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

Public (e)Health, Digital Epidemiology and Public Health Informatics (725) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Smoking Cessation (556) Machine Learning (1465) Mass Media/Social Media Communication and Campaigns (461) Infodemiology and Infoveillance (1349) Natural Language Processing (692) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Peer-to-Peer Support and Online Communities (636) Prevention and Health Promotion (986)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42863, first published .
Social Media Data Mining of Antitobacco Campaign Messages: Machine Learning Analysis of Facebook Posts

Social Media Data Mining of Antitobacco Campaign Messages: Machine Learning Analysis of Facebook Posts

Social Media Data Mining of Antitobacco Campaign Messages: Machine Learning Analysis of Facebook Posts

Authors of this article:

Shuo-Yu Lin1 Author Orcid Image ;   Xiaolu Cheng2 Author Orcid Image ;   Jun Zhang3 Author Orcid Image ;   Jaya Sindhu Yannam1 Author Orcid Image ;   Andrew J Barnes4 Author Orcid Image ;   J Randy Koch5, 6 Author Orcid Image ;   Rashelle Hayes7 Author Orcid Image ;   Gilbert Gimm1 Author Orcid Image ;   Xiaoquan Zhao8 Author Orcid Image ;   Hemant Purohit9 Author Orcid Image ;   Hong Xue1 Author Orcid Image
Article Authors Cited by (7) Tweetations (10) Metrics

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  1. Wusylko C, Weisberg L, Opoku R, Abramowitz B, Williams J, Xing W, Vu T, Vu M. Using machine learning techniques to investigate learner engagement with TikTok media literacy campaigns. Journal of Research on Technology in Education 2024;56(1):72 View
  2. Jawed A, Hogan A. Lessons Learned From Telltale Testimonies: A Descriptive Study Assessing Coverage of the Tips From Former Smokers Campaign on YouTube. American Journal of Health Education 2024;55(2):108 View
  3. Gupta A, Velagapuri V, Xue H, Purohit H. Intent mining framework for understanding online conversations on vaping to inform social media-based intervention design. Behaviour & Information Technology 2024:1 View
  4. Lin S, Tulabandu S, Koch J, Hayes R, Barnes A, Purohit H, Chen S, Han B, Xue H. Public Response to Federal Electronic Cigarette Regulations Analyzed Using Social Media Data Through Natural Language Processing: Topic Modeling Study. Journal of Medical Internet Research 2024;26:e58919 View
  5. Yip Y, Makmor-Bakry M, Chong W. Elements influencing user engagement in social media posts on lifestyle risk factors: a systematic review (Preprint). Journal of Medical Internet Research 2024 View
  6. Jerfy A, Selden O, Balkrishnan R. The Growing Impact of Natural Language Processing in Healthcare and Public Health. INQUIRY: The Journal of Health Care Organization, Provision, and Financing 2024;61 View
  7. Kim J, Cai Z, Chen M, Rezaei S, Onyeka S, Rodriguez C, Hernandez-Boussard T, Filkov V, Whitmer R, Linos E, Choi Y. Mental health care needs of caregivers of people with Alzheimer’s disease from online forum analysis. npj Mental Health Research 2024;3(1) View