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

Infodemiology and Infoveillance (1278) Public (e)Health, Digital Epidemiology and Public Health Informatics (646) Outbreak and Pandemic Preparedness and Management (1679) Vaccination and Immunization in the Digital Age (347) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1573) Infectious Diseases (non-STD/STI) (1490) Early Results from COVID-19 Studies (281)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/49753, first published .
Deep Learning Analysis of COVID-19 Vaccine Hesitancy and Confidence Expressed on Twitter in 6 High-Income Countries: Longitudinal Observational Study

Deep Learning Analysis of COVID-19 Vaccine Hesitancy and Confidence Expressed on Twitter in 6 High-Income Countries: Longitudinal Observational Study

Deep Learning Analysis of COVID-19 Vaccine Hesitancy and Confidence Expressed on Twitter in 6 High-Income Countries: Longitudinal Observational Study

Authors of this article:

Xinyu Zhou1, 2, 3 Author Orcid Image ;   Suhang Song4 Author Orcid Image ;   Ying Zhang1, 2 Author Orcid Image ;   Zhiyuan Hou1, 2 Author Orcid Image
Article Authors Cited by (1) Tweetations (10) Metrics

Journals

  1. Ward J, Peretti-Watel P, Dubé E, Verger P, Attwell K. Context matters: How to research vaccine attitudes and uptake after the COVID-19 crisis. Human Vaccines & Immunotherapeutics 2024;20(1) View