A systematic literature review of hate speech identification on Arabic Twitter data: research challenges and future directions

Abstract

The automatic speech identification in Arabic tweets has generated substantial attention among academics in the fields of text mining and natural language processing (NLP). The quantity of studies done on this subject has experienced significant growth. This study aims to provide an overview of this field by conducting a systematic review of literature that focuses on automatic hate speech identification, particularly in the Arabic language. The goal is to examine the research trends in Arabic hate speech identification and offer guidance to researchers by highlighting the most significant studies published between 2018 and 2023. This systematic study addresses five specific research questions concerning the types of the Arabic language used, hate speech categories, classification techniques, feature engineering techniques, performance metrics, validation methods, existing challenges faced by researchers, and potential future research directions. Through a comprehensive search across nine academic databases, 24 studies that met the predefined inclusion criteria and quality assessment were identified. The review findings revealed the existence of many Arabic linguistic varieties used in hate speech on Twitter, with modern standard Arabic (MSA) being the most prominent. In identification techniques, machine learning categories are the most used technique for Arabic hate speech identification. The result also shows different feature engineering techniques used and indicates that N-gram and CBOW are the most used techniques. F1-score, precision, recall, and accuracy were also identified as the most used performance metric. The review also shows that the most used validation method is the train/test split method. Therefore, the findings of this study can serve as valuable guidance for researchers in enhancing the efficacy of their models in future investigations. Besides, algorithm development, policy rule regulation, community management, and legal and ethical consideration are other real-world applications that can be reaped from this research.

Code-mixing unveiled: Enhancing the hate speech detection in Arabic dialect tweets using machine learning models

“…Moreover, the existing studies did not identify research gaps in the methodology of the reviewed literature at the preprocessing and classification levels. Building on the recent published literature review on hate speech detection in Arabic Twitter data [ 32 ], this research seeks to investigate the use and effects of machine learning techniques in translating and identifying hate speech in Arabic dialectic language, especially in code-mixed language scenarios. This can be done by demonstrating if the approach performs well on HS datasets by comparing three feature engineering, five ML classifiers on standard Arabic tweets, and code-mixing tweets on HS datasets.…”