ETHOS: an Online Hate Speech Detection Dataset
Ioannis Mollas Aristotle University of Thessaloniki Thessaloniki, 54636, Greece、Zoe Chrysopoulou Aristotle University of Thessaloniki Thessaloniki, 54636, Greece、Stamatis Karlos Aristotle University of Thessaloniki Thessaloniki, 54636, Greece、Grigorios Tsoumakas Aristotle University of Thessaloniki Thessaloniki, 54636, Greece
Abstract
Online hate speech is a recent problem in our society that is rising at a steady pace by leveraging the vulnerabilities of the corresponding regimes that characterise most social media platforms. This phenomenon is primarily fostered by offensive comments, either during user interaction or in the form of a posted multimedia context. Nowadays, giant corporations own platforms where millions of users log in every day, and protection from exposure to similar phenomena appears to be necessary in order to comply with the corresponding legislation and maintain a high level of service quality. A robust and reliable system for detecting and preventing the uploading of relevant content will have a significant impact on our digitally interconnected society. Several aspects of our daily lives are undeniably linked to our social profiles, making us vulnerable to abusive behaviours. As a result, the lack of accurate hate speech detection mechanisms would severely degrade the overall user experience, although its erroneous operation would pose many ethical concerns. In this paper, we present ‘ETHOS’, a textual dataset with two variants: binary and multi-label, based on YouTube and Reddit comment
中文速览
网络仇恨言论（hate speech）的泛滥给社交媒体平台带来了严峻的内容治理挑战，而现有数据集普遍存在类别严重不平衡、信息冗余、以及只支持二分类而非多标签分类的缺陷。为此，研究者提出了一套主动采样（active sampling）标注流程，并据此构建了名为 ETHOS 的文本数据集——以 YouTube 和 Reddit 评论为原料，通过 Figure-Eight 众包平台验证，分别提供二分类和多标签两个版本，多标签版本可同时标记一条评论所涉及的种族、性别、宗教、残障等多个仇恨维度。在此数据集上，研究者系统测试了从传统机器学习、集成模型到含预训练词向量的神经网络等多类基线方法，证明经过精心平衡与去冗余处理的小规模数据集同样能为模型提供有效的学习信号。这项工作的价值在于：它不仅为仇恨言论检测提供了一个标注质量更高、类别分布更均衡的基准资源，其标注协议本身也具有通用性，可迁移到其他需要主动学习与多标签建模的文本分类场景。
原文 arXiv:2006.08328;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2006.08328v2