  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.
