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Unveiling Roots of Chinese Adolescent Cyberbullying Through Explainable Machine Learning Approach
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DOI：10.1111/desc.70230.png)
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Abstract
En 中文
Cyberbullying poses a substantial threat to adolescents’ well-being, yet prevention efforts remain limited by insufficient understanding of its multilevel determinants. Guided by ecological systems theory, this study applies explainable machine learning (ML) to examine factors associated with cyberbullying perpetration across individual, family, peer, class, school, and online contexts. Questionnaire data from 2286 adolescents (Mage = 13.46 years, SD = 0.93; 11–16 years) were analyzed. Random Forest and XGBoost achieved out-of-sample accuracies of 87.35% and 85.95%, respectively. Model-based importance analyses consistently highlighted Childhood Psychological Abuse, Adverse Peer Interactions, and Cyberbullying Victimization as the highest-ranked predictors. At the system level, variables from the Family, Individual and Cyber contexts accounted for a substantial share of model importance, indicating their salience for intervention design. These findings prioritize psychosocial targets for prevention and demonstrate how explainable ML can synthesize questionnaire data to inform multi-tiered strategies against adolescent cyberbullying.
Keywords:
cyberbullying perpetration
ecosystem theory
explainable machine learning
predictive modeling
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