The Enforcement and Feasibility of Hate Speech Moderation

arXiv:2604.12289 · cs.CY, cs.CL · Submitted 2026-04-14 · Read on arXiv

cs.CY, cs.CL

Submitted: 2026-04-14

Updated: 2026-09-01

Code: https://github.com/manueltonneau/twitter-hate-moderation

License: http://creativecommons.org/licenses/by/4.0/

The gist: Online hate speech is associated with harms ranging from deteriorating mental health to violence, yet how consistently platforms moderate hate, and whether enforcement is feasible at scale, remain

Terminology

Abstract

Online hate speech is associated with harms ranging from deteriorating mental health to violence, yet how consistently platforms moderate hate, and whether enforcement is feasible at scale, remain poorly understood. We audit hate speech moderation on Twitter (now X) using 540,000 tweets annotated by trained native speakers, representative of a full day on the platform. Five months after posting, 80% of hateful tweets, including violent ones, remained online. Removal was only marginally more likely than for non-hateful tweets, far below scams or adult content, and insensitive to severity and reach. Automated detection could not reliably classify hate but ranked it highly, enabling human triage. Simulating this workflow, current staffing curbed little exposure, yet substantial reductions proved financially feasible, far below applicable regulatory fines. Persistent hate reflects resource allocation, not technical limits.

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