In the computational detection of cyberbullying, existing work largely focused on building generic classifiers that rely exclusively on text analysis of social media sessions. Despite their empirical success, we argue that a critical missing piece is the model explainability, i.e., why a particular piece of media session is detected as cyberbullying. In this paper, therefore, we propose a novel deep model, HEterogeneous Neural Interaction Networks (HENIN), for explainable cyberbullying detection. HENIN contains the following components: a comment encoder, a post-comment co-attention sub-network, and session-session and post-post interaction extractors. Extensive experiments conducted on real datasets exhibit not only the promising performance of HENIN, but also highlight evidential comments so that one can understand why a media session is identified as cyberbullying.

提出了一种深度学习模型HEterogeneous Neural Interaction Networks (HENIN)，用于社交媒体中的可解释网络欺凌检测，其包含评论编码器、帖子-评论共注意力子网络以及会话-会话以及帖子-帖子交互提取器。在真实数据集上的实验表明了HENIN的优越性能及其突出的可解释性。

HENIN: 在社交媒体上学习异质神经相互作用网络用于可解释的网络欺凌检测