Exploratory Analysis of Electronic Cigarette-Related Videos on YouTube: Observational Study

Differences in Attentional Bias Toward e-Cigarette Cues Between e-Cigarette Users and Nonusers

ABSTRACT

The use of electronic nicotine delivery systems (ENDS) has increased rapidly in recent years, particularly among young adults. There is a dearth of research on the cognitive factors that contribute to ENDS use. One of the possible cognitive mechanisms involved with addictive behavior is attentional bias (AB). AB can manifest as either facilitated attention engagement toward or delayed attention disengagement from a relevant stimulus. The purpose of this study was to examine the difference in AB toward ENDS-related cues between ENDS users and non-ENDS users. ENDS users (n = 29) and nonusers (n = 24) between the ages of 18 and 29 years participated in the dot-probe and eye-tracking picture-viewing tasks. The results showed that there was a significant difference in the variance of AB between the two groups. In the eye-tracking task, ENDS users displayed significantly greater net dwell time and fixation time at time frames of 6-9, 9-12, and 12-15 seconds, compared to nonusers. It is noteworthy that ENDS users exhibited attentional fluctuation toward ENDS cues as well as difficulties disengaging attention from ENDS cues. The current findings offer insight into the nature of attentional processes associated with ENDS cues and provide useful data to guide the development of a nurse-led cognitive intervention focusing on biased attentional processing related to ENDS cues.

Vaping: Public Health, Social Media, and Toxicity

This viewpoint aims to provide a comprehensive understanding of vaping from various perspectives that contribute to the invention, development, spread, and consequences of e-cigarette products and vaping. Our analysis showed that the specific characteristics of e-cigarette products as well as marketing strategies, especially social media marketing, fostered the spread of vaping and the subsequent effects on human health and toxicity. We analyzed the components of e-cigarette devices and e-liquids, including the latest variants whose impacts were often overlooked. The different forms of nicotine, including salts and freebase nicotine, tobacco-derived nicotine, tobacco-free nicotine, and cooling agents (WS3 and WS23), have brought more choices for vapers along with more ways for e-cigarette manufacturers to advertise false understandings and present a greater threat to vapers' health. Our work emphasized the products of brands that have gained significant influence recently, which are contributing to severe public health issues. On the other hand, we also discussed in detail the toxicity of e-liquid components and proposed a toxicity mechanism. We also noticed that nicotine and other chemicals in e-liquids promote each other's negative effects through the oxidative stress and inflammatory nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) pathway, a mechanism leading to pulmonary symptoms and addiction. The impact of government regulations on the products themselves, including flavor bans or regulations, has been limited. Therefore, we proposed further interventions or harm reduction strategies from a public health perspective.

Social Network Analysis of e-Cigarette-Related Social Media Influencers on Twitter/X: Observational Study

BACKGROUND

An e-cigarette uses a battery to heat a liquid that generates an aerosol for consumers to inhale. e-Cigarette use (vaping) has been associated with respiratory disease, cardiovascular disease, and cognitive functions. Recently, vaping has become increasingly popular, especially among youth and young adults.

OBJECTIVE

The aim of this study was to understand the social networks of Twitter (now rebranded as X) influencers related to e-cigarettes through social network analysis.

METHODS

Through the Twitter streaming application programming interface, we identified 3,617,766 unique Twitter accounts posting e-cigarette-related tweets from May 3, 2021, to June 10, 2022. Among these, we identified 33 e-cigarette influencers. The followers of these influencers were grouped according to whether or not they post about e-cigarettes themselves; specifically, the former group was defined as having posted at least five e-cigarette-related tweets in the past year, whereas the latter group was defined as followers that had not posted any e-cigarette-related tweets in the past 3 years. We randomly sampled 100 user accounts among each group of e-cigarette influencer followers and created corresponding social networks for each e-cigarette influencer. We compared various network measures (eg, clustering coefficient) between the networks of the two follower groups.

RESULTS

Major topics from e-cigarette-related tweets posted by the 33 e-cigarette influencers included advocating against vaping policy (48.0%), vaping as a method to quit smoking (28.0%), and vaping product promotion (24.0%). The follower networks of these 33 influencers showed more connections for those who also post about e-cigarettes than for followers who do not post about e-cigarettes, with significantly higher clustering coefficients for the former group (0.398 vs 0.098; P=.005). Further, networks of followers who post about e-cigarettes exhibited substantially more incoming and outgoing connections than those of followers who do not post about e-cigarettes, with significantly higher in-degree (0.273 vs 0.084; P=.02), closeness (0.452 vs 0.137; P=.04), betweenness (0.036 vs 0.008; P=.001), and out-of-degree (0.097 vs 0.014; P=.02) centrality values. The followers who post about e-cigarettes also had a significantly (P<.001) higher number of followers (n=322) than that of followers who do not post about e-cigarettes (n=201). The number of tweets in the networks of followers who post about e-cigarettes was significantly higher than that in the networks of followers who do not post about e-cigarettes (93 vs 43; P<.001). Two major topics discussed in the networks of followers who post about e-cigarettes included promoting e-cigarette products or vaping activity (55.7%) and vaping being a help for smoking cessation and harm reduction (44.3%).

CONCLUSIONS

Followers of e-cigarette influencers who also post about e-cigarettes have more closely connected networks than those of followers who do not themselves post about e-cigarettes. These findings provide a potentially practical intervention approach for future antivaping campaigns.

Characterizing Anti-Vaping Posts for Effective Communication on Instagram Using Multimodal Deep Learning

INTRODUCTION

Instagram is a popular social networking platform for sharing photos with a large proportion of youth and young adult users. We aim to identify key features in anti-vaping Instagram image posts associated with high social media user engagement by artificial intelligence.

AIMS AND METHODS

We collected 8972 anti-vaping Instagram image posts and hand-coded 2200 Instagram images to identify nine image features such as warning signs and person-shown vaping. We utilized a deep-learning model, the OpenAI: contrastive language-image pre-training with ViT-B/32 as the backbone and a 5-fold cross-validation model evaluation, to extract similar features from the Instagram image and further trained logistic regression models for multilabel classification. Latent Dirichlet Allocation model and Valence Aware Dictionary and sEntiment Reasoner were used to extract the topics and sentiment from the captions. Negative binomial regression models were applied to identify features associated with the likes and comments count of posts.

RESULTS

Several features identified in anti-vaping Instagram image posts were significantly associated with high social media user engagement (likes or comments), such as educational warnings and warning signs. Instagram posts with captions about health risks associated with vaping received significantly more likes or comments than those about help quitting smoking or vaping. Compared to the model based on 2200 hand-coded Instagram image posts, more significant features have been identified from 8972 AI-labeled Instagram image posts.

CONCLUSION

Features identified from anti-vaping Instagram image posts will provide a potentially effective way to communicate with the public about the health effects of e-cigarette use.

IMPLICATIONS

Considering the increasing popularity of social media and the current vaping epidemic, especially among youth and young adults, it becomes necessary to understand e-cigarette-related content on social media. Although pro-vaping messages dominate social media, anti-vaping messages are limited and often have low user engagement. Using advanced deep-learning and statistical models, we identified several features in anti-vaping Instagram image posts significantly associated with high user engagement. Our findings provide a potential approach to effectively communicate with the public about the health risks of vaping to protect public health.

Monitoring the Official YouTube Channels of E-Cigarette Companies: A Thematic Analysis

BACKGROUND

E-cigarette companies use YouTube to foster brand awareness, market their products, and interact with current and future tobacco users. However, research on the official YouTube channels of e-cigarette companies is limited. This study determined the themes of, and degree of user engagement with, videos posted to the official channels of e-cigarette companies on YouTube.

METHODS

Data were collected from the official YouTube channels of seven e-cigarette companies by scraping (i.e., electronically copying) the videos. The earliest video was posted on October 10, 2013, and the most recent video was posted on April 22, 2021 (n = 260). An inductive approach was used to identify themes in the data. User engagement with posts including number of likes, dislikes, and comments were also collected.

RESULTS

Prevalent themes included branding (n = 250 of 260 videos, 96%), youth use (n = 222, 85%), and tobacco use (n = 210, 81%), while less common themes included misleading health statements (n = 4, 2%), personal choice (n = 4, 2%), and antitobacco (n = 2, 1%). Videos that contained the themes testimonial, product design features, and instructional received the highest mean number of likes. Videos that contained the themes antitobacco, cessation, and testimonial received the highest mean number of dislikes. The 260 videos in this study were collectively viewed 6,619,700 times as of May 5, 2021.

CONCLUSIONS

Videos from the official YouTube channels of seven e-cigarette companies often focused on branding and user experience but rarely mentioned cessation. While videos about cessation were rare, they received the second highest mean number of dislikes. Future research should assess the impact of exposure to e-cigarette-related content on YouTube and e-cigarette-related attitudes and behaviors.

Characterizing Electronic Cigarette-Related Videos on TikTok: Observational Study (Preprint)

BACKGROUND

As a popular social networking platform for sharing short videos, TikTok has been widely used for sharing e-cigarettes or vaping-related videos, especially among the youth.

OBJECTIVE

This study aims to characterize e-cigarette or vaping-related videos and their user engagement on TikTok through descriptive analysis.

METHODS

From TikTok, a total of 417 short videos, posted between October 4, 2018, and February 27, 2021, were collected using e-cigarette or vaping-related hashtags. Two human coders independently hand-coded the video category and the attitude toward vaping (provaping or antivaping) for each vaping-related video. The social media user engagement measures (eg, the comment count, like count, and share count) for each video category were compared within provaping and antivaping groups. The user accounts posting these videos were also characterized.

RESULTS

Among 417 vaping-related TikTok videos, 387 (92.8%) were provaping, and 30 (7.2%) were antivaping videos. Among provaping TikTok videos, the most popular category is vaping tricks (n=107, 27.65%), followed by advertisement (n=85, 21.95%), customization (n=75, 19.38%), TikTok trend (n=70, 18.09%), others (n=44, 11.37%), and education (n=6, 1.55%). By comparison, videos showing the TikTok trend had significantly higher user engagement (like count per video) than other provaping videos. Antivaping videos included 15 (50%) videos with the TikTok trend, 10 (33.33%) videos on education, and 5 (16.67%) videos about others. Videos with education have a significantly lower number of likes than other antivaping videos. Most TikTok users posting vaping-related videos are personal accounts (119/203, 58.62%).

CONCLUSIONS

Vaping-related TikTok videos are dominated by provaping videos focusing on vaping tricks, advertisement, customization, and TikTok trend. Videos with the TikTok trend have higher user engagement than other video categories. Our findings provide important information on vaping-related videos shared on TikTok and their user engagement levels, which might provide valuable guidance on future policy making, such as possible restrictions on provaping videos posted on TikTok, as well as how to effectively communicate with the public about the potential health risks of vaping.

Characteristics of and User Engagement With Antivaping Posts on Instagram: Observational Study

BACKGROUND

Although government agencies acknowledge that messages about the adverse health effects of e-cigarette use should be promoted on social media, effectively delivering those health messages is challenging. Instagram is one of the most popular social media platforms among US youth and young adults, and it has been used to educate the public about the potential harm of vaping through antivaping posts.

OBJECTIVE

We aim to analyze the characteristics of and user engagement with antivaping posts on Instagram to inform future message development and information delivery.

METHODS

A total of 11,322 Instagram posts were collected from November 18, 2019, to January 2, 2020, by using antivaping hashtags including #novape, #novaping, #stopvaping, #dontvape, #antivaping, #quitvaping, #antivape, #stopjuuling, #dontvapeonthepizza, and #escapethevape. Among those posts, 1025 posts were randomly selected and 500 antivaping posts were further identified by hand coding. The image type, image content, and account type of antivaping posts were hand coded, the text information in the caption was explored by topic modeling, and the user engagement of each category was compared.

RESULTS

Analyses found that antivaping images of the educational/warning type were the most common (253/500; 50.6%). The average likes of the educational/warning type (15 likes/post) were significantly lower than the catchphrase image type (these emphasized a slogan such as "athletesdontvape" in the image; 32.5 likes/post; P<.001). The majority of the antivaping posts contained the image content element text (n=332, 66.4%), followed by the image content element people/person (n=110, 22%). The images containing people/person elements (32.8 likes/post) had more likes than the images containing other elements (13.8-21.1 likes/post). The captions of the antivaping Instagram posts covered topics including "lung health," "teen vaping," "stop vaping," and "vaping death cases." Among the 500 antivaping Instagram posts, while most posts were from the antivaping community (n=177, 35.4%) and personal account types (n=182, 36.4%), the antivaping community account type had the highest average number of posts (1.69 posts/account). However, there was no difference in the number of likes among different account types.

CONCLUSIONS

Multiple features of antivaping Instagram posts may be related to user engagement and perception. This study identified the critical elements associated with high user engagement, which could be used to design antivaping posts to deliver health-related information more efficiently.