An Automated Hate Speech Detection Using Deep Learning on Arabic Corpus

Abdulrahman Alzahrani

:: International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

http://TuEngr.com

ISSN 2228-9860
eISSN 1906-9642
CODEN: ITJEA8

FEATURE PEER-REVIEWED ARTICLE

Vol.15(4) (2024)

An Automated Hate Speech Detection Using Deep Learning on Arabic Corpus

Abdulrahman Alzahrani (Department of Computer Science and Engineering, College of Computer Science and Engineering, University of Hafr Al Batin, SAUDI ARABIA).

Discipline: Computer Science and Engineering.
➤ FullText

doi: 10.14456/ITJEMAST.2024.25

Keywords: Arabic Hate Speech; Offensive Language; Deep Learning; BiGRU; Arithmetic Optimization Algorithm; AraBERT; AHOSD-DLAC; Machine Learning; AOA; Natural Language Processing; Speech Detection; NLP.

Abstract

Hate speech on social media, particularly in the Arabic language take witnessed a dramatic increase and caused significant destruction. Several researches have been undertaken on social media adopted by people to share their thoughts. Due to the diverse and broad linguistic terrain, Hate Speech Detection in Arabic brings about substantial hurdles. With its rich cultural subtleties and multiple dialects, Arabic requires certain measures to successfully address hate speech online. To overcome these issues, developers have applied ML algorithms and NLP systems adapted to the difficulties of Arabic text. This work offers the design of Automated Hate and Offensive Speech Detection using Deep Learning on Arabic Corpus (AHOSD-DLAC) technique. The objective of AHOSD-DLAC is to design an approach that is capable of handling the recognition and classification of offensive language and Arabic hate speech. The AHOSD-DLAC is composed of pre-processing, AraBERT-based feature extraction, and bidirectional gated recurrent unit (BiGRU) classification to identify hate speech. An arithmetic optimizer algorithm (AOA) is used for hyperparameter tuning to improve the performance of the AHOSD-DLAC. We accompanied a set of experiments on X data in order to evaluate the AHOSD-DLAC. The AHOSD-DLAC shows potential outcomes on hate speech recognition than preliminary works on Arabic hate-speech recognition.

Paper ID: 15A4F

Cite this article:

Zaini, A. I., Awang Sulong, A. H., Abu Bakar, R., and Jyi, M.W.H. (2024). An Automated Hate Speech Detection Using Deep Learning on Arabic Corpus. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 15(4), 15A4F, 1-14. http://doi.org/10.14456/ITJEMAST.2024.25

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