针对网络欺凌中隐性攻击检测难问题，提出情绪自适应训练框架提升模型性能。
Detecting harassment and defamation in cyberbullying with emotion-adaptive training
- 引入情绪感知机制，通过跨领域知识迁移增强模型对隐性攻击的识别能力。
- 在低资源条件下，多类任务平均宏F1提升20%，显著优于传统方法。
- 适用于社交媒体内容安全、舆情监控等需要精准识别隐性攻击的场景。
现有社交平台网络欺凌检测研究主要聚焦于显性骚扰，通常作为二分类任务处理。然而，网络欺凌形式多样，包括贬损和骚扰，尤其针对公众人物。当前针对这些多样形式的标注数据稀缺。本文构建了一个包含骚扰与诽谤两类事件的名人网络欺凌数据集，并评估了多种基于Transformer的模型（如RoBERTa、Bert、DistilBert、Electra、XLNet、Mpnet、T5、Llama2和Llama3）在低资源条件下的表现。结果表明，这些模型在显性骚扰二分类任务上表现良好，但在骚扰与贬损的多分类任务中性能显著下降。为此，本文提出情绪自适应训练框架（EAT），将情绪识别领域的知识迁移到网络欺凌检测中，以识别间接欺凌行为。EAT在九种不同模型上均使平均宏F1、精确率和召回率提升20%，且理论分析与大量实验验证了其有效性。
原文摘要 · Abstract (English)
Existing research on detecting cyberbullying incidents on social media has primarily concentrated on harassment and is typically approached as a binary classification task. However, cyberbullying encompasses various forms, such as denigration and harassment, which celebrities frequently face. Furthermore, suitable training data for these diverse forms of cyberbullying remains scarce. In this study, we first develop a celebrity cyberbullying dataset that encompasses two distinct types of incidents: harassment and defamation. We investigate various types of transformer-based models, namely masked (RoBERTa, Bert and DistilBert), replacing(Electra), autoregressive (XLnet), masked&permuted (Mpnet), text-text (T5) and large language models (Llama2 and Llama3) under low source settings. We find that they perform competitively on explicit harassment binary detection. However, their performance is substantially lower on harassment and denigration multi-classification tasks. Therefore, we propose an emotion-adaptive training framework (EAT) that helps transfer knowledge from the domain of emotion detection to the domain of cyberbullying detection to help detect indirect cyberbullying events. EAT consistently improves the average macro F1, precision and recall by 20% in cyberbullying detection tasks across nine transformer-based models under low-resource settings. Our claims are supported by intuitive theoretical insights and extensive experiments.
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