ArticleSubstance use & misuse2025
Identification and Classification of Images in e-Cigarette-Related Content on TikTok: Unsupervised Machine Learning Image Clustering Approach.
Article in Substance use & misuse, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundPrevious studies identified e-cigarette content on popular video and image-based social media platforms such as TikTok. While machine learning approaches have been increasingly used with text-based social media data, image-based analysis such as image-clustering has been rarely used on TikTok. Image clustering can identify underlying patterns and structures across large sets of images, enabling more streamlined distillation and analysis of visual data on TikTok. This study used image-clustering approaches to examine e-cigarette-related images on TikTok.
methodsWe searched for 13 hashtags related to e-cigarettes in November 2021 (e.g., vape, vapelife). We scraped up to 1000 posts per hashtag depending on the number of available posts, for 12,599 posts in total. After randomly selecting 13% of posts and excluding non-English (
resultsWe identified
conclusionsThis 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.
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