Ahmad, A.; Azzeh, M.; Alnagi, E.; Abu Al-Haija, Q.; Halabi, D.; Aref, A.; AbuHour, Y. Hate Speech Detection in the Arabic Language: Corpus Design, Construction, and Evaluation. Frontiers in Artificial Intelligence 2024, 7, doi:10.3389/frai.2024.1345445.
Ahmad, A.; Azzeh, M.; Alnagi, E.; Abu Al-Haija, Q.; Halabi, D.; Aref, A.; AbuHour, Y. Hate Speech Detection in the Arabic Language: Corpus Design, Construction, and Evaluation. Frontiers in Artificial Intelligence 2024, 7, doi:10.3389/frai.2024.1345445.
Ahmad, A.; Azzeh, M.; Alnagi, E.; Abu Al-Haija, Q.; Halabi, D.; Aref, A.; AbuHour, Y. Hate Speech Detection in the Arabic Language: Corpus Design, Construction, and Evaluation. Frontiers in Artificial Intelligence 2024, 7, doi:10.3389/frai.2024.1345445.
Ahmad, A.; Azzeh, M.; Alnagi, E.; Abu Al-Haija, Q.; Halabi, D.; Aref, A.; AbuHour, Y. Hate Speech Detection in the Arabic Language: Corpus Design, Construction, and Evaluation. Frontiers in Artificial Intelligence 2024, 7, doi:10.3389/frai.2024.1345445.
Abstract
Hate Speech Detection in Arabic presents a multifaceted challenge due to the broad and diverse linguistic terrain. With its multiple dialects and rich cultural subtleties, Arabic requires particular measures to address hate speech online successfully. To address this issue, academics and developers have used natural language processing (NLP) methods and machine learning algorithms adapted to the complexities of Arabic text. However, many proposed methods were hampered by a lack of a comprehensive dataset/corpus of Arabic hate speech. In this research, we propose a novel multi-class public Arabic dataset comprised of 403,688 annotated tweets categorized as extremely positive, positive, neutral, or negative based on the presence of hate speech. Using our developed dataset, we additionally characterize the performance of multiple machine learning models for Hate speech identification in Arabic Jordanian dialect tweets. Specifically, the Word2Vec, TF-IDF, and AraBert text representation models have been applied to produce word vectors. With the help of these models, we can provide classification models with vectors representing text. After that, seven Machine learning classifiers have been evaluated: Support Vector Machine (SVM), Logistic Regression (LR), Naive Bays (NB), Random Forest (RF), AdaBoost (Ada), XGBoost (XGB), and CatBoost (CatB). In light of this, the experimental evaluation revealed that, in this challenging and unstructured setting, our gathered and annotated datasets were rather efficient and generated encouraging assessment outcomes. This will enable academics to delve further into this crucial field of study.
Keywords
Arabic Hate Speech; Natural Language Processing (NLP); Machine Learning; Arabic 18 Hate Speech Detection; Arabic Hate Speech Corpus
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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