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. Journal of Medical Internet Research 2021;23(6):e26655 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
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  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
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  125. Rotter D, Doebler P, Schmitz F. Interests, Motives, and Psychological Burdens in Times of Crisis and Lockdown: Google Trends Analysis to Inform Policy Makers. Journal of Medical Internet Research 2021;23(6):e26385 View
  126. Schweinberger M, Haugh M, Hames S. Analysing discourse around COVID-19 in the Australian Twittersphere: A real-time corpus-based analysis. Big Data & Society 2021;8(1):205395172110214 View
  127. Kydros D, Argyropoulou M, Vrana V. A Content and Sentiment Analysis of Greek Tweets during the Pandemic. Sustainability 2021;13(11):6150 View
  128. Ilyas H, Anwar A, Yaqub U, Alzamil Z, Appelbaum D. Analysis and visualization of COVID-19 discourse on Twitter using data science: a case study of the USA, the UK and India. Global Knowledge, Memory and Communication 2021;ahead-of-print(ahead-of-print) View
  129. Asare A, Yap R, Truong N, Sarpong E. The pandemic semesters: Examining public opinion regarding online learning amidst COVID ‐19. Journal of Computer Assisted Learning 2021 View
  130. Priyadarshini I, Mohanty P, Kumar R, Sharma R, Puri V, Singh P. A study on the sentiments and psychology of twitter users during COVID-19 lockdown period. Multimedia Tools and Applications 2021 View
  131. Tri Sakti A, Mohamad E, Azlan A. Mining of Opinions on COVID-19 Large-Scale Social Restrictions in Indonesia: Public Sentiment and Emotion Analysis on Online Media. Journal of Medical Internet Research 2021;23(8):e28249 View
  132. Cohrdes C, Yenikent S, Wu J, Ghanem B, Franco-Salvador M, Vogelgesang F. Indications of Depressive Symptoms During the COVID-19 Pandemic in Germany: Comparison of National Survey and Twitter Data. JMIR Mental Health 2021;8(6):e27140 View
  133. 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
  134. Han J, Park J, Lee H. Effect of exposure to COVID‐19 infodemic on infection‐preventive intentions among Korean adults. Nursing Open 2021 View
  135. Abd-Alrazaq A, Hassan A, Abuelezz I, Ahmed A, Alzubaidi M, Shah U, Alhuwail D, Giannicchi A, Househ M. An overview of technologies implemented during the first wave of COVID-19: A scoping review (Preprint). Journal of Medical Internet Research 2021 View
  136. Tran H, Lu S, Tran H, Nguyen B. Social Media Insights During the COVID-19 Pandemic: Infodemiology Study Using Big Data. JMIR Medical Informatics 2021;9(7):e27116 View
  137. El-Rashidy N, Abdelrazik S, Abuhmed T, Amer E, Ali F, Hu J, El-Sappagh S. Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic. Diagnostics 2021;11(7):1155 View
  138. Choudrie J, Patil S, Kotecha K, Matta N, Pappas I. Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study. Information Systems Frontiers 2021 View
  139. ÜNVER O, DİNÇ H, ÇETİN E, ARGAN M. Kaygılarım Kabusum Olmasın! Futbolcuların Covid-19 Pandemisi Sürecindeki Kaygılarının Fotoses Yöntemiyle İncelenmesi. International Journal of Sport, Exercise & Training Sciences 2021 View
  140. Hanschmidt F, Kersting A. Emotions in Covid-19 Twitter discourse following the introduction of social contact restrictions in Central Europe. Journal of Public Health 2021 View
  141. Lama Y, Nan X, Quinn S. General and health-related social media use among adults with children in the household: Findings from a national survey in the United States. Patient Education and Counseling 2021 View
  142. Naveed M, Malik A, Mahmood K. Impact of conspiracy beliefs on Covid-19 fear and health protective behavior: a case of university students. Library Hi Tech 2021;ahead-of-print(ahead-of-print) View
  143. Al-Rawi A, Grepin K, Li X, Morgan R, Wenham C, Smith J. Investigating Public Discourses Around Gender and COVID-19: a Social Media Analysis of Twitter Data. Journal of Healthcare Informatics Research 2021;5(3):249 View
  144. Jong W, Liang O, Yang C. The Exchange of Informational Support in Online Health Communities at the Onset of the COVID-19 Pandemic: Content Analysis. JMIRx Med 2021;2(3):e27485 View
  145. Elyashar A, Plochotnikov I, Cohen I, Puzis R, Cohen O. The State of Mind of Healthcare Professionals in the Light of the COVID-19: Insights from Text Analysis of Twitter’s Online Discourses (Preprint). Journal of Medical Internet Research 2021 View
  146. 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
  147. Gupta P, Kumar S, Suman R, Kumar V. Sentiment Analysis of Lockdown in India During COVID-19: A Case Study on Twitter. IEEE Transactions on Computational Social Systems 2021;8(4):992 View
  148. 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):1959728 View
  149. Boucher J, Cornelson K, Benham J, Fullerton M, Tang T, Constantinescu C, Mourali M, Oxoby R, Marshall D, Hemmati H, Badami A, Hu J, Lang R. Analyzing Social Media to Explore the Attitudes and Behaviors Following the Announcement of Successful COVID-19 Vaccine Trials: Infodemiology Study. JMIR Infodemiology 2021;1(1):e28800 View
  150. Melo M, Tupinambás U, Ferri P, Godoy S, Torres R, Palmeira V, Rocha G, Reis Z. Covid-19: e-Learning as a tool for improving the knowledge. Revista Brasileira de Educação Médica 2021;45(3) View
  151. Motahari-Nezhad H, Shekofteh M, Andalib-Kondori M. Social media as a platform for information and support for coronavirus: analysis ofCOVID-19 Facebook groups. Global Knowledge, Memory and Communication 2021;ahead-of-print(ahead-of-print) View

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
  4. Casillo M, Colace F, Conte D, De Santo M, Lombardi M, Mottola S, Santaniello D. Computational Data and Social Networks. View
  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
  8. Lähdeaho O, Hilmola O. Human Centred Intelligent Systems. View
  9. Sabuncu I. Handbook of Research on the Impacts and Implications of COVID-19 on the Tourism Industry. View
  10. Chire Saire J, Pineda-Briseño A. Artificial Intelligence for COVID-19. View
  11. Chauhan B, Jaiswar A, Bedi A, Verma S, Shrivastaw V, Vedrtnam A. Artificial Intelligence for COVID-19. View
  12. Abd-Alrazaq A, Schneider J, Alhuwail D, Hamdi M, Al-Kuwari S, Al-Thani D, Househ M. Multiple Perspectives on Artificial Intelligence in Healthcare. View