Valence of Media Coverage About Electronic Cigarettes and Other Tobacco Products From 2014 to 2017: Evidence From Automated Content Analysis

IQOS news media coverage in Israel: a comparison across three subpopulations

BACKGROUND

Philip Morris International's (PMI) IQOS, a leading heated tobacco product globally, entered the Israeli market in 2016. IQOS and/or electronic cigarette use is higher in Israel's Arab population (2.8% vs 1.2% of Jews). However, previous research indicated possible targeting of the Ultra-orthodox Jewish population with more IQOS paid ads. This paper examined how IQOS is framed in news media articles directed at three subpopulations in Israel: Arab, Ultra-orthodox Jews and general public.

METHODS

Media articles (January-October 2020) were obtained from Ifat media and were coded using abductive coding. Characteristics of articles (photo and article content) targeting each subpopulation were compared using χ2, Fisher's exact test, one-way analysis of variance and median test, as appropriate.

RESULTS

Of the 63 unique articles identified, 16 targeted Arab, 24 Ultra-orthodox Jews and 23 general public. Arab and Ultra-orthodox Jewish media significantly differed from the general public's media in their positive framing of PMI (100% Arab and 75% Ultra-orthodox Jews vs 52% general public, p=0.004), and IQOS (100% Arab and 88% Ultra-orthodox Jews vs 61% general public, p=0.006). Arab media differed from others by highlighting IQOS' retail locations (81% vs 17% Ultra-orthodox Jews and 13% general public), social benefits (88% vs 8% Ultra-orthodox Jews and 17% general public) and reflecting content from PMI's press release (100% vs 46% Ultra-orthodox Jews and 35% general public; ps <0.001).

CONCLUSIONS

IQOS was framed more positively in media targeting minority populations (Arab and Ultra-orthodox Jews), compared with general public. Arabic media in particular emphasised IQOS' retail accessibility and social benefits. These findings highlight the need for media surveillance and regulation, especially of minority-oriented media.

Understanding the Associations Between Adolescents' Exposure to E-Cigarette Information and Vaping Behavior Through the Theory of Planned Behavior

Adolescents have actively looked for and passively scanned information about electronic cigarettes (e-cigarettes) from a variety of media and interpersonal sources. Despite the evidence that exposure to e-cigarette information is associated with youth's increased vaping intention, there is a paucity of scholarship that differentiates the sources where adolescents obtain e-cigarette information in their investigation, which limits our understanding of the unique association between vaping intention and e-cigarette information acquisition from specific sources. In addition, few studies have systematically examined the mechanism of the aforementioned associations. To fill the gap, an online national survey on a panel of adolescents between 13 to 17 years old was conducted. After controlling for potential confounders, several significant indirect effects were observed. Specifically, adolescents' vaping intention was negatively associated with e-cigarette information seeking from health professionals but positively with e-cigarette information exposure from family and friends, outdoors advertisements, social media, and other online channels, with the theory of planned behavior (TPB) constructs mediating these relationships. The findings not only contribute to the body of scholarship on TPB but also provide important suggestions for regulating outdoor and online e-cigarette information and designing persuasive interventions and campaigns.

Understanding e-cigarette content and promotion on YouTube through machine learning

INTRODUCTION

YouTube is a popular social media used by youth and has electronic cigarette (e-cigarette) content. We used machine learning to identify the content of e-cigarette videos, featured e-cigarette products, video uploaders, and marketing and sales of e-cigarette products.

METHODS

We identified e-cigarette content using 18 search terms (eg, e-cig) using fictitious youth viewer profiles and predicted four models using the metadata as the input to supervised machine learning: (1) video themes, (2) featured e-cigarette products, (3) channel type (ie, video uploaders) and (4) discount/sales. We assessed the association between engagement data and the four models.

RESULTS

3830 English videos were included in the supervised machine learning. The most common video theme was 'product review' (48.9%), followed by 'instruction' (eg, 'how to' use/modify e-cigarettes; 17.3%); diverse e-cigarette products were featured; 'vape enthusiasts' most frequently posted e-cigarette videos (54.0%), followed by retailers (20.3%); 43.2% of videos had discount/sales of e-cigarettes; and the most common sales strategy was external links for purchasing (34.1%). 'Vape trick' was the least common theme but had the highest engagement (eg, >2 million views). 'Cannabis' (53.9%) and 'instruction' (49.9%) themes were more likely to have external links for purchasing (p<0.001). The four models achieved an F1 score (a measure of model accuracy) of up to 0.87.

DISCUSSION

Our findings indicate that on YouTube videos accessible to youth, a variety of e-cigarette products are featured through diverse videos themes, with discount/sales. The findings highlight the need to regulate the promotion of e-cigarettes on social media platforms.

Disclosure Standards for Social Media and Generative Artificial Intelligence Research: Toward Transparency and Replicability

Social media dominate today's information ecosystem and provide valuable information for social research. Market researchers, social scientists, policymakers, government entities, public health researchers, and practitioners recognize the potential for social data to inspire innovation, support products and services, characterize public opinion, and guide decisions. The appeal of mining these rich datasets is clear. However, there is potential risk of data misuse, underscoring an equally huge and fundamental flaw in the research: there are no procedural standards and little transparency. Transparency across the processes of collecting and analyzing social media data is often limited due to proprietary algorithms. Spurious findings and biases introduced by artificial intelligence (AI) demonstrate the challenges this lack of transparency poses for research. Social media research remains a virtual "wild west," with no clear standards for reporting regarding data retrieval, preprocessing steps, analytic methods, or interpretation. Use of emerging generative AI technologies to augment social media analytics can undermine validity and replicability of findings, potentially turning this research into a "black box" enterprise. Clear guidance for social media analyses and reporting is needed to assure the quality of the resulting research. In this article, we propose criteria for evaluating the quality of studies using social media data, grounded in established scientific practice. We offer clear documentation guidelines to ensure that social data are used properly and transparently in research and applications. A checklist of disclosure elements to meet minimal reporting standards is proposed. These criteria will make it possible for scholars and practitioners to assess the quality, credibility, and comparability of research findings using digital data.

Scanned information exposure and support for tobacco regulations among US youth and young adult tobacco product users and non-users

The influences of information exposure on youth and young adults' (YYA) support for smoking/vaping regulations have been understudied. This study examines (i) the relationships between routine exposure to (i.e. scanning) anti-smoking/pro-vaping information and YYA support for anti-smoking/vaping regulations and (ii) whether these relationships differ across YYA users and non-users of tobacco products. We analyzed the data from a nationally representative two-wave rolling cross-sectional survey of YYA in the United States, collected from 2014 to 2017 (baseline n = 10 642; follow-up n = 4001). Less than 5% of the participants ever scanned pro-smoking and anti-vaping information. Scanning anti-smoking information had significant positive relationships with support for all anti-smoking policies cross-sectionally, and this pattern was longitudinally significant in two anti-smoking policy contexts. Scanning pro-vaping information had significant negative associations with support for anti-vaping policies cross-sectionally, but not longitudinally. The lagged positive relationships between scanning anti-smoking information and support for anti-smoking regulations were stronger among YYA smokers than among YYA non-smokers, whereas evidence from adult data suggested the opposite pattern. The findings suggest that scanning information can affect YYA support for tobacco regulations. Future efforts are required to investigate mechanisms underlying the influences of scanned information on YYA support for tobacco regulations.

How Does Social Media Exposure and Engagement Influence College Students' Use of ENDS Products? A Cross-lagged Longitudinal Study

Electronic nicotine delivery systems (ENDS) products have been marketed heavily on social media throughout the past years, which exerts great influence on young adults' ENDS use. Despite scholars' pioneering efforts in investigating the influence of tobacco and nicotine products marketing on young adults' vaping behavior, scholarly attention has been paid primarily to passive exposure to rather than active engagement with the information on social media. In addition, the majority of existing research has been cross-sectional or focused on the unidirectional path from marketing information to behavior. To extend previous research in tobacco regulatory science on new media, we examined the bidirectional associations between self-reported exposure to and engagement with tobacco and nicotine products messaging on social media, and subsequent use of ENDS products one year later among a large, diverse sample of young adults. Results from cross-lagged panel analyses indicated that pro-tobacco/ENDS engagement and advertising exposure elevated risk whereas anti-tobacco/ENDS engagement decreased risk for the subsequent use of ENDS products one year later. On the other hand, the use of ENDS products positively predicted both pro- and anti-tobacco/ENDS engagement one year later. Findings provide empirical support for the reasoned action approach and the confirmation bias rooted in cognitive dissonance theory through rigorous longitudinal examination. Our findings not only point to the imperativeness of and offer guidance for regulating marketing information on social media, but also suggest social media as a promising platform to prevent young adults from initiating ENDS product use.

Machine learning applications in tobacco research: a scoping review

OBJECTIVE

Identify and review the body of tobacco research literature that self-identified as using machine learning (ML) in the analysis.

DATA SOURCES

MEDLINE, EMABSE, PubMed, CINAHL Plus, APA PsycINFO and IEEE Xplore databases were searched up to September 2020. Studies were restricted to peer-reviewed, English-language journal articles, dissertations and conference papers comprising an empirical analysis where ML was identified to be the method used to examine human experience of tobacco. Studies of genomics and diagnostic imaging were excluded.

STUDY SELECTION

Two reviewers independently screened the titles and abstracts. The reference list of articles was also searched. In an iterative process, eligible studies were classified into domains based on their objectives and types of data used in the analysis.

DATA EXTRACTION

Using data charting forms, two reviewers independently extracted data from all studies. A narrative synthesis method was used to describe findings from each domain such as study design, objective, ML classes/algorithms, knowledge users and the presence of a data sharing statement. Trends of publication were visually depicted.

DATA SYNTHESIS

74 studies were grouped into four domains: ML-powered technology to assist smoking cessation (n=22); content analysis of tobacco on social media (n=32); smoker status classification from narrative clinical texts (n=6) and tobacco-related outcome prediction using administrative, survey or clinical trial data (n=14). Implications of these studies and future directions for ML researchers in tobacco control were discussed.

CONCLUSIONS

ML represents a powerful tool that could advance the research and policy decision-making of tobacco control. Further opportunities should be explored.

The Effects of Tobacco Coverage in the Public Communication Environment on Young People's Decisions to Smoke Combustible Cigarettes

In today's complex media environment, does media coverage influence youth and young adults' (YYA) tobacco use and intentions? We conceptualize the "public communication environment" and effect mediators, then ask whether over time variation in exogenously measured tobacco media coverage from mass and social media sources predicts daily YYA cigarette smoking intentions measured in a rolling nationally representative phone survey (N = 11,847 on 1,147 days between May 2014 and June 2017). Past week anti-tobacco and pro-tobacco content from Twitter, newspapers, broadcast news, Associated Press, and web blogs made coherent scales (thetas = 0.77 and 0.79). Opportunities for exposure to anti-tobacco content in the past week predicted lower intentions to smoke (Odds ratio [OR] = 0.95, p < .05, 95% confidence interval [CI] = 0.91-1.00). The effect was stronger among current smokers than among nonsmokers (interaction OR = 0.88, p < .05, 95% CI = 0.77-1.00). These findings support specific effects of anti-tobacco media coverage and illustrate a productive general approach to conceptualizing and assessing effects in the complex media environment.

Determining the prevalence of cannabis, tobacco, and vaping device mentions in online communities using natural language processing

INTRODUCTION

The relationship between cannabis, tobacco, and vaping devices is both rapidly changing and poorly understood, with consumers rapidly shifting between use of all three product types. Given this dynamic and evolving landscape, there is an urgent need to monitor and better understand co-use, dual-use, and transition patterns between these products. This study describes work that utilizes social media - in this case, Reddit - in conjunction with automated Natural Language Processing (NLP) methods to better understand cannabis, tobacco, and vaping device product usage patterns.

METHODS

We collected Reddit data from the period 2013-2018, sourced from eight popular, high-volume Reddit communities (subreddits) related to the three product categories. We then manually annotated (coded) a set of 2640 Reddit posts and trained a machine learning-based NLP algorithm to automatically identify and disambiguate between cannabis or tobacco mentions (both smoking and vaping) in Reddit posts. This classifier was then applied to all data derived from the eight subreddits, 767,788 posts in total.

RESULTS

The NLP algorithm achieved an overall moderate performance (overall F-score of 0.77). When applied to our large corpus of Reddit posts, we discovered that over 10% of posts in the smoking cessation subreddit r/stopsmoking were classified as referring to vaping nicotine, and that only 2% of posts from the subreddits r/electronic_cigarette and r/vaping were classified as referring to smoking (tobacco) cessation.

CONCLUSIONS

This study presents the results of applying an NLP algorithm designed to identify and distinguish between cannabis and tobacco mentions (both smoking and vaping) in Reddit posts, hence contributing to our currently limited understanding of co-use, dual-use, and transition patterns between these products.

Flavors And Implied Reduced-Risk Descriptors In Cigar Ads At Stores Near Schools

INTRODUCTION

Although the FDA prohibits using inaccurate, reduced-risk descriptors on tobacco product advertising, descriptors that imply reduced-risk or an enhanced user experience may be present on cigar product advertising in retail outlets near schools. Therefore, to inform the development of federal labeling and advertising requirements that reduce youth appeal of cigars, we conducted a content analysis of cigar ads in retailers near schools to document the presence of implied health claims and other selling propositions that may convey enhanced smoking experience.

METHODS

Up to four interior and exterior LCC advertisements were photographed in a random sample of licensed tobacco retailers (n=530) near California middle and high schools. Unique ads (n= 234) were coded for brand, flavor, and presence of implicit health claims, premium branding descriptors, and sensory descriptors. Logistic regressions assessed the association among flavored ads and presence of implicit health claims, premium branding, or sensory descriptors.

RESULTS

Seventeen cigar brands were advertised near schools; Black & Mild (20.1%) and Swisher Sweets (20.1%) were most common. Flavor was featured in 64.5% of ads, with explicit flavor names (e.g., grape) being more prevalent than ambiguous names (e.g., Jazz) (49.6% vs. 34.2%). Compared to ads without flavors, ads with ambiguous flavors were more likely to feature implicit health claims (OR=1.83, 95%CI=1.06, 3.19) and sensory descriptors (OR=2.64, 95%CI=1.39, 5.04); ads with explicit flavors were more likely to feature premium branding (OR=2.84, 95%CI=1.53, 5.41).

CONCLUSIONS

Cigar ads that featured implicit health claims and premium branding, and sensory selling propositions are present at retailer stores near schools.

Will E-Cigarette Modified Risk Messages with a Nicotine Warning Polarize Smokers' Beliefs about the Efficacy of Switching Completely to E-Cigarettes in Reducing Smoking-Related Risks?

In the U.S., e-cigarette companies can apply for permission to use reduced or modified risk messages (MRMs) in their marketing materials. Because e-cigarette marketing materials should have a nicotine addictiveness warning, MRMs and a nicotine warning could appear together-resulting in a conflicting message. When reading a conflicting message, individuals assimilate evidence supporting their pre-existing beliefs and eventually develop stronger beliefs, diverging more from those with different pre-existing beliefs (i.e., polarization). This study examined if exposure to e-cigarette MRMs with a nicotine warning polarizes smokers' initially opposing beliefs about the efficacy of switching completely to e-cigarettes in reducing smoking-related risks, and if this polarization depends on individuals' need for closure. An online experiment randomized 761 U.S. adult smokers to either three MRMs with a nicotine warning or three control messages. People reported their perceived efficacy of switching completely to e-cigarettes at pre- and posttest and need for closure at pretest. Linear regression showed no polarization effects. Nonetheless, need for closure and pretest efficacy beliefs influenced message response: MRMs with a nicotine warning only enhanced efficacy beliefs of smokers with low pretest efficacy beliefs and low need for closure. Evaluation of e-cigarette mixed communication should consider individuals' motivational and cognitive differences.

Modelling Public Sentiments about Juul Flavors on Twitter through Machine Learning

INTRODUCTION

The availability of a variety of e-cigarettes flavors is one of the frequently cited reasons for their adoption. An active stream of discussion about flavoring can be observed online. Analyzing these real-time conversations offers nuanced insights into key factors related to the adoption of flavors, subsequently supporting public health interventions.

METHODS

Google's BERT, a state-of-the-art deep learning method was employed to model the first sentiment corpus on JUUL flavors. BERT, which is pre-trained with the complete English Wikipedia was fine-tuned by integrating a classification model, with human labeled Tweets, as training data. A collection of 30,075 Tweets about JUUL flavors was classified into positive and negative sentiments. Finally, using topic models, we identify and grouped thematic areas into positive and negative Tweets.

RESULTS

With an average of 89% cross-validation precision for classifying tweets, the finetuned BERT model classified 24,114 Tweets as positive and 5,961 Tweets as negative. Through the topic modeling approach 10 thematic topics were identified from the predicted positive and negative sentiments expressed in the Tweets.

CONCLUSIONS

JUUL flavors, notably mango, mint, and cucumber, provoke overwhelmingly positive sentiments indicating a strong likeness due to favoarble taste and odor. Negative discourse about JUUL flavors revolve around addictiveness, high nicotine content, and youth targeted marketing.

IMPLICATIONS

Limiting the content related to flavors and positive perceptions on social media is necessary to minimize exposure to youth. The novel methodology used in this study may be adopted to monitor e-cigarette discourse periodically, as well as other critical public health phenomena online.

Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review

BACKGROUND

Modifiable risky health behaviors, such as tobacco use, excessive alcohol use, being overweight, lack of physical activity, and unhealthy eating habits, are some of the major factors for developing chronic health conditions. Social media platforms have become indispensable means of communication in the digital era. They provide an opportunity for individuals to express themselves, as well as share their health-related concerns with peers and health care providers, with respect to risky behaviors. Such peer interactions can be utilized as valuable data sources to better understand inter-and intrapersonal psychosocial mediators and the mechanisms of social influence that drive behavior change.

OBJECTIVE

The objective of this review is to summarize computational and quantitative techniques facilitating the analysis of data generated through peer interactions pertaining to risky health behaviors on social media platforms.

METHODS

We performed a systematic review of the literature in September 2020 by searching three databases-PubMed, Web of Science, and Scopus-using relevant keywords, such as "social media," "online health communities," "machine learning," "data mining," etc. The reporting of the studies was directed by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Two reviewers independently assessed the eligibility of studies based on the inclusion and exclusion criteria. We extracted the required information from the selected studies.

RESULTS

The initial search returned a total of 1554 studies, and after careful analysis of titles, abstracts, and full texts, a total of 64 studies were included in this review. We extracted the following key characteristics from all of the studies: social media platform used for conducting the study, risky health behavior studied, the number of posts analyzed, study focus, key methodological functions and tools used for data analysis, evaluation metrics used, and summary of the key findings. The most commonly used social media platform was Twitter, followed by Facebook, QuitNet, and Reddit. The most commonly studied risky health behavior was nicotine use, followed by drug or substance abuse and alcohol use. Various supervised and unsupervised machine learning approaches were used for analyzing textual data generated from online peer interactions. Few studies utilized deep learning methods for analyzing textual data as well as image or video data. Social network analysis was also performed, as reported in some studies.

CONCLUSIONS

Our review consolidates the methodological underpinnings for analyzing risky health behaviors and has enhanced our understanding of how social media can be leveraged for nuanced behavioral modeling and representation. The knowledge gained from our review can serve as a foundational component for the development of persuasive health communication and effective behavior modification technologies aimed at the individual and population levels.

A Toxic Blend: Assessing the Effects of Cross-Source Media Coverage of Flavored E-Cigarettes on Youth and Young Adult Perceptions

Flavored e-cigarettes have received high attention across social and news media. How does exposure to e-cigarette flavors across multiple sources in the media environment influence youth e-cigarette perceptions? To address this question, we identified e-cigarette flavor mentions on 24.3 million Twitter posts and 11,691 longform texts (newspapers, broadcast news, and websites) disseminated over 3 years (2014-2017). During the same period, we measured e-cigarette beliefs through a nationally representative randomly sampled rolling survey of 13-26-year-olds (N = 4,470, 1013 days). We estimated the association between flavor-specific content on Twitter and longform sources in the 28 days prior to each survey date and perceptions that e-cigarettes taste good. The interaction of coverage on Twitter and longform sources was significantly associated with more favorable perceptions of e-cigarette taste (OR = 1.21; 95% CI, 1.04-1.41); the main effects of each source type were not significant. This study presents a novel approach to evaluating the effects of cross-source coverage in today's complex media landscape and may strengthen claims for media influence on e-cigarette use.