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Discontentment trumps Euphoria: Interacting with European Politicians’ migration-related messages on social media

Author

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

Abstract

We investigate user engagement with politicians' migration discourses on social media. In particular, we study the effects of message framing and support base attitudes on interactions on Facebook and Twitter in five European countries. Enriching automated analysis of social media content with survey data in a multilevel negative binomial regression approach, findings show that migration-related messages tend to elicit more interactions than other kinds of messages. Furthermore, the presence of a security frame in a migration-related message positively relates to user engagement. However, additional analyses suggest that the relevance of these frames differ between different political parties. In fact, a message gets an even higher number of interactions, when the dimension of the migration issue included in those framed messages is perceived more negatively by a party's support base. The findings have important implications for communication strategies of political actors and the state of migration discourses on social media.

Suggested Citation

  • Heidenreich, Tobias & Eberl, Jakob-Moritz & Lind, Fabienne & Boomgaarden, Hajo G., 2022. "Discontentment trumps Euphoria: Interacting with European Politicians’ migration-related messages on social media," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, issue OnlineFir, pages 1-1.
    • Handle: RePEc:zbw:espost:250896
    DOI: 10.1177/14614448221074648
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    References listed on IDEAS

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    1. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    2. 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).
    3. de Vries, Erik & Schoonvelde, Martijn & Schumacher, Gijs, 2018. "No Longer Lost in Translation: Evidence that Google Translate Works for Comparative Bag-of-Words Text Applications," Political Analysis, Cambridge University Press, vol. 26(4), pages 417-430, October.
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