Hierarchical Cyber Troll Detection with Text and User Behavior

The cyber trolls in social media have threatened users' personal rights and social order. By publishing offensive and disgusting comments on social media, cyber trolls try to shift the focus of the discussion, provoke others, and even trigger antagonistic behaviors among groups. Most of existing studies were based on English scenes. These methods mainly distinguished the cyber trolls from ordinary users according to whether the comments were offensive or not. But the studies ignored the diversity and concealment of cyber trolls, so it was difficult to identify them pertinently and finely. This paper builds a new Chinese cyber troll dataset and presents a hierarchical cyber troll detection method based on text and user behavior. Starting from the behavior motivation of cyber trolls, we divide users into two levels: inactive and active. For each level of users, this paper proposes some new behavior indicators based on the user statistical features, and selects the text features with significant influence from the comments. Next, these two types of features are input into the XGBoost model for detection. Finally, the detected cyber trolls at each level are combined as the final detection result. Experiments on our dataset show that our method is superior to other baseline methods.