Chinese Cyberbullying Detection Based on Hierarchical Multi-Committee Ensemble

Chinese social media cyberbullying detection faces three major challenges: implicit aggressive semantics, diverse adversarial expression patterns, and severe long-tailed class imbalance. To address these issues, this paper proposes a Hierarchical Multi-Committee Ensemble (HMCE) framework for Chinese cyberbullying detection. At the representation level, a hybrid architecture integrating pre-trained language models, multi-scale TextCNN, and attention pooling is designed to capture both global contextual dependencies and fine-grained local N-gram offensive cues. At the decision level, a three-tier progressive ensemble architecture is constructed. Specifically, the Tier-0 Committee Ensemble Layer introduces heterogeneous committees with differentiated training strategies to generate diverse Out-of-Fold (OOF) probability features. The Tier-1 Aggregation Layer employs an XGBoost-based meta-learner to model nonlinear complementary relationships among committees and improve decision fusion capability. Furthermore, the Tier-2 Calibration Layer incorporates probability calibration and adaptive threshold optimization to alleviate prediction bias under long-tailed distributions and improve minority-class recognition performance. Experiments conducted on a real-world dataset containing 23,080 Chinese social media posts demonstrate that the proposed HMCE framework achieves a Macro-F1 score of 90.26% and an accuracy of 92.50%, outperforming conventional pre-trained language models and existing ensemble approaches. The results validate the effectiveness, robustness, and generalization performance of HMCE for cyberbullying detection in complex Chinese linguistic environments.