Towards generalisable hate speech detection: a review on obstacles and solutions

• Published in: PeerJ. Computer science Vol. 7; p. e598
• Main Authors: Yin, Wenjie, Zubiaga, Arkaitz
• Format: Journal Article
• Language: English
• Published: San Diego PeerJ. Ltd 17.06.2021; PeerJ, Inc; PeerJ Inc
• Subjects: Abusive language; Artificial Intelligence; Barriers; Computational Linguistics; Data Mining and Machine Learning; Datasets; Error analysis; Generalisation; Hate speech; Keywords; Language; Language processing; Literature review; Natural Language and Speech; Natural language interfaces; Natural language processing; Profanity; Social Computing; Social media; Social networks; Speeches; Surveys; Text classification; User generated content; Workshops; United Kingdom
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.598

Hate speech is one type of harmful online content which directly attacks or promotes hate towards a group or an individual member based on their actual or perceived aspects of identity, such as ethnicity, religion, and sexual orientation. With online hate speech on the rise, its automatic detection as a natural language processing task is gaining increasing interest. However, it is only recently that it has been shown that existing models generalise poorly to unseen data. This survey paper attempts to summarise how generalisable existing hate speech detection models are and the reasons why hate speech models struggle to generalise, sums up existing attempts at addressing the main obstacles, and then proposes directions of future research to improve generalisation in hate speech detection.