Psychological Network Analysis for Risk and Protective Factors of Problematic Social Media Use

Decision support systems relying on computer-based information analysis have great potential to improve healthcare. For a host of complex issues in the society, such as problematic social media use (PSMU), figuring out the interactions between the different factors is challenging. Previous research has investigated risk and protective factors for PSMU among adolescents using theory-driven (i.e., confirmatory-explanatory) approaches, such as regression models. However, few studies have simultaneously examined personal, peer, and parent characteristics in predicting PSMU, and none have explored the structural relationships among potential risk and protective factors using data-driven (i.e., inductive-exploratory) approaches. Using three waves of cross-sectional data from over two thousand secondary school students, the current study employs two methods to investigate which factors appear most relevant in identifying at-risk/problematic SMU among adolescents. Logistic regression reveals that fear of missing out (FoMO), impulsivity, depressive symptoms, intensity of meeting with friends, and reactive parental rules explain unique variance in at-risk/problematic SMU. Psychological network analysis identifies self-esteem, attention problems, impulsivity, depressive symptoms, and life satisfaction as the central nodes most strongly connected in networks. Our findings demonstrate that theory- and data-driven approaches emphasize different factors involved in the emergence of at-risk/problematic SMU among adolescents, illuminating how psychological network analysis can be used to generate novel hypotheses about causal processes.