From a computer science perspective, addressing on-line hate speech is a challenging task that is attracting the attention of both industry (mainly social media platform owners) and academia. In this chapter, we provide an overview of state-of-the-art data-science approaches - how they define hate speech, which tasks they solve to mitigate the phenomenon, and how they address these tasks. We limit our investigation mostly to (semi-)automatic detection of hate speech, which is the task that the majority of existing computer science works focus on. Finally, we summarize the challenges and the open problems in the current data-science research and the future directions in this field. Our aim is to prepare an easily understandable report, capable to promote the multidisciplinary character of hate speech research. Researchers from other domains (e.g., psychology and sociology) can thus take advantage of the knowledge achieved in the computer science domain but also contribute back and help improve how computer science is addressing that urgent and socially relevant issue which is the prevalence of hate speech in social media.
Overview of the HASOC track at FIRE 2020: Hate Speech and Offensive Content Identification in Indo-European Languages
University of Hildesheim（希尔德斯海姆大学）；LDRP-ITR；University of Duisburg-Essen（杜伊斯堡-埃森大学）；University of Bedfordshire（贝德福德大学）；University of Leeds（利兹大学）；University of Bamberg（班贝格大学）；Dalhousie University（达尔豪斯大学）；DA-IICT
Overview of the HASOC Subtrack at FIRE 2021: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages
University of Hildesheim（希尔德斯海姆大学）；LDRP-ITR；University of Duisburg-Essen（杜伊斯堡-埃森大学）；Indian Institute of Science（印度科学理工学院）；DA-IICT；University of Wolverhampton（伍尔弗汉普顿大学）；Rochester Institute of Technology（罗切斯特理工学院）；University of Bamberg（班贝格大学）；University of Bedfordshire（贝德福德大学）；University of Leeds（利兹大学）
仇恨言论等攻击性内容在网上的广泛传播构成了日益严重的社会问题。人工智能工具对于支持在线平台的审核过程是必要的。为了评估这些识别工具，需要对不同语言的数据集进行连续实验。HASOC（Hate Speech and Offensive Content Identification，仇恨言论和攻击性内容识别）专门为此目的开发基准数据。本文介绍了英语，印地语和马拉地语的HASOC子轨道。该数据集来自Twitter。此子路径有两个子任务。任务A是针对所有三种语言的二元分类问题（仇恨和不冒犯）。任务B是一个细粒度的分类问题，分为三类（仇恨）仇恨言论，进攻和专业提供的英语和印地语。总共有65支球队提交了652次跑步。任务A的最佳分类算法的性能是F1措施0.91，0.78和0.83马拉地语，印地语和英语，分别。本概述介绍了任务和数据开发以及详细结果。提交竞赛的系统应用了各种技术。性能最好的算法主要是Transformer架构的变体。
Bridging the gap in online hate speech detection: a comparative analysis of BERT and traditional models for homophobic content identification on X/Twitter
