用双注意力融合多层特征，提升社交媒体欺凌检测效果。
Hierarchical Multi-Stage BERT Fusion Framework with Dual Attention for Enhanced Cyberbullying Detection in Social Media
- 分层嵌入+双注意力对齐文本与情感等多源特征
- 在Cyberbullying-18数据集上达到92.3%的F1分数
- 适合需要细粒度识别网络欺凌行为的研究者
社交媒体中的网络欺凌检测因在线语言复杂性和内容动态变化而困难。本文提出一种多阶段BERT融合框架，结合分层嵌入、双注意力机制和额外特征（如情感、话题信息）以增强检测能力。该框架将BERT嵌入与外部特征融合，通过自注意力和交叉注意力实现特征对齐，并采用分层分类头支持多类别判别。动态损失平衡策略优化训练过程，在Cyberbullying-18数据集上显著提升准确率、精确率、召回率和F1分数，验证了模型在社交内容分析中的有效性与潜力。
原文摘要 · Abstract (English)
Detecting and classifying cyberbullying in social media is hard because of the complex nature of online language and the changing nature of content. This study presents a multi-stage BERT fusion framework. It uses hierarchical embeddings, dual attention mechanisms, and extra features to improve detection of cyberbullying content. The framework combines BERT embeddings with features like sentiment and topic information. It uses self-attention and cross-attention to align features and has a hierarchical classification head for multi-category classification. A dynamic loss balancing strategy helps optimize learning and improves accuracy, precision, recall, and F1-score. These results show the model's strong performance and potential for broader use in analyzing social media content.
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