论文标题
通过参与者角色增强网络欺凌的识别
Enhancing the Identification of Cyberbullying through Participant Roles
论文作者
论文摘要
网络欺凌是一个普遍的社会问题，对受害者的健康和安全造成了不利的后果，例如心理困扰，反社会行为和自杀。网络欺凌检测的自动化是一个最近但广泛研究的问题，目前的研究非常关注欺凌与非欺凌的二元分类。本文提出了一种通过角色建模来增强网络欺凌检测的新方法。我们利用来自AskFM的数据集执行多级分类来检测参与者角色（例如受害者，骚扰者）。我们的初步结果表明了有希望的性能，包括分别用于网络欺凌和角色分类的0.83和0.76的F1得分，表现优于基层。
Cyberbullying is a prevalent social problem that inflicts detrimental consequences to the health and safety of victims such as psychological distress, anti-social behaviour, and suicide. The automation of cyberbullying detection is a recent but widely researched problem, with current research having a strong focus on a binary classification of bullying versus non-bullying. This paper proposes a novel approach to enhancing cyberbullying detection through role modeling. We utilise a dataset from ASKfm to perform multi-class classification to detect participant roles (e.g. victim, harasser). Our preliminary results demonstrate promising performance including 0.83 and 0.76 of F1-score for cyberbullying and role classification respectively, outperforming baselines.
