Comparative Analysis of Classification Models for Cyberbullying Detection in University Environments

Cyberbullying is a growing concern in university settings, with significant consequences on students’ mental, physical, and social health. This study evaluates the structural quality of the “CyberBullying University Students” dataset, which collects sociodemographic and psychological variables as well as cyberbullying experiences, in 615 German university students. Data quality assessment metrics were applied, and seven supervised classification models were trained to detect cyberbullying cases. Decision Tree models were the best-performing algorithms (achieving an average accuracy of 99.1% with outstanding evaluation metrics: 100% precision, 98.6% recall, and 99.2% F1 score). The findings suggest that this model can be a useful tool for the early detection of cyberbullying in educational settings, provided that it is integrated with an ethical approach and professional support. It is concluded that the use of artificial intelligence models can strengthen institutional prevention strategies, especially when combined with educational programs that promote empathy and institutional justice.