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

Infoveillance and Social Listening (209) Infodemiology and Infoveillance (1349) Machine Learning (1465) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Information Quality in Digital Media (340) Smoking Cessation (556) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Data Sources and Open Data for Infodemiology (15)

Published on in Vol 3 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42218, first published .
Influence of User Profile Attributes on e-Cigarette–Related Searches on YouTube: Machine Learning Clustering and Classification

Influence of User Profile Attributes on e-Cigarette–Related Searches on YouTube: Machine Learning Clustering and Classification

Influence of User Profile Attributes on e-Cigarette–Related Searches on YouTube: Machine Learning Clustering and Classification

Authors of this article:

Dhiraj Murthy1 Author Orcid Image ;   Juhan Lee2 Author Orcid Image ;   Hassan Dashtian1 Author Orcid Image ;   Grace Kong2 Author Orcid Image
Article Authors Cited by (4) Tweetations (2) Metrics

Journals

  1. Li W, Kong G, Davis D, Bold K, Krishnan-Sarin S, Camenga D, Morean M, Demant D. Sources of exposure to non-tobacco nicotine electronic nicotine delivery systems and associations with susceptibility to use and use behaviors among young adults in the United States. PLOS ONE 2024;19(5):e0299533 View
  2. Lee J, Ouellette R, Morean M, Kong G. Adolescents and Young Adults Use of Social Media and Following of e-Cigarette Influencers. Substance Use & Misuse 2024;59(9):1424 View
  3. Jung S, Murthy D, Bateineh B, Loukas A, Wilkinson A. The Normalization of Vaping on TikTok Using Computer Vision, Natural Language Processing, and Qualitative Thematic Analysis: Mixed Methods Study. Journal of Medical Internet Research 2024;26:e55591 View