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

JMIR Theme Issue: COVID-19 Special Issue (2084) Infodemiology and Infoveillance (999) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1268) Machine Learning (996) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (816) Outbreak and Pandemic Preparedness and Management (1450) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1328)

Published on in Vol 22 , No 11 (2020) :November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/20550, first published .
Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach

Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach

Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach

Authors of this article:

Jia Xue 1, 2 Author Orcid Image ;   Junxiang Chen 3 Author Orcid Image ;   Ran Hu 1 Author Orcid Image ;   Chen Chen 4 Author Orcid Image ;   Chengda Zheng 2 Author Orcid Image ;   Yue Su 5, 6 Author Orcid Image ;   Tingshao Zhu 5 Author Orcid Image
Article Authors Cited by (150) Tweetations (12) Metrics

Journals

  1. Sontayasara T, Jariyapongpaiboon S, Promjun A, Seelpipat N, Saengtabtim K, Tang J, Leelawat N. Twitter Sentiment Analysis of Bangkok Tourism During COVID-19 Pandemic Using Support Vector Machine Algorithm. Journal of Disaster Research 2021;16(1):24 View
  2. 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
  3. 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
  4. Cotfas L, Delcea C, Roxin I, Ioanas C, Gherai D, Tajariol F. The Longest Month: Analyzing COVID-19 Vaccination Opinions Dynamics From Tweets in the Month Following the First Vaccine Announcement. IEEE Access 2021;9:33203 View
  5. 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
  6. Siddique S, Chow J. Machine Learning in Healthcare Communication. Encyclopedia 2021;1(1):220 View
  7. 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
  8. 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
  9. 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
  10. 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;169:120849 View
  11. Satu M, Khan M, Mahmud M, Uddin S, Summers M, Quinn J, Moni M. TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets. Knowledge-Based Systems 2021;226:107126 View
  12. Batra R, Imran A, Kastrati Z, Ghafoor A, Daudpota S, Shaikh S. Evaluating Polarity Trend Amidst the Coronavirus Crisis in Peoples’ Attitudes toward the Vaccination Drive. Sustainability 2021;13(10):5344 View
  13. 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
  14. Criss S, Nguyen T, Norton S, Virani I, Titherington E, Tillmanns E, Kinnane C, Maiolo G, Kirby A, Gee G. Advocacy, Hesitancy, and Equity: Exploring U.S. Race-Related Discussions of the COVID-19 Vaccine on Twitter. International Journal of Environmental Research and Public Health 2021;18(11):5693 View
  15. Kydros D, Argyropoulou M, Vrana V. A Content and Sentiment Analysis of Greek Tweets during the Pandemic. Sustainability 2021;13(11):6150 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. Otero P, Gago J, Quintas P. Twitter data analysis to assess the interest of citizens on the impact of marine plastic pollution. Marine Pollution Bulletin 2021;170:112620 View
  18. Chilman N, Morant N, Lloyd-Evans B, Wackett J, Johnson S. Twitter Users’ Views on Mental Health Crisis Resolution Team Care Compared With Stakeholder Interviews and Focus Groups: Qualitative Analysis. JMIR Mental Health 2021;8(6):e25742 View
  19. Zhu Y, Park H. Development of a COVID-19 Web Information Transmission Structure Based on a Quadruple Helix Model: Webometric Network Approach Using Bing. Journal of Medical Internet Research 2021;23(8):e27681 View
  20. Luo C, Ji K, Tang Y, Du Z. Exploring the Expression Differences Between Professionals and Laypeople Toward the COVID-19 Vaccine: Text Mining Approach. Journal of Medical Internet Research 2021;23(8):e30715 View
  21. 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
  22. Ghaleb M, Almurtadha Y, Algarni F, Abdullah M, Felemban E, M. Alsharafi A, Othman M, Ghilan K. Mining the Chatbot Brain to Improve COVID-19 Bot Response Accuracy. Computers, Materials & Continua 2022;70(2):2619 View
  23. 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
  24. Zhang M, Qi X, Chen Z, Liu J. Social Bots’ Involvement in the COVID-19 Vaccine Discussions on Twitter. International Journal of Environmental Research and Public Health 2022;19(3):1651 View
  25. Shankar S, Tewari V. Understanding the Emotional Intelligence Discourse on Social Media: Insights from the Analysis of Twitter. Journal of Intelligence 2021;9(4):56 View
  26. Luo L, Wang Y, Mo D. Identifying COVID-19 Personal Health Mentions From Tweets Using Masked Attention Model. IEEE Access 2022;10:59068 View
  27. Heyerdahl L, Lana B, Giles-Vernick T. The Impact of the Online COVID-19 Infodemic on French Red Cross Actors’ Field Engagement and Protective Behaviors: Mixed Methods Study. JMIR Infodemiology 2021;1(1):e27472 View
  28. Magazzino C, Mele M, Coccia M. A machine learning algorithm to analyse the effects of vaccination on COVID-19 mortality. Epidemiology and Infection 2022;150 View
  29. Guo D, Zhao Q, Chen Q, Wu J, Li L, Gao H. Comparison between sentiments of people from affected and non-affected regions after the flood. Geomatics, Natural Hazards and Risk 2021;12(1):3346 View
  30. Koren A, Alam M, Koneru S, DeVito A, Abdallah L, Liu B. Nursing Perspectives on the Impacts of COVID-19: Social Media Content Analysis. JMIR Formative Research 2021;5(12):e31358 View
  31. 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
  32. Ilbeigipour S, Albadvi A, Akhondzadeh Noughabi E. Cluster-based analysis of COVID-19 cases using self-organizing map neural network and K-means methods to improve medical decision-making. Informatics in Medicine Unlocked 2022;32:101005 View
  33. Oliveira F, Haque A, Mougouei D, Evans S, Sichman J, Singh M. Investigating the Emotional Response to COVID-19 News on Twitter: A Topic Modeling and Emotion Classification Approach. IEEE Access 2022;10:16883 View
  34. Farahat R, Yassin M, Al-Tawfiq J, Bejan C, Abdelazeem B. Public perspectives of monkeypox in Twitter: A social media analysis using machine learning. New Microbes and New Infections 2022;49-50:101053 View
  35. Arce-García S, Díaz-Campo J, Cambronero-Saiz B. Online hate speech and emotions on Twitter: a case study of Greta Thunberg at the UN Climate Change Conference COP25 in 2019. Social Network Analysis and Mining 2023;13(1) View
  36. Sarirete A. Sentiment analysis tracking of COVID-19 vaccine through tweets. Journal of Ambient Intelligence and Humanized Computing 2022 View
  37. Ritschl V, Eibensteiner F, Mosor E, Omara M, Sperl L, Nawaz F, Siva Sai C, Cenanovic M, Devkota H, Hribersek M, De R, Klager E, Schaden E, Kletecka-Pulker M, Völkl-Kernstock S, Willschke H, Aufricht C, Atanasov A, Stamm T. Mandatory Vaccination Against COVID-19: Twitter Poll Analysis on Public Health Opinion. JMIR Formative Research 2022;6(6):e35754 View
  38. Nakanishi M, Sakai M, Takagi G, Toshi K, Wakashima K, Yoshii H. The Association Between COVID-19 Information Sources and Stigma Against Health Care Workers Among College Students: Cross-sectional, Observational Study. JMIR Formative Research 2022;6(7):e35806 View
  39. Winter R, Lavis A. The Impact of COVID-19 on Young People’s Mental Health in the UK: Key Insights from Social Media Using Online Ethnography. International Journal of Environmental Research and Public Health 2021;19(1):352 View
  40. Michailidis P. Visualizing Social Media Research in the Age of COVID-19. Information 2022;13(8):372 View
  41. Wu J, Wang L, Hua Y, Li M, Zhou L, Bates D, Yang J. Trend and Co-occurrence Network of COVID-19 Symptoms From Large-Scale Social Media Data: Infoveillance Study. Journal of Medical Internet Research 2023;25:e45419 View
  42. Rezapour M, Elmshaeuser S, Vellido A. Artificial intelligence-based analytics for impacts of COVID-19 and online learning on college students’ mental health. PLOS ONE 2022;17(11):e0276767 View
  43. Casillano N. Discovering Sentiments and Latent Themes in the Views of Faculty Members towards the Shift from Conventional to Online Teaching Using VADER and Latent Dirichlet Allocation. International Journal of Information and Education Technology 2022;12(4):290 View
  44. Kahanek A, Yu X, Hong L, Cleveland A, Philbrick J. Temporal Variations and Spatial Disparities in Public Sentiment Toward COVID-19 and Preventive Practices in the United States: Infodemiology Study of Tweets. JMIR Infodemiology 2021;1(1):e31671 View
  45. Monzani D, Vergani L, Pizzoli S, Marton G, Pravettoni G. Emotional Tone, Analytical Thinking, and Somatosensory Processes of a Sample of Italian Tweets During the First Phases of the COVID-19 Pandemic: Observational Study. Journal of Medical Internet Research 2021;23(10):e29820 View
  46. 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
  47. Wang H, Sun K, Wang Y. Exploring the Chinese Public’s Perception of Omicron Variants on Social Media: LDA-Based Topic Modeling and Sentiment Analysis. International Journal of Environmental Research and Public Health 2022;19(14):8377 View
  48. Li W, Deng X, Shao H, Wang X. Deep Learning Applications for COVID-19 Analysis: A State-of-the-Art Survey. Computer Modeling in Engineering & Sciences 2021;129(1):65 View
  49. 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
  50. Baird A, Xia Y, Cheng Y. Consumer perceptions of telehealth for mental health or substance abuse: a Twitter-based topic modeling analysis. JAMIA Open 2022;5(2) View
  51. 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
  52. 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
  53. Huang Y, Liu H, Zhang L, Li S, Wang W, Ren Z, Zhou Z, Ma X. The Psychological and Behavioral Patterns of Online Psychological Help-Seekers before and during COVID-19 Pandemic: A Text Mining-Based Longitudinal Ecological Study. International Journal of Environmental Research and Public Health 2021;18(21):11525 View
  54. Wong J, Yang J, Liu Z. It’s the Thoughts That Count: How Psychological Distance and Affect Heuristic Influence Support for Aid Response Measures During the COVID-19 Pandemic. Health Communication 2022:1 View
  55. Aritenang A. Evaluating city-scale urban mobility restriction in Jakarta due to the COVID-19 pandemic: the impact on subjective wellbeing. Urban, Planning and Transport Research 2021;9(1):519 View
  56. Cabrera-Barona P, Barragán-Ochoa F, Carrión A, Valdez F, López-Sandoval M. Emociones, espacio público e imágenes urbanas en el contexto de COVID-19. Universitas 2022;(36):149 View
  57. ASLAN S. BiGRU-CNN Tabanlı Derin Öğrenme Modeliyle Türkiye’deki Covid-19 Aşılarına Yönelik Twitter Duygu Analizi. International Journal of Pure and Applied Sciences 2022 View
  58. 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
  59. Mir A, Rathinam S, Gul S. Public perception of COVID-19 vaccines from the digital footprints left on Twitter: analyzing positive, neutral and negative sentiments of Twitterati. Library Hi Tech 2022;40(2):340 View
  60. Teague S, Shatte A, Weller E, Fuller-Tyszkiewicz M, Hutchinson D. Methods and Applications of Social Media Monitoring of Mental Health During Disasters: Scoping Review. JMIR Mental Health 2022;9(2):e33058 View
  61. Jiang H, Castellanos A, Castillo A, Gomes P, Li J, VanderMeer D. Nurses’ Work Concerns and Disenchantment During the COVID-19 Pandemic: Machine Learning Analysis of Web-Based Discussions. JMIR Nursing 2023;6:e40676 View
  62. Gille F, Smith S, Mays N. Evidence-based guiding principles to build public trust in personal data use in health systems. DIGITAL HEALTH 2022;8:205520762211119 View
  63. Chiang Y, Chu M, Lin S, Cai X, Chen Q, Wang H, Li A, Rui J, Zhang X, Xie F, Lee C, Chen T. Capturing the Trajectory of Psychological Status and Analyzing Online Public Reactions During the Coronavirus Disease 2019 Pandemic Through Weibo Posts in China. Frontiers in Psychology 2021;12 View
  64. 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
  65. Al-Qerem W, Al Bawab A, Hammad A, Ling J, Alasmari F. Willingness of the Jordanian Population to Receive a COVID-19 Booster Dose: A Cross-Sectional Study. Vaccines 2022;10(3):410 View
  66. 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
  67. Cano-Marin E, Mora-Cantallops M, Sanchez-Alonso S. The power of big data analytics over fake news: A scientometric review of Twitter as a predictive system in healthcare. Technological Forecasting and Social Change 2023;190:122386 View
  68. Bastani P, Hakimzadeh S, Bahrami M. Designing a conceptual framework for misinformation on social media: a qualitative study on COVID-19. BMC Research Notes 2021;14(1) View
  69. Madni H, Umer M, Abuzinadah N, Hu Y, Saidani O, Alsubai S, Hamdi M, Ashraf I. Improving Sentiment Prediction of Textual Tweets Using Feature Fusion and Deep Machine Ensemble Model. Electronics 2023;12(6):1302 View
  70. 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
  71. Etta G, Galeazzi A, Hutchings J, James Smith C, Conti M, Quattrociocchi W, Riva G, Jalloh M. COVID-19 infodemic on Facebook and containment measures in Italy, United Kingdom and New Zealand. PLOS ONE 2022;17(5):e0267022 View
  72. Yum S. The COVID-19 Response in North America. Disaster Medicine and Public Health Preparedness 2023;17 View
  73. Russell P, Frackowiak M, Cohen-Chen S, Rusconi P, Fasoli F. Induced gratitude and hope, and experienced fear, but not experienced disgust, facilitate COVID-19 prevention. Cognition and Emotion 2023:1 View
  74. Blanco G, Lourenço A. Optimism and pessimism analysis using deep learning on COVID-19 related twitter conversations. Information Processing & Management 2022;59(3):102918 View
  75. Sinha C, Meheli S, Kadaba M. Understanding Digital Mental Health Needs and Usage With an Artificial Intelligence–Led Mental Health App (Wysa) During the COVID-19 Pandemic: Retrospective Analysis. JMIR Formative Research 2023;7:e41913 View
  76. Hammad A, Al-Qerem W, Abu Zaid A, Khdair S, Hall F. Misconceptions Related to COVID 19 Vaccines Among the Jordanian Population: Myth and Public Health. Disaster Medicine and Public Health Preparedness 2023;17 View
  77. Martínez-Martínez F, Roldán-Álvarez D, Martín E, Hoppe H. An analytics approach to health and healthcare in citizen science communications on Twitter. DIGITAL HEALTH 2023;9:205520762211453 View
  78. Fang H, Xu G, Long Y, Tang W. An Effective ELECTRA-Based Pipeline for Sentiment Analysis of Tourist Attraction Reviews. Applied Sciences 2022;12(21):10881 View
  79. Chang V, Ng C, Xu Q, Guizani M, Hossain M. How Do People View COVID-19 Vaccines. Journal of Global Information Management 2022;30(10):1 View
  80. 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
  81. 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
  82. Yang J, Liu Z, Wong J. Information seeking and information sharing during the COVID-19 pandemic. Communication Quarterly 2022;70(1):1 View
  83. Alqarni A, Rahman A. Arabic Tweets-Based Sentiment Analysis to Investigate the Impact of COVID-19 in KSA: A Deep Learning Approach. Big Data and Cognitive Computing 2023;7(1):16 View
  84. Liu Z, Yang J. Public Support for COVID-19 Responses: Cultural Cognition, Risk Perception, and Emotions. Health Communication 2023;38(4):648 View
  85. Delmelle E, Desjardins M, Jung P, Owusu C, Lan Y, Hohl A, Dony C. Uncertainty in geospatial health: challenges and opportunities ahead. Annals of Epidemiology 2022;65:15 View
  86. 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
  87. Zheng H, Goh D, Lee E, Lee C, Theng Y. Understanding the effects of message cues on COVID ‐19 information sharing on Twitter. Journal of the Association for Information Science and Technology 2022;73(6):847 View
  88. Nia Z, Asgary A, Bragazzi N, Mellado B, Orbinski J, Wu J, Kong J. Nowcasting unemployment rate during the COVID-19 pandemic using Twitter data: The case of South Africa. Frontiers in Public Health 2022;10 View
  89. 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
  90. Kumar V. Spatiotemporal sentiment variation analysis of geotagged COVID-19 tweets from India using a hybrid deep learning model. Scientific Reports 2022;12(1) View
  91. Huang X, Wang S, Zhang M, Hu T, Hohl A, She B, Gong X, Li J, Liu X, Gruebner O, Liu R, Li X, Liu Z, Ye X, Li Z. Social media mining under the COVID-19 context: Progress, challenges, and opportunities. International Journal of Applied Earth Observation and Geoinformation 2022;113:102967 View
  92. Zhou X, Li Y. Forecasting the COVID-19 vaccine uptake rate: an infodemiological study in the US. Human Vaccines & Immunotherapeutics 2022;18(1) View
  93. León-Sandoval E, Zareei M, Barbosa-Santillán L, Falcón Morales L, Pareja Lora A, Ochoa Ruiz G, Hošovský A. Monitoring the Emotional Response to the COVID-19 Pandemic Using Sentiment Analysis: A Case Study in Mexico. Computational Intelligence and Neuroscience 2022;2022:1 View
  94. Lim S, Ng Q, Xin X, Lim Y, Boon E, Liew T. Public Discourse Surrounding Suicide during the COVID-19 Pandemic: An Unsupervised Machine Learning Analysis of Twitter Posts over a One-Year Period. International Journal of Environmental Research and Public Health 2022;19(21):13834 View
  95. Daghriri T, Proctor M, Matthews S. Evolution of Select Epidemiological Modeling and the Rise of Population Sentiment Analysis: A Literature Review and COVID-19 Sentiment Illustration. International Journal of Environmental Research and Public Health 2022;19(6):3230 View
  96. Muis K, Sinatra G, Pekrun R, Kendeou P, Mason L, Jacobson N, Van Tilburg W, Orcutt E, Zaccoletti S, Losenno K. Flattening the COVID-19 curve: Emotions mediate the effects of a persuasive message on preventive action. Frontiers in Psychology 2022;13 View
  97. Gao H, Zhao Q, Ning C, Guo D, Wu J, Li L. Does the COVID-19 Vaccine Still Work That “Most of the Confirmed Cases Had Been Vaccinated”? A Content Analysis of Vaccine Effectiveness Discussion on Sina Weibo during the Outbreak of COVID-19 in Nanjing. International Journal of Environmental Research and Public Health 2021;19(1):241 View
  98. Atabekova A, Lutskovskaia L, Kalashnikova E. Axiology of Covid-19 as a linguistic phenomenon. Journal of Information Science 2022:016555152210915 View
  99. Cai M, Luo H, Meng X, Cui Y, Wang W. Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 2023;60(2):103197 View
  100. Chai C. Comparison of text preprocessing methods. Natural Language Engineering 2022:1 View
  101. Benis A, Chatsubi A, Levner E, Ashkenazi S. Change in Threads on Twitter Regarding Influenza, Vaccines, and Vaccination During the COVID-19 Pandemic: Artificial Intelligence–Based Infodemiology Study. JMIR Infodemiology 2021;1(1):e31983 View
  102. Vyas P, Reisslein M, Rimal B, Vyas G, Basyal G, Muzumdar P. Automated Classification of Societal Sentiments on Twitter With Machine Learning. IEEE Transactions on Technology and Society 2022;3(2):100 View
  103. Nash C. Fear-Responses to Bat-Originating Coronavirus Pandemics with Respect to Quarantines Gauged in Relation to Postmodern Thought—Implications and Recommendations. COVID 2022;2(10):1303 View
  104. Vernikou S, Lyras A, Kanavos A. Multiclass sentiment analysis on COVID-19-related tweets using deep learning models. Neural Computing and Applications 2022;34(22):19615 View
  105. Gao J, Guo Y, Ademu L. Associations between Public Fear of COVID-19 and Number of COVID-19 Vaccinations: A County-Level Longitudinal Analysis. Vaccines 2022;10(9):1422 View
  106. 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
  107. Baj-Rogowska A. Mapping of the Covid-19 Vaccine Uptake Determinants From Mining Twitter Data. IEEE Access 2021;9:134929 View
  108. Hossain M, Asadullah M, Rahaman A, Miah M, Hasan M, Paul T, Hossain M. Prediction on Domestic Violence in Bangladesh during the COVID-19 Outbreak Using Machine Learning Methods. Applied System Innovation 2021;4(4):77 View
  109. Karami A, Zhu M, Goldschmidt B, Boyajieff H, Najafabadi M. COVID-19 Vaccine and Social Media in the U.S.: Exploring Emotions and Discussions on Twitter. Vaccines 2021;9(10):1059 View
  110. Sheng X, Huo W, Zhang C, Zhang X, Han Y. A paper quality and comment consistency detection model based on feature dimensionality reduction. Alexandria Engineering Journal 2022;61(12):10395 View
  111. Pan W, Han Y, Li J, Zhang E, He B. The positive energy of netizens: development and application of fine-grained sentiment lexicon and emotional intensity model. Current Psychology 2022 View
  112. 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 2022 View
  113. Greyling T, Rossouw S, Gesser-Edelsburg A. Positive attitudes towards COVID-19 vaccines: A cross-country analysis. PLOS ONE 2022;17(3):e0264994 View
  114. León-Sandoval E, Zareei M, Barbosa-Santillán L, Falcón Morales L. Measuring the Impact of Language Models in Sentiment Analysis for Mexico’s COVID-19 Pandemic. Electronics 2022;11(16):2483 View
  115. Kendrick K, Isaac M. Overview of behavioural and psychological consequences of COVID 19. Current Opinion in Psychiatry 2021;34(5):477 View
  116. 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
  117. Ghasemyani S, Khodayari-Zarnaq R. COVID-19 Pandemic Tweets by Iranian Political Elites: A Content Analysis Study. Depiction of Health 2021;12(4):298 View
  118. Egger R, Yu J. A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts. Frontiers in Sociology 2022;7 View
  119. Benítez-Andrades J, García-Ordás M, Russo M, Sakor A, Fernandes Rotger L, Vidal M, Kondylakis H, Rao P, Stefanidis K. Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts. Semantic Web 2023:1 View
  120. 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
  121. He S, Li D, Liu C, Xiong Y, Liu D, Feng J, Wen J, Napoli C. Crisis communication in the WHO COVID-19 press conferences: A retrospective analysis. PLOS ONE 2023;18(3):e0282855 View

Books/Policy Documents

  1. Ganguly C, Nayak S, Gupta A. Artificial Intelligence, Machine Learning, and Mental Health in Pandemics. View
  2. Tan A, Estuar M, Co N, Tan H, Abao R, Aureus J. Social Computing and Social Media: Design, User Experience and Impact. View
  3. Esparza J, Bejarano G, Ramesh A, Seetharam A. Computational Data and Social Networks. View
  4. Ghosal A, Gupta N, Nandi E, Somolu H. Artificial Intelligence and Machine Learning Methods in COVID-19 and Related Health Diseases. View
  5. Kovalchuk O, Slobodzian V, Sobko O, Molchanova M, Mazurets O, Barmak O, Krak I, Savina N. Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making. View
  6. Mallikarjuna B, D. J. A, M. S, Sabharwal M. Handbook of Research on Advances in Data Analytics and Complex Communication Networks. View
  7. Dhandapani A, Balasubramaniam A, Balasubramaniam T, Paul A. Machine Intelligence and Data Science Applications. View
  8. Utsu K, Yagi N, Fukushima A, Takemori Y, Okazaki A, Uchida O. Information Technology in Disaster Risk Reduction. View
  9. Yuan K, Zhang M. Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. View
  10. Dhir K, Singh P, Dwivedi Y, Sawhney S, Sawhney R. Co-creating for Context in the Transfer and Diffusion of IT. View
  11. Apolinario-Arzube O, García-Díaz J, Roldán D, Prieto-González L, Casal G, Valencia-García R. Technologies and Innovation. View
  12. Dzitac D. Data Science in Applications. View