A Literature Review of Textual Hate Speech Detection Methods and Datasets

Online toxic discourses could result in conflicts between groups or harm to online communities. Hate speech is complex and multifaceted harmful or offensive content targeting individuals or groups. Existing literature reviews have generally focused on a particular category of hate speech, and to the best of our knowledge, no review has been dedicated to hate speech datasets. This paper systematically reviews textual hate speech detection systems and highlights their primary datasets, textual features, and machine learning models. The results of this literature review are integrated with content analysis, resulting in several themes for 138 relevant papers. This study shows several approaches that do not provide consistent results in various hate speech categories. The most dominant sets of methods combine more than one deep learning model. Moreover, the analysis of several hate speech datasets shows that many datasets are small in size and are not reliable for various tasks of hate speech detection. Therefore, this study provides the research community with insights and empirical evidence on the intrinsic properties of hate speech and helps communities identify topics for future work.

Automated summary

Online toxic discourses can lead to conflicts and harm to online communities. Existing research has largely focused on specific types of hate speech, and there is limited review of hate speech datasets. This paper provides a systematic review of textual hate speech detection systems and identifies primary datasets, textual features, and machine learning models. The findings highlight the lack of consistency in approaches across different hate speech categories. Most approaches combine multiple deep learning models. Additionally, the analysis of hate speech datasets reveals that many are small in size and not reliable for various hate speech detection tasks. This study offers insights and empirical evidence on hate speech to the research community, aiding in identifying areas for future work.