Addressing Hate Speech with Data Science: An Overview from Computer Science Perspective

TL;DR

This paper reviews state-of-the-art data science methods for detecting online hate speech, highlighting challenges, open problems, and future directions to foster multidisciplinary research and social impact.

Contribution

It provides a comprehensive overview of computer science approaches to hate speech detection, emphasizing the need for interdisciplinary collaboration and outlining future research directions.

Findings

Most research focuses on semi-automatic hate speech detection

Current methods face challenges with context and nuance understanding

Open problems include dataset biases and multilingual detection

Abstract

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.