Abstract
BACKGROUND: Digital addiction in adolescents is often conceptualized as a latent construct, masking the complex interplay between specific symptoms and psychological resources. This study aims to map the specific symptom-level interactions between digital addiction (Social Media Addiction, Nomophobia) and cognitive resources (Self-Regulation, Self-Efficacy) using a large-scale network analysis approach. METHOD: The study employed a split-half cross-validation strategy with a total sample of 1,497 adolescents (M(age) = 16.07). The dataset was randomly partitioned into Discovery (n = 748) and Validation (n = 749) groups. Network structure was estimated using the Gaussian Graphical Model (GGM) with polychoric correlations. Robustness was assessed via Network Comparison Test (NCT), sensitivity analysis controlling for demographics, and case-dropping bootstrapping. RESULTS: The network analysis revealed a distinct topological separation between addiction symptoms and cognitive resources. Centrality analysis identified “intolerance of environmental restriction” (Nomophobia item d6) as the most influential node in the network (highest EI = 1.81 relative to other nodes). Bridge centrality analysis highlighted “withdrawal upon prohibition” (Social Media Addiction item b5) as the critical bridge as the strongest bridge node (highest BS = 0.61) associating addiction symptoms with cognitive resources. Conversely, “self-monitoring” (a8) emerged as the most prominent inversely associated (BS = 0.47). The network structure was highly consistent across subsamples (NCT: p > 0.05), robust to demographic covariates (r = 0.98), and stable [CS((cor=0.70)) = 0.60]. CONCLUSIONS: The findings suggest that adolescent digital addiction is characterized centrally by restriction intolerance and withdrawal anxiety rather than mere usage gratification. Interventions targeting self-monitoring skills and building tolerance to disconnection may be strategically positioned to destabilize the psychopathological network.