ArticleJournal of medical Internet research2019
Promotion of Vape Tricks on YouTube: Content Analysis.
Article in Journal of medical Internet research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
46 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Examining Tobacco-Related Social Media Research in Government Policy Documents: Systematic Review.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2024Pooled it
- #TurntTrending: a systematic review of substance use portrayals on social media platforms.Addiction (Abingdon, England) · 2023Pooled it
- Narrative Review of Novel Nicotine-Delivery Systems and Emerging Pharmaceuticals for Tobacco Cessation.Medicines (Basel, Switzerland) · 2026Review
- Exploring #Zyn-Related Video Content Posted by Zynfluencers on TikTok.Substance use & misuse · 2026Article
- Demographic Trends in E-Cigarette Social Media Marketing: Perceiving Gender Presentation and Facial Age Via Computer Vision.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2025Article
- Remediated marketing: leveraging computer vision and rule-based classification models to detect e-cigarette warning labels across social media.Information, communication and society · 2025Article
- E-Cigarette and Cannabis Social Media Posts and Adolescent Substance Use.JAMA network open · 2025Article
- 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
- Concerns Over Vuse e-Cigarette Digital Marketing and Implications for Public Health Regulation: Content Analysis.JMIR formative research · 2024Article
- 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
- The e-cigarette assessment for youth-Revised (EAsY-R): Preliminary results of a pilot study of measure refinement via cognitive interviewing.Journal of research on adolescence : the official journal of the Society for Research on Adolescence · 2024Article
- Article
- Characteristics of Adolescents' and Young Adults' Exposure to and Engagement with Nicotine and Tobacco Product Content on Social Media.Journal of health communication · 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
- Perceptions of social media harms and potential management strategies: vaping case study.BMC public health · 2024Article
- Effectiveness of an intervention program to develop e-cigarette control leaders at the University in Lampang Province, Thailand.Tobacco prevention & cessation · 2024Article
- Content Analysis of YouTube Videos Related to E-Cigarettes and COVID-19.Substance use & misuse · 2024Article
- Understanding e-cigarette content and promotion on YouTube through machine learning.Tobacco control · 2023Article
- Electronic Nicotine Delivery Systems and E-Liquid Modifications to Vape Cannabis Depicted in Online Videos.JAMA network open · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundThe ability to perform vape tricks (ie, blowing large vapor clouds or shapes like rings) using e-cigarettes appeals to youth. Vape tricks are promoted on social media, but the promotion of vape tricks on social media is not well understood.
objectiveThe aim of this study was to examine how vape tricks were promoted on YouTube to youth.
methodsVideos on vape tricks that could be accessed by underage youth were identified. The videos were coded for number of views, likes, dislikes, and content (ie, description of vape tricks, e-cigarette devices used for this purpose, video sponsors [private or industry], brand marketing, and contextual characteristics [eg, model characteristics, music, and profanity]).
resultsAn analysis of 59 sample videos on vape tricks identified 25 distinct vape tricks. These videos had more likes than dislikes (11 to 1 ratio) and a 32,017 median view count. 48% (28/59) of the videos were posted by industry accounts (27% [16/59] provaping organizations, 15% [9/59] online shops, and 3% [2/59] vape shops) and 53% by private accounts (55% [17/31] private users, 26% [8/31] vape enthusiasts, and 19% [6/31] YouTube influencers); 53% (31/59) of the videos promoted a brand of e-cigarette devices, e-liquids, or online/vape shops, and 99% of the devices used for vape tricks were advanced generation devices. The models in the videos were 80.2% (160/198) male, 51.5% white (102/198), and 61.6% (122/198) aged 18 to 24 years; 85% (50/59) of the videos had electronic dance music and hip hop, and 32% (19/59) had profanity.
conclusionsVape trick videos on YouTube, about half of which were industry sponsored, were accessible to youth. Restrictions of e-cigarette marketing on social media, such as YouTube, are needed.
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What OpenQuestion holds
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.