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

Pediatrics (139) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1666) Pediatrics (205) Early Results from COVID-19 Studies (309) Outbreak and Pandemic Preparedness and Management (1743) Vaccination and Immunization in the Digital Age (378) Infodemiology and Infoveillance (1369) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1666) Infodemiology and Infoveillance (1369) Outbreak and Pandemic Preparedness and Management (1743) Vaccination and Immunization in the Digital Age (378) Early Results from COVID-19 Studies (309) Pediatrics (205)

Published on in Vol 6 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48004, first published .
Detection and Characterization of Web-Based Pediatric COVID-19 Vaccine Discussions and Racial and Ethnic Minority Topics: Retrospective Analysis of Twitter Data

Detection and Characterization of Web-Based Pediatric COVID-19 Vaccine Discussions and Racial and Ethnic Minority Topics: Retrospective Analysis of Twitter Data

Detection and Characterization of Web-Based Pediatric COVID-19 Vaccine Discussions and Racial and Ethnic Minority Topics: Retrospective Analysis of Twitter Data

Authors of this article:

Tiana McMann1, 2, 3 Author Orcid Image ;   Christine Wenzel3 Author Orcid Image ;   Nicolette Le1, 2 Author Orcid Image ;   Zhuoran Li2, 3 Author Orcid Image ;   Qing Xu2, 3 Author Orcid Image ;   Raphael E Cuomo2, 4 Author Orcid Image ;   Tim Mackey1, 2, 3 Author Orcid Image
Article Authors Cited by (1) Tweetations (6) 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