KMAN Publication Inc. (KMANPUB) Journal of Adolescent and Youth Psychological Studies (JAYPS) 2981-2526 6 Serial Number 40 2025 12 10 Identifying Psychological Predictors of Cyberbullying Involvement in Adolescents Using Machine Learning Identifying Psychological Predictors of Cyberbullying Involvement in Adolescents Using Machine Learning 1 11 10.61838/kman.jayps.4909 EN Journal Article 2025 07 23 <table> <tbody> <tr> <td> <p><strong>Objective:</strong> The objective of this study was to identify and model the key psychological predictors of cyberbullying involvement among adolescents using supervised and explainable machine learning approaches.</p> <p><strong>Methods and Materials: </strong>This cross-sectional study was conducted among secondary school adolescents in Bulgaria using a multi-stage cluster sampling strategy. Participants completed a comprehensive battery of validated self-report instruments assessing cyberbullying involvement, moral disengagement, aggression, impulsivity, emotion regulation difficulties, empathy, loneliness, perceived peer support, problematic internet use, and online disinhibition. Data were analyzed using multiple supervised machine learning algorithms, including regularized logistic regression, support vector machines, random forest, gradient boosting, and extreme gradient boosting. Model performance was evaluated using stratified cross-validation and inferential performance metrics, and explainability analyses were conducted using SHAP values to determine the relative contribution of psychological predictors.</p> <p><strong>Findings: </strong>Inferential machine learning results demonstrated that ensemble-based models significantly outperformed linear models in predicting cyberbullying involvement, indicating the presence of nonlinear and interactive psychological effects. Moral disengagement, online disinhibition, trait aggression, and emotion regulation difficulties emerged as the most influential predictors, showing strong positive associations with cyberbullying involvement. Problematic internet use and impulsivity also contributed significantly to model predictions, while empathy and perceived peer support showed weaker but statistically meaningful protective effects. Subgroup analyses revealed distinct psychological profiles across victims, perpetrators, and perpetrator–victims, with the latter group exhibiting the highest cumulative risk across multiple psychological dimensions. Model robustness analyses indicated stable predictive performance across gender and age groups.</p> <p><strong>Conclusion: </strong>The findings demonstrate that cyberbullying involvement among adolescents can be effectively predicted using machine learning models integrating multidimensional psychological data.</p> </td> </tr> </tbody> </table> https://journals.kmanpub.com/index.php/jayps/article/download/4909/9308