The topic of cyberbullying in social networks such as Facebook, X, Instagram and others was analyzed, in addition to the ways and tools to detect it. The problem is that although there are many tools that have been developed to detect and prevent cyberbullying, many do not meet the requirements to adapt to different social groups subjected to any particular type of research. The goal is to develop a customized tool specifically designed to diagnose and prevent cyber- bullying, focusing on tailored functionalities and their effectiveness in various social contexts. The result was a prototype tool to detect cyberbullying. The application was developed for educational and research purposes within 3 institutions, using Python as programming language and integrating several libraries to facilitate web data extraction, data processing and user interface design. It was concluded that the developed tool provides a comprehensive solution for the automated collection and analysis of online comments, with a specific focus on detecting potential cyberbullying cases. This purpose aligns with the growing need to address the problem of harassment and harmful behavior in virtual environments, where early identification and appropriate intervention are critical to ensure the safety and well-being of users
