Ecological Predictors and Network Patterns of Cyberbullying Victimisation Among Chinese Adolescents: Integrating Machine Learning and Network Analysis

ABSTRACT

Cyberbullying victimisation (CBV) is an important concern for adolescent well‐being and child protection, yet its predictors are often examined separately across ecological domains. Guided by an expanded social‐ecological perspective, this study integrated machine learning and network analysis to examine CBV among 2006 Chinese adolescents. Twenty‐two predictors spanning individual, family, peer, school and digital‐behavioural factors were included, and six supervised machine learning models were compared after hyperparameter tuning. Results showed that random forest achieved the best performance on the held‐out test. SHAP analysis identified traditional bullying victimisation, social media addiction, risky internet behaviours, difficulties in emotion regulation and perceived school climate as the most salient predictors; cyberbullying aggression, gaming addiction, child neglect and inferiority also ranked highly. Network analysis showed that traditional bullying victimisation had the highest betweenness centrality, whereas coping self‐efficacy had the highest strength centrality. These findings position CBV within adolescents' wider developmental and safeguarding ecology rather than as a problem confined to online behaviour. Effective prevention therefore requires an ecologically informed safeguarding response that integrates early identification of offline peer victimisation and digital risk with coordinated school‐based protection, family support and the strengthening of adolescents' coping resources.