Ideology and free speech values predict content moderation preferences: cross-national evidence across targets of hate speech

With over 4 billion people using social media platforms daily, decisions about what content to allow or moderate have profound implications for global public discourse, democratic deliberation, and users' psychological well-being. This study examines cross-national variation in content moderation preferences by exposing participants in 10 countries to a standardized example of hate speech: a hateful post modeled on text that X (formerly Twitter) had cited in its content moderation guidelines. Across contexts, exposure to such content triggers widespread support for intervention, with minimal differences based on the experimentally varied identity of the target. However, significant variation emerges across countries. The United States stands out for its comparatively lower support for moderation of hate speech, while countries like France, Brazil, and South Africa exhibit much stronger demand. Within most countries, political ideology predicts support: individuals on the right are less likely to endorse removal or suspension than those on the left. Crucially, values around freedom of speech versus harm prevention consistently shape preferences across all contexts. Those who prioritize prevention from harm are markedly more likely to support hard forms of moderation (e.g., account suspension and content take-downs). These findings highlight that moderation preferences are shaped less by the specific target than by individuals' deeper commitments regarding free expression and harm prevention.

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Publication Details

Journal
Scientific Reports
Published
2026-06-17
DOI
https://doi.org/10.1038/s41598-026-56054-y
Primary Topic
Hate Speech and Cyberbullying Detection
Type
article
Field-Weighted Citation Impact
0.00

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article

Ideology and free speech values predict content moderation preferences: cross-national evidence across targets of hate speech

Jan Zilinsky, Spyros Kosmidis, Yannis Theocharis, Friederike Quint
Scientific Reports
Hate Speech and Cyberbullying Detection
article

Ideology and free speech values predict content moderation preferences: cross-national evidence across targets of hate speech

Jan Zilinsky, Spyros Kosmidis, Yannis Theocharis, Friederike Quint
article en

Abstract

With over 4 billion people using social media platforms daily, decisions about what content to allow or moderate have profound implications for global public discourse, democratic deliberation, and users' psychological well-being. This study examines cross-national variation in content moderation preferences by exposing participants in 10 countries to a standardized example of hate speech: a hateful post modeled on text that X (formerly Twitter) had cited in its content moderation guidelines. Across contexts, exposure to such content triggers widespread support for intervention, with minimal differences based on the experimentally varied identity of the target. However, significant variation emerges across countries. The United States stands out for its comparatively lower support for moderation of hate speech, while countries like France, Brazil, and South Africa exhibit much stronger demand. Within most countries, political ideology predicts support: individuals on the right are less likely to endorse removal or suspension than those on the left. Crucially, values around freedom of speech versus harm prevention consistently shape preferences across all contexts. Those who prioritize prevention from harm are markedly more likely to support hard forms of moderation (e.g., account suspension and content take-downs). These findings highlight that moderation preferences are shaped less by the specific target than by individuals' deeper commitments regarding free expression and harm prevention.

Scientific ReportsVol. 16(1)
Slovak Academy of Sciences (SK), University of Oxford (GB), Centre of Social and Psychological Sciences of the Slovak Academy of Sciences (SK), Technical University of Munich (DE)
University of Oxford, Technische Universität München
Peace, Justice and strong institutions
Openalex Percentile: Top 5%
Hate Speech and Cyberbullying Detection
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