# A study of text representations in Hate Speech Detection

arXiv: https://arxiv.org/abs/2102.04521 · HTML: https://mlsanity.com/paper/2102.04521/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 8 Feb 2021.

## Builds on (most cited first, 5 of 5)

- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Deep Learning for Hate Speech Detection in Tweets](https://mlsanity.com/paper/1706.00188/) (2017)
- [Analyzing the Targets of Hate in Online Social Media](https://mlsanity.com/paper/1603.07709/) (2016)
- [One-step and Two-step Classification for Abusive Language Detection on Twitter](https://mlsanity.com/paper/1706.01206/) (2017)
- [A Web of Hate: Tackling Hateful Speech in Online Social Spaces](https://mlsanity.com/paper/1709.10159/) (2017)

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

- [The Unappreciated Role of Intent in Algorithmic Moderation of Social Media Content](https://mlsanity.com/paper/2405.11030/) (2024)

## Abstract (arXiv)

The pervasiveness of the Internet and social media have enabled the rapid and anonymous spread of Hate Speech content on microblogging platforms such as Twitter. Current EU and US legislation against hateful language, in conjunction with the large amount of data produced in these platforms has led to automatic tools being a necessary component of the Hate Speech detection task and pipeline. In this study, we examine the performance of several, diverse text representation techniques paired with multiple classification algorithms, on the automatic Hate Speech detection and abusive language discrimination task. We perform an experimental evaluation on binary and multiclass datasets, paired with significance testing. Our results show that simple hate-keyword frequency features (BoW) work best, followed by pre-trained word embeddings (GLoVe) as well as N-gram graphs (NGGs): a graph-based representation which proved to produce efficient, very low-dimensional but rich features for this task. A combination of these representations paired with Logistic Regression or 3-layer neural network classifiers achieved the best detection performance, in terms of micro and macro F-measure.