Changes in Viewer Engagement and Accessibility of Popular Vaping Videos on TikTok: A 12-Month Prospective Study

US adolescents' response to nicotine warning labels in influencer e-cigarette marketing social media posts: a survey-based randomised between-subject experiment

BACKGROUND

E-cigarette promotional posts with misleading nicotine warning labels on social media may lower adolescents' risk perceptions of e-cigarettes. A between-subject experiment assessed effects of influencers' e-cigarette promotional posts with different nicotine warning labels on adolescents' e-cigarette harm perceptions, susceptibility to e-cigarette use and perceptions of influencer credibility.

METHODS

In 2022, adolescents (N=1864, Mage=17 (SD=0.6)) were randomised to view six images and videos featuring influencers promoting e-cigarettes in one of five experimental conditions: two conditions with a 'tobacco-free nicotine' warning label (placed on an e-cigarette in the post or at the top of the post), two conditions with the US Food and Drug Administration (FDA) nicotine warning label (on an e-cigarette in the post or at the top of the post (control)) and one no-label condition. Participants rated influencers' credibility (eg, honesty). Harm perceptions of, and susceptibility to use, e-cigarettes were then assessed.

RESULTS

Participants who viewed posts with the FDA warning label at the top of the post perceived influencers as more honest, trustworthy and informed than in all the other conditions. E-cigarette never-users who viewed posts with a 'tobacco-free nicotine' warning label pictured on an e-cigarette (compared with the FDA label at the top of a post) were more likely to report susceptibility to e-cigarette use (adjusted OR 1.79, 95% CI 1.17 to 2.73).

CONCLUSIONS

Alterations of the required nicotine warning labels can make them less effective in deterring e-cigarette use behaviour. Tobacco control authorities could increase enforcement of the FDA-required nicotine warning labels and harness influencers for antitobacco messaging.

Scalable Surveillance of E-Cigarette Products on Instagram and TikTok Using Computer Vision

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.

Perceptions of social media harms and potential management strategies: vaping case study

BACKGROUND

The social media landscape is now ubiquitous in people's everyday lives. It is a space where culture, politics, economics and sociological and public health discourses occur. There is mounting evidence that e-cigarette products are being promoted and advertised on social media, a media platform particularly popular with young people. Our research aimed to understand industry professionals' perceptions of social media harms and potential management strategies using vaping as a case study.

METHODS

A critical realist perspective guided reflexive thematic analysis of the qualitative in depth, semi structured interviews. Data collection occurred in January and February 2023 with 13 participants working in the areas of public health, digital media, law, governance, tobacco control and advocacy.

RESULTS

Two superordinate themes emerged from the data: (1) Fathoming a complex system (social media) that contained the subordinate themes of Traversing Boundaries (crossing borders, crossing sectors) and Ungovernable (global and local landscapes, vested interests, self-regulation and opacity). (2) Addressing complexity (social media)- that contained the subordinate themes of Strengthening Institutions (global to local, policy and legislation, individuals and organisations); Defanging Industry (responsibility and transparency, moderation and algorithms, complaints); and Engaging Citizens (raising awareness, framing messaging).

CONCLUSIONS

There was consensus among participants that e-cigarette related social media content can be harmful and government action is urgently needed. There was an identified need for the development of government led national-level regulatory frameworks, with government led appropriate legislation; identification of an organisation or organisations with suitable levels of regulatory power and resources to monitor, enforce and penalise noncompliant social media companies; accompanied by increased community awareness raising of harmful social media content and improved digital literacy.

Viral Vaping: A systematic review and meta analysis of e-cigarette and Tobacco-Related social media content and its influence on youth behaviours and attitudes

AIMS

E-cigarette and tobacco-related content on social media continues to rise from lax restrictions on both personal and promotional posts. This content has been linked to various mechanisms of increased e-cigarette and tobacco use (i.e., lower risk perceptions and increased susceptibility). This study aimed to synthesis the association between exposure to e-cigarette and tobacco-related content and youth behaviours and attitudes.

METHODS

A comprehensive search was conducted on PubMed, Scopus, PsycINFO and Web of Science. Studies published post-2004 reporting effect estimates for exposure or engagement with e-cigarette or tobacco content on social media and behaviour or attitude outcomes were included.

RESULTS

Thirty-two studies (N = 274,283, aged 9 to 25 years) were included for synthesis. Meta-analyses revealed significant associations between engagement with tobacco content and use (OR 2.21; 95% CI = 1.27-3.82, p =.005; I2 = 96.4%), exposure to tobacco content and never users' lower risk perceptions (OR 0.68; 95% CI = 0.49-0.91; p =.011; I2 = 78.2%), and exposure to e-cigarette content and use (OR 1.37; 95% CI = 0.99-1.88; p = 0.058; I2 = 64.4%). There was no observed relationship between exposure to tobacco content and ever users' risk perceptions (OR 0.83; 95% CI = 0.61-1.13; p =.231; I2 = 83.5%). Qualitative synthesis found significant associations between tobacco exposure and increased current use and pro-tobacco attitudes; e-cigarette exposure and increased susceptibility and lower risk perceptions; tobacco engagement and increased susceptibility; e-cigarette engagement and increased use; dual exposure and increased susceptibility; and dual engagement and increased dual use. Mixed findings were identified for the influence of e-cigarette exposure on attitudes, tobacco exposure on susceptibility, dual exposure on dual use behaviours, and dual engagement on dual susceptibility.

CONCLUSIONS

Findings suggest an association between exposure and engagement to e-cigarette or tobacco products on social media and use or pro-use attitudes among youth. Further substantive research in the area of youth-specific use and attitudes following exposure and engagement with e-cigarette and tobacco content is needed to quantify this association.

Promotion of E-Cigarettes on TikTok and Regulatory Considerations

E-cigarettes are promoted extensively on TikTok and other social media platforms. Platform policies to restrict e-cigarette promotion seem insufficient and are poorly enforced. This paper aims to understand how e-cigarettes are being promoted on TikTok and provide insights into the effectiveness of current TikTok policies. Seven popular hashtag-based keywords were used to identify TikTok accounts and associated videos related to e-cigarettes. Posts were independently coded by two trained coders. Collectively, the 264 videos received 2,470,373 views, 166,462 likes and 3426 comments. The overwhelming majority of videos (97.7%) portrayed e-cigarettes positively, and these posts received 98.7% of the total views and 98.2% of the total likes. A total of 69 posts (26.1%) clearly violated TikTok's own content policy. The findings of the current study suggest that a variety of predominantly pro-vaping content is available on TikTok. Current policies and moderation processes appear to be insufficient in restricting the spread of pro-e-cigarette content on TikTok, putting predominantly young users at potential risk of e-cigarette use.

#TurntTrending: a systematic review of substance use portrayals on social media platforms

AIMS

There is a growing body of literature exploring the types of substance-related content and their portrayals on various social media platforms. We aimed to summarize how content related to substances is portrayed on various social media platforms.

METHODS

This systematic review was pre-registered on PROSPERO (ref: CRD42021291853). A comprehensive search was conducted in the databases of PubMed, Scopus, PsycINFO and Web of Science in April 2021. Original qualitative studies published post-2004 that included thematic and sentiment analyses of social media content on tobacco, alcohol, psychostimulant, e-cigarette, cannabis, opiate, stimulant/amphetamine, inhalant and novel psychoactive substance were included. Social media platforms were defined as online web- or application-based platforms that allowed users to generate content and interact via 'liking', comment or messaging features. Only studies that included summative and/or thematic content analyses of substance-related social media content were included.

RESULTS

A total of 73 studies, which covered 15 905 182 substance-related posts on Twitter, YouTube, Instagram, Pinterest, TikTok and Weibo, were identified. A total of 76.3% of all substance-related content was positive in its depiction of substance use, with 20.2% of content depicting use negatively. Sentiment regarding opiate use however was commonly negative (55.5%). Most studies identified themes relating to Health, Safety and Harms (65.0%) of substance use. Themes relating to Promotions/Advertisements (63.3%), Informative content (55.0%) and Use behaviours (43.3%) were also frequently identified.

CONCLUSIONS

Substance-related content that promotes engagement with substance use or actively depicts use appears to be widely available on social media. The large public presence of this content may have concerning influences on attitudes, behaviours and risk perceptions relating to substance use, particularly among the most vulnerable and heaviest users of social media-adolescents and young adults.

Assessing the Quality of Hearing Aids-Related Videos on TikTok

Hearing aids are effective at improving listening ability and health-related quality of life. Recently, we observed that there are many hearing aids-related videos published on TikTok. However, the quality of the information they offer remains unstudied. This study aimed to evaluate the information quality of hearing aids videos on TikTok. We collected a sample of 155 hearing aids-related videos in Chinese and extracted the basic information. First, we identified the source of each video. Two independent raters assessed the quality of the information in the videos, using the PEMAT-A/V tool and DISCERN instrument. Regarding content, the results showed that the video contents on TikTok mainly about features, functionalities, and suggestions of purchase or fitting of hearing aids, while the information about the disadvantages and complications of hearing aids was limited. The overall quality of the hearing aids-related videos was acceptable on average, although the quality varies greatly depending on the type of source. Patients should be cautious in obtaining information about hearing aids on TikTok.

Deconstructing TikTok Videos on Mental Health: Cross-sectional, Descriptive Content Analysis

Background

Social media platforms that are based on the creation of visual media, such as TikTok, are increasingly popular with adolescents. Online social media networks provide valuable opportunities to connect with each other to share experiences and strategies for health and wellness.

Objective

The aim of this study was to describe the content of the hashtag #mentalhealth on TikTok.

Methods

This cross-sectional, descriptive content analysis study included 100 videos with the hashtag #mentalhealth on TikTok. All videos that included the hashtag #mentalhealth were analyzed and coded for the presence of content categories. Additionally, the comments to each video were viewed and coded for content in the following themes: offering support or validation; mentioning experience with suicide or suicidal ideation; mentioning experience with self-harm; describing an experience with hospitalization for mental health issues; describing other mental health issues; and sharing coping strategies, experiences of healing, or ways to feel better.

Results

Collectively, the 100 videos studied received 1,354,100,000 views; 266,900,000 likes; and 2,515,954 comments. On average, each video received 13,406,930.69 (SD 8,728,095.52) views; 2,657,425.74 (SD 1,449,920.45) likes; and 24,910.44 (SD 21,035.06) comments. The only content category observed in most (51/100, 51%) of the videos included in the sample was “general mental health.” The remaining content categories appeared in less than 50% of the sample. In total, 32% (32/100) of the videos sampled received more than the overall average number of likes (ie, more that 2.67 million likes). Among these 32 videos, 23 (72%) included comments offering support or validation and 20 (62%) included comments that described other mental health issues or struggles.

Conclusions

With over 1 billion cumulative views, almost half of the assessed TikTok videos included in this study reported or expressed symptoms of mental distress. Future research should focus on the potential role of intervention by health care professionals on social media.