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

Infodemiology and Infoveillance (1349) JMIR Theme Issue: COVID-19 Special Issue (2452) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Outbreak and Pandemic Preparedness and Management (1727) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517) Infectious Diseases (non-STD/STI) (1575) Behavioural Surveillance for Public Health (229)

Published on in Vol 23, No 5 (2021): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27059, first published .
Mining and Validating Social Media Data for COVID-19–Related Human Behaviors Between January and July 2020: Infodemiology Study

Mining and Validating Social Media Data for COVID-19–Related Human Behaviors Between January and July 2020: Infodemiology Study

Mining and Validating Social Media Data for COVID-19–Related Human Behaviors Between January and July 2020: Infodemiology Study

Authors of this article:

Ashlynn R Daughton1 Author Orcid Image ;   Courtney D Shelley1 Author Orcid Image ;   Martha Barnard1 Author Orcid Image ;   Dax Gerts1 Author Orcid Image ;   Chrysm Watson Ross1, 2 Author Orcid Image ;   Isabel Crooker1 Author Orcid Image ;   Gopal Nadiga1 Author Orcid Image ;   Nilesh Mukundan1 Author Orcid Image ;   Nidia Yadira Vaquera Chavez1, 2 Author Orcid Image ;   Nidhi Parikh1 Author Orcid Image ;   Travis Pitts1 Author Orcid Image ;   Geoffrey Fairchild1 Author Orcid Image
Article Authors Cited by (7) Tweetations (3) Metrics

Journals

  1. Gao Z, Fujita S, Shimizu N, Liew K, Murayama T, Yada S, Wakamiya S, Aramaki E. Measuring Public Concern About COVID-19 in Japanese Internet Users Through Search Queries: Infodemiological Study. JMIR Public Health and Surveillance 2021;7(7):e29865 View
  2. Chen S, Yin S, Guo Y, Ge Y, Janies D, Dulin M, Brown C, Robinson P, Zhang D. Content and sentiment surveillance (CSI): A critical component for modeling modern epidemics. Frontiers in Public Health 2023;11 View
  3. Bravo C, Castells V, Zietek-Gutsch S, Bodin P, Molony C, Frühwein M. Using social media listening and data mining to understand travellers’ perspectives on travel disease risks and vaccine-related attitudes and behaviours. Journal of Travel Medicine 2022;29(2) View
  4. Etienne D, Archambault P, Aziaka D, Chipenda-Dansokho S, Dubé E, Fallon C, Hakim H, Kindrachuk J, Krecoum D, MacDonald S, Ndjaboue R, Noubi M, Paquette J, Parent E, Witteman H. A Personalized Avatar-Based Web Application to Help People Understand How Social Distancing Can Reduce the Spread of COVID-19: Cross-sectional, Observational, Pre-Post Study. JMIR Formative Research 2023;7:e38430 View
  5. Dupuy-Zini A, Audeh B, Gérardin C, Duclos C, Gagneux-Brunon A, Bousquet C. Users’ Reactions to Announced Vaccines Against COVID-19 Before Marketing in France: Analysis of Twitter Posts. Journal of Medical Internet Research 2023;25:e37237 View
  6. 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
  7. Denis-Robichaud J, Rees E, Daley P, Zarowsky C, Diouf A, Nasri B, de Montigny S, Carabin H. Linking Opinions Shared on Social Media About COVID-19 Public Health Measures to Adherence: Repeated Cross-Sectional Surveys of Twitter Use in Canada. Journal of Medical Internet Research 2024;26:e51325 View