推特上仇恨言论管控的执行与可行性
The Enforcement and Feasibility of Hate Speech Moderation
AI总结：
研究通过全球审计揭示推特仇恨言论管控的执行情况，发现仇恨言论在平台上持续存在，且去除率与非仇恨言论无显著差异，表明技术限制外还有制度因素影响管控效果。
摘要：
在线仇恨言论与重大社会危害相关，但平台对仇恨言论政策的执行一致性及大规模执行的可行性仍不清楚。本文通过全球审计推特（现X）上的仇恨言论管控，利用24小时公开推文快照构建了包含54万条推文的代表性样本，由训练有素的注释员标注仇恨言论。五个月后，80%的仇恨推文仍在线上，包括显性暴力仇恨言论。这些推文的删除率与非仇恨推文无显著差异，严重性或可见性并未增加删除的可能性。随后研究探讨这些执行差距是否反映大规模管控系统的技术限制。虽然完全自动化的检测系统无法可靠地识别仇恨言论而不产生大量误报，但它们有效地优先处理可能的违规内容供人工审核。模拟人类-人工智能审核流程表明，大幅减少用户接触仇恨言论在经济上是可行的，成本低于现有监管处罚。这些结果表明，网络上仇恨言论的持续存在不能仅由技术限制解释，还反映了在审核资源分配上的制度选择。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
