SocialBullyAlert: A Web Application for Cyberbullying Detection on Minors’ Social Media

Abstract

The severe problem of cyberbullying towards minors is addressed, which has been shown to have significant impacts on the mental and emotional health of children and adolescents. Subsequently, the effectiveness of existing artificial intelligence models and neural networks in detecting cyberbullying on social media is analyzed. In response, a web platform is developed whose contribution is to identify offensive content, adapt to various slangs and idioms, and offer an intuitive interface with high usability in terms of user experience (UX) and user interface (UI) design. The application was validated with cyberbullying experts (teachers, principals, and psychologists), and the UI/UX design was also validated with users (parents). Limitations and future challenges are discussed, including varying cyberbullying regulations, the need for constant updates, and adapting to multiple languages and cultural contexts. This highlights the importance of ongoing research to enhance parental control tools in digital environments.