Published on in Vol 22, No 4 (2020): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19016, first published .
Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study

Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study

Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study

Journals

  1. Rovetta A, Bhagavathula A. Global Infodemiology of COVID-19: Analysis of Google Web Searches and Instagram Hashtags. Journal of Medical Internet Research 2020;22(8):e20673 View
  2. Domínguez-Salas S, Gómez-Salgado J, Andrés-Villas M, Díaz-Milanés D, Romero-Martín M, Ruiz-Frutos C. Psycho-Emotional Approach to the Psychological Distress Related to the COVID-19 Pandemic in Spain: A Cross-Sectional Observational Study. Healthcare 2020;8(3):190 View
  3. Badell-Grau R, Cuff J, Kelly B, Waller-Evans H, Lloyd-Evans E. Investigating the Prevalence of Reactive Online Searching in the COVID-19 Pandemic: Infoveillance Study. Journal of Medical Internet Research 2020;22(10):e19791 View
  4. Alvarez-Risco A, Mejia C, Delgado-Zegarra J, Del-Aguila-Arcentales S, Arce-Esquivel A, Valladares-Garrido M, Rosas del Portal M, Villegas L, Curioso W, Sekar M, Yáñez J. The Peru Approach against the COVID-19 Infodemic: Insights and Strategies. The American Journal of Tropical Medicine and Hygiene 2020;103(2):583 View
  5. OLIVEIRA L, ZANATTA F. Self-reported dental treatment needs during the COVID-19 outbreak in Brazil: an infodemiological study. Brazilian Oral Research 2020;34 View
  6. Qazi U, Imran M, Ofli F. GeoCoV19. SIGSPATIAL Special 2020;12(1):6 View
  7. Doogan C, Buntine W, Linger H, Brunt S. Public Perceptions and Attitudes Toward COVID-19 Nonpharmaceutical Interventions Across Six Countries: A Topic Modeling Analysis of Twitter Data. Journal of Medical Internet Research 2020;22(9):e21419 View
  8. Hecht N, Wessels L, Werft F, Schneider U, Czabanka M, Vajkoczy P. Need for ensuring care for neuro-emergencies—lessons learned from the COVID-19 pandemic. Acta Neurochirurgica 2020;162(8):1795 View
  9. De Santis E, Martino A, Rizzi A. An Infoveillance System for Detecting and Tracking Relevant Topics From Italian Tweets During the COVID-19 Event. IEEE Access 2020;8:132527 View
  10. Warin T. Global Research on Coronaviruses: An R Package. Journal of Medical Internet Research 2020;22(8):e19615 View
  11. Laato S, Islam A, Farooq A, Dhir A. Unusual purchasing behavior during the early stages of the COVID-19 pandemic: The stimulus-organism-response approach. Journal of Retailing and Consumer Services 2020;57:102224 View
  12. de Melo T, Figueiredo C. A first public dataset from Brazilian twitter and news on COVID-19 in Portuguese. Data in Brief 2020;32:106179 View
  13. Abrams E, Greenhawt M. Mitigating Misinformation and Changing the Social Narrative. The Journal of Allergy and Clinical Immunology: In Practice 2020;8(10):3261 View
  14. Ming L, Untong N, Aliudin N, Osili N, Kifli N, Tan C, Goh K, Ng P, Al-Worafi Y, Lee K, Goh H. Mobile Health Apps on COVID-19 Launched in the Early Days of the Pandemic: Content Analysis and Review. JMIR mHealth and uHealth 2020;8(9):e19796 View
  15. Larrouquere L, Gabin M, Poingt E, Mouffak A, Hlavaty A, Lepelley M, Khouri C, Bellier A, Alexandre J, Bedouch P, Bertoletti L, Bordet R, Bouhanick B, Jonville‐Bera A, Laporte S, Le Jeunne C, Letinier L, Micallef J, Naudet F, Roustit M, Molimard M, Richard V, Cracowski J. Genesis of an emergency public drug information website by the French Society of Pharmacology and Therapeutics during the COVID‐19 pandemic. Fundamental & Clinical Pharmacology 2020;34(3):389 View
  16. Rovetta A, Bhagavathula A. COVID-19-Related Web Search Behaviors and Infodemic Attitudes in Italy: Infodemiological Study. JMIR Public Health and Surveillance 2020;6(2):e19374 View
  17. Pobiruchin M, Zowalla R, Wiesner M. Temporal and Location Variations, and Link Categories for the Dissemination of COVID-19–Related Information on Twitter During the SARS-CoV-2 Outbreak in Europe: Infoveillance Study. Journal of Medical Internet Research 2020;22(8):e19629 View
  18. Kaya T. The changes in the effects of social media use of Cypriots due to COVID-19 pandemic. Technology in Society 2020;63:101380 View
  19. Cignarelli A, Sansone A, Caruso I, Perrini S, Natalicchio A, Laviola L, Jannini E, Giorgino F. Diabetes in the Time of COVID-19: A Twitter-Based Sentiment Analysis. Journal of Diabetes Science and Technology 2020;14(6):1131 View
  20. Chen E, Lerman K, Ferrara E. Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set. JMIR Public Health and Surveillance 2020;6(2):e19273 View
  21. Mackey T, Purushothaman V, Li J, Shah N, Nali M, Bardier C, Liang B, Cai M, Cuomo R. Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study. JMIR Public Health and Surveillance 2020;6(2):e19509 View
  22. 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
  23. Qazi U, Imran M, Ofli F. GeoCoV19. SIGSPATIAL Special 2020;12(1):6 View
  24. Fagherazzi G, Goetzinger C, Rashid M, Aguayo G, Huiart L. Digital Health Strategies to Fight COVID-19 Worldwide: Challenges, Recommendations, and a Call for Papers. Journal of Medical Internet Research 2020;22(6):e19284 View
  25. Chen L, Chang K, Chung H. A Novel Statistic-Based Corpus Machine Processing Approach to Refine a Big Textual Data: An ESP Case of COVID-19 News Reports. Applied Sciences 2020;10(16):5505 View
  26. Budhwani H, Sun R. Creating COVID-19 Stigma by Referencing the Novel Coronavirus as the “Chinese virus” on Twitter: Quantitative Analysis of Social Media Data. Journal of Medical Internet Research 2020;22(5):e19301 View
  27. Campos-Castillo C, Laestadius L. Racial and Ethnic Digital Divides in Posting COVID-19 Content on Social Media Among US Adults: Secondary Survey Analysis. Journal of Medical Internet Research 2020;22(7):e20472 View
  28. Zhu B, Zheng X, Liu H, Li J, Wang P. Analysis of spatiotemporal characteristics of big data on social media sentiment with COVID-19 epidemic topics. Chaos, Solitons & Fractals 2020;140:110123 View
  29. González-Padilla D, Tortolero-Blanco L. Social media influence in the COVID-19 Pandemic. International braz j urol 2020;46(suppl 1):120 View
  30. Ruiz-Frutos C, Ortega-Moreno M, Dias A, Bernardes J, García-Iglesias J, Gómez-Salgado J. Information on COVID-19 and Psychological Distress in a Sample of Non-Health Workers during the Pandemic Period. International Journal of Environmental Research and Public Health 2020;17(19):6982 View
  31. Tsai J, Phua J, Pan S, Yang C. Intergroup Contact, COVID-19 News Consumption, and the Moderating Role of Digital Media Trust on Prejudice Toward Asians in the United States: Cross-Sectional Study. Journal of Medical Internet Research 2020;22(9):e22767 View
  32. Al-Rawi A, Shukla V. Bots as Active News Promoters: A Digital Analysis of COVID-19 Tweets. Information 2020;11(10):461 View
  33. Kimhi S, Marciano H, Eshel Y, Adini B. Recovery from the COVID-19 pandemic: Distress and resilience. International Journal of Disaster Risk Reduction 2020;50:101843 View
  34. Vlasschaert C, Topf J, Hiremath S. Proliferation of Papers and Preprints During the Coronavirus Disease 2019 Pandemic: Progress or Problems With Peer Review?. Advances in Chronic Kidney Disease 2020;27(5):418 View
  35. Kamiński M, Szymańska C, Nowak J. Whose Tweets on COVID-19 Gain the Most Attention: Celebrities, Political, or Scientific Authorities?. Cyberpsychology, Behavior, and Social Networking 2021;24(2):123 View
  36. Duong T, Pham K, Do B, Kim G, Dam H, Le V, Nguyen T, Nguyen H, Nguyen T, Le T, Do H, Yang S. Digital Healthy Diet Literacy and Self-Perceived Eating Behavior Change during COVID-19 Pandemic among Undergraduate Nursing and Medical Students: A Rapid Online Survey. International Journal of Environmental Research and Public Health 2020;17(19):7185 View
  37. Chang C, Monselise M, Yang C. What Are People Concerned About During the Pandemic? Detecting Evolving Topics about COVID-19 from Twitter. Journal of Healthcare Informatics Research 2021;5(1):70 View
  38. Massey D, Huang C, Lu Y, Cohen A, Oren Y, Moed T, Matzner P, Mahajan S, Caraballo C, Kumar N, Xue Y, Ding Q, Dreyer R, Roy B, Krumholz H. Engagement with COVID-19 Public Health Measures in the United States: A Cross-Sectional Social Media Analysis from June to November 2020 (Preprint). Journal of Medical Internet Research 2020 View
  39. Grabowski D, Overgaard M, Meldgaard J, Johansen L, Willaing I. Disrupted Self-Management and Adaption to New Diabetes Routines: A Qualitative Study of How People with Diabetes Managed Their Illness during the COVID-19 Lockdown. Diabetology 2021;2(1):1 View
  40. Zhou X, Song Y, Jiang H, Wang Q, Qu Z, Zhou X, Jit M, Hou Z, Lin L. Comparison of Public Responses to Containment Measures During the Initial Outbreak and Resurgence of COVID-19 in China: Infodemiology Study. Journal of Medical Internet Research 2021;23(4):e26518 View
  41. Shen T, Chen A, Bovonratwet P, Shen C, Su E. COVID-19–Related Internet Search Patterns Among People in the United States: Exploratory Analysis. Journal of Medical Internet Research 2020;22(11):e22407 View
  42. Salvi C, Iannello P, Cancer A, McClay M, Rago S, Dunsmoor J, Antonietti A. Going Viral: How Fear, Socio-Cognitive Polarization and Problem-Solving Influence Fake News Detection and Proliferation During COVID-19 Pandemic. Frontiers in Communication 2021;5 View
  43. Nsoesie E, Cesare N, Müller M, Ozonoff A. COVID-19 Misinformation Spread in Eight Countries: Exponential Growth Modeling Study. Journal of Medical Internet Research 2020;22(12):e24425 View
  44. Basch C, Fera J, Pierce I, Basch C. Promoting Mask Use on TikTok: Descriptive, Cross-sectional Study. JMIR Public Health and Surveillance 2021;7(2):e26392 View
  45. Farsi D. Social Media and Health Care, Part I: Literature Review of Social Media Use by Health Care Providers. Journal of Medical Internet Research 2021;23(4):e23205 View
  46. Singh T, Roberts K, Cohen T, Cobb N, Wang J, Fujimoto K, Myneni S. Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review. JMIR Public Health and Surveillance 2020;6(4):e21660 View
  47. Carnot M, Bernardino J, Laranjeiro N, Gonçalo Oliveira H. Applying Text Analytics for Studying Research Trends in Dependability. Entropy 2020;22(11):1303 View
  48. Gencoglu O, Gruber M. Causal Modeling of Twitter Activity during COVID-19. Computation 2020;8(4):85 View
  49. Chandrasekaran R, Mehta V, Valkunde T, Moustakas E. Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study. Journal of Medical Internet Research 2020;22(10):e22624 View
  50. Chintalapudi N, Battineni G, Amenta F. Sentimental Analysis of COVID-19 Tweets Using Deep Learning Models. Infectious Disease Reports 2021;13(2):329 View
  51. Petersen K, Gerken J. #Covid-19: An exploratory investigation of hashtag usage on Twitter. Health Policy 2021;125(4):541 View
  52. Gencoglu O. Large-Scale, Language-Agnostic Discourse Classification of Tweets During COVID-19. Machine Learning and Knowledge Extraction 2020;2(4):603 View
  53. Reuter K, Deodhar A, Makri S, Zimmer M, Berenbaum F, Nikiphorou E. The impact of the COVID-19 pandemic on people with rheumatic and musculoskeletal diseases: insights from patient-generated data on social media. Rheumatology 2021 View
  54. Shah A, Yan X, Qayyum A, Naqvi R, Shah S. Mining topic and sentiment dynamics in physician rating websites during the early wave of the COVID-19 pandemic: Machine learning approach. International Journal of Medical Informatics 2021;149:104434 View
  55. Rustam F, Khalid M, Aslam W, Rupapara V, Mehmood A, Choi G, Mumtaz W. A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis. PLOS ONE 2021;16(2):e0245909 View
  56. Yang M, Han C. Revealing industry challenge and business response to Covid-19: a text mining approach. International Journal of Contemporary Hospitality Management 2021;33(4):1230 View
  57. Tsao S, Chen H, Tisseverasinghe T, Yang Y, Li L, Butt Z. What social media told us in the time of COVID-19: a scoping review. The Lancet Digital Health 2021;3(3):e175 View
  58. Cauberghe V, Van Wesenbeeck I, De Jans S, Hudders L, Ponnet K. How Adolescents Use Social Media to Cope with Feelings of Loneliness and Anxiety During COVID-19 Lockdown. Cyberpsychology, Behavior, and Social Networking 2021;24(4):250 View
  59. Do B, Tran T, Phan D, Nguyen H, Nguyen T, Nguyen H, Ha T, Dao H, Trinh M, Do T, Nguyen H, Vo T, Nguyen N, Tran C, Tran K, Duong T, Pham H, Nguyen L, Nguyen K, Chang P, Duong T. Health Literacy, eHealth Literacy, Adherence to Infection Prevention and Control Procedures, Lifestyle Changes, and Suspected COVID-19 Symptoms Among Health Care Workers During Lockdown: Online Survey. Journal of Medical Internet Research 2020;22(11):e22894 View
  60. Sharma S, Sharma S. Analyzing the depression and suicidal tendencies of people affected by COVID-19’s lockdown using sentiment analysis on social networking websites. Journal of Statistics and Management Systems 2021;24(1):115 View
  61. Chen S, Zhou L, Song Y, Xu Q, Wang P, Wang K, Ge Y, Janies D. A Novel Machine Learning Framework for Comparison of Viral COVID-19–Related Sina Weibo and Twitter Posts: Workflow Development and Content Analysis. Journal of Medical Internet Research 2021;23(1):e24889 View
  62. Xue J, Chen J, Hu R, Chen C, Zheng C, Su Y, Zhu T. Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach. Journal of Medical Internet Research 2020;22(11):e20550 View
  63. . Genèse d’un site d’information sur le bon usage du médicament au cours de la pandémie. Actualités Pharmaceutiques 2020;59(599):34 View
  64. Alomari E, Katib I, Albeshri A, Mehmood R. COVID-19: Detecting Government Pandemic Measures and Public Concerns from Twitter Arabic Data Using Distributed Machine Learning. International Journal of Environmental Research and Public Health 2021;18(1):282 View
  65. Garcia K, Berton L. Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA. Applied Soft Computing 2021;101:107057 View
  66. Schück S, Foulquié P, Mebarki A, Faviez C, Khadhar M, Texier N, Katsahian S, Burgun A, Chen X. Concerns Discussed on Chinese and French Social Media During the COVID-19 Lockdown: Comparative Infodemiology Study Based on Topic Modeling. JMIR Formative Research 2021;5(4):e23593 View
  67. Zhao Y, Xi H, Zhang C. Exploring Occupation Differences in Reactions to COVID-19 Pandemic on Twitter. Data and Information Management 2021;5(1):110 View
  68. Xue J, Chen J, Chen C, Hu R, Zhu T. The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets. Journal of Medical Internet Research 2020;22(11):e24361 View
  69. Petrocchi S, Iannello P, Ongaro G, Antonietti A, Pravettoni G. The interplay between risk and protective factors during the initial height of the COVID-19 crisis in Italy: The role of risk aversion and intolerance of ambiguity on distress. Current Psychology 2021 View
  70. Al-Khalifa K, AlSheikh R, Alsahafi Y, Alkhalifa A, Sadaf S, Muazen Y, Al-Moumen S, Yermal A. Dental care during the COVID-19 Pandemic: An Arabic tweets analysis (Preprint). JMIR Public Health and Surveillance 2020 View
  71. Piccinelli S, Moro S, Rita P. Air-travelers' concerns emerging from online comments during the COVID-19 outbreak. Tourism Management 2021;85:104313 View
  72. Cordoș A, Bolboacă S. Lockdown, Social Media exposure regarding COVID‐19 and the relation with self‐assessment depression and anxiety. Is the medical staff different?. International Journal of Clinical Practice 2021;75(4) View
  73. Älgå A, Eriksson O, Nordberg M. Analysis of Scientific Publications During the Early Phase of the COVID-19 Pandemic: Topic Modeling Study. Journal of Medical Internet Research 2020;22(11):e21559 View
  74. Alnajashi H, Jabbad R, Lavorgna L. Behavioral practices of patients with multiple sclerosis during Covid-19 pandemic. PLOS ONE 2020;15(10):e0241103 View
  75. de Melo T, Figueiredo C. Comparing News Articles and Tweets About COVID-19 in Brazil: Sentiment Analysis and Topic Modeling Approach. JMIR Public Health and Surveillance 2021;7(2):e24585 View
  76. Gupta V, Jain N, Katariya P, Kumar A, Mohan S, Ahmadian A, Ferrara M. An Emotion Care Model using Multimodal Textual Analysis on COVID-19. Chaos, Solitons & Fractals 2021;144:110708 View
  77. Chen N, Zhong Z, Pang J. An Exploratory Study of COVID-19 Information on Twitter in the Greater Region. Big Data and Cognitive Computing 2021;5(1):5 View
  78. Chakraborty K, Bhatia S, Bhattacharyya S, Platos J, Bag R, Hassanien A. Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media. Applied Soft Computing 2020;97:106754 View
  79. Zhang C, Xu S, Li Z, Hu S. Understanding Concerns, Sentiments, and Disparities Among Population Groups During the COVID-19 Pandemic Via Twitter Data Mining: Large-scale Cross-sectional Study. Journal of Medical Internet Research 2021;23(3):e26482 View
  80. Lindemann I, Simonetti A, Amaral C, Riffel R, Simon T, Stobbe J, Acrani G. Percepção do medo de ser contaminado pelo novo coronavírus. Jornal Brasileiro de Psiquiatria 2021;70(1):3 View
  81. Halabowski D, Rzymski P. Taking a lesson from the COVID-19 pandemic: Preventing the future outbreaks of viral zoonoses through a multi-faceted approach. Science of The Total Environment 2021;757:143723 View
  82. Mavragani A, Gkillas K. COVID-19 predictability in the United States using Google Trends time series. Scientific Reports 2020;10(1) View
  83. Yu S, Eisenman D, Han Z. Temporal Dynamics of Public Emotions During the COVID-19 Pandemic at the Epicenter of the Outbreak: Sentiment Analysis of Weibo Posts From Wuhan. Journal of Medical Internet Research 2021;23(3):e27078 View
  84. Ashfield S, Donelle L. Parental Online Information Access and Childhood Vaccination Decisions in North America: Scoping Review. Journal of Medical Internet Research 2020;22(10):e20002 View
  85. Chatibura D. Travellers’ top comments during the COVID-19 pandemic in Botswana. Research in Hospitality Management 2020;10(2):123 View
  86. Jang H, Rempel E, Roth D, Carenini G, Janjua N. Tracking COVID-19 Discourse on Twitter in North America: Infodemiology Study Using Topic Modeling and Aspect-Based Sentiment Analysis. Journal of Medical Internet Research 2021;23(2):e25431 View
  87. Abrams E, Singer A, Greenhawt M, Stukus D, Shaker M. Ten tips for improving your clinical practice during the COVID-19 pandemic. Current Opinion in Pediatrics 2021;33(2):260 View
  88. Hussain A, Tahir A, Hussain Z, Sheikh Z, Gogate M, Dashtipour K, Ali A, Sheikh A. 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 of Medical Internet Research 2021;23(4):e26627 View
  89. Niknam F, Samadbeik M, Fatehi F, Shirdel M, Rezazadeh M, Bastani P. COVID-19 on Instagram: A content analysis of selected accounts. Health Policy and Technology 2021;10(1):165 View
  90. Pandey D, Bansal S, Goyal S, Garg A, Sethi N, Pothiyill D, Sreelakshmi E, Sayyad M, Sethi R, Santana G. Psychological impact of mass quarantine on population during pandemics—The COVID-19 Lock-Down (COLD) study. PLOS ONE 2020;15(10):e0240501 View
  91. Gamsızkan Z, Sungur M, Erdemir G. How do older age, gender and risk groups affect protective behaviours and mental health in the COVID‐19 pandemic?. International Journal of Clinical Practice 2021 View
  92. Alshalan R, Al-Khalifa H, Alsaeed D, Al-Baity H, Alshalan S. Detection of Hate Speech in COVID-19–Related Tweets in the Arab Region: Deep Learning and Topic Modeling Approach. Journal of Medical Internet Research 2020;22(12):e22609 View
  93. Park S, Han S, Kim J, Molaie M, Vu H, Singh K, Han J, Lee W, Cha M. COVID-19 Discourse on Twitter in Four Asian Countries: Case Study of Risk Communication. Journal of Medical Internet Research 2021;23(3):e23272 View
  94. Al-Laith A, Alenezi M. Monitoring People’s Emotions and Symptoms from Arabic Tweets during the COVID-19 Pandemic. Information 2021;12(2):86 View
  95. 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
  96. Kubb C, Foran H. Measuring COVID-19 Related Anxiety in Parents: Psychometric Comparison of Four Different Inventories. JMIR Mental Health 2020;7(12):e24507 View
  97. Wang J, Zhou Y, Zhang W, Evans R, Zhu C. Concerns Expressed by Chinese Social Media Users During the COVID-19 Pandemic: Content Analysis of Sina Weibo Microblogging Data. Journal of Medical Internet Research 2020;22(11):e22152 View
  98. Dalili Shoaei M, Dastani M. The Role of Twitter During the COVID-19 Crisis: A Systematic Literature Review. Acta Informatica Pragensia 2020;9(2):154 View
  99. Lopreite M, Panzarasa P, Puliga M, Riccaboni M. Early warnings of COVID-19 outbreaks across Europe from social media. Scientific Reports 2021;11(1) View
  100. Viñán-Ludeña M, de Campos L. Analyzing tourist data on Twitter: a case study in the province of Granada at Spain. Journal of Hospitality and Tourism Insights 2021;ahead-of-print(ahead-of-print) View
  101. 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
  102. Wicke P, Bolognesi M. Covid-19 Discourse on Twitter: How the Topics, Sentiments, Subjectivity, and Figurative Frames Changed Over Time. Frontiers in Communication 2021;6 View
  103. Karami A, Anderson M. Social media and COVID ‐19: Characterizing anti‐quarantine comments on Twitter. Proceedings of the Association for Information Science and Technology 2020;57(1) View
  104. Boon-Itt S, Skunkan Y. Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study. JMIR Public Health and Surveillance 2020;6(4):e21978 View
  105. Long V, Koh W, Saw Y, Liu J. Vulnerability to rumours during the COVID-19 pandemic in Singapore. Annals of the Academy of Medicine, Singapore 2021;50(3):232 View
  106. Thomas M, Lal V, Baby A, Rabeeh VP M, James A, Raj A. Can technological advancements help to alleviate COVID-19 pandemic? a review. Journal of Biomedical Informatics 2021;117:103787 View
  107. Gabarron E, Rivera-Romero O, Miron-Shatz T, Grainger R, Denecke K. Role of Participatory Health Informatics in Detecting and Managing Pandemics: Literature Review. Yearbook of Medical Informatics 2021 View
  108. Tao C, Diaz D, Xie Z, Chen L, Li D, O’ Connor R. Potential impact of a COVID-19 and smoking paper on Twitter users’ attitudes toward smoking: Observational Study (Preprint). JMIR Formative Research 2020 View
  109. Fiok K, Karwowski W, Gutierrez E, Saeidi M, Aljuaid A, Davahli M, Taiar R, Marek T, Sawyer B. A Study of the Effects of the COVID-19 Pandemic on the Experience of Back Pain Reported on Twitter® in the United States: A Natural Language Processing Approach. International Journal of Environmental Research and Public Health 2021;18(9):4543 View
  110. 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
  111. Han C, Yang M, Piterou A. Do news media and citizens have the same agenda on COVID-19? An empirical comparison of Twitter posts. Technological Forecasting and Social Change 2021:120849 View
  112. Shah A, Naqvi R, Jeong O. Detecting Topic and Sentiment Trends in Physician Rating Websites: Analysis of Online Reviews Using 3-Wave Datasets. International Journal of Environmental Research and Public Health 2021;18(9):4743 View
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Books/Policy Documents

  1. Barua R, Datta S, Bardhan N. Handbook of Research on Representing Health and Medicine in Modern Media. View
  2. Shah C, Sebastian M. Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation. View
  3. Sabuncu I, Aydin M. Data Science Advancements in Pandemic and Outbreak Management. View
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  5. Diván M, Singh M. Intelligent Human Computer Interaction. View
  6. Chen Z, Li Z, Ji G, Stacks D, Yook B. Communicating Science in Times of Crisis. View
  7. Saire J, Cruz J. Information Management and Big Data. View