: The objectives of our study were to quantify HPV vaccine communication on Twitter, and to develop a novel methodology to improve the collection and analysis of Twitter data. Methods: We collected Twitter data using 10 keywords related to HPV vaccination from August 1, 2014 to July 31, 2015. Prospective data collection used the Twitter Search API and retrospective data collection used Twitter Firehose. Using a codebook to characterize tweet sentiment and content, we coded a subsample of tweets by hand to develop classification models to code the entire sample using machine learning procedures. We also documented the words in the 140-character tweet text most associated with each keyword. We used chi-square tests, analysis of variance, and nonparametric equality of medians to test for significant differences in tweet characteristic by sentiment. Results: A total of 193,379 English-language tweets were collected, classified, and analyzed. Associated words varied with each keyword, with more positive and preventive words associated with ``HPV vaccine'' and more negative words associated with name-brand vaccines. Positive sentiment was the largest type of sentiment in the sample, with 75,393 positive tweets (38.99\% of the sample), followed by negative sentiment with 48,940 tweets (25.31\% of the sample). Positive and neutral tweets constituted the largest percentage of tweets mentioning prevention or protection (20,425/75,393, 27.09\% and 6477/25,110, 25.79\%, respectively), compared with only 11.5\% of negative tweets (5647/48,940; P<.001). Nearly one-half (22,726/48,940, 46.44\%) of negative tweets mentioned side effects, compared with only 17.14\% (12,921/75,393) of positive tweets and 15.08\% of neutral tweets (3787/25,110; P<.001). Conclusions: Examining social media to detect health trends, as well as to communicate important health information, is a growing area of research in public health. Understanding the content and implications of conversations that form around HPV vaccination on social media can aid health organizations and health-focused Twitter users in creating a meaningful exchange of ideas and in having a significant impact on vaccine uptake. This area of research is inherently interdisciplinary, and this study supports this movement by applying public health, health communication, and data science approaches to extend methodologies across fields. ", doi="10.2196/jmir.6670", url="http://www.jmir.org/2016/12/e318/", url="http://www.ncbi.nlm.nih.gov/pubmed/27919863" } @Article{info:doi/10.2196/publichealth.5384, author="Nishimoto, Naoki and Ota, Mizuki and Yagahara, Ayako and Ogasawara, Katsuhiko", title="Estimating the Duration of Public Concern After the Fukushima Dai-ichi Nuclear Power Station Accident From the Occurrence of Radiation Exposure-Related Terms on Twitter: A Retrospective Data Analysis", journal="JMIR Public Health Surveill", year="2016", month="Nov", day="25", volume="2", number="2", pages="e168", keywords="Twitter", keywords="social media", keywords="public concern", keywords="nuclear power plants", keywords="survival analysis", keywords="Kaplan-Meier estimate", keywords="infodemiology", keywords="radiation", abstract="Background: After the Fukushima Dai-ichi Nuclear Power Station accident in Japan on March 11, 2011, a large number of comments, both positive and negative, were posted on social media. Objective: The objective of this study was to clarify the characteristics of the trend in the number of tweets posted on Twitter, and to estimate how long public concern regarding the accident continued. We surveyed the attenuation period of the first term occurrence related to radiation exposure as a surrogate endpoint for the duration of concern. Methods: We retrieved 18,891,284 tweets from Twitter data between March 11, 2011 and March 10, 2012, containing 143 variables in Japanese. We selected radiation, radioactive, Sievert (Sv), Becquerel (Bq), and gray (Gy) as keywords to estimate the attenuation period of public concern regarding radiation exposure. These data, formatted as comma-separated values, were transferred into a Statistical Analysis System (SAS) dataset for analysis, and survival analysis methodology was followed using the SAS LIFETEST procedure. This study was approved by the institutional review board of Hokkaido University and informed consent was waived. Results: A Kaplan-Meier curve was used to show the rate of Twitter users posting a message after the accident that included one or more of the keywords. The term Sv occurred in tweets up to one year after the first tweet. Among the Twitter users studied, 75.32\% (880,108/1,168,542) tweeted the word radioactive and 9.20\% (107,522/1,168,542) tweeted the term Sv. The first reduction was observed within the first 7 days after March 11, 2011. The means and standard errors (SEs) of the duration from the first tweet on March 11, 2011 were 31.9 days (SE 0.096) for radioactive and 300.6 days (SE 0.181) for Sv. These keywords were still being used at the end of the study period. The mean attenuation period for radioactive was one month, and approximately one year for radiation and radiation units. The difference in mean duration between the keywords was attributed to the effect of mass media. Regularly posted messages, such as daily radiation dose reports, were relatively easy to detect from their time and formatted contents. The survival estimation indicated that public concern about the nuclear power plant accident remained after one year. Conclusions: Although the simple plot of the number of tweets did not show clear results, we estimated the mean attenuation period as approximately one month for the keyword radioactive, and found that the keywords were still being used in posts at the end of the study period. Further research is required to quantify the effect of other phrases in social media data. The results of this exploratory study should advance progress in influencing and quantifying the communication of risk. ", doi="10.2196/publichealth.5384", url="http://publichealth.jmir.org/2016/2/e168/", url="http://www.ncbi.nlm.nih.gov/pubmed/27888168" } @Article{info:doi/10.2196/publichealth.6586, author="Tangherlini, R. Timothy and Roychowdhury, Vwani and Glenn, Beth and Crespi, M. Catherine and Bandari, Roja and Wadia, Akshay and Falahi, Misagh and Ebrahimzadeh, Ehsan and Bastani, Roshan", title="``Mommy Blogs'' and the Vaccination Exemption Narrative: Results From A Machine-Learning Approach for Story Aggregation on Parenting Social Media Sites", journal="JMIR Public Health Surveill", year="2016", month="Nov", day="22", volume="2", number="2", pages="e166", keywords="vaccination", keywords="social media", keywords="machine learning", keywords="personal narratives", keywords="Internet", keywords="health knowledge", keywords="attitudes", keywords="practice", abstract="Background: Social media offer an unprecedented opportunity to explore how people talk about health care at a very large scale. Numerous studies have shown the importance of websites with user forums for people seeking information related to health. Parents turn to some of these sites, colloquially referred to as ``mommy blogs,'' to share concerns about children's health care, including vaccination. Although substantial work has considered the role of social media, particularly Twitter, in discussions of vaccination and other health care--related issues, there has been little work on describing the underlying structure of these discussions and the role of persuasive storytelling, particularly on sites with no limits on post length. Understanding the role of persuasive storytelling at Internet scale provides useful insight into how people discuss vaccinations, including exemption-seeking behavior, which has been tied to a recent diminution of herd immunity in some communities. Objective: To develop an automated and scalable machine-learning method for story aggregation on social media sites dedicated to discussions of parenting. We wanted to discover the aggregate narrative frameworks to which individuals, through their exchange of experiences and commentary, contribute over time in a particular topic domain. We also wanted to characterize temporal trends in these narrative frameworks on the sites over the study period. Methods: To ensure that our data capture long-term discussions and not short-term reactions to recent events, we developed a dataset of 1.99 million posts contributed by 40,056 users and viewed 20.12 million times indexed from 2 parenting sites over a period of 105 months. Using probabilistic methods, we determined the topics of discussion on these parenting sites. We developed a generative statistical-mechanical narrative model to automatically extract the underlying stories and story fragments from millions of posts. We aggregated the stories into an overarching narrative framework graph. In our model, stories were represented as network graphs with actants as nodes and their various relationships as edges. We estimated the latent stories circulating on these sites by modeling the posts as a sampling of the hidden narrative framework graph. Temporal trends were examined based on monthly user-poststatistics. Results: We discovered that discussions of exemption from vaccination requirements are highly represented. We found a strong narrative framework related to exemption seeking and a culture of distrust of government and medical institutions. Various posts reinforced part of the narrative framework graph in which parents, medical professionals, and religious institutions emerged as key nodes, and exemption seeking emerged as an important edge. In the aggregate story, parents used religion or belief to acquire exemptions to protect their children from vaccines that are required by schools or government institutions, but (allegedly) cause adverse reactions such as autism, pain, compromised immunity, and even death. Although parents joined and left the discussion forums over time, discussions and stories about exemptions were persistent and robust to these membership changes. Conclusions: Analyzing parent forums about health care using an automated analytic approach, such as the one presented here, allows the detection of widespread narrative frameworks that structure and inform discussions. In most vaccination stories from the sites we analyzed, it is taken for granted that vaccines and not vaccine preventable diseases (VPDs) pose a threat to children. Because vaccines are seen as a threat, parents focus on sharing successful strategies for avoiding them, with exemption being the foremost among these strategies. When new parents join such sites, they may be exposed to this endemic narrative framework in the threads they read and to which they contribute, which may influence their health care decision making. ", doi="10.2196/publichealth.6586", url="http://publichealth.jmir.org/2016/2/e166/", url="http://www.ncbi.nlm.nih.gov/pubmed/27876690" } @Article{info:doi/10.2196/jmir.5439, author="Ben-Sasson, Ayelet and Yom-Tov, Elad", title="Online Concerns of Parents Suspecting Autism Spectrum Disorder in Their Child: Content Analysis of Signs and Automated Prediction of Risk", journal="J Med Internet Res", year="2016", month="Nov", day="22", volume="18", number="11", pages="e300", keywords="online queries", keywords="autistic disorders", keywords="parents", keywords="machine learning", keywords="early detection", abstract="Background: Online communities are used as platforms by parents to verify developmental and health concerns related to their child. The increasing public awareness of autism spectrum disorders (ASD) leads more parents to suspect ASD in their child. Early identification of ASD is important for early intervention. Objective: To characterize the symptoms mentioned in online queries posed by parents who suspect that their child might have ASD and determine whether they are age-specific. To test the efficacy of machine learning tools in classifying the child's risk of ASD based on the parent's narrative. Methods: To this end, we analyzed online queries posed by parents who were concerned that their child might have ASD and categorized the warning signs they mentioned according to ASD-specific and non-ASD--specific domains. We then used the data to test the efficacy with which a trained machine learning tool classified the degree of ASD risk. Yahoo Answers, a social site for posting queries and finding answers, was mined for queries of parents asking the community whether their child has ASD. A total of 195 queries were sampled for this study (mean child age=38.0 months; 84.7\% [160/189] boys). Content text analysis of the queries aimed to categorize the types of symptoms described and obtain clinical judgment of the child's ASD-risk level. Results: Concerns related to repetitive and restricted behaviors and interests (RRBI) were the most prevalent (75.4\%, 147/195), followed by concerns related to language (61.5\%, 120/195) and emotional markers (50.3\%, 98/195). Of the 195 queries, 18.5\% (36/195) were rated by clinical experts as low-risk, 30.8\% (60/195) as medium-risk, and 50.8\% (99/195) as high-risk. Risk groups differed significantly (P<.001) in the rate of concerns in the language, social, communication, and RRBI domains. When testing whether an automatic classifier (decision tree) could predict if a query was medium- or high-risk based on the text of the query and the coded symptoms, performance reached an area under the receiver operating curve (ROC) curve of 0.67 (CI 95\% 0.50-0.78), whereas predicting from the text and the coded signs resulted in an area under the curve of 0.82 (0.80-0.86). Conclusions: Findings call for health care providers to closely listen to parental ASD-related concerns, as recommended by screening guidelines. They also demonstrate the need for Internet-based screening systems that utilize parents' narratives using a decision tree questioning method. ", doi="10.2196/jmir.5439", url="http://www.jmir.org/2016/11/e300/", url="http://www.ncbi.nlm.nih.gov/pubmed/27876688" } @Article{info:doi/10.2196/jmir.5811, author="Hand, K. Rosa and Kenne, Deric and Wolfram, M. Taylor and Abram, K. Jenica and Fleming, Michael", title="Assessing the Viability of Social Media for Disseminating Evidence-Based Nutrition Practice Guideline Through Content Analysis of Twitter Messages and Health Professional Interviews: An Observational Study", journal="J Med Internet Res", year="2016", month="Nov", day="15", volume="18", number="11", pages="e295", keywords="social media", keywords="information dissemination", keywords="medical nutrition therapy", keywords="evidence-based medicine", keywords="heart failure", abstract="Background: Given the high penetration of social media use, social media has been proposed as a method for the dissemination of information to health professionals and patients. This study explored the potential for social media dissemination of the Academy of Nutrition and Dietetics Evidence-Based Nutrition Practice Guideline (EBNPG) for Heart Failure (HF). Objectives: The objectives were to (1) describe the existing social media content on HF, including message content, source, and target audience, and (2) describe the attitude of physicians and registered dietitian nutritionists (RDNs) who care for outpatient HF patients toward the use of social media as a method to obtain information for themselves and to share this information with patients. Methods: The methods were divided into 2 parts. Part 1 involved conducting a content analysis of tweets related to HF, which were downloaded from Twitonomy and assigned codes for message content (19 codes), source (9 codes), and target audience (9 codes); code frequency was described. A comparison in the popularity of tweets (those marked as favorites or retweeted) based on applied codes was made using t tests. Part 2 involved conducting phone interviews with RDNs and physicians to describe health professionals' attitude toward the use of social media to communicate general health information and information specifically related to the HF EBNPG. Interviews were transcribed and coded; exemplar quotes representing frequent themes are presented. Results: The sample included 294 original tweets with the hashtag ``\#heartfailure.'' The most frequent message content codes were ``HF awareness'' (166/294, 56.5\%) and ``patient support'' (97/294, 33.0\%). The most frequent source codes were ``professional, government, patient advocacy organization, or charity'' (112/277, 40.4\%) and ``patient or family'' (105/277, 37.9\%). The most frequent target audience codes were ``unable to identify'' (111/277, 40.1\%) and ``other'' (55/277, 19.9\%). Significant differences were found in the popularity of tweets with (mean 1, SD 1.3 favorites) or without (mean 0.7, SD 1.3 favorites), the content code being ``HF research'' (P=.049). Tweets with the source code ``professional, government, patient advocacy organizations, or charities'' were significantly more likely to be marked as a favorite and retweeted than those without this source code (mean 1.2, SD 1.4 vs mean 0.8, SD 1.2, P=.03) and (mean 1.5, SD 1.8 vs mean 0.9, SD 2.0, P=.03). Interview participants believed that social media was a useful way to gather professional information. They did not believe that social media was useful for communicating with patients due to privacy concerns and the fact that the information had to be kept general rather than be tailored for a specific patient and the belief that their patients did not use social media or technology. Conclusions: Existing Twitter content related to HF comes from a combination of patients and evidence-based organizations; however, there is little nutrition content. That gap may present an opportunity for EBNPG dissemination. Health professionals use social media to gather information for themselves but are skeptical of its value when communicating with patients, particularly due to privacy concerns and misconceptions about the characteristics of social media users. ", doi="10.2196/jmir.5811", url="http://www.jmir.org/2016/11/e295/", url="http://www.ncbi.nlm.nih.gov/pubmed/27847349" } @Article{info:doi/10.2196/diabetes.6256, author="Liu, Yang and Mei, Qiaozhu and Hanauer, A. David and Zheng, Kai and Lee, M. Joyce", title="Use of Social Media in the Diabetes Community: An Exploratory Analysis of Diabetes-Related Tweets", journal="JMIR Diabetes", year="2016", month="Nov", day="07", volume="1", number="2", pages="e4", keywords="social media", keywords="Twitter, DSMA", keywords="diabetes community", keywords="spatiotemporal analysis", keywords="content analysis", abstract="Background: Use of social media is becoming ubiquitous, and disease-related communities are forming online, including communities of interest around diabetes. Objective: Our objective was to examine diabetes-related participation on Twitter by describing the frequency and timing of diabetes-related tweets, the geography of tweets, and the types of participants over a 2-year sample of 10\% of all tweets. Methods: We identified tweets with diabetes-related search terms and hashtags in a dataset of 29.6 billion tweets for the years 2013 and 2014 and extracted the text, time, location, retweet, and user information. We assessed the frequencies of tweets used across different search terms and hashtags by month and day of week and, for tweets that provided location information, by country. We also performed these analyses for a subset of tweets that used the hashtag \#dsma, a social media advocacy community focused on diabetes. Random samples of user profiles in the 2 groups were also drawn and reviewed to understand the types of stakeholders participating online. Results: We found 1,368,575 diabetes-related tweets based on diabetes-related terms and hashtags. There was a seasonality to tweets; a higher proportion occurred during the month of November, which is when World Diabetes Day occurs. The subset of tweets with the \#dsma were most frequent on Thursdays (coordinated universal time), which is consistent with the timing of a weekly chat organized by this online community. Approximately 2\% of tweets carried geolocation information and were most prominent in the United States (on the east and west coasts), followed by Indonesia and the United Kingdom. For the user profiles randomly selected among overall tweets, we could not identify a relationship to diabetes for the majority of users; for the profiles using the \#dsma hashtag, we found that patients with type 1 diabetes and their caregivers represented the largest proportion of individuals. Conclusions: Twitter is increasingly becoming a space for online conversations about diabetes. Further qualitative and quantitative content analysis is needed to understand the nature and purpose of these conversations. ", doi="10.2196/diabetes.6256", url="http://diabetes.jmir.org/2016/2/e4/", url="http://www.ncbi.nlm.nih.gov/pubmed/30291053" } @Article{info:doi/10.2196/publichealth.6327, author="Daniulaityte, Raminta and Chen, Lu and Lamy, R. Francois and Carlson, G. Robert and Thirunarayan, Krishnaprasad and Sheth, Amit", title="``When `Bad' is `Good''': Identifying Personal Communication and Sentiment in Drug-Related Tweets", journal="JMIR Public Health Surveill", year="2016", month="Oct", day="24", volume="2", number="2", pages="e162", keywords="social media", keywords="Twitter", keywords="cannabis", keywords="synthetic cannabinoids", keywords="machine learning", keywords="sentiment analysis", keywords="eDrugTrends", abstract="Background: To harness the full potential of social media for epidemiological surveillance of drug abuse trends, the field needs a greater level of automation in processing and analyzing social media content. Objectives: The objective of the study is to describe the development of supervised machine-learning techniques for the eDrugTrends platform to automatically classify tweets by type/source of communication (personal, official/media, retail) and sentiment (positive, negative, neutral) expressed in cannabis- and synthetic cannabinoid--related tweets. Methods: Tweets were collected using Twitter streaming Application Programming Interface and filtered through the eDrugTrends platform using keywords related to cannabis, marijuana edibles, marijuana concentrates, and synthetic cannabinoids. After creating coding rules and assessing intercoder reliability, a manually labeled data set (N=4000) was developed by coding several batches of randomly selected subsets of tweets extracted from the pool of 15,623,869 collected by eDrugTrends (May-November 2015). Out of 4000 tweets, 25\% (1000/4000) were used to build source classifiers and 75\% (3000/4000) were used for sentiment classifiers. Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machines (SVM) were used to train the classifiers. Source classification (n=1000) tested Approach 1 that used short URLs, and Approach 2 where URLs were expanded and included into the bag-of-words analysis. For sentiment classification, Approach 1 used all tweets, regardless of their source/type (n=3000), while Approach 2 applied sentiment classification to personal communication tweets only (2633/3000, 88\%). Multiclass and binary classification tasks were examined, and machine-learning sentiment classifier performance was compared with Valence Aware Dictionary for sEntiment Reasoning (VADER), a lexicon and rule-based method. The performance of each classifier was assessed using 5-fold cross validation that calculated average F-scores. One-tailed t test was used to determine if differences in F-scores were statistically significant. Results: In multiclass source classification, the use of expanded URLs did not contribute to significant improvement in classifier performance (0.7972 vs 0.8102 for SVM, P=.19). In binary classification, the identification of all source categories improved significantly when unshortened URLs were used, with personal communication tweets benefiting the most (0.8736 vs 0.8200, P<.001). In multiclass sentiment classification Approach 1, SVM (0.6723) performed similarly to NB (0.6683) and LR (0.6703). In Approach 2, SVM (0.7062) did not differ from NB (0.6980, P=.13) or LR (F=0.6931, P=.05), but it was over 40\% more accurate than VADER (F=0.5030, P<.001). In multiclass task, improvements in sentiment classification (Approach 2 vs Approach 1) did not reach statistical significance (eg, SVM: 0.7062 vs 0.6723, P=.052). In binary sentiment classification (positive vs negative), Approach 2 (focus on personal communication tweets only) improved classification results, compared with Approach 1, for LR (0.8752 vs 0.8516, P=.04) and SVM (0.8800 vs 0.8557, P=.045). Conclusions: The study provides an example of the use of supervised machine learning methods to categorize cannabis- and synthetic cannabinoid--related tweets with fairly high accuracy. Use of these content analysis tools along with geographic identification capabilities developed by the eDrugTrends platform will provide powerful methods for tracking regional changes in user opinions related to cannabis and synthetic cannabinoids use over time and across different regions. ", doi="10.2196/publichealth.6327", url="http://publichealth.jmir.org/2016/2/e162/", url="http://www.ncbi.nlm.nih.gov/pubmed/27777215" } @Article{info:doi/10.2196/publichealth.5901, author="Sharpe, Danielle J. and Hopkins, S. Richard and Cook, L. Robert and Striley, W. Catherine", title="Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis", journal="JMIR Public Health Surveill", year="2016", month="Oct", day="20", volume="2", number="2", pages="e161", keywords="Internet", keywords="social media", keywords="Bayes theorem", keywords="public health surveillance", keywords="influenza, human", abstract="Background: Traditional influenza surveillance relies on influenza-like illness (ILI) syndrome that is reported by health care providers. It primarily captures individuals who seek medical care and misses those who do not. Recently, Web-based data sources have been studied for application to public health surveillance, as there is a growing number of people who search, post, and tweet about their illnesses before seeking medical care. Existing research has shown some promise of using data from Google, Twitter, and Wikipedia to complement traditional surveillance for ILI. However, past studies have evaluated these Web-based sources individually or dually without comparing all 3 of them, and it would be beneficial to know which of the Web-based sources performs best in order to be considered to complement traditional methods. Objective: The objective of this study is to comparatively analyze Google, Twitter, and Wikipedia by examining which best corresponds with Centers for Disease Control and Prevention (CDC) ILI data. It was hypothesized that Wikipedia will best correspond with CDC ILI data as previous research found it to be least influenced by high media coverage in comparison with Google and Twitter. Methods: Publicly available, deidentified data were collected from the CDC, Google Flu Trends, HealthTweets, and Wikipedia for the 2012-2015 influenza seasons. Bayesian change point analysis was used to detect seasonal changes, or change points, in each of the data sources. Change points in Google, Twitter, and Wikipedia that occurred during the exact week, 1 preceding week, or 1 week after the CDC's change points were compared with the CDC data as the gold standard. All analyses were conducted using the R package ``bcp'' version 4.0.0 in RStudio version 0.99.484 (RStudio Inc). In addition, sensitivity and positive predictive values (PPV) were calculated for Google, Twitter, and Wikipedia. Results: During the 2012-2015 influenza seasons, a high sensitivity of 92\% was found for Google, whereas the PPV for Google was 85\%. A low sensitivity of 50\% was calculated for Twitter; a low PPV of 43\% was found for Twitter also. Wikipedia had the lowest sensitivity of 33\% and lowest PPV of 40\%. Conclusions: Of the 3 Web-based sources, Google had the best combination of sensitivity and PPV in detecting Bayesian change points in influenza-related data streams. Findings demonstrated that change points in Google, Twitter, and Wikipedia data occasionally aligned well with change points captured in CDC ILI data, yet these sources did not detect all changes in CDC data and should be further studied and developed. ", doi="10.2196/publichealth.5901", url="http://publichealth.jmir.org/2016/2/e161/", url="http://www.ncbi.nlm.nih.gov/pubmed/27765731" } @Article{info:doi/10.2196/publichealth.5869, author="Nguyen, C. Quynh and Li, Dapeng and Meng, Hsien-Wen and Kath, Suraj and Nsoesie, Elaine and Li, Feifei and Wen, Ming", title="Building a National Neighborhood Dataset From Geotagged Twitter Data for Indicators of Happiness, Diet, and Physical Activity", journal="JMIR Public Health Surveill", year="2016", month="Oct", day="17", volume="2", number="2", pages="e158", keywords="social media", keywords="Twitter messaging", keywords="health behavior", keywords="happiness", keywords="food", keywords="physical activity", abstract="Background: Studies suggest that where people live, play, and work can influence health and well-being. However, the dearth of neighborhood data, especially data that is timely and consistent across geographies, hinders understanding of the effects of neighborhoods on health. Social media data represents a possible new data resource for neighborhood research. Objective: The aim of this study was to build, from geotagged Twitter data, a national neighborhood database with area-level indicators of well-being and health behaviors. Methods: We utilized Twitter's streaming application programming interface to continuously collect a random 1\% subset of publicly available geolocated tweets for 1 year (April 2015 to March 2016). We collected 80 million geotagged tweets from 603,363 unique Twitter users across the contiguous United States. We validated our machine learning algorithms for constructing indicators of happiness, food, and physical activity by comparing predicted values to those generated by human labelers. Geotagged tweets were spatially mapped to the 2010 census tract and zip code areas they fall within, which enabled further assessment of the associations between Twitter-derived neighborhood variables and neighborhood demographic, economic, business, and health characteristics. Results: Machine labeled and manually labeled tweets had a high level of accuracy: 78\% for happiness, 83\% for food, and 85\% for physical activity for dichotomized labels with the F scores 0.54, 0.86, and 0.90, respectively. About 20\% of tweets were classified as happy. Relatively few terms (less than 25) were necessary to characterize the majority of tweets on food and physical activity. Data from over 70,000 census tracts from the United States suggest that census tract factors like percentage African American and economic disadvantage were associated with lower census tract happiness. Urbanicity was related to higher frequency of fast food tweets. Greater numbers of fast food restaurants predicted higher frequency of fast food mentions. Surprisingly, fitness centers and nature parks were only modestly associated with higher frequency of physical activity tweets. Greater state-level happiness, positivity toward physical activity, and positivity toward healthy foods, assessed via tweets, were associated with lower all-cause mortality and prevalence of chronic conditions such as obesity and diabetes and lower physical inactivity and smoking, controlling for state median income, median age, and percentage white non-Hispanic. Conclusions: Machine learning algorithms can be built with relatively high accuracy to characterize sentiment, food, and physical activity mentions on social media. Such data can be utilized to construct neighborhood indicators consistently and cost effectively. Access to neighborhood data, in turn, can be leveraged to better understand neighborhood effects and address social determinants of health. We found that neighborhoods with social and economic disadvantage, high urbanicity, and more fast food restaurants may exhibit lower happiness and fewer healthy behaviors. ", doi="10.2196/publichealth.5869", url="http://publichealth.jmir.org/2016/2/e158/", url="http://www.ncbi.nlm.nih.gov/pubmed/27751984" } @Article{info:doi/10.2196/publichealth.6504, author="Ling, Rebecca and Lee, Joon", title="Disease Monitoring and Health Campaign Evaluation Using Google Search Activities for HIV and AIDS, Stroke, Colorectal Cancer, and Marijuana Use in Canada: A Retrospective Observational Study", journal="JMIR Public Health Surveill", year="2016", month="Oct", day="12", volume="2", number="2", pages="e156", keywords="public health informatics", keywords="Internet", keywords="information seeking behavior", abstract="Background: Infodemiology can offer practical and feasible health research applications through the practice of studying information available on the Web. Google Trends provides publicly accessible information regarding search behaviors in a population, which may be studied and used for health campaign evaluation and disease monitoring. Additional studies examining the use and effectiveness of Google Trends for these purposes remain warranted. Objective: The objective of our study was to explore the use of infodemiology in the context of health campaign evaluation and chronic disease monitoring. It was hypothesized that following a launch of a campaign, there would be an increase in information seeking behavior on the Web. Second, increasing and decreasing disease patterns in a population would be associated with search activity patterns. This study examined 4 different diseases: human immunodeficiency virus (HIV) infection, stroke, colorectal cancer, and marijuana use. Methods: Using Google Trends, relative search volume data were collected throughout the period of February 2004 to January 2015. Campaign information and disease statistics were obtained from governmental publications. Search activity trends were graphed and assessed with disease trends and the campaign interval. Pearson product correlation statistics and joinpoint methodology analyses were used to determine significance. Results: Disease patterns and online activity across all 4 diseases were significantly correlated: HIV infection (r=.36, P<.001), stroke (r=.40, P<.001), colorectal cancer (r= ?.41, P<.001), and substance use (r=.64, P<.001). Visual inspection and the joinpoint analysis showed significant correlations for the campaigns on colorectal cancer and marijuana use in stimulating search activity. No significant correlations were observed for the campaigns on stroke and HIV regarding search activity. Conclusions: The use of infoveillance shows promise as an alternative and inexpensive solution to disease surveillance and health campaign evaluation. Further research is needed to understand Google Trends as a valid and reliable tool for health research. ", doi="10.2196/publichealth.6504", url="http://publichealth.jmir.org/2016/2/e156/", url="http://www.ncbi.nlm.nih.gov/pubmed/27733330" } @Article{info:doi/10.2196/jmir.6240, author="Agarwal, Vibhu and Zhang, Liangliang and Zhu, Josh and Fang, Shiyuan and Cheng, Tim and Hong, Chloe and Shah, H. Nigam", title="Impact of Predicting Health Care Utilization Via Web Search Behavior: A Data-Driven Analysis", journal="J Med Internet Res", year="2016", month="Sep", day="21", volume="18", number="9", pages="e251", keywords="search behavior", keywords="geotagged search logs", keywords="health care utilization", keywords="utility", keywords="health care costs", keywords="Internet", abstract="Background: By recent estimates, the steady rise in health care costs has deprived more than 45 million Americans of health care services and has encouraged health care providers to better understand the key drivers of health care utilization from a population health management perspective. Prior studies suggest the feasibility of mining population-level patterns of health care resource utilization from observational analysis of Internet search logs; however, the utility of the endeavor to the various stakeholders in a health ecosystem remains unclear. Objective: The aim was to carry out a closed-loop evaluation of the utility of health care use predictions using the conversion rates of advertisements that were displayed to the predicted future utilizers as a surrogate. The statistical models to predict the probability of user's future visit to a medical facility were built using effective predictors of health care resource utilization, extracted from a deidentified dataset of geotagged mobile Internet search logs representing searches made by users of the Baidu search engine between March 2015 and May 2015. Methods: We inferred presence within the geofence of a medical facility from location and duration information from users' search logs and putatively assigned medical facility visit labels to qualifying search logs. We constructed a matrix of general, semantic, and location-based features from search logs of users that had 42 or more search days preceding a medical facility visit as well as from search logs of users that had no medical visits and trained statistical learners for predicting future medical visits. We then carried out a closed-loop evaluation of the utility of health care use predictions using the show conversion rates of advertisements displayed to the predicted future utilizers. In the context of behaviorally targeted advertising, wherein health care providers are interested in minimizing their cost per conversion, the association between show conversion rate and predicted utilization score, served as a surrogate measure of the model's utility. Results: We obtained the highest area under the curve (0.796) in medical visit prediction with our random forests model and daywise features. Ablating feature categories one at a time showed that the model performance worsened the most when location features were dropped. An online evaluation in which advertisements were served to users who had a high predicted probability of a future medical visit showed a 3.96\% increase in the show conversion rate. Conclusions: Results from our experiments done in a research setting suggest that it is possible to accurately predict future patient visits from geotagged mobile search logs. Results from the offline and online experiments on the utility of health utilization predictions suggest that such prediction can have utility for health care providers. ", doi="10.2196/jmir.6240", url="http://www.jmir.org/2016/9/e251/", url="http://www.ncbi.nlm.nih.gov/pubmed/27655225" } @Article{info:doi/10.2196/publichealth.5739, author="Marcon, R. Alessandro and Klostermann, Philip and Caulfield, Timothy", title="Chiropractic and Spinal Manipulation Therapy on Twitter: Case Study Examining the Presence of Critiques and Debates", journal="JMIR Public Health Surveill", year="2016", month="Sep", day="16", volume="2", number="2", pages="e153", keywords="spinal manipulation", keywords="manipulation therapy", keywords="chiropractic", keywords="alternative medicine", keywords="Twitter", keywords="social media", keywords="infodemiology", abstract="Background: Spinal manipulation therapy (SMT) is a popular though controversial practice. The debates surrounding efficacy and risk of SMT are only partially evident in popular discourse. Objective: This study aims to investigate the presence of critiques and debates surrounding efficacy and risk of SMT on the social media platform Twitter. The study examines whether there is presence of debate and whether critical information is being widely disseminated. Methods: An initial corpus of 31,339 tweets was compiled through Twitter's Search Application Programming Interface using the query terms ``chiropractic,'' ``chiropractor,'' and ``spinal manipulation therapy.'' Tweets were collected for the month of December 2015. Post removal of tweets made by bots and spam, the corpus totaled 20,695 tweets, of which a sample (n=1267) was analyzed for skeptical or critical tweets. Additional criteria were also assessed. Results: There were 34 tweets explicitly containing skepticism or critique of SMT, representing 2.68\% of the sample (n=1267). As such, there is a presence of 2.68\% of tweets in the total corpus, 95\% CI 0-6.58\% displaying explicitly skeptical or critical perspectives of SMT. In addition, there are numerous tweets highlighting the health benefits of SMT for health issues such as attention deficit hyperactivity disorder (ADHD), immune system, and blood pressure that receive scant critical attention. The presence of tweets in the corpus highlighting the risks of ``stroke'' and ``vertebral artery dissection'' is also minute (0.1\%). Conclusions: In the abundance of tweets substantiating and promoting chiropractic and SMT as sound health practices and valuable business endeavors, the debates surrounding the efficacy and risks of SMT on Twitter are almost completely absent. Although there are some critical voices of SMT proving to be influential, issues persist regarding how widely this information is being disseminated. ", doi="10.2196/publichealth.5739", url="http://publichealth.jmir.org/2016/2/e153/", url="http://www.ncbi.nlm.nih.gov/pubmed/27637456" } @Article{info:doi/10.2196/jmir.5802, author="Zhang, Zhu and Zheng, Xiaolong and Zeng, Dajun Daniel and Leischow, J. Scott", title="Tracking Dabbing Using Search Query Surveillance: A Case Study in the United States", journal="J Med Internet Res", year="2016", month="Sep", day="16", volume="18", number="9", pages="e252", keywords="marijuana", keywords="information seeking behavior", keywords="surveillance", keywords="search engine", keywords="time series analysis", keywords="spatial analysis", abstract="Background: Dabbing is an emerging method of marijuana ingestion. However, little is known about dabbing owing to limited surveillance data on dabbing. Objective: The aim of the study was to analyze Google search data to assess the scope and breadth of information seeking on dabbing. Methods: Google Trends data about dabbing and related topics (eg, electronic nicotine delivery system [ENDS], also known as e-cigarettes) in the United States between January 2004 and December 2015 were collected by using relevant search terms such as ``dab rig.'' The correlation between dabbing (including topics: dab and hash oil) and ENDS (including topics: vaping and e-cigarette) searches, the regional distribution of dabbing searches, and the impact of cannabis legalization policies on geographical location in 2015 were analyzed. Results: Searches regarding dabbing increased in the United States over time, with 1,526,280 estimated searches during 2015. Searches for dab and vaping have very similar temporal patterns, where the Pearson correlation coefficient (PCC) is .992 (P<.001). Similar phenomena were also obtained in searches for hash oil and e-cigarette, in which the corresponding PCC is .931 (P<.001). Dabbing information was searched more in some western states than other regions. The average dabbing searches were significantly higher in the states with medical and recreational marijuana legalization than in the states with only medical marijuana legalization (P=.02) or the states without medical and recreational marijuana legalization (P=.01). Conclusions: Public interest in dabbing is increasing in the United States. There are close associations between dabbing and ENDS searches. The findings suggest greater popularity of dabs in the states that legalized medical and recreational marijuana use. This study proposes a novel and timely way of cannabis surveillance, and these findings can help enhance the understanding of the popularity of dabbing and provide insights for future research and informed policy making on dabbing. ", doi="10.2196/jmir.5802", url="http://www.jmir.org/2016/9/e252/", url="http://www.ncbi.nlm.nih.gov/pubmed/27637361" } @Article{info:doi/10.2196/jmir.6045, author="Surian, Didi and Nguyen, Quoc Dat and Kennedy, Georgina and Johnson, Mark and Coiera, Enrico and Dunn, G. Adam", title="Characterizing Twitter Discussions About HPV Vaccines Using Topic Modeling and Community Detection", journal="J Med Internet Res", year="2016", month="Aug", day="29", volume="18", number="8", pages="e232", keywords="topic modelling", keywords="graph algorithms analysis", keywords="social media", keywords="public health surveillance", abstract="Background: In public health surveillance, measuring how information enters and spreads through online communities may help us understand geographical variation in decision making associated with poor health outcomes. Objective: Our aim was to evaluate the use of community structure and topic modeling methods as a process for characterizing the clustering of opinions about human papillomavirus (HPV) vaccines on Twitter. Methods: The study examined Twitter posts (tweets) collected between October 2013 and October 2015 about HPV vaccines. We tested Latent Dirichlet Allocation and Dirichlet Multinomial Mixture (DMM) models for inferring topics associated with tweets, and community agglomeration (Louvain) and the encoding of random walks (Infomap) methods to detect community structure of the users from their social connections. We examined the alignment between community structure and topics using several common clustering alignment measures and introduced a statistical measure of alignment based on the concentration of specific topics within a small number of communities. Visualizations of the topics and the alignment between topics and communities are presented to support the interpretation of the results in context of public health communication and identification of communities at risk of rejecting the safety and efficacy of HPV vaccines. Results: We analyzed 285,417 Twitter posts (tweets) about HPV vaccines from 101,519 users connected by 4,387,524 social connections. Examining the alignment between the community structure and the topics of tweets, the results indicated that the Louvain community detection algorithm together with DMM produced consistently higher alignment values and that alignments were generally higher when the number of topics was lower. After applying the Louvain method and DMM with 30 topics and grouping semantically similar topics in a hierarchy, we characterized 163,148 (57.16\%) tweets as evidence and advocacy, and 6244 (2.19\%) tweets describing personal experiences. Among the 4548 users who posted experiential tweets, 3449 users (75.84\%) were found in communities where the majority of tweets were about evidence and advocacy. Conclusions: The use of community detection in concert with topic modeling appears to be a useful way to characterize Twitter communities for the purpose of opinion surveillance in public health applications. Our approach may help identify online communities at risk of being influenced by negative opinions about public health interventions such as HPV vaccines. ", doi="10.2196/jmir.6045", url="http://www.jmir.org/2016/8/e232/", url="http://www.ncbi.nlm.nih.gov/pubmed/27573910" } @Article{info:doi/10.2196/publichealth.5333, author="Meaney, Sarah and Cussen, Leanne and Greene, A. Richard and O'Donoghue, Keelin", title="Reaction on Twitter to a Cluster of Perinatal Deaths: A Mixed Method Study", journal="JMIR Public Health Surveill", year="2016", month="Jul", day="27", volume="2", number="2", pages="e36", keywords="social media", keywords="health care services", keywords="maternity", keywords="perinatal death", keywords="Twitter", keywords="infodemiology", keywords="infoveillance", abstract="Background: Participation in social networking sites is commonplace and the micro-blogging site Twitter can be considered a platform for the rapid broadcasting of news stories. Objective: The aim of this study was to explore the Twitter status updates and subsequent responses relating to a number of perinatal deaths which occurred in a small maternity unit in Ireland. Methods: An analysis of Twitter status updates, over a two month period from January to March 2014, was undertaken to identify the key themes arising in relation to the perinatal deaths. Results: Our search identified 3577 tweets relating to the reported perinatal deaths. At the height of the controversy, Twitter updates generated skepticism in relation to the management of not only of the unit in question, which was branded as unsafe, but also the governance of the entire Irish maternity service. Themes of concern and uncertainty arose whereby the professional motives of the obstetric community and staffing levels in the maternity services were called into question. Conclusions: Twitter activity provides a useful insight into attitudes towards health-related events. The role of the media in influencing opinion is well-documented and this study underscores the challenges that clinicians face in light of an obstetric media scandal. Further study to identify how the obstetric community could develop tools to utilize Twitter to disseminate valid health information could be beneficial. ", doi="10.2196/publichealth.5333", url="http://publichealth.jmir.org/2016/2/e36/", url="http://www.ncbi.nlm.nih.gov/pubmed/27466002" } @Article{info:doi/10.2196/jmir.4955, author="Woo, Hyekyung and Cho, Youngtae and Shim, Eunyoung and Lee, Jong-Koo and Lee, Chang-Gun and Kim, Hwan Seong", title="Estimating Influenza Outbreaks Using Both Search Engine Query Data and Social Media Data in South Korea", journal="J Med Internet Res", year="2016", month="Jul", day="04", volume="18", number="7", pages="e177", keywords="influenza", keywords="surveillance", keywords="population surveillance", keywords="infodemiology", keywords="infoveillance", keywords="Internet search", keywords="query", keywords="social media", keywords="big data", keywords="forecasting", keywords="epidemiology", keywords="early response", abstract="Background: As suggested as early as in 2006, logs of queries submitted to search engines seeking information could be a source for detection of emerging influenza epidemics if changes in the volume of search queries are monitored (infodemiology). However, selecting queries that are most likely to be associated with influenza epidemics is a particular challenge when it comes to generating better predictions. Objective: In this study, we describe a methodological extension for detecting influenza outbreaks using search query data; we provide a new approach for query selection through the exploration of contextual information gleaned from social media data. Additionally, we evaluate whether it is possible to use these queries for monitoring and predicting influenza epidemics in South Korea. Methods: Our study was based on freely available weekly influenza incidence data and query data originating from the search engine on the Korean website Daum between April 3, 2011 and April 5, 2014. To select queries related to influenza epidemics, several approaches were applied: (1) exploring influenza-related words in social media data, (2) identifying the chief concerns related to influenza, and (3) using Web query recommendations. Optimal feature selection by least absolute shrinkage and selection operator (Lasso) and support vector machine for regression (SVR) were used to construct a model predicting influenza epidemics. Results: In total, 146 queries related to influenza were generated through our initial query selection approach. A considerable proportion of optimal features for final models were derived from queries with reference to the social media data. The SVR model performed well: the prediction values were highly correlated with the recent observed influenza-like illness (r=.956; P<.001) and virological incidence rate (r=.963; P<.001). Conclusions: These results demonstrate the feasibility of using search queries to enhance influenza surveillance in South Korea. In addition, an approach for query selection using social media data seems ideal for supporting influenza surveillance based on search query data. ", doi="10.2196/jmir.4955", url="http://www.jmir.org/2016/7/e177/", url="http://www.ncbi.nlm.nih.gov/pubmed/27377323" } @Article{info:doi/10.2196/jmir.5585, author="Klembczyk, Jeffrey Joseph and Jalalpour, Mehdi and Levin, Scott and Washington, E. Raynard and Pines, M. Jesse and Rothman, E. Richard and Dugas, Freyer Andrea", title="Google Flu Trends Spatial Variability Validated Against Emergency Department Influenza-Related Visits", journal="J Med Internet Res", year="2016", month="Jun", day="28", volume="18", number="6", pages="e175", keywords="influenza", keywords="surveillance", keywords="emergency department", keywords="google flu trends", keywords="infoveillance", abstract="Background: Influenza is a deadly and costly public health problem. Variations in its seasonal patterns cause dangerous surges in emergency department (ED) patient volume. Google Flu Trends (GFT) can provide faster influenza surveillance information than traditional CDC methods, potentially leading to improved public health preparedness. GFT has been found to correlate well with reported influenza and to improve influenza prediction models. However, previous validation studies have focused on isolated clinical locations. Objective: The purpose of the study was to measure GFT surveillance effectiveness by correlating GFT with influenza-related ED visits in 19 US cities across seven influenza seasons, and to explore which city characteristics lead to better or worse GFT effectiveness. Methods: Using Healthcare Cost and Utilization Project data, we collected weekly counts of ED visits for all patients with diagnosis (International Statistical Classification of Diseases 9) codes for influenza-related visits from 2005-2011 in 19 different US cities. We measured the correlation between weekly volume of GFT searches and influenza-related ED visits (ie, GFT ED surveillance effectiveness) per city. We evaluated the relationship between 15 publically available city indicators (11 sociodemographic, two health care utilization, and two climate) and GFT surveillance effectiveness using univariate linear regression. Results: Correlation between city-level GFT and influenza-related ED visits had a median of .84, ranging from .67 to .93 across 19 cities. Temporal variability was observed, with median correlation ranging from .78 in 2009 to .94 in 2005. City indicators significantly associated (P<.10) with improved GFT surveillance include higher proportion of female population, higher proportion with Medicare coverage, higher ED visits per capita, and lower socioeconomic status. Conclusions: GFT is strongly correlated with ED influenza-related visits at the city level, but unexplained variation over geographic location and time limits its utility as standalone surveillance. GFT is likely most useful as an early signal used in conjunction with other more comprehensive surveillance techniques. City indicators associated with improved GFT surveillance provide some insight into the variability of GFT effectiveness. For example, populations with lower socioeconomic status may have a greater tendency to initially turn to the Internet for health questions, thus leading to increased GFT effectiveness. GFT has the potential to provide valuable information to ED providers for patient care and to administrators for ED surge preparedness. ", doi="10.2196/jmir.5585", url="http://www.jmir.org/2016/6/e175/", url="http://www.ncbi.nlm.nih.gov/pubmed/27354313" } @Article{info:doi/10.2196/resprot.5621, author="Rastegar-Mojarad, Majid and Liu, Hongfang and Nambisan, Priya", title="Using Social Media Data to Identify Potential Candidates for Drug Repurposing: A Feasibility Study", journal="JMIR Res Protoc", year="2016", month="Jun", day="16", volume="5", number="2", pages="e121", keywords="social media", keywords="drug repurposing", keywords="natural language processing", keywords="patient comments", abstract="Background: Drug repurposing (defined as discovering new indications for existing drugs) could play a significant role in drug development, especially considering the declining success rates of developing novel drugs. Typically, new indications for existing medications are identified by accident. However, new technologies and a large number of available resources enable the development of systematic approaches to identify and validate drug-repurposing candidates. Patients today report their experiences with medications on social media and reveal side effects as well as beneficial effects of those medications. Objective: Our aim was to assess the feasibility of using patient reviews from social media to identify potential candidates for drug repurposing. Methods: We retrieved patient reviews of 180 medications from an online forum, WebMD. Using dictionary-based and machine learning approaches, we identified disease names in the reviews. Several publicly available resources were used to exclude comments containing known indications and adverse drug effects. After manually reviewing some of the remaining comments, we implemented a rule-based system to identify beneficial effects. Results: The dictionary-based system and machine learning system identified 2178 and 6171 disease names respectively in 64,616 patient comments. We provided a list of 10 common patterns that patients used to report any beneficial effects or uses of medication. After manually reviewing the comments tagged by our rule-based system, we identified five potential drug repurposing candidates. Conclusions: To our knowledge, this is the first study to consider using social media data to identify drug-repurposing candidates. We found that even a rule-based system, with a limited number of rules, could identify beneficial effect mentions in patient comments. Our preliminary study shows that social media has the potential to be used in drug repurposing. ", doi="10.2196/resprot.5621", url="http://www.researchprotocols.org/2016/2/e121/", url="http://www.ncbi.nlm.nih.gov/pubmed/27311964" } @Article{info:doi/10.2196/jmir.5521, author="Mazzocut, Mauro and Truccolo, Ivana and Antonini, Marialuisa and Rinaldi, Fabio and Omero, Paolo and Ferrarin, Emanuela and De Paoli, Paolo and Tasso, Carlo", title="Web Conversations About Complementary and Alternative Medicines and Cancer: Content and Sentiment Analysis", journal="J Med Internet Res", year="2016", month="Jun", day="16", volume="18", number="6", pages="e120", keywords="complementary and alternative medicine", keywords="Internet", keywords="neoplasms", keywords="health information online", keywords="website content analysis", keywords="barriers to patient-doctor communication", keywords="misinformation", keywords="sentiment analysis", keywords="data mining", abstract="Background: The use of complementary and alternative medicine (CAM) among cancer patients is widespread and mostly self-administrated. Today, one of the most relevant topics is the nondisclosure of CAM use to doctors. This general lack of communication exposes patients to dangerous behaviors and to less reliable information channels, such as the Web. The Italian context scarcely differs from this trend. Today, we are able to mine and analyze systematically the unstructured information available in the Web, to get an insight of people's opinions, beliefs, and rumors concerning health topics. Objective: Our aim was to analyze Italian Web conversations about CAM, identifying the most relevant Web sources, therapies, and diseases and measure the related sentiment. Methods: Data have been collected using the Web Intelligence tool ifMONITOR. The workflow consisted of 6 phases: (1) eligibility criteria definition for the ifMONITOR search profile; (2) creation of a CAM terminology database; (3) generic Web search and automatic filtering, the results have been manually revised to refine the search profile, and stored in the ifMONITOR database; (4) automatic classification using the CAM database terms; (5) selection of the final sample and manual sentiment analysis using a 1-5 score range; (6) manual indexing of the Web sources and CAM therapies type retrieved. Descriptive univariate statistics were computed for each item: absolute frequency, percentage, central tendency (mean sentiment score [MSS]), and variability (standard variation $\sigma$). Results: Overall, 212 Web sources, 423 Web documents, and 868 opinions have been retrieved. The overall sentiment measured tends to a good score (3.6 of 5). Quite a high polarization in the opinions of the conversation partaking emerged from standard variation analysis ($\sigma$?1). In total, 126 of 212 (59.4\%) Web sources retrieved were nonhealth-related. Facebook (89; 21\%) and Yahoo Answers (41; 9.7\%) were the most relevant. In total, 94 CAM therapies have been retrieved. Most belong to the ``biologically based therapies or nutrition'' category: 339 of 868 opinions (39.1\%), showing an MSS of 3.9 ($\sigma$=0.83). Within nutrition, ``diets'' collected 154 opinions (18.4\%) with an MSS of 3.8 ($\sigma$=0.87); ``food as CAM'' overall collected 112 opinions (12.8\%) with a MSS of 4 ($\sigma$=0.68). Excluding diets and food, the most discussed CAM therapy is the controversial Italian ``Di Bella multitherapy'' with 102 opinions (11.8\%) with an MSS of 3.4 ($\sigma$=1.21). Breast cancer was the most mentioned disease: 81 opinions of 868. Conclusions: Conversations about CAM and cancer are ubiquitous. There is a great concern about the biologically based therapies, perceived as harmless and useful, under-rating all risks related to dangerous interactions or malnutrition. Our results can be useful to doctors to be aware of the implications of these beliefs for the clinical practice. Web conversation exploitation could be a strategy to gain insights of people's perspective for other controversial topics. ", doi="10.2196/jmir.5521", url="http://www.jmir.org/2016/6/e120/", url="http://www.ncbi.nlm.nih.gov/pubmed/27311444" } @Article{info:doi/10.2196/publichealth.5308, author="Lyles, Rees Courtney and Godbehere, Andrew and Le, Gem and El Ghaoui, Laurent and Sarkar, Urmimala", title="Applying Sparse Machine Learning Methods to Twitter: Analysis of the 2012 Change in Pap Smear Guidelines. A Sequential Mixed-Methods Study", journal="JMIR Public Health Surveill", year="2016", month="Jun", day="10", volume="2", number="1", pages="e21", keywords="Twitter", keywords="machine learning", keywords="social media", keywords="cervical cancer", keywords="qualitative research", abstract="Background: It is difficult to synthesize the vast amount of textual data available from social media websites. Capturing real-world discussions via social media could provide insights into individuals' opinions and the decision-making process. Objective: We conducted a sequential mixed methods study to determine the utility of sparse machine learning techniques in summarizing Twitter dialogues. We chose a narrowly defined topic for this approach: cervical cancer discussions over a 6-month time period surrounding a change in Pap smear screening guidelines. Methods: We applied statistical methodologies, known as sparse machine learning algorithms, to summarize Twitter messages about cervical cancer before and after the 2012 change in Pap smear screening guidelines by the US Preventive Services Task Force (USPSTF). All messages containing the search terms ``cervical cancer,'' ``Pap smear,'' and ``Pap test'' were analyzed during: (1) January 1--March 13, 2012, and (2) March 14--June 30, 2012. Topic modeling was used to discern the most common topics from each time period, and determine the singular value criterion for each topic. The results were then qualitatively coded from top 10 relevant topics to determine the efficiency of clustering method in grouping distinct ideas, and how the discussion differed before vs. after the change in guidelines . Results: This machine learning method was effective in grouping the relevant discussion topics about cervical cancer during the respective time periods ({\textasciitilde}20\% overall irrelevant content in both time periods). Qualitative analysis determined that a significant portion of the top discussion topics in the second time period directly reflected the USPSTF guideline change (eg, ``New Screening Guidelines for Cervical Cancer''), and many topics in both time periods were addressing basic screening promotion and education (eg, ``It is Cervical Cancer Awareness Month! Click the link to see where you can receive a free or low cost Pap test.'') Conclusions: It was demonstrated that machine learning tools can be useful in cervical cancer prevention and screening discussions on Twitter. This method allowed us to prove that there is publicly available significant information about cervical cancer screening on social media sites. Moreover, we observed a direct impact of the guideline change within the Twitter messages. ", doi="10.2196/publichealth.5308", url="http://publichealth.jmir.org/2016/1/e21/", url="http://www.ncbi.nlm.nih.gov/pubmed/27288093" } @Article{info:doi/10.2196/publichealth.5814, author="Majumder, S. Maimuna and Santillana, Mauricio and Mekaru, R. Sumiko and McGinnis, P. Denise and Khan, Kamran and Brownstein, S. John", title="Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak", journal="JMIR Public Health Surveill", year="2016", month="Jun", day="01", volume="2", number="1", pages="e30", keywords="Zika virus disease", keywords="digital disease surveillance", keywords="mathematical modeling", keywords="reproductive number", keywords="transmission dynamics", abstract="Background: Approximately 40 countries in Central and South America have experienced local vector-born transmission of Zika virus, resulting in nearly 300,000 total reported cases of Zika virus disease to date. Of the cases that have sought care thus far in the region, more than 70,000 have been reported out of Colombia. Objective: In this paper, we use nontraditional digital disease surveillance data via HealthMap and Google Trends to develop near real-time estimates for the basic (R0) and observed (Robs) reproductive numbers associated with Zika virus disease in Colombia. We then validate our results against traditional health care-based disease surveillance data. Methods: Cumulative reported case counts of Zika virus disease in Colombia were acquired via the HealthMap digital disease surveillance system. Linear smoothing was conducted to adjust the shape of the HealthMap cumulative case curve using Google search data. Traditional surveillance data on Zika virus disease were obtained from weekly Instituto Nacional de Salud (INS) epidemiological bulletin publications. The Incidence Decay and Exponential Adjustment (IDEA) model was used to estimate R0 and Robs for both data sources. Results: Using the digital (smoothed HealthMap) data, we estimated a mean R0 of 2.56 (range 1.42-3.83) and a mean Robs of 1.80 (range 1.42-2.30). The traditional (INS) data yielded a mean R0 of 4.82 (range 2.34-8.32) and a mean Robs of 2.34 (range 1.60-3.31). Conclusions: Although modeling using the traditional (INS) data yielded higher R0 estimates than the digital (smoothed HealthMap) data, modeled ranges for Robs were comparable across both data sources. As a result, the narrow range of possible case projections generated by the traditional (INS) data was largely encompassed by the wider range produced by the digital (smoothed HealthMap) data. Thus, in the absence of traditional surveillance data, digital surveillance data can yield similar estimates for key transmission parameters and should be utilized in other Zika virus-affected countries to assess outbreak dynamics in near real time. ", doi="10.2196/publichealth.5814", url="http://publichealth.jmir.org/2016/1/e30/", url="http://www.ncbi.nlm.nih.gov/pubmed/27251981" } @Article{info:doi/10.2196/mental.4822, author="Braithwaite, R. Scott and Giraud-Carrier, Christophe and West, Josh and Barnes, D. Michael and Hanson, Lee Carl", title="Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality", journal="JMIR Mental Health", year="2016", month="May", day="16", volume="3", number="2", pages="e21", keywords="suicide", keywords="social media", keywords="twitter", keywords="machine learning", abstract="Background: One of the leading causes of death in the United States (US) is suicide and new methods of assessment are needed to track its risk in real time. Objective: Our objective is to validate the use of machine learning algorithms for Twitter data against empirically validated measures of suicidality in the US population. Methods: Using a machine learning algorithm, the Twitter feeds of 135 Mechanical Turk (MTurk) participants were compared with validated, self-report measures of suicide risk. Results: Our findings show that people who are at high suicidal risk can be easily differentiated from those who are not by machine learning algorithms, which accurately identify the clinically significant suicidal rate in 92\% of cases (sensitivity: 53\%, specificity: 97\%, positive predictive value: 75\%, negative predictive value: 93\%). Conclusions: Machine learning algorithms are efficient in differentiating people who are at a suicidal risk from those who are not. Evidence for suicidality can be measured in nonclinical populations using social media data. ", doi="10.2196/mental.4822", url="http://mental.jmir.org/2016/2/e21/", url="http://www.ncbi.nlm.nih.gov/pubmed/27185366" } @Article{info:doi/10.2196/resprot.4203, author="Kandadai, Venk and Yang, Haodong and Jiang, Ling and Yang, C. Christopher and Fleisher, Linda and Winston, Koplin Flaura", title="Measuring Health Information Dissemination and Identifying Target Interest Communities on Twitter: Methods Development and Case Study of the @SafetyMD Network", journal="JMIR Res Protoc", year="2016", month="May", day="05", volume="5", number="2", pages="e50", keywords="Twitter", keywords="health information", keywords="dissemination", keywords="health communication", keywords="digital health", abstract="Background: Little is known about the ability of individual stakeholder groups to achieve health information dissemination goals through Twitter. Objective: This study aimed to develop and apply methods for the systematic evaluation and optimization of health information dissemination by stakeholders through Twitter. Methods: Tweet content from 1790 followers of @SafetyMD (July-November 2012) was examined. User emphasis, a new indicator of Twitter information dissemination, was defined and applied to retweets across two levels of retweeters originating from @SafetyMD. User interest clusters were identified based on principal component analysis (PCA) and hierarchical cluster analysis (HCA) of a random sample of 170 followers. Results: User emphasis of keywords remained across levels but decreased by 9.5 percentage points. PCA and HCA identified 12 statistically unique clusters of followers within the @SafetyMD Twitter network. Conclusions: This study is one of the first to develop methods for use by stakeholders to evaluate and optimize their use of Twitter to disseminate health information. Our new methods provide preliminary evidence that individual stakeholders can evaluate the effectiveness of health information dissemination and create content-specific clusters for more specific targeted messaging. ", doi="10.2196/resprot.4203", url="http://www.researchprotocols.org/2016/2/e50/", url="http://www.ncbi.nlm.nih.gov/pubmed/27151100" } @Article{info:doi/10.2196/cancer.5212, author="Foroughi, Forough and Lam, K-Y Alfred and Lim, S.C Megan and Saremi, Nassim and Ahmadvand, Alireza", title="``Googling'' for Cancer: An Infodemiological Assessment of Online Search Interests in Australia, Canada, New Zealand, the United Kingdom, and the United States", journal="JMIR Cancer", year="2016", month="May", day="04", volume="2", number="1", pages="e5", keywords="cancer", keywords="neoplasms", keywords="infodemiology", keywords="epidemiology", keywords="geographic mapping", keywords="Google Trends", keywords="Internet", keywords="consumer health information", abstract="Background: The infodemiological analysis of queries from search engines to shed light on the status of various noncommunicable diseases has gained increasing popularity in recent years. Objective: The aim of the study was to determine the international perspective on the distribution of information seeking in Google regarding ``cancer'' in major English-speaking countries. Methods: We used Google Trends service to assess people's interest in searching about ``Cancer'' classified as ``Disease,'' from January 2004 to December 2015 in Australia, Canada, New Zealand, the United Kingdom, and the United States. Then, we evaluated top cities and their relative search volumes (SVs) and country-specific ``Top searches'' and ``Rising searches.'' We also evaluated the cross-country correlations of SVs for cancer, as well as rank correlations of SVs from 2010 to 2014 with the incidence of cancer in 2012 in the abovementioned countries. Results: From 2004 to 2015, the United States (relative SV [from 100]: 63), Canada (62), and Australia (61) were the top countries searching for cancer in Google, followed by New Zealand (54) and the United Kingdom (48). There was a consistent seasonality pattern in searching for cancer in the United States, Canada, Australia, and New Zealand. Baltimore (United States), St John's (Canada), Sydney (Australia), Otaika (New Zealand), and Saint Albans (United Kingdom) had the highest search interest in their corresponding countries. ``Breast cancer'' was the cancer entity that consistently appeared high in the list of top searches in all 5 countries. The ``Rising searches'' were ``pancreatic cancer'' in Canada and ``ovarian cancer'' in New Zealand. Cross-correlation of SVs was strong between the United States, Canada, and Australia (>.70, P<.01). Conclusions: Cancer maintained its popularity as a search term for people in the United States, Canada, and Australia, comparably higher than New Zealand and the United Kingdom. The increased interest in searching for keywords related to cancer shows the possible effectiveness of awareness campaigns in increasing societal demand for health information on the Web, to be met in community-wide communication or awareness interventions. ", doi="10.2196/cancer.5212", url="http://cancer.jmir.org/2016/1/e5/", url="http://www.ncbi.nlm.nih.gov/pubmed/28410185" } @Article{info:doi/10.2196/publichealth.5205, author="Xu, Songhua and Markson, Christopher and Costello, L. Kaitlin and Xing, Y. Cathleen and Demissie, Kitaw and Llanos, AM Adana", title="Leveraging Social Media to Promote Public Health Knowledge: Example of Cancer Awareness via Twitter", journal="JMIR Public Health Surveill", year="2016", month="Apr", day="28", volume="2", number="1", pages="e17", keywords="awareness", keywords="breast cancer", keywords="colorectal cancer", keywords="disparities", keywords="lung cancer", keywords="prostate cancer", keywords="social media", keywords="Twitter", abstract="Background: As social media becomes increasingly popular online venues for engaging in communication about public health issues, it is important to understand how users promote knowledge and awareness about specific topics. Objective: The aim of this study is to examine the frequency of discussion and differences by race and ethnicity of cancer-related topics among unique users via Twitter. Methods: Tweets were collected from April 1, 2014 through January 21, 2015 using the Twitter public streaming Application Programming Interface (API) to collect 1\% of public tweets. Twitter users were classified into racial and ethnic groups using a new text mining approach applied to English-only tweets. Each ethnic group was then analyzed for frequency in cancer-related terms within user timelines, investigated for changes over time and across groups, and measured for statistical significance. Results: Observable usage patterns of the terms ``cancer'', ``breast cancer'', ``prostate cancer'', and ``lung cancer'' between Caucasian and African American groups were evident across the study period. We observed some variation in the frequency of term usage during months known to be labeled as cancer awareness months, particularly September, October, and November. Interestingly, we found that of the terms studied, ``colorectal cancer'' received the least Twitter attention. Conclusions: The findings of the study provide evidence that social media can serve as a very powerful and important tool in implementing and disseminating critical prevention, screening, and treatment messages to the community in real-time. The study also introduced and tested a new methodology of identifying race and ethnicity among users of the social media. Study findings highlight the potential benefits of social media as a tool in reducing racial and ethnic disparities. ", doi="10.2196/publichealth.5205", url="http://publichealth.jmir.org/2016/1/e17/", url="http://www.ncbi.nlm.nih.gov/pubmed/27227152" } @Article{info:doi/10.2196/publichealth.5304, author="Ayers, W. John and Westmaas, Lee J. and Leas, C. Eric and Benton, Adrian and Chen, Yunqi and Dredze, Mark and Althouse, M. Benjamin", title="Leveraging Big Data to Improve Health Awareness Campaigns: A Novel Evaluation of the Great American Smokeout", journal="JMIR Public Health Surveill", year="2016", month="Mar", day="31", volume="2", number="1", pages="e16", keywords="big data", keywords="evaluation", keywords="health communication", keywords="mass media", keywords="social media", keywords="tobacco control", keywords="infodemiology", keywords="infoveillence", keywords="twitter", keywords="smoking cessation", abstract="Background: Awareness campaigns are ubiquitous, but little is known about their potential effectiveness because traditional evaluations are often unfeasible. For 40 years, the ``Great American Smokeout'' (GASO) has encouraged media coverage and popular engagement with smoking cessation on the third Thursday of November as the nation's longest running awareness campaign. Objective: We proposed a novel evaluation framework for assessing awareness campaigns using the GASO as a case study by observing cessation-related news reports and Twitter postings, and cessation-related help seeking via Google, Wikipedia, and government-sponsored quitlines. Methods: Time trends (2009-2014) were analyzed using a quasi-experimental design to isolate spikes during the GASO by comparing observed outcomes on the GASO day with the simulated counterfactual had the GASO not occurred. Results: Cessation-related news typically increased by 61\% (95\% CI 35-87) and tweets by 13\% (95\% CI ?21 to 48) during the GASO compared with what was expected had the GASO not occurred. Cessation-related Google searches increased by 25\% (95\% CI 10-40), Wikipedia page visits by 22\% (95\% CI ?26 to 67), and quitline calls by 42\% (95\% CI 19-64). Cessation-related news media positively coincided with cessation tweets, Internet searches, and Wikipedia visits; for example, a 50\% increase in news for any year predicted a 28\% (95\% CI ?2 to 59) increase in tweets for the same year. Increases on the day of the GASO rivaled about two-thirds of a typical New Year's Day---the day that is assumed to see the greatest increases in cessation-related activity. In practical terms, there were about 61,000 more instances of help seeking on Google, Wikipedia, or quitlines on GASO each year than would normally be expected. Conclusions: These findings provide actionable intelligence to improve the GASO and model how to rapidly, cost-effectively, and efficiently evaluate hundreds of awareness campaigns, nearly all for the first time. ", doi="10.2196/publichealth.5304", url="http://publichealth.jmir.org/2016/1/e16/", url="http://www.ncbi.nlm.nih.gov/pubmed/27227151" } @Article{info:doi/10.2196/jmir.5409, author="Risson, Val{\'e}ry and Saini, Deepanshu and Bonzani, Ian and Huisman, Alice and Olson, Melvin", title="Patterns of Treatment Switching in Multiple Sclerosis Therapies in US Patients Active on Social Media: Application of Social Media Content Analysis to Health Outcomes Research", journal="J Med Internet Res", year="2016", month="Mar", day="17", volume="18", number="3", pages="e62", keywords="Internet", keywords="multiple sclerosis", keywords="outcomes assessment", keywords="drug switching", abstract="Background: Social media analysis has rarely been applied to the study of specific questions in outcomes research. Objective: The aim was to test the applicability of social media analysis to outcomes research using automated listening combined with filtering and analysis of data by specialists. After validation, the process was applied to the study of patterns of treatment switching in multiple sclerosis (MS). Methods: A comprehensive listening and analysis process was developed that blended automated listening with filtering and analysis of data by life sciences-qualified analysts and physicians. The population was patients with MS from the United States. Data sources were Facebook, Twitter, blogs, and online forums. Sources were searched for mention of specific oral, injectable, and intravenous (IV) infusion treatments. The representativeness of the social media population was validated by comparison with community survey data and with data from three large US administrative claims databases: MarketScan, PharMetrics Plus, and Department of Defense. Results: A total of 10,260 data points were sampled for manual review: 3025 from Twitter, 3771 from Facebook, 2773 from Internet forums, and 691 from blogs. The demographics of the social media population were similar to those reported from community surveys and claims databases. Mean age was 39 (SD 11) years and 14.56\% (326/2239) of the population was older than 50 years. Women, patients aged 30 to 49 years, and those diagnosed for more than 10 years were represented by more data points than other patients were. Women also accounted for a large majority (82.6\%, 819/991) of reported switches. Two-fifths of switching patients had lived with their disease for more than 10 years since diagnosis. Most reported switches (55.05\%, 927/1684) were from injectable to oral drugs with switches from IV therapies to orals the second largest switch (15.38\%, 259/1684). Switches to oral drugs accounted for more than 80\% (927/1114) of the switches away from injectable therapies. Four reasons accounted for more than 90\% of all switches: severe side effects, lack of efficacy, physicians' advice, and greater ease of use. Side effects were the main reason for switches to oral or to injectable therapies and search for greater efficacy was the most important factor in switches to IV therapies. Cost of medication was the reason for switching in less than 0.5\% of patients. Conclusions: Social intelligence can be applied to outcomes research with power to analyze MS patients' personal experiences of treatments and to chart the most common reasons for switching between therapies. ", doi="10.2196/jmir.5409", url="http://www.jmir.org/2016/3/e62/", url="http://www.ncbi.nlm.nih.gov/pubmed/26987964" } @Article{info:doi/10.2196/publichealth.5264, author="Du, Li and Rachul, Christen and Guo, Zhaochen and Caulfield, Timothy", title="Gordie Howe's ``Miraculous Treatment'': Case Study of Twitter Users' Reactions to a Sport Celebrity's Stem Cell Treatment", journal="JMIR Public Health Surveill", year="2016", month="Mar", day="09", volume="2", number="1", pages="e8", keywords="Gordie Howe", keywords="stem cell treatment", keywords="stem cell tourism", keywords="social network", keywords="Twitter", keywords="infodemiology", keywords="infoveillance", abstract="Background: Former Detroit Red Wing Gordie Howe received stem cell (SC) treatment in Mexico in December 2014 for a stroke he suffered in October 2014. The news about his positive response to the SC treatment prompted discussion on social networks like Twitter. Objective: This study aims to provide information about discussions that took place on Twitter regarding Howe's SC treatment and SC treatment in general. In particular, this study examines whether tweets portrayed a positive or negative attitude towards Howe's SC treatment, whether or not tweets mention that the treatment is unproven, and whether the tweets mention risks associated with the SC treatment. Methods: This is an infodemiology study, harnessing big data published on the Internet for public health research and analysis of public engagement. A corpus of 2783 tweets about Howe's SC treatment was compiled using a program that collected English-language tweets from December 19, 2014 at 00:00 to February 7, 2015 at 00:00. A content analysis of the corpus was conducted using a coding framework developed through a two-stage process. Results: 78.87\% (2195/2783) of tweets mentioned improvements to Howe's health. Only one tweet explicitly mentioned that Howe's SC treatment was unproven, and 3 tweets warned that direct-to-consumer SC treatments lacked scientific evidence. In addition, 10.31\% (287/2783) of tweets mentioned challenges with SC treatment that have been raised by scientists and researchers, and 3.70\% (103/2783) of tweets either defined Howe as a ``stem cell tourist'' or claimed that his treatment was part of ``stem cell tourism''. In general, 71.79\% (1998/2783) of tweets portrayed a positive attitude towards Howe's SC treatment. Conclusions: Our study found the responses to Howe's treatment on Twitter to be overwhelmingly positive. There was far less attention paid to the lack of scientific evidence regarding the efficacy of the treatment. Unbalanced and uncritical discussion on Twitter regarding SC treatments is another example of inaccurate representations of SC treatments that may create unrealistic expectations that will facilitate the market for unproven stem cell therapies. ", doi="10.2196/publichealth.5264", url="http://publichealth.jmir.org/2016/1/e8/", url="http://www.ncbi.nlm.nih.gov/pubmed/27227162" } @Article{info:doi/10.2196/jmir.5033, author="Priest, Chad and Knopf, Amelia and Groves, Doyle and Carpenter, S. Janet and Furrey, Christopher and Krishnan, Anand and Miller, R. Wendy and Otte, L. Julie and Palakal, Mathew and Wiehe, Sarah and Wilson, Jeffrey", title="Finding the Patient's Voice Using Big Data: Analysis of Users' Health-Related Concerns in the ChaCha Question-and-Answer Service (2009--2012)", journal="J Med Internet Res", year="2016", month="Mar", day="09", volume="18", number="3", pages="e44", keywords="social meda", keywords="health information seeking", keywords="adolescent", keywords="sexual health", keywords="patient engagement", keywords="ChaCha", keywords="big data", keywords="question-and-answer service", keywords="infodemiology", keywords="infoveillance", abstract="Background: The development of effective health care and public health interventions requires a comprehensive understanding of the perceptions, concerns, and stated needs of health care consumers and the public at large. Big datasets from social media and question-and-answer services provide insight into the public's health concerns and priorities without the financial, temporal, and spatial encumbrances of more traditional community-engagement methods and may prove a useful starting point for public-engagement health research (infodemiology). Objective: The objective of our study was to describe user characteristics and health-related queries of the ChaCha question-and-answer platform, and discuss how these data may be used to better understand the perceptions, concerns, and stated needs of health care consumers and the public at large. Methods: We conducted a retrospective automated textual analysis of anonymous user-generated queries submitted to ChaCha between January 2009 and November 2012. A total of 2.004 billion queries were read, of which 3.50\% (70,083,796/2,004,243,249) were missing 1 or more data fields, leaving 1.934 billion complete lines of data for these analyses. Results: Males and females submitted roughly equal numbers of health queries, but content differed by sex. Questions from females predominantly focused on pregnancy, menstruation, and vaginal health. Questions from males predominantly focused on body image, drug use, and sexuality. Adolescents aged 12--19 years submitted more queries than any other age group. Their queries were largely centered on sexual and reproductive health, and pregnancy in particular. Conclusions: The private nature of the ChaCha service provided a perfect environment for maximum frankness among users, especially among adolescents posing sensitive health questions. Adolescents' sexual health queries reveal knowledge gaps with serious, lifelong consequences. The nature of questions to the service provides opportunities for rapid understanding of health concerns and may lead to development of more effective tailored interventions. ", doi="10.2196/jmir.5033", url="http://www.jmir.org/2016/3/e44/", url="http://www.ncbi.nlm.nih.gov/pubmed/26960745" } @Article{info:doi/10.2196/jmir.5391, author="Hamad, O. Eradah and Savundranayagam, Y. Marie and Holmes, D. Jeffrey and Kinsella, Anne Elizabeth and Johnson, M. Andrew", title="Toward a Mixed-Methods Research Approach to Content Analysis in The Digital Age: The Combined Content-Analysis Model and its Applications to Health Care Twitter Feeds", journal="J Med Internet Res", year="2016", month="Mar", day="08", volume="18", number="3", pages="e60", keywords="health care social media", keywords="Twitter feeds", keywords="health care tweets", keywords="mixed methods research", keywords="content analysis", keywords="coding", keywords="computer-aided content analysis", keywords="infodemiology", keywords="infoveillance", keywords="digital disease detection", abstract="Background: Twitter's 140-character microblog posts are increasingly used to access information and facilitate discussions among health care professionals and between patients with chronic conditions and their caregivers. Recently, efforts have emerged to investigate the content of health care-related posts on Twitter. This marks a new area for researchers to investigate and apply content analysis (CA). In current infodemiology, infoveillance and digital disease detection research initiatives, quantitative and qualitative Twitter data are often combined, and there are no clear guidelines for researchers to follow when collecting and evaluating Twitter-driven content. Objective: The aim of this study was to identify studies on health care and social media that used Twitter feeds as a primary data source and CA as an analysis technique. We evaluated the resulting 18 studies based on a narrative review of previous methodological studies and textbooks to determine the criteria and main features of quantitative and qualitative CA. We then used the key features of CA and mixed-methods research designs to propose the combined content-analysis (CCA) model as a solid research framework for designing, conducting, and evaluating investigations of Twitter-driven content. Methods: We conducted a PubMed search to collect studies published between 2010 and 2014 that used CA to analyze health care-related tweets. The PubMed search and reference list checks of selected papers identified 21 papers. We excluded 3 papers and further analyzed 18. Results: Results suggest that the methods used in these studies were not purely quantitative or qualitative, and the mixed-methods design was not explicitly chosen for data collection and analysis. A solid research framework is needed for researchers who intend to analyze Twitter data through the use of CA. Conclusions: We propose the CCA model as a useful framework that provides a straightforward approach to guide Twitter-driven studies and that adds rigor to health care social media investigations. We provide suggestions for the use of the CCA model in elder care-related contexts. ", doi="10.2196/jmir.5391", url="http://www.jmir.org/2016/3/e60/", url="http://www.ncbi.nlm.nih.gov/pubmed/26957477" } @Article{info:doi/10.2196/jmir.4738, author="Kim, Yoonsang and Huang, Jidong and Emery, Sherry", title="Garbage in, Garbage Out: Data Collection, Quality Assessment and Reporting Standards for Social Media Data Use in Health Research, Infodemiology and Digital Disease Detection", journal="J Med Internet Res", year="2016", month="Feb", day="26", volume="18", number="2", pages="e41", keywords="social media", keywords="precision and recall", keywords="sensitivity and specificity", keywords="search filter", keywords="Twitter", keywords="standard reporting", keywords="infodemiology", keywords="infoveillance", keywords="digital disease detection", abstract="Background: Social media have transformed the communications landscape. People increasingly obtain news and health information online and via social media. Social media platforms also serve as novel sources of rich observational data for health research (including infodemiology, infoveillance, and digital disease detection detection). While the number of studies using social data is growing rapidly, very few of these studies transparently outline their methods for collecting, filtering, and reporting those data. Keywords and search filters applied to social data form the lens through which researchers may observe what and how people communicate about a given topic. Without a properly focused lens, research conclusions may be biased or misleading. Standards of reporting data sources and quality are needed so that data scientists and consumers of social media research can evaluate and compare methods and findings across studies. Objective: We aimed to develop and apply a framework of social media data collection and quality assessment and to propose a reporting standard, which researchers and reviewers may use to evaluate and compare the quality of social data across studies. Methods: We propose a conceptual framework consisting of three major steps in collecting social media data: develop, apply, and validate search filters. This framework is based on two criteria: retrieval precision (how much of retrieved data is relevant) and retrieval recall (how much of the relevant data is retrieved). We then discuss two conditions that estimation of retrieval precision and recall rely on---accurate human coding and full data collection---and how to calculate these statistics in cases that deviate from the two ideal conditions. We then apply the framework on a real-world example using approximately 4 million tobacco-related tweets collected from the Twitter firehose. Results: We developed and applied a search filter to retrieve e-cigarette--related tweets from the archive based on three keyword categories: devices, brands, and behavior. The search filter retrieved 82,205 e-cigarette--related tweets from the archive and was validated. Retrieval precision was calculated above 95\% in all cases. Retrieval recall was 86\% assuming ideal conditions (no human coding errors and full data collection), 75\% when unretrieved messages could not be archived, 86\% assuming no false negative errors by coders, and 93\% allowing both false negative and false positive errors by human coders. Conclusions: This paper sets forth a conceptual framework for the filtering and quality evaluation of social data that addresses several common challenges and moves toward establishing a standard of reporting social data. Researchers should clearly delineate data sources, how data were accessed and collected, and the search filter building process and how retrieval precision and recall were calculated. The proposed framework can be adapted to other public social media platforms. ", doi="10.2196/jmir.4738", url="http://www.jmir.org/2016/2/e41/", url="http://www.ncbi.nlm.nih.gov/pubmed/26920122" } @Article{info:doi/10.2196/jmir.4033, author="Jankowski, Wojciech and Hoffmann, Marcin", title="Can Google Searches Predict the Popularity and Harm of Psychoactive Agents?", journal="J Med Internet Res", year="2016", month="Feb", day="25", volume="18", number="2", pages="e38", keywords="drugs", keywords="narcotics", keywords="Internet", keywords="psychoactive agents", keywords="forecasting", keywords="trends", abstract="Background: Predicting the popularity of and harm caused by psychoactive agents is a serious problem that would be difficult to do by a single simple method. However, because of the growing number of drugs it is very important to provide a simple and fast tool for predicting some characteristics of these substances. We were inspired by the Google Flu Trends study on the activity of the influenza virus, which showed that influenza virus activity worldwide can be monitored based on queries entered into the Google search engine. Objective: Our aim was to propose a fast method for ranking the most popular and most harmful drugs based on easily available data gathered from the Internet. Methods: We used the Google search engine to acquire data for the ranking lists. Subsequently, using the resulting list and the frequency of hits for the respective psychoactive drugs combined with the word ``harm'' or ``harmful'', we estimated quickly how much harm is associated with each drug. Results: We ranked the most popular and harmful psychoactive drugs. As we conducted the research over a period of several months, we noted that the relative popularity indexes tended to change depending on when we obtained them. This suggests that the data may be useful in monitoring changes over time in the use of each of these psychoactive agents. Conclusions: Our data correlate well with the results from a multicriteria decision analysis of drug harms in the United Kingdom. We showed that Google search data can be a valuable source of information to assess the popularity of and harm caused by psychoactive agents and may help in monitoring drug use trends. ", doi="10.2196/jmir.4033", url="http://www.jmir.org/2016/2/e38/", url="http://www.ncbi.nlm.nih.gov/pubmed/26916984" } @Article{info:doi/10.2196/jmir.4981, author="Lee, Donghyun and Lee, Hojun and Choi, Munkee", title="Examining the Relationship Between Past Orientation and US Suicide Rates: An Analysis Using Big Data-Driven Google Search Queries", journal="J Med Internet Res", year="2016", month="Feb", day="11", volume="18", number="2", pages="e35", keywords="attitude", keywords="big data", keywords="Google search query", keywords="Internet search", keywords="past orientation", keywords="suicide", abstract="Background: Internet search query data reflect the attitudes of the users, using which we can measure the past orientation to commit suicide. Examinations of past orientation often highlight certain predispositions of attitude, many of which can be suicide risk factors. Objective: To investigate the relationship between past orientation and suicide rate by examining Google search queries. Methods: We measured the past orientation using Google search query data by comparing the search volumes of the past year and those of the future year, across the 50 US states and the District of Columbia during the period from 2004 to 2012. We constructed a panel dataset with independent variables as control variables; we then undertook an analysis using multiple ordinary least squares regression and methods that leverage the Akaike information criterion and the Bayesian information criterion. Results: It was found that past orientation had a positive relationship with the suicide rate (P?.001) and that it improves the goodness-of-fit of the model regarding the suicide rate. Unemployment rate (P?.001 in Models 3 and 4), Gini coefficient (P?.001), and population growth rate (P?.001) had a positive relationship with the suicide rate, whereas the gross state product (P?.001) showed a negative relationship with the suicide rate. Conclusions: We empirically identified the positive relationship between the suicide rate and past orientation, which was measured by big data-driven Google search query. ", doi="10.2196/jmir.4981", url="http://www.jmir.org/2016/2/e35/", url="http://www.ncbi.nlm.nih.gov/pubmed/26868917" } @Article{info:doi/10.2196/publichealth.5059, author="Radzikowski, Jacek and Stefanidis, Anthony and Jacobsen, H. Kathryn and Croitoru, Arie and Crooks, Andrew and Delamater, L. Paul", title="The Measles Vaccination Narrative in Twitter: A Quantitative Analysis", journal="JMIR Public Health Surveill", year="2016", month="Jan", day="04", volume="2", number="1", pages="e1", keywords="social media", keywords="health narrative", keywords="geographic characteristics", keywords="data analysis", keywords="health informatics", keywords="GIS (geographic information systems)", abstract="Background: The emergence of social media is providing an alternative avenue for information exchange and opinion formation on health-related issues. Collective discourse in such media leads to the formation of a complex narrative, conveying public views and perceptions. Objective: This paper presents a study of Twitter narrative regarding vaccination in the aftermath of the 2015 measles outbreak, both in terms of its cyber and physical characteristics. We aimed to contribute to the analysis of the data, as well as presenting a quantitative interdisciplinary approach to analyze such open-source data in the context of health narratives. Methods: We collected 669,136 tweets referring to vaccination from February 1 to March 9, 2015. These tweets were analyzed to identify key terms, connections among such terms, retweet patterns, the structure of the narrative, and connections to the geographical space. Results: The data analysis captures the anatomy of the themes and relations that make up the discussion about vaccination in Twitter. The results highlight the higher impact of stories contributed by news organizations compared to direct tweets by health organizations in communicating health-related information. They also capture the structure of the antivaccination narrative and its terms of reference. Analysis also revealed the relationship between community engagement in Twitter and state policies regarding child vaccination. Residents of Vermont and Oregon, the two states with the highest rates of non-medical exemption from school-entry vaccines nationwide, are leading the social media discussion in terms of participation. Conclusions: The interdisciplinary study of health-related debates in social media across the cyber-physical debate nexus leads to a greater understanding of public concerns, views, and responses to health-related issues. Further coalescing such capabilities shows promise towards advancing health communication, thus supporting the design of more effective strategies that take into account the complex and evolving public views of health issues. ", doi="10.2196/publichealth.5059", url="http://publichealth.jmir.org/2016/1/e1/", url="http://www.ncbi.nlm.nih.gov/pubmed/27227144" } @Article{info:doi/10.2196/jmir.5038, author="Gabarron, Elia and Lau, YS Annie and Wynn, Rolf", title="Is There a Weekly Pattern for Health Searches on Wikipedia and Is the Pattern Unique to Health Topics?", journal="J Med Internet Res", year="2015", month="Dec", day="22", volume="17", number="12", pages="e286", keywords="information-seeking behavior", keywords="health information--seeking behavior", keywords="periodicity", keywords="Wikipedia", keywords="chlamydia", keywords="gonorrhea", keywords="HIV", keywords="AIDS", keywords="influenza", keywords="diabetes", abstract="Background: Online health information--seeking behaviors have been reported to be more common at the beginning of the workweek. This behavior pattern has been interpreted as a kind of ``healthy new start'' or ``fresh start'' due to regrets or attempts to compensate for unhealthy behavior or poor choices made during the weekend. However, the observations regarding the most common health information--seeking day were based only on the analyses of users' behaviors with websites on health or on online health-related searches. We wanted to confirm if this pattern could be found in searches of Wikipedia on health-related topics and also if this search pattern was unique to health-related topics or if it could represent a more general pattern of online information searching---which could be of relevance even beyond the health sector. Objective: The aim was to examine the degree to which the search pattern described previously was specific to health-related information seeking or whether similar patterns could be found in other types of information-seeking behavior. Methods: We extracted the number of searches performed on Wikipedia in the Norwegian language for 911 days for the most common sexually transmitted diseases (chlamydia, gonorrhea, herpes, human immunodeficiency virus [HIV], and acquired immune deficiency syndrome [AIDS]), other health-related topics (influenza, diabetes, and menopause), and 2 nonhealth-related topics (footballer Lionel Messi and pop singer Justin Bieber). The search dates were classified according to the day of the week and ANOVA tests were used to compare the average number of hits per day of the week. Results: The ANOVA tests showed that the sexually transmitted disease queries had their highest peaks on Tuesdays (P<.001) and the fewest searches on Saturdays. The other health topics also showed a weekly pattern, with the highest peaks early in the week and lower numbers on Saturdays (P<.001). Footballer Lionel Messi had the highest mean number of hits on Tuesdays and Wednesdays, whereas pop singer Justin Bieber had the most hits on Tuesdays. Both these tracked search queries also showed significantly lower numbers on Saturdays (P<.001). Conclusions: Our study supports prior studies finding an increase in health information searching at the beginning of the workweek. However, we also found a similar pattern for 2 randomly chosen nonhealth-related terms, which may suggest that the search pattern is not unique to health-related searches. The results are potentially relevant beyond the field of health and our preliminary findings need to be further explored in future studies involving a broader range of nonhealth-related searches. ", doi="10.2196/jmir.5038", url="http://www.jmir.org/2015/12/e286/", url="http://www.ncbi.nlm.nih.gov/pubmed/26693859" } @Article{info:doi/10.2196/jmir.5144, author="Katsuki, Takeo and Mackey, Ken Tim and Cuomo, Raphael", title="Establishing a Link Between Prescription Drug Abuse and Illicit Online Pharmacies: Analysis of Twitter Data", journal="J Med Internet Res", year="2015", month="Dec", day="16", volume="17", number="12", pages="e280", keywords="social media", keywords="surveillance", keywords="prescription drug abuse", keywords="twitter", keywords="eHealth", keywords="illicit Internet pharmacies", keywords="cyberpharmacies", keywords="infodemiology", keywords="infoveillance", abstract="Background: Youth and adolescent non-medical use of prescription medications (NUPM) has become a national epidemic. However, little is known about the association between promotion of NUPM behavior and access via the popular social media microblogging site, Twitter, which is currently used by a third of all teens. Objective: In order to better assess NUPM behavior online, this study conducts surveillance and analysis of Twitter data to characterize the frequency of NUPM-related tweets and also identifies illegal access to drugs of abuse via online pharmacies. Methods: Tweets were collected over a 2-week period from April 1-14, 2015, by applying NUPM keyword filters for both generic/chemical and street names associated with drugs of abuse using the Twitter public streaming application programming interface. Tweets were then analyzed for relevance to NUPM and whether they promoted illegal online access to prescription drugs using a protocol of content coding and supervised machine learning. Results: A total of 2,417,662 tweets were collected and analyzed for this study. Tweets filtered for generic drugs names comprised 232,108 tweets, including 22,174 unique associated uniform resource locators (URLs), and 2,185,554 tweets (376,304 unique URLs) filtered for street names. Applying an iterative process of manual content coding and supervised machine learning, 81.72\% of the generic and 12.28\% of the street NUPM datasets were predicted as having content relevant to NUPM respectively. By examining hyperlinks associated with NUPM relevant content for the generic Twitter dataset, we discovered that 75.72\% of the tweets with URLs included a hyperlink to an online marketing affiliate that directly linked to an illicit online pharmacy advertising the sale of Valium without a prescription. Conclusions: This study examined the association between Twitter content, NUPM behavior promotion, and online access to drugs using a broad set of prescription drug keywords. Initial results are concerning, as our study found over 45,000 tweets that directly promoted NUPM by providing a URL that actively marketed the illegal online sale of prescription drugs of abuse. Additional research is needed to further establish the link between Twitter content and NUPM, as well as to help inform future technology-based tools, online health promotion activities, and public policy to combat NUPM online. ", doi="10.2196/jmir.5144", url="http://www.jmir.org/2015/12/e280/", url="http://www.ncbi.nlm.nih.gov/pubmed/26677966" } @Article{info:doi/10.2196/publichealth.5014, author="Albalawi, Yousef and Sixsmith, Jane", title="Agenda Setting for Health Promotion: Exploring an Adapted Model for the Social Media Era", journal="JMIR Public Health Surveill", year="2015", month="Nov", day="25", volume="1", number="2", pages="e21", keywords="agenda setting, health promotion, social media, Twitter, health communication, Saudi Arabia, road traffic accidents", abstract="Background: The foundation of best practice in health promotion is a robust theoretical base that informs design, implementation, and evaluation of interventions that promote the public's health. This study provides a novel contribution to health promotion through the adaptation of the agenda-setting approach in response to the contribution of social media. This exploration and proposed adaptation is derived from a study that examined the effectiveness of Twitter in influencing agenda setting among users in relation to road traffic accidents in Saudi Arabia. Objective: The proposed adaptations to the agenda-setting model to be explored reflect two levels of engagement: agenda setting within the social media sphere and the position of social media within classic agenda setting. This exploratory research aims to assess the veracity of the proposed adaptations on the basis of the hypotheses developed to test these two levels of engagement. Methods: To validate the hypotheses, we collected and analyzed data from two primary sources: Twitter activities and Saudi national newspapers. Keyword mentions served as indicators of agenda promotion; for Twitter, interactions were used to measure the process of agenda setting within the platform. The Twitter final dataset comprised 59,046 tweets and 38,066 users who contributed by tweeting, replying, or retweeting. Variables were collected for each tweet and user. In addition, 518 keyword mentions were recorded from six popular Saudi national newspapers. Results: The results showed significant ratification of the study hypotheses at both levels of engagement that framed the proposed adaptions. The results indicate that social media facilitates the contribution of individuals in influencing agendas (individual users accounted for 76.29\%, 67.79\%, and 96.16\% of retweet impressions, total impressions, and amplification multipliers, respectively), a component missing from traditional constructions of agenda-setting models. The influence of organizations on agenda setting is also highlighted (in the data of user interactions, organizational accounts registered 17\% and 14.74\% as source and target of interactions, respectively). In addition, 13 striking similarities showed the relationship between newspapers and Twitter on the mentions trends line. Conclusions: The effective use of social media platforms in health promotion intervention programs requires new strategies that consider the limitations of traditional communication channels. Conducting research is vital to establishing a strong basis for modifying, designing, and developing new health promotion strategies and approaches. ", doi="10.2196/publichealth.5014", url="http://publichealth.jmir.org/2015/2/e21/", url="http://www.ncbi.nlm.nih.gov/pubmed/27227139" } @Article{info:doi/10.2196/publichealth.4809, author="Cawkwell, B. Philip and Lee, Lily and Weitzman, Michael and Sherman, E. Scott", title="Tracking Hookah Bars in New York: Utilizing Yelp as a Powerful Public Health Tool", journal="JMIR Public Health Surveill", year="2015", month="Nov", day="20", volume="1", number="2", pages="e19", keywords="hookah", keywords="hookah bar", keywords="Internet", keywords="public health", keywords="Yelp", abstract="Background: While cigarette use has seen a steady decline in recent years, hookah (water pipe) use has rapidly increased in popularity. While anecdotal reports have noted a rise in hookah bars, methodological difficulties have prevented researchers from drawing definitive conclusions about the number of hookah bars in any given location. There is no publicly available database that has been shown to reliably provide this information. It is now possible to analyze Internet trends as a measure of population behavior and health-related phenomena. Objective: The objective of the study was to investigate whether Yelp can be used to accurately identify the number of hookah bars in New York State, assess the distribution and characteristics of hookah bars, and monitor temporal trends in their presence. Methods: Data were obtained from Yelp that captures a variety of parameters for every business listed in their database as of October 28, 2014, that was tagged as a ``hookah bar'' and operating in New York State. Two algebraic models were created: one estimated the date of opening of a hookah bar based on the first Yelp review received and the other estimated whether the bar was open or closed based on the date of the most recent Yelp review. These findings were then compared with empirical data obtained by Internet searches. Results: From 2014 onward, the date of the first Yelp review predicts the opening date of new hookah bars to within 1 month. Yelp data allow the estimate of such venues and demonstrate that new bars are not randomly distributed, but instead are clustered near colleges and in specific racial/ethnic neighborhoods. New York has seen substantially more new hookah bars in 2012-2014 compared with the number that existed prior to 2009. Conclusions: Yelp is a powerful public health tool that allows for the investigation of various trends and characteristics of hookah bars. New York is experiencing tremendous growth in hookah bars, a worrying phenomenon that necessitates further investigation. ", doi="10.2196/publichealth.4809", url="http://publichealth.jmir.org/2015/2/e19/", url="http://www.ncbi.nlm.nih.gov/pubmed/27227137" } @Article{info:doi/10.2196/jmir.4516, author="Wang, Ho-Wei and Chen, Duan-Rung and Yu, Hsiao-Wei and Chen, Ya-Mei", title="Forecasting the Incidence of Dementia and Dementia-Related Outpatient Visits With Google Trends: Evidence From Taiwan", journal="J Med Internet Res", year="2015", month="Nov", day="19", volume="17", number="11", pages="e264", keywords="dementia", keywords="Alzheimer's disease", keywords="Google Trends", keywords="big data", keywords="incidence", keywords="early detection", keywords="self-diagnosis", keywords="Internet search", keywords="health-seeking behaviors", abstract="Background: Google Trends has demonstrated the capability to both monitor and predict epidemic outbreaks. The connection between Internet searches for dementia information and dementia incidence and dementia-related outpatient visits remains unknown. Objective: This study aimed to determine whether Google Trends could provide insight into trends in dementia incidence and related outpatient visits in Taiwan. We investigated and validated the local search terms that would be the best predictors of new dementia cases and outpatient visits. We further evaluated the nowcasting (ie, forecasting the present) and forecasting effects of Google Trends search trends for new dementia cases and outpatient visits. The long-term goal is to develop a surveillance system to help early detection and interventions for dementia in Taiwan. Methods: This study collected (1) dementia data from Taiwan's National Health Insurance Research Database and (2) local Internet search data from Google Trends, both from January 2009 to December 2011. We investigated and validated search terms that would be the best predictors of new dementia cases and outpatient visits. We then evaluated both the nowcasting and the forecasting effects of Google Trends search trends through cross-correlation analysis of the dementia incidence and outpatient visit data with the Google Trends data. Results: The search term ``dementia + Alzheimer's disease'' demonstrated a 3-month lead effect for new dementia cases and a 6-month lead effect for outpatient visits (r=.503, P=.002; r=.431, P=.009, respectively). When gender was included in the analysis, the search term ``dementia'' showed 6-month predictive power for new female dementia cases (r=.520, P=.001), but only a nowcasting effect for male cases (r=.430, P=.009). The search term ``neurology'' demonstrated a 3-month leading effect for new dementia cases (r=.433, P=.008), for new male dementia cases (r=.434, P=.008), and for outpatient visits (r=.613, P<.001). Conclusions: Google Trends established a plausible relationship between search terms and new dementia cases and dementia-related outpatient visits in Taiwan. This data may allow the health care system in Taiwan to prepare for upcoming outpatient and dementia screening visits. In addition, the validated search term results can be used to provide caregivers with caregiving-related health, skills, and social welfare information by embedding dementia-related search keywords in relevant online articles. ", doi="10.2196/jmir.4516", url="http://www.jmir.org/2015/11/e264/", url="http://www.ncbi.nlm.nih.gov/pubmed/26586281" } @Article{info:doi/10.2196/jmir.4466, author="Kim, E. Annice and Hopper, Timothy and Simpson, Sean and Nonnemaker, James and Lieberman, J. Alicea and Hansen, Heather and Guillory, Jamie and Porter, Lauren", title="Using Twitter Data to Gain Insights into E-cigarette Marketing and Locations of Use: An Infoveillance Study", journal="J Med Internet Res", year="2015", month="Nov", day="06", volume="17", number="11", pages="e251", keywords="electronic cigarettes", keywords="social media", keywords="tobacco", keywords="marketing", keywords="natural language processing", abstract="Background: Marketing and use of electronic cigarettes (e-cigarettes) and other electronic nicotine delivery devices have increased exponentially in recent years fueled, in part, by marketing and word-of-mouth communications via social media platforms, such as Twitter. Objective: This study examines Twitter posts about e-cigarettes between 2008 and 2013 to gain insights into (1) marketing trends for selling and promoting e-cigarettes and (2) locations where people use e-cigarettes. Methods: We used keywords to gather tweets about e-cigarettes between July 1, 2008 and February 28, 2013. A randomly selected subset of tweets was manually coded as advertising (eg, marketing, advertising, sales, promotion) or nonadvertising (eg, individual users, consumers), and classification algorithms were trained to code the remaining data into these 2 categories. A combination of manual coding and natural language processing methods was used to indicate locations where people used e-cigarettes. Additional metadata were used to generate insights about users who tweeted most frequently about e-cigarettes. Results: We identified approximately 1.7 million tweets about e-cigarettes between 2008 and 2013, with the majority of these tweets being advertising (93.43\%, 1,559,508/1,669,123). Tweets about e-cigarettes increased more than tenfold between 2009 and 2010, suggesting a rapid increase in the popularity of e-cigarettes and marketing efforts. The Twitter handles tweeting most frequently about e-cigarettes were a mixture of e-cigarette brands, affiliate marketers, and resellers of e-cigarette products. Of the 471 e-cigarette tweets mentioning a specific place, most mentioned e-cigarette use in class (39.1\%, 184/471) followed by home/room/bed (12.5\%, 59/471), school (12.1\%, 57/471), in public (8.7\%, 41/471), the bathroom (5.7\%, 27/471), and at work (4.5\%, 21/471). Conclusions: Twitter is being used to promote e-cigarettes by different types of entities and the online marketplace is more diverse than offline product offerings and advertising strategies. E-cigarettes are also being used in public places, such as schools, underscoring the need for education and enforcement of policies banning e-cigarette use in public places. Twitter data can provide new insights on e-cigarettes to help inform future research, regulations, surveillance, and enforcement efforts. ", doi="10.2196/jmir.4466", url="http://www.jmir.org/2015/11/e251/", url="http://www.ncbi.nlm.nih.gov/pubmed/26545927" } @Article{info:doi/10.2196/jmir.4969, author="Cole-Lewis, Heather and Pugatch, Jillian and Sanders, Amy and Varghese, Arun and Posada, Susana and Yun, Christopher and Schwarz, Mary and Augustson, Erik", title="Social Listening: A Content Analysis of E-Cigarette Discussions on Twitter", journal="J Med Internet Res", year="2015", month="Oct", day="27", volume="17", number="10", pages="e243", keywords="social media", keywords="Twitter", keywords="e-cigarettes", keywords="content analysis", abstract="Background: Electronic cigarette (e-cigarette) use has increased in the United States, leading to active debate in the public health sphere regarding e-cigarette use and regulation. To better understand trends in e-cigarette attitudes and behaviors, public health and communication professionals can turn to the dialogue taking place on popular social media platforms such as Twitter. Objective: The objective of this study was to conduct a content analysis to identify key conversation trends and patterns over time using historical Twitter data. Methods: A 5-category content analysis was conducted on a random sample of tweets chosen from all publicly available tweets sent between May 1, 2013, and April 30, 2014, that matched strategic keywords related to e-cigarettes. Relevant tweets were isolated from the random sample of approximately 10,000 tweets and classified according to sentiment, user description, genre, and theme. Descriptive analyses including univariate and bivariate associations, as well as correlation analyses were performed on all categories in order to identify patterns and trends. Results: The analysis revealed an increase in e-cigarette--related tweets from May 2013 through April 2014, with tweets generally being positive; 71\% of the sample tweets were classified as having a positive sentiment. The top two user categories were everyday people (65\%) and individuals who are part of the e-cigarette community movement (16\%). These two user groups were responsible for a majority of informational (79\%) and news tweets (75\%), compared to reputable news sources and foundations or organizations, which combined provided 5\% of informational tweets and 12\% of news tweets. Personal opinion (28\%), marketing (21\%), and first person e-cigarette use or intent (20\%) were the three most common genres of tweets, which tended to have a positive sentiment. Marketing was the most common theme (26\%), and policy and government was the second most common theme (20\%), with 86\% of these tweets coming from everyday people and the e-cigarette community movement combined, compared to 5\% of policy and government tweets coming from government, reputable news sources, and foundations or organizations combined. Conclusions: Everyday people and the e-cigarette community are dominant forces across several genres and themes, warranting continued monitoring to understand trends and their implications regarding public opinion, e-cigarette use, and smoking cessation. Analyzing social media trends is a meaningful way to inform public health practitioners of current sentiments regarding e-cigarettes, and this study contributes a replicable methodology. ", doi="10.2196/jmir.4969", url="http://www.jmir.org/2015/10/e243/", url="http://www.ncbi.nlm.nih.gov/pubmed/26508089" } @Article{info:doi/10.2196/jmir.4517, author="Chen, T. Annie and Zhu, Shu-Hong and Conway, Mike", title="What Online Communities Can Tell Us About Electronic Cigarettes and Hookah Use: A Study Using Text Mining and Visualization Techniques", journal="J Med Internet Res", year="2015", month="Sep", day="29", volume="17", number="9", pages="e220", keywords="electronic cigarettes", keywords="hookah smoking", keywords="cigarettes", keywords="tobacco products", keywords="social media", keywords="text mining", abstract="Background: The rise in popularity of electronic cigarettes (e-cigarettes) and hookah over recent years has been accompanied by some confusion and uncertainty regarding the development of an appropriate regulatory response towards these emerging products. Mining online discussion content can lead to insights into people's experiences, which can in turn further our knowledge of how to address potential health implications. In this work, we take a novel approach to understanding the use and appeal of these emerging products by applying text mining techniques to compare consumer experiences across discussion forums. Objective: This study examined content from the websites Vapor Talk, Hookah Forum, and Reddit to understand people's experiences with different tobacco products. Our investigation involves three parts. First, we identified contextual factors that inform our understanding of tobacco use behaviors, such as setting, time, social relationships, and sensory experience, and compared the forums to identify the ones where content on these factors is most common. Second, we compared how the tobacco use experience differs with combustible cigarettes and e-cigarettes. Third, we investigated differences between e-cigarette and hookah use. Methods: In the first part of our study, we employed a lexicon-based extraction approach to estimate prevalence of contextual factors, and then we generated a heat map based on these estimates to compare the forums. In the second and third parts of the study, we employed a text mining technique called topic modeling to identify important topics and then developed a visualization, Topic Bars, to compare topic coverage across forums. Results: In the first part of the study, we identified two forums, Vapor Talk Health \& Safety and the Stopsmoking subreddit, where discussion concerning contextual factors was particularly common. The second part showed that the discussion in Vapor Talk Health \& Safety focused on symptoms and comparisons of combustible cigarettes and e-cigarettes, and the Stopsmoking subreddit focused on psychological aspects of quitting. Last, we examined the discussion content on Vapor Talk and Hookah Forum. Prominent topics included equipment, technique, experiential elements of use, and the buying and selling of equipment. Conclusions: This study has three main contributions. Discussion forums differ in the extent to which their content may help us understand behaviors with potential health implications. Identifying dimensions of interest and using a heat map visualization to compare across forums can be helpful for identifying forums with the greatest density of health information. Additionally, our work has shown that the quitting experience can potentially be very different depending on whether or not e-cigarettes are used. Finally, e-cigarette and hookah forums are similar in that members represent a ``hobbyist culture'' that actively engages in information exchange. These differences have important implications for both tobacco regulation and smoking cessation intervention design. ", doi="10.2196/jmir.4517", url="http://www.jmir.org/2015/9/e220/", url="http://www.ncbi.nlm.nih.gov/pubmed/26420469" } @Article{info:doi/10.2196/jmir.4396, author="Adusumalli, Swarnaseetha and Lee, HueyTyng and Hoi, Qiangze and Koo, Si-Lin and Tan, Beehuat Iain and Ng, Crystal Pauline", title="Assessment of Web-Based Consumer Reviews as a Resource for Drug Performance", journal="J Med Internet Res", year="2015", month="Aug", day="28", volume="17", number="8", pages="e211", keywords="consumer drug reviews", keywords="online drug ratings", keywords="WebMD", keywords="online health websites", abstract="Background: Some health websites provide a public forum for consumers to post ratings and reviews on drugs. Drug reviews are easily accessible and comprehensible, unlike clinical trials and published literature. Because the public increasingly uses the Internet as a source of medical information, it is important to know whether such information is reliable. Objective: We aim to examine whether Web-based consumer drug ratings and reviews can be used as a resource to compare drug performance. Methods: We analyzed 103,411 consumer-generated reviews on 615 drugs used to treat 249 disease conditions from the health website WebMD. Statistical analysis identified 427 drug pairs from 24 conditions for which two drugs treating the same condition had significantly and substantially different satisfaction ratings (with at least a half-point difference between Web-based ratings and P<.01). PubMed and Google Scholar were searched for publications that were assessed for concordance with findings online. Results: Scientific literature was found for 77 out of the 427 drug pairs and compared to findings online. Nearly two-thirds (48/77, 62\%) of the online drug trends with at least a half-point difference in online ratings were supported by published literature (P=.02). For a 1-point online rating difference, the concordance rate increased to 68\% (15/22) (P=.07). The discrepancies between scientific literature and findings online were further examined to obtain more insights into the usability of Web-based consumer-generated reviews. We discovered that (1) drugs with FDA black box warnings or used off-label were rated poorly in Web-based reviews, (2) drugs with addictive properties were rated higher than their counterparts in Web-based reviews, and (3) second-line or alternative drugs were rated higher. In addition, Web-based ratings indicated drug delivery problems. If FDA black box warning labels are used to resolve disagreements between publications and online trends, the concordance rate increases to 71\% (55/77) (P<.001) for a half-point rating difference and 82\% (18/22) for a 1-point rating difference (P=.002). Our results suggest that Web-based reviews can be used to inform patients' drug choices, with certain caveats. Conclusions: Web-based reviews can be viewed as an orthogonal source of information for consumers, physicians, and drug manufacturers to assess the performance of a drug. However, one should be cautious to rely solely on consumer reviews as ratings can be strongly influenced by the consumer experience. ", doi="10.2196/jmir.4396", url="http://www.jmir.org/2015/8/e211/", url="http://www.ncbi.nlm.nih.gov/pubmed/26319108" } @Article{info:doi/10.2196/jmir.4392, author="Cole-Lewis, Heather and Varghese, Arun and Sanders, Amy and Schwarz, Mary and Pugatch, Jillian and Augustson, Erik", title="Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning", journal="J Med Internet Res", year="2015", month="Aug", day="25", volume="17", number="8", pages="e208", keywords="social media", keywords="Twitter", keywords="e-cigarette", keywords="machine learning", abstract="Background: Electronic cigarettes (e-cigarettes) continue to be a growing topic among social media users, especially on Twitter. The ability to analyze conversations about e-cigarettes in real-time can provide important insight into trends in the public's knowledge, attitudes, and beliefs surrounding e-cigarettes, and subsequently guide public health interventions. Objective: Our aim was to establish a supervised machine learning algorithm to build predictive classification models that assess Twitter data for a range of factors related to e-cigarettes. Methods: Manual content analysis was conducted for 17,098 tweets. These tweets were coded for five categories: e-cigarette relevance, sentiment, user description, genre, and theme. Machine learning classification models were then built for each of these five categories, and word groupings (n-grams) were used to define the feature space for each classifier. Results: Predictive performance scores for classification models indicated that the models correctly labeled the tweets with the appropriate variables between 68.40\% and 99.34\% of the time, and the percentage of maximum possible improvement over a random baseline that was achieved by the classification models ranged from 41.59\% to 80.62\%. Classifiers with the highest performance scores that also achieved the highest percentage of the maximum possible improvement over a random baseline were Policy/Government (performance: 0.94; \% improvement: 80.62\%), Relevance (performance: 0.94; \% improvement: 75.26\%), Ad or Promotion (performance: 0.89; \% improvement: 72.69\%), and Marketing (performance: 0.91; \% improvement: 72.56\%). The most appropriate word-grouping unit (n-gram) was 1 for the majority of classifiers. Performance continued to marginally increase with the size of the training dataset of manually annotated data, but eventually leveled off. Even at low dataset sizes of 4000 observations, performance characteristics were fairly sound. Conclusions: Social media outlets like Twitter can uncover real-time snapshots of personal sentiment, knowledge, attitudes, and behavior that are not as accessible, at this scale, through any other offline platform. Using the vast data available through social media presents an opportunity for social science and public health methodologies to utilize computational methodologies to enhance and extend research and practice. This study was successful in automating a complex five-category manual content analysis of e-cigarette-related content on Twitter using machine learning techniques. The study details machine learning model specifications that provided the best accuracy for data related to e-cigarettes, as well as a replicable methodology to allow extension of these methods to additional topics. ", doi="10.2196/jmir.4392", url="http://www.jmir.org/2015/8/e208/", url="http://www.ncbi.nlm.nih.gov/pubmed/26307512" } @Article{info:doi/10.2196/jmir.4427, author="Callahan, Alison and Pernek, Igor and Stiglic, Gregor and Leskovec, Jure and Strasberg, R. Howard and Shah, Haresh Nigam", title="Analyzing Information Seeking and Drug-Safety Alert Response by Health Care Professionals as New Methods for Surveillance", journal="J Med Internet Res", year="2015", month="Aug", day="20", volume="17", number="8", pages="e204", keywords="Internet log analysis", keywords="data mining", keywords="physicians", keywords="information-seeking behavior", keywords="drug safety surveillance", abstract="Background: Patterns in general consumer online search logs have been used to monitor health conditions and to predict health-related activities, but the multiple contexts within which consumers perform online searches make significant associations difficult to interpret. Physician information-seeking behavior has typically been analyzed through survey-based approaches and literature reviews. Activity logs from health care professionals using online medical information resources are thus a valuable yet relatively untapped resource for large-scale medical surveillance. Objective: To analyze health care professionals' information-seeking behavior and assess the feasibility of measuring drug-safety alert response from the usage logs of an online medical information resource. Methods: Using two years (2011-2012) of usage logs from UpToDate, we measured the volume of searches related to medical conditions with significant burden in the United States, as well as the seasonal distribution of those searches. We quantified the relationship between searches and resulting page views. Using a large collection of online mainstream media articles and Web log posts we also characterized the uptake of a Food and Drug Administration (FDA) alert via changes in UpToDate search activity compared with general online media activity related to the subject of the alert. Results: Diseases and symptoms dominate UpToDate searches. Some searches result in page views of only short duration, while others consistently result in longer-than-average page views. The response to an FDA alert for Celexa, characterized by a change in UpToDate search activity, differed considerably from general online media activity. Changes in search activity appeared later and persisted longer in UpToDate logs. The volume of searches and page view durations related to Celexa before the alert also differed from those after the alert. Conclusions: Understanding the information-seeking behavior associated with online evidence sources can offer insight into the information needs of health professionals and enable large-scale medical surveillance. Our Web log mining approach has the potential to monitor responses to FDA alerts at a national level. Our findings can also inform the design and content of evidence-based medical information resources such as UpToDate. ", doi="10.2196/jmir.4427", url="http://www.jmir.org/2015/8/e204/", url="http://www.ncbi.nlm.nih.gov/pubmed/26293444" } @Article{info:doi/10.2196/publichealth.4488, author="Adrover, Cosme and Bodnar, Todd and Huang, Zhuojie and Telenti, Amalio and Salath{\'e}, Marcel", title="Identifying Adverse Effects of HIV Drug Treatment and Associated Sentiments Using Twitter", journal="JMIR Public Health Surveill", year="2015", month="Jul", day="27", volume="1", number="2", pages="e7", keywords="Twitter", keywords="HIV", keywords="AIDS", keywords="pharmacovigilance", keywords="mTurk", keywords="mechanical Turk", abstract="Background: Social media platforms are increasingly seen as a source of data on a wide range of health issues. Twitter is of particular interest for public health surveillance because of its public nature. However, the very public nature of social media platforms such as Twitter may act as a barrier to public health surveillance, as people may be reluctant to publicly disclose information about their health. This is of particular concern in the context of diseases that are associated with a certain degree of stigma, such as HIV/AIDS. Objective: The objective of the study is to assess whether adverse effects of HIV drug treatment and associated sentiments can be determined using publicly available data from social media. Methods: We describe a combined approach of machine learning and crowdsourced human assessment to identify adverse effects of HIV drug treatment solely on individual reports posted publicly on Twitter. Starting from a large dataset of 40 million tweets collected over three years, we identify a very small subset (1642; 0.004\%) of individual reports describing personal experiences with HIV drug treatment. Results: Despite the small size of the extracted final dataset, the summary representation of adverse effects attributed to specific drugs, or drug combinations, accurately captures well-recognized toxicities. In addition, the data allowed us to discriminate across specific drug compounds, to identify preferred drugs over time, and to capture novel events such as the availability of preexposure prophylaxis. Conclusions: The effect of limited data sharing due to the public nature of the data can be partially offset by the large number of people sharing data in the first place, an observation that may play a key role in digital epidemiology in general. ", doi="10.2196/publichealth.4488", url="http://publichealth.jmir.org/2015/2/e7/", url="http://www.ncbi.nlm.nih.gov/pubmed/27227141" } @Article{info:doi/10.2196/jmir.4304, author="Lardon, J{\'e}r{\'e}my and Abdellaoui, Redhouane and Bellet, Florelle and Asfari, Hadyl and Souvignet, Julien and Texier, Nathalie and Jaulent, Marie-Christine and Beyens, Marie-No{\"e}lle and Burgun, Anita and Bousquet, C{\'e}dric", title="Adverse Drug Reaction Identification and Extraction in Social Media: A Scoping Review", journal="J Med Internet Res", year="2015", month="Jul", day="10", volume="17", number="7", pages="e171", keywords="pharmacovigilance", keywords="adverse drug reaction", keywords="Internet", keywords="Web 2.0", keywords="social media", keywords="text mining", keywords="scoping review", keywords="adverse event", abstract="Background: The underreporting of adverse drug reactions (ADRs) through traditional reporting channels is a limitation in the efficiency of the current pharmacovigilance system. Patients' experiences with drugs that they report on social media represent a new source of data that may have some value in postmarketing safety surveillance. Objective: A scoping review was undertaken to explore the breadth of evidence about the use of social media as a new source of knowledge for pharmacovigilance. Methods: Daubt et al's recommendations for scoping reviews were followed. The research questions were as follows: How can social media be used as a data source for postmarketing drug surveillance? What are the available methods for extracting data? What are the different ways to use these data? We queried PubMed, Embase, and Google Scholar to extract relevant articles that were published before June 2014 and with no lower date limit. Two pairs of reviewers independently screened the selected studies and proposed two themes of review: manual ADR identification (theme 1) and automated ADR extraction from social media (theme 2). Descriptive characteristics were collected from the publications to create a database for themes 1 and 2. Results: Of the 1032 citations from PubMed and Embase, 11 were relevant to the research question. An additional 13 citations were added after further research on the Internet and in reference lists. Themes 1 and 2 explored 11 and 13 articles, respectively. Ways of approaching the use of social media as a pharmacovigilance data source were identified. Conclusions: This scoping review noted multiple methods for identifying target data, extracting them, and evaluating the quality of medical information from social media. It also showed some remaining gaps in the field. Studies related to the identification theme usually failed to accurately assess the completeness, quality, and reliability of the data that were analyzed from social media. Regarding extraction, no study proposed a generic approach to easily adding a new site or data source. Additional studies are required to precisely determine the role of social media in the pharmacovigilance system. ", doi="10.2196/jmir.4304", url="http://www.jmir.org/2015/7/e171/", url="http://www.ncbi.nlm.nih.gov/pubmed/26163365" } @Article{info:doi/10.2196/resprot.3433, author="Rastegar-Mojarad, Majid and Ye, Zhan and Wall, Daniel and Murali, Narayana and Lin, Simon", title="Collecting and Analyzing Patient Experiences of Health Care From Social Media", journal="JMIR Res Protoc", year="2015", month="Jul", day="02", volume="4", number="3", pages="e78", keywords="patient satisfaction", keywords="social media", keywords="health care", keywords="natural language processing", keywords="consumer health information", abstract="Background: Social Media, such as Yelp, provides rich information of consumer experience. Previous studies suggest that Yelp can serve as a new source to study patient experience. However, the lack of a corpus of patient reviews causes a major bottleneck for applying computational techniques. Objective: The objective of this study is to create a corpus of patient experience (COPE) and report descriptive statistics to characterize COPE. Methods: Yelp reviews about health care-related businesses were extracted from the Yelp Academic Dataset. Natural language processing (NLP) tools were used to split reviews into sentences, extract noun phrases and adjectives from each sentence, and generate parse trees and dependency trees for each sentence. Sentiment analysis techniques and Hadoop were used to calculate a sentiment score of each sentence and for parallel processing, respectively. Results: COPE contains 79,173 sentences from 6914 patient reviews of 985 health care facilities near 30 universities in the United States. We found that patients wrote longer reviews when they rated the facility poorly (1 or 2 stars). We demonstrated that the computed sentiment scores correlated well with consumer-generated ratings. A consumer vocabulary to describe their health care experience was constructed by a statistical analysis of word counts and co-occurrences in COPE. Conclusions: A corpus called COPE was built as an initial step to utilize social media to understand patient experiences at health care facilities. The corpus is available to download and COPE can be used in future studies to extract knowledge of patients' experiences from their perspectives. Such information can subsequently inform and provide opportunity to improve the quality of health care. ", doi="10.2196/resprot.3433", url="http://www.researchprotocols.org/2015/3/e78/", url="http://www.ncbi.nlm.nih.gov/pubmed/26137885" } @Article{info:doi/10.2196/jmir.4103, author="Koschack, Janka and Weibezahl, Lara and Friede, Tim and Himmel, Wolfgang and Makedonski, Philip and Grabowski, Jens", title="Scientific Versus Experiential Evidence: Discourse Analysis of the Chronic Cerebrospinal Venous Insufficiency Debate in a Multiple Sclerosis Forum", journal="J Med Internet Res", year="2015", month="Jul", day="01", volume="17", number="7", pages="e159", keywords="multiple sclerosis", keywords="venous insufficiency", keywords="Internet", keywords="social media", keywords="cognitive dissonance", keywords="qualitative research", abstract="Background: The vascular hypothesis of multiple sclerosis (MS), called chronic cerebrospinal venous insufficiency (CCSVI), and its treatment (known as liberation therapy) was immediately rejected by experts but enthusiastically gripped by patients who shared their experiences with other patients worldwide by use of social media, such as patient online forums. Contradictions between scientific information and lay experiences may be a source of distress for MS patients, but we do not know how patients perceive and deal with these contradictions. Objective: We aimed to understand whether scientific and experiential knowledge were experienced as contradictory in MS patient online forums and, if so, how these contradictions were resolved and how patients tried to reconcile the CCSVI debate with their own illness history and experience. Methods: By using critical discourse analysis, we studied CCSVI-related posts in the patient online forum of the German MS Society in a chronological order from the first post mentioning CCSVI to the time point when saturation was reached. For that time period, a total of 117 CCSVI-related threads containing 1907 posts were identified. We analyzed the interaction and communication practices of and between individuals, looked for the relation between concrete subtopics to identify more abstract discourse strands, and tried to reveal discourse positions explaining how users took part in the CCSVI discussion. Results: There was an emotionally charged debate about CCSVI which could be generalized to 2 discourse strands: (1) the ``downfall of the professional knowledge providers'' and (2) the ``rise of the nonprofessional treasure trove of experience.'' The discourse strands indicated that the discussion moved away from the question whether scientific or experiential knowledge had more evidentiary value. Rather, the question whom to trust (ie, scientists, fellow sufferers, or no one at all) was of fundamental significance. Four discourse positions could be identified by arranging them into the dimensions ``trust in evidence-based knowledge,'' ``trust in experience-based knowledge,'' and ``subjectivity'' (ie, the emotional character of contributions manifested by the use of popular rhetoric that seemed to mask a deep personal involvement). Conclusions: By critical discourse analysis of the CCSVI discussion in a patient online forum, we reconstruct a lay discourse about the evidentiary value of knowledge. We detected evidence criteria in this lay discourse that are different from those in the expert discourse. But we should be cautious to interpret this dissociation as a sign of an intellectual incapability to understand scientific evidence or a na{\"i}ve trust in experiential knowledge. Instead, it might be an indication of cognitive dissonance reduction to protect oneself against contradictory information. ", doi="10.2196/jmir.4103", url="http://www.jmir.org/2015/7/e159/", url="http://www.ncbi.nlm.nih.gov/pubmed/26133525" } @Article{info:doi/10.2196/publichealth.3953, author="Weeg, Christopher and Schwartz, Andrew H. and Hill, Shawndra and Merchant, M. Raina and Arango, Catalina and Ungar, Lyle", title="Using Twitter to Measure Public Discussion of Diseases: A Case Study", journal="JMIR Public Health Surveill", year="2015", month="Jun", day="26", volume="1", number="1", pages="e6", keywords="bias", keywords="data mining", keywords="demographics", keywords="disease", keywords="epidemiology", keywords="prevalence", keywords="public health", keywords="social media", abstract="Background: Twitter is increasingly used to estimate disease prevalence, but such measurements can be biased, due to both biased sampling and inherent ambiguity of natural language. Objective: We characterized the extent of these biases and how they vary with disease. Methods: We correlated self-reported prevalence rates for 22 diseases from Experian's Simmons National Consumer Study (n=12,305) with the number of times these diseases were mentioned on Twitter during the same period (2012). We also identified and corrected for two types of bias present in Twitter data: (1) demographic variance between US Twitter users and the general US population; and (2) natural language ambiguity, which creates the possibility that mention of a disease name may not actually refer to the disease (eg, ``heart attack'' on Twitter often does not refer to myocardial infarction). We measured the correlation between disease prevalence and Twitter disease mentions both with and without bias correction. This allowed us to quantify each disease's overrepresentation or underrepresentation on Twitter, relative to its prevalence. Results: Our sample included 80,680,449 tweets. Adjusting disease prevalence to correct for Twitter demographics more than doubles the correlation between Twitter disease mentions and disease prevalence in the general population (from .113 to .258, P <.001). In addition, diseases varied widely in how often mentions of their names on Twitter actually referred to the diseases, from 14.89\% (3827/25,704) of instances (for stroke) to 99.92\% (5044/5048) of instances (for arthritis). Applying ambiguity correction to our Twitter corpus achieves a correlation between disease mentions and prevalence of .208 ( P <.001). Simultaneously applying correction for both demographics and ambiguity more than triples the baseline correlation to .366 ( P <.001). Compared with prevalence rates, cancer appeared most overrepresented in Twitter, whereas high cholesterol appeared most underrepresented. Conclusions: Twitter is a potentially useful tool to measure public interest in and concerns about different diseases, but when comparing diseases, improvements can be made by adjusting for population demographics and word ambiguity. ", doi="10.2196/publichealth.3953", url="http://publichealth.jmir.org/2015/1/e6/", url="http://www.ncbi.nlm.nih.gov/pubmed/26925459" } @Article{info:doi/10.2196/jmir.4220, author="Kendra, Lynn Rachel and Karki, Suman and Eickholt, Lee Jesse and Gandy, Lisa", title="Characterizing the Discussion of Antibiotics in the Twittersphere: What is the Bigger Picture?", journal="J Med Internet Res", year="2015", month="Jun", day="19", volume="17", number="6", pages="e154", keywords="Twitter messaging", keywords="social media", keywords="Internet", keywords="Web mining", keywords="semi-supervised learning", keywords="neural network", abstract="Background: User content posted through Twitter has been used for biosurveillance, to characterize public perception of health-related topics, and as a means of distributing information to the general public. Most of the existing work surrounding Twitter and health care has shown Twitter to be an effective medium for these problems but more could be done to provide finer and more efficient access to all pertinent data. Given the diversity of user-generated content, small samples or summary presentations of the data arguably omit a large part of the virtual discussion taking place in the Twittersphere. Still, managing, processing, and querying large amounts of Twitter data is not a trivial task. This work describes tools and techniques capable of handling larger sets of Twitter data and demonstrates their use with the issue of antibiotics. Objective: This work has two principle objectives: (1) to provide an open-source means to efficiently explore all collected tweets and query health-related topics on Twitter, specifically, questions such as what users are saying and how messages are spread, and (2) to characterize the larger discourse taking place on Twitter with respect to antibiotics. Methods: Open-source software suites Hadoop, Flume, and Hive were used to collect and query a large number of Twitter posts. To classify tweets by topic, a deep network classifier was trained using a limited number of manually classified tweets. The particular machine learning approach used also allowed the use of a large number of unclassified tweets to increase performance. Results: Query-based analysis of the collected tweets revealed that a large number of users contributed to the online discussion and that a frequent topic mentioned was resistance. A number of prominent events related to antibiotics led to a number of spikes in activity but these were short in duration. The category-based classifier developed was able to correctly classify 70\% of manually labeled tweets (using a 10-fold cross validation procedure and 9 classes). The classifier also performed well when evaluated on a per category basis. Conclusions: Using existing tools such as Hive, Flume, Hadoop, and machine learning techniques, it is possible to construct tools and workflows to collect and query large amounts of Twitter data to characterize the larger discussion taking place on Twitter with respect to a particular health-related topic. Furthermore, using newer machine learning techniques and a limited number of manually labeled tweets, an entire body of collected tweets can be classified to indicate what topics are driving the virtual, online discussion. The resulting classifier can also be used to efficiently explore collected tweets by category and search for messages of interest or exemplary content. ", doi="10.2196/jmir.4220", url="http://www.jmir.org/2015/6/e154/", url="http://www.ncbi.nlm.nih.gov/pubmed/26091775" } @Article{info:doi/10.2196/jmir.4343, author="Dunn, G. Adam and Leask, Julie and Zhou, Xujuan and Mandl, D. Kenneth and Coiera, Enrico", title="Associations Between Exposure to and Expression of Negative Opinions About Human Papillomavirus Vaccines on Social Media: An Observational Study", journal="J Med Internet Res", year="2015", month="Jun", day="10", volume="17", number="6", pages="e144", keywords="HPV vaccines", keywords="Twitter messaging", keywords="social media", keywords="public health surveillance", keywords="social networks", abstract="Background: Groups and individuals that seek to negatively influence public opinion about the safety and value of vaccination are active in online and social media and may influence decision making within some communities. Objective: We sought to measure whether exposure to negative opinions about human papillomavirus (HPV) vaccines in Twitter communities is associated with the subsequent expression of negative opinions by explicitly measuring potential information exposure over the social structure of Twitter communities. Methods: We hypothesized that prior exposure to opinions rejecting the safety or value of HPV vaccines would be associated with an increased risk of posting similar opinions and tested this hypothesis by analyzing temporal sequences of messages posted on Twitter (tweets). The study design was a retrospective analysis of tweets related to HPV vaccines and the social connections between users. Between October 2013 and April 2014, we collected 83,551 English-language tweets that included terms related to HPV vaccines and the 957,865 social connections among 30,621 users posting or reposting the tweets. Tweets were classified as expressing negative or neutral/positive opinions using a machine learning classifier previously trained on a manually labeled sample. Results: During the 6-month period, 25.13\% (20,994/83,551) of tweets were classified as negative; among the 30,621 users that tweeted about HPV vaccines, 9046 (29.54\%) were exposed to a majority of negative tweets. The likelihood of a user posting a negative tweet after exposure to a majority of negative opinions was 37.78\% (2780/7361) compared to 10.92\% (1234/11,296) for users who were exposed to a majority of positive and neutral tweets corresponding to a relative risk of 3.46 (95\% CI 3.25-3.67, P<.001). Conclusions: The heterogeneous community structure on Twitter appears to skew the information to which users are exposed in relation to HPV vaccines. We found that among users that tweeted about HPV vaccines, those who were more often exposed to negative opinions were more likely to subsequently post negative opinions. Although this research may be useful for identifying individuals and groups currently at risk of disproportionate exposure to misinformation about HPV vaccines, there is a clear need for studies capable of determining the factors that affect the formation and adoption of beliefs about public health interventions. ", doi="10.2196/jmir.4343", url="http://www.jmir.org/2015/6/e144/", url="http://www.ncbi.nlm.nih.gov/pubmed/26063290" } @Article{info:doi/10.2196/jmir.4476, author="McIver, J. David and Hawkins, B. Jared and Chunara, Rumi and Chatterjee, K. Arnaub and Bhandari, Aman and Fitzgerald, P. Timothy and Jain, H. Sachin and Brownstein, S. John", title="Characterizing Sleep Issues Using Twitter", journal="J Med Internet Res", year="2015", month="Jun", day="08", volume="17", number="6", pages="e140", keywords="sleep issues", keywords="social media", keywords="insomnia", keywords="novel methods", keywords="sentiment", keywords="depression", abstract="Background: Sleep issues such as insomnia affect over 50 million Americans and can lead to serious health problems, including depression and obesity, and can increase risk of injury. Social media platforms such as Twitter offer exciting potential for their use in studying and identifying both diseases and social phenomenon. Objective: Our aim was to determine whether social media can be used as a method to conduct research focusing on sleep issues. Methods: Twitter posts were collected and curated to determine whether a user exhibited signs of sleep issues based on the presence of several keywords in tweets such as insomnia, ``can't sleep'', Ambien, and others. Users whose tweets contain any of the keywords were designated as having self-identified sleep issues (sleep group). Users who did not have self-identified sleep issues (non-sleep group) were selected from tweets that did not contain pre-defined words or phrases used as a proxy for sleep issues. Results: User data such as number of tweets, friends, followers, and location were collected, as well as the time and date of tweets. Additionally, the sentiment of each tweet and average sentiment of each user were determined to investigate differences between non-sleep and sleep groups. It was found that sleep group users were significantly less active on Twitter (P=.04), had fewer friends (P<.001), and fewer followers (P<.001) compared to others, after adjusting for the length of time each user's account has been active. Sleep group users were more active during typical sleeping hours than others, which may suggest they were having difficulty sleeping. Sleep group users also had significantly lower sentiment in their tweets (P<.001), indicating a possible relationship between sleep and pyschosocial issues. Conclusions: We have demonstrated a novel method for studying sleep issues that allows for fast, cost-effective, and customizable data to be gathered. ", doi="10.2196/jmir.4476", url="http://www.jmir.org/2015/6/e140/", url="http://www.ncbi.nlm.nih.gov/pubmed/26054530" } @Article{info:doi/10.2196/jmir.4305, author="Yin, Zhijun and Fabbri, Daniel and Rosenbloom, Trent S. and Malin, Bradley", title="A Scalable Framework to Detect Personal Health Mentions on Twitter", journal="J Med Internet Res", year="2015", month="Jun", day="05", volume="17", number="6", pages="e138", keywords="consumer health", keywords="information retrieval", keywords="machine learning", keywords="social media", keywords="twitter", keywords="infodemiology", abstract="Background: Biomedical research has traditionally been conducted via surveys and the analysis of medical records. However, these resources are limited in their content, such that non-traditional domains (eg, online forums and social media) have an opportunity to supplement the view of an individual's health. Objective: The objective of this study was to develop a scalable framework to detect personal health status mentions on Twitter and assess the extent to which such information is disclosed. Methods: We collected more than 250 million tweets via the Twitter streaming API over a 2-month period in 2014. The corpus was filtered down to approximately 250,000 tweets, stratified across 34 high-impact health issues, based on guidance from the Medical Expenditure Panel Survey. We created a labeled corpus of several thousand tweets via a survey, administered over Amazon Mechanical Turk, that documents when terms correspond to mentions of personal health issues or an alternative (eg, a metaphor). We engineered a scalable classifier for personal health mentions via feature selection and assessed its potential over the health issues. We further investigated the utility of the tweets by determining the extent to which Twitter users disclose personal health status. Results: Our investigation yielded several notable findings. First, we find that tweets from a small subset of the health issues can train a scalable classifier to detect health mentions. Specifically, training on 2000 tweets from four health issues (cancer, depression, hypertension, and leukemia) yielded a classifier with precision of 0.77 on all 34 health issues. Second, Twitter users disclosed personal health status for all health issues. Notably, personal health status was disclosed over 50\% of the time for 11 out of 34 (33\%) investigated health issues. Third, the disclosure rate was dependent on the health issue in a statistically significant manner (P<.001). For instance, more than 80\% of the tweets about migraines (83/100) and allergies (85/100) communicated personal health status, while only around 10\% of the tweets about obesity (13/100) and heart attack (12/100) did so. Fourth, the likelihood that people disclose their own versus other people's health status was dependent on health issue in a statistically significant manner as well (P<.001). For example, 69\% (69/100) of the insomnia tweets disclosed the author's status, while only 1\% (1/100) disclosed another person's status. By contrast, 1\% (1/100) of the Down syndrome tweets disclosed the author's status, while 21\% (21/100) disclosed another person's status. Conclusions: It is possible to automatically detect personal health status mentions on Twitter in a scalable manner. These mentions correspond to the health issues of the Twitter users themselves, but also other individuals. Though this study did not investigate the veracity of such statements, we anticipate such information may be useful in supplementing traditional health-related sources for research purposes. ", doi="10.2196/jmir.4305", url="http://www.jmir.org/2015/6/e138/", url="http://www.ncbi.nlm.nih.gov/pubmed/26048075" } @Article{info:doi/10.2196/publichealth.4472, author="Broniatowski, Andre David and Dredze, Mark and Paul, J. Michael and Dugas, Andrea", title="Using Social Media to Perform Local Influenza Surveillance in an Inner-City Hospital: A Retrospective Observational Study", journal="JMIR Public Health Surveill", year="2015", month="May", day="29", volume="1", number="1", pages="e5", keywords="Web mining", keywords="social computing", keywords="time series analysis", abstract="Background: Public health officials and policy makers in the United States expend significant resources at the national, state, county, and city levels to measure the rate of influenza infection. These individuals rely on influenza infection rate information to make important decisions during the course of an influenza season driving vaccination campaigns, clinical guidelines, and medical staffing. Web and social media data sources have emerged as attractive alternatives to supplement existing practices. While traditional surveillance methods take 1-2 weeks, and significant labor, to produce an infection estimate in each locale, web and social media data are available in near real-time for a broad range of locations. Objective: The objective of this study was to analyze the efficacy of flu surveillance from combining data from the websites Google Flu Trends and HealthTweets at the local level. We considered both emergency department influenza-like illness cases and laboratory-confirmed influenza cases for a single hospital in the City of Baltimore. Methods: This was a retrospective observational study comparing estimates of influenza activity of Google Flu Trends and Twitter to actual counts of individuals with laboratory-confirmed influenza, and counts of individuals presenting to the emergency department with influenza-like illness cases. Data were collected from November 20, 2011 through March 16, 2014. Each parameter was evaluated on the municipal, regional, and national scale. We examined the utility of social media data for tracking actual influenza infection at the municipal, state, and national levels. Specifically, we compared the efficacy of Twitter and Google Flu Trends data. Results: We found that municipal-level Twitter data was more effective than regional and national data when tracking actual influenza infection rates in a Baltimore inner-city hospital. When combined, national-level Twitter and Google Flu Trends data outperformed each data source individually. In addition, influenza-like illness data at all levels of geographic granularity were best predicted by national Google Flu Trends data. Conclusions: In order to overcome sensitivity to transient events, such as the news cycle, the best-fitting Google Flu Trends model relies on a 4-week moving average, suggesting that it may also be sacrificing sensitivity to transient fluctuations in influenza infection to achieve predictive power. Implications for influenza forecasting are discussed in this report. ", doi="10.2196/publichealth.4472", url="http://publichealth.jmir.org/2015/1/e5/", url="http://www.ncbi.nlm.nih.gov/pubmed/27014744" } @Article{info:doi/10.2196/jmir.3863, author="Mollema, Liesbeth and Harmsen, Anhai Irene and Broekhuizen, Emma and Clijnk, Rutger and De Melker, Hester and Paulussen, Theo and Kok, Gerjo and Ruiter, Robert and Das, Enny", title="Disease Detection or Public Opinion Reflection? Content Analysis of Tweets, Other Social Media, and Online Newspapers During the Measles Outbreak in the Netherlands in 2013", journal="J Med Internet Res", year="2015", month="May", day="26", volume="17", number="5", pages="e128", keywords="Internet", keywords="Web 2.0", keywords="measles", keywords="infectious disease outbreak", keywords="Netherlands", keywords="vaccination", abstract="Background: In May 2013, a measles outbreak began in the Netherlands among Orthodox Protestants who often refuse vaccination for religious reasons. Objective: Our aim was to compare the number of messages expressed on Twitter and other social media during the measles outbreak with the number of online news articles and the number of reported measles cases to answer the question if and when social media reflect public opinion patterns versus disease patterns. Methods: We analyzed measles-related tweets, other social media messages, and online newspaper articles over a 7-month period (April 15 to November 11, 2013) with regard to topic and sentiment. Thematic analysis was used to structure and analyze the topics. Results: There was a stronger correlation between the weekly number of social media messages and the weekly number of online news articles (P<.001 for both tweets and other social media messages) than between the weekly number of social media messages and the weekly number of reported measles cases (P=.003 and P=.048 for tweets and other social media messages, respectively), especially after the summer break. All data sources showed 3 large peaks, possibly triggered by announcements about the measles outbreak by the Dutch National Institute for Public Health and the Environment and statements made by well-known politicians. Most messages informed the public about the measles outbreak (ie, about the number of measles cases) (93/165, 56.4\%) followed by messages about preventive measures taken to control the measles spread (47/132, 35.6\%). The leading opinion expressed was frustration regarding people who do not vaccinate because of religious reasons (42/88, 48\%). Conclusions: The monitoring of online (social) media might be useful for improving communication policies aiming to preserve vaccination acceptability among the general public. Data extracted from online (social) media provide insight into the opinions that are at a certain moment salient among the public, which enables public health institutes to respond immediately and appropriately to those public concerns. More research is required to develop an automatic coding system that captures content and user's characteristics that are most relevant to the diseases within the National Immunization Program and related public health events and can inform official responses. ", doi="10.2196/jmir.3863", url="http://www.jmir.org/2015/5/e128/", url="http://www.ncbi.nlm.nih.gov/pubmed/26013683" } @Article{info:doi/10.2196/cancer.3883, author="Sato, Akira and Aramaki, Eiji and Shimamoto, Yumiko and Tanaka, Shiro and Kawakami, Koji", title="Blog Posting After Lung Cancer Notification: Content Analysis of Blogs Written by Patients or Their Families", journal="JMIR Cancer", year="2015", month="May", day="18", volume="1", number="1", pages="e5", keywords="blog", keywords="lung cancer", keywords="notification", keywords="content analysis", keywords="communication", keywords="Internet", abstract="Background: The advent and spread of the Internet has changed the way societies communicate. A portion of information on the Internet may constitute an important source of information concerning the experiences and thoughts of patients and their families. Patients and their families use blogs to obtain updated information, search for alternative treatments, facilitate communication with other patients, and receive emotional support. However, much of this information has yet to be actively utilized by health care professionals. Objective: We analyzed health-related information in blogs from Japan, focusing on the feelings and satisfaction levels of lung cancer patients or their family members after being notified of their disease. Methods: We collected 100 blogs written in Japanese by patients (or their families) who had been diagnosed with lung cancer by a physician. These 100 blogs posts were searchable between June 1 and June 30, 2013. We focused on blog posts that addressed the lung cancer notification event. We analyzed the data using two different approaches (Analysis A and Analysis B). Analysis A was blog content analysis in which we analyzed the content addressing the disease notification event in each blog. Analysis B was patient's dissatisfaction and anxiety analysis. Detailed blog content regarding patient's dissatisfaction and anxiety at the individual sentence level was coded and analyzed. Results: The 100 blog posts were written by 48 men, 46 women, and 6 persons whose sex was undisclosed. The average age of the blog authors was 52.4 years. With regard to cancer staging, there were 5 patients at Stage I, 3 patients at Stage II, 14 patients at Stage III, 21 patients at Stage IV, and 57 patients without a disclosed cancer stage. The results of Analysis A showed that the proportion of patients who were dissatisfied with the level of health care exceeded that of satisfied patients (22\% vs 8\%). From the 2499 sentences in the 100 blog posts analyzed, we identified expressions of dissatisfaction and anxiety in 495 sentences. Our results showed that there were substantially more posts concerning ``Way of living, reasons for living, set of values'' and ``Relationships with medical staff (own hospital)'' than in previous studies (Analysis B). Conclusions: This study provides insight into the feelings of dissatisfaction and anxieties held by lung cancer patients and their families, including those regarding the ``Way of living, reasons for living, set of values'' and ``Relationship with medical staff (own hospital),'' which were inaccessible in previous survey analyses. When comparing information obtained from patients' voluntary records and those from previous surveys conducted by health care institutions, it is likely that the former would be more indicative of patients' actual opinions and feelings. Therefore, it is important to utilize such records as an information resource. ", doi="10.2196/cancer.3883", url="https://cancer.jmir.org/2015/1/e5/", url="http://www.ncbi.nlm.nih.gov/pubmed/28410169" } @Article{info:doi/10.2196/mental.4113, author="Krueger, A. Evan and Young, D. Sean", title="Twitter: A Novel Tool for Studying the Health and Social Needs of Transgender Communities", journal="JMIR Mental Health", year="2015", month="May", day="07", volume="2", number="2", pages="e16", keywords="Twitter", keywords="social media", keywords="transgender", keywords="health", abstract="Background: Limited research has examined the health and social needs of transgender and gender nonconforming populations. Due to high levels of stigma, transgender individuals may avoid disclosing their identities to researchers, hindering this type of work. Further, researchers have traditionally relied on clinic-based sampling methods, which may mask the true heterogeneity of transgender and gender nonconforming communities. Online social networking websites present a novel platform for studying this diverse, difficult-to-reach population. Objective: The objective of this study was to attempt to examine the perceived health and social needs of transgender and gender nonconforming communities by examining messages posted to the popular microblogging platform, Twitter. Methods: Tweets were collected from 13 transgender-related hashtags on July 11, 2014. They were read and coded according to general themes addressed, and a content analysis was performed. Qualitative and descriptive statistics are presented. Results: There were 1135 tweets that were collected in total. Both ``positive'' and ``negative'' events were discussed, in both personal and social contexts. Violence, discrimination, suicide, and sexual risk behavior were discussed. There were 34.36\% (390/1135) of tweets that addressed transgender-relevant current events, and 60.79\% (690/1135) provided a link to a relevant news article or resource. Conclusions: This study found that transgender individuals and allies use Twitter to discuss health and social needs relevant to the population. Real-time social media sites like Twitter can be used to study issues relevant to transgender communities. ", doi="10.2196/mental.4113", url="http://mental.jmir.org/2015/2/e16/", url="http://www.ncbi.nlm.nih.gov/pubmed/26082941" } @Article{info:doi/10.2196/jmir.3970, author="Gittelman, Steven and Lange, Victor and Gotway Crawford, A. Carol and Okoro, A. Catherine and Lieb, Eugene and Dhingra, S. Satvinder and Trimarchi, Elaine", title="A New Source of Data for Public Health Surveillance: Facebook Likes", journal="J Med Internet Res", year="2015", month="Apr", day="20", volume="17", number="4", pages="e98", keywords="big data", keywords="social networks", keywords="surveillance", keywords="chronic illness", abstract="Background: Investigation into personal health has become focused on conditions at an increasingly local level, while response rates have declined and complicated the process of collecting data at an individual level. Simultaneously, social media data have exploded in availability and have been shown to correlate with the prevalence of certain health conditions. Objective: Facebook likes may be a source of digital data that can complement traditional public health surveillance systems and provide data at a local level. We explored the use of Facebook likes as potential predictors of health outcomes and their behavioral determinants. Methods: We performed principal components and regression analyses to examine the predictive qualities of Facebook likes with regard to mortality, diseases, and lifestyle behaviors in 214 counties across the United States and 61 of 67 counties in Florida. These results were compared with those obtainable from a demographic model. Health data were obtained from both the 2010 and 2011 Behavioral Risk Factor Surveillance System (BRFSS) and mortality data were obtained from the National Vital Statistics System. Results: Facebook likes added significant value in predicting most examined health outcomes and behaviors even when controlling for age, race, and socioeconomic status, with model fit improvements (adjusted R2) of an average of 58\% across models for 13 different health-related metrics over basic sociodemographic models. Small area data were not available in sufficient abundance to test the accuracy of the model in estimating health conditions in less populated markets, but initial analysis using data from Florida showed a strong model fit for obesity data (adjusted R2=.77). Conclusions: Facebook likes provide estimates for examined health outcomes and health behaviors that are comparable to those obtained from the BRFSS. Online sources may provide more reliable, timely, and cost-effective county-level data than that obtainable from traditional public health surveillance systems as well as serve as an adjunct to those systems. ", doi="10.2196/jmir.3970", url="http://www.jmir.org/2015/4/e98/", url="http://www.ncbi.nlm.nih.gov/pubmed/25895907" } @Article{info:doi/10.2196/jmir.3646, author="Jung, Yuchul and Hur, Cinyoung and Jung, Dain and Kim, Minki", title="Identifying Key Hospital Service Quality Factors in Online Health Communities", journal="J Med Internet Res", year="2015", month="Apr", day="07", volume="17", number="4", pages="e90", keywords="hospital service factors", keywords="online health communities", keywords="social media-based key quality factors for hospitals", keywords="recommendation type classification", keywords="quality factor analysis", keywords="healthcare policy", abstract="Background: The volume of health-related user-created content, especially hospital-related questions and answers in online health communities, has rapidly increased. Patients and caregivers participate in online community activities to share their experiences, exchange information, and ask about recommended or discredited hospitals. However, there is little research on how to identify hospital service quality automatically from the online communities. In the past, in-depth analysis of hospitals has used random sampling surveys. However, such surveys are becoming impractical owing to the rapidly increasing volume of online data and the diverse analysis requirements of related stakeholders. Objective: As a solution for utilizing large-scale health-related information, we propose a novel approach to identify hospital service quality factors and overtime trends automatically from online health communities, especially hospital-related questions and answers. Methods: We defined social media--based key quality factors for hospitals. In addition, we developed text mining techniques to detect such factors that frequently occur in online health communities. After detecting these factors that represent qualitative aspects of hospitals, we applied a sentiment analysis to recognize the types of recommendations in messages posted within online health communities. Korea's two biggest online portals were used to test the effectiveness of detection of social media--based key quality factors for hospitals. Results: To evaluate the proposed text mining techniques, we performed manual evaluations on the extraction and classification results, such as hospital name, service quality factors, and recommendation types using a random sample of messages (ie, 5.44\% (9450/173,748) of the total messages). Service quality factor detection and hospital name extraction achieved average F1 scores of 91\% and 78\%, respectively. In terms of recommendation classification, performance (ie, precision) is 78\% on average. Extraction and classification performance still has room for improvement, but the extraction results are applicable to more detailed analysis. Further analysis of the extracted information reveals that there are differences in the details of social media--based key quality factors for hospitals according to the regions in Korea, and the patterns of change seem to accurately reflect social events (eg, influenza epidemics). Conclusions: These findings could be used to provide timely information to caregivers, hospital officials, and medical officials for health care policies. ", doi="10.2196/jmir.3646", url="http://www.jmir.org/2015/4/e90/", url="http://www.ncbi.nlm.nih.gov/pubmed/25855612" } @Article{info:doi/10.2196/jmir.3769, author="Tighe, J. Patrick and Goldsmith, C. Ryan and Gravenstein, Michael and Bernard, Russell H. and Fillingim, B. Roger", title="The Painful Tweet: Text, Sentiment, and Community Structure Analyses of Tweets Pertaining to Pain", journal="J Med Internet Res", year="2015", month="Apr", day="02", volume="17", number="4", pages="e84", keywords="Twitter messaging", keywords="emotions", keywords="text mining", keywords="social networks", abstract="Background: Despite the widespread popularity of social media, little is known about the extent or context of pain-related posts by users of those media. Objective: The aim was to examine the type, context, and dissemination of pain-related tweets. Methods: We used content analysis of pain-related tweets from 50 cities to unobtrusively explore the meanings and patterns of communications about pain. Content was examined by location and time of day, as well as within the context of online social networks. Results: The most common terms published in conjunction with the term ``pain'' included feel (n=1504), don't (n=702), and love (n=649). The proportion of tweets with positive sentiment ranged from 13\% in Manila to 56\% in Los Angeles, CA, with a median of 29\% across cities. Temporally, the proportion of tweets with positive sentiment ranged from 24\% at 1600 to 38\% at 2400, with a median of 32\%. The Twitter-based social networks pertaining to pain exhibited greater sparsity and lower connectedness than did those social networks pertaining to common terms such as apple, Manchester United, and Obama. The number of word clusters in proportion to node count was greater for emotion terms such as tired (0.45), happy (0.43), and sad (0.4) when compared with objective terms such as apple (0.26), Manchester United (0.14), and Obama (0.25). Conclusions: Taken together, our results suggest that pain-related tweets carry special characteristics reflecting unique content and their communication among tweeters. Further work will explore how geopolitical events and seasonal changes affect tweeters' perceptions of pain and how such perceptions may affect therapies for pain. ", doi="10.2196/jmir.3769", url="http://www.jmir.org/2015/4/e84/", url="http://www.ncbi.nlm.nih.gov/pubmed/25843553" } @Article{info:doi/10.2196/jmir.3875, author="Wang, Shiliang and Paul, J. Michael and Dredze, Mark", title="Social Media as a Sensor of Air Quality and Public Response in China", journal="J Med Internet Res", year="2015", month="Mar", day="26", volume="17", number="3", pages="e22", keywords="air pollution", keywords="public health surveillance", keywords="social media", keywords="data mining", keywords="text mining", keywords="natural language processing", abstract="Background: Recent studies have demonstrated the utility of social media data sources for a wide range of public health goals, including disease surveillance, mental health trends, and health perceptions and sentiment. Most such research has focused on English-language social media for the task of disease surveillance. Objective: We investigated the value of Chinese social media for monitoring air quality trends and related public perceptions and response. The goal was to determine if this data is suitable for learning actionable information about pollution levels and public response. Methods: We mined a collection of 93 million messages from Sina Weibo, China's largest microblogging service. We experimented with different filters to identify messages relevant to air quality, based on keyword matching and topic modeling. We evaluated the reliability of the data filters by comparing message volume per city to air particle pollution rates obtained from the Chinese government for 74 cities. Additionally, we performed a qualitative study of the content of pollution-related messages by coding a sample of 170 messages for relevance to air quality, and whether the message included details such as a reactive behavior or a health concern. Results: The volume of pollution-related messages is highly correlated with particle pollution levels, with Pearson correlation values up to .718 (n=74, P<.001). Our qualitative results found that 67.1\% (114/170) of messages were relevant to air quality and of those, 78.9\% (90/114) were a firsthand report. Of firsthand reports, 28\% (32/90) indicated a reactive behavior and 19\% (17/90) expressed a health concern. Additionally, 3 messages of 170 requested that action be taken to improve quality. Conclusions: We have found quantitatively that message volume in Sina Weibo is indicative of true particle pollution levels, and we have found qualitatively that messages contain rich details including perceptions, behaviors, and self-reported health effects. Social media data can augment existing air pollution surveillance data, especially perception and health-related data that traditionally requires expensive surveys or interviews. ", doi="10.2196/jmir.3875", url="http://www.jmir.org/2015/3/e22/", url="http://www.ncbi.nlm.nih.gov/pubmed/25831020" } @Article{info:doi/10.2196/publichealth.3310, author="Mahoney, Meghan L. and Tang, Tang and Ji, Kai and Ulrich-Schad, Jessica", title="The Digital Distribution of Public Health News Surrounding the Human Papillomavirus Vaccination: A Longitudinal Infodemiology Study", journal="JMIR Public Health Surveill", year="2015", month="Mar", day="18", volume="1", number="1", pages="e2", keywords="new media", keywords="public health dissemination", keywords="health communication", keywords="social media", keywords="HPV vaccination", keywords="infodemiology", keywords="infoveillance", abstract="Background: New media changes the dissemination of public health information and misinformation. During a guest appearance on the Today Show, US Representative Michele Bachmann claimed that human papillomavirus (HPV) vaccines could cause ``mental retardation''. Objective: The purpose of this study is to explore how new media influences the type of public health information users access, as well as the impact to these platforms after a major controversy. Specifically, this study aims to examine the similarities and differences in the dissemination of news articles related to the HPV vaccination between Google News and Twitter, as well as how the content of news changed after Michele Bachmann's controversial comment. Methods: This study used a purposive sampling to draw the first 100 news articles that appeared on Google News and the first 100 articles that appeared on Twitter from August 1-October 31, 2011. Article tone, source, topics, concerns, references, publication date, and interactive features were coded. The intercoder reliability had a total agreement of .90. Results: Results indicate that 44.0\% of the articles (88/200) about the HPV vaccination had a positive tone, 32.5\% (65/200) maintained a neutral tone, while 23.5\% (47/200) presented a negative tone. Protection against diseases 82.0\% (164/200), vaccine eligibility for females 75.5\% (151/200), and side effects 59.0\% (118/200) were the top three topics covered by these articles. Google News and Twitter articles significantly differed in article tone, source, topics, concerns covered, types of sources referenced in the article, and uses of interactive features. Most notably, topic focus changed from public health information towards political conversation after Bachmann's comment. Before the comment, the HPV vaccine news talked more often about vaccine dosing (P<.001), duration (P=.005), vaccine eligibility for females (P=.03), and protection against diseases (P=.04) than did the later pieces. After the controversy, the news topic shifted towards politics (P=.01) and talked more about HPV vaccine eligibility for males (P=.01). Conclusions: This longitudinal infodemiology study suggests that new media influences public health communication, knowledge transaction, and poses potential problems in the amount of misinformation disseminated during public health campaigns. In addition, the study calls for more research to adopt an infodemiology approach to explore relationships between online information supply and public health decisions. ", doi="10.2196/publichealth.3310", url="http://publichealth.jmir.org/2015/1/e2/", url="http://www.ncbi.nlm.nih.gov/pubmed/27227125" } @Article{info:doi/10.2196/jmir.3812, author="Wong, A. Charlene and Sap, Maarten and Schwartz, Andrew and Town, Robert and Baker, Tom and Ungar, Lyle and Merchant, M. Raina", title="Twitter Sentiment Predicts Affordable Care Act Marketplace Enrollment", journal="J Med Internet Res", year="2015", month="Feb", day="23", volume="17", number="2", pages="e51", keywords="affordable care act", keywords="social media", keywords="health insurance marketplace", abstract="Background: Traditional metrics of the impact of the Affordable Care Act (ACA) and health insurance marketplaces in the United States include public opinion polls and marketplace enrollment, which are published with a lag of weeks to months. In this rapidly changing environment, a real-time barometer of public opinion with a mechanism to identify emerging issues would be valuable. Objective: We sought to evaluate Twitter's role as a real-time barometer of public sentiment on the ACA and to determine if Twitter sentiment (the positivity or negativity of tweets) could be predictive of state-level marketplace enrollment. Methods: We retrospectively collected 977,303 ACA-related tweets in March 2014 and then tested a correlation of Twitter sentiment with marketplace enrollment by state. Results: A 0.10 increase in the sentiment score was associated with an 8.7\% increase in enrollment at the state level (95\% CI 1.32-16.13; P=.02), a correlation that remained significant when adjusting for state Medicaid expansion (P=.02) or use of a state-based marketplace (P=.03). Conclusions: This correlation indicates Twitter's potential as a real-time monitoring strategy for future marketplace enrollment periods; marketplaces could systematically track Twitter sentiment to more rapidly identify enrollment changes and potentially emerging issues. As a repository of free and accessible consumer-generated opinions, this study reveals a novel role for Twitter in the health policy landscape. ", doi="10.2196/jmir.3812", url="http://www.jmir.org/2015/2/e51/", url="http://www.ncbi.nlm.nih.gov/pubmed/25707038" } @Article{info:doi/10.2196/jmir.4082, author="Yom-Tov, Elad and Borsa, Diana and Hayward, C. Andrew and McKendry, A. Rachel and Cox, J. Ingemar", title="Automatic Identification of Web-Based Risk Markers for Health Events", journal="J Med Internet Res", year="2015", month="Jan", day="27", volume="17", number="1", pages="e29", keywords="Information retrieval query processing", keywords="epidemiology", keywords="self-controlled case series", keywords="Machine Learning", keywords="Web search engines", abstract="Background: The escalating cost of global health care is driving the development of new technologies to identify early indicators of an individual's risk of disease. Traditionally, epidemiologists have identified such risk factors using medical databases and lengthy clinical studies but these are often limited in size and cost and can fail to take full account of diseases where there are social stigmas or to identify transient acute risk factors. Objective: Here we report that Web search engine queries coupled with information on Wikipedia access patterns can be used to infer health events associated with an individual user and automatically generate Web-based risk markers for some of the common medical conditions worldwide, from cardiovascular disease to sexually transmitted infections and mental health conditions, as well as pregnancy. Methods: Using anonymized datasets, we present methods to first distinguish individuals likely to have experienced specific health events, and classify them into distinct categories. We then use the self-controlled case series method to find the incidence of health events in risk periods directly following a user's search for a query category, and compare to the incidence during other periods for the same individuals. Results: Searches for pet stores were risk markers for allergy. We also identified some possible new risk markers; for example: searching for fast food and theme restaurants was associated with a transient increase in risk of myocardial infarction, suggesting this exposure goes beyond a long-term risk factor but may also act as an acute trigger of myocardial infarction. Dating and adult content websites were risk markers for sexually transmitted infections, such as human immunodeficiency virus (HIV). Conclusions: Web-based methods provide a powerful, low-cost approach to automatically identify risk factors, and support more timely and personalized public health efforts to bring human and economic benefits. ", doi="10.2196/jmir.4082", url="http://www.jmir.org/2015/1/e29/", url="http://www.ncbi.nlm.nih.gov/pubmed/25626480" } @Article{info:doi/10.2196/jmir.3617, author="Conway, Mike", title="Ethical Issues in Using Twitter for Public Health Surveillance and Research: Developing a Taxonomy of Ethical Concepts From the Research Literature", journal="J Med Internet Res", year="2014", month="Dec", day="22", volume="16", number="12", pages="e290", keywords="social media", keywords="twitter messaging", keywords="ethics", abstract="Background: The rise of social media and microblogging platforms in recent years, in conjunction with the development of techniques for the processing and analysis of ``big data'', has provided significant opportunities for public health surveillance using user-generated content. However, relatively little attention has been focused on developing ethically appropriate approaches to working with these new data sources. Objective: Based on a review of the literature, this study seeks to develop a taxonomy of public health surveillance-related ethical concepts that emerge when using Twitter data, with a view to: (1) explicitly identifying a set of potential ethical issues and concerns that may arise when researchers work with Twitter data, and (2) providing a starting point for the formation of a set of best practices for public health surveillance through the development of an empirically derived taxonomy of ethical concepts. Methods: We searched Medline, Compendex, PsycINFO, and the Philosopher's Index using a set of keywords selected to identify Twitter-related research papers that reference ethical concepts. Our initial set of queries identified 342 references across the four bibliographic databases. We screened titles and abstracts of these references using our inclusion/exclusion criteria, eliminating duplicates and unavailable papers, until 49 references remained. We then read the full text of these 49 articles and discarded 36, resulting in a final inclusion set of 13 articles. Ethical concepts were then identified in each of these 13 articles. Finally, based on a close reading of the text, a taxonomy of ethical concepts was constructed based on ethical concepts discovered in the papers. Results: From these 13 articles, we iteratively generated a taxonomy of ethical concepts consisting of 10 top level categories: privacy, informed consent, ethical theory, institutional review board (IRB)/regulation, traditional research vs Twitter research, geographical information, researcher lurking, economic value of personal information, medical exceptionalism, and benefit of identifying socially harmful medical conditions. Conclusions: In summary, based on a review of the literature, we present a provisional taxonomy of public health surveillance-related ethical concepts that emerge when using Twitter data. ", doi="10.2196/jmir.3617", url="http://www.jmir.org/2014/12/e290/", url="http://www.ncbi.nlm.nih.gov/pubmed/25533619" } @Article{info:doi/10.2196/jmir.3680, author="Seo, Dong-Woo and Jo, Min-Woo and Sohn, Hwan Chang and Shin, Soo-Yong and Lee, JaeHo and Yu, Maengsoo and Kim, Young Won and Lim, Soo Kyoung and Lee, Sang-Il", title="Cumulative Query Method for Influenza Surveillance Using Search Engine Data", journal="J Med Internet Res", year="2014", month="Dec", day="16", volume="16", number="12", pages="e289", keywords="syndromic surveillance system", keywords="influenza", keywords="influenza-like illness", keywords="Google Flu Trends", keywords="Internet search", keywords="query", abstract="Background: Internet search queries have become an important data source in syndromic surveillance system. However, there is currently no syndromic surveillance system using Internet search query data in South Korea. Objectives: The objective of this study was to examine correlations between our cumulative query method and national influenza surveillance data. Methods: Our study was based on the local search engine, Daum (approximately 25\% market share), and influenza-like illness (ILI) data from the Korea Centers for Disease Control and Prevention. A quota sampling survey was conducted with 200 participants to obtain popular queries. We divided the study period into two sets: Set 1 (the 2009/10 epidemiological year for development set 1 and 2010/11 for validation set 1) and Set 2 (2010/11 for development Set 2 and 2011/12 for validation Set 2). Pearson's correlation coefficients were calculated between the Daum data and the ILI data for the development set. We selected the combined queries for which the correlation coefficients were .7 or higher and listed them in descending order. Then, we created a cumulative query method n representing the number of cumulative combined queries in descending order of the correlation coefficient. Results: In validation set 1, 13 cumulative query methods were applied, and 8 had higher correlation coefficients (min=.916, max=.943) than that of the highest single combined query. Further, 11 of 13 cumulative query methods had an r value of ?.7, but 4 of 13 combined queries had an r value of ?.7. In validation set 2, 8 of 15 cumulative query methods showed higher correlation coefficients (min=.975, max=.987) than that of the highest single combined query. All 15 cumulative query methods had an r value of ?.7, but 6 of 15 combined queries had an r value of ?.7. Conclusions: Cumulative query method showed relatively higher correlation with national influenza surveillance data than combined queries in the development and validation set. ", doi="10.2196/jmir.3680", url="http://www.jmir.org/2014/12/e289/", url="http://www.ncbi.nlm.nih.gov/pubmed/25517353" } @Article{info:doi/10.2196/jmir.3523, author="Delaney, P. Kevin and Kramer, R. Michael and Waller, A. Lance and Flanders, Dana W. and Sullivan, S. Patrick", title="Using a Geolocation Social Networking Application to Calculate the Population Density of Sex-Seeking Gay Men for Research and Prevention Services", journal="J Med Internet Res", year="2014", month="Nov", day="18", volume="16", number="11", pages="e249", keywords="Internet", keywords="HIV", keywords="MSM", keywords="sampling, location services", abstract="Background: In the United States, human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) continues to have a heavy impact on men who have sex with men (MSM). Among MSM, black men under the age of 30 are at the most risk for being diagnosed with HIV. The US National HIV/AIDS strategy recommends intensifying efforts in communities that are most heavily impacted; to do so requires new methods for identifying and targeting prevention resources to young MSM, especially young MSM of color. Objective: We piloted a methodology for using the geolocation features of social and sexual networking applications as a novel approach to calculating the local population density of sex-seeking MSM and to use self-reported age and race from profile postings to highlight areas with a high density of minority and young minority MSM in Atlanta, Georgia. Methods: We collected data from a geographically systematic sample of points in Atlanta. We used a sexual network mobile phone app and collected application profile data, including age, race, and distance from each point, for either the 50 closest users or for all users within a 2-mile radius of sampled points. From these data, we developed estimates of the spatial density of application users in the entire city, stratified by race. We then compared the ratios and differences between the spatial densities of black and white users and developed an indicator of areas with the highest density of users of each race. Results: We collected data from 2666 profiles at 79 sampled points covering 883 square miles; overlapping circles of data included the entire 132.4 square miles in Atlanta. Of the 2666 men whose profiles were observed, 1563 (58.63\%) were white, 810 (30.38\%) were black, 146 (5.48\%) were another race, and 147 (5.51\%) did not report a race in their profile. The mean age was 31.5 years, with 591 (22.17\%) between the ages of 18-25, and 496 (18.60\%) between the ages of 26-30. The mean spatial density of observed profiles was 33 per square mile, but the distribution of profiles observed across the 79 sampled points was highly skewed (median 17, range 1-208). Ratio, difference, and distribution outlier measures all provided similar information, highlighting areas with higher densities of minority and young minority MSM. Conclusions: Using a limited number of sampled points, we developed a geospatial density map of MSM using a social-networking sex-seeking app. This approach provides a simple method to describe the density of specific MSM subpopulations (users of a particular app) for future HIV behavioral surveillance and allow targeting of prevention resources such as HIV testing to populations and areas of highest need. ", doi="10.2196/jmir.3523", url="http://www.jmir.org/2014/11/e249/", url="http://www.ncbi.nlm.nih.gov/pubmed/25406722" } @Article{info:doi/10.2196/jmir.3532, author="Aslam, A. Anosh{\'e} and Tsou, Ming-Hsiang and Spitzberg, H. Brian and An, Li and Gawron, Mark J. and Gupta, K. Dipak and Peddecord, Michael K. and Nagel, C. Anna and Allen, Christopher and Yang, Jiue-An and Lindsay, Suzanne", title="The Reliability of Tweets as a Supplementary Method of Seasonal Influenza Surveillance", journal="J Med Internet Res", year="2014", month="Nov", day="14", volume="16", number="11", pages="e250", keywords="Twitter", keywords="tweets", keywords="infoveillance", keywords="infodemiology", keywords="syndromic surveillance", keywords="influenza", keywords="Internet", abstract="Background: Existing influenza surveillance in the United States is focused on the collection of data from sentinel physicians and hospitals; however, the compilation and distribution of reports are usually delayed by up to 2 weeks. With the popularity of social media growing, the Internet is a source for syndromic surveillance due to the availability of large amounts of data. In this study, tweets, or posts of 140 characters or less, from the website Twitter were collected and analyzed for their potential as surveillance for seasonal influenza. Objective: There were three aims: (1) to improve the correlation of tweets to sentinel-provided influenza-like illness (ILI) rates by city through filtering and a machine-learning classifier, (2) to observe correlations of tweets for emergency department ILI rates by city, and (3) to explore correlations for tweets to laboratory-confirmed influenza cases in San Diego. Methods: Tweets containing the keyword ``flu'' were collected within a 17-mile radius from 11 US cities selected for population and availability of ILI data. At the end of the collection period, 159,802 tweets were used for correlation analyses with sentinel-provided ILI and emergency department ILI rates as reported by the corresponding city or county health department. Two separate methods were used to observe correlations between tweets and ILI rates: filtering the tweets by type (non-retweets, retweets, tweets with a URL, tweets without a URL), and the use of a machine-learning classifier that determined whether a tweet was ``valid'', or from a user who was likely ill with the flu. Results: Correlations varied by city but general trends were observed. Non-retweets and tweets without a URL had higher and more significant (P<.05) correlations than retweets and tweets with a URL. Correlations of tweets to emergency department ILI rates were higher than the correlations observed for sentinel-provided ILI for most of the cities. The machine-learning classifier yielded the highest correlations for many of the cities when using the sentinel-provided or emergency department ILI as well as the number of laboratory-confirmed influenza cases in San Diego. High correlation values (r=.93) with significance at P<.001 were observed for laboratory-confirmed influenza cases for most categories and tweets determined to be valid by the classifier. Conclusions: Compared to tweet analyses in the previous influenza season, this study demonstrated increased accuracy in using Twitter as a supplementary surveillance tool for influenza as better filtering and classification methods yielded higher correlations for the 2013-2014 influenza season than those found for tweets in the previous influenza season, where emergency department ILI rates were better correlated to tweets than sentinel-provided ILI rates. Further investigations in the field would require expansion with regard to the location that the tweets are collected from, as well as the availability of more ILI data. ", doi="10.2196/jmir.3532", url="http://www.jmir.org/2014/11/e250/", url="http://www.ncbi.nlm.nih.gov/pubmed/25406040" } @Article{info:doi/10.2196/jmir.3416, author="Nagar, Ruchit and Yuan, Qingyu and Freifeld, C. Clark and Santillana, Mauricio and Nojima, Aaron and Chunara, Rumi and Brownstein, S. John", title="A Case Study of the New York City 2012-2013 Influenza Season With Daily Geocoded Twitter Data From Temporal and Spatiotemporal Perspectives", journal="J Med Internet Res", year="2014", month="Oct", day="20", volume="16", number="10", pages="e236", keywords="influenza", keywords="Twitter", keywords="New York City", keywords="spatiotemporal", keywords="Google Flu Trends", keywords="infodemiology", keywords="mHealth", keywords="social media, natural language processing", keywords="medical informatics", abstract="Background: Twitter has shown some usefulness in predicting influenza cases on a weekly basis in multiple countries and on different geographic scales. Recently, Broniatowski and colleagues suggested Twitter's relevance at the city-level for New York City. Here, we look to dive deeper into the case of New York City by analyzing daily Twitter data from temporal and spatiotemporal perspectives. Also, through manual coding of all tweets, we look to gain qualitative insights that can help direct future automated searches. Objective: The intent of the study was first to validate the temporal predictive strength of daily Twitter data for influenza-like illness emergency department (ILI-ED) visits during the New York City 2012-2013 influenza season against other available and established datasets (Google search query, or GSQ), and second, to examine the spatial distribution and the spread of geocoded tweets as proxies for potential cases. Methods: From the Twitter Streaming API, 2972 tweets were collected in the New York City region matching the keywords ``flu'', ``influenza'', ``gripe'', and ``high fever''. The tweets were categorized according to the scheme developed by Lamb et al. A new fourth category was added as an evaluator guess for the probability of the subject(s) being sick to account for strength of confidence in the validity of the statement. Temporal correlations were made for tweets against daily ILI-ED visits and daily GSQ volume. The best models were used for linear regression for forecasting ILI visits. A weighted, retrospective Poisson model with SaTScan software (n=1484), and vector map were used for spatiotemporal analysis. Results: Infection-related tweets (R=.763) correlated better than GSQ time series (R=.683) for the same keywords and had a lower mean average percent error (8.4 vs 11.8) for ILI-ED visit prediction in January, the most volatile month of flu. SaTScan identified primary outbreak cluster of high-probability infection tweets with a 2.74 relative risk ratio compared to medium-probability infection tweets at P=.001 in Northern Brooklyn, in a radius that includes Barclay's Center and the Atlantic Avenue Terminal. Conclusions: While others have looked at weekly regional tweets, this study is the first to stress test Twitter for daily city-level data for New York City. Extraction of personal testimonies of infection-related tweets suggests Twitter's strength both qualitatively and quantitatively for ILI-ED prediction compared to alternative daily datasets mixed with awareness-based data such as GSQ. Additionally, granular Twitter data provide important spatiotemporal insights. A tweet vector-map may be useful for visualization of city-level spread when local gold standard data are otherwise unavailable. ", doi="10.2196/jmir.3416", url="http://www.jmir.org/2014/10/e236/", url="http://www.ncbi.nlm.nih.gov/pubmed/25331122" } @Article{info:doi/10.2196/jmir.3622, author="Harris, K. Jenine and Moreland-Russell, Sarah and Choucair, Bechara and Mansour, Raed and Staub, Mackenzie and Simmons, Kendall", title="Tweeting for and Against Public Health Policy: Response to the Chicago Department of Public Health's Electronic Cigarette Twitter Campaign", journal="J Med Internet Res", year="2014", month="Oct", day="16", volume="16", number="10", pages="e238", keywords="Twitter", keywords="health departments", keywords="e-cigarette", abstract="Background: In January 2014, the Chicago City Council scheduled a vote on local regulation of electronic cigarettes as tobacco products. One week prior to the vote, the Chicago Department of Public Health (CDPH) released a series of messages about electronic cigarettes (e-cigarettes) through its Twitter account. Shortly after the messages, or tweets, were released, the department's Twitter account became the target of a ``Twitter bomb'' by Twitter users sending more than 600 tweets in one week against the proposed regulation. Objective: The purpose of our study was to examine the messages and tweet patterns in the social media response to the CDPH e-cigarette campaign. Methods: We collected all tweets mentioning the CDPH in the week between the e-cigarette campaign and the vote on the new local e-cigarette policy. We conducted a content analysis of the tweets, used descriptive statistics to examine characteristics of involved Twitter users, and used network visualization and descriptive statistics to identify Twitter users prominent in the conversation. Results: Of the 683 tweets mentioning CDPH during the week, 609 (89.2\%) were anti-policy. More than half of anti-policy tweets were about use of electronic cigarettes for cessation as a healthier alternative to combustible cigarettes (358/609, 58.8\%). Just over one-third of anti-policy tweets asserted that the health department was lying or disseminating propaganda (224/609, 36.8\%). Approximately 14\% (96/683, 14.1\%) of the tweets used an account or included elements consistent with ``astroturfing''---a strategy employed to promote a false sense of consensus around an idea. Few Twitter users were from the Chicago area; Twitter users from Chicago were significantly more likely than expected to tweet in support of the policy. Conclusions: Our findings may assist public health organizations to anticipate, recognize, and respond to coordinated social media campaigns. ", doi="10.2196/jmir.3622", url="http://www.jmir.org/2014/10/e238/", url="http://www.ncbi.nlm.nih.gov/pubmed/25320863" } @Article{info:doi/10.2196/jmir.3765, author="Lee, L. Joy and DeCamp, Matthew and Dredze, Mark and Chisolm, S. Margaret and Berger, D. Zackary", title="What Are Health-Related Users Tweeting? A Qualitative Content Analysis of Health-Related Users and Their Messages on Twitter", journal="J Med Internet Res", year="2014", month="Oct", day="15", volume="16", number="10", pages="e237", keywords="communication", keywords="consumer health informatics", keywords="health information technology", keywords="social media", abstract="Background: Twitter is home to many health professionals who send messages about a variety of health-related topics. Amid concerns about physicians posting inappropriate content online, more in-depth knowledge about these messages is needed to understand health professionals' behavior on Twitter. Objective: Our goal was to characterize the content of Twitter messages, specifically focusing on health professionals and their tweets relating to health. Methods: We performed an in-depth content analysis of 700 tweets. Qualitative content analysis was conducted on tweets by health users on Twitter. The primary objective was to describe the general type of content (ie, health-related versus non-health related) on Twitter authored by health professionals and further to describe health-related tweets on the basis of the type of statement made. Specific attention was given to whether a tweet was personal (as opposed to professional) or made a claim that users would expect to be supported by some level of medical evidence (ie, a ``testable'' claim). A secondary objective was to compare content types among different users, including patients, physicians, nurses, health care organizations, and others. Results: Health-related users are posting a wide range of content on Twitter. Among health-related tweets, 53.2\% (184/346) contained a testable claim. Of health-related tweets by providers, 17.6\% (61/346) were personal in nature; 61\% (59/96) made testable statements. While organizations and businesses use Twitter to promote their services and products, patient advocates are using this tool to share their personal experiences with health. Conclusions: Twitter users in health-related fields tweet about both testable claims and personal experiences. Future work should assess the relationship between testable tweets and the actual level of evidence supporting them, including how Twitter users---especially patients---interpret the content of tweets posted by health providers. ", doi="10.2196/jmir.3765", url="http://www.jmir.org/2014/10/e237/", url="http://www.ncbi.nlm.nih.gov/pubmed/25591063" } @Article{info:doi/10.2196/jmir.3763, author="Mao, Chen and Wu, Xin-Yin and Fu, Xiao-Hong and Di, Meng-Yang and Yu, Yuan-Yuan and Yuan, Jin-Qiu and Yang, Zu-Yao and Tang, Jin-Ling", title="An Internet-Based Epidemiological Investigation of the Outbreak of H7N9 Avian Influenza A in China Since Early 2013", journal="J Med Internet Res", year="2014", month="Sep", day="25", volume="16", number="9", pages="e221", keywords="influenza A virus, H7N9 subtype", keywords="Internet", keywords="big data", keywords="disease outbreaks", keywords="epidemiology", abstract="Background: In early 2013, a new type of avian influenza, H7N9, emerged in China. It quickly became an issue of great public concern and a widely discussed topic on the Internet. A considerable volume of relevant information was made publicly available on the Internet through various sources. Objective: This study aimed to describe the outbreak of H7N9 in China based on data openly available on the Internet and to validate our investigation by comparing our findings with a well-conducted conventional field epidemiologic study. Methods: We searched publicly accessible Internet data on the H7N9 outbreak primarily from government and major mass media websites in China up to February 10, 2014. Two researchers independently extracted, compared, and confirmed the information of each confirmed H7N9 case using a self-designed data extraction form. We summarized the epidemiological and clinical characteristics of confirmed H7N9 cases and compared them with those from the field study. Results: According to our data updated until February 10, 2014, 334 confirmed H7N9 cases were identified. The median age was 58 years and 67.0\% (219/327) were males. Cases were reported in 15 regions in China. Five family clusters were found. Of the 16.8\% (56/334) of the cases with relevant data, 69.6\% (39/56) reported a history of exposure to animals. Of the 1751 persons with a close contact with a confirmed case, 0.6\% (11/1751) of them developed respiratory symptoms during the 7-day surveillance period. In the 97.9\% (327/334) of the cases with relevant data, 21.7\% (71/327) died, 20.8\% (68/327) were discharged from a hospital, and 57.5\% (188/327) were of uncertain status. We compared our findings before February 10, 2014 and those before December 1, 2013 with those from the conventional field study, which had the latter cutoff date of ours in data collection. Our study showed most epidemiological and clinical characteristics were similar to those in the field study, except for case fatality (71/327, 21.7\% for our data before February 10; 45/138, 32.6\% for our data before December 1; 47/139, 33.8\% for the field study), time from illness onset to first medical care (4 days, 3 days, and 1 day), and time from illness onset to death (16.5 days, 17 days, and 21 days). Conclusions: Findings from our Internet-based investigation were similar to those from the conventional field study in most epidemiological and clinical aspects of the outbreak. Importantly, publicly available Internet data are open to any interested researchers and can thus greatly facilitate the investigation and control of such outbreaks. With improved efforts for Internet data provision, Internet-based investigation has a great potential to become a quick, economical, novel approach to investigating sudden issues of great public concern that involve a relatively small number of cases like this H7N9 outbreak. ", doi="10.2196/jmir.3763", url="http://www.jmir.org/2014/9/e221/", url="http://www.ncbi.nlm.nih.gov/pubmed/25257217" } @Article{info:doi/10.2196/jmir.3203, author="Zheluk, Andrey and Quinn, Casey and Meylakhs, Peter", title="Internet Search and Krokodil in the Russian Federation: An Infoveillance Study", journal="J Med Internet Res", year="2014", month="Sep", day="18", volume="16", number="9", pages="e212", keywords="Russia", keywords="search engine", keywords="surveillance", keywords="controlled substances", keywords="designer drugs", keywords="street drugs", abstract="Background: Krokodil is an informal term for a cheap injectable illicit drug domestically prepared from codeine-containing medication (CCM). The method of krokodil preparation may produce desomorphine as well as toxic reactants that cause extensive tissue necrosis. The first confirmed report of krokodil use in Russia took place in 2004. In 2012, reports of krokodil-related injection injuries began to appear beyond Russia in Western Europe and the United States. Objective: This exploratory study had two main objectives: (1) to determine if Internet search patterns could detect regularities in behavioral responses to Russian CCM policy at the population level, and (2) to determine if complementary data sources could explain the regularities we observed. Methods: First, we obtained krokodil-related search pattern data for each Russia subregion (oblast) between 2011 and 2012. Second, we analyzed several complementary data sources included krokodil-related court cases, and related search terms on both Google and Yandex to evaluate the characteristics of terms accompanying krokodil-related search queries. Results: In the 6 months preceding CCM sales restrictions, 21 of Russia's 83 oblasts had search rates higher than the national average (mean) of 16.67 searches per 100,000 population for terms associated with krokodil. In the 6 months following restrictions, mean national searches dropped to 9.65 per 100,000. Further, the number of oblasts recording a higher than average search rate dropped from 30 to 16. Second, we found krokodil-related court appearances were moderately positively correlated (Spearman correlation=.506, P?.001) with behaviors consistent with an interest in the production and use of krokodil across Russia. Finally, Google Trends and Google and Yandex related terms suggested consistent public interest in the production and use of krokodil as well as for CCM as analgesic medication during the date range covered by this study. Conclusions: Illicit drug use data are generally regarded as difficult to obtain through traditional survey methods. Our analysis suggests it is plausible that Yandex search behavior served as a proxy for patterns of krokodil production and use during the date range we investigated. More generally, this study demonstrates the application of novel methods recently used by policy makers to both monitor illicit drug use and influence drug policy decision making. ", doi="10.2196/jmir.3203", url="http://www.jmir.org/2014/9/e212/", url="http://www.ncbi.nlm.nih.gov/pubmed/25236385" } @Article{info:doi/10.2196/jmir.3247, author="Cavazos-Rehg, Patricia and Krauss, Melissa and Grucza, Richard and Bierut, Laura", title="Characterizing the Followers and Tweets of a Marijuana-Focused Twitter Handle", journal="J Med Internet Res", year="2014", month="Jun", day="27", volume="16", number="6", pages="e157", keywords="Twitter", keywords="social media", keywords="marijuana", abstract="Background: Twitter is a popular social media forum for sharing personal experiences, interests, and opinions. An improved understanding of the discourse on Twitter that encourages marijuana use can be helpful for tailoring and targeting online and offline prevention messages. Objectives: The intent of the study was to assess the content of ``tweets'' and the demographics of followers of a popular pro-marijuana Twitter handle (@stillblazingtho). Methods: We assessed the sentiment and content of tweets (sent from May 1 to December 31, 2013), as well as the demographics of consumers that follow a popular pro-marijuana Twitter handle (approximately 1,000,000 followers) using Twitter analytics from Demographics Pro. This analytics company estimates demographic characteristics based on Twitter behavior/usage, relying on multiple data signals from networks, consumption, and language and requires confidence of 95\% or above to make an estimate of a single demographic characteristic. Results: A total of 2590 tweets were sent from @stillblazingtho during the 8-month period and 305 (11.78\%) replies to another Twitter user were excluded for qualitative analysis. Of the remaining 2285 tweets, 1875 (82.06\%) were positive about marijuana, 403 (17.64\%) were neutral, and 7 (0.31\%) appeared negative about marijuana. Approximately 1101 (58.72\%) of the positive marijuana tweets were perceived as jokes or humorous, 340 (18.13\%) implied that marijuana helps you to feel good or relax, 294 (15.68\%) mentioned routine, frequent, or heavy use, 193 (10.29\%) mentioned blunts, marijuana edibles, or paraphernalia (eg, bongs, vaporizers), and 186 (9.92\%) mentioned other risky health behaviors (eg, tobacco, alcohol, other drugs, sex). The majority (699,103/959,143; 72.89\%) of @stillblazingtho followers were 19 years old or younger. Among people ages 17 to 19 years, @stillblazingtho was in the top 10\% of all Twitter handles followed. More followers of @stillblazingtho in the United States were African American (323,107/759,407; 42.55\%) or Hispanic (90,732/759,407; 11.95\%) than the Twitter median average (African American 22.4\%, inter-quartile ratio [IQR] 5.1-62.5\%; Hispanic 5.4\%, IQR 3.0-10.8\%) and among Hispanics, @stillblazingtho was in the top 30\% of all Twitter handles followed. Conclusions: Young people are especially responsive to social media influences and often establish substance use patterns during this phase of development. Our findings underscore the need for surveillance efforts to monitor the pro-marijuana content reaching young people on Twitter. ", doi="10.2196/jmir.3247", url="http://www.jmir.org/2014/6/e157/", url="http://www.ncbi.nlm.nih.gov/pubmed/24974893" } @Article{info:doi/10.2196/jmir.2868, author="Troullos, Emanuel and Baird, Lisa and Jayawardena, Shyamalie", title="Common Cold Symptoms in Children: Results of an Internet-Based Surveillance Program", journal="J Med Internet Res", year="2014", month="Jun", day="19", volume="16", number="6", pages="e144", keywords="common cold", keywords="pediatric", keywords="sleep", keywords="surveillance", keywords="symptoms", abstract="Background: Conducting and analyzing clinical studies of cough and cold medications is challenging due to the rapid onset and short duration of the symptoms. The use of Internet-based surveillance tools is a new approach in clinical studies that is gradually becoming popular and may become a useful method of recruitment. As part of an initiative to assess the safety and efficacy of cough and cold ingredients in children 6-11 years of age, a surveillance program was proposed as a means to identify and recruit pediatric subjects for clinical studies. Objective: The objective of the study was to develop an Internet-based surveillance system and to assess the feasibility of using such a system to recruit children for common cold clinical studies, record the natural history of their cold symptoms, and determine the willingness of parents to have their children participate in clinical studies. Methods: Healthy potential subjects were recruited via parental contact online. During the 6-week surveillance period, parents completed daily surveys to record details of any cold symptoms in their children. If a child developed a cold, symptoms were followed via survey for 10 days. Additional questions evaluated the willingness of parents to have their children participate in a clinical study shortly after onset of symptoms. Results: The enrollment target of 248 children was reached in approximately 1 week. Children from 4 distinct geographic regions of the United States were recruited. Parents reported cold symptoms in 163 children, and 134 went on to develop colds. The most prevalent symptoms were runny nose, stuffed-up nose, and sneezing. The most severe symptoms were runny nose, stuffed-up nose, and sore/scratchy throat. The severity of most symptoms peaked 1--2 days after onset. Up to 54\% of parents expressed willingness to bring a sick child to a clinical center shortly after the onset of symptoms. Parents found the Internet-based surveys easy to complete. Conclusions: Internet-based surveillance and recruitment can be useful tools to follow colds in children and enroll subjects in clinical studies. However, study designs should account for a potentially high dropout rate and low rate of adherence to study procedures. ", doi="10.2196/jmir.2868", url="http://www.jmir.org/2014/6/e144/", url="http://www.ncbi.nlm.nih.gov/pubmed/24945090" } @Article{info:doi/10.2196/jmir.3156, author="Yom-Tov, Elad and Borsa, Diana and Cox, J. Ingemar and McKendry, A. Rachel", title="Detecting Disease Outbreaks in Mass Gatherings Using Internet Data", journal="J Med Internet Res", year="2014", month="Jun", day="18", volume="16", number="6", pages="e154", keywords="mass gatherings", keywords="infodemiology", keywords="infectious disease", keywords="information retrieval", keywords="data mining", abstract="Background: Mass gatherings, such as music festivals and religious events, pose a health care challenge because of the risk of transmission of communicable diseases. This is exacerbated by the fact that participants disperse soon after the gathering, potentially spreading disease within their communities. The dispersion of participants also poses a challenge for traditional surveillance methods. The ubiquitous use of the Internet may enable the detection of disease outbreaks through analysis of data generated by users during events and shortly thereafter. Objective: The intent of the study was to develop algorithms that can alert to possible outbreaks of communicable diseases from Internet data, specifically Twitter and search engine queries. Methods: We extracted all Twitter postings and queries made to the Bing search engine by users who repeatedly mentioned one of nine major music festivals held in the United Kingdom and one religious event (the Hajj in Mecca) during 2012, for a period of 30 days and after each festival. We analyzed these data using three methods, two of which compared words associated with disease symptoms before and after the time of the festival, and one that compared the frequency of these words with those of other users in the United Kingdom in the days following the festivals. Results: The data comprised, on average, 7.5 million tweets made by 12,163 users, and 32,143 queries made by 1756 users from each festival. Our methods indicated the statistically significant appearance of a disease symptom in two of the nine festivals. For example, cough was detected at higher than expected levels following the Wakestock festival. Statistically significant agreement (chi-square test, P<.01) between methods and across data sources was found where a statistically significant symptom was detected. Anecdotal evidence suggests that symptoms detected are indeed indicative of a disease that some users attributed to being at the festival. Conclusions: Our work shows the feasibility of creating a public health surveillance system for mass gatherings based on Internet data. The use of multiple data sources and analysis methods was found to be advantageous for rejecting false positives. Further studies are required in order to validate our findings with data from public health authorities. ", doi="10.2196/jmir.3156", url="http://www.jmir.org/2014/6/e154/", url="http://www.ncbi.nlm.nih.gov/pubmed/24943128" } @Article{info:doi/10.2196/jmir.2846, author="Nakada, Haruka and Yuji, Koichiro and Tsubokura, Masaharu and Ohsawa, Yukio and Kami, Masahiro", title="Development of a National Agreement on Human Papillomavirus Vaccination in Japan: An Infodemiology Study", journal="J Med Internet Res", year="2014", month="May", day="15", volume="16", number="5", pages="e129", keywords="cervical cancer", keywords="health policy", keywords="human papillomavirus", keywords="public health", keywords="vaccination", abstract="Background: A national agreement on human papillomavirus (HPV) vaccination was achieved relatively quickly in Japan as compared to the United States and India. Objective: The objective was to identify the role of print and online media references, including references to celebrities or other informants, as factors potentially responsible for the relatively rapid national acceptance of HPV vaccination in Japan. Methods: A method of text mining was performed to select keywords, representing the context of the target documents, from articles relevant to the promotion of HPV vaccination appearing in major Japanese newspapers and Web pages between January 2009 and July 2010. The selected keywords were classified as positive, negative, or neutral, and the transition of the frequency of their appearance was analyzed. Results: The number of positive and neutral keywords appearing in newspaper articles increased sharply in early 2010 while the number of negative keywords remained low. The numbers of positive, neutral, and negative keywords appearing in Web pages increased gradually and did not significantly differ by category. Neutral keywords, such as ``vaccine'' and ``prevention,'' appeared more frequently in newspaper articles, whereas negative keywords, such as ``infertility'' and ``side effect,'' appeared more frequently in Web pages. The extraction of the positive keyword ``signature campaign'' suggests that vaccine beneficiaries cooperated with providers in promoting HPV vaccination. Conclusions: The rapid development of a national agreement regarding HPV vaccination in Japan may be primarily attributed to the advocacy of vaccine beneficiaries, supported by advocacy by celebrities and positive reporting by print and online media. ", doi="10.2196/jmir.2846", url="http://www.jmir.org/2014/5/e129/", url="http://www.ncbi.nlm.nih.gov/pubmed/24834471" } @Article{info:doi/10.2196/jmir.3397, author="McNaughton, C. Emily and Coplan, M. Paul and Black, A. Ryan and Weber, E. Sarah and Chilcoat, D. Howard and Butler, F. Stephen", title="Monitoring of Internet Forums to Evaluate Reactions to the Introduction of Reformulated OxyContin to Deter Abuse", journal="J Med Internet Res", year="2014", month="May", day="02", volume="16", number="5", pages="e119", keywords="Internet", keywords="opioid analgesic", keywords="drug abuse", keywords="prescription drug", keywords="OxyContin", keywords="epidemiology", keywords="surveillance", keywords="social media", keywords="qualitative research", abstract="Background: Reformulating opioid analgesics to deter abuse is one approach toward improving their benefit-risk balance. To assess sentiment and attempts to defeat these products among difficult-to-reach populations of prescription drug abusers, evaluation of posts on Internet forums regarding reformulated products may be useful. A reformulated version of OxyContin (extended-release oxycodone) with physicochemical properties to deter abuse presented an opportunity to evaluate posts about the reformulation in online discussions. Objective: The objective of this study was to use messages on Internet forums to evaluate reactions to the introduction of reformulated OxyContin and to identify methods aimed to defeat the abuse-deterrent properties of the product. Methods: Posts collected from 7 forums between January 1, 2008 and September 30, 2013 were evaluated before and after the introduction of reformulated OxyContin on August 9, 2010. A quantitative evaluation of discussion levels across the study period and a qualitative coding of post content for OxyContin and 2 comparators for the 26 month period before and after OxyContin reformulation were conducted. Product endorsement was estimated for each product before and after reformulation as the ratio of endorsing-to-discouraging posts (ERo). Post-to-preintroduction period changes in ERos (ie, ratio of ERos) for each product were also calculated. Additionally, post content related to recipes for defeating reformulated OxyContin were evaluated from August 9, 2010 through September 2013. Results: Over the study period, 45,936 posts related to OxyContin, 18,685 to Vicodin (hydrocodone), and 23,863 to Dilaudid (hydromorphone) were identified. The proportion of OxyContin-related posts fluctuated between 6.35 and 8.25 posts per 1000 posts before the reformulation, increased to 10.76 in Q3 2010 when reformulated OxyContin was introduced, and decreased from 9.14 in Q4 2010 to 3.46 in Q3 2013 in the period following the reformulation. The sentiment profile for OxyContin changed following reformulation; the post-to-preintroduction change in the ERo indicated reformulated OxyContin was discouraged significantly more than the original formulation (ratio of ERos=0.43, P<.001). A total of 37 recipes for circumventing the abuse-deterrent characteristics of reformulated OxyContin were observed; 32 were deemed feasible (ie, able to abuse). The frequency of posts reporting abuse of reformulated OxyContin via these recipes was low and decreased over time. Among the 5677 posts mentioning reformulated OxyContin, 825 posts discussed recipes and 498 reported abuse of reformulated OxyContin by such recipes (41 reported injecting and 128 reported snorting). Conclusions: After introduction of physicochemical properties to deter abuse, changes in discussion of OxyContin on forums occurred reflected by a reduction in discussion levels and endorsing content. Despite discussion of recipes, there is a relatively small proportion of reported abuse of reformulated OxyContin via recipes, particularly by injecting or snorting routes. Analysis of Internet discussion is a valuable tool for monitoring the impact of abuse-deterrent formulations. ", doi="10.2196/jmir.3397", url="http://www.jmir.org/2014/5/e119/", url="http://www.ncbi.nlm.nih.gov/pubmed/24800858" } @Article{info:doi/10.2196/jmir.3099, author="Timpka, Toomas and Spreco, Armin and Dahlstr{\"o}m, {\"O}rjan and Eriksson, Olle and Gursky, Elin and Ekberg, Joakim and Blomqvist, Eva and Str{\"o}mgren, Magnus and Karlsson, David and Eriksson, Henrik and Nyce, James and Hinkula, Jorma and Holm, Einar", title="Performance of eHealth Data Sources in Local Influenza Surveillance:A 5-Year Open Cohort Study", journal="J Med Internet Res", year="2014", month="Apr", day="28", volume="16", number="4", pages="e116", keywords="influenza", keywords="infectious disease surveillance", keywords="Internet", keywords="eHealth", keywords="Google Flu Trends", keywords="telenursing call centers", keywords="website usage", keywords="open cohort design", keywords="public health", abstract="Background: There is abundant global interest in using syndromic data from population-wide health information systems---referred to as eHealth resources---to improve infectious disease surveillance. Recently, the necessity for these systems to achieve two potentially conflicting requirements has been emphasized. First, they must be evidence-based; second, they must be adjusted for the diversity of populations, lifestyles, and environments. Objective: The primary objective was to examine correlations between data from Google Flu Trends (GFT), computer-supported telenursing centers, health service websites, and influenza case rates during seasonal and pandemic influenza outbreaks. The secondary objective was to investigate associations between eHealth data, media coverage, and the interaction between circulating influenza strain(s) and the age-related population immunity. Methods: An open cohort design was used for a five-year study in a Swedish county (population 427,000). Syndromic eHealth data were collected from GFT, telenursing call centers, and local health service website visits at page level. Data on mass media coverage of influenza was collected from the major regional newspaper. The performance of eHealth data in surveillance was measured by correlation effect size and time lag to clinically diagnosed influenza cases. Results: Local media coverage data and influenza case rates showed correlations with large effect sizes only for the influenza A (A) pH1N1 outbreak in 2009 (r=.74, 95\% CI .42-.90; P<.001) and the severe seasonal A H3N2 outbreak in 2011-2012 (r=.79, 95\% CI .42-.93; P=.001), with media coverage preceding case rates with one week. Correlations between GFT and influenza case data showed large effect sizes for all outbreaks, the largest being the seasonal A H3N2 outbreak in 2008-2009 (r=.96, 95\% CI .88-.99; P<.001). The preceding time lag decreased from two weeks during the first outbreaks to one week from the 2009 A pH1N1 pandemic. Telenursing data and influenza case data showed correlations with large effect sizes for all outbreaks after the seasonal B and A H1 outbreak in 2007-2008, with a time lag decreasing from two weeks for the seasonal A H3N2 outbreak in 2008-2009 (r=.95, 95\% CI .82-.98; P<.001) to none for the A p H1N1 outbreak in 2009 (r=.84, 95\% CI .62-.94; P<.001). Large effect sizes were also observed between website visits and influenza case data. Conclusions: Correlations between the eHealth data and influenza case rates in a Swedish county showed large effect sizes throughout a five-year period, while the time lag between signals in eHealth data and influenza rates changed. Further research is needed on analytic methods for adjusting eHealth surveillance systems to shifts in media coverage and to variations in age-group related immunity between virus strains. The results can be used to inform the development of alert-generating eHealth surveillance systems that can be subject for prospective evaluations in routine public health practice. ", doi="10.2196/jmir.3099", url="http://www.jmir.org/2014/4/e116/", url="http://www.ncbi.nlm.nih.gov/pubmed/24776527" } @Article{info:doi/10.2196/jmir.3265, author="Nascimento, D. Thiago and DosSantos, F. Marcos and Danciu, Theodora and DeBoer, Misty and van Holsbeeck, Hendrik and Lucas, R. Sarah and Aiello, Christine and Khatib, Leen and Bender, A. MaryCatherine and and Zubieta, Jon-Kar and DaSilva, F. Alexandre", title="Real-Time Sharing and Expression of Migraine Headache Suffering on Twitter: A Cross-Sectional Infodemiology Study", journal="J Med Internet Res", year="2014", month="Apr", day="03", volume="16", number="4", pages="e96", keywords="migraine", keywords="headache", keywords="epidemiology", keywords="social media", keywords="Twitter", abstract="Background: Although population studies have greatly improved our understanding of migraine, they have relied on retrospective self-reports that are subject to memory error and experimenter-induced bias. Furthermore, these studies also lack specifics from the actual time that attacks were occurring, and how patients express and share their ongoing suffering. Objective: As technology and language constantly evolve, so does the way we share our suffering. We sought to evaluate the infodemiology of self-reported migraine headache suffering on Twitter. Methods: Trained observers in an academic setting categorized the meaning of every single ``migraine'' tweet posted during seven consecutive days. The main outcome measures were prevalence, life-style impact, linguistic, and timeline of actual self-reported migraine headache suffering on Twitter. Results: From a total of 21,741 migraine tweets collected, only 64.52\% (14,028/21,741 collected tweets) were from users reporting their migraine headache attacks in real-time. The remainder of the posts were commercial, re-tweets, general discussion or third person's migraine, and metaphor. The gender distribution available for the actual migraine posts was 73.47\% female (10,306/14,028), 17.40\% males (2441/14,028), and 0.01\% transgendered (2/14,028). The personal impact of migraine headache was immediate on mood (43.91\%, 6159/14,028), productivity at work (3.46\%, 486/14,028), social life (3.45\%, 484/14,028), and school (2.78\%, 390/14,028). The most common migraine descriptor was ``Worst'' (14.59\%, 201/1378) and profanity, the ``F-word'' (5.3\%, 73/1378). The majority of postings occurred in the United States (58.28\%, 3413/5856), peaking on weekdays at 10:00h and then gradually again at 22:00h; the weekend had a later morning peak. Conclusions: Twitter proved to be a powerful source of knowledge for migraine research. The data in this study overlap large-scale epidemiological studies, avoiding memory bias and experimenter-induced error. Furthermore, linguistics of ongoing migraine reports on social media proved to be highly heterogeneous and colloquial in our study, suggesting that current pain questionnaires should undergo constant reformulations to keep up with modernization in the expression of pain suffering in our society. In summary, this study reveals the modern characteristics and broad impact of migraine headache suffering on patients' lives as it is spontaneously shared via social media. ", doi="10.2196/jmir.3265", url="http://www.jmir.org/2014/4/e96/", url="http://www.ncbi.nlm.nih.gov/pubmed/24698747" } @Article{info:doi/10.2196/med20.3237, author="Genes, Nicholas and Chary, Michael and Chason, Kevin", title="Analysis of Twitter Users' Sharing of Official New York Storm Response Messages", journal="Med 2.0", year="2014", month="Mar", day="20", volume="3", number="1", pages="e1", keywords="social media", keywords="disaster response", keywords="emergencies", keywords="public health", keywords="emergency management", abstract="Background: Twitter is a social network where users read, send, and share snippets of text (``tweets''). Tweets can be disseminated through multiple means; on desktop computers, laptops, and mobile devices, over ethernet, Wi-Fi or cellular networks. This redundancy positions Twitter as a useful tool for disseminating information to the public during emergencies or disasters.Previous research on dissemination of information using Twitter has mostly investigated the characteristics of tweets that are most effective in raising consumer awareness about a new product or event. In particular, they describe characteristics that increase the chance the messages will be shared (``retweeted'') by users. In comparison, little has been published on how information from municipal or state government agencies spreads on Twitter during emergency situations. Retweeting these messages is a way to enhance public awareness of potentially important instructions from public officials in a disaster. Objective: The aim of this study is to (1) describe the tweets of select New York State and New York City agencies by public officials surrounding two notable recent winter storms that required a large-scale emergency response, and (2) identify the characteristics of the tweets of public officials that were most disseminated (retweeted). Methods: For one week surrounding Superstorm Sandy (October 2012) and the winter blizzard Nemo (February 2013), we collected (1) tweets from the official accounts for six New York governmental agencies, and (2) all tweets containing the hashtags \#sandy (or \#nemo) and \#nyc. From these data we calculated how many times a tweet was retweeted, controlling for differences in baseline activity in each account. We observed how many hashtags and links each tweet contained. We also calculated the lexical diversity of each tweet, a measure of the range of vocabulary used. Results: During the Sandy storm, 3242 shared (retweeted) messages from public officials were collected. The lexical diversity of official tweets was similar (2.25-2.49) and well below the average for non-official tweets mentioning \#sandy and \#nyc (3.82). Most official tweets were with substantial retweets including a link for further reading. Of the 448 tweets analyzed from six official city and state Twitter accounts from the Nemo blizzard, 271 were related to the storm, and 174 had actionable information for the public. Actionable storm messages were retweeted approximately 24x per message, compared to 31x per message for general storm information. Conclusions: During two weather emergencies, New York public officials were able to convey storm-related information that was shared widely beyond existing follower bases, potentially improving situational awareness and disaster response. Official Sandy tweets, characterized by a lower lexical diversity score than other city- and Sandy-related tweets, were likely easier to understand, and often linked to further information and resources. Actionable information in the Nemo blizzard, such as specific instructions and cancellation notices, was not shared as often as more general warnings and ``fun facts,'' suggesting agencies mix important instructions with more general news and trivia, as a way of reaching the broadest audience during a disaster. ", doi="10.2196/med20.3237", url="http://www.medicine20.com/2014/1/e1/", url="http://www.ncbi.nlm.nih.gov/pubmed/25075245" } @Article{info:doi/10.2196/jmir.2838, author="Koh, Sukjin and Gordon, S. Andrew and Wienberg, Christopher and Sood, O. Sara and Morley, Stephanie and Burke, M. Deborah", title="Stroke Experiences in Weblogs: A Feasibility Study of Sex Differences", journal="J Med Internet Res", year="2014", month="Mar", day="19", volume="16", number="3", pages="e84", keywords="cerebral stroke", keywords="signs and symptoms", keywords="sex differences", keywords="Internet", keywords="blogging", abstract="Background: Research on cerebral stroke symptoms using hospital records has reported that women experience more nontraditional symptoms of stroke (eg, mental status change, pain) than men do. This is an important issue because nontraditional symptoms may delay the decision to get medical assistance and increase the difficulty of correct diagnosis. In the present study, we investigate sex differences in the stroke experience as described in stories on weblogs. Objective: The goal of this study was to investigate the feasibility of using the Internet as a source of data for basic research on stroke experiences. Methods: Stroke experiences described in blogs were identified by using StoryUpgrade, a program that searches blog posts using a fictional prototype story. In this study, the prototype story was a description of a stroke experience. Retrieved stories coded by the researchers as relevant were used to update the search query and retrieve more stories using relevance feedback. Stories were coded for first- or third-person narrator, traditional and nontraditional patient symptoms, type of stroke, patient sex and age, delay before seeking medical assistance, and delay at hospital and in treatment. Results: There were 191 relevant stroke stories of which 174 stories reported symptoms (52.3\% female and 47.7\% male patients). There were no sex differences for each traditional or nontraditional stroke symptom by chi-square analysis (all Ps>.05). Type of narrator, however, affected report of traditional and nontraditional symptoms. Female first-person narrators (ie, the patient) were more likely to report mental status change (56.3\%, 27/48) than male first-person narrators (36.4\%, 16/44), a marginally significant effect by logistic regression (P=.056), whereas reports of third-person narrators did not differ for women (27.9\%, 12/43) and men (28.2\%, 11/39) patients. There were more reports of at least 1 nontraditional symptom in the 92 first-person reports (44.6\%, 41/92) than in the 82 third-person reports (25.6\%, 21/82, P=.006). Ischemic or hemorrhagic stroke was reported in 67 and 29 stories, respectively. Nontraditional symptoms varied with stroke type with 1 or more nontraditional symptoms reported for 79.3\% (23/29) of hemorrhagic stroke patients and 53.7\% (36/67) of ischemic stroke patients (P=.001). Conclusions: The results replicate previous findings based on hospital interview data supporting the reliability of findings from weblogs. New findings include the effect of first- versus third-person narrator on sex differences in the report of nontraditional symptoms. This result suggests that narrator is an important variable to be examined in future studies. A fragmentary data problem limits some conclusions because important information, such as age, was not consistently reported. Age trends strengthen the feasibility of using the Internet for stroke research because older adults have significantly increased their Internet use in recent years. ", doi="10.2196/jmir.2838", url="http://www.jmir.org/2014/3/e84/", url="http://www.ncbi.nlm.nih.gov/pubmed/24647327" } @Article{info:doi/10.2196/jmir.2664, author="Yom-Tov, Elad and White, W. Ryen and Horvitz, Eric", title="Seeking Insights About Cycling Mood Disorders via Anonymized Search Logs", journal="J Med Internet Res", year="2014", month="Feb", day="25", volume="16", number="2", pages="e65", keywords="information retrieval", keywords="mood disorders", keywords="bipolar disorder", keywords="machine learning", abstract="Background: Mood disorders affect a significant portion of the general population. Cycling mood disorders are characterized by intermittent episodes (or events) of the disease. Objective: Using anonymized Web search logs, we identify a population of people with significant interest in mood stabilizing drugs (MSD) and seek evidence of mood swings in this population. Methods: We extracted queries to the Microsoft Bing search engine made by 20,046 Web searchers over six months, separately explored searcher demographics using data from a large external panel of users, and sought supporting information from people with mood disorders via a survey. We analyzed changes in information needs over time relative to searches on MSD. Results: Queries for MSD focused on side effects and their relation to the disease. We found evidence of significant changes in search behavior and interests coinciding with days that MSD queries are made. These include large increases (>100\%) in the access of nutrition information, commercial information, and adult materials. A survey of patients diagnosed with mood disorders provided evidence that repeated queries on MSD may come with exacerbations of mood disorder. A classifier predicting the occurrence of such queries one day before they are observed obtains strong performance (AUC=0.78). Conclusions: Observed patterns in search behavior align with known behaviors and those highlighted by survey respondents. These observations suggest that searchers showing intensive interest in MSD may be patients who have been prescribed these drugs. Given behavioral dynamics, we surmise that the days on which MSD queries are made may coincide with commencement of mania or depression. Although we do not have data on mood changes and whether users have been diagnosed with bipolar illness, we see evidence of cycling in people who show interest in MSD and further show that we can predict impending shifts in behavior and interest. ", doi="10.2196/jmir.2664", url="http://www.jmir.org/2014/2/e65/", url="http://www.ncbi.nlm.nih.gov/pubmed/24568936" } @Article{info:doi/10.2196/jmir.2694, author="Ozan-Rafferty, E. Margaret and Johnson, A. James and Shah, H. Gulzar and Kursun, Attila", title="In the Words of the Medical Tourist: An Analysis of Internet Narratives by Health Travelers to Turkey", journal="J Med Internet Res", year="2014", month="Feb", day="06", volume="16", number="2", pages="e43", keywords="medical tourism", keywords="Turkey", keywords="travel blogging", keywords="personal narratives", keywords="qualitative research", keywords="patient satisfaction", keywords="delivery of health care", keywords="globalization", keywords="social media internationality", abstract="Background: Patients regularly travel to the West for advanced medical care, but now the trend is also shifting in the opposite direction. Many people from Western countries now seek care outside of their country. This phenomenon has been labeled medical tourism or health travel. Information regarding health travelers' actual outcomes, experiences, and perceptions is lacking or insufficient. However, advanced Internet technology and apps provide information on medical tourism and are a vehicle for patients to share their experiences. Turkey has a large number of internationally accredited hospitals, is a top tourism destination, and is positioning itself to attract international patients. Objective: The objective of this research was to identify the important individual characteristics of health travelers, outline the push and pull factors for seeking health care in Turkey, identify satisfaction with the outcomes and the results of these individuals' treatments, and note positive and negative factors influencing their perceptions and overall experiences about patients' health travel. Methods: This research uses qualitative data from Internet narratives of medical tourists to Turkey. Ethical considerations of using Internet narratives were reviewed. Narratives for analysis were obtained by using the Google search engine and using multiple search terms to obtain publicly posted blogs and discussion board postings of health travelers via purposeful sampling. Narratives were included if they were written in English, described travel to Turkey for health care, and were publicly accessible. Exclusion criteria included narratives that were on medical tourism facilitator or provider promotional websites, not in English, and did not describe an experience of a medical tourist. Medical tourists' written words were analyzed in an iterative analytic process using narrative analysis theory principles. Three stages of coding (open, axial, and selective) were conducted to identify characteristics and themes using qualitative analysis software. Results: The narrative posts of 36 individuals undergoing 47 procedures who traveled to Turkey for medical care between 2007 and 2012 were analyzed. The narratives came from 13 countries, not including the narratives for which patient origin could not be determined. Travelers were predominantly from Europe (16/36, 44\%) and North America (10/36, 28\%). Factors driving travelers away from their home country (push factors) were cost and lack of treatment options or insufficient insurance coverage in their home country. Leading factors attracting patients to destination (pull factors) were lower costs, physician's expertise and responsiveness, and familiarity or interest in Turkey. Health travelers to Turkey were generally satisfied with the outcomes of their procedures and care provided by their physicians, many noting intent to return. Communication challenges, food, transportation, and gaps in customer service emerged as key areas for improvement. Conclusions: This analysis provides an understanding of the insights of medical tourists through the words of actual health travelers. This nonintrusive methodology provides candid insights of common themes of health travelers and may be applied to study other patient experiences. The findings of this research expands the body of knowledge in medical tourism and serves as a platform for further qualitative and quantitative research on health travelers' experiences. ", doi="10.2196/jmir.2694", url="http://www.jmir.org/2014/2/e43/", url="http://www.ncbi.nlm.nih.gov/pubmed/24513565" } @Article{info:doi/10.2196/jmir.3078, author="Kim, Minki and Jung, Yuchul and Jung, Dain and Hur, Cinyoung", title="Investigating the Congruence of Crowdsourced Information With Official Government Data: The Case of Pediatric Clinics", journal="J Med Internet Res", year="2014", month="Feb", day="03", volume="16", number="2", pages="e29", keywords="online health community", keywords="crowdsourcing", keywords="risk of misinformation", keywords="public health", abstract="Background: Health 2.0 is a benefit to society by helping patients acquire knowledge about health care by harnessing collective intelligence. However, any misleading information can directly affect patients' choices of hospitals and drugs, and potentially exacerbate their health condition. Objective: This study investigates the congruence between crowdsourced information and official government data in the health care domain and identifies the determinants of low congruence where it exists. In-line with infodemiology, we suggest measures to help the patients in the regions vulnerable to inaccurate health information. Methods: We text-mined multiple online health communities in South Korea to construct the data for crowdsourced information on public health services (173,748 messages). Kendall tau and Spearman rank order correlation coefficients were used to compute the differences in 2 ranking systems of health care quality: actual government evaluations of 779 hospitals and mining results of geospecific online health communities. Then we estimated the effect of sociodemographic characteristics on the level of congruence by using an ordinary least squares regression. Results: The regression results indicated that the standard deviation of married women's education (P=.046), population density (P=.01), number of doctors per pediatric clinic (P=.048), and birthrate (P=.002) have a significant effect on the congruence of crowdsourced data (adjusted R2=.33). Specifically, (1) the higher the birthrate in a given region, (2) the larger the variance in educational attainment, (3) the higher the population density, and (4) the greater the number of doctors per clinic, the more likely that crowdsourced information from online communities is congruent with official government data. Conclusions: To investigate the cause of the spread of misleading health information in the online world, we adopted a unique approach by associating mining results on hospitals from geospecific online health communities with the sociodemographic characteristics of corresponding regions. We found that the congruence of crowdsourced information on health care services varied across regions and that these variations could be explained by geospecific demographic factors. This finding can be helpful to governments in reducing the potential risk of misleading online information and the accompanying safety issues. ", doi="10.2196/jmir.3078", url="http://www.jmir.org/2014/2/e29/", url="http://www.ncbi.nlm.nih.gov/pubmed/24496094" } @Article{info:doi/10.2196/jmir.2998, author="Nsoesie, O. Elaine and Buckeridge, L. David and Brownstein, S. John", title="Guess Who's Not Coming to Dinner? Evaluating Online Restaurant Reservations for Disease Surveillance", journal="J Med Internet Res", year="2014", month="Jan", day="22", volume="16", number="1", pages="e22", keywords="population surveillance", keywords="restaurants", keywords="epidemics", keywords="outbreaks", abstract="Background: Alternative data sources are used increasingly to augment traditional public health surveillance systems. Examples include over-the-counter medication sales and school absenteeism. Objective: We sought to determine if an increase in restaurant table availabilities was associated with an increase in disease incidence, specifically influenza-like illness (ILI). Methods: Restaurant table availability was monitored using OpenTable, an online restaurant table reservation site. A daily search was performed for restaurants with available tables for 2 at the hour and at half past the hour for 22 distinct times: between 11:00 am-3:30 pm for lunch and between 6:00-11:30 PM for dinner. In the United States, we examined table availability for restaurants in Boston, Atlanta, Baltimore, and Miami. For Mexico, we studied table availabilities in Cancun, Mexico City, Puebla, Monterrey, and Guadalajara. Time series of restaurant use was compared with Google Flu Trends and ILI at the state and national levels for the United States and Mexico using the cross-correlation function. Results: Differences in restaurant use were observed across sampling times and regions. We also noted similarities in time series trends between data on influenza activity and restaurant use. In some settings, significant correlations greater than 70\% were noted between data on restaurant use and ILI trends. Conclusions: This study introduces and demonstrates the potential value of restaurant use data for event surveillance. ", doi="10.2196/jmir.2998", url="http://www.jmir.org/2014/1/e22/", url="http://www.ncbi.nlm.nih.gov/pubmed/24451921" } @Article{info:doi/10.2196/jmir.2911, author="Gu, Hua and Chen, Bin and Zhu, Honghong and Jiang, Tao and Wang, Xinyi and Chen, Lei and Jiang, Zhenggang and Zheng, Dawei and Jiang, Jianmin", title="Importance of Internet Surveillance in Public Health Emergency Control and Prevention: Evidence From a Digital Epidemiologic Study During Avian Influenza A H7N9 Outbreaks", journal="J Med Internet Res", year="2014", month="Jan", day="17", volume="16", number="1", pages="e20", keywords="influenza A virus, H7N9 subtype", keywords="Internet", keywords="surveillance", keywords="disease outbreak", abstract="Background: Outbreaks of human infection with a new avian influenza A H7N9 virus occurred in China in the spring of 2013. Control and prevention of a new human infectious disease outbreak can be strongly affected by public reaction and social impact through the Internet and social media. Objective: This study aimed to investigate the potential roles of Internet surveillance in control and prevention of the human H7N9 outbreaks. Methods: Official data for the human H7N9 outbreaks were collected via the China National Health and Family Planning Committee website from March 31 to April 24, 2013. We obtained daily posted and forwarded number of blogs for the keyword ``H7N9'' from Sina microblog website and a daily Baidu Attention Index (BAI) from Baidu website, which reflected public attention to the outbreak. Rumors identified and confirmed by the authorities were collected from Baidu search engine. Results: Both daily posted and forwarded number and BAI for keyword H7N9 increased quickly during the first 3 days of the outbreaks and remained at a high level for 5 days. The total daily posted and forwarded number for H7N9 on Sina microblog peaked at 850,000 on April 3, from zero blogs before March 31, increasing to 97,726 on April 1 and to 370,607 on April 2, and remaining above 500,000 from April 5-8 before declining to 208,524 on April 12. The total daily BAI showed a similar pattern of change to the total daily posted and forwarded number over time from March 31 to April 12. When the outbreak locations spread, especially into other areas of the same province/city and the capital, Beijing, daily posted and forwarded number and BAI increased again to a peak at 368,500 and 116,911, respectively. The median daily BAI during the studied 25 days was significantly higher among the 7 provinces/cities with reported human H7N9 cases than the 2 provinces without any cases (P<.001). So were the median daily posted and forwarded number and daily BAI in each province/city except Anhui province. We retrieved a total of 32 confirmed rumors spread across 19 provinces/cities in China. In all, 84\% (27/32) of rumors were disseminated and transmitted by social media. Conclusions: The first 3 days of an epidemic is a critical period for the authorities to take appropriate action through Internet surveillance to prevent and control the epidemic, including preparation of personnel, technology, and other resources; information release; collection of public opinion and reaction; and clarification, prevention, and control of rumors. Internet surveillance can be used as an efficient and economical tool to prevent and control public health emergencies, such as H7N9 outbreaks. ", doi="10.2196/jmir.2911", url="http://www.jmir.org/2014/1/e20/", url="http://www.ncbi.nlm.nih.gov/pubmed/24440770" } @Article{info:doi/10.2196/jmir.2875, author="Sudau, Fabian and Friede, Tim and Grabowski, Jens and Koschack, Janka and Makedonski, Philip and Himmel, Wolfgang", title="Sources of Information and Behavioral Patterns in Online Health Forums: Observational Study", journal="J Med Internet Res", year="2014", month="Jan", day="14", volume="16", number="1", pages="e10", keywords="Internet utilization", keywords="information dissemination", keywords="data mining", keywords="social media", keywords="social networks", keywords="multiple sclerosis", keywords="CCSVI", abstract="Background: Increasing numbers of patients are raising their voice in online forums. This shift is welcome as an act of patient autonomy, reflected in the term ``expert patient''. At the same time, there is considerable concern that patients can be easily misguided by pseudoscientific research and debate. Little is known about the sources of information used in health-related online forums, how users apply this information, and how they behave in such forums. Objective: The intent of the study was to identify (1) the sources of information used in online health-related forums, and (2) the roles and behavior of active forum visitors in introducing and disseminating this information. Methods: This observational study used the largest German multiple sclerosis (MS) online forum as a database, analyzing the user debate about the recently proposed and controversial Chronic Cerebrospinal Venous Insufficiency (CCSVI) hypothesis. After extracting all posts and then filtering relevant CCSVI posts between 01 January 2008 and 17 August 2012, we first identified hyperlinks to scientific publications and other information sources used or referenced in the posts. Employing k-means clustering, we then analyzed the users' preference for sources of information and their general posting habits. Results: Of 139,912 posts from 11,997 threads, 8628 posts discussed or at least mentioned CCSVI. We detected hyperlinks pointing to CCSVI-related scientific publications in 31 posts. In contrast, 2829 different URLs were posted to the forum, most frequently referring to social media, such as YouTube or Facebook. We identified a total of 6 different roles of hyperlink posters including Social Media Fans, Organization Followers, and Balanced Source Users. Apart from the large and nonspecific residual category of the ``average user'', several specific behavior patterns were identified, such as the small but relevant groups of CCSVI-Focused Responders or CCSVI Activators. Conclusions: The bulk of the observed contributions were not based on scientific results, but on various social media sources. These sources seem to contain mostly opinions and personal experience. A small group of people with distinct behavioral patterns played a core role in fuelling the discussion about CCSVI. ", doi="10.2196/jmir.2875", url="http://www.jmir.org/2014/1/e10/", url="http://www.ncbi.nlm.nih.gov/pubmed/24425598" } @Article{info:doi/10.2196/jmir.2870, author="Zhang, Ni and Campo, Shelly and Janz, F. Kathleen and Eckler, Petya and Yang, Jingzhen and Snetselaar, G. Linda and Signorini, Alessio", title="Electronic Word of Mouth on Twitter About Physical Activity in the United States: Exploratory Infodemiology Study", journal="J Med Internet Res", year="2013", month="Nov", day="20", volume="15", number="11", pages="e261", keywords="Twitter messaging", keywords="social marketing", keywords="motor activity", abstract="Background: Twitter is a widely used social medium. However, its application in promoting health behaviors is understudied. Objective: In order to provide insights into designing health marketing interventions to promote physical activity on Twitter, this exploratory infodemiology study applied both social cognitive theory and the path model of online word of mouth to examine the distribution of different electronic word of mouth (eWOM) characteristics among personal tweets about physical activity in the United States. Methods: This study used 113 keywords to retrieve 1 million public tweets about physical activity in the United States posted between January 1 and March 31, 2011. A total of 30,000 tweets were randomly selected and sorted based on numbers generated by a random number generator. Two coders scanned the first 16,100 tweets and yielded 4672 (29.02\%) tweets that they both agreed to be about physical activity and were from personal accounts. Finally, 1500 tweets were randomly selected from the 4672 tweets (32.11\%) for further coding. After intercoder reliability scores reached satisfactory levels in the pilot coding (100 tweets separate from the final 1500 tweets), 2 coders coded 750 tweets each. Descriptive analyses, Mann-Whitney U tests, and Fisher exact tests were performed. Results: Tweets about physical activity were dominated by neutral sentiments (1270/1500, 84.67\%). Providing opinions or information regarding physical activity (1464/1500, 97.60\%) and chatting about physical activity (1354/1500, 90.27\%) were found to be popular on Twitter. Approximately 60\% (905/1500, 60.33\%) of the tweets demonstrated users' past or current participation in physical activity or intentions to participate in physical activity. However, social support about physical activity was provided in less than 10\% of the tweets (135/1500, 9.00\%). Users with fewer people following their tweets (followers) (P=.02) and with fewer accounts that they followed (followings) (P=.04) were more likely to talk positively about physical activity on Twitter. People with more followers were more likely to post neutral tweets about physical activity (P=.04). People with more followings were more likely to forward tweets (P=.04). People with larger differences between number of followers and followings were more likely to mention companionship support for physical activity on Twitter (P=.04). Conclusions: Future health marketing interventions promoting physical activity should segment Twitter users based on their number of followers, followings, and gaps between the number of followers and followings. The innovative application of both marketing and public health theory to examine tweets about physical activity could be extended to other infodemiology or infoveillance studies on other health behaviors (eg, vaccinations). ", doi="10.2196/jmir.2870", url="http://www.jmir.org/2013/11/e261/", url="http://www.ncbi.nlm.nih.gov/pubmed/24257325" } @Article{info:doi/10.2196/jmir.2936, author="Zheluk, Andrey and Quinn, Casey and Hercz, Daniel and Gillespie, A. James", title="Internet Search Patterns of Human Immunodeficiency Virus and the Digital Divide in the Russian Federation: Infoveillance Study", journal="J Med Internet Res", year="2013", month="Nov", day="12", volume="15", number="11", pages="e256", keywords="Russia", keywords="search engine", keywords="human immunodeficiency virus", keywords="surveillance", abstract="Background: Human immunodeficiency virus (HIV) is a serious health problem in the Russian Federation. However, the true scale of HIV in Russia has long been the subject of considerable debate. Using digital surveillance to monitor diseases has become increasingly popular in high income countries. But Internet users may not be representative of overall populations, and the characteristics of the Internet-using population cannot be directly ascertained from search pattern data. This exploratory infoveillance study examined if Internet search patterns can be used for disease surveillance in a large middle-income country with a dispersed population. Objective: This study had two main objectives: (1) to validate Internet search patterns against national HIV prevalence data, and (2) to investigate the relationship between search patterns and the determinants of Internet access. Methods: We first assessed whether online surveillance is a valid and reliable method for monitoring HIV in the Russian Federation. Yandex and Google both provided tools to study search patterns in the Russian Federation. We evaluated the relationship between both Yandex and Google aggregated search patterns and HIV prevalence in 2011 at national and regional tiers. Second, we analyzed the determinants of Internet access to determine the extent to which they explained regional variations in searches for the Russian terms for ``HIV'' and ``AIDS''. We sought to extend understanding of the characteristics of Internet searching populations by data matching the determinants of Internet access (age, education, income, broadband access price, and urbanization ratios) and searches for the term ``HIV'' using principal component analysis (PCA). Results: We found generally strong correlations between HIV prevalence and searches for the terms ``HIV'' and ``AIDS''. National correlations for Yandex searches for ``HIV'' were very strongly correlated with HIV prevalence (Spearman rank-order coefficient [rs]=.881, P?.001) and strongly correlated for ``AIDS'' (rs=.714, P?.001). The strength of correlations varied across Russian regions. National correlations in Google for the term ``HIV'' (rs=.672, P=.004) and ``AIDS'' (rs=.584, P?.001) were weaker than for Yandex. Second, we examined the relationship between the determinants of Internet access and search patterns for the term ``HIV'' across Russia using PCA. At the national level, we found Principal Component 1 loadings, including age (-0.56), HIV search (-0.533), and education (-0.479) contributed 32\% of the variance. Principal Component 2 contributed 22\% of national variance (income, -0.652 and broadband price, -0.460). Conclusions: This study contributes to the methodological literature on search patterns in public health. Based on our preliminary research, we suggest that PCA may be used to evaluate the relationship between the determinants of Internet access and searches for health problems beyond high-income countries. We believe it is in middle-income countries that search methods can make the greatest contribution to public health. ", doi="10.2196/jmir.2936", url="http://www.jmir.org/2013/11/e256/", url="http://www.ncbi.nlm.nih.gov/pubmed/24220250" } @Article{info:doi/10.2196/jmir.2721, author="Greaves, Felix and Ramirez-Cano, Daniel and Millett, Christopher and Darzi, Ara and Donaldson, Liam", title="Use of Sentiment Analysis for Capturing Patient Experience From Free-Text Comments Posted Online", journal="J Med Internet Res", year="2013", month="Nov", day="01", volume="15", number="11", pages="e239", keywords="Internet", keywords="patient experience", keywords="quality", keywords="machine learning", abstract="Background: There are large amounts of unstructured, free-text information about quality of health care available on the Internet in blogs, social networks, and on physician rating websites that are not captured in a systematic way. New analytical techniques, such as sentiment analysis, may allow us to understand and use this information more effectively to improve the quality of health care. Objective: We attempted to use machine learning to understand patients' unstructured comments about their care. We used sentiment analysis techniques to categorize online free-text comments by patients as either positive or negative descriptions of their health care. We tried to automatically predict whether a patient would recommend a hospital, whether the hospital was clean, and whether they were treated with dignity from their free-text description, compared to the patient's own quantitative rating of their care. Methods: We applied machine learning techniques to all 6412 online comments about hospitals on the English National Health Service website in 2010 using Weka data-mining software. We also compared the results obtained from sentiment analysis with the paper-based national inpatient survey results at the hospital level using Spearman rank correlation for all 161 acute adult hospital trusts in England. Results: There was 81\%, 84\%, and 89\% agreement between quantitative ratings of care and those derived from free-text comments using sentiment analysis for cleanliness, being treated with dignity, and overall recommendation of hospital respectively (kappa scores: .40--.74, P<.001 for all). We observed mild to moderate associations between our machine learning predictions and responses to the large patient survey for the three categories examined (Spearman rho 0.37-0.51, P<.001 for all). Conclusions: The prediction accuracy that we have achieved using this machine learning process suggests that we are able to predict, from free-text, a reasonably accurate assessment of patients' opinion about different performance aspects of a hospital and that these machine learning predictions are associated with results of more conventional surveys. ", doi="10.2196/jmir.2721", url="http://www.jmir.org/2013/11/e239/", url="http://www.ncbi.nlm.nih.gov/pubmed/24184993" } @Article{info:doi/10.2196/jmir.2705, author="Nagel, C. Anna and Tsou, Ming-Hsiang and Spitzberg, H. Brian and An, Li and Gawron, Mark J. and Gupta, K. Dipak and Yang, Jiue-An and Han, Su and Peddecord, Michael K. and Lindsay, Suzanne and Sawyer, H. Mark", title="The Complex Relationship of Realspace Events and Messages in Cyberspace: Case Study of Influenza and Pertussis Using Tweets", journal="J Med Internet Res", year="2013", month="Oct", day="26", volume="15", number="10", pages="e237", keywords="Twitter", keywords="infoveillance", keywords="infodemiology", keywords="cyberspace", keywords="syndromic surveillance", keywords="influenza", keywords="pertussis", keywords="whooping cough", abstract="Background: Surveillance plays a vital role in disease detection, but traditional methods of collecting patient data, reporting to health officials, and compiling reports are costly and time consuming. In recent years, syndromic surveillance tools have expanded and researchers are able to exploit the vast amount of data available in real time on the Internet at minimal cost. Many data sources for infoveillance exist, but this study focuses on status updates (tweets) from the Twitter microblogging website. Objective: The aim of this study was to explore the interaction between cyberspace message activity, measured by keyword-specific tweets, and real world occurrences of influenza and pertussis. Tweets were aggregated by week and compared to weekly influenza-like illness (ILI) and weekly pertussis incidence. The potential effect of tweet type was analyzed by categorizing tweets into 4 categories: nonretweets, retweets, tweets with a URL Web address, and tweets without a URL Web address. Methods: Tweets were collected within a 17-mile radius of 11 US cities chosen on the basis of population size and the availability of disease data. Influenza analysis involved all 11 cities. Pertussis analysis was based on the 2 cities nearest to the Washington State pertussis outbreak (Seattle, WA and Portland, OR). Tweet collection resulted in 161,821 flu, 6174 influenza, 160 pertussis, and 1167 whooping cough tweets. The correlation coefficients between tweets or subgroups of tweets and disease occurrence were calculated and trends were presented graphically. Results: Correlations between weekly aggregated tweets and disease occurrence varied greatly, but were relatively strong in some areas. In general, correlation coefficients were stronger in the flu analysis compared to the pertussis analysis. Within each analysis, flu tweets were more strongly correlated with ILI rates than influenza tweets, and whooping cough tweets correlated more strongly with pertussis incidence than pertussis tweets. Nonretweets correlated more with disease occurrence than retweets, and tweets without a URL Web address correlated better with actual incidence than those with a URL Web address primarily for the flu tweets. Conclusions: This study demonstrates that not only does keyword choice play an important role in how well tweets correlate with disease occurrence, but that the subgroup of tweets used for analysis is also important. This exploratory work shows potential in the use of tweets for infoveillance, but continued efforts are needed to further refine research methods in this field. ", doi="10.2196/jmir.2705", url="http://www.jmir.org/2013/10/e237/", url="http://www.ncbi.nlm.nih.gov/pubmed/24158773" } @Article{info:doi/10.2196/jmir.2741, author="Hanson, Lee Carl and Cannon, Ben and Burton, Scott and Giraud-Carrier, Christophe", title="An Exploration of Social Circles and Prescription Drug Abuse Through Twitter", journal="J Med Internet Res", year="2013", month="Sep", day="06", volume="15", number="9", pages="e189", keywords="prescription drug abuse", keywords="social media", keywords="social circles", keywords="Twitter", abstract="Background: Prescription drug abuse has become a major public health problem. Relationships and social context are important contributing factors. Social media provides online channels for people to build relationships that may influence attitudes and behaviors. Objective: To determine whether people who show signs of prescription drug abuse connect online with others who reinforce this behavior, and to observe the conversation and engagement of these networks with regard to prescription drug abuse. Methods: Twitter statuses mentioning prescription drugs were collected from November 2011 to November 2012. From this set, 25 Twitter users were selected who discussed topics indicative of prescription drug abuse. Social circles of 100 people were discovered around each of these Twitter users; the tweets of the Twitter users in these networks were collected and analyzed according to prescription drug abuse discussion and interaction with other users about the topic. Results: From November 2011 to November 2012, 3,389,771 mentions of prescription drug terms were observed. For the 25 social circles (n=100 for each circle), on average 53.96\% (SD 24.3) of the Twitter users used prescription drug terms at least once in their posts, and 37.76\% (SD 20.8) mentioned another Twitter user by name in a post with a prescription drug term. Strong correlation was found between the kinds of drugs mentioned by the index user and his or her network (mean r=0.73), and between the amount of interaction about prescription drugs and a level of abusiveness shown by the network (r=0.85, P<.001). Conclusions: Twitter users who discuss prescription drug abuse online are surrounded by others who also discuss it---potentially reinforcing a negative behavior and social norm. ", doi="10.2196/jmir.2741", url="http://www.jmir.org/2013/9/e189/", url="http://www.ncbi.nlm.nih.gov/pubmed/24014109" } @Article{info:doi/10.2196/jmir.2180, author="Chan, KL and Ho, SY and Lam, TH", title="Infodemiology of Alcohol Use in Hong Kong Mentioned on Blogs: Infoveillance Study", journal="J Med Internet Res", year="2013", month="Sep", day="02", volume="15", number="9", pages="e192", keywords="alcohol drinking", keywords="blogging", keywords="blog search", keywords="Chinese", keywords="Hong Kong", keywords="infodemiology", keywords="infoveillance", keywords="Internet", abstract="Background: In 2007 and 2008, the beer and wine tax in Hong Kong was halved and then abolished, resulting in an increase of alcohol consumption. The prevalence of the Internet and a high blogging rate by adolescents and adults present a unique opportunity to study drinking patterns by infodemiology. Objective: To assess and explain the online use of alcohol-related Chinese keywords and to validate blog searching as an infoveillance method for surveying changes in drinking patterns (eg, alcohol type) in Hong Kong people (represented by bloggers on a Hong Kong--based Chinese blogging site) in 2005-2010. Methods: Blog searching was done using a blog search engine, Google Blog Search, in the archives of a Hong Kong--based blog service provider, MySinaBlog from 2005-2010. Three groups of Chinese keywords, each representing a specific alcohol-related concept, were used: (1) ``alcohol'' (ie, the control concept), (2) ``beer or wine'', and (3) ``spirit''. The resulting blog posts were analyzed quantitatively using infodemiological metrics and correlation coefficients, and qualitatively by manual effort. The infodemiological metrics were (1) apparent prevalence, (2) actual prevalence, (3) prevalence rate, and (4) prevalence ratio. Pearson and Spearman correlations were calculated for prevalence rates and ratios with respect to per capita alcohol consumption. Manual analysis focused on (1) blog author characteristics (ie, authorship, sex, and age), and (2) blog content (ie, frequency of keywords, description of a discrete episode of alcohol drinking, drinking amount, and genres). Results: The online use of alcohol-related concepts increased noticeably for ``alcohol'' in 2008 and ``spirit'' in 2008-2009 but declined for ``beer or wine'' over the years. Correlation between infodemiological and epidemiological data was only significant for the ``alcohol'' prevalence rate. Most blogs were managed by single authors. Their sex distribution was even, and the majority were aged 18 and above. Not all Chinese keywords were found. Many of the blog posts did not describe a discrete episode of alcohol drinking and were classified as personal diary, opinion, or emotional outlets. The rest lacked information on drinking amount, which hindered assessment of binge drinking. Conclusions: The prevalence of alcohol-related Chinese keywords online was attributed to many different factors, including spam, and hence not a specific reflection of local drinking patterns. Correlation between infodemiological data (represented by prevalence rates and ratios of alcohol-related concepts) and epidemiological data (represented by per capita alcohol consumption) was poor. Many blog posts were affective rather than informative in nature. Semantic analysis of blog content was recommended given enough expertise and resources. ", doi="10.2196/jmir.2180", url="http://www.jmir.org/2013/9/e192/", url="http://www.ncbi.nlm.nih.gov/pubmed/23999327" } @Article{info:doi/10.2196/jmir.2534, author="Mysl{\'i}n, Mark and Zhu, Shu-Hong and Chapman, Wendy and Conway, Mike", title="Using Twitter to Examine Smoking Behavior and Perceptions of Emerging Tobacco Products", journal="J Med Internet Res", year="2013", month="Aug", day="29", volume="15", number="8", pages="e174", keywords="social media", keywords="twitter messaging", keywords="smoking", keywords="natural language processing", abstract="Background: Social media platforms such as Twitter are rapidly becoming key resources for public health surveillance applications, yet little is known about Twitter users' levels of informedness and sentiment toward tobacco, especially with regard to the emerging tobacco control challenges posed by hookah and electronic cigarettes. Objective: To develop a content and sentiment analysis of tobacco-related Twitter posts and build machine learning classifiers to detect tobacco-relevant posts and sentiment towards tobacco, with a particular focus on new and emerging products like hookah and electronic cigarettes. Methods: We collected 7362 tobacco-related Twitter posts at 15-day intervals from December 2011 to July 2012. Each tweet was manually classified using a triaxial scheme, capturing genre, theme, and sentiment. Using the collected data, machine-learning classifiers were trained to detect tobacco-related vs irrelevant tweets as well as positive vs negative sentiment, using Na{\"i}ve Bayes, k-nearest neighbors, and Support Vector Machine (SVM) algorithms. Finally, phi contingency coefficients were computed between each of the categories to discover emergent patterns. Results: The most prevalent genres were first- and second-hand experience and opinion, and the most frequent themes were hookah, cessation, and pleasure. Sentiment toward tobacco was overall more positive (1939/4215, 46\% of tweets) than negative (1349/4215, 32\%) or neutral among tweets mentioning it, even excluding the 9\% of tweets categorized as marketing. Three separate metrics converged to support an emergent distinction between, on one hand, hookah and electronic cigarettes corresponding to positive sentiment, and on the other hand, traditional tobacco products and more general references corresponding to negative sentiment. These metrics included correlations between categories in the annotation scheme (phihookah-positive=0.39; phie-cigs-positive=0.19); correlations between search keywords and sentiment ($\chi$24=414.50, P<.001, Cramer's V=0.36), and the most discriminating unigram features for positive and negative sentiment ranked by log odds ratio in the machine learning component of the study. In the automated classification tasks, SVMs using a relatively small number of unigram features (500) achieved best performance in discriminating tobacco-related from unrelated tweets (F score=0.85). Conclusions: Novel insights available through Twitter for tobacco surveillance are attested through the high prevalence of positive sentiment. This positive sentiment is correlated in complex ways with social image, personal experience, and recently popular products such as hookah and electronic cigarettes. Several apparent perceptual disconnects between these products and their health effects suggest opportunities for tobacco control education. Finally, machine classification of tobacco-related posts shows a promising edge over strictly keyword-based approaches, yielding an improved signal-to-noise ratio in Twitter data and paving the way for automated tobacco surveillance applications. ", doi="10.2196/jmir.2534", url="http://www.jmir.org/2013/8/e174/", url="http://www.ncbi.nlm.nih.gov/pubmed/23989137" } @Article{info:doi/10.2196/jmir.2740, author="Bernardo, Marie Theresa and Rajic, Andrijana and Young, Ian and Robiadek, Katie and Pham, T. Mai and Funk, A. Julie", title="Scoping Review on Search Queries and Social Media for Disease Surveillance: A Chronology of Innovation", journal="J Med Internet Res", year="2013", month="Jul", day="18", volume="15", number="7", pages="e147", keywords="disease", keywords="surveillance", keywords="social media", keywords="review", abstract="Background: The threat of a global pandemic posed by outbreaks of influenza H5N1 (1997) and Severe Acute Respiratory Syndrome (SARS, 2002), both diseases of zoonotic origin, provoked interest in improving early warning systems and reinforced the need for combining data from different sources. It led to the use of search query data from search engines such as Google and Yahoo! as an indicator of when and where influenza was occurring. This methodology has subsequently been extended to other diseases and has led to experimentation with new types of social media for disease surveillance. Objective: The objective of this scoping review was to formally assess the current state of knowledge regarding the use of search queries and social media for disease surveillance in order to inform future work on early detection and more effective mitigation of the effects of foodborne illness. Methods: Structured scoping review methods were used to identify, characterize, and evaluate all published primary research, expert review, and commentary articles regarding the use of social media in surveillance of infectious diseases from 2002-2011. Results: Thirty-two primary research articles and 19 reviews and case studies were identified as relevant. Most relevant citations were peer-reviewed journal articles (29/32, 91\%) published in 2010-11 (28/32, 88\%) and reported use of a Google program for surveillance of influenza. Only four primary research articles investigated social media in the context of foodborne disease or gastroenteritis. Most authors (21/32 articles, 66\%) reported that social media-based surveillance had comparable performance when compared to an existing surveillance program. The most commonly reported strengths of social media surveillance programs included their effectiveness (21/32, 66\%) and rapid detection of disease (21/32, 66\%). The most commonly reported weaknesses were the potential for false positive (16/32, 50\%) and false negative (11/32, 34\%) results. Most authors (24/32, 75\%) recommended that social media programs should primarily be used to support existing surveillance programs. Conclusions: The use of search queries and social media for disease surveillance are relatively recent phenomena (first reported in 2006). Both the tools themselves and the methodologies for exploiting them are evolving over time. While their accuracy, speed, and cost compare favorably with existing surveillance systems, the primary challenge is to refine the data signal by reducing surrounding noise. Further developments in digital disease surveillance have the potential to improve sensitivity and specificity, passively through advances in machine learning and actively through engagement of users. Adoption, even as supporting systems for existing surveillance, will entail a high level of familiarity with the tools and collaboration across jurisdictions. ", doi="10.2196/jmir.2740", url="http://www.jmir.org/2013/7/e147/", url="http://www.ncbi.nlm.nih.gov/pubmed/23896182" } @Article{info:doi/10.2196/jmir.2146, author="Kostkova, Patty and Fowler, David and Wiseman, Sue and Weinberg, R. Julius", title="Major Infection Events Over 5 Years: How Is Media Coverage Influencing Online Information Needs of Health Care Professionals and the Public?", journal="J Med Internet Res", year="2013", month="Jul", day="15", volume="15", number="7", pages="e107", keywords="information seeking behavior", keywords="weblogs analysis", keywords="online information needs", keywords="data mining", keywords="infectious outbreaks", abstract="Background: The last decade witnessed turbulent events in public health. Emerging infections, increase of antimicrobial resistance, deliberately released threats and ongoing battles with common illnesses were amplified by the spread of disease through increased international travel. The Internet has dramatically changed the availability of information about outbreaks; however, little research has been done in comparing the online behavior of public and professionals around the same events and the effect of media coverage of outbreaks on information needs. Objective: To investigate professional and public online information needs around major infection outbreaks and correlate these with media coverage. Questions include (1) How do health care professionals' online needs for public health and infection control information differ from those of the public?, (2) Does dramatic media coverage of outbreaks contribute to the information needs among the public?, and (3) How do incidents of diseases and major policy events relate to the information needs of professionals? Methods: We used three longitudinal time-based datasets from mid-2006 until end of 2010: (1) a unique record of professional online behavior on UK infection portals: National electronic Library of Infection and National Resource of Infection Control (NeLI/NRIC), (2) equivalent public online information needs (Google Trends), and (3) relevant media coverage (LexisNexis). Analysis of NeLI/NRIC logs identified the highest interest around six major infectious diseases: Clostridium difficile (C difficile)/Methicillin-resistant Staphylococcus aureus (MRSA), tuberculosis, meningitis, norovirus, and influenza. After pre-processing, the datasets were analyzed and triangulated with each other. Results: Public information needs were more static, following the actual disease occurrence less than those of professionals, whose needs increase with public health events (eg, MRSA/C difficile) and the release of major national policies or important documents. Media coverage of events resulted in major public interest (eg, the 2007/2008 UK outbreak of C difficile/MRSA). An exception was norovirus, showing a seasonal pattern for both public and professionals, which matched the periodic disease occurrence. Meningitis was a clear example of a disease with heightened media coverage tending to focus on individual and celebrity cases. Influenza was a major concern during the 2009 H1N1 outbreak creating massive public interest in line with the spring and autumn peaks in cases; although in autumn 2009, there was no corresponding increase in media coverage. Online resources play an increasing role in fulfilling professionals' and public information needs. Conclusions: Significant factors related to a surge of professional interest around a disease were typically key publications and major policy changes. Public interests seem more static and correlate with media influence but to a lesser extent than expected. The only exception was norovirus, exhibiting online public and professional interest correlating with seasonal occurrences of the disease. Public health agencies with responsibility for risk communication of public health events, in particular during outbreaks and emergencies, need to collaborate with media in order to ensure the coverage is high quality and evidence-based, while professionals' information needs remain mainly fulfilled by online open access to key resources. ", doi="10.2196/jmir.2146", url="http://www.jmir.org/2013/7/e107/", url="http://www.ncbi.nlm.nih.gov/pubmed/23856364" } @Article{info:doi/10.2196/jmir.2613, author="Hingle, Melanie and Yoon, Donella and Fowler, Joseph and Kobourov, Stephen and Schneider, Lee Michael and Falk, Daniel and Burd, Randy", title="Collection and Visualization of Dietary Behavior and Reasons for Eating Using Twitter", journal="J Med Internet Res", year="2013", month="Jun", day="24", volume="15", number="6", pages="e125", keywords="dietary behavior", keywords="data visualization", keywords="social media", keywords="mobile health", keywords="mHealth", abstract="Background: Increasing an individual's awareness and understanding of their dietary habits and reasons for eating may help facilitate positive dietary changes. Mobile technologies allow individuals to record diet-related behavior in real time from any location; however, the most popular software applications lack empirical evidence supporting their efficacy as health promotion tools. Objective: The purpose of this study was to test the feasibility and acceptability of a popular social media software application (Twitter) to capture young adults' dietary behavior and reasons for eating. A secondary aim was to visualize data from Twitter using a novel analytic tool designed to help identify relationships among dietary behaviors, reasons for eating, and contextual factors. Methods: Participants were trained to record all food and beverages consumed over 3 consecutive days (2 weekdays and 1 weekend day) using their mobile device's native Twitter application. A list of 24 hashtags (\#) representing food groups and reasons for eating were provided to participants to guide reporting (eg, \#protein, \#mood). Participants were encouraged to annotate hashtags with contextual information using photos, text, and links. User experience was assessed through a combination of email reports of technical challenges and a 9-item exit survey. Participant data were captured from the public Twitter stream, and frequency of hashtag occurrence and co-occurrence were determined. Contextual data were further parsed and qualitatively analyzed. A frequency matrix was constructed to identify food and behavior hashtags that co-occurred. These relationships were visualized using GMap algorithmic mapping software. Results: A total of 50 adults completed the study. In all, 773 tweets including 2862 hashtags (1756 foods and 1106 reasons for eating) were reported. Frequently reported food groups were \#grains (n=365 tweets), \#dairy (n=221), and \#protein (n=307). The most frequently cited reasons for eating were \#social (activity) (n=122), \#taste (n=146), and \#convenience (n=173). Participants used a combination of study-provided hash tags and their own hash tags to describe behavior. Most rated Twitter as easy to use for the purpose of reporting diet-related behavior. ``Maps'' of hash tag occurrences and co-occurrences were developed that suggested time-varying diet and behavior patterns. Conclusions: Twitter combined with an analytical software tool provides a method for capturing real-time food consumption and diet-related behavior. Data visualization may provide a method to identify relationships between dietary and behavioral factors. These findings will inform the design of a study exploring the use of social media and data visualization to identify relationships between food consumption, reasons for engaging in specific food-related behaviors, relevant contextual factors, and weight and health statuses in diverse populations. ", doi="10.2196/jmir.2613", url="http://www.jmir.org/2013/6/e125/", url="http://www.ncbi.nlm.nih.gov/pubmed/23796439" } @Article{info:doi/10.2196/jmir.2614, author="Yom-Tov, Elad and Gabrilovich, Evgeniy", title="Postmarket Drug Surveillance Without Trial Costs: Discovery of Adverse Drug Reactions Through Large-Scale Analysis of Web Search Queries", journal="J Med Internet Res", year="2013", month="Jun", day="18", volume="15", number="6", pages="e124", keywords="machine learning", keywords="information retrieval", keywords="side effects", keywords="infoveillance", keywords="infodemiology", abstract="Background: Postmarket drug safety surveillance largely depends on spontaneous reports by patients and health care providers; hence, less common adverse drug reactions---especially those caused by long-term exposure, multidrug treatments, or those specific to special populations---often elude discovery. Objective: Here we propose a low cost, fully automated method for continuous monitoring of adverse drug reactions in single drugs and in combinations thereof, and demonstrate the discovery of heretofore-unknown ones. Methods: We used aggregated search data of large populations of Internet users to extract information related to drugs and adverse reactions to them, and correlated these data over time. We further extended our method to identify adverse reactions to combinations of drugs. Results: We validated our method by showing high correlations of our findings with known adverse drug reactions (ADRs). However, although acute early-onset drug reactions are more likely to be reported to regulatory agencies, we show that less acute later-onset ones are better captured in Web search queries. Conclusions: Our method is advantageous in identifying previously unknown adverse drug reactions. These ADRs should be considered as candidates for further scrutiny by medical regulatory authorities, for example, through phase 4 trials. ", doi="10.2196/jmir.2614", url="http://www.jmir.org/2013/6/e124/", url="http://www.ncbi.nlm.nih.gov/pubmed/23778053" } @Article{ref1, url="" } @Article{info:doi/10.2196/jmir.2503, author="Hanson, L. Carl and Burton, H. Scott and Giraud-Carrier, Christophe and West, H. Josh and Barnes, D. Michael and Hansen, Bret", title="Tweaking and Tweeting: Exploring Twitter for Nonmedical Use of a Psychostimulant Drug (Adderall) Among College Students", journal="J Med Internet Res", year="2013", month="Apr", day="17", volume="15", number="4", pages="e62", keywords="Adderall", keywords="Twitter", keywords="social media", keywords="prescription drug abuse", abstract="Background: Adderall is the most commonly abused prescription stimulant among college students. Social media provides a real-time avenue for monitoring public health, specifically for this population. Objective: This study explores discussion of Adderall on Twitter to identify variations in volume around college exam periods, differences across sets of colleges and universities, and commonly mentioned side effects and co-ingested substances. Methods: Public-facing Twitter status messages containing the term ``Adderall'' were monitored from November 2011 to May 2012. Tweets were examined for mention of side effects and other commonly abused substances. Tweets from likely students containing GPS data were identified with clusters of nearby colleges and universities for regional comparison. Results: 213,633 tweets from 132,099 unique user accounts mentioned ``Adderall.'' The number of Adderall tweets peaked during traditional college and university final exam periods. Rates of Adderall tweeters were highest among college and university clusters in the northeast and south regions of the United States. 27,473 (12.9\%) mentioned an alternative motive (eg, study aid) in the same tweet. The most common substances mentioned with Adderall were alcohol (4.8\%) and stimulants (4.7\%), and the most common side effects were sleep deprivation (5.0\%) and loss of appetite (2.6\%). Conclusions: Twitter posts confirm the use of Adderall as a study aid among college students. Adderall discussions through social media such as Twitter may contribute to normative behavior regarding its abuse. ", doi="10.2196/jmir.2503", url="http://www.jmir.org/2013/4/e62/", url="http://www.ncbi.nlm.nih.gov/pubmed/23594933" } @Article{info:doi/10.2196/jmir.2313, author="Robillard, M. Julie and Whiteley, Louise and Johnson, Wade Thomas and Lim, Jonathan and Wasserman, W. Wyeth and Illes, Judy", title="Utilizing Social Media to Study Information-Seeking and Ethical Issues in Gene Therapy", journal="J Med Internet Res", year="2013", month="Mar", day="04", volume="15", number="3", pages="e44", keywords="gene therapy", keywords="social media", keywords="content analysis", keywords="ethics", keywords="public opinion", abstract="Background: The field of gene therapy is rapidly evolving, and while hopes of treating disorders of the central nervous system and ethical concerns have been articulated within the academic community, little is known about views and opinions of different stakeholder groups. Objective: To address this gap, we utilized social media to investigate the kind of information public users are seeking about gene therapy and the hopes, concerns, and attitudes they express. Methods: We conducted a content analysis of questions containing the keywords ``gene therapy'' from the Q\&A site ``Yahoo! Answers'' for the 5-year period between 2006 and 2010. From the pool of questions retrieved (N=903), we identified those containing at least one theme related to ethics, environment, economics, law, or society (n=173) and then characterized the content of relevant answers (n=399) through emergent coding. Results: The results show that users seek a wide range of information regarding gene therapy, with requests for scientific information and ethical issues at the forefront of enquiry. The question sample reveals high expectations for gene therapy that range from cures for genetic and nongenetic diseases to pre- and postnatal enhancement of physiological attributes. Ethics questions are commonly expressed as fears about the impact of gene therapy on self and society. The answer sample echoes these concerns but further suggests that the acceptability of gene therapy varies depending on the specific application. Conclusions: Overall, the findings highlight the powerful role of social media as a rich resource for research into attitudes toward biomedicine and as a platform for knowledge exchange and public engagement for topics relating to health and disease. ", doi="10.2196/jmir.2313", url="http://www.jmir.org/2013/3/e44/", url="http://www.ncbi.nlm.nih.gov/pubmed/23470490" } @Article{info:doi/10.2196/jmir.2181, author="Wong, Wai-Ching Paul and Fu, King-Wa and Yau, Sai-Pong Rickey and Ma, Hei-Man Helen and Law, Yik-Wa and Chang, Shu-Sen and Yip, Siu-Fai Paul", title="Accessing Suicide-Related Information on the Internet: A Retrospective Observational Study of Search Behavior", journal="J Med Internet Res", year="2013", month="Jan", day="11", volume="15", number="1", pages="e3", keywords="Internet search", keywords="Pagerank", keywords="Suicide information", keywords="Information seeking", keywords="Search behavior", keywords="Information retrieval", abstract="Background: The Internet's potential impact on suicide is of major public health interest as easy online access to pro-suicide information or specific suicide methods may increase suicide risk among vulnerable Internet users. Little is known, however, about users' actual searching and browsing behaviors of online suicide-related information. Objective: To investigate what webpages people actually clicked on after searching with suicide-related queries on a search engine and to examine what queries people used to get access to pro-suicide websites. Methods: A retrospective observational study was done. We used a web search dataset released by America Online (AOL). The dataset was randomly sampled from all AOL subscribers' web queries between March and May 2006 and generated by 657,000 service subscribers. Results: We found 5526 search queries (0.026\%, 5526/21,000,000) that included the keyword ``suicide''. The 5526 search queries included 1586 different search terms and were generated by 1625 unique subscribers (0.25\%, 1625/657,000). Of these queries, 61.38\% (3392/5526) were followed by users clicking on a search result. Of these 3392 queries, 1344 (39.62\%) webpages were clicked on by 930 unique users but only 1314 of those webpages were accessible during the study period. Each clicked-through webpage was classified into 11 categories. The categories of the most visited webpages were: entertainment (30.13\%; 396/1314), scientific information (18.31\%; 240/1314), and community resources (14.53\%; 191/1314). Among the 1314 accessed webpages, we could identify only two pro-suicide websites. We found that the search terms used to access these sites included ``commiting suicide with a gas oven'', ``hairless goat'', ``pictures of murder by strangulation'', and ``photo of a severe burn''. A limitation of our study is that the database may be dated and confined to mainly English webpages. Conclusions: Searching or browsing suicide-related or pro-suicide webpages was uncommon, although a small group of users did access websites that contain detailed suicide method information. ", doi="10.2196/jmir.2181", url="http://www.jmir.org/2013/1/e3/", url="http://www.ncbi.nlm.nih.gov/pubmed/23305632" } @Article{info:doi/10.2196/jmir.2270, author="Zheluk, Andrey and Gillespie, A. James and Quinn, Casey", title="Searching for Truth: Internet Search Patterns as a Method of Investigating Online Responses to a Russian Illicit Drug Policy Debate", journal="J Med Internet Res", year="2012", month="Dec", day="13", volume="14", number="6", pages="e165", keywords="Russia", keywords="search engine", keywords="drug dependence", keywords="policy", abstract="Background: This is a methodological study investigating the online responses to a national debate over an important health and social problem in Russia. Russia is the largest Internet market in Europe, exceeding Germany in the absolute number of users. However, Russia is unusual in that the main search provider is not Google, but Yandex. Objective: This study had two main objectives. First, to validate Yandex search patterns against those provided by Google, and second, to test this method's adequacy for investigating online interest in a 2010 national debate over Russian illicit drug policy. We hoped to learn what search patterns and specific search terms could reveal about the relative importance and geographic distribution of interest in this debate. Methods: A national drug debate, centering on the anti-drug campaigner Egor Bychkov, was one of the main Russian domestic news events of 2010. Public interest in this episode was accompanied by increased Internet search. First, we measured the search patterns for 13 search terms related to the Bychkov episode and concurrent domestic events by extracting data from Google Insights for Search (GIFS) and Yandex WordStat (YaW). We conducted Spearman Rank Correlation of GIFS and YaW search data series. Second, we coded all 420 primary posts from Bychkov's personal blog between March 2010 and March 2012 to identify the main themes. Third, we compared GIFS and Yandex policies concerning the public release of search volume data. Finally, we established the relationship between salient drug issues and the Bychkov episode. Results: We found a consistent pattern of strong to moderate positive correlations between Google and Yandex for the terms ``Egor Bychkov'' (rs = 0.88, P < .001), ``Bychkov'' (rs = .78, P < .001) and ``Khimki''(rs = 0.92, P < .001). Peak search volumes for the Bychkov episode were comparable to other prominent domestic political events during 2010. Monthly search counts were 146,689 for ``Bychkov'' and 48,084 for ``Egor Bychkov'', compared to 53,403 for ``Khimki'' in Yandex. We found Google potentially provides timely search results, whereas Yandex provides more accurate geographic localization. The correlation was moderate to strong between search terms representing the Bychkov episode and terms representing salient drug issues in Yandex--``illicit drug treatment'' (rs = .90, P < .001), ``illicit drugs'' (rs = .76, P < .001), and ``drug addiction'' (rs = .74, P < .001). Google correlations were weaker or absent--``illicit drug treatment'' (rs = .12, P = .58), ``illicit drugs '' (rs = -0.29, P = .17), and ``drug addiction'' (rs = .68, P < .001). Conclusions: This study contributes to the methodological literature on the analysis of search patterns for public health. This paper investigated the relationship between Google and Yandex, and contributed to the broader methods literature by highlighting both the potential and limitations of these two search providers. We believe that Yandex Wordstat is a potentially valuable, and underused data source for researchers working on Russian-related illicit drug policy and other public health problems. The Russian Federation, with its large, geographically dispersed, and politically engaged online population presents unique opportunities for studying the evolving influence of the Internet on politics and policy, using low cost methods resilient against potential increases in censorship. ", doi="10.2196/jmir.2270", url="http://www.jmir.org/2012/6/e165/", url="http://www.ncbi.nlm.nih.gov/pubmed/23238600" } @Article{info:doi/10.2196/jmir.2121, author="Burton, H. Scott and Tanner, W. Kesler and Giraud-Carrier, G. Christophe and West, H. Joshua and Barnes, D. Michael", title="``Right Time, Right Place'' Health Communication on Twitter: Value and Accuracy of Location Information", journal="J Med Internet Res", year="2012", month="Nov", day="15", volume="14", number="6", pages="e156", keywords="Twitter", keywords="GPS Location", keywords="Infodemiology", keywords="Surveillance", keywords="Intervention", keywords="Social Media", abstract="Background: Twitter provides various types of location data, including exact Global Positioning System (GPS) coordinates, which could be used for infoveillance and infodemiology (ie, the study and monitoring of online health information), health communication, and interventions. Despite its potential, Twitter location information is not well understood or well documented, limiting its public health utility. Objective: The objective of this study was to document and describe the various types of location information available in Twitter. The different types of location data that can be ascertained from Twitter users are described. This information is key to informing future research on the availability, usability, and limitations of such location data. Methods: Location data was gathered directly from Twitter using its application programming interface (API). The maximum tweets allowed by Twitter were gathered (1\% of the total tweets) over 2 separate weeks in October and November 2011. The final dataset consisted of 23.8 million tweets from 9.5 million unique users. Frequencies for each of the location options were calculated to determine the prevalence of the various location data options by region of the world, time zone, and state within the United States. Data from the US Census Bureau were also compiled to determine population proportions in each state, and Pearson correlation coefficients were used to compare each state's population with the number of Twitter users who enable the GPS location option. Results: The GPS location data could be ascertained for 2.02\% of tweets and 2.70\% of unique users. Using a simple text-matching approach, 17.13\% of user profiles in the 4 continental US time zones were able to be used to determine the user's city and state. Agreement between GPS data and data from the text-matching approach was high (87.69\%). Furthermore, there was a significant correlation between the number of Twitter users per state and the 2010 US Census state populations (r???0.97, P?