Scalable surveillance of e-cigarette products on Instagram and TikTok using computer vision.

Prior research identified themes in e-cigarette-related social media posts using qualitative or text-based machine learning methods. We developed an image-based computer vision model to identify e-cigarette products in social media images and videos.We trained a DyHead object detection model using a Swin-Large backbone, evaluated the model's performance on 20 Instagram and TikTok videos featuring at least two e-cigarette objects, and applied the model to 14,072 e-cigarette-related promotional TikTok videos (2019-2022; 10,276,485 frames).The deep learning model can be used for automated, scalable surveillance of image- and video-based e-cigarette-related promotional content on social media, providing valuable data for tobacco regulatory science. Social media platforms could use computer vision to identify tobacco-related imagery and remove it promptly, which could reduce adolescents' exposure to tobacco content online.