Web(er) of Hate: 仇恨言论如何被类型化的一项综述
Web(er) of Hate: A Survey on How Hate Speech Is Typed
AI总结：
本文综述仇恨言论数据集的策展设计，借鉴韦伯理想类型理论，倡导研究者反思自身价值判断，以提升数据集可靠性与方法论严谨性。
摘要：
仇恨言论数据集的策展涉及复杂的设计决策，需在相互竞争的优先事项间取得平衡。本文批判性地审视了多种数据集中的这些方法论选择，强调了常见主题与实践及其对数据集可靠性的影响。借鉴马克斯·韦伯的理想类型概念，我们主张在数据集创建中采取反思性方法，敦促研究者在数据集构建过程中承认自身的价值判断，以促进透明度和方法论严谨性。
英文摘要：
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.
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。
