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

JMIR Theme Issue: COVID-19 Special Issue (2458) Infodemiology and Infoveillance (1365) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1660) Machine Learning (1491) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1049) Outbreak and Pandemic Preparedness and Management (1740) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1520)

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 Xue1, 2 Author Orcid Image ;   Junxiang Chen3 Author Orcid Image ;   Ran Hu1 Author Orcid Image ;   Chen Chen4 Author Orcid Image ;   Chengda Zheng2 Author Orcid Image ;   Yue Su5, 6 Author Orcid Image ;   Tingshao Zhu5 Author Orcid Image
Article Authors Cited by (229) Tweetations (17) 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 2023;14(11):14661 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 2023;38(12):2702 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;8(2):312 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;37(2):196 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 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 2024;50(1):245 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 2023;29(3):509 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 2023;42(32):27901 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 2023;19(3):317 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;14(5):873 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
  122. Tanner A, Di Cara N, Maggio V, Thomas R, Boyd A, Sloan L, Al Baghal T, Macleod J, Haworth C, Davis O. Epicosm—a framework for linking online social media in epidemiological cohorts. International Journal of Epidemiology 2023;52(3):952 View
  123. Davidson P, Muniandy T, Karmegam D. Perception of COVID-19 vaccination among Indian Twitter users: computational approach. Journal of Computational Social Science 2023;6(2):541 View
  124. Czeranowska O, Chlasta K, Miłkowski P, Grabowska I, Kocoń J, Hwaszcz K, Wieczorek J, Jastrzębowska A. Migrants vs. stayers in the pandemic – A sentiment analysis of Twitter content. Telematics and Informatics Reports 2023;10:100059 View
  125. Xue J, Zhang B, Zhang Q, Hu R, Jiang J, Liu N, Peng Y, Li Z, Logan J. Using Twitter-Based Data for Sexual Violence Research: Scoping Review. Journal of Medical Internet Research 2023;25:e46084 View
  126. Laureate C, Buntine W, Linger H. A systematic review of the use of topic models for short text social media analysis. Artificial Intelligence Review 2023;56(12):14223 View
  127. Clark S, Lomax N. Using e-petition data to quantify public concerns during the COVID-19 pandemic: a case study of England. Policy Studies 2024;45(2):159 View
  128. Ciolfi Felice M, Søndergaard M, Balaam M. Analyzing User Reviews of the First Digital Contraceptive: Mixed Methods Study. Journal of Medical Internet Research 2023;25:e47131 View
  129. Segev E. Sharing Feelings and User Engagement on Twitter: It’s All About Me and You. Social Media + Society 2023;9(2) View
  130. Wang H, Wang X. Sentiment analysis of tweets and government translations: Assessing China’s post-COVID-19 landscape for signs of withering or booming. Global Media and China 2023;8(2):213 View
  131. Kodati D, Dasari C. Negative emotion detection on social media during the peak time of COVID-19 through deep learning with an auto-regressive transformer. Engineering Applications of Artificial Intelligence 2024;127:107361 View
  132. Akande O, Lawrence M, Ogedebe P. Application of bidirectional LSTM deep learning technique for sentiment analysis of COVID-19 tweets: post-COVID vaccination era. Journal of Electrical Systems and Information Technology 2023;10(1) View
  133. Butt M, Malik A, Qamar N, Yar S, Malik A, Rauf U. A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media. Sensors 2023;23(12):5543 View
  134. Lee J, Kalny C, Demetriades S, Walter N. Angry Content for Angry People: How Anger Appeals Facilitate Health Misinformation Recall on Social Media. Media Psychology 2024;27(5):639 View
  135. Oliveira F, Mougouei D, Haque A, Sichman J, Dam H, Evans S, Ghose A, Singh M. Beyond fear and anger: A global analysis of emotional response to Covid-19 news on Twitter. Online Social Networks and Media 2023;36:100253 View
  136. Alvarez-Mon M, Pereira-Sanchez V, Hooker E, Sanchez F, Alvarez-Mon M, Teo A. Content and User Engagement of Health-Related Behavior Tweets Posted by Mass Media Outlets From Spain and the United States Early in the COVID-19 Pandemic: Observational Infodemiology Study. JMIR Infodemiology 2023;3:e43685 View
  137. Aslan S. A deep learning-based sentiment analysis approach (MF-CNN-BILSTM) and topic modeling of tweets related to the Ukraine–Russia conflict. Applied Soft Computing 2023;143:110404 View
  138. Çiçek Korkmaz A. Public’s perception on nursing education during the COVID-19 pandemic: SENTIMENT analysis of Twitter data. International Journal of Disaster Risk Reduction 2023;99:104127 View
  139. Guo L, Wang W, Wu Y. What Do Scholars Propose for Future COVID-19 Research in Academic Publications? A Topic Analysis Based on Autoencoder. Sage Open 2023;13(2) View
  140. Stefanis C, Giorgi E, Kalentzis K, Tselemponis A, Nena E, Tsigalou C, Kontogiorgis C, Kourkoutas Y, Chatzak E, Dokas I, Constantinidis T, Bezirtzoglou E. Sentiment analysis of epidemiological surveillance reports on COVID-19 in Greece using machine learning models. Frontiers in Public Health 2023;11 View
  141. Kassen M. Curbing the COVID-19 digital infodemic: strategies and tools. Journal of Public Health Policy 2023;44(4):643 View
  142. Arazzi M, Murer D, Nicolazzo S, Nocera A. How COVID-19 affects user interaction with online streaming service providers on twitter. Social Network Analysis and Mining 2023;13(1) View
  143. Xia X, Zhang Y, Jiang W, Wu C. Staying Home, Tweeting Hope: Mixed Methods Study of Twitter Sentiment Geographical Index During US Stay-At-Home Orders. Journal of Medical Internet Research 2023;25:e45757 View
  144. Chaudhary M, Kosyluk K, Thomas S, Neal T. On the use of aspect-based sentiment analysis of Twitter data to explore the experiences of African Americans during COVID-19. Scientific Reports 2023;13(1) View
  145. Huang X, Zhou Y, Du Y. A Novel Bi-Dual Inference Approach for Detecting Six-Element Emotions. Applied Sciences 2023;13(17):9957 View
  146. Isip Tan I, Cleofas J, Solano G, Pillejera J, Catapang J. Interdisciplinary Approach to Identify and Characterize COVID-19 Misinformation on Twitter: Mixed Methods Study. JMIR Formative Research 2023;7:e41134 View
  147. Marres N, Colombo G, Bounegru L, Gray J, Gerlitz C, Tripp J. Testing and Not Testing for Coronavirus on Twitter: Surfacing Testing Situations Across Scales With Interpretative Methods. Social Media + Society 2023;9(3) View
  148. Dong L, Liu Y. Frontiers of policy and governance research in a smart city and artificial intelligence: an advanced review based on natural language processing. Frontiers in Sustainable Cities 2023;5 View
  149. Beierle F, Pryss R, Aizawa A. Sentiments about Mental Health on Twitter—Before and during the COVID-19 Pandemic. Healthcare 2023;11(21):2893 View
  150. Córdova-Palomera A, Siffel C, DeBoever C, Wong E, Diogo D, Szalma S, Ulgen A. Assessing the potential of polygenic scores to strengthen medical risk prediction models of COVID-19. PLOS ONE 2023;18(5):e0285991 View
  151. Fogarty B, Massie K, Svistova J. Unmasking twitter discourse: an infodemiology study of covid-19 mitigation practices. Atlantic Journal of Communication 2024;32(1):124 View
  152. Saleh S, McDonald S, Basit M, Kumar S, Arasaratnam R, Perl T, Lehmann C, Medford R. Public perception of COVID-19 vaccines through analysis of Twitter content and users. Vaccine 2023;41(33):4844 View
  153. Andreu-Sánchez C, Martín-Pascual M. Positive and Negative Affect Schedule in early COVID-19 pandemic. Scientific Data 2023;10(1) View
  154. Cooper J, Theivendrampillai S, Lee T, Marquez C, Lau M, Straus S, Fahim C. Exploring perceptions and experiences of stigma in Canada during the COVID-19 pandemic: a qualitative study. BMC Global and Public Health 2023;1(1) View
  155. Lwin M, Yang S, Sheldenkar A, Yang X, Lee B. Assessing consumer rationality during a pandemic: Panic buying behaviours and its association with online social media discourse. Computers in Human Behavior Reports 2023:100361 View
  156. Terry K, Yang F, Yao Q, Liu C. The role of social media in public health crises caused by infectious disease: a scoping review. BMJ Global Health 2023;8(12):e013515 View
  157. Doğan B, Balcioglu Y, Elçi M. Multidimensional sentiment analysis method on social media data: comparison of emotions during and after the COVID-19 pandemic. Kybernetes 2024 View
  158. Nguyen A, Longa A, Luca M, Kaul J, Lopez G. Emotion Analysis Using Multilayered Networks for Graphical Representation of Tweets. IEEE Access 2022;10:99467 View
  159. Haque A, Singh K, Kaphle S, Panchasara H, Tseng W. Shifting Workplace Paradigms: Twitter Sentiment Insights on Work from Home. Sustainability 2024;16(2):871 View
  160. Jeyaraj S, T. R. Covid based question criticality prediction with domain adaptive BERT embeddings. Engineering Applications of Artificial Intelligence 2024;132:107913 View
  161. Aldosery A, Carruthers R, Kay K, Cave C, Reynolds P, Kostkova P. Enhancing public health response: a framework for topics and sentiment analysis of COVID-19 in the UK using Twitter and the embedded topic model. Frontiers in Public Health 2024;12 View
  162. Cheung L, Lau A, Lam K, Ng P. A Review of Environmental Factors for an Ontology-Based Risk Analysis for Pandemic Spread. COVID 2024;4(4):466 View
  163. Whitfield C, Liu Y, Anwar M. Impact of COVID-19 Pandemic on Social Determinants of Health Issues of Marginalized Black and Asian Communities: A Social Media Analysis Empowered by Natural Language Processing. Journal of Racial and Ethnic Health Disparities 2024 View
  164. Chepo M, Martin S, Déom N, Khalid A, Vindrola-Padros C. Twitter Analysis of Health Care Workers’ Sentiment and Discourse Regarding Post–COVID-19 Condition in Children and Young People: Mixed Methods Study. Journal of Medical Internet Research 2024;26:e50139 View
  165. Muis K, Kendeou P, Kohatsu M, Wang S. “Let’s get back to normal”: emotions mediate the effects of persuasive messages on willingness to vaccinate for COVID-19. Frontiers in Public Health 2024;12 View
  166. Xue J, Shier M, Chen J, Wang Y, Zheng C, Chen C. A Typology of Social Media Use by Human Service Nonprofits: Mixed Methods Study. Journal of Medical Internet Research 2024;26:e51698 View
  167. Chatzimina M, Papadaki H, Pontikoglou C, Tsiknakis M. A Comparative Sentiment Analysis of Greek Clinical Conversations Using BERT, RoBERTa, GPT-2, and XLNet. Bioengineering 2024;11(6):521 View
  168. Gencoglu O. Large-Scale, Language-Agnostic Discourse Classification of Tweets During COVID-19. Machine Learning and Knowledge Extraction 2020;2(4):603 View
  169. Ariza-Colpas P, Piñeres-Melo M, Urina-Triana M, Barceló-Martinez E, Barceló-Castellanos C, Roman F. Machine Learning Applied to the Analysis of Prolonged COVID Symptoms: An Analytical Review. Informatics 2024;11(3):48 View
  170. Sarracino F, Greyling T, O'Connor K, Peroni C, Rossouw S. Trust predicts compliance with COVID-19 containment policies: Evidence from ten countries using big data. Economics & Human Biology 2024;54:101412 View
  171. Repke T, Callaghan M, Lamb W, Lück S, Müller-Hansen F, Minx J. How global crises compete for our attention: Insights from 13.5 million tweets on climate change during COVID-19. Energy Research & Social Science 2024;116:103668 View
  172. Ignaccolo C, Wibisono K, Sutto M, Plunz R. Tweeting during the Pandemic in New York City: Unveiling the Evolving Sentiment Landscape of NYC through a Spatiotemporal Analysis of Geolocated Tweets. Journal of Urban Technology 2024;31(3):3 View
  173. Mirzaei T, Amini L, Esmaeilzadeh P. Clinician voices on ethics of LLM integration in healthcare: a thematic analysis of ethical concerns and implications. BMC Medical Informatics and Decision Making 2024;24(1) View
  174. Merayo N, Ayuso-Lanchares A, González-Sanguino C. Machine learning and natural language processing to assess the emotional impact of influencers’ mental health content on Instagram. PeerJ Computer Science 2024;10:e2251 View
  175. Sharma A, Verbeke W. Influence of gender dimorphism on audience engagement in podcasts: a machine learning analysis of dynamic affective linguistic and paralinguistic features. Frontiers in Communication 2024;9 View
  176. Kusuma I, Visnyovszki Á, Bahar M. Mapping the Mpox discourse: A network and sentiment analysis. Exploratory Research in Clinical and Social Pharmacy 2024;16:100521 View
  177. Kang X, Stamolampros P. Unveiling public perceptions at the beginning of lockdown: an application of structural topic modeling and sentiment analysis in the UK and India. BMC Public Health 2024;24(1) View
  178. Ouyang Q, Chen H, Liu S, Pu L, Ge D, Fan K. DMHANT: DropMessage Hypergraph Attention Network for Information Propagation Prediction. Big Data 2024 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
  13. Kaur J, Patel S, Vasani M, Saini J. Advances in Information Communication Technology and Computing. View
  14. Morgan M, Kulkarni A. Social Computing and Social Media. View
  15. Vasileiou E, Koutrakos P. Data Analytics for Management, Banking and Finance. View
  16. Tekumalla R, Banda J. HCI International 2023 – Late Breaking Papers. View
  17. Osop H, Wong J, Lwin S, Lee C. Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration. View
  18. Gyftopoulos S, Drosatos G, Pecchia L, Fico G, Kaldoudi E. MEDICON’23 and CMBEBIH’23. View
  19. Jafari A, Farahbakhsh R, Salehi M, Crespi N. Proceedings of Data Analytics and Management. View
  20. Kędzierska M, Spytek M, Kurek M, Sawicki J, Ganzha M, Paprzycki M. Big Data Analytics in Astronomy, Science, and Engineering. View
  21. Rossouw S, Greyling T. Resistance to COVID-19 Vaccination. View