Predicting Cyberbullying Perpetration from Moral Disengagement, Online Disinhibition, Trait Aggression, and Social Network Density Using Random Forests - Ouellet, Marcus; Dlamini, Nomvula (Journal of Adolescent and Youth Psychological Studies (JAYPS), 2026)
Objective: The aim of this study was to predict cyberbullying perpetration from moral disengagement, online disinhibition, trait aggression, and social network density using a Random Forest machine learning algorithm among a sample of South African adolescents and young adults.Methods and Materials: The study employed a quantitative, cross-sectional predictive research design with a sample of participants from three South African provinces. Data were collected using the Cyberbullying Offending Scale, Cyberbullying Moral Disengagement Scale, Online Disinhibition Scale, Buss-Perry Aggression Questionnaire, and a structural proxy questionnaire for social network density. A Random Forest model was trained on of the data and tested on the remaining , utilizing -fold cross-validation for hyperparameter tuning.Findings: The Random Forest model demonstrated high predictive performance on the testing set ( ), achieving an Accuracy of , Precision of , Recall of , F1-score of , and . Variable importance metrics revealed that trait aggression was the strongest predictor of cyberbullying perpetration (Mean Decrease Gini ), followed by moral disengagement (Mean Decrease Gini ) and online disinhibition. Social network density was the weakest predictor in the model (Mean Decrease Gini ).Conclusion: Individual psychological factors, specifically trait aggression and moral disengagement, overwhelmingly drive cyberbullying perpetration compared to digital structural environments, emphasizing the need for interventions focused on anger management and cognitive restructuring.