Toward Multi-Modal Approach for Identification and Detection of Cyberbullying in Social Networks

Given the widespread use of social networks in people's everyday lives, cyberbullying has emerged as a major threat, especially affecting younger users on these platforms. This matter has generated significant societal apprehensions. Prior studies have primarily concentrated on analyzing text in relation to cyberbullying. However, the dynamic nature of cyberbullying covers many goals, communication platforms, and manifestations. Conventional text analysis approaches are not effective in dealing with the wide range of bullying data seen in social networks. In order to tackle this difficulty, our suggested multi-modal detection approach integrates data from diverse sources including photos, videos, comments, and temporal information from social networks. In addition to textual data, our approach employs hierarchical attention networks to record session features and encode various media information. The resulting multi-modal cyberbullying detection platform provides a comprehensive approach to address this emerging kind of cyberbullying. By conducting experimental analysis on two actual datasets, our framework exhibits greater performance in comparison to many state-of-the-art models. This highlights its effectiveness in dealing with the intricate nature of cyberbullying in social networks.