Meta-Analysis of Anger and Persuasion: An Empirical Integration of Four Models

Despite the increasing use of anger in persuasive messaging, such as political ads and health campaigns, very little is known about when and how anger affects persuasion. Building on theoretical propositions derived from four theoretical models that have addressed the link between anger and persuasion, the current meta-analysis (k = 55, N = 6,805) finds a weak impact of anger on behavior (r = .15, p = .04) and nonsignificant effects on attitudes (r = −.03, p = .30) and intent (r = .06, p = .13). Yet a closer look reveals a more complicated reality, where positive effects are identified with the presence of strong arguments, relevant anger, and the inclusion of efficacy appeals. Further, the study identifies an interplay between emotional intensity and argument strength, such that argument strength plays an important role only at lower levels of anger. The study concludes by integrating the results and proposing three promising areas for future research into anger and persuasion

Trump, Twitter, and news media responsiveness: a media systems approach

How populists engage with media of various types, and are treated by those media, are questions of international interest. In the United States, Donald Trump stands out for both his populism-inflected campaign style and his success at attracting media attention. This article examines how interactions between candidate communications, social media, partisan media, and news media combined to shape attention to Trump, Clinton, Cruz, and Sanders during the 2015–2016 American presidential primary elections. We identify six major components of the American media system and measure candidates’ efforts to gain attention from them. Our results demonstrate that social media activity, in the form of retweets of candidate posts, provided a significant boost to news media coverage of Trump, but no comparable boost for other candidates. Furthermore, Trump tweeted more at times when he had recently garnered less of a relative advantage in news attention, suggesting he strategically used Twitter to trigger coverage.Accepted manuscrip

Voter Typology Cluster Analysis

Despite changes in the U.S. political landscape, many scholars still rely on the traditional left-right binary to understand voters. Such a reductive typology obfuscates voter differences, particularly regarding subgroups within political parties, as evident by 2016 U.S. Presidential election. This data comes from a survey distributed right before November 8, 2016 and includes which includes items about 17 enduring political values and worldviews. Studies using this dataset apply clustering techniques to typologize voters, both holistically and as part of specific groups (e.g., Independents). Results from this data emphasis the need to re-consider how best to communicate with different clusters of the American electorate

The temporal turn in communication research: time series analyses sing computational approaches

Some of the most pioneering work in our field is occurring where emerging computational approaches are meeting time series analytic techniques. Combining these methods is helping scholars improve our understanding of phenomena as varied as news and issue attention cycles, physiological responses to communication exposure, changes in mass opinion, and the dynamics between social media and legacy news media. In this article, we summarize the current state of computational communication science techniques to generate sequential data for use in time series analysis and suggest directions for further development. In particular, we consider the long-standing place of temporal dynamics for our field’s main theories; overview recent work combining computational science with time series analysis; present narrative accounts of two major research programs in this area; and review techniques of time series analysis, including major concerns for communication researchers working in the area.Published versio