Systematic partisan content skews in TikTok during the 2024 US elections
Open Access
- 6 May 2026
- journal article
- research article
- Published by Springer Nature in Nature
- Vol. 654 (8120) , 1004-1011
Abstract
Social media platforms increasingly mediate political information exposure, yet the role of algorithmic curation in shaping political exposure remains contested 1,2 . This question is difficult to resolve on platforms in which users retain substantial control over their feeds 3,4 . The ‘For You’ feed of TikTok, which delivers content almost entirely through algorithmic recommendation, offers a setting in which user agency is sharply constrained. Here we show, through 323 audit experiments with controlled ‘sock puppet’ accounts seeded with Democratic or Republican content across three US states, that accounts seeded with partisan content exhibited systematic, asymmetric differences in partisan exposure. Across more than 280,000 recommendations collected over 27 weeks during the 2024 US presidential election campaign, Republican-seeded accounts received about 11.5% more co-partisan content than Democratic-seeded accounts, whereas Democratic-seeded accounts were exposed to about 7.5% more cross-partisan content—largely anti-Democratic material—even after adjusting for engagement metrics. These asymmetries are concentrated among high-reach Republican channels and in specific policy domains, including immigration, crime and foreign policy for Democrats, and abortion for Republicans. Our findings show partisan imbalances in political information exposure on a platform dominated by algorithmic recommendations, with implications for platform governance and democratic discourse.Keywords
This publication has 36 references indexed in Scilit:
- Evaluating large language models for user stance detection on X (Twitter)Machine Learning, 2024
- The effects of Facebook and Instagram on the 2020 election: A deactivation experimentProceedings of the National Academy of Sciences, 2024
- Causally estimating the effect of YouTube’s recommender system using counterfactual botsProceedings of the National Academy of Sciences, 2024
- Auditing YouTube’s recommendation system for ideologically congenial, extreme, and problematic recommendationsProceedings of the National Academy of Sciences, 2023
- Like-minded sources on Facebook are prevalent but not polarizingNature, 2023
- Algorithmic amplification of politics on TwitterProceedings of the National Academy of Sciences, 2021
- Examining the consumption of radical content on YouTubeProceedings of the National Academy of Sciences, 2021
- Exposure to news grows less fragmented with an increase in mobile accessProceedings of the National Academy of Sciences, 2020
- Comparative Media Regulation in the United States and Europe2020
- Personality and political orientation: Meta-analysis and test of a Threat-Constraint ModelJournal of Research in Personality, 2012