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      Partisan Selective Exposure in Times of Political and Technological Upheaval: A Social Media Field Experiment

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      Media and Communication
      Cogitatio

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          Abstract

          Contemporary democracies are increasingly shaped by a surge of populism, posing serious threats to the idea of liberal democracy. Particularly in the run-up to elections, knowledge of such threats is essential for citizens to cast an informed vote. Against this background, the present study examined the likelihood of media users to engage with political news providing critical perspectives on populist movements in a 24-hour social media field experiment during the 2017 federal election campaign in Germany (<em>N</em> = 210). Based on two selective exposure measures, findings suggest that exposure to critical news is contingent upon the conceptualization of populist partisanship as a political orientation of either high commitment (i.e., voting intention) or high affinity (i.e., sympathy for a party). While high commitment triggered a rather classic confirmation bias, especially regarding click decisions, high affinity caused selection patterns to be more strongly guided by informational utility, particularly during newsfeed browsing, with counter-attitudinal information receiving more attention. When public sentiment cues were present, however, attitudinal patterns disappeared. These findings imply that partisan news use in times of political upheaval is best gauged by taking a closer look at the particular type of partisanship that guides selective exposure, as both types of partisanship caused contrary exposure patterns, and that today’s news environments potentially override attitudinal influences by providing additional social monitoring cues.

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          Contributors
          (View ORCID Profile)
          Journal
          Media and Communication
          MaC
          Cogitatio
          2183-2439
          July 30 2019
          July 30 2019
          : 7
          : 3
          : 42-53
          Article
          10.17645/mac.v7i3.2183
          c99c028c-b49c-4155-b840-7143d71010f4
          © 2019

          http://creativecommons.org/licenses/by/4.0

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