Cyberbullying on social networks has emerged as a global problem with serious consequences on the mental health of victims, mainly children, and adolescents. Although there are AI-based solutions to address this issue, they face limitations such as a lack of multilingual datasets, detecting sarcasm, and detecting idioms. Research presents an innovative approach to effective cyberbullying detection using a fine-tuned GPT-3.5 model. Our main contribution is the creation of an extensive multi-label dataset of approximately 60,000 data in English, and Spanish, spanning diverse dialects. This data set was obtained by combining and processing multiple datasets from reliable sources. In addition, we developed a fine-tuned model based on GPT-3.5, capable of identifying hate speech, and offensive language in textual content on social networks. We conducted a thorough evaluation comparing our mode...

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 a...