CyDeBoost: A SHAP-Guided Boosting Strategy Framework for Cyberbullying Detection through Mistake-Driven Learning

The widespread use of online social media has increased the urgency of detecting and mitigating harmful language, such as cyberbullying. Traditional detection systems often struggle with semantically ambiguous and context-dependent expressions, leading to frequent misclassifications. This paper presents CyDeBoost, a mistake-driven boosting framework for cyberbullying detection. The core idea of CyDeBoost is to identify and learn from misclassified instances by leveraging SHAP (SHapley Additive exPlanations) to uncover key lexical features responsible for prediction errors. Based on these insights, the framework uses large language models to generate semantically contrastive augmented samples, which are then used to fine-tune the model through a modular continual learning strategy. Specifically, lightweight adapter modules are stacked iteratively to simulate the boosting process while mitigating catastrophic forgetting. Experimental results demonstrate that CyDeBoost improves classification robustness across multiple cyberbullying categories and maintains generalization under low-resource settings. This framework aligns with the goals of content moderation on dynamic web platforms by offering interpretable, adaptive, and resource-efficient language safety solutions.