Combining Crowd-Sourcing and Automated Content Methods to Improve Estimates of Overall Media Coverage: Theme Mentions in E-cigarette and Other Tobacco Coverage

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.

Harnessing Artificial Intelligence for Health Message Generation: The Folic Acid Message Engine

Background

Communication campaigns using social media can raise public awareness; however, they are difficult to sustain. A barrier is the need to generate and constantly post novel but on-topic messages, which creates a resource-intensive bottleneck.

Objective

In this study, we aim to harness the latest advances in artificial intelligence (AI) to build a pilot system that can generate many candidate messages, which could be used for a campaign to suggest novel, on-topic candidate messages. The issue of folic acid, a B-vitamin that helps prevent major birth defects, serves as an example; however, the system can work with other issues that could benefit from higher levels of public awareness.

Methods

We used the Generative Pretrained Transformer-2 architecture, a machine learning model trained on a large natural language corpus, and fine-tuned it using a data set of autodownloaded tweets about #folicacid. The fine-tuned model was then used as a message engine, that is, to create new messages about this topic. We conducted a web-based study to gauge how human raters evaluate AI-generated tweet messages compared with original, human-crafted messages.

Results

We found that the Folic Acid Message Engine can easily create several hundreds of new messages that appear natural to humans. Web-based raters evaluated the clarity and quality of a human-curated sample of AI-generated messages as on par with human-generated ones. Overall, these results showed that it is feasible to use such a message engine to suggest messages for web-based campaigns that focus on promoting awareness.

Conclusions

The message engine can serve as a starting point for more sophisticated AI-guided message creation systems for health communication. Beyond the practical potential of such systems for campaigns in the age of social media, they also hold great scientific potential for the quantitative analysis of message characteristics that promote successful communication. We discuss future developments and obvious ethical challenges that need to be addressed as AI technologies for health persuasion enter the stage.

Do Longitudinal Trends in Tobacco 21-Related Media Coverage Correlate with Policy Support? an Exploratory Analysis Using Supervised and Unsupervised Machine Learning Methods

Media coverage can impact support for health policies and, ultimately, compliance with those policies. Prior research found consistent, high support for Tobacco 21 policies, which raise the minimum legal age of tobacco purchase to 21, among adults and nonsmoking youth. However, a recent study found support (i.e., agreement with the statement: "The legal age to buy tobacco cigarettes should be increased from 18 to 21") among 13-20-year-old smokers increased from 2014 until mid-2016 and then declined steadily through mid-2017. To assess whether media coverage could be related to young smokers' changing support, we conducted an exploratory content analysis to identify texts about Tobacco 21 in a large corpus of tobacco texts (N = 135,691) published in four popular media sources from 2014 to 2017. For this content analysis, we developed a novel methodological approach that combined supervised and unsupervised machine learning methods and could be useful in other areas of communication research. We found that the prevalence of Tobacco 21 media coverage and Tobacco 21 support among young smokers exhibited similar temporal patterns for much of the study period. These findings highlight the need for continued research into the effects of media coverage on Tobacco 21 support among young smokers, a group that must comply with Tobacco 21 policies in order to ensure maximum effectiveness. This research is of particular utility following the 2019 passage of a federal Tobacco 21 regulation, as the public health impact of this regulation could be limited by low public support, and thus low rates of policy compliance.

Assessing open science and citizen science in addictions and substance use research: A scoping review

BACKGROUND

The EU promotes 'Open Science' as a public good. Complementary to its implementation is Citizen Science, which redefines the relationship between the scientific community, civic society and the individual. Open Science and Citizen Science poses challenges for the substance use and addictions research community but may provide positive opportunities for future European addiction research. This paper explores both current barriers and potential facilitators for the implementation of Open Science and Citizen Science in substance use and addictions research.

METHODOLOGY

A scoping review was used to examine barriers and facilitators identified in the substance use and addiction research literature for the adoption of Open Science and Citizen Science.

RESULTS

'Technical' facilitators included the pre-registration of study protocols; publication of open-source datasets; open peer review and online tools. 'Motivational' facilitators included enhanced reputation; embracing co-creation; engaged citizenship and gamification. 'Economic' facilitators included the use of free tools and balanced remuneration of crowdworkers. 'Political' facilitators included better informed debates through the 'triple helix' approach and trust-generating transparency. 'Legal' facilitators included epidemiologically informed law enforcement; better policy surveillance and the validation of other datasets. 'Ethical' facilitators included the 'democratisation of science' and opportunities to explore new concepts of ethics in addiction research.

CONCLUSION

Open Science and Citizen Science in substance use and addictions research may provide a range of benefits in relation to the democratisation of science; transparency; efficiency and the reliability/validity of data. However, its implementation raises a range of research integrity and ethical issues that need be considered. These include issues related to participant recruitment; privacy; confidentiality; security; cost and industry involvement. Progressive journal policies to support Open Science practices; a shift in researcher norms; the use of free tools and the greater availability of methodological and ethical standards are likely to increase adoption in the field.

Celebrity Politicians as Health-Promoting Role Models in the Media: the Cases of Vladimir Putin, Donald Trump, and Benjamin Netanyahu

In recent years, scholars have increasingly revealed the importance of celebrities in society, among them celebrity politicians. These celebrities not only influence political attitudes but also serve as role models for many individuals. Yet, little is known regarding what types of role models' politicians serve as in the context of health. To fill this gap, we examined three influential contemporary political leaders: Vladimir Putin, Donald Trump and Benjamin Netanyahu, and the type of mediated role models each possibly comprise in the context of healthy living. By conducting a qualitative content analysis, we analyzed 90 articles from 2018 to 2019 from two leading newspapers in each of the three politician's countries, namely Russia, the USA, and Israel as well as the respective politician's Facebook pages. Our findings point to three types of potential role models' as political leaders: a health-promoting model (Putin), a hybrid model (Trump), and a model of non-existence (Netanyahu), as the literature points to mediated role models influencing individuals' beliefs and behaviors. This study contributes to the understanding of mediated types of role models' politicians potentially serve as in the context of health vital to people's personal health.

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.

Breadth of Media Scanning Leads to Vaping among Youth and Young Adults: Evidence of Direct and Indirect Pathways from a National Longitudinal Survey

Electronic cigarette use among youth and young adults has reached an epidemic proportion of growth. This study examined the direct and indirect effects of the breadth of media scanning about e-cigarette use on subsequent vaping behavior through interpersonal communication and changes in descriptive norm perceptions. We conducted a nationally representative longitudinal phone survey of 13- to 25-year-olds from June 2014 to March 2017, with 11,013 respondents who completed a baseline survey, among which 3,212 completed a follow-up 6 months later. The results from both cross-sectional and lagged analyses provided robust evidence to suggest that passive routine exposure to e-cigarette use content from more media outlets predicted increased likelihood of vaping among youth and young adults. High scanners were about twice as likely to vape as non-scanners (17% versus 9%). Mediation models using bootstrapping procedures found that breadth of scanning predicted higher descriptive norm perceptions which were associated with subsequent vaping; in addition, interpersonal communication mediated the relationship between breadth of scanning and changes in descriptive norm perceptions. These findings highlight the important roles of scanning, norm perceptions and interpersonal discussions in shaping cognition and behavior changes. The results also suggest an overall pro-e-cigarette public communication environment, which warrants further examination.

Toward an Aggregate, Implicit, and Dynamic Model of Norm Formation: Capturing Large-Scale Media Representations of Dynamic Descriptive Norms Through Automated and Crowdsourced Content Analysis

Media content can shape people's descriptive norm perceptions by presenting either population-level prevalence information or descriptions of individuals' behaviors. Supervised machine learning and crowdsourcing can be combined to answer new, theoretical questions about the ways in which normative perceptions form and evolve through repeated, incidental exposure to normative mentions emanating from the media environment. Applying these methods, this study describes tobacco and e-cigarette norm prevalence and trends over 37 months through an examination of a census of 135,764 long-form media texts, 12,262 popular YouTube videos, and 75,322,911 tweets. Long-form texts mentioned tobacco population norms (4-5%) proportionately less often than e-cigarette population norms (20%). Individual use norms were common across sources, particularly YouTube (tobacco long-form: 34%; Twitter: 33%; YouTube: 88%; e-cigarette long form: 17%; Twitter: 16%; YouTube: 96%). The capacity to capture aggregated prevalence and temporal dynamics of normative media content permits asking population-level media effects questions that would otherwise be infeasible to address.