Predicting Cyberbullying Perpetration from Moral Disengagement, Online Disinhibition, Trait Aggression, and Social Network Density Using Random Forests

Authors

    Marcus Ouellet Department of Applied Psychology, Dalhousie University, Halifax, Canada
    Nomvula Dlamini * Department of Psychology, University of Cape Town, Cape Town, South Africa nomvula.dlamini@uct.ac.za
https://doi.org/10.61838/

Keywords:

Cyberbullying, Random Forest, Moral Disengagement, Trait Aggression, Online Disinhibition, Machine Learning

Abstract

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.

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Published

2026-04-10

Submitted

2025-10-16

Revised

2026-01-26

Accepted

2026-02-05

How to Cite

Ouellet, M., & Dlamini, N. (2026). Predicting Cyberbullying Perpetration from Moral Disengagement, Online Disinhibition, Trait Aggression, and Social Network Density Using Random Forests. Journal of Adolescent and Youth Psychological Studies (JAYPS), 7(4), 1-10. https://doi.org/10.61838/