Ensemble of pre-trained language models and data augmentation for hate speech detection from Arabic tweets

TL;DR

This paper introduces an ensemble of pre-trained language models combined with data augmentation techniques to improve hate speech detection accuracy in Arabic tweets, addressing challenges of limited performance and data imbalance.

Contribution

It presents a novel ensemble and semi-supervised data augmentation approach that outperforms existing methods in Arabic hate speech classification.

Findings

Ensemble learning with pre-trained models outperforms previous methods.

Data augmentation significantly improves detection accuracy.

Achieved encouraging results in Arabic hate speech detection.

Abstract

Today, hate speech classification from Arabic tweets has drawn the attention of several researchers. Many systems and techniques have been developed to resolve this classification task. Nevertheless, two of the major challenges faced in this context are the limited performance and the problem of imbalanced data. In this study, we propose a novel approach that leverages ensemble learning and semi-supervised learning based on previously manually labeled. We conducted experiments on a benchmark dataset by classifying Arabic tweets into 5 distinct classes: non-hate, general hate, racial, religious, or sexism. Experimental results show that: (1) ensemble learning based on pre-trained language models outperforms existing related works; (2) Our proposed data augmentation improves the accuracy results of hate speech detection from Arabic tweets and outperforms existing related works. Our main contribution is the achievement of encouraging results in Arabic hate speech detection.