Using Deep Learning for Behavioral Pattern Recognition for Avoiding Cyberbullying: A Comprehensive Review
Keywords:
Cyberbullying, Deep learning, Multimedia analysis, Natural language processing, Online harassment, Pattern recognition, Transformer modelsAbstract
Cyberbullying poses severe psychological and emotional risks worldwide. Traditional monitoring and rule-based detection methods struggle with scalability and contextual adaptability. This review critically examines deep learning-based behavioral pattern recognition models as a scalable and adaptive solution for cyberbullying detection. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Transformer models enable fine-grained feature extraction from multimodal social data, effectively capturing linguistic, temporal, and visual cues of online aggression. The term “psychometrically validated” is replaced with “empirically validated through benchmarked datasets,” ensuring methodological transparency. Comparative analyses of recent deep learning studies demonstrate accuracy improvements of 5–12% over traditional models when applied to benchmark datasets such as Kaggle’s “Cyberbullying Detection” and Twitter Hate Speech datasets. Key challenges, including data imbalance, interpretability, and privacy, are linked to emerging research directions involving fairness-aware, explainable, and privacy-preserving deep architectures.
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