When Visual Cues Activate Moral Foundations: Unintended Effects of Visual Portrayals of Vaping within Electronic Cigarette Video Advertisements

Advocating for Mask-Wearing Across the Aisle: Applying Moral Reframing in Health Communication

During the COVID-19 pandemic, the United States public polarized along political lines in their willingness to adopt various health-protective measures. To bridge these political divides, we tested moral reframing as a tool for advocating for wearing face masks when audiences vary in their moral priorities. We additionally address a gap in prior moral reframing research by comparing responses to a topic-relevant non-moral appeal. Across two studies, we examined effects on perceived message effectiveness, intentions to wear masks, support for a nationwide mask mandate, and willingness to share messages on social media. We find support for the efficacy of ideology-matched moral arguments and generally find support for the boomerang effect of ideology-mismatched moral arguments. However, these effects were restricted to relatively liberal audiences; politically conservative message recipients did not differentiate between message conditions. We discuss these asymmetric effects and their implications for theory in moral rhetoric.

Moralization of E-cigarette Use and Regulation: A Mixed-Method Computational Analysis of Opinion Polarization

E-cigarette use, or vaping, is undergoing a process of moralization in which issues about vaping evolve from being morally neutral to having discernible moral implications. Using Moral Foundations Theory, this study compared the moral narratives underlying polarized views about e-cigarette use and regulation. We integrated computational and human strategies by conducting the Chow test on the time series data and classification, topic modeling, and Chi-square tests on posts (N = 2,669) from 26 pro-vaping and 19 anti-vaping Facebook Pages. The observation period (August 1, 2019 to March 5, 2020) encompassed the outbreak of "e-cigarette or vaping product use associated lung injury" (EVALI), deaths and subsequent legislation. Results revealed that pro-vaping posts were more likely than anti-vaping posts to mention Fairness/cheating and Authority/subversion, involving a conspiracy belief in an "e-cigarettes vs. Big Tobacco" rivalry, while anti-vaping posts were more likely to mention Sanctity/degradation. There were no significant differences between pro-vaping and anti-vaping posts in the likelihood of mentioning Care/harm or Loyalty/betrayal. Nevertheless, according to the topic modeling results, the use of moral foundations varied between pro-vaping and anti-vaping narratives, with the meanings of Care/harm and Loyalty/betrayal dependent on the post author's group affiliation. Health interventions can tailor persuasive messages to different moral values and debunk misinformation about public health policies to mitigate the vaping epidemic. Theoretical implications are also discussed.

Twitter as research data Tools, costs, skill sets, and lessons learned

Scholars increasingly use Twitter data to study the life sciences and politics. However, Twitter data collection tools often pose challenges for scholars who are unfamiliar with their operation. Equally important, although many tools indicate that they offer representative samples of the full Twitter archive, little is known about whether the samples are indeed representative of the targeted population of tweets. This article evaluates such tools in terms of costs, training, and data quality as a means to introduce Twitter data as a research tool. Further, using an analysis of COVID-19 and moral foundations theory as an example, we compared the distributions of moral discussions from two commonly used tools for accessing Twitter data (Twitter's standard APIs and third-party access) to the ground truth, the Twitter full archive. Our results highlight the importance of assessing the comparability of data sources to improve confidence in findings based on Twitter data. We also review the major new features of Twitter's API version 2.