E-Cigarette-Related Nicotine Misinformation on Social Media

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.

Twitter Misinformation Discourses About Vaping: Systematic Content Analysis

BACKGROUND

While there has been substantial analysis of social media content deemed to spread misinformation about electronic nicotine delivery systems use, the strategic use of misinformation accusations to undermine opposing views has received limited attention.

OBJECTIVE

This study aims to fill this gap by analyzing how social media users discuss the topic of misinformation related to electronic nicotine delivery systems, notably vaping products. Additionally, this study identifies and analyzes the actors commonly blamed for spreading such misinformation and how these claims support both the provaping and antivaping narratives.

METHODS

Using Twitter's (subsequently rebranded as X) academic application programming interface, we collected tweets referencing #vape and #vaping and keywords associated with fake news and misinformation. This study uses systematic content analysis to analyze the tweets and identify common themes and actors who discuss or possibly spread misinformation.

RESULTS

This study found that provape users dominate the platform regarding discussions about misinformation about vaping, with provaping tweets being more frequent and having higher overall user engagement. The most common narrative for provape tweets surrounds the conversation of vaping being perceived as safe. On the other hand, the most common topic from the antivape narrative is that vaping is indeed harmful. This study also points to a general distrust in authority figures, with news outlets, public health authorities, and political actors regularly accused of spreading misinformation, with both placing blame. However, specific actors differ depending on their positionalities. The vast number of accusations from provaping advocates is found to shape what is considered misinformation and works to silence other narratives. Additionally, allegations against reliable and proven sources, such as public health authorities, work to discredit assessments about the health impacts, which is detrimental to public health overall for both provaping and antivaping advocates.

CONCLUSIONS

We conclude that the spread of misinformation and the accusations of misinformation dissemination using terms such as "fact check," "misinformation," "fake news," and "disinformation" have become weaponized and co-opted by provaping actors to delegitimize criticisms about vaping and to increase confusion about the potential health risks. The study discusses the mixed types of impact of vaping on public health for both smokers and nonsmokers. Additionally, we discuss the implications for effective health education and communication about vaping and how misinformation claims can affect evidence-based discourse on Twitter as well as informed vaping decisions.

To Tweet or Not to Tweet: Tweets About Tobacco Regulation can Help Disseminate Anti-regulatory Messages

INTRODUCTION

Twitter enables public organizations to engage the public in health policy discourse. However, documented hostility towards tobacco control proposals on Twitter suggests that a closer examination of the nature of interaction with such content is warranted.

AIMS AND METHODS

We scraped tweets from government bodies with tobacco control interests between July and November of 2021 (N = 3889), 2 months before and after the Food and Drug Administration's (FDA) Premarket Tobacco Authorization Act's (PMTA) September deadline. PMTA is a review process for authorizing the sale of new and existing e-cigarette or vaping products. Tweets related to PMTA were identified (n = 52) using a keyword filter. A content analysis of quote tweets and replies examined the amplification of pro and anti-policy sentiment via likes and retweets.

RESULTS

Replies were overwhelmingly anti-policy (96.7%). Moreover, the amplification of these replies, including 83.3% of likes and 65.6% of retweets, amplified anti-policy replies. Quote tweets, which allow users to add their own commentary to an existing tweet, were 77.9% (n = 120) anti-policy, receiving 87.7% of likes (n = 1708) and 86.2% of retweets (n = 726) compared to pro-policy quote tweets (n = 240 likes and n = 116 retweets). Regression analyses showed a significantly greater amplification of anti-policy content.

CONCLUSIONS

Communicating about tobacco policy on Twitter carries risks. Anti-policy advocates can weaponize quote tweets for easy construction of messages designed in accordance with evidence-based guidelines for conferring resistance to persuasion. Future research should examine whether public health organizations can adapt this strategy to counter anti-regulatory advocates on Twitter.

IMPLICATIONS

The primary implications of this research are that communication about tobacco policy on Twitter should be part of a broader public engagement strategy with quantifiable metrics of success. The information environment on Twitter is demonstrably hostile to pro-tobacco regulatory policy positions. As a result, efforts to engage on the platform by regulatory institutions like the FDA can inadvertently provide materials that are easily leveraged as effective counter-messaging. Moreover, this counter-messaging can disseminate more broadly than the original message.

Community and Opinion Leadership Effects on Vaping Discourse: A Network Analysis of Online Reddit Threads

Reddit is a popular hub for discussing vaping. A deeper understanding of the factors that influence this online discourse could inform public health messaging efforts targeting this platform. Using a network analysis framework, we sought to investigate the role of opinion leaders and online communities in facilitating vaping discussions on Reddit. We collected Reddit submissions about vaping posted in May 2021 and used these submissions to generate subreddit-level (N=261) and thread-level (N=8,377) data sets. We coded subreddits into four community categories: 1) Vaping, 2) Substance use, 3) Cessation, and 4) Non-specific. We used sociometric in-degree centrality statistics to identify subreddit opinion leaders. We computed non-parametric ANOVAs and negative binomial regressions to test associations between opinion leadership and subreddit community category variables on subreddit network composition (comprised of subreddit-level network nodes and edges) and the number of commenters on Reddit threads about vaping (thread-level). Subreddit network composition was largely dependent on opinion leaders in Non-specific communities, and less so in Vaping and Substance use communities. At the thread-level, the rate of commenters was higher among threads initiated by opinion leaders than non-opinion leaders (adjusted rate ratio [aRR]=4.84). Furthermore, threads posted in Vaping (aRR=1.64), Substance use (aRR=1.92), and Cessation (aRR=1.21) communities had higher commenter rates than those posted in Non-specific communities. Communities and opinion leaders play a key role in the composition and reach of vaping discussions on Reddit. These findings provide a foundation for public health campaigns and interventions targeting Reddit and perhaps other social media platforms.

Perceptions of a prescription model for accessing nicotine vaping products: an examination of submissions made by self-reported e-cigarette users to an Australian consultation

In response to rapid and substantial increases in rates of e-cigarette use among young people, Australia's Therapeutic Goods Administration (TGA) made changes to the regulations governing nicotine vaping products. As part of the regulatory change process, Australians were invited to comment on the proposed regulations, which featured the introduction of a prescription model for nicotine vaping products. To inform strategies to enhance compliance with the tightened regulations, this study examined submissions made by self-reported e-cigarette users to the TGA's public consultation (n = 1405). A content analysis was conducted to identify and quantify key arguments. Claims about possible negative consequences associated with the regulations (e.g. people will return to smoking, inconvenience) featured in most submissions (84%). Around half (55%) of submissions mentioned perceived benefits of e-cigarettes, including favourable health outcomes (e.g. improved breathing) and enhanced tobacco cessation. Around half (52%) featured concerns about inconsistency in treatment and the argument that e-cigarettes should not be restricted when more harmful tobacco products are readily available. Alternative approaches to a prescription model were offered in nearly one-third (31%) of submissions. One-quarter (26%) included text provided by an industry-led astroturfing campaign. The arguments made in the analysed submissions suggest a lack of appreciation of (i) the negative health outcomes associated with e-cigarette use and (ii) evidence linking these devices to smoking relapse. Results highlight the need for targeted health campaigns that address (i) gaps in consumers' knowledge and (ii) vaping-related misinformation being promulgated by the industry and its allies.

Viewer Reactions to EVALI Storylines on Popular Medical Dramas: A Thematic Analysis of Twitter Messages

Previous research has found an association between awareness of e-cigarette, or vaping, product-use associated lung injury (EVALI) and lower intention to use e-cigarettes among young people. This study utilized Twitter data to evaluate if the January 2020 depiction of EVALI on New Amsterdam, Chicago Med, and Grey's Anatomy-three popular primetime medical dramas-could be a potential innovative avenue to raise awareness of EVALI. We obtained tweets containing e-cigarette-related search strings from 1/21/2020 to 02/18/2020 and filtered these with storyline-specific keywords, resulting in 1,493 tweets for qualitative coding by two trained human coders. Content codes were informed by prior research, theories of narrative influence, and e-cigarette related outcomes. Of 641 (42.9%) relevant tweets, the most frequent content codes were perceived realism (n = 292, 45.6%) and negative response (n = 264, 41.2%). A common theme among these tweets was that storylines were unrealistic because none of the characters with EVALI used THC-containing products. Approximately 12% of tweets (n = 78) mentioned e-cigarette knowledge and 28 (4.4%) mentioned behavior, including quitting e-cigarettes because of viewing the storylines. Implications for health communication research utilizing social media data and maximizing the achievement of positive health-related outcomes for storylines depicting current health topics are discussed.

Toward a more expansive and inclusive definition of women's health: A content analysis of Twitter conversations

To develop a nuanced understanding of women's health on social media, we conducted a content analysis of Twitter data in early 2020, during the early days of the COVID-19 pandemic. Included tweets (N = 1,714) fell into 15 overarching themes. "Politics and Women's Health" was most discussed, demonstrating the politicization of women's health, followed by "Maternal, Reproductive, and Sexual Health." COVID-19 was a crosscutting issue for 12 themes, suggesting widespread effects on women's health. Overall, diverse conversations unfolded on social media, including variation geographically, highlighting the need for a more expansive and inclusive definition of women's health. This work supports further investigation into the role of politics and COVID-19 across women's health domains.

Content Analysis of Idiopathic Pulmonary Fibrosis-related Information on Twitter

Background

Information regarding idiopathic pulmonary fibrosis (IPF) on the internet is often outdated, inaccurate, and potentially harmful. Twitter is a social media platform that allows users to post content in the form of "tweets".

Objective

We sought to assess the prevalence of inaccurate information regarding IPF on Twitter. We hypothesized that foundations and medical organizations would be the least likely to post inaccurate information and that inaccurate tweets would have higher user engagement.

Methods

All tweets posted between 2011 and 2019 were gathered using "snscrape" on Python 3.8 while searching for the phrase "idiopathic pulmonary fibrosis". Quantitative analysis was performed to describe trends in IPF-related tweet frequency over time. A subset of tweets made between 2018 and 2019 was screened for verifiable medical statements, which were then analyzed for accuracy compared with contemporary clinical practice guidelines, with descriptive statistics reported. Logistic regression was used to compare tweet accuracy and recommendation of nonindicated therapies across sources, with adjustment for tweet age and character count. Wilcoxon rank-sum tests were used to determine if user engagement (favorites, retweets, and replies) differed between accurate and inaccurate tweets.

Results

A total of 16,787 tweets were identified between 2011 and 2019. Between 2018 and 2019, 4,861 tweets were included, of which 1,612 (33%) contained verifiable medical statements. Tweets from sources other than foundations or medical organizations were more likely to contain inaccurate information and to recommend nonindicated therapies in both unadjusted and adjusted analyses. News and media sources had the highest odds of communicating potentially harmful information in both adjusted (odds ratio [OR], 12.00; 95% confidence interval [CI], 5.87-27.16) and unadjusted (OR, 11.62; 95% CI, 5.70-26.21) analyses when compared with foundations and medical organizations. Tweets containing inaccurate information had significantly lower numbers of favorites and retweets (P < 0.001 for both).

Conclusion

Misinformation regarding IPF is present on Twitter and is more often presented by news and media sources. Medically inaccurate tweets displayed less user engagement than accurate tweets. This differs from findings on IPF-related information on YouTube and Facebook, which may reflect differences in both author and consumer qualities across social media platforms.

The Future of Infodemic Surveillance as Public Health Surveillance

Public health systems need to be able to detect and respond to infodemics (outbreaks of misinformation, disinformation, information overload, or information voids). Drawing from our experience at the US Centers for Disease Control and Prevention, the COVID-19 State of Vaccine Confidence Insight Reporting System has been created as one of the first public health infodemic surveillance systems. Key functions of infodemic surveillance systems include monitoring the information environment by person, place, and time; identifying infodemic events with digital analytics; conducting offline community-based assessments; and generating timely routine reports. Although specific considerations of several system attributes of infodemic surveillance system must be considered, infodemic surveillance systems share several similarities with traditional public health surveillance systems. Because both information and pathogens are spread more readily in an increasingly hyperconnected world, sustainable and routine systems must be created to ensure that timely interventions can be deployed for both epidemic and infodemic response.

Interventions to mitigate vaping misinformation: protocol for a scoping review

BACKGROUND

The impact of misinformation about vapes' relative harms compared with smoking may lead to increased tobacco-related burden of disease. To date, no systematic efforts have been made to chart interventions that mitigate vaping-related misinformation. We plan to conduct a scoping review that seeks to fill gaps in the current knowledge of interventions that mitigate vaping-related misinformation.

METHODS

A scoping review focusing on interventions that mitigate vaping-related misinformation will be conducted. We will search (no date restrictions) MEDLINE, Scopus, EMBASE, CINAHL, PsycINFO, Web of Science Core Collection, Global Health, ERIC, and Sociological Abstracts. Gray literature will be identified using Disaster Lit, Google Scholar, Open Science Framework, governmental websites, and preprint servers (e.g., EuropePMC, PsyArXiv, MedRxiv, JMIR Preprints). Study selection will conform to Joanna Briggs Institute Reviewers' Manual 2020 Methodology for JBI Scoping Reviews. Only English language, original studies will be considered for inclusion. Two reviewers will independently screen all citations, full-text articles, and abstract data. A narrative summary of findings will be conducted. Data analysis will involve quantitative (e.g., frequencies) and qualitative (e.g., content and thematic analysis) methods. Where possible, a single effect size of exposure to the mitigation of vaping-related misinformation will be calculated per sample. Similarly, where possible, each study will be coded for moderating characteristics to find and account for systematic differences in the size of the effect or outcome that is being analyzed. Quality will be appraised with the study quality assessment tools utilized by the National Heart, Lung, and Blood Institute. Findings will be subjected to several different publication bias tests: Egger's regression test, Begg and Mazumdar's ran correlation test, and generation of a funnel plot with effect sizes plotted against a corresponding standard error.

DISCUSSION

Original research is urgently needed to design interventions to mitigate vaping-related misinformation. The planned scoping review will help to address this gap.

SYSTEMATIC REVIEW REGISTRATION

Open Science Framework osf/io/hy3tk.

The influence of pro-vaping “gatewatchers” on the dissemination of COVID-19 misinformation on Twitter (Preprint)

Background

Objective

Methods

Results

Conclusions