Identification and Classification of Images in e-Cigarette-Related Content on TikTok: Unsupervised Machine Learning Image Clustering Approach

This study using the state-of-the-art image-clustering method successfully identified various e-cigarette-related images on TikTok. This study suggests that novel methodologies can be helpful to tobacco regulatory agencies looking to conduct rapid surveillance of e-cigarette content on social media.

Authors

Rachel R. Ouellette (ORCID: https://orcid.org/0000-0001-6982-0830)
Tanvi Anand (ORCID: https://orcid.org/0000-0001-5976-8581)
Dhiraj Murthy (ORCID: https://orcid.org/0000-0001-9734-1124)
Grace Kong (ORCID: https://orcid.org/0000-0002-9269-3435)
Juhan Lee (ORCID: https://orcid.org/0000-0003-0860-3327)

Institutions

Yale University (US)
The University of Texas at Austin (US)

Publication Details

Journal: Substance Use & Misuse
Published: 2024-12-30
DOI: https://doi.org/10.1080/10826084.2024.2447415
Citations: 3
Primary Topic: Misinformation and Its Impacts
Type: article
Field-Weighted Citation Impact: 3.40

Funders

National Institutes of Health
National Institute on Drug Abuse
Center for Tobacco Products

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article

Identification and Classification of Images in e-Cigarette-Related Content on TikTok: Unsupervised Machine Learning Image Clustering Approach

Rachel R. Ouellette, Tanvi Anand, Dhiraj Murthy, Grace Kong et al.

Dec 30, 2024

3 citations

Substance Use & Misuse

Misinformation and Its Impacts

3.40

article

Identification and Classification of Images in e-Cigarette-Related Content on TikTok: Unsupervised Machine Learning Image Clustering Approach

Rachel R. Ouellette, Tanvi Anand, Dhiraj Murthy, Grace Kong, Juhan Lee

December 30, 2024article en

3 citations

Abstract

This study using the state-of-the-art image-clustering method successfully identified various e-cigarette-related images on TikTok. This study suggests that novel methodologies can be helpful to tobacco regulatory agencies looking to conduct rapid surveillance of e-cigarette content on social media.

Substance Use & MisuseVol. 60(5)

Yale University (US), The University of Texas at Austin (US)

National Institutes of Health, National Institute on Drug Abuse, Center for Tobacco Products

Openalex Percentile: Top 7%

Misinformation and Its Impacts

3.40

DOI Open Access PDF OpenAlex

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Identification and Classification of Images in e-Cigarette-Related Content on TikTok: Unsupervised Machine Learning Image Clustering Approach — Rachel R. Ouellette, Tanvi Anand, et al. · Substance Use & Misuse (2024) | TGRS Research Map | TGRS