Performance analysis of transformer-based architectures and their ensembles to detect trait-based cyberbullying

The influence of social media is one of the most dominating characteristics of the current era, and this has led cyberbullying to grow into a more serious social issue. As a result, automated cyberbullying detection systems need to be an integral part of almost all social media platforms. Past studies on this domain have primarily focused on hand-picked features and traditional machine learning approaches for cyberbullying detection from user comments on social media. Recently, transformers have been proved to be quite effective in various language-related tasks; however, their effectiveness has not been extensively explored in this particular domain. In this study, we evaluate the individual performance of several well-known transformer-based architectures and aim to contribute to the development of automated cyberbullying detection systems by proposing our own transformer-based ensemble framework. Our proposed framework is evaluated on a balanced and an imbalanced dataset, both of which are constructed from a large collection of Twitter comments and are publicly available. Our proposed architecture outperforms all the baseline models, as well the individual standalone classifier networks that are used in our ensemble, obtaining an average F1-score of 95.92% on the balanced dataset and an average F1-score of 87.51% on the imbalanced dataset. We further investigate the cases where our proposed architecture misclassifies samples from both datasets, preventing it from achieving a perfect score. Our models and code have been made publicly available (https://​github.​com/​tasnim7ahmed/​Extended-Cyberbullying-Detection/​).