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My Voters Should See This! What News Items Are Shared by Politicians on Facebook?

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  • Heidenreich, Tobias
  • Eberl, Jakob-Moritz
  • Tolochko, Petro
  • Lind, Fabienne
  • Boomgaarden, Hajo G.

Abstract

Political actors play an increasingly important role in the dissemination of political information on social media. However, relatively little is known about the mechanisms why specific news items are shared with the support base instead of others. For a timespan between December 2017 and the end of 2018, we combine the analysis of Facebook content from 1,022 politicians associated with 20 political parties from Germany, Spain, and the UK, with an automated content analysis of media coverage from 22 major online news outlets, and survey data in a multilevel binomial regression approach. By comparing news items that have been shared by one or several political parties with news items that have not been shared by any of them, we overcome the selection biases of previous studies in the field of news dissemination. Findings show that a news item's likelihood to be shared by a politician increases (1) if that politician's party is mentioned in the news item, (2) the more salient their party's owned issues are in the news item, and (3) the more party supporters tend to read the news outlet in which the news item is published. We contextualize these findings in light of political actors’ multi-faceted motivations for news sharing on social media and discuss how this process potentially reinforces an information bias that may contribute to the polarization and fragmentation of audiences.

Suggested Citation

  • Heidenreich, Tobias & Eberl, Jakob-Moritz & Tolochko, Petro & Lind, Fabienne & Boomgaarden, Hajo G., 2022. "My Voters Should See This! What News Items Are Shared by Politicians on Facebook?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, issue OnlineFir, pages 1-1.
    • Handle: RePEc:zbw:espost:259790
    DOI: 10.1177/19401612221104740
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    1. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    2. Han, Kyung Joon, 2020. "Beclouding Party Position as an Electoral Strategy: Voter Polarization, Issue Priority and Position Blurring," British Journal of Political Science, Cambridge University Press, vol. 50(2), pages 653-675, April.
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