ArticleTobacco control2023
Understanding e-cigarette content and promotion on YouTube through machine learning.
Article in Tobacco control, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Temporal Relationships between Smartphone Application Use and Online Substance Procurement in U.S. Youth.medRxiv : the preprint server for health sciences · 2026Article
- Effects of Nicotine-Free E-Cigarettes on Gastrointestinal System: A Systematic Review.Biomedicines · 2025Review
- Harnessing machine learning in contemporary tobacco research.Toxicology reports · 2025Review
- Generative artificial intelligence and machine learning methods to screen social media content.PeerJ. Computer science · 2025Article
- Identification and Classification of Images in e-Cigarette-Related Content on TikTok: Unsupervised Machine Learning Image Clustering Approach.Substance use & misuse · 2025Article
- Identifying E-cigarette Content on TikTok: Using a BERTopic Modeling Approach.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2024Article
- Use of ENDS devices and modifications to vape cannabis among adolescents and young adults who also vape nicotine.Addictive behaviors · 2024Article
- Associations between social media engagement and young adults' subsequent onset of ENDS dependence symptoms one year later.Addictive behaviors · 2024Article
- Chemical Composition of Electronic Vaping Products From School Grounds in California.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2024Article
- Modification of Electronic Nicotine Delivery Systems Among Adolescents and Young Adults.Pediatrics · 2024Article
- Scalable Surveillance of E-Cigarette Products on Instagram and TikTok Using Computer Vision.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2024Article
- Exposure to E-Cigarette Advertisements or Reviews and E-Cigarette Use Progression: A Longitudinal Examination of Short-Term and Long-Term Associations among US Young Adults.International journal of environmental research and public health · 2024Article
- A multimodal deep learning architecture for smoking detection with a small data approach.Frontiers in artificial intelligence · 2024Article
- Content Analysis of YouTube Videos Related to E-Cigarettes and COVID-19.Substance use & misuse · 2024Article
- Electronic Nicotine Delivery Systems and E-Liquid Modifications to Vape Cannabis Depicted in Online Videos.JAMA network open · 2023Article
- Longitudinal relationship between social media and e-cigarette use among adolescents: the roles of internalizing problems and academic performance.BMC public health · 2023Article
- Social Media Use and Subsequent E-Cigarette Susceptibility, Initiation, and Continued Use Among US Adolescents.Preventing chronic disease · 2023Article
- Social media use and cannabis vaping initiation among US youth.Drug and alcohol dependence · 2023Article
- Characteristics Associated with Young Adults' Intentions to Engage with Anti-Vaping Instagram Posts.International journal of environmental research and public health · 2023Article
- Exposure to e-cigarette advertisements and non-advertising content in relation to use behaviors and perceptions among US and Israeli adults.Tobacco prevention & cessation · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
introductionYouTube is a popular social media used by youth and has electronic cigarette (e-cigarette) content. We used machine learning to identify the content of e-cigarette videos, featured e-cigarette products, video uploaders, and marketing and sales of e-cigarette products.
methodsWe identified e-cigarette content using 18 search terms (eg, e-cig) using fictitious youth viewer profiles and predicted four models using the metadata as the input to supervised machine learning: (1) video themes, (2) featured e-cigarette products, (3) channel type (ie, video uploaders) and (4) discount/sales. We assessed the association between engagement data and the four models.
results3830 English videos were included in the supervised machine learning. The most common video theme was 'product review' (48.9%), followed by 'instruction' (eg, 'how to' use/modify e-cigarettes; 17.3%); diverse e-cigarette products were featured; 'vape enthusiasts' most frequently posted e-cigarette videos (54.0%), followed by retailers (20.3%); 43.2% of videos had discount/sales of e-cigarettes; and the most common sales strategy was external links for purchasing (34.1%). 'Vape trick' was the least common theme but had the highest engagement (eg, >2 million views). 'Cannabis' (53.9%) and 'instruction' (49.9%) themes were more likely to have external links for purchasing (p<0.001). The four models achieved an F1 score (a measure of model accuracy) of up to 0.87. DISCUSSION: Our findings indicate that on YouTube videos accessible to youth, a variety of e-cigarette products are featured through diverse videos themes, with discount/sales. The findings highlight the need to regulate the promotion of e-cigarettes on social media platforms.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.