| Résumé : |
This study addresses concerns surrounding the inadvertent promotion of tobacco-related products on TikTok by introducing an efficient deep learning-based video analysis system. Our approach focuses on categorizing TikTok videos based on tobacco-related cues, including content related to e-cigarettes, vapes, cigarettes, various tobacco flavors, and accessories that may bypass tobacco control policies.
The proposed two-stage process begins with the extraction of essential cues using speech-to-text, Optical Character Recognition (OCR), and video classification techniques. This initial
phase comprehensively captures textual and visual information associated with tobacco products, forming the foundation for understanding video content. Subsequently, in the second stage,
the extracted cues are integrated into a vision-language model alongside the input video. This stage trains the model to analyze contextual nuances, achieving a detailed understanding of the
nuanced elements associated with tobacco promotion on TikTok.
The system classifies input videos into predefined classes (cigarette, e-cigarette/vapes, pouches, or others) and provides detailed analyses. This capability enables a granular examination
of diverse tobacco-related content on TikTok, proving valuable for regulatory agencies like the FDA in quickly identifying potential illegal promotion and sales of non-compliant tobacco
products and accessories.
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Assessing TikTok videos content of tobacco usage by leveraging deep learning methods (2024)
