Digital attention disruption and academic impact in adolescents: a data-driven network and machine learning analysis

Academic impact associated with digital media use is a topic of considerable concern in the fields of adolescent mental health and education; however, the role of attention status in this context remains largely unknown. The present study aimed to systematically investigate the multidimensional association patterns between attention-related characteristics and academic impact among adolescents using data mining approaches. The data were derived from two publicly available cross-sectional survey datasets, and a final sample of 799 adolescents aged 19 years and below was included after age screening. The analytical framework integrated LASSO regression, regularized partial correlation network analysis based on EBICglasso, XGBoost predictive modeling with SHAP interpretation, and latent profile analysis. Results showed that LASSO regression identified stress level (coefficient = 0.384) and social media use duration (coefficient = 0.297) as the strongest predictors of academic impact. Network analysis revealed a three-community structure, corresponding to the "digital distraction load," "psychological-academic exhaustion," and "academic engagement" dimensions, with academic impact located at the intersection of the first two communities. The XGBoost model achieved an AUC of 0.842, and SHAP dependence plots further revealed a nonlinear pattern with an inflection point at approximately 3.5 hours for daily social media use duration and a nonlinear effect of stress level. Latent profile analysis identified three subgroups, among which the "high stress-exhaustion" group showed a probability of academic impact as high as 79.4%. These findings indicate that attention-related characteristics can effectively predict academic impact status in adolescents, with stress level, social media use duration, and number of notifications constituting the most influential set of predictors. The results provide data-driven theoretical evidence and practical implications for the early identification and stratified intervention of academic impact in adolescents.