Hierarchical Attention Networks for Cyberbullying Detection on the Instagram Social Network

Cyberbullying has become one of the most pressing online risks for young people and has raised serious concerns in society. Emerging literature identifies cyberbullying as repetitive temporal acts rather than one-off incidents. Yet, there has been relatively little work to model the hierarchical structure of social media sessions and the temporal dynamics of cyberbullying in online social network sessions. We propose a hierarchical attention network for cyberbullying detection that takes these aspects of cyberbullying into account. The primary distinctive characteristics of our approach include: (i) a hierarchical structure that mirrors the structure of a social media session; (ii) two levels of attention mechanisms applied to the word and comment level, enabling the model to pay different amount of attention to words and comments dependent on the context; and (iii) a cyberbullying detection task that also predicts the interval of time between two adjacent comments. This allows the model to exploit the commonalities and differences across these two tasks to improve the performance of cyberbullying detection. Experiments on a real-world dataset from Instagram, the social media platform on which the highest percentage of users have reported experiencing cyberbullying, reveal that the proposed architecture outperforms the state-of-the-art method.