论文标题
激进，重复，有意，可见和不平衡：网络欺凌分类的精炼表示
Aggressive, Repetitive, Intentional, Visible, and Imbalanced: Refining Representations for Cyberbullying Classification
论文作者
论文摘要
网络欺凌是在线社区的普遍问题。为了确定大型社交网络中的网络欺凌案例，内容主持人依靠机器学习分类器来自动进行网络欺凌检测。但是，现有模型仍然不适合现实世界应用，这主要是由于缺乏公开可用的培训数据以及缺乏分配地面真实标签的标准标准。在这项研究中，我们使用原始注释框架解决了对可靠数据的需求。受社会科学研究的启发，我们表征了使用五个明确的因素代表其社会和语言方面的网络欺凌问题的细微问题。我们使用社交网络和基于语言的功能对此行为进行建模，从而改善了分类器的性能。这些结果证明了将网络欺凌作为一种社会现象的重要性。
Cyberbullying is a pervasive problem in online communities. To identify cyberbullying cases in large-scale social networks, content moderators depend on machine learning classifiers for automatic cyberbullying detection. However, existing models remain unfit for real-world applications, largely due to a shortage of publicly available training data and a lack of standard criteria for assigning ground truth labels. In this study, we address the need for reliable data using an original annotation framework. Inspired by social sciences research into bullying behavior, we characterize the nuanced problem of cyberbullying using five explicit factors to represent its social and linguistic aspects. We model this behavior using social network and language-based features, which improve classifier performance. These results demonstrate the importance of representing and modeling cyberbullying as a social phenomenon.
