Published on in Vol 22, No 10 (2020): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/22624, first published .
Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study

Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study

Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study

Journals

  1. Álvarez-Mon M, Rodríguez-Quiroga A, de Anta L, Quintero J. Aplicaciones médicas de las redes sociales. Aspectos específicos de la pandemia de la COVID-19. Medicine - Programa de Formación Médica Continuada Acreditado 2020;13(23):1305 View
  2. Lyu J, Luli G. Understanding the Public Discussion About the Centers for Disease Control and Prevention During the COVID-19 Pandemic Using Twitter Data: Text Mining Analysis Study. Journal of Medical Internet Research 2021;23(2):e25108 View
  3. Chum A, Nielsen A, Bellows Z, Farrell E, Durette P, Banda J, Cupchik G. Changes in public response associated with various COVID-19 restrictions in Ontario, Canada: an observational study using social media time series data (Preprint). Journal of Medical Internet Research 2021 View
  4. Pang P, Cai Q, Jiang W, Chan K. Engagement of Government Social Media on Facebook during the COVID-19 Pandemic in Macao. International Journal of Environmental Research and Public Health 2021;18(7):3508 View
  5. 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
  6. Ghasiya P, Okamura K. Investigating COVID-19 News Across Four Nations: A Topic Modeling and Sentiment Analysis Approach. IEEE Access 2021;9:36645 View
  7. Margus C, Brown N, Hertelendy A, Safferman M, Hart A, Ciottone G. Emergency Physician Twitter Use in the COVID-19 Pandemic as a Potential Predictor of Impending Surge: Retrospective Observational Study. Journal of Medical Internet Research 2021;23(7):e28615 View
  8. González L, Devís-Devís J, Pellicer-Chenoll M, Pans M, Pardo-Ibañez A, García-Massó X, Peset F, Garzón-Farinós F, Pérez-Samaniego V. The Impact of COVID-19 on Sport in Twitter: A Quantitative and Qualitative Content Analysis. International Journal of Environmental Research and Public Health 2021;18(9):4554 View
  9. Miller M, Romine W, Oroszi T. Public Discussion of Anthrax on Twitter: Using Machine Learning to Identify Relevant Topics and Events. JMIR Public Health and Surveillance 2021;7(6):e27976 View
  10. Bhatnagar S, Choubey N. Making sense of tweets using sentiment analysis on closely related topics. Social Network Analysis and Mining 2021;11(1) View
  11. Obadimu A, Khaund T, Mead E, Marcoux T, Agarwal N. Developing a socio-computational approach to examine toxicity propagation and regulation in COVID-19 discourse on YouTube. Information Processing & Management 2021;58(5):102660 View
  12. Mohamed Ridhwan K, Hargreaves C. Leveraging Twitter data to understand public sentiment for the COVID‐19 outbreak in Singapore. International Journal of Information Management Data Insights 2021;1(2):100021 View
  13. 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
  14. Hansen N, Treider J, Swarbrick D, Bamford J, Wilson J, Vuoskoski J. A Crowd-Sourced Database of Coronamusic: Documenting Online Making and Sharing of Music During the COVID-19 Pandemic. Frontiers in Psychology 2021;12 View
  15. Alsudias L, Rayson P. Social Media Monitoring of the COVID-19 Pandemic and Influenza ‎Epidemic: Adapting for Informal Language in Arabic Twitter (Preprint). JMIR Medical Informatics 2021 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. 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
  18. 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
  19. 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
  20. Liu S, Li J, Liu J. Leveraging Transfer Learning to Analyze Opinions, Attitudes, and Behavioral Intentions Toward COVID-19 Vaccines: Social Media Content and Temporal Analysis. Journal of Medical Internet Research 2021;23(8):e30251 View