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

Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1902) Misinformation and Disinformation Outbreaks and Information Prevalence Studies (89) HIV/AIDS/STI Prevention and Care (1027) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1132) Mass Media/Social Media Communication and Campaigns (571) Infodemiology and Infoveillance (1554) Behavioral Surveillance for Population and Public Health Informatics (57)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/77424, first published .
Public Attention to Mpox in China During the Pandemic: Qualitative Analysis of TikTok Data Using Latent Dirichlet Allocation Topic Modeling

Public Attention to Mpox in China During the Pandemic: Qualitative Analysis of TikTok Data Using Latent Dirichlet Allocation Topic Modeling

Public Attention to Mpox in China During the Pandemic: Qualitative Analysis of TikTok Data Using Latent Dirichlet Allocation Topic Modeling

Authors of this article:

DongHang Luo1 Author Orcid Image ;   Jie Xu1 Author Orcid Image ;   Yaoyao Jiang1 Author Orcid Image ;   Mengnan Tan1 Author Orcid Image ;   Yaping Yao2 Author Orcid Image ;   Lin He2 Author Orcid Image ;   Jing Ma3 Author Orcid Image ;   Wei Dong1 Author Orcid Image ;   Wei Luo1 Author Orcid Image ;   Chu Zhou1 Author Orcid Image
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