Title: Toward Arabic social networks unmasking toxicity using machine learning and deep learning models

Authors: Anis Mezghani; Mohamed Elleuch; Salwa Gasmi; Monji Kherallah

Addresses: Higher Institute of Industrial Management, University of Sfax, Tunisia ' National School of Computer Science, University of Manouba, Tunisia ' Faculty of Sciences, University of Gafsa, Tunisia ' Advanced Technologies for Environment and Smart Cities (ATES Unit), Faculty of Sciences, University of Sfax, Tunisia

Abstract: With the rapid expansion of social media usage, the issue of racism has gained momentum, leading to an increased prevalence of racist discussions. Despite the efforts made by international organisations and prominent social media platforms like Twitter and Facebook to combat racism, it persists as a real-world problem. Consequently, multitudes of researchers are now directing their attention towards the detection of hate speech and racism on social media, particularly in the context of the Arabic language. We therefore propose the creation of an intelligent system for detecting and classifying toxic comments in Tunisian dialect using deep learning and machine learning models. Specifically, we use LR, linear SVC, SVM and BLSTM. This analysis has been performed using the potent technique for text analysis named NLP. The best performances were provided by combining BLSTM and SVM models with an accuracy of 99.83% for binary classification and 98.42% for ternary classification.

Keywords: racism; Arabic content; social media; natural language processing; NLP; machine learning; deep learning.

DOI: 10.1504/IJISTA.2024.140948

International Journal of Intelligent Systems Technologies and Applications, 2024 Vol.22 No.3, pp.260 - 280

Received: 04 Oct 2023
Accepted: 27 Nov 2023
Published online: 04 Sep 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article