Communication Theory and Methodology 2019 Abstracts
Open Call Competition
Social Networking for Interpersonal Life: Facebook Use and the Forms of Competence • Brandon Bouchillon • This study considered associations between Facebook use, computer-mediated communication competence, and interpersonal competence over time. Results indicate CMC competence contributed to interpersonal competence, and interpersonal competence related to CMC competence. Facebook use related to CMC competence as well, but not to interpersonal competence, at least not directly. Facebook use did contribute to interpersonal competence indirectly, through increasing CMC competence over time. Social networking can facilitate real-world interactional capability by first adding to its online counterpart.
Beyond the What to the Who: Advancing Archetype Theory to Improve Branded Communication • Katie Wiliams; Karissa Skerda; Jared Brickman, Carnegie Dartlet • Archetypal theories have been long studied to better define human personality. Concurrently, brands looking to separate from their peers have attempted communication strategies that take advantage of emotional storytelling through the voice of a personified protagonist. However, bringing consensus around this vision is difficult in organizations with varied stakeholders. This paper extends archetypal theory in communication specific to organizations while also proposing a method for consensus-driven research to uncover the “who” of higher education institutions.
Mediation analysis in communication science: Examining the study of indirect effects in communication journals between 1996-2017 • Michael Chan; Panfeng Hu; Macau K. F. Mak, The Chinese University of Hong Kong • Mediation analysis is one of the most popular techniques in communication research. However, a systematic synthesis of the trends, tools and statistical methods used to conduct mediation analysis in the field is lacking. This content analysis examined 595 journal articles published in 14 communication journals from 1996 to 2017. Results showed an exponential increase in the number of studies employing mediation analyses in the past two decades using both regression and SEM-based approaches. The proportion of studies using regression-based approaches in particular has grown rapidly in the second decade, due to the popularity of user-friendly macros that simplifies necessary procedures to test indirect effects. Bootstrapping has become the most popular method for testing indirect effects while uses of the Baron & Kenny and Sobel approaches have declined over time. Many studies though claim mediating mechanisms without formally testing the indirect effects, and others report the indirect effects, but not how they were tested. Findings and implications for practice are discussed.
Climate frame dynamics over time: Computer-assisted detection and identification of news frames • Yingying Chen, College of Communication Arts and Sciences, Michigan State University; Kjerstin Thorson; John Andrew Lavaccare • We analyze the evolution of news frames about climate change over the course of four years, between 2012-2015. We use a structural topic model combined with human coding to detect frames in news coverage of twelve climate-related events between 2012-2015. Findings suggest that frame usage strongly varies by event, and that some events seem to constrain the diversity of news media framings. Journalists also consistently rely on a small set of “default” frames about climate.
That’s not news: Audience perceptions of ‘news-ness’ and why it matters • Stephanie Edgerly; Emily Vraga • How do people identify news on social media sites? This study uses an experimental design to isolate two features of a headline shared on Twitter to determine the impact on audience ratings of ‘news-ness.’ We find that headline story type (breaking, exclusive, opinion, fact check) and source (AP, MSNBC, Fox News) separately impact news-ness, with partisanship conditioning the influence of source on news-ness. News-ness then mediates these effects on outcomes of tweet credibility and verification.
What makes gun violence a prominent issue? A computational analysis of compelling arguments and partisanship • Lei Guo, Boston University; Kate Mays; Yiyan Zhang, Boston University; Margrit Betke; Derry Wijaya • Drawing upon theories of compelling arguments and selective exposure, this study examines the impact of mainstream and partisan media on U.S. public opinion regarding a highly polarized issue: gun violence. Results demonstrate that episodic framing of gun violence in the mainstream media increases the issue prominence among conservatives than liberals, thus to some extent narrowing the opinion polarization. Exposure to conservative media, however, makes people believe gun violence is a less important issue.
Priming Postpartum Prejudice: Comparing Media Effects and Embodied Risk to Accessibility of Mental Illness Concepts • Lynette Holman, Appalachian State University; Robert McKeever, University of South Carolina • A between-subjects experimental replication (N = 581) was conducted to ascertain whether a media exemplar could prime a stereotype of mental illness among women who are pregnant or of child-bearing age and how that media effect compared to the effect of one’s health status. The findings suggest that the provocation of the exemplars alone was not significant predictor of participant perceptions of risk. Pregnancy prominently predicted mental illness stereotypes, increased risk perceptions, and treatment avoidance.
Foundations for the development of communication that works with, not against, stakeholders’ existing viewpoints • Sadie Hundemer, University of Florida; Martha Monroe, University of Florida • The scientific and social complexity of natural resources issues can yield perspectives that vary substantially among stakeholder groups. This diversity can make it difficult to structure communication that promotes outreach objectives and cross-group collaboration while also attending to existing viewpoints. This study uses cultural domain analysis to examine stakeholders’ mental models of regional water challenges and explore ways natural resources communicators can use this information to bridge cognitive divides.
Mapping the Corporate Social Responsibility Research in Communication: A Network and Bibliometric Analysis • Grace Ji, Virginia Commonwealth University; Weiting Tao, University of Miami; Hyejoon Rim • This study evaluates how scholarly research on corporate social responsibility (CSR) in communication has developed over the last four decades and discovers the pattern of knowledge diffusion during this process. Comprehensive bibliometric analyses were conducted with 290 peer-reviewed articles published between 1980 to 2018 by 490 authors in 61 communication journals. Taking a network perspective, invisible colleges of CSR research were unveiled via co-authorship and co-citation analyses. The study identifies the studies and publication sources that have the most significant influence over the construction of CSR scholarship in communication and uncovered the social networks of scholarly collaboration. Results empirically demonstrate the area of CSR research in communication is notably multidisciplinary, which is investigated by scholars from public relations, advertising, organizational communication, and environmental communication. In addition, results also show the joint impact from management and marketing literature to CSR scholarship and their transformation into the communication field via public relations and advertising research. Future paths of CSR research in communication are suggested.
Inferential statistical analysis with Inaccurate self-reports Comparing correlational outcomes with self reported and logged mobile data • Mo Jones-Jang; Yu-Jin Heo, University of South Carolina; Robert McKeever, University of South Carolina; Leigh Moscowitz; David Moscowitz, School of Journalism and Mass • Research on the social and psychological effects of mobile phone use primarily employs self-report measures. However, recent findings suggest that such data contain a significant amount of measurement errors. The key question of this study is not only to examine discrepancies between survey and logged data, but also to compare correlational outcomes resulting from two different measures. Two hundred ninety seven college students participated in this study by providing both self-reported and digital trace data of daily minutes of screen time and number of phone screen unlocks over seven days. We specifically examined correlations between smartphone use and four social variables, including bridging, bonding, well-being, and problematic use of smartphone. The results indicate that the effect sizes of correlations using self-reported data are in fact smaller compared to inferential statistical results with logged data. Theoretical and methodological implications are discussed.
Classifying Twitter Bots • Michael Kearney, School of Journalism | Informatics Institute | University of Missouri; Lingshu Hu, University of Missouri; Iuliia Alieva, University of Missouri – Columbia • The current study sought to leverage public Twitter lists in order to develop a machine learning classifier of “bot” accounts. In addition to developing a Twitter bot detecting-classifier, we have also exported this classifier in two ways. First, we have exported the classifier as an interactive Shiny web application. Second, we have exported the classifier as an R package, tweetbotornot. As a programatic tool, it is possible to leverage the classifier to get probability estimates of up to 90,000 Twitter accounts every fifteen minutes. Through our work to develop and share list-leveraging Twitter bot classifier tools, we threfore offer three major contributions. First, we provide a novel and flexible approach to the classification of Twitter accounts. While accounts were initially labelled using well-known “bot” accounts and lists published in previous research, additional labelling was achieved via a new snowballing method wherein additional accounts were identified if they appeared on similar Twitter lists. Second, we provide a transparent, user friendly (Shiny web application), and scalable tools (R packages) for classifying Twitter accounts. These tools can be used by members of the public and academics alike. Finally, we provide a template for a flexible and dynamic approach to the construction of Twitter classifiers. Twitter lists can similarly be leveraged for other types of accounts, allowing researchers to further maximize information gleaned from the large trove of Twitter data.
Culling on Social Media: Antecedents and Consequences of Unfriending and Unsubscribing • Dam Hee Kim; Kate Kenski; Mo Jones-Jang • This paper investigates whether selective avoidance actions on social media such as unfriending and unsubscribing in the context of elections are determined by motivated reasoning styles. Analyses of a two-wave national survey collected before and after the 2018 midterm election revealed that individuals with a high need for cognition and a high need to evaluate were most likely to selectively avoid over time, which then positively predicted political expression on social media.
Framing Effects of Numerical Information in Communicating Risk • ByungGu Lee; Jiawei Liu, University of Wisconsin-Madison; Hyesun Choung; Douglas McLeod • In messages that present information about risk, the same piece of information can be presented in alternative ways. This article investigates the interplay between risk statements (i.e., positive versus negative portrayal of risks) and number formats (i.e., raw frequency versus percentage) in influencing readers’ comprehension of numerical information and their subsequent emotional and cognitive evaluations. Experimental findings across two issue contexts (impaired driving and endangered species) showed that statistics in the form of percentages reduced the effects of positive versus negative risk statements and produced more accurate and reliable comprehension of risks as compared to statistics with raw frequency formats. Moreover, the comprehension of risk statistics determined the level of emotions readers experienced, which in turn affected their risk perceptions. Implications are discussed.
Highlights of Two U.S. Presidential Debates: Identifying Candidate Insults that Go Viral • Josephine Lukito, UW Madison; Prathusha Sarma, UW Madison; Jordan Foley, UW Madison; Jon Pevehouse, UW Madison; Aman Abhishek; Dhavan Shah, UW Madison; Erik Bucy, Texas Tech University; Chris Wells, Boston University • This study analyzes social media discourse and debate rhetoric during two U.S. Presidential debates: the first 2012 debate between Barack Obama and Mitt Romney, and the first 2016 debate between Hillary Clinton and Donald Trump. Using a combined strategy involving time series analysis to identify potential viral moments in social media and natural language processing to determine whether words shifted in use in the pre-viral and post-viral moment, we find and examine several viral moments in both debates. Notably, the majority of 2016 debate viral moments were insults or remarks about a scandal, rather than gaffs or mistakes. Overall, the results of both 2012 and 2016 suggest that candidates can induce viral moments on social media to temporarily increase attention towards themselves.
Processing News on Social Media. The Political Incidental News Exposure Model (PINE) • Joerg Matthes, U of Vienna; Andreas Nanz, U of Vienna; Raffael Heiss, Management Center Innsbruck; Marlis Stubenvoll • This paper outlines the Political Incidental News Exposure Model (PINE). The PINE model understands incidental news exposure (IE) as a dynamic process by distinguishing two levels of IE: the passive scanning of incidentally encountered political information (first level) and the intentional processing of incidentally encountered content (second level). The PINE model further differentiates intention-based and topic-based IE, and it conceptualizes IE with respect to political and non-political content. Theoretical and methodological implications are discussed.
Agenda Setting by News and by the Audience in a News Portal Experiment • Martina Santia; Raymond Pingree, Louisiana State University; Kirill Bryanov, Louisiana State University; Brian Watson • A 12-day experiment embedded in a purpose-built online news portal tested effects of the news agenda and the “user agenda.” Participants were randomly assigned to encounter more or fewer real, timely news stories on particular topics (the news agenda) and altered rankings of stories in a recommended or trending sidebar (the user agenda). News agenda setting effects were found only on education. A user agenda emphasizing racism increased perceived importance of immigration, particularly among Republicans.
Why Defining Automation in Journalism is not Automatic • Jia Yao Lim, Nanyang Technological University Singapore; Ruoming Zheng, Nanyang Technological University Singapore; Edson Tandoc, Nanyang Technological University Singapore; Andrew Prahl, Nanyang Technological University Singapore; Shangyuan Wu, Nanyang Technological University Singapore • This paper examined the ways automation has been defined in manufacturing, education, healthcare, and journalism, arguing that the journalism industry can learn from the experience of other industries when it comes to understanding the impact of automation. In the context of journalism, most definitions include references to an autonomous system that entails the replacement of humans, consistent with fears that algorithms might displace human journalists when it comes to writing stories. However, many of these definitions have focused on automated writing, when journalism is more than just writing articles.
Personality factors differentiating selective exposure, selective avoidance and the belief in the importance of silencing others: Further evidence for discriminant validity • Yariv Tsfati, University of Haifa • Recent research proposed self-report measures tapping three different strategies used by people to place themselves within an ideologically homogeneous information environment: selective exposure, selective avoidance and the belief in the importance of silencing others (BISO). However, demonstrating that people are able to answer survey questions about these strategies falls short of establishing that people are able to distinguish between them. Using online survey data collected in Israel (n = 749), the present investigation explores the discriminant validity of these constructs. Confirmatory factor models and model comparisons support their empirical differentiation. In addition, it is argued that the constructs are empirically different given the fact that they correlate differently with personality factors. BISO is more strongly and positively associated with authoritarianism. Selective avoidance is more strongly negatively associated with openness to experience. Selective exposure was positively associated with empathy, with which selective avoidance was negatively associated. Further differences in the correlates of these constructs are discussed.
Theorizing News Literacy: A Proposed Framework for Unifying a Fractured Field • Emily Vraga; Melissa Tully; Adam Maksl, Indiana University Southeast; Stephanie Craft, University of Illinois at Urbana Champaign; Seth Ashley, Boise State University • News literacy research has received increased attention as we consider the role of news in a chaotic public sphere. However, existing research lacks a consistent definition of news literacy or guiding theoretical framework. Therefore, building from the Theory of Planned Behavior, we propose a model for exploring critical news consumption in which we add news literacy – defined as knowledge and skills in five domains – to the model. We conclude by proposing a continued research agenda.
News About Victims’ Delayed Sexual Harassment Accusations and Effects on Victim Blaming: A Mediation Model • Christian von Sikorski; Melanie Saumer • We lack research on how news about delayed sexual harassment accusations affect victim blaming. Drawing from construal level theory and attribution theory, we experimentally tested how participants react to news about a victim’s delayed accusations (harassment occurred years ago), non-delayed accusations (harassment occurred days ago), or accusations with no time cue. Findings showed that delayed accusations resulted in the attribution of negative motives toward the victim. Negative motives, in turn, increased victim blaming.
Testing the Role of Positive News in the Empathy-Helping Relationship • Masahiro Yamamoto, University at Albany; Chun Yang, Louisiana State University • This study integrates two types of news consumption, positive and crime news, into the Empathy-Altruism Hypothesis. Data from an online survey of Japanese citizens reveal that positive news use at Time 1 has a positive effect on helping at Time 2. Data also indicate that positive news use does not have a significant effect on empathic concern and personal distress. Rather, empathic concern at Time 1 has a positive effect on both positive news use and crime news use at Time 2. Implications are discussed for the role of positive news in promoting prosocial, helping behaviors.
Exploring Genetic Contributions to Motives for News Use: A Study of Identical and Fraternal Twins • Chance York, Kent State University; Paul Haridakis, Kent State University • Prior research conducted within the Uses and Gratifications theoretical framework has considered the contribution of numerous social and psychological (e.g., personality) differences to media use and effects. In this study, we explore whether an additional fundamental source of individual differences—genes—also may explain motives to use media. Utilizing original data collected on identical and fraternal twins, we find differences in underlying genetic traits explained 35% of the variance in news consumption for surveillance purposes.
Understanding privacy concern in using social media: The extension of Marshall McLuhan • Bu Zhong, Pennsylvania State University; Tao Sun, University of Vermont; Yakun Huang, Jinan University; Yu Zhou, South China University of Technology • The advances of information and communication technologies (ICT) have reinvigorated a long tradition of searching for the links between the dominant communication technology of an age and the key features of society. Guided by Marshall McLuhan’s media ecology theory highlighting the ICT role in defining historical stages, this study analyzes the Internet privacy concern (IPC) among social media users (N = 1,340) from the United States and China. It has found a generation gap concerning IPC between people growing up with social media as a dominate media platform and those who did not. A significant correlation between the power use of social media and IPC was also identified, which was further moderated by respondents’ cultural background. The findings expand media ecology theory by providing empirical support to it in terms of the impact of dominant ICT on societal perceptions, thus contributing to the understanding of IPC in a cross-cultural setting.
Student Paper Competition
Realtime Distributed Cognition: A Conceptual Framework • Wes Hartley, Regent University • While the broad framework of distributed cognition has proven to be a versatile theoretical lens through which to view team problem-solving structures, this broad use of distributed cognition theory has, perhaps, allowed the theory to drift away from some of the root ideas that grounded the original concept. This paper seeks to advance the distributed cognition framework by narrowing the parameters of the theory and providing a new conceptual framework with clear boundaries for identifying distributed cognition units. Two real-world scenarios will be evaluated using this new conceptual framework in order to demonstrate its functionality.
An Approach for Measuring Partisan Segregation in Political Media Consumption • Jacob Long • Despite the amount of research on the topic, there are few direct measurements of partisan segregation in media use. Of those that do exist, none are easily transferable to multi-party systems. Using a network analytic approach, I use data from a nationally representative survey of the United States to describe the amount of partisan segregation in media consumption and discuss further applications for these measures.
Improving the Generalizability of Inferences in Quantitative Communication Research • Jacob Long • This paper discusses the quality of quantitative communication research in light of the so-called “replicability crisis” that has affected neighboring disciplines. I discuss some of the problems these fields have faced and suggest implementing some of their solutions in communication research. I then argue for greater consideration of generalizability and propose a theoretical framework for assessing the quality of studies suited to a variable field like communication.
A Territorial Dispute or An Agenda Battle? A Cross-National Examination of the Network and Intermedia Agenda-Setting Effects between Newspapers and Twitter on Diaoyu Islands Dispute • Yan Su, The Edward R. Murrow College of Communication, Washington State University; Jun Hu, Department of Earth Sciences, University of Southern California • Within the theoretical frameworks of Network Agenda-Setting (NAS) model and intermedia agenda-setting, this study analyzed the media agendas in China, Japan, and the U.S. on the Diaoyu Islands dispute – a geopolitical issue involved multiple subjects, in terms of the theme, directionality, and valence of present relationships. Further, the study analyzed the discussions on Twitter and probed the intermedia power flow between Twitter and the selected media. The findings showed that the Chinese media’s depictions were more biased. Although reciprocities emerged, Twitter exhibited an overall stronger power in predicting traditional media’s agenda. Moreover, Chinese and the U.S. media had stronger transnational intermedia effects, whereas Japanese media is less likely to exert influence across the national boundaries.
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