Despite the pervasiveness of digital disinformation in society, little is known about the individual characteristics that make some users more susceptible to erroneous information uptake than others, effectively dividing the media audience into prone and resistant groups. This study identifies and tests procedural news knowledge as a consequential civic resource with the capacity to inoculate audiences from disinformation and close this “resistance gap.” Engaging the persuasion knowledge model, the study utilizes data from two national surveys to demonstrate that possessing working knowledge of how the news media operate aids in the identification and effects of fabricated news and native advertising.

Populism, as many have observed, is a communication phenomenon as much as a coherent ideology whose mass appeal stems from the fiery articulation of core positions, notably hostility toward “others,” bias against elites in favor of “the people,” and the transgressive delivery of those messages. Yet much of what we know about populist communication is based on analysis of candidate pronouncements, the verbal message conveyed at political events and over social media, rather than transgressive performances—the visual and tonal markers of outrage—that give populism its distinctive flair. The present study addresses this gap in the literature by using detailed verbal, tonal, and nonverbal coding of the first US presidential debate of 2016 between Donald Trump and Hillary Clinton to show how Trump’s transgressive style—his violation of normative boundaries, particularly those related to protocol and politeness, and open displays of frustration and anger—can be operationalized from a communication standpoint and used in statistical modeling to predict the volume of Twitter response to both candidates during the debate. Our findings support the view that Trump’s norm-violating transgressive style, a type of political performance, resonated with viewers significantly more than Clinton’s more controlled approach and garnered Trump substantial second-screen attention.

The news media’s increased reliance on visual communication to illustrate complex processes and promote learning stresses the importance of investigating how visual content impacts the understanding of scientific issues. In this paper, we investigate how members of the public interpret and make sense of differentially framed images of hydraulic fracturing (fracking) depicting environmental risk, economic benefit, or issue protest. For the analysis, a repeated measures online experiment was conducted with 250 participants to evaluate 40 photographs of fracking operations and consequences. Quantitative coding and thematic analysis of open-ended responses to the images reveal that standing attitudes, operationalized as support, opposition, or indecision about fracking, segments viewers into distinct groups and shapes interpretations of environmental risk and economic benefit. Issue opponents are more likely to indicate concern for the environment regardless of frame shown, whereas undecideds and supporters cite the impact on human health more frequently, largely in relation to job site safety. Supporters also see the least ambiguity, and most economic gains, in images about the controversial production practice.

As with the first televised debates in 1960, the 2012 US presidential debates accentuated the importance of nonverbal behavior in political competition, with President Obama receiving widespread criticism for his disengaged and arguably inappropriate communication style in the first debate. To investigate the perceptual impact of such nonverbal expectancy violations, this study first employs an experimental design to examine the consequence of inappropriate leader displays, operationalized as nonverbal behaviors that are incongruent with the rhetorical setting. Theoretical explanations about the evaluative consequences of inappropriate leader displays are described in light of expectancy violations theory. Results of a repeated measures eye-tracking experiment find support for the prediction that inappropriate facial expressions increase visual attention on the source of violation, prompt critical scrutiny, and elicit negative evaluations. These findings are further explored with qualitative analysis of focus group responses to key moments from the first and third presidential debates. The discussion considers the broader implications of nonverbal communication in politics and how expressive leader displays serve as meaningful cues for citizens when making sense of televised political encounters.

Drawing on propositions from the HAII-TIME (Human–artificial intelligence [AI] Interaction and the Theory of Interactive Media Effects) and Persuasion Knowledge Model, this study examines how knowledge of automated journalism (AJ) moderates the evaluation of algorithmically generated news. Experiment 1 demonstrates the utility of process-related knowledge in user evaluations of agency: individuals with little knowledge of AJ prefer attributions of human authorship over news stories attributed to algorithms, whereas individuals with high AJ knowledge have an equal or stronger preference for news that is described as algorithmically generated. Experiment 2 conditions these effects to show how prior characterizations of AJ—whether more machine- or human-like—shape evaluations of algorithmically generated news contingent on user age and knowledge level. Effects are found for differing age groups at lower levels of AJ knowledge, where machine-like characterizations enhance evaluations of algorithmically generated news for younger users but ascribing human-like traits enhances evaluations of automated news for older users.

Recent decades have witnessed an increased growth in data generated by information, communication, and technological systems, giving birth to the ‘Big Data’ paradigm. Despite the profusion of raw data being captured by social media platforms, Big Data require specialized skills to parse and analyze — and even with the requisite skills, social media data are not readily available to download. Thus, the Big Data paradigm has not produced a coincidental explosion of research opportunities for the typical scholar. The promising world of unprecedented precision and predictive accuracy that Big Data conjure remains out of reach for most communication and technology researchers, a problem that traditional platforms, namely mass media, did not present. In this paper, we evaluate the system architecture that supports the storage and retrieval of big social data, distinguishing between overt and covert data types, and how both the cost and control of social media data limit opportunities for research. Ultimately, we illuminate a curious but growing ‘scholarly divide’ between researchers with the technical know-how, funding, or institutional connections to extract big social data and the mass of researchers who merely hear big social data invoked as the latest, exciting trend in unattainable scholarship.