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

Journals

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  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 2023:1 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

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