剖析仇恨言论数据集设计中的隐性价值判断，推动更透明的研究方法。
Web(er) of Hate: A Survey on How Hate Speech Is Typed
- 基于理想类型理论，反思数据集构建中的价值取向
- 揭示主流数据集共性设计问题及其对可靠性的影响
- 适合关注伦理与方法论的AI安全研究者
仇恨言论数据集的构建涉及复杂的权衡决策。本文批判性审视多种数据集中存在的方法学选择，揭示其共同模式与实践，及其对数据集可靠性的潜在影响。借鉴马克斯·韦伯的理想类型概念，主张在数据集创建中采取反思性方法，呼吁研究者承认自身价值判断的存在，以提升研究的透明度与方法严谨性。
原文摘要 · Abstract (English)
The curation of hate speech datasets involves complex design decisions that balance competing priorities. This paper critically examines these methodological choices in a diverse range of datasets, highlighting common themes and practices, and their implications for dataset reliability. Drawing on Max Weber's notion of ideal types, we argue for a reflexive approach in dataset creation, urging researchers to acknowledge their own value judgments during dataset construction, fostering transparency and methodological rigour.
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