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SYSTEMATIC REVIEW article

Front. Artif. Intell.

Sec. Natural Language Processing

Volume 8 - 2025 | doi: 10.3389/frai.2025.1666349

This article is part of the Research TopicEmerging Techniques in Arabic Natural Language ProcessingView all 9 articles

CYBERBULLYING DETECTION APPROACHES FOR ARABIC TEXTS: SYSTEMATIC LITERATURE REVIEW

Provisionally accepted
Hooayda  AllwaibedHooayda Allwaibed1*Mohammed  AnbarMohammed Anbar1Selvakumar  ManickamSelvakumar Manickam1Annisa  BintangAnnisa Bintang2
  • 1National Advanced IPv6 (NAv6) Centre, Universiti Sains Malaysia (USM), Penang, Malaysia
  • 2Universitas Indonesia Fakultas Ilmu Komputer, Depok, Indonesia

The final, formatted version of the article will be published soon.

This study presents a comprehensive review of current methodologies, trends, and challenges in cyberbullying detection within Arabic-language contexts, with a focus on the unique linguistic and cultural factors associated with Arabic. This study reviews 35 peer-reviewed articles about the identification of cyberbullying in Arabic text. Reported accuracies across datasets and platforms range from approximately 73% to 96%, with precision frequently surpassing recall, suggesting that systems are more adept at identifying blatant bullying than at encompassing all pertinent instances. Methodologically, conventional machine learning utilizing Arabic-specific characteristics remains effective on smaller datasets, however deep neural architectures— especially CNN/BiLSTM—and transformer models like AraBERT yield superior outcomes when dialectal heterogeneity and orthographic noise are mitigated. Evaluation methodologies differ; research using a neutral class frequently indicates exaggerated accuracy, underscoring the necessity to emphasize macro-averaged F1 and per-class metrics. The evidence underscores deficiencies in dialectal representativeness, the uniformity of bullying notions compared to general abuse, and the transparency of annotation processes. Ethical and deployment considerations— privacy preservation, dialectal bias, and real-time robustness—are becoming increasingly significant. We integrate trends (models and features), standards (labeling and metrics), and future work directions, encompassing dialect-robust pretraining, cross-dataset evaluation, context-aware modeling, and human-in-the-loop frameworks. The review offers a comprehensive basis for researchers and practitioners pursuing culturally and linguistically tailored approaches to Arabic cyberbullying detection..

Keywords: Cyberbullying detection, Arabic language, Systematic Literature Review, Machine Learning (ML), deep learning (DL), support vector machines (SVM), Convolutional Neural Networks (CNN)

Received: 25 Jul 2025; Accepted: 29 Sep 2025.

Copyright: © 2025 Allwaibed, Anbar, Manickam and Bintang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Hooayda Allwaibed, hooaydaallwaibed@gmail.com

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