BBCS-Net: A BERT-Based CNN-BiLSTM-SVM Hybrid Architecture for Fine-Grained Cyberbullying Detection

Abstract

Cyberbullying detection remains challenging due to diverse linguistic patterns used by offenders. The in- ductive biases of deep learning architectures add to this challenge. Single-model approaches often capture only part of abusive language. This results in distinct but partially overlapping error spaces, limiting their effectiveness in real-world scenarios. To address this issue, we propose BBCS-Net. This is a modular hybrid framework that leverages diverse inductive biases. It combines Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (BiLSTM) networks, and a margin-based Support Vec- tor Machine (SVM) classifier. In the proposed framework, a Bidirectional Encoder Representations from Transformers (BERT) model is first fine-tuned on a publicly available fine-grained cyberbullying dataset consisting of approximately 47,000 samples across six classes. This generates contextualized word repre- sentations. CNN and BiLSTM models are then trained independently. These models learn complementary feature representations. CNNs capture localized contextual cues. BiLSTMs capture long-range sequential dependencies. Penultimate-layer features from both models are fused at the feature level and classified using an SVM. The proposed approach achieves an average accuracy and F1 Score of about 98.87% us- ing nested cross-validation for model selection. On a held-out test set, it achieves 94.75% accuracy and a macro-F1 score of 94.75%. Error-space analysis shows that BBCS-Net recovers many model-specific errors. It corrects about 70.37% of CNN-only errors and 57.69% of BiLSTM-only errors. This highlights the effectiveness of feature-level hybridization in overcoming inductive-bias-driven failure modes. Latency analysis confirms the framework’s practical feasibility. It achieves a single-sample inference latency of 17.75 ms. During batch processing, the ONNX runtime reduces this to about 4.18 ms per sample. This supports near real-time cyberbullying moderation.