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

JMIR Theme Issue: COVID-19 Special Issue (2452) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Hygiene and Infection Prevention (317) Infodemiology and Infoveillance (1349) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517) Mass Media/Social Media Communication and Campaigns (461) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Outbreak and Pandemic Preparedness and Management (1727) Infectious Diseases (non-STD/STI) (1575) Vaccination and Immunization in the Digital Age (374) Innovative Methods in Public Health and Surveillance (332) Statistical Methods for Surveillance and Population Health (156) GIS (Geographic Information Systems) Applications in Public Health and Spatial Epidemiology (111) Quality/Credibility of eHealth Information and Trust Issues (531) Vaccination Sentiment and Anti-Vaccination Infodemiology (58) COVID-19 Vaccination (146)

Published on in Vol 23, No 9 (2021): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30854, first published .
Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective

Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective

Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective

Authors of this article:

Tao Hu1, 2 Author Orcid Image ;   Siqin Wang3 Author Orcid Image ;   Wei Luo4 Author Orcid Image ;   Mengxi Zhang5 Author Orcid Image ;   Xiao Huang6 Author Orcid Image ;   Yingwei Yan4 Author Orcid Image ;   Regina Liu7 Author Orcid Image ;   Kelly Ly8 Author Orcid Image ;   Viraj Kacker9 Author Orcid Image ;   Bing She10 Author Orcid Image ;   Zhenlong Li11 Author Orcid Image
Article Authors Cited by (92) Tweetations (8) Metrics

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