Abstract

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.

Highlights

  • In recent years, cyberbullying has become one of the most pressing online risks among youth and raised serious concerns in society

  • The results suggest that modeling interactions between sessions and between posts through graph convolutional networks (GCN) in HEterogeneous Neural Interaction Networks (HENIN) is important

  • To answer EQ4, we evaluate the performance of the explainability of our HENIN model from the perspective of comments

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Summary

Introduction

In recent years, cyberbullying has become one of the most pressing online risks among youth and raised serious concerns in society. Research from the American Psychological Association and the White House has revealed more than 40% of young people in the US indicate that they have been bullied on social media platforms (Dinakar et al, 2012). Such a growing prevalence of cyberbullying on social media has detrimental societal effects, such as victims may experience lower self-esteem, increased suicidal ideation, and a variety of negative emotional responses (Hinduja and Patchin, 2014). It has become critically important to be able to detect and prevent cyberbullying

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