A study of text representations in Hate Speech Detection

TL;DR

This paper evaluates various text representation methods for hate speech detection on social media, finding that simple keyword frequency features combined with classifiers yield the best results.

Contribution

It systematically compares multiple text representations and classifiers, highlighting the effectiveness of simple features like BoW and N-gram graphs for hate speech detection.

Findings

BoW features outperform other representations.

Pre-trained embeddings like GLoVe are effective.

Combining representations with Logistic Regression yields top performance.

Abstract

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.