Remediated marketing

Leveraging computer vision and rule-based classification models to detect e-cigarette warning labels across social media

Bibliographic Data

ID20509154
AuthorsKellen Sharp (0009-0005-5519-9787, The University of Texas at Austin, corresponding author), Marzieh Babaeianjelodar (Yale School of Medicine), Dhiraj Murthy (0000-0001-9734-1124, Moody College of Communication), Rachel R Ouellette (0000-0001-6982-0830, Yale School of Medicine), Juhan Lee (0000-0003-0860-3327, Yale School of Medicine), Amanda de la Noval (Yale School of Medicine), Neil Kamdar (0000-0002-1898-1594, The University of Texas at Austin), Grace Kong (0000-0002-9269-3435, Yale School of Medicine)
Year2025
Pages1-20
Publication date2025-06-22
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueInformation Communication & Society (JOURNAL)
Journal identifiersISSN: 1369-118X • E-ISSN: 1468-4462
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/1369118x.2025.2500485
PMID42344228
OpenAlexW4411532706
LanguageEN
Citations received1
References cited53

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

Advertising · Business · Data science · Social media · World Wide Web · Computer Science · Smoking Behavior and Cessation · Artificial Intelligence