The Enforcement and Feasibility of Hate Speech Moderation on Twitter

TL;DR

This study audits Twitter's hate speech moderation, revealing persistent hateful content online and analyzing the technical and institutional factors affecting enforcement effectiveness.

Contribution

It provides a comprehensive global audit of hate speech enforcement on Twitter, highlighting technical limitations and institutional resource allocation issues.

Findings

80% of hateful tweets remain online after five months

Automated detection systems struggle with false positives but aid human review

Reducing user exposure to hate speech is economically feasible with current moderation strategies

Abstract

Online hate speech is associated with substantial social harms, yet it remains unclear how consistently platforms enforce hate speech policies or whether enforcement is feasible at scale. We address these questions through a global audit of hate speech moderation on Twitter (now X). Using a complete 24-hour snapshot of public tweets, we construct representative samples comprising 540,000 tweets annotated for hate speech by trained annotators across eight major languages. Five months after posting, 80% of hateful tweets remain online, including explicitly violent hate speech. Such tweets are no more likely to be removed than non-hateful tweets, with neither severity nor visibility increasing the likelihood of removal. We then examine whether these enforcement gaps reflect technical limits of large-scale moderation systems. While fully automated detection systems cannot reliably identify hate speech without generating large numbers of false positives, they effectively prioritize likely violations for human review. Simulations of a human-AI moderation pipeline indicate that substantially reducing user exposure to hate speech is economically feasible at a cost below existing regulatory penalties. These results suggest that the persistence of online hate cannot be explained by technical constraints alone but also reflects institutional choices in the allocation of moderation resources.