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

Public (e)Health, Digital Epidemiology and Public Health Informatics (743) Infodemiology and Infoveillance (1361) Quality/Credibility of eHealth Information and Trust Issues (542) Peer-to-Peer Support and Online Communities (644) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1656) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1048) Information Seeking, Information Needs (477) Machine Learning (53) Artificial Intelligence, Machine Learning, and Natural Language Processing for Public Health (37)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/45019, first published .
Hot Topic Recognition of Health Rumors Based on Anti-Rumor Articles on the WeChat Official Account Platform: Topic Modeling

Hot Topic Recognition of Health Rumors Based on Anti-Rumor Articles on the WeChat Official Account Platform: Topic Modeling

Hot Topic Recognition of Health Rumors Based on Anti-Rumor Articles on the WeChat Official Account Platform: Topic Modeling

Authors of this article:

Ziyu Li1 Author Orcid Image ;   Xiaoqian Wu1, 2 Author Orcid Image ;   Lin Xu1, 3 Author Orcid Image ;   Ming Liu1 Author Orcid Image ;   Cheng Huang1 Author Orcid Image
Article Authors Cited by (4) Tweetations (4) Metrics

Journals

  1. Liu X, Zhang L, Sun L, Liu R. Survival analysis of the duration of rumors during the COVID-19 pandemic. BMC Public Health 2024;24(1) View
  2. Loeb S, Langford A, Bragg M, Sherman R, Chan J. Cancer misinformation on social media. CA: A Cancer Journal for Clinicians 2024;74(5):453 View
  3. Luo Y, Miao Y, Zhao Y, Li J, Chen Y, Yue Y, Wu Y. Comparing the Accuracy of Two Generated Large Language Models in Identifying Health-Related Rumors or Misconceptions and the Applicability in Health Science Popularization: Proof-of-Concept Study. JMIR Formative Research 2024;8:e63188 View