The political effects of X’s feed algorithm

Feed algorithms are widely suspected to influence political attitudes. However, previous evidence from switching off the algorithm on Meta platforms found no political effects<sup>1</sup>. Here we present results from a 2023 field experiment on Elon Musk's platform X shedding light on this puzzle. We assigned active US-based users randomly to either an algorithmic or a chronological feed for 7 weeks, measuring political attitudes and online behaviour. Switching from a chronological to an algorithmic feed increased engagement and shifted political opinion towards more conservative positions, particularly regarding policy priorities, perceptions of criminal investigations into Donald Trump and views on the war in Ukraine. In contrast, switching from the algorithmic to the chronological feed had no comparable effects. Neither switching the algorithm on nor switching it off significantly affected affective polarization or self-reported partisanship. To investigate the mechanism, we analysed users' feed content and behaviour. We found that the algorithm promotes conservative content and demotes posts by traditional media. Exposure to algorithmic content leads users to follow conservative political activist accounts, which they continue to follow even after switching off the algorithm, helping explain the asymmetry in effects. These results suggest that initial exposure to X's algorithm has persistent effects on users' current political attitudes and account-following behaviour, even in the absence of a detectable effect on partisanship.

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Institutions

Publication Details

Journal
Nature
Published
2026-02-18
DOI
https://doi.org/10.1038/s41586-026-10098-2
Citations
1
Primary Topic
Social Media and Politics
Type
article
Field-Weighted Citation Impact
62.22

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article

The political effects of X’s feed algorithm

Ekaterina Zhuravskaya, Philine Widmer, Germain Gauthier, Roland Hodler
1 citations
Nature
Social Media and Politics
62.22
article

The political effects of X’s feed algorithm

Ekaterina Zhuravskaya, Philine Widmer, Germain Gauthier, Roland Hodler
article en
1 citations

Abstract

Feed algorithms are widely suspected to influence political attitudes. However, previous evidence from switching off the algorithm on Meta platforms found no political effects<sup>1</sup>. Here we present results from a 2023 field experiment on Elon Musk's platform X shedding light on this puzzle. We assigned active US-based users randomly to either an algorithmic or a chronological feed for 7 weeks, measuring political attitudes and online behaviour. Switching from a chronological to an algorithmic feed increased engagement and shifted political opinion towards more conservative positions, particularly regarding policy priorities, perceptions of criminal investigations into Donald Trump and views on the war in Ukraine. In contrast, switching from the algorithmic to the chronological feed had no comparable effects. Neither switching the algorithm on nor switching it off significantly affected affective polarization or self-reported partisanship. To investigate the mechanism, we analysed users' feed content and behaviour. We found that the algorithm promotes conservative content and demotes posts by traditional media. Exposure to algorithmic content leads users to follow conservative political activist accounts, which they continue to follow even after switching off the algorithm, helping explain the asymmetry in effects. These results suggest that initial exposure to X's algorithm has persistent effects on users' current political attitudes and account-following behaviour, even in the absence of a detectable effect on partisanship.

Nature
University of St.Gallen (CH), Paris School of Economics (FR), Bocconi University (IT), École des hautes études en sciences sociales (FR)
National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Peace, Justice and strong institutions
Openalex Percentile: Top 0%
Social Media and Politics
62.22
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The political effects of X’s feed algorithm — Ekaterina Zhuravskaya, Philine Widmer, et al. · Nature (2026) | TGRS Research Map | TGRS