Hate speech detection in algerian dialect using deep learning

TL;DR

This paper presents a deep learning-based approach for detecting hate speech in Algerian dialect social media messages, addressing a gap in dialect-specific hate speech detection with promising results.

Contribution

It introduces a new Algerian dialect corpus and evaluates multiple deep learning architectures for hate speech detection in this under-studied language variant.

Findings

Deep learning models achieved high accuracy on the Algerian dialect corpus.

The created corpus contains over 13,500 labeled messages from social networks.

The approach demonstrates effective hate speech detection in Algerian social media texts.

Abstract

With the proliferation of hate speech on social networks under different formats, such as abusive language, cyberbullying, and violence, etc., people have experienced a significant increase in violence, putting them in uncomfortable situations and threats. Plenty of efforts have been dedicated in the last few years to overcome this phenomenon to detect hate speech in different structured languages like English, French, Arabic, and others. However, a reduced number of works deal with Arabic dialects like Tunisian, Egyptian, and Gulf, mainly the Algerian ones. To fill in the gap, we propose in this work a complete approach for detecting hate speech on online Algerian messages. Many deep learning architectures have been evaluated on the corpus we created from some Algerian social networks (Facebook, YouTube, and Twitter). This corpus contains more than 13.5K documents in Algerian dialect written in Arabic, labeled as hateful or non-hateful. Promising results are obtained, which show the efficiency of our approach.