Published on in Vol 15, No 8 (2013): August

Using Twitter to Examine Smoking Behavior and Perceptions of Emerging Tobacco Products

Using Twitter to Examine Smoking Behavior and Perceptions of Emerging Tobacco Products

Using Twitter to Examine Smoking Behavior and Perceptions of Emerging Tobacco Products

Journals

  1. Kazemi D, Borsari B, Levine M, Dooley B. Systematic review of surveillance by social media platforms for illicit drug use. Journal of Public Health 2017;39(4):763 View
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  3. Zhu Y. Pro-smoking information scanning using social media predicts young adults' smoking behavior. Computers in Human Behavior 2017;77:19 View
  4. Hswen Y, Naslund J, Chandrashekar P, Siegel R, Brownstein J, Hawkins J. Exploring online communication about cigarette smoking among Twitter users who self-identify as having schizophrenia. Psychiatry Research 2017;257:479 View
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  6. Conway M, O’Connor D. Social media, big data, and mental health: current advances and ethical implications. Current Opinion in Psychology 2016;9:77 View
  7. Cole-Lewis H, Pugatch J, Sanders A, Varghese A, Posada S, Yun C, Schwarz M, Augustson E. Social Listening: A Content Analysis of E-Cigarette Discussions on Twitter. Journal of Medical Internet Research 2015;17(10):e243 View
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  9. Zhu S, Gamst A, Lee M, Cummins S, Yin L, Zoref L, Blum A. The Use and Perception of Electronic Cigarettes and Snus among the U.S. Population. PLoS ONE 2013;8(10):e79332 View
  10. Majmundar A, Cornelis E, Moran M. Examining the vulnerability of ambivalent young adults to e-cigarette messages. Health Marketing Quarterly 2020;37(1):73 View
  11. Glasser A, Collins L, Pearson J, Abudayyeh H, Niaura R, Abrams D, Villanti A. Overview of Electronic Nicotine Delivery Systems: A Systematic Review. American Journal of Preventive Medicine 2017;52(2):e33 View
  12. Burke-Garcia A, Stanton C. A tale of two tools: Reliability and feasibility of social media measurement tools examining e-cigarette twitter mentions. Informatics in Medicine Unlocked 2017;8:8 View
  13. Thackeray R, Neiger B, Burton S, Thackeray C. Analysis of the Purpose of State Health Departments' Tweets: Information Sharing, Engagement, and Action. Journal of Medical Internet Research 2013;15(11):e255 View
  14. Zhang L, Fan H, Peng C, Rao G, Cong Q. Sentiment Analysis Methods for HPV Vaccines Related Tweets Based on Transfer Learning. Healthcare 2020;8(3):307 View
  15. Lienemann B, Unger J, Cruz T, Chu K. Methods for Coding Tobacco-Related Twitter Data: A Systematic Review. Journal of Medical Internet Research 2017;19(3):e91 View
  16. Paul M, Dredze M. Social Monitoring for Public Health. Synthesis Lectures on Information Concepts, Retrieval, and Services 2017;9(5):1 View
  17. Mowery D, Smith H, Cheney T, Stoddard G, Coppersmith G, Bryan C, Conway M. Understanding Depressive Symptoms and Psychosocial Stressors on Twitter: A Corpus-Based Study. Journal of Medical Internet Research 2017;19(2):e48 View
  18. Cavazos-Rehg P, Krauss M, Sowles S, Bierut L. Marijuana-Related Posts on Instagram. Prevention Science 2016;17(6):710 View
  19. Liu J, Ho M, Lu L, Xiao G. Recent Themes in Social Networking Service Research. PLOS ONE 2017;12(1):e0170293 View
  20. Ashford R, Curtis B. Commentary on Cohn and Colleagues: Discussions of Alcohol Use in an Online Social Network for Smoking Cessation: Analysis of Topics, Sentiment, and Social Network Centrality (ACER, 2019). Alcoholism: Clinical and Experimental Research 2019;43(3):401 View
  21. Colditz J, Welling J, Smith N, James A, Primack B. World Vaping Day: Contextualizing Vaping Culture in Online Social Media Using a Mixed Methods Approach. Journal of Mixed Methods Research 2019;13(2):196 View
  22. Hart K, Perlis R, McCoy T. What do patients learn about psychotropic medications on the web? A natural language processing study. Journal of Affective Disorders 2020;260:366 View
  23. Daniulaityte R, Chen L, Lamy F, Carlson R, Thirunarayan K, Sheth A. “When ‘Bad’ is ‘Good’”: Identifying Personal Communication and Sentiment in Drug-Related Tweets. JMIR Public Health and Surveillance 2016;2(2):e162 View
  24. Chan B, Lopez A, Sarkar U, Hildt E. The Canary in the Coal Mine Tweets: Social Media Reveals Public Perceptions of Non-Medical Use of Opioids. PLOS ONE 2015;10(8):e0135072 View
  25. Kendra R, Karki S, Eickholt J, Gandy L. Characterizing the Discussion of Antibiotics in the Twittersphere: What is the Bigger Picture?. Journal of Medical Internet Research 2015;17(6):e154 View
  26. Allem J, Dharmapuri L, Leventhal A, Unger J, Boley Cruz T. Hookah-Related Posts to Twitter From 2017 to 2018: Thematic Analysis. Journal of Medical Internet Research 2018;20(11):e11669 View
  27. Mullins C, ffrench-O'Carroll R, Lane J, O'Connor T. Sharing the pain: an observational analysis of Twitter and pain in Ireland. Regional Anesthesia & Pain Medicine 2020;45(8):597 View
  28. Nascimento T, DosSantos M, Danciu T, DeBoer M, van Holsbeeck H, Lucas S, Aiello C, Khatib L, Bender M, Zubieta J, DaSilva A. Real-Time Sharing and Expression of Migraine Headache Suffering on Twitter: A Cross-Sectional Infodemiology Study. Journal of Medical Internet Research 2014;16(4):e96 View
  29. Visweswaran S, Colditz J, O’Halloran P, Han N, Taneja S, Welling J, Chu K, Sidani J, Primack B. Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study. Journal of Medical Internet Research 2020;22(8):e17478 View
  30. Rose S, Jo C, Binns S, Buenger M, Emery S, Ribisl K. Perceptions of Menthol Cigarettes Among Twitter Users: Content and Sentiment Analysis. Journal of Medical Internet Research 2017;19(2):e56 View
  31. Doan S, Ritchart A, Perry N, Chaparro J, Conway M. How Do You #relax When You’re #stressed? A Content Analysis and Infodemiology Study of Stress-Related Tweets. JMIR Public Health and Surveillance 2017;3(2):e35 View
  32. Sabus C, Johns B, Schultz N, Gagnon K. Exploration of Content and Reach of Physical Therapy-Related Discussion on Twitter. Physical Therapy 2019;99(8):1048 View
  33. Cortese D, Szczypka G, Emery S, Wang S, Hair E, Vallone D. Smoking Selfies: Using Instagram to Explore Young Women’s Smoking Behaviors. Social Media + Society 2018;4(3):205630511879076 View
  34. Kim M, Kim J, Kim S, Jeong J. Twitter Analysis of the Nonmedical Use and Side Effects of Methylphenidate: Machine Learning Study. Journal of Medical Internet Research 2020;22(2):e16466 View
  35. Chu K, Colditz J, Malik M, Yates T, Primack B. Identifying Key Target Audiences for Public Health Campaigns: Leveraging Machine Learning in the Case of Hookah Tobacco Smoking. Journal of Medical Internet Research 2019;21(7):e12443 View
  36. Allem J, Chu K, Cruz T, Unger J. Waterpipe Promotion and Use on Instagram: #Hookah. Nicotine & Tobacco Research 2017:ntw329 View
  37. Park A, Conway M, Chen A. Examining thematic similarity, difference, and membership in three online mental health communities from reddit: A text mining and visualization approach. Computers in Human Behavior 2018;78:98 View
  38. Kamiński M, Muth A, Bogdański P. Smoking, Vaping, and Tobacco Industry During COVID-19 Pandemic: Twitter Data Analysis. Cyberpsychology, Behavior, and Social Networking 2020;23(12):811 View
  39. Little R, West B, Boonstra P, Hu J. Measures of the Degree of Departure from Ignorable Sample Selection. Journal of Survey Statistics and Methodology 2020;8(5):932 View
  40. Kelley D, Brown M, Murray A, Blake K. Prevalence and Characteristics of Twitter Posts About Court-Ordered, Tobacco-Related Corrective Statements: Descriptive Content Analysis. JMIR Public Health and Surveillance 2019;5(4):e12878 View
  41. Chu K, Colditz J, Sidani J, Zimmer M, Primack B. Re-evaluating standards of human subjects protection for sensitive health data in social media networks. Social Networks 2021;67:41 View
  42. Lazard A, Saffer A, Wilcox G, Chung A, Mackert M, Bernhardt J. E-Cigarette Social Media Messages: A Text Mining Analysis of Marketing and Consumer Conversations on Twitter. JMIR Public Health and Surveillance 2016;2(2):e171 View
  43. Gohil S, Vuik S, Darzi A. Sentiment Analysis of Health Care Tweets: Review of the Methods Used. JMIR Public Health and Surveillance 2018;4(2):e43 View
  44. Akl E, Ward K, Bteddini D, Khaliel R, Alexander A, Lotfi T, Alaouie H, Afifi R. The allure of the waterpipe: a narrative review of factors affecting the epidemic rise in waterpipe smoking among young persons globally. Tobacco Control 2015;24(Suppl 1):i13 View
  45. Chen A, Zhu S, Conway M. What Online Communities Can Tell Us About Electronic Cigarettes and Hookah Use: A Study Using Text Mining and Visualization Techniques. Journal of Medical Internet Research 2015;17(9):e220 View
  46. Nguyen Q, Li D, Meng H, Kath S, Nsoesie E, Li F, Wen M. Building a National Neighborhood Dataset From Geotagged Twitter Data for Indicators of Happiness, Diet, and Physical Activity. JMIR Public Health and Surveillance 2016;2(2):e158 View
  47. Finfgeld-Connett D. Twitter and Health Science Research. Western Journal of Nursing Research 2015;37(10):1269 View
  48. Nguyen Q, McCullough M, Meng H, Paul D, Li D, Kath S, Loomis G, Nsoesie E, Wen M, Smith K, Li F. Geotagged US Tweets as Predictors of County-Level Health Outcomes, 2015–2016. American Journal of Public Health 2017;107(11):1776 View
  49. Nguyen J, Gilbert L, Priede L, Heckman C. The Reach of the “Don’t Fry Day” Twitter Campaign: Content Analysis. JMIR Dermatology 2019;2(1):e14137 View
  50. Kim Y, Huang J, Emery S. 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 of Medical Internet Research 2016;18(2):e41 View
  51. Petersen C, Halter R, Kotz D, Loeb L, Cook S, Pidgeon D, Christensen B, Batsis J. Using Natural Language Processing and Sentiment Analysis to Augment Traditional User-Centered Design: Development and Usability Study. JMIR mHealth and uHealth 2020;8(8):e16862 View
  52. Mikal J, Hurst S, Conway M. Ethical issues in using Twitter for population-level depression monitoring: a qualitative study. BMC Medical Ethics 2016;17(1) View
  53. Doan S, Yang E, Tilak S, Li P, Zisook D, Torii M. Extracting health-related causality from twitter messages using natural language processing. BMC Medical Informatics and Decision Making 2019;19(S3) View
  54. Krauss M, Sowles S, Moreno M, Zewdie K, Grucza R, Bierut L, Cavazos-Rehg P. Hookah-Related Twitter Chatter: A Content Analysis. Preventing Chronic Disease 2015;12 View
  55. Ahmed S, Jaidka K, Cho J. The 2014 Indian elections on Twitter: A comparison of campaign strategies of political parties. Telematics and Informatics 2016;33(4):1071 View
  56. Allem J, Ramanujam J, Lerman K, Chu K, Boley Cruz T, Unger J. Identifying Sentiment of Hookah-Related Posts on Twitter. JMIR Public Health and Surveillance 2017;3(4):e74 View
  57. Escobedo P, Cruz T, Tsai K, Allem J, Soto D, Kirkpatrick M, Pattarroyo M, Unger J. Monitoring Tobacco Brand Websites to Understand Marketing Strategies Aimed at Tobacco Product Users and Potential Users. Nicotine & Tobacco Research 2018;20(11):1393 View
  58. Alotaibi S, Mehmood R, Katib I, Rana O, Albeshri A. Sehaa: A Big Data Analytics Tool for Healthcare Symptoms and Diseases Detection Using Twitter, Apache Spark, and Machine Learning. Applied Sciences 2020;10(4):1398 View
  59. Benson R, Hu M, Chen A, Nag S, Zhu S, Conway M. Investigating the Attitudes of Adolescents and Young Adults Towards JUUL: Computational Study Using Twitter Data. JMIR Public Health and Surveillance 2020;6(3):e19975 View
  60. Shutler L, Nelson L, Portelli I, Blachford C, Perrone J. Drug Use in the Twittersphere: A Qualitative Contextual Analysis of Tweets About Prescription Drugs. Journal of Addictive Diseases 2015;34(4):303 View
  61. Chung J. A Smoking Cessation Campaign on Twitter: Understanding the Use of Twitter and Identifying Major Players in a Health Campaign. Journal of Health Communication 2016;21(5):517 View
  62. Alghamdi A, Abumelha K, Allarakia J, Al-Shehri A. Conversations and Misconceptions About Chemotherapy in Arabic Tweets: Content Analysis. Journal of Medical Internet Research 2020;22(7):e13979 View
  63. Kim K, Gibson L, Williams S, Kim Y, Binns S, Emery S, Hornik R. Valence of Media Coverage About Electronic Cigarettes and Other Tobacco Products From 2014 to 2017: Evidence From Automated Content Analysis. Nicotine & Tobacco Research 2020;22(10):1891 View
  64. Cawkwell P, Lee L, Weitzman M, Sherman S. Tracking Hookah Bars in New York: Utilizing Yelp as a Powerful Public Health Tool. JMIR Public Health and Surveillance 2015;1(2):e19 View
  65. Foufi V, Timakum T, Gaudet-Blavignac C, Lovis C, Song M. Mining of Textual Health Information from Reddit: Analysis of Chronic Diseases With Extracted Entities and Their Relations. Journal of Medical Internet Research 2019;21(6):e12876 View
  66. Fogel J, Travis Y. Twitter use related to reality television characters: Association with increased marijuana use. Journal of Organizational Computing and Electronic Commerce 2017;27(2):152 View
  67. Haddad L, El-Shahawy O, Ghadban R, Barnett T, Johnson E. Waterpipe Smoking and Regulation in the United States: A Comprehensive Review of the Literature. International Journal of Environmental Research and Public Health 2015;12(6):6115 View
  68. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206 View
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  72. Conway M. Ethical Issues in Using Twitter for Public Health Surveillance and Research: Developing a Taxonomy of Ethical Concepts From the Research Literature. Journal of Medical Internet Research 2014;16(12):e290 View
  73. Fogel J, Shlivko A. Reality Television Programs Are Associated With Illegal Drug Use and Prescription Drug Misuse Among College Students. Substance Use & Misuse 2016;51(1):62 View
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  138. Netzel L, Heldt S, Denecke M. Analyzing Twitter communication about heavy precipitation events to improve future risk communication and disaster reduction in Germany. Urban Water Journal 2021;18(5):310 View
  139. Alnazzawi N, Fiorini N. Building a semantically annotated corpus for chronic disease complications using two document types. PLOS ONE 2021;16(3):e0247319 View
  140. Oyebode O, Lomotey R, Orji R. “I Tried to Breastfeed but…”: Exploring Factors Influencing Breastfeeding Behaviours Based on Tweets Using Machine Learning and Thematic Analysis. IEEE Access 2021;9:61074 View
  141. Lossio-Ventura J, Gonzales S, Morzan J, Alatrista-Salas H, Hernandez-Boussard T, Bian J. Evaluation of clustering and topic modeling methods over health-related tweets and emails. Artificial Intelligence in Medicine 2021;117:102096 View
  142. Shah A, Yan X, Qayyum A. Social Network Analysis of an Online Smoking Cessation Community to Identify Users’ Smoking Status. Healthcare Informatics Research 2021;27(2):116 View
  143. Sivrikaya E, Yilmaz O, Sivrikaya P. Dentist–patient communication on dental anxiety using the social media: A randomized controlled trial. Scandinavian Journal of Psychology 2021;62(6):780 View
  144. Rutherford B, Lim C, Johnson B, Cheng B, Chung J, Huang S, Sun T, Leung J, Stjepanović D, Chan G. #TurntTrending: a systematic review of substance use portrayals on social media platforms. Addiction 2023;118(2):206 View
  145. Xu Q, Yang J, Haupt M, Cai M, Nali M, Mackey T. Digital Surveillance to Identify California Alternative and Emerging Tobacco Industry Policy Influence and Mobilization on Facebook. International Journal of Environmental Research and Public Health 2021;18(21):11150 View
  146. Benson R, Hu M, Chen A, Zhu S, Conway M. Examining Cannabis, Tobacco, and Vaping Discourse on Reddit: An Exploratory Approach Using Natural Language Processing. Frontiers in Public Health 2022;9 View
  147. El-Amin S, Kinnunen J, Rimpelä A. Adolescents’ Perceptions of Harmfulness of Tobacco and Tobacco-like Products in Finland. International Journal of Environmental Research and Public Health 2022;19(3):1485 View
  148. Hassan L, Elkaref M, de Mel G, Bogdanovica I, Nenadic G. Text mining tweets on e-cigarette risks and benefits using machine learning following a vaping related lung injury outbreak in the USA. Healthcare Analytics 2022;2:100066 View
  149. Gao C, Espinoza Suarez N, Toloza F, Malaga Zuniga A, McCarthy S, Boehmer K, Yao L, Fu S, Brito J. Patients’ Perspective About the Cost of Diabetes Management: An Analysis of Online Health Communities. Mayo Clinic Proceedings: Innovations, Quality & Outcomes 2021;5(5):898 View
  150. 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
  151. Pilipiec P, Liwicki M, Bota A. Using Machine Learning for Pharmacovigilance: A Systematic Review. Pharmaceutics 2022;14(2):266 View
  152. Lane J, Habib D, Curtis B. Linguistic Methodologies to Surveil the Leading Causes of Mortality: Scoping Review of Twitter for Public Health Data. Journal of Medical Internet Research 2023;25:e39484 View
  153. Mittal R, Mittal A, Aggarwal I. Identification of affective valence of Twitter generated sentiments during the COVID-19 outbreak. Social Network Analysis and Mining 2021;11(1) View
  154. Chen J, Xue S, Xie Z, Li D. Perceptions and Discussions of Snus on Twitter: Observational Study. JMIR Medical Informatics 2022;10(8):e38174 View
  155. Fu R, Kundu A, Mitsakakis N, Elton-Marshall T, Wang W, Hill S, Bondy S, Hamilton H, Selby P, Schwartz R, Chaiton M. Machine learning applications in tobacco research: a scoping review. Tobacco Control 2023;32(1):99 View
  156. Roemmich K, Andalibi N. Data Subjects' Conceptualizations of and Attitudes Toward Automatic Emotion Recognition-Enabled Wellbeing Interventions on Social Media. Proceedings of the ACM on Human-Computer Interaction 2021;5(CSCW2):1 View
  157. Ren Y, Wu D, Singh A, Kasson E, Huang M, Cavazos-Rehg P. Automated Detection of Vaping-Related Tweets on Twitter During the 2019 EVALI Outbreak Using Machine Learning Classification. Frontiers in Big Data 2022;5 View
  158. Jeong H, Bayro A, Umesh S, Mamgain K, Lee M. Social Media Users’ Perceptions of a Wearable Mixed Reality Headset During the COVID-19 Pandemic: Aspect-Based Sentiment Analysis. JMIR Serious Games 2022;10(3):e36850 View
  159. Cui J, Wang Z, Ho S, Cambria E. Survey on sentiment analysis: evolution of research methods and topics. Artificial Intelligence Review 2023;56(8):8469 View
  160. DURMUŞOĞLU Z, KOCABEY ÇİFTÇİ P. Socio-demographic determinants of smoking: A data mining analysis of the Global Adult Tobacco Surveys. Türkiye Halk Sağlığı Dergisi 2021 View
  161. Baker W, Colditz J, Dobbs P, Mai H, Visweswaran S, Zhan J, Primack B. Classification of Twitter Vaping Discourse Using BERTweet: Comparative Deep Learning Study. JMIR Medical Informatics 2022;10(7):e33678 View
  162. Wang Y, Wang G, Li H, Gong L, Wu Z. Mapping and analyzing the construction noise pollution in China using social media platforms. Environmental Impact Assessment Review 2022;97:106863 View
  163. dos Santos B, Steiner M, Lima R. Proposal of a method to classify female smokers based on data mining techniques. Computers & Industrial Engineering 2022;170:108363 View
  164. Zhou R, Tang Q, Xie Z, Li D. Public Perceptions of the Food and Drug Administration’s Proposed Rules Prohibiting Menthol Cigarettes on Twitter: Observational Study. JMIR Formative Research 2023;7:e42706 View
  165. Hu M, Benson R, Chen A, Zhu S, Conway M. Determining the prevalence of cannabis, tobacco, and vaping device mentions in online communities using natural language processing. Drug and Alcohol Dependence 2021;228:109016 View
  166. Haupt M, Xu Q, Yang J, Cai M, Mackey T. Characterizing Vaping Industry Political Influence and Mobilization on Facebook: Social Network Analysis. Journal of Medical Internet Research 2021;23(10):e28069 View
  167. Hébert E, Vandewater E, Businelle M, Harrell M, Kelder S, Perry C. Tobacco advertising exposure and product use among young adults: An ecological momentary assessment approach. Addictive Behaviors 2023;139:107601 View
  168. Pilipiec P, Samsten I, Bota A, Rocha L. Surveillance of communicable diseases using social media: A systematic review. PLOS ONE 2023;18(2):e0282101 View
  169. Kim I, Begay C, Ma H, Orozco F, Rogers C, Valente T, Unger J. E-Cigarette–Related Health Beliefs Expressed on Twitter Within the U.S.. AJPM Focus 2023;2(2):100067 View
  170. Berg C, Abroms L, Levine H, Romm K, Khayat A, Wysota C, Duan Z, Bar-Zeev Y. IQOS Marketing in the US: The Need to Study the Impact of FDA Modified Exposure Authorization, Marketing Distribution Channels, and Potential Targeting of Consumers. International Journal of Environmental Research and Public Health 2021;18(19):10551 View
  171. Thakur N. Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets from 2017–2022 and 100 Research Questions. Analytics 2022;1(2):72 View
  172. Stekelenburg N, Horsham C, O’Hara M, Janda M. Using Social Media to Determine the Affective and Cognitive Components of Tweets about Sunburn. Dermatology 2020;236(2):75 View
  173. Groseclose S, Buckeridge D. Public Health Surveillance Systems: Recent Advances in Their Use and Evaluation. Annual Review of Public Health 2017;38(1):57 View
  174. Russell A, Colditz J, Barry A, Davis R, Shields S, Ortega J, Primack B. Analyzing Twitter Chatter About Tobacco Use Within Intoxication-related Contexts of Alcohol Use: “Can Someone Tell Me Why Nicotine is So Fire When You’re Drunk?”. Nicotine & Tobacco Research 2022;24(8):1193 View
  175. 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
  176. Malakar K, Majumder P, Lu C. Twitterati on COVID-19 pandemic-environment linkage: Insights from mining one year of tweets. Environmental Development 2023;46:100835 View
  177. Salvatore C. Inference with non-probability samples and survey data integration: a science mapping study. METRON 2023;81(1):83 View
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  179. Elkaim L, Levett J, Niazi F, Alvi M, Shlobin N, Linzey J, Robertson F, Bokhari R, Alotaibi N, Lasry O. Cervical Myelopathy and Social Media: Mixed Methods Analysis. Journal of Medical Internet Research 2023;25:e42097 View
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Books/Policy Documents

  1. Khan I, Naqvi S, Alam M, Rizvi S. Big Data Analytics. View
  2. Grover P, Kar A, Dwivedi Y, Janssen M. Digital Nations – Smart Cities, Innovation, and Sustainability. View
  3. Optican A, Cavazos-Rehg P. Child and Adolescent Psychiatry and the Media. View
  4. Bibi S, Hussain S, Ahmed M, Zeb M. New Knowledge in Information Systems and Technologies. View
  5. Godea A, Caragea C, Bulgarov F, Ramisetty-Mikler S. Artificial Intelligence in Medicine. View
  6. Mayer M, Fernández-Luque L, Leis A. Participatory Health Through Social Media. View
  7. Shah G, Alfonso M, Jolani N. Public Health and Welfare. View
  8. Hu H, Phan N, Geller J, Vo H, Manasi B, Huang X, Di Lorio S, Dinh T, Chun S. Computational Data and Social Networks. View
  9. Epure E, Deneckere R, Salinesi C. Artificial Intelligence in Medicine. View
  10. Nguyen A, Pham H, Nguyen D, Tran T. Public Health Intelligence and the Internet. View
  11. Lazar J, Feng J, Hochheiser H. Research Methods in Human Computer Interaction. View
  12. Anwar M, Yuan Z. Smart Health. View
  13. Shah G, Alfonso M, Jolani N. Implications of Social Media Use in Personal and Professional Settings. View
  14. Lombi L. Clinical Handbook of Air Pollution-Related Diseases. View
  15. Amrani G, Khennou F, Chaoui N. Information and Software Technologies. View
  16. Ohki Y, Ikeda Y, Iyetomi H. Big Data Analysis on Global Community Formation and Isolation. View
  17. Yang Q, Rains S. The International Encyclopedia of Health Communication. View