Discovery of cybervictimization-associated factors among adolescents: Using machine learning and network analysis

The issue of cybervictimization among adolescents is escalating, presenting a significant public concern. Recent research has turned to use a more robust method, machine learning, to explore important predictors for adolescent cybervictimization. The current study tested an extreme gradient boosting (XGBoost) machine learning algorithm to detect cybervictimization-associated factors among adolescents and used network analysis to explore associations between these factors for future targeted interventions. By combining a 6-month longitudinal design, a total of 1181 Chinese adolescents (the average age was 15.78 +/- 1.67 years, 55.9% girls) participated in the study. The XGBoost model with satisfactory performance selected the top 10 features from 22 variables associated with cybervictimization by using SHAP value. The network analysis results indicated that maladaptive cognitive emotion regulation strategy is a central node and it has positive correlations with negative self-schema and depression. The XGBoost model and network analysis were useful methods for discovering and understanding cybervictimization-related factors among adolescents. Moreover, these essential factors could offer insights into future interventions for cybervictimization.