Hate speech should be tackled and prosecuted based on how it is operationalized. However, the existing theoretical definitions of hate speech are not sufficiently fleshed out or easily operable. To overcome this inadequacy, and with the help of interdisciplinary experts, we propose an empirical definition of hate speech by providing a list of 10 hate speech indicators and the rationale behind them (the indicators refer to specific, observable, and measurable characteristics that offer a practical definition of hate speech). A preliminary exploratory examination of the structure of hate speech, with the focus on comments related to migrants (one of the most reported grounds of hate speech), revealed that two indicators in particular, denial of human rights and promoting violent behavior, occupy a central role in the network of indicators. Furthermore, we discuss the practical implications of the proposed hate speech indicators—especially (semi-)automatic detection using the latest natural language processing (NLP) and machine learning (ML) methods. Having a set of quantifiable indicators could benefit researchers, human right activists, educators, analysts, and regulators by providing them with a pragmatic approach to hate speech assessment and detection.

仇恨言论应根据其操作方式加以处理和起诉。然而，现有的仇恨言论理论定义不够充实或易于操作。为了克服这一不足，在跨学科专家的帮助下，我们提出了仇恨言论的实证定义，提供了 10 个仇恨言论指标及其背后的基本原理（这些指标是指提供特定、可观察和可衡量的特征）仇恨言论的实际定义）。对仇恨言论结构的初步探索性审查，重点是与移民有关的评论（仇恨言论报告最多的理由之一），揭示了两个指标，特别是否认人权和促进暴力行为，在指标网络中占据核心地位。此外，我们讨论了所提议的仇恨言论指标的实际意义——尤其是使用最新的自然语言处理 (NLP) 和机器学习 (ML) 方法的（半）自动检测。拥有一套可量化的指标可以为研究人员、人权活动家、教育工作者、分析师和监管机构提供实用的仇恨言论评估和检测方法，从而使他们受益。