A two-stage cyberbullying detection based on multi-view features and decision fusion strategy

Abstract

Cyberbullying has emerged as a pressing concern across various social platforms due to the escalating usage of online networks. Cyberbullying may lead victims to depression, self-harm, and even suicide. In this research, a two-stage cyberbullying detection framework based on multi-view features and decision fusion strategies is proposed. The first stage is to discover cyberbullying texts in social media, and the second stage delves into categorizing the specific forms of bullying present in the identified texts. In the two-stage detection process, features are constructed from multiple views, including Content view, Profanity view, and User view, to portray the bullying behavior. Furthermore, a decision fusion strategy is designed, incorporating both single-view features and multi-view features to enhance detection effectiveness. Finally, the research explains the complex mechanism of multi-view features in two-stage cyberbullying detection by calculating their SHAP values. The experimental results demonstrate the effectiveness of the multi-view feature and decision fusion strategy in cyberbullying detection. Notably, this framework yields impressive results, boasting an F1-score of 89.66% and an AUC of 95.98% in Stage I, while achieving an F1-score of 74.25% and an Accuracy of 79.01% in Stage II. The interpretability analysis of features affirms the pivotal role played by multi-view features, with the Content view features emerging as especially significant in the pursuit of effective cyberbullying detection.