A Multimodal Correlation and Interaction-based Method for Cyberbullying Detection

Lin Zhu, Xiaoyu Sean Lu, Rui Zhang, Bo Huang

Abstract: The rapid development and explosive usage of social media make cyberbullying detection a major concern for society. However, many existing studies only focus on textual contents and ignore multimodal social media data, including texts, images, videos, etc. Although there are a few studies on multimodal cyberbullying detection, they suffer from the following limitations: 1) fail to extract features of each modality sufficiently; 2) largely ignore the significance of multimodal correlation for cyberbullying detection; 3) consider each social media session as independent. To address these issues, we propose a novel Multimodal Correlation and Interaction-based Cyberbullying Detection method (MCICD). Specifically, we construct a post-image co-attention sub-network to capture correlations between texts and images, and design a heterogeneous user-postimage interaction sub-network to explore dependencies among social media sessions, which contain multimodal information. Experimental results on two real-world session-level social media datasets demonstrate the effectiveness of our proposed method. Additionally, it is also verified that our method performs well on the early detection of cyberbullying.