Cyberbullying detection approaches for Arabic texts: a systematic literature review

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.