CYBY24 and Step-Wise Model for Thread-Based Fine-Grained Cyberbullying Detection

Abstract

The prevalence rate of cyberbullying on social networking sites (SNSs) is a severe issue for online safety. In literature, cyberbullying detection models are mostly modeled on datasets labeled based on standalone tweets. However, bystander roles play an important role in the severity of cyberbullying events. Involving the bystander roles to the detection of the fine-grained cyberbullying could aid in interpreting the intention of short text in standalone tweets. This work extends previous research efforts in the field of cyberbullying detection by considering the whole thread of conversation to capture the bystander roles features. In particular, we introduce the first dataset CYBY24 for thread-based fine-grained cyberbullying detection with bystander roles feature. Furthermore, we tested different detection models with different text representations. Zero-shot classification with LLMs attained less accuracy than the models with access to training data. The proposed step-wise fine-grained cyberbullying detection proved to be the optimal model using BERT and BiLSTM for the first and second classification steps, respectively. We compared the model performance with and without using the proposed bystander roles feature. As a result, the inclusion of bystander roles feature helped the model to improve its predictions.