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

Vaccination Sentiment and Anti-Vaccination Infodemiology (58) JMIR Theme Issue: COVID-19 Special Issue (2452) Infodemiology and Infoveillance (1349) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Vaccination and Immunization in the Digital Age (374)

Published on in Vol 3 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/41582, first published .
Public Officials’ Engagement on Social Media During the Rollout of the COVID-19 Vaccine: Content Analysis of Tweets

Public Officials’ Engagement on Social Media During the Rollout of the COVID-19 Vaccine: Content Analysis of Tweets

Public Officials’ Engagement on Social Media During the Rollout of the COVID-19 Vaccine: Content Analysis of Tweets

Authors of this article:

Husayn Marani1, 2 Author Orcid Image ;   Melodie Yunju Song2, 3 Author Orcid Image ;   Margaret Jamieson1, 2 Author Orcid Image ;   Monika Roerig1, 2 Author Orcid Image ;   Sara Allin1, 2 Author Orcid Image
Article Authors Cited by (1) Tweetations (5) Metrics

Journals

  1. Deiner M, Honcharov V, Li J, Mackey T, Porco T, Sarkar U. Large Language Models Can Enable Inductive Thematic Analysis of a Social Media Corpus in a Single Prompt: Human Validation Study. JMIR Infodemiology 2024;4:e59641 View