Remediated marketing: leveraging computer vision and rule-based classification models to detect e-cigarette warning labels across social media

Abstract

Big Tobacco and other stakeholders, such as vape shops and smaller e-cigarette manufacturers, have adapted traditional tobacco marketing techniques to digital platforms. Warning labels are essential for informing consumers about the potential harms of tobacco use, including e-cigarettes. However, in a rapidly changing digital landscape, social media platform policies often lag behind, leaving digital marketing largely unchecked. This has allowed Big Tobacco to modernize traditional cigarette marketing in the digital sphere with e-cigarettes, a phenomenon we term ‘remediated marketing’. Without adequate warning labels, exposure to tobacco promotion may increase e-cigarette use among youth, who engage with social media at particularly high rates. This article presents a rule-based classifier developed to detect warning labels in TikTok and YouTube videos by combining computer vision technology with rule-based classification. Our classifier achieved 97.33% accuracy in detecting posts with warning labels. However, only 2.32% of YouTube video frames (240 out of 10,344 frames) and 1.32% of TikTok video frames (61 out of 4639 frames) contained warning labels, suggesting that warning messages are infrequent across e-cigarette content on platforms popular among youth, including TikTok and YouTube. Among the detected warning labels, there was notable diversity in wording and length, indicating a lack of standardization. Additionally, within YouTube and TikTok video frames, 63.7% and 30.0% of the warnings appeared in the first five seconds of the videos, respectively. These results highlight the need for improved policies and standardized warning labels to better protect young adults from e-cigarette promotion on social media.