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

arXiv: https://arxiv.org/abs/2103.10489 · HTML: https://mlsanity.com/paper/2103.10489/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 18 Mar 2021.

## Builds on (most cited first, 9 of 9)

- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [Multilingual and Multi-Aspect Hate Speech Analysis](https://mlsanity.com/paper/1908.11049/) (2019)
- [Understanding Abuse: A Typology of Abusive Language Detection Subtasks](https://mlsanity.com/paper/1705.09899/) (2017)
- [Spread of hate speech in online social media](https://mlsanity.com/paper/1812.01693/) (2018)
- [Hate Speech Detection: A Solved Problem? The Challenging Case of Long Tail on Twitter](https://mlsanity.com/paper/1803.03662/) (2018)
- [A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1610.08815/) (2016)
- [Improving Moderation of Online Discussions via Interpretable Neural Models](https://mlsanity.com/paper/1809.06906/) (2018)
- [Hate is not Binary: Studying Abusive Behavior of #GamerGate on Twitter](https://mlsanity.com/paper/1705.03345/) (2017)

## Built on by (most cited first, 1 of 1)

- [Detecting Hate Speech with GPT-3](https://mlsanity.com/paper/2103.12407/) (2021)

## Abstract (arXiv)

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.