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      How Do Partisans Consume News on Social Media? A Comparison of Self-Reports With Digital Trace Measures Among Twitter Users

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      Social Media + Society
      SAGE Publications

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          Abstract

          There are growing concerns that social media exacerbates the selective exposure of audience members to content that supports their political views. However, despite the hype, the existing literature does not fully address the extent to which social media users selectively consume like-minded news stories, in part due to different methodologies. In an attempt to move toward a common framework, this study examined the partisan selective exposure of a representative sample of Twitter users by combining survey data with digital trace data. Specifically, the study linked survey responses ( n = 558) from Twitter users with their media following and exposure to news via their friends. The study found that selectivity bias was present in all types of data, including self-reported media consumption (survey), media following (Twitter), and indirect exposure to media (Twitter). However, the study found some differences between self-reports and digital measures such that the overlaps in media diets between partisan groups were much larger based on the digital trace data than the self-reported data. In addition, the study observed an asymmetric pattern of selective exposure between conservatives and liberals in the digital trace data, but not in the self-reported data. The implications of these findings are discussed with reference to the contemporary news environment, hostile media effects, and normative assumptions of selective exposure.

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          Political science. Exposure to ideologically diverse news and opinion on Facebook.

          Exposure to news, opinion, and civic information increasingly occurs through social media. How do these online networks influence exposure to perspectives that cut across ideological lines? Using deidentified data, we examined how 10.1 million U.S. Facebook users interact with socially shared news. We directly measured ideological homophily in friend networks and examined the extent to which heterogeneous friends could potentially expose individuals to cross-cutting content. We then quantified the extent to which individuals encounter comparatively more or less diverse content while interacting via Facebook's algorithmically ranked News Feed and further studied users' choices to click through to ideologically discordant content. Compared with algorithmic ranking, individuals' choices played a stronger role in limiting exposure to cross-cutting content.
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            Social Desirability Bias in CATI, IVR, and Web Surveys: The Effects of Mode and Question Sensitivity

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              Exposure to opposing views on social media can increase political polarization

              Significance Social media sites are often blamed for exacerbating political polarization by creating “echo chambers” that prevent people from being exposed to information that contradicts their preexisting beliefs. We conducted a field experiment that offered a large group of Democrats and Republicans financial compensation to follow bots that retweeted messages by elected officials and opinion leaders with opposing political views. Republican participants expressed substantially more conservative views after following a liberal Twitter bot, whereas Democrats’ attitudes became slightly more liberal after following a conservative Twitter bot—although this effect was not statistically significant. Despite several limitations, this study has important implications for the emerging field of computational social science and ongoing efforts to reduce political polarization online.
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                Author and article information

                Contributors
                Journal
                Social Media + Society
                Social Media + Society
                SAGE Publications
                2056-3051
                2056-3051
                October 2020
                December 18 2020
                October 2020
                : 6
                : 4
                : 205630512098103
                Affiliations
                [1 ]University of Florida, USA
                Article
                10.1177/2056305120981039
                6b66aa0f-d24c-4d81-86d2-16d5c33c91d2
                © 2020

                https://creativecommons.org/licenses/by-nc/4.0/

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