Background Exposure to visual posts featuring e-cigarette products on social media is associated with increased e-cigarette use among US adolescents. Instagram is the largest source of e-cigarette social media marketing, where influencers—for example, bloggers, brand ambassadors—post promotional materials. This study analysed the network of e-cigarette brands and influencers on Instagram, characterising the most central players in e-cigarette social media marketing. Methods We tracked influencers with public profiles on Instagram who posted promotional e-cigarette content in 2020, had over 1000 followers and high user engagement rate (ratio of likes and comments to followers) of 1%–25% per post. By conducting a social network analysis, we identified the most central (highly involved in promotional activities) influencers and e-cigarette brands. The number of the influencers’ followers aged 13–17 years old and the age verification practices restricting youth access were also assessed. Results There is a highly interconnected network of engaging e-cigarette influencers (n=55) worldwide who collaborated with over 600 e-cigarette brands in 2020. The Asian and US influencers had five to six times more teenage followers compared with the European influencers. 75% of the influencers did not restrict youth access to their promotional content on Instagram. The brands Voopotech, Innokin, Geekvape, Lost Vape, Smok and Vaporesso collaborated with the largest number of influencers (mean n=20). Conclusions It is important to understand associations among influencers and e-cigarette use behaviours, especially youth, to inform effective public health communication and potential policies that could regulate social media marketing sponsored by e-cigarette companies.

Tobacco promotion is prolific on social media, with each platform setting their own restrictions on tobacco promotion and sales. We evaluated the policies related to tobacco product promotion and sales on 11 sites that are popular with youth in May 2021: Discord, Facebook, Instagram, Pinterest, Reddit, Snapchat, TikTok, Tumblr, Twitch, Twitter and YouTube. Nine of the 11 sites prohibitedpaid advertisingfor tobacco products. However, only three of them clearly prohibitedsponsored content(ie, social influencers) that promotes tobacco. Six platforms restricted content thatsells tobacco productsand three tried toprohibit underage accessto content that promotes or sells tobacco products. Although most platform policies prohibited paid tobacco advertising, few addressed more novel strategies, such as sponsored/influencer content and few had age-gating to prevent youth access. There is a pressing need to regulate tobacco promotion on social media platforms.

• Using TikTok several times per day was significantly associated with e-cigarette ever-use, current (past 30 days) use and initiation among adolescents. • Adolescents had higher odds of e-cigarette ever-use if they reported seeing tobacco or nicotine posts, including e-cigarettes, on TikTok at least weekly. • Students had higher odds of e-cigarette ever-use (not including current use) if they reported seeing tobacco or nicotine posts, including e-cigarettes, on Instagram at least weekly.

Introduction: Nicotine/tobacco social media content may increase young people’s risk for use. This study examined prospective associations between exposure to and engagement with nicotine/tobacco-related social media content and nicotine/tobacco use among young adults. Aims and Methods: Young adults (N = 2080) originally recruited from Southern California high schools for a prospective cohort study reported frequency of viewing and posting nicotine/tobacco content on four social media platforms (Facebook, Instagram, Twitter, and YouTube). Participants also reported frequency of seeing nicotine/tobacco posts from friends, seeing nicotine/tobacco posts from influencers or celebrities, and liking nicotine/tobacco posts. Within subsamples of nicotine/tobacco never users (n = 794), past users (n = 897) and current users (n = 389), analyses examined associations of baseline (May–October 2020) social media content exposure and engagement with follow-up (January–June 2021) tobacco use initiation (among never users), resumption (among past users), and continuation (among current users), adjusting for sociodemographic and socioenvironmental characteristics. Results: Never users who saw nicotine/tobacco posts from friends (Adjusted Odds Ratio [AOR] = 2.91 [95% Confidence Interval [CI]: 1.46, 5.82], p = .002) or from celebrities or influencers (AOR = 2.77 [1.32, 5.84], p = .007) were significantly more likely to initiate nicotine/tobacco use than their peers. Among past users, posting nicotine/tobacco content at baseline was associated with use resumption (AOR = 1.77 [1.12, 2.80], p = .014). Content exposure and engagement were not associated with nicotine/tobacco use continuation among current users (p-values > .05). Conclusions: Seeing nicotine/tobacco posts from friends, influencers, or celebrities was associated with greater odds of tobacco use initiation, but not resumption or continuation, 6 months later. Young adults with past nicotine/tobacco use who post about nicotine/tobacco may be at elevated risk for resuming use. Implications: Young adults with exposure to nicotine/tobacco social media content were more likely than their peers to initiate nicotine/tobacco use 6 months later. Past nicotine/tobacco users who reported posting about nicotine/tobacco on social media at baseline were more likely than their peers to resume nicotine/tobacco use. Among young adults with current nicotine/tobacco use at baseline, social media activity did not predict odds of nicotine/tobacco use continuation at follow-up. Nicotine/tobacco content on social media should be restricted to reduce young people’s chances of nicotine/tobacco use initiation or resumption.

Introduction: Instagram and TikTok, video-based social media platforms popular among adolescents, contain tobacco-related content despite the platforms’ policies prohibiting substance-related posts. 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. Aims and Methods: We created a data set of 6999 Instagram images labeled for 8 object classes: mod or pod devices, e-juice containers, packaging boxes, nicotine warning labels, e-juice flavors, e-cigarette brand names, and smoke clouds. We trained a DyHead object detection model using a Swin-Large backbone, evaluated the model’s performance on 20 Instagram and TikTok videos, and applied the model to 14 072 e-cigarette-related promotional TikTok videos (2019–2022; 10 276 485 frames). Results: The model achieved the following mean average precision scores on the image test set: e-juice container: 0.89; pod device: 0.67; mod device: 0.54; packaging box: 0.84; nicotine warning label: 0.86; e-cigarette brand name: 0.71; e-juice flavor name: 0.89; and smoke cloud: 0.46. The prevalence of pod devices in promotional TikTok videos increased by 15% from 2019 to 2022. The prevalence of e-juices increased by 33% from 2021 to 2022. The prevalence of e-juice flavor names and e-cigarette brand names increased by about 100% from 2019 to 2022. Conclusions: Deep learning-based object detection technology enables automated analysis of visual posts on social media. Our computer vision model can detect the presence of e-cigarettes products in images and videos, providing valuable surveillance data for tobacco regulatory science (TRS). Implications: 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 TRS. 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.