JUCS - Journal of Universal Computer Science 29(11): 1319-1335, doi: 10.3897/jucs.112604

Deep Random Forest and AraBert for Hate Speech Detection from Arabic Tweets

Kheir Eddine Daouadi^‡, Yaakoub Boualleg^‡, Oussama Guehairia^§

‡ Echahid Cheikh Larbi Tebessi University, Tebessa, Algeria§ Mohamed Khider University of Biskra, Biskra, Algeria

Corresponding author: Kheir Eddine Daouadi ( kheireddine.daouadi@univ-tebessa.dz )

This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY-ND 4.0). This license allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use.

Citation: Daouadi KE, Boualleg Y, Guehairia O (2023) Deep Random Forest and AraBert for Hate Speech Detection from Arabic Tweets. JUCS - Journal of Universal Computer Science 29(11): 1319-1335. https://doi.org/10.3897/jucs.112604

Abstract

Nowadays, hate speech detection from Arabic tweets attracts the attention of many researchers. Numerous systems and techniques have been proposed to address this classification challenge. Nonetheless, three major limits persist: the use of deep learning models with an excess of hyperparameters, the reliance on hand-crafted features, and the requirement for a huge amount of training data to achieve satisfactory performance. In this study, we propose Contextual Deep Random Forest (CDRF), a hate speech detection approach that combines contextual embedding and Deep Random Forest. From the experimental findings, the Arabic contextual embedding model proves to be highly effective in hate speech detection, outperforming the static embedding models. Additionally, we prove that the proposed CDRF significantly enhances the performance of Arabic hate speech classification.

Keywords

Twitter, Hate Speech Detection, Arabic Tweet Classification, Contextual Deep Ran-dom Forest, Fine-tuning, Pre-trained Contextual Embedding