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

JMIR Theme Issue: COVID-19 Special Issue (2391) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1013) Vaccination and Immunization in the Digital Age (346) Vaccination Sentiment and Anti-Vaccination Infodemiology (50) COVID-19 Vaccination (128) Mass Media/Social Media Communication and Campaigns (448) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1482) Outbreak and Pandemic Preparedness and Management (1675) Peer-to-Peer Support and Online Communities (597) Information Quality in Digital Media (318) Quality/Credibility of eHealth Information and Trust Issues (489) Infodemiology and Infoveillance (1274) Natural Language Processing (644)

Published on in Vol 23, No 10 (2021): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30765, first published .
Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis

Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis

Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis

Authors of this article:

Michal Monselise1 Author Orcid Image ;   Chia-Hsuan Chang2 Author Orcid Image ;   Gustavo Ferreira1 Author Orcid Image ;   Rita Yang3 Author Orcid Image ;   Christopher C Yang1 Author Orcid Image
Article Authors Cited by (41) Tweetations (3) Metrics

Journals

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  23. Gatto J, Seegmiller P, Johnston G, Preum S. Identifying the Perceived Severity of Patient-Generated Telemedical Queries Regarding COVID: Developing and Evaluating a Transfer Learning–Based Solution. JMIR Medical Informatics 2022;10(9):e37770 View
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