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How does a political text's complexity – as a crucial characteristic of a text – uniquely affect readers' political information processing? Based on a metacognitive response framework and extant discussions based on the Heuristic-Systematic Model vs. the Elaboration Likelihood Model, we tested whether a reader's initial expectation of a message characteristic and stable individual differences intersect with the metacognitive processing fluency experience. Do people recall more information from the news when presented with articles that are more – or less – complex than they expected, or do they recall more information when expectations of article complexity are met? And for whom is this more likely to occur? Our results provide strong support for the notion that objective complexity is indeed the main driver of metacognitive perceptual processing fluency and information recall, while also rending partial support for the role of (in)congruency in this process. The results also showed that individual differences in processing capabilities play a significant role in shaping information recall. We conclude by discussing the theoretical and practical implications. [ABSTRACT FROM AUTHOR]

Citizen's exposure to disagreement – whether intentional or incidental – is a central concept in communication research, yet the precise degree to which citizens are exposed to opposing views online and the antecedents to this phenomenon continue to be debated. Despite the theoretical importance of this question, empirical assessments of cross-cutting exposure, especially those involving online settings, are largely based on individuals' perception of their own behavior. Therefore, we know little regarding response bias in self-reports of cross-cutting exposure online. Combining digital trace data with a panel survey, we observe overreporting of self-reported online cross-cutting exposure. We then demonstrate that self-reported exposure to disagreement is retrospectively conditioned by the perception of the opinion climate in a given context. Finally, using Monte Carlo simulations, we examine the consequences of relying on (potentially imperfect) self-reported measures. [ABSTRACT FROM AUTHOR]

Drawing from the literature on the psychological harm of hate speech and the source magnification framework, we investigated the effects of hate messages from multiple sources on target group members in the context of Twitter. An online experiment conducted with Black Americans living in the United States provided evidence for source magnification of anti-Black messages. In the experiment, participants were exposed to one of three sets of tweets: hate tweets from multiple sources, hate tweets (of identical content) from a single source, and non-hate (control) tweets. The results showed that hate tweets from multiple sources, when compared to the identical hate messages from a single source and to the non-hate tweets, led to greater emotional distress, which, in turn, resulted in greater likelihood of attributing ambiguous social situations to racial prejudice. The findings provide initial evidence for source magnification of hate messages in the context of communication based on social media where simultaneous exposure to asynchronously posted hate messages from multiple sources is likely to happen. The findings also suggest that multiple-source hate messages, when compared to single-source hate messages, are more likely to indirectly result in potential relational harm. [ABSTRACT FROM AUTHOR]

Political communication has become one of the central arenas of innovation in the application of automated analysis approaches to ever-growing quantities of digitized texts. However, although researchers routinely and conveniently resort to certain forms of human coding to validate the results derived from automated procedures, in practice the actual "quality assurance" of such a "gold standard" often goes unchecked. Contemporary practices of validation via manual annotations are far from being acknowledged as best practices in the literature, and the reporting and interpretation of validation procedures differ greatly. We systematically assess the connection between the quality of human judgment in manual annotations and the relative performance evaluations of automated procedures against true standards by relying on large-scale Monte Carlo simulations. The results from the simulations confirm that there is a substantially greater risk of a researcher reaching an incorrect conclusion regarding the performance of automated procedures when the quality of manual annotations used for validation is not properly ensured. Our contribution should therefore be regarded as a call for the systematic application of high-quality manual validation materials in any political communication study, drawing on automated text analysis procedures. [ABSTRACT FROM AUTHOR]

As citizens inadvertently "encounter" news and political information through digital media and social networking sites, they might perceive themselves to be well informed about politics without actively seeking political information, which has been labeled as the "News Finds Me" (NFM) perception. We attempt to explicate and further advance the conceptualization and corresponding measurement of "News Finds Me" perception. Using a nationally representative survey conducted in Austria, our analysis shows that social media news use may contribute to cynicism towards politics, and that such a relationship is critically mediated through NFM perceptions, but to a differential degree depending on specific sub-dimensions of NFM perceptions. [ABSTRACT FROM AUTHOR]

Communication of political information is vital for a well-functioning democratic system, and texts are one of the main mediums of politics. Most studies dealing with political text consider how such texts communicate content, rather than the structural characteristics of texts themselves. The current study focuses on complexity as one of the focal structural characteristics of political text. Previous research showed that different types of textual complexity affect learning processes. Such prior studies are, however, limited both conceptually and empirically. This study addresses these gaps by employing a large-scale experimental design (N= 822), investigating how different dimensions of textual complexity affect factual and structural political knowledge, and whether such relationships are mediated through perceived complexity. Results indicate that different levels (high vs. low) and different dimensions of textual complexity (semantic vs. syntactic) influence reader's perception of text, as well as their factual and structural knowledge. Only semantic complexity has an effect on one's perceived complexity, which in turn negatively affects factual and structural knowledge. Syntactic complexity directly lowered one's factual knowledge acquisition, while there was no direct effect of syntactic complexity on structural knowledge. The results suggest that text complexity indeed plays an important role in political information acquisition, and our findings also highlight the importance of perception in mediating the structural effects of the text. [ABSTRACT FROM AUTHOR]

Most existing evidence suggests that accuracy in perceptions of political preferences within communication networks is reasonably high, but closer examination suggests this may not be true. Perceptions may be accurate, inaccurate, or respondents may offer no perceptions to evaluate for accuracy due to uncertainty. We re-analyze data from several published studies to evaluate whether different treatments of "don't know" (DK) and similar ambiguous responses matters when evaluating accuracy levels. It does, sometimes leading to dramatically altered conclusions regarding accuracy. We also reanalyze recent data to evaluate the individual and group-level factors (with an emphasis on communication and homophily) that lead to such DK responses, and consider the implications of the treatment of DK responses for inferences about the role of communication in producing truly accurate perceptions. [ABSTRACT FROM AUTHOR]

The substantial increase in nested data such as egocentric network data pose unique challenges to researchers, including how to adequately model the mechanisms and processes in which hypothesized effects operates within the complex interdependencies among social actors. While there is a growing interest in identifying the causal mechanisms and their contingencies using such nested structured data, the direct application of principles of (moderated) mediation analysis within traditional multilevel modeling framework is ambiguous and controversial. In this article, I provide a tutorial illustrating an approach to assessing mediation hypotheses based on path-analytic framework popularized by Hayes (2013). Focusing on estimation and inference of indirect effects with regard to nested data structure, I provide examples of the analysis within the context of egocentric network data, and illustrate implementation usingMplussoftware. [ABSTRACT FROM AUTHOR]

Recent advances in partisan selective exposure research have provided compelling evidence for the distinction between selective approach and selective avoidance. Yet the questions of whether, how, and to what extent discrete emotions systematically shape either of these patterns has not been sufficiently addressed. This study explores the differential roles of fear, anger, and enthusiasm in selective approach to and selective avoidance of partisan news programs, focusing on partisan differences in regard to a person’s general approach versus avoidance tendencies to external stimuli as a possible moderating mechanism. A secondary analysis of the 2012 American National Election Studies data suggested that fear and anger both significantly increased proattitudinal news exposure, whereas only anger decreased counterattitudinal news exposure. In addition, Republicans exhibit these patterns to a greater extent than Democrats. Furthermore, enthusiasm significantly predicted exposure to proattitudinal news for both Republicans and Democrats, whereas Democrats were significantly more likely than Republicans to increase their counterattitudinal news exposure as a function of enthusiasm. Theoretical and methodological implications are discussed. [ABSTRACT FROM PUBLISHER]

The present study focuses on how different features of discussion networks and the contexts in which they reside are related to political ambivalence, participation, and knowledge. Using full sociometric network data derived from 25 different student activity organizations, we reveal complex implications of various facets of discussion networks and their contextual settings. Ambivalence is amplified by being highly central within a discussion network and a political minority within the group. Diversity of one’s egocentric discussion network increases participation, whereas closeness centrality was negatively correlated with participation. High levels of betweenness centrality were most positively associated with knowledge in groups that were relatively political in their orientation compared to those that were apolitical. The results support the argument that communication networks and the contextual settings in which they are situated should be more fully considered in the study of political communication effects. [ABSTRACT FROM PUBLISHER]

This research examined whether people make correspondent inferences more readily for others' expression of unhappiness than happiness. Study 1 measured people's lay theory of the range of emotional expressions that happy and unhappy people enact. We found that people perceive that a person who is unhappy has a wider range of emotional expression than a person who is happy. Participants in Study 2 inferred that another's description of his or her own unhappy life was more revealing of that person's true attitudes and feelings than another's description of his or her own happy life. Following up on these findings, Study 3 showed that people's inferences regarding another's level of happiness were more affected by situational information when the person described a happy life compared to an unhappy life. Together, these three studies point to a negativity effect in person inference that also applies to the expression and communication of happiness and unhappiness. [ABSTRACT FROM AUTHOR]