Time-User Heterogeneous Neural Interaction Network For Cyberbullying Detection

Guangkun Zhou, Xiaoyu Sean Lu, Rui Zhang, Haoyue Liu, Bo Huang

Abstract: In the field of cyberbullying detection, it is crucial to understand and analyze the structural features of media sessions. However, most studies ignore the dynamic correlation between comments and temporal information as well as temporal pattern matching among sessions. To address the problem, this study proposes a Time-User Heterogeneous Neural Interaction Network (TUHIN) model. It consists of five modules: session encoding module, comment interaction module, session-time attention module, user interaction module and aggregation module. Specifically, considering comment and temporal information, we adopt a hierarchical structure to extract features from the comment and session levels of media sessions. Then, in order to analyze the interaction patterns among users, we use graph convolutional networks to model the influential situations of users participation in media sessions. Comparing with the baseline models on public Instagram and Vine datasets, our model performs better on both Recall and F1.