Promoting Security and Trust on Social Networks: Explainable Cyberbullying Detection Using Large Language Models in a Stream-Based Machine Learning Framework

Personal use of this material is permitted. Abstract --Social media platforms enable instant and ubiquitous connectivity and are essential to social interaction and communication in our technological society. Apart from its advantages, these platforms have given rise to negative behaviors in the online community, the so-called cyberbullying. An explainability dashboard is provided to promote the system's trustworthiness, reliability, and accountability. Results on experimental data report promising performance close to 90 % in all evaluation metrics and surpassing those obtained by competing works in the literature. Ultimately, our proposal contributes to the safety of online communities by timely detecting abusive behavior to prevent long-lasting harassment and reduce the negative consequences in society. Online communication has become an essential feature of social interaction thanks to the proliferation of new communities and networks [1]. More in detail, 4.90 billion people reported using social media worldwide in 2023, and this figure is expected to rise to 5.85 billion by 2027 In contrast, 15 % have experienced abuse related to sharing sensitive or private content, receiving threatening comments and messages, or being the target of false information and rumors spreading.