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Political content and news exposure of German TikTok users during the 2025 Federal German Elections

[working paper]

Wedel, Lion
Pfiffner, Nico
Ohme, Jakob
Friemel, Thomas N.
Gaisbauer, Felix
Lehner, Marco
Schöffel, Robert
Koenigsdorff, Simon
Plavec, Jan Georg
Wolf, Julika

Abstract

The impact of TikTok on recent elections around the globe has been a significant topic of public discourse, including debates over banning the platform (US) or regulating it (EU), specifically the possibility of asymmetric algorithmic amplification of certain political content by "the TikTok algorit... view more

The impact of TikTok on recent elections around the globe has been a significant topic of public discourse, including debates over banning the platform (US) or regulating it (EU), specifically the possibility of asymmetric algorithmic amplification of certain political content by "the TikTok algorithm". By using digital traces of 624 participants’ watch histories collected through data donations, this study provides large-scale insights into the content voters have been exposed to during the German Federal Election campaign in 2025. We explore the prevalence of political content from official party accounts, political influencers, traditional news accounts, and party-referencing content in TikTok Feeds during the German Federal Election 2025. Overall, political content makes up on average 5% of videos in user watch histories in the two months prior to elections with an increase closer to election date - while the share of legacy news stays constant around 1% throughout the whole time period. We do not find clear evidence for asymmetric algorithmic curation of extreme political content. Instead, our findings indicate that most of the political content users see on TikTok aligns with their stated political preferences and we cannot observe a disproportional prevalence of populist or right-wing content.... view less

Keywords
social media; algorithm; political influence; voting behavior; election to the Bundestag

Classification
Interactive, electronic Media
Impact Research, Recipient Research

Free Keywords
TikTok; content exposure; algorithmic selection; recommender systems; German Federal Election 2025; data donation

Document language
English

Publication Year
2026

Page/Pages
43 p.

Status
Preprint; not reviewed

Licence
Creative Commons - Attribution-Noncommercial-No Derivative Works 4.0


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© 2007 - 2026 Social Science Open Access Repository (SSOAR).
Based on DSpace, Copyright (c) 2002-2022, DuraSpace. All rights reserved.