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

Journal: Substance use & misuse
PMID: 40019898

Abstract

BACKGROUND: Previous 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.

Authors

  • Juhan Lee
    Department of Surgery, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Republic of Korea.
  • Dhiraj Murthy
    School of Journalism and Media, University of Texas at Austin, Austin, TX, United States.
  • Rachel Ouellette
    Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States.
  • Tanvi Anand
    School of Journalism and Media, University of Texas Austin, Austin, TX, United States.
  • Grace Kong
    Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

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