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

Theme Issue 2023: Exploring the Intersection Between Health Information, Misinformation, and Generative AI Technologies (9) Infoveillance and Social Listening (265) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1812) Generative Language Models Including ChatGPT (535)

Published on in Vol 4 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/59641, first published .
Large Language Models Can Enable Inductive Thematic Analysis of a Social Media Corpus in a Single Prompt: Human Validation Study

Large Language Models Can Enable Inductive Thematic Analysis of a Social Media Corpus in a Single Prompt: Human Validation Study

Large Language Models Can Enable Inductive Thematic Analysis of a Social Media Corpus in a Single Prompt: Human Validation Study

Authors of this article:

Michael S Deiner1 Author Orcid Image ;   Vlad Honcharov2, 3 Author Orcid Image ;   Jiawei Li4 Author Orcid Image ;   Tim K Mackey4, 5 Author Orcid Image ;   Travis C Porco6 Author Orcid Image ;   Urmimala Sarkar2, 3 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Li F, Yang Y. Impact of Artificial Intelligence–Generated Content Labels On Perceived Accuracy, Message Credibility, and Sharing Intentions for Misinformation: Web-Based, Randomized, Controlled Experiment. JMIR Formative Research 2024;8:e60024 View
  2. Ferizaj D, Lalk C, Lahmann N, Strube-Lahmann S, Rubel J. Identifying Yalom’s group therapeutic factors in anonymous mental health discussions on Reddit: a mixed-methods analysis using large language models, topic modeling and human supervision. Frontiers in Psychiatry 2025;16 View
  3. Morgan D. Query-Based Analysis: A Strategy for Analyzing Qualitative Data Using ChatGPT. Qualitative Health Research 2025 View