This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2388) Infodemiology and Infoveillance (1269) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1552) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1010) Outbreak and Pandemic Preparedness and Management (1673) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1481) Infoveillance and Social Listening (178)

Published on in Vol 22, No 10 (2020): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/22624, first published .
Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study

Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study

Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study

Authors of this article:

Ranganathan Chandrasekaran1 Author Orcid Image ;   Vikalp Mehta1 Author Orcid Image ;   Tejali Valkunde1 Author Orcid Image ;   Evangelos Moustakas2 Author Orcid Image
Article Authors Cited by (135) Tweetations (3) Metrics

Journals

  1. Álvarez-Mon M, Rodríguez-Quiroga A, de Anta L, Quintero J. Aplicaciones médicas de las redes sociales. Aspectos específicos de la pandemia de la COVID-19. Medicine - Programa de Formación Médica Continuada Acreditado 2020;13(23):1305 View
  2. Lyu J, Luli G. Understanding the Public Discussion About the Centers for Disease Control and Prevention During the COVID-19 Pandemic Using Twitter Data: Text Mining Analysis Study. Journal of Medical Internet Research 2021;23(2):e25108 View
  3. Chum A, Nielsen A, Bellows Z, Farrell E, Durette P, Banda J, Cupchik G. Changes in Public Response Associated With Various COVID-19 Restrictions in Ontario, Canada: Observational Infoveillance Study Using Social Media Time Series Data. Journal of Medical Internet Research 2021;23(8):e28716 View
  4. Pang P, Cai Q, Jiang W, Chan K. Engagement of Government Social Media on Facebook during the COVID-19 Pandemic in Macao. International Journal of Environmental Research and Public Health 2021;18(7):3508 View
  5. Adikari A, Nawaratne R, De Silva D, Ranasinghe S, Alahakoon O, Alahakoon D. Emotions of COVID-19: Content Analysis of Self-Reported Information Using Artificial Intelligence. Journal of Medical Internet Research 2021;23(4):e27341 View
  6. Ghasiya P, Okamura K. Investigating COVID-19 News Across Four Nations: A Topic Modeling and Sentiment Analysis Approach. IEEE Access 2021;9:36645 View
  7. Margus C, Brown N, Hertelendy A, Safferman M, Hart A, Ciottone G. Emergency Physician Twitter Use in the COVID-19 Pandemic as a Potential Predictor of Impending Surge: Retrospective Observational Study. Journal of Medical Internet Research 2021;23(7):e28615 View
  8. González L, Devís-Devís J, Pellicer-Chenoll M, Pans M, Pardo-Ibañez A, García-Massó X, Peset F, Garzón-Farinós F, Pérez-Samaniego V. The Impact of COVID-19 on Sport in Twitter: A Quantitative and Qualitative Content Analysis. International Journal of Environmental Research and Public Health 2021;18(9):4554 View
  9. Miller M, Romine W, Oroszi T. Public Discussion of Anthrax on Twitter: Using Machine Learning to Identify Relevant Topics and Events. JMIR Public Health and Surveillance 2021;7(6):e27976 View
  10. Bhatnagar S, Choubey N. Making sense of tweets using sentiment analysis on closely related topics. Social Network Analysis and Mining 2021;11(1) View
  11. Obadimu A, Khaund T, Mead E, Marcoux T, Agarwal N. Developing a socio-computational approach to examine toxicity propagation and regulation in COVID-19 discourse on YouTube. Information Processing & Management 2021;58(5):102660 View
  12. Mohamed Ridhwan K, Hargreaves C. Leveraging Twitter data to understand public sentiment for the COVID‐19 outbreak in Singapore. International Journal of Information Management Data Insights 2021;1(2):100021 View
  13. Kharlamov A, Raskhodchikov A, Pilgun M. Smart City Data Sensing during COVID-19: Public Reaction to Accelerating Digital Transformation. Sensors 2021;21(12):3965 View
  14. Hansen N, Treider J, Swarbrick D, Bamford J, Wilson J, Vuoskoski J. A Crowd-Sourced Database of Coronamusic: Documenting Online Making and Sharing of Music During the COVID-19 Pandemic. Frontiers in Psychology 2021;12 View
  15. Alsudias L, Rayson P. Social Media Monitoring of the COVID-19 Pandemic and Influenza Epidemic With Adaptation for Informal Language in Arabic Twitter Data: Qualitative Study. JMIR Medical Informatics 2021;9(9):e27670 View
  16. Viviani M, Crocamo C, Mazzola M, Bartoli F, Carrà G, Pasi G. Assessing vulnerability to psychological distress during the COVID-19 pandemic through the analysis of microblogging content. Future Generation Computer Systems 2021;125:446 View
  17. Elyashar A, Plochotnikov I, Cohen I, Puzis R, Cohen O. The State of Mind of Health Care Professionals in Light of the COVID-19 Pandemic: Text Analysis Study of Twitter Discourses. Journal of Medical Internet Research 2021;23(10):e30217 View
  18. Wang Y, Shi M, Zhang J, Feng G. What public health campaigns can learn from people’s Twitter reactions on mask-wearing and COVID-19 Vaccines: a topic modeling approach. Cogent Social Sciences 2021;7(1) View
  19. Stevens H, Oh Y, Taylor L. Desensitization to Fear-Inducing COVID-19 Health News on Twitter: Observational Study. JMIR Infodemiology 2021;1(1):e26876 View
  20. Liu S, Li J, Liu J. Leveraging Transfer Learning to Analyze Opinions, Attitudes, and Behavioral Intentions Toward COVID-19 Vaccines: Social Media Content and Temporal Analysis. Journal of Medical Internet Research 2021;23(8):e30251 View
  21. Qiao S, Li Z, Liang C, Li X, Rudisill C. Three dimensions of COVID‐19 risk perceptions and their socioeconomic correlates in the United States: A social media analysis. Risk Analysis 2023;43(6):1174 View
  22. Ghanem A, Asaad C, Hafidi H, Moukafih Y, Guermah B, Sbihi N, Zakroum M, Ghogho M, Dairi M, Cherqaoui M, Baina K. Real-Time Infoveillance of Moroccan Social Media Users’ Sentiments towards the COVID-19 Pandemic and Its Management. International Journal of Environmental Research and Public Health 2021;18(22):12172 View
  23. Wu D, Kasson E, Singh A, Ren Y, Kaiser N, Huang M, Cavazos-Rehg P. Topics and Sentiment Surrounding Vaping on Twitter and Reddit During the 2019 e-Cigarette and Vaping Use–Associated Lung Injury Outbreak: Comparative Study. Journal of Medical Internet Research 2022;24(12):e39460 View
  24. Wang J, Aaron A, Baidya A, Chan C, Wetzler E, Savage K, Joseph M, Kang Y. Gender differences in psychosocial status of adolescents during COVID-19: a six-country cross-sectional survey in Asia Pacific. BMC Public Health 2021;21(1) View
  25. Shi J, Li W, Yongchareon S, Yang Y, Bai Q. Graph-based joint pandemic concern and relation extraction on Twitter. Expert Systems with Applications 2022;195:116538 View
  26. Chandrasekaran R, Desai R, Shah H, Kumar V, Moustakas E. Examining Public Sentiments and Attitudes Toward COVID-19 Vaccination: Infoveillance Study Using Twitter Posts. JMIR Infodemiology 2022;2(1):e33909 View
  27. Jun J, Zain A, Chen Y, Kim S. Adverse Mentions, Negative Sentiment, and Emotions in COVID-19 Vaccine Tweets and Their Association with Vaccination Uptake: Global Comparison of 192 Countries. Vaccines 2022;10(5):735 View
  28. Shahi T, Sitaula C, Paudel N, G T. A Hybrid Feature Extraction Method for Nepali COVID-19-Related Tweets Classification. Computational Intelligence and Neuroscience 2022;2022:1 View
  29. Huang S, Tsao S, Chen H, Bin Noon G, Li L, Yang Y, Butt Z. Topic Modelling and Sentiment Analysis of Tweets Related to Freedom Convoy 2022 in Canada. International Journal of Public Health 2022;67 View
  30. Kwon S, Park A. Understanding user responses to the COVID-19 pandemic on Twitter from a terror management theory perspective: Cultural differences among the US, UK and India. Computers in Human Behavior 2022;128:107087 View
  31. Maghsoudi A, Nowakowski S, Agrawal R, Sharafkhaneh A, Kunik M, Naik A, Xu H, Razjouyan J. Sentiment Analysis of Insomnia-Related Tweets via a Combination of Transformers Using Dempster-Shafer Theory: Pre– and Peri–COVID-19 Pandemic Retrospective Study. Journal of Medical Internet Research 2022;24(12):e41517 View
  32. Huang C, Bandyopadhyay A, Fan W, Miller A, Gilbertson-White S, Chen Z. Mental toll on working women during the COVID-19 pandemic: An exploratory study using Reddit data. PLOS ONE 2023;18(1):e0280049 View
  33. Liu Y, Yin Z, Ni C, Yan C, Wan Z, Malin B. Examining Rural and Urban Sentiment Difference in COVID-19–Related Topics on Twitter: Word Embedding–Based Retrospective Study. Journal of Medical Internet Research 2023;25:e42985 View
  34. Ainapure B, Pise R, Reddy P, Appasani B, Srinivasulu A, Khan M, Bizon N. Sentiment Analysis of COVID-19 Tweets Using Deep Learning and Lexicon-Based Approaches. Sustainability 2023;15(3):2573 View
  35. Sohail M, Yang M, Maresova P, Mustafa S. An SEM-ANN approach to evaluate public awareness about COVID, A pathway toward adaptation effective strategies for sustainable development. Frontiers in Public Health 2022;10 View
  36. Gupta M, Gupta A, Cousins K. Toward the understanding of the constituents of organizational culture: The embedded topic modeling analysis of publicly available employee‐generated reviews of two major U.S.‐based retailers. Production and Operations Management 2022;31(10):3668 View
  37. Ueda M, Watanabe K, Sueki H. 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 of Medical Internet Research 2023;25:e44965 View
  38. Li J, Pang P, Xiao Y, Wong D. Changes in Doctor–Patient Relationships in China during COVID-19: A Text Mining Analysis. International Journal of Environmental Research and Public Health 2022;19(20):13446 View
  39. Singhal A, Baxi M, Mago V. Synergy Between Public and Private Health Care Organizations During COVID-19 on Twitter: Sentiment and Engagement Analysis Using Forecasting Models. JMIR Medical Informatics 2022;10(8):e37829 View
  40. Kasson E, Singh A, Huang M, Wu D, Cavazos-Rehg P. Using a mixed methods approach to identify public perception of vaping risks and overall health outcomes on Twitter during the 2019 EVALI outbreak. International Journal of Medical Informatics 2021;155:104574 View
  41. Boukobza A, Burgun A, Roudier B, Tsopra R. Deep Neural Networks for Simultaneously Capturing Public Topics and Sentiments During a Pandemic: Application on a COVID-19 Tweet Data Set. JMIR Medical Informatics 2022;10(5):e34306 View
  42. Abrams M, Pelullo A, Meisel Z, Merchant R, Purtle J, Agarwal A. 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. JMIR Infodemiology 2023;3:e38676 View
  43. Ntompras C, Drosatos G, Kaldoudi E. A high-resolution temporal and geospatial content analysis of Twitter posts related to the COVID-19 pandemic. Journal of Computational Social Science 2022;5(1):687 View
  44. Zhou Z, Song Z, Xiao H, Ren T. Multi-source data driven cryptocurrency price movement prediction and portfolio optimization. Expert Systems with Applications 2023;219:119600 View
  45. Cai M, Luo H, Meng X, Cui Y, Wang W. Influence of information attributes on information dissemination in public health emergencies. Humanities and Social Sciences Communications 2022;9(1) View
  46. Arenas Gaitán J, Ramírez-Correa P. COVID-19 and telemedicine: A netnography approach. Technological Forecasting and Social Change 2023;190:122420 View
  47. Luo C, Chen A, Cui B, Liao W. Exploring public perceptions of the COVID-19 vaccine online from a cultural perspective: Semantic network analysis of two social media platforms in the United States and China. Telematics and Informatics 2021;65:101712 View
  48. Liew T, Lee C. Examining the Utility of Social Media in COVID-19 Vaccination: Unsupervised Learning of 672,133 Twitter Posts. JMIR Public Health and Surveillance 2021;7(11):e29789 View
  49. Liu L, Fu Y. Study on the mechanism of public attention to a major event: The outbreak of COVID-19 in China. Sustainable Cities and Society 2022;81:103811 View
  50. Delir Haghighi P, Burstein F, Urquhart D, Cicuttini F. Investigating Individuals’ Perceptions Regarding the Context Around the Low Back Pain Experience: Topic Modeling Analysis of Twitter Data. Journal of Medical Internet Research 2021;23(12):e26093 View
  51. Xavier T, Lambert J. Sentiment and emotion trends in nurses' tweets about the COVID‐19 pandemic. Journal of Nursing Scholarship 2022;54(5):613 View
  52. Mandl T, Jaki S, Mitera H, Schmidt F. Interdisciplinary Analysis of Science Communication on Social Media during the COVID-19 Crisis. Knowledge 2023;3(1):97 View
  53. Li M, Hua Y, Liao Y, Zhou L, Li X, Wang L, Yang J. Tracking the Impact of COVID-19 and Lockdown Policies on Public Mental Health Using Social Media: Infoveillance Study. Journal of Medical Internet Research 2022;24(10):e39676 View
  54. Beliga S, Martinčić-Ipšić S, Matešić M, Petrijevčanin Vuksanović I, Meštrović A. Infoveillance of the Croatian Online Media During the COVID-19 Pandemic: One-Year Longitudinal Study Using Natural Language Processing. JMIR Public Health and Surveillance 2021;7(12):e31540 View
  55. Radic A, Koo B, Kim J, Ariza-Montes A, Vega-Muñoz A, Han H. The Effects of Media Encouragements on Coronavirus Vaccination Decision and Public Interest in Traveling Abroad. Information 2022;13(3):157 View
  56. Roe C, Lowe M, Williams B, Miller C. Public Perception of SARS-CoV-2 Vaccinations on Social Media: Questionnaire and Sentiment Analysis. International Journal of Environmental Research and Public Health 2021;18(24):13028 View
  57. Athanasiou M, Fragkozidis G, Zarkogianni K, Nikita K. Long Short-term Memory–Based Prediction of the Spread of Influenza-Like Illness Leveraging Surveillance, Weather, and Twitter Data: Model Development and Validation. Journal of Medical Internet Research 2023;25:e42519 View
  58. Tsao S, MacLean A, Chen H, Li L, Yang Y, Butt Z. Public Attitudes During the Second Lockdown: Sentiment and Topic Analyses Using Tweets From Ontario, Canada. International Journal of Public Health 2022;67 View
  59. Scarpino I, Zucco C, Vallelunga R, Luzza F, Cannataro M. Investigating Topic Modeling Techniques to Extract Meaningful Insights in Italian Long COVID Narration. BioTech 2022;11(3):41 View
  60. Zhang J, Wang Y, Shi M, Wang X. Factors Driving the Popularity and Virality of COVID-19 Vaccine Discourse on Twitter: Text Mining and Data Visualization Study. JMIR Public Health and Surveillance 2021;7(12):e32814 View
  61. Randler C, Kalb N, Tryjanowski P. Sentiment Analysis of Comments of American Birders during Two Waves of the COVID-19 Pandemic Reveal More Negative Sentiments in the Context of Birding. International Journal of Environmental Research and Public Health 2021;18(24):13142 View
  62. Mathayomchan B, Taecharungroj V, Wattanacharoensil W. Evolution of COVID-19 tweets about Southeast Asian Countries: topic modelling and sentiment analyses. Place Branding and Public Diplomacy 2023;19(3):317 View
  63. Gamal N, Ghoniemy S, Faheem H, Seada N. Sentiment-Based Spatiotemporal Prediction Framework for Pandemic Outbreaks Awareness Using Social Networks Data Classification. IEEE Access 2022;10:76434 View
  64. Luo H, Meng X, Zhao Y, Cai M. Exploring the impact of sentiment on multi-dimensional information dissemination using COVID-19 data in China. Computers in Human Behavior 2023;144:107733 View
  65. Wang A, Lan J, Wang M, Yu C. The Evolution of Rumors on a Closed Social Networking Platform During COVID-19: Algorithm Development and Content Study. JMIR Medical Informatics 2021;9(11):e30467 View
  66. Lamsal R, Harwood A, Read M. Socially Enhanced Situation Awareness from Microblogs Using Artificial Intelligence: A Survey. ACM Computing Surveys 2023;55(4):1 View
  67. Chin H, Lima G, Shin M, Zhunis A, Cha C, Choi J, Cha M. User-Chatbot Conversations During the COVID-19 Pandemic: Study Based on Topic Modeling and Sentiment Analysis. Journal of Medical Internet Research 2023;25:e40922 View
  68. Ke S, Neeley-Tass E, Barnes M, Hanson C, Giraud-Carrier C, Snell Q. COVID-19 Health Beliefs Regarding Mask Wearing and Vaccinations on Twitter: Deep Learning Approach. JMIR Infodemiology 2022;2(2):e37861 View
  69. Srinivasan R, Jha A, Verma N. To talk or not?: An analysis of firm‐initiated social media communication's impact on firm value preservation during a massive disruption across multiple firms and industries. Decision Sciences 2023;54(4):410 View
  70. Jones R, Mougouei D, Evans S. Understanding the emotional response to COVID‐19 information in news and social media: A mental health perspective. Human Behavior and Emerging Technologies 2021;3(5):832 View
  71. Sitaula C, Basnet A, Mainali A, Shahi T, G T. Deep Learning-Based Methods for Sentiment Analysis on Nepali COVID-19-Related Tweets. Computational Intelligence and Neuroscience 2021;2021:1 View
  72. Gunasekeran D, Chew A, Chandrasekar E, Rajendram P, Kandarpa V, Rajendram M, Chia A, Smith H, Leong C. The Impact and Applications of Social Media Platforms for Public Health Responses Before and During the COVID-19 Pandemic: Systematic Literature Review. Journal of Medical Internet Research 2022;24(4):e33680 View
  73. Al-Rashedi A, Al-Hagery M. Deep Learning Algorithms for Forecasting COVID-19 Cases in Saudi Arabia. Applied Sciences 2023;13(3):1816 View
  74. AL-Ahdal T, Coker D, Awad H, Reda A, Żuratyński P, Khailaie S. Improving Public Health Policy by Comparing the Public Response during the Start of COVID-19 and Monkeypox on Twitter in Germany: A Mixed Methods Study. Vaccines 2022;10(12):1985 View
  75. Babić K, Petrović M, Beliga S, Martinčić-Ipšić S, Matešić M, Meštrović A. Characterisation of COVID-19-Related Tweets in the Croatian Language: Framework Based on the Cro-CoV-cseBERT Model. Applied Sciences 2021;11(21):10442 View
  76. Xu W, Tshimula J, Dubé È, Graham J, Greyson D, MacDonald N, Meyer S. Unmasking the Twitter Discourses on Masks During the COVID-19 Pandemic: User Cluster–Based BERT Topic Modeling Approach. JMIR Infodemiology 2022;2(2):e41198 View
  77. Hu M, Conway M. Perspectives of the COVID-19 Pandemic on Reddit: Comparative Natural Language Processing Study of the United States, the United Kingdom, Canada, and Australia. JMIR Infodemiology 2022;2(2):e36941 View
  78. Evans S, Jones R, Alkan E, Sichman J, Haque A, de Oliveira F, Mougouei D, Yan Z. The Emotional Impact of COVID-19 News Reporting: A Longitudinal Study Using Natural Language Processing. Human Behavior and Emerging Technologies 2023;2023:1 View
  79. Morita P, Zakir Hussain I, Kaur J, Lotto M, Butt Z. 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 of Medical Internet Research 2023;25:e44356 View
  80. Chen S, Yin S, Guo Y, Ge Y, Janies D, Dulin M, Brown C, Robinson P, Zhang D. Content and sentiment surveillance (CSI): A critical component for modeling modern epidemics. Frontiers in Public Health 2023;11 View
  81. Sukhavasi N, Misra J, Kaulgud V, Podder S. Geo-sentiment trends analysis of tweets in context of economy and employment during COVID-19. Journal of Computational Social Science 2023;6(2):411 View
  82. Vaiyapuri T, Jagannathan S, Ahmed M, Ramya K, Joshi G, Lee S, Lee G. Sustainable Artificial Intelligence-Based Twitter Sentiment Analysis on COVID-19 Pandemic. Sustainability 2023;15(8):6404 View
  83. Zhou Z, Song Z, Xiao H, Ren T. Multi-Source Data Driven Cryptocurrency Price Movement Prediction and Portfolio Optimization. SSRN Electronic Journal 2022 View
  84. Fu J, Li C, Zhou C, Li W, Lai J, Deng S, Zhang Y, Guo Z, Wu Y. Methods for Analyzing the Contents of Social Media for Health Care: Scoping Review. Journal of Medical Internet Research 2023;25:e43349 View
  85. Su L, Chen T, Ng Y, Gong Z, Wang Y. Integrating Human Insights Into Text Analysis: Semi-Supervised Topic Modeling of Emerging Food-Technology Businesses’ Brand Communication on Social Media. Social Science Computer Review 2024;42(2):416 View
  86. Swank L. Vague news and fake news. Journal of Economic Behavior & Organization 2023;215:89 View
  87. Milhazes-Cunha J, Oliveira L. Doctors for the Truth: Echo Chambers of Disinformation, Hate Speech, and Authority Bias on Social Media. Societies 2023;13(10):226 View
  88. Sun W, Kobayashi H, Nakao S, Schmöcker J. On the Relationship Between Crowdsourced Sentiments and Mobility Trends During COVID-19: A Case Study of Kyoto. Data Science for Transportation 2023;5(3) View
  89. González-Malabet M, Sanandres Campis E, May R, Molinares Guerrero I, Durán-Oviedo S. The hybrid political role of feminism on Twitter during COVID-19: SISMA Mujer in Colombia. Women's Studies International Forum 2023;99:102778 View
  90. Fogarty B, Massie K, Svistova J. Unmasking twitter discourse: an infodemiology study of covid-19 mitigation practices. Atlantic Journal of Communication 2024;32(1):124 View
  91. Luo H, Meng X, Zhao Y, Cai M. Rise of social bots: The impact of social bots on public opinion dynamics in public health emergencies from an information ecology perspective. Telematics and Informatics 2023;85:102051 View
  92. Xia X, Zhang Y, Jiang W, Wu C. Staying Home, Tweeting Hope: Mixed Methods Study of Twitter Sentiment Geographical Index During US Stay-At-Home Orders. Journal of Medical Internet Research 2023;25:e45757 View
  93. Reisinezhad P, Fakhrahmad M. Induction of knowledge, attitude and practice of people toward a pandemic from Twitter: a comprehensive model based on opinion mining. Kybernetes 2023;52(7):2507 View
  94. Yim D, Khuntia J, King E, Treskon M, Galiatsatos P. Expert Credibility and Sentiment in Infodemiology of Hydroxychloroquine’s Efficacy on Cable News Programs: Empirical Analysis. JMIR Infodemiology 2023;3:e45392 View
  95. Shankar K, Chandrasekaran R, Jeripity Venkata P, Miketinas D. Investigating the Role of Nutrition in Enhancing Immunity During the COVID-19 Pandemic: Twitter Text-Mining Analysis. Journal of Medical Internet Research 2023;25:e47328 View
  96. Alvarez-Mon M, Pereira-Sanchez V, Hooker E, Sanchez F, Alvarez-Mon M, Teo A. 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. JMIR Infodemiology 2023;3:e43685 View
  97. Singhal A, Mago V. Exploring How Healthcare Organizations Use Twitter: A Discourse Analysis. Informatics 2023;10(3):65 View
  98. Sitaula C, Shahi T. Multi-channel CNN to classify Nepali COVID-19 related tweets using hybrid features. Journal of Ambient Intelligence and Humanized Computing 2024;15(3):2047 View
  99. Chandrasekaran R, Bapat P, Venkata P, Moustakas E. Face time with physicians: How do patients assess providers in video-visits?. Heliyon 2023;9(6):e16883 View
  100. Weerasinghe S, Oyebode O, Orji R, Matwin S. Dynamics of emotion trends in Canadian Twitter users during COVID-19 confinement in relation to caseloads: Artificial intelligence-based emotion detection approach. DIGITAL HEALTH 2023;9:205520762311714 View
  101. Saleh S, McDonald S, Basit M, Kumar S, Arasaratnam R, Perl T, Lehmann C, Medford R. Public perception of COVID-19 vaccines through analysis of Twitter content and users. Vaccine 2023;41(33):4844 View
  102. Lamba N, Khokhlova O, Bhatia A, McHugh C. Mental health hygiene during a health crisis: Exploring factors associated with media-induced secondary trauma in relation to the COVID-19 pandemic. Health Psychology Open 2023;10(2) View
  103. Faviez C, Talmatkadi M, Foulquié P, Mebarki A, Schück S, Burgun A, Chen X. Assessment of the Early Detection of Anosmia and Ageusia Symptoms in COVID-19 on Twitter: Retrospective Study. JMIR Infodemiology 2023;3:e41863 View
  104. Isip Tan I, Cleofas J, Solano G, Pillejera J, Catapang J. Interdisciplinary Approach to Identify and Characterize COVID-19 Misinformation on Twitter: Mixed Methods Study. JMIR Formative Research 2023;7:e41134 View
  105. Sun D, Zhao G, Al-Awaida W. The impact of Public Health Emergency (PHE) on the news dissemination strength: Evidence from Chinese-Speaking Vloggers on YouTube. PLOS ONE 2023;18(11):e0294665 View
  106. Bui H, Ekşioğlu S, Proano R, Nurre Pinkley S. An analysis of COVID-19 vaccine hesitancy in the U.S.. IISE Transactions 2024:1 View
  107. Snellman J, Barreiro N, Barrio R, Ventura C, Govezensky T, Kaski K, Korpi-Lagg M. Socio-economic pandemic modelling: case of Spain. Scientific Reports 2024;14(1) View
  108. Fazal U, Khan M, Maqbool M, Bibi H, Nazeer R. Sentiment Analysis of Omicron Tweets by using Machine Learning Models. VFAST Transactions on Software Engineering 2023;11(1):67 View
  109. Atilla F, Zwaan R. Impact of spatial distance on public attention and sentiment during the spread of COVID-19. Informatics in Medicine Unlocked 2024;45:101463 View
  110. Chandrasekaran R, Konaraddi K, Sharma S, Moustakas E. Text-Mining and Video Analytics of COVID-19 Narratives Shared by Patients on YouTube. Journal of Medical Systems 2024;48(1) View
  111. Choi S. Perceived Challenges and Emotional Responses in the Daily Lives of Older Adults With Disabilities: A Text Mining Study. Gerontology and Geriatric Medicine 2024;10 View
  112. Li Y, Chen M, Lee H. Health communication on social media at the early stage of the pandemic: Examining health professionals’ COVID-19 related tweets. Social Science & Medicine 2024;347:116748 View
  113. Kusumaningrum R, Khoerunnisa S, Khadijah K, Syafrudin M. Exploring Community Awareness of Mangrove Ecosystem Preservation through Sentence-BERT and K-Means Clustering. Information 2024;15(3):165 View
  114. Chu A, Kwok P, Chan J, So M. COVID-19 Pandemic Risk Assessment: Systematic Review. Risk Management and Healthcare Policy 2024;Volume 17:903 View
  115. Batool A, Byun Y. Enhanced Sentiment Analysis and Topic Modeling During the Pandemic Using Automated Latent Dirichlet Allocation. IEEE Access 2024;12:81206 View

Books/Policy Documents

  1. Babić K, Petrović M, Beliga S, Martinčić-Ipšić S, Jarynowski A, Meštrović A. Proceedings of Sixth International Congress on Information and Communication Technology. View
  2. Amofa S, Gao J, Asante-Mensah M, Haruna C, Qi X. Frontiers in Cyber Security. View
  3. Thakur O, Saritha S, Jain S. Machine Learning, Image Processing, Network Security and Data Sciences. View
  4. Nimanthika S, Kuhaneswaran B, Wijeratne A, Kumara S. Handbook of Research on Advancements of Contactless Technology and Service Innovation in Library and Information Science. View
  5. Pousset R. Senizid. View
  6. Saha S, Showrov M, Rahman M, Majumder M. Machine Intelligence and Emerging Technologies. View
  7. Martinis M, Scarpino I, Zucco C, Cannataro M. Computational Science – ICCS 2023. View
  8. Chen S. Communicating COVID-19. View
  9. Maulana F, Adi P, Lestari D, Purnomo A, Wangean D. Proceedings of the 4th International Conference on Electronics, Biomedical Engineering, and Health Informatics. View