Identifying Latent Psychological Subtypes of Adolescents With Internet Addiction Using Unsupervised Machine Learning Techniques
Keywords:
Internet addiction, adolescents, unsupervised machine learning, psychological subtypes, clustering analysisAbstract
Objective: The objective of this study was to identify empirically derived latent psychological subtypes of adolescents with internet addiction by applying unsupervised machine learning techniques to multidimensional emotional, behavioral, and contextual data.
Methods and Materials: This cross-sectional study was conducted among secondary school adolescents in Tunisia using a multi-stage cluster sampling strategy. Participants completed standardized self-report measures assessing internet addiction severity, depressive and anxiety symptoms, perceived stress, emotion regulation difficulties, impulsivity, loneliness, self-esteem, family support, academic engagement, and sleep quality. After data preprocessing, including standardization and handling of missing values, unsupervised machine learning techniques were applied. Dimensionality reduction was used to address multicollinearity, followed by multiple clustering algorithms, including k-means, hierarchical clustering, and Gaussian mixture modeling. Internal validation indices and stability analyses were employed to determine the optimal clustering solution, and external variables were used to profile and validate the resulting subtypes.
Findings: Unsupervised analyses identified four distinct latent psychological subtypes. Inferential comparisons demonstrated significant between-cluster differences in internet addiction severity, emotional distress, impulsivity, loneliness, self-esteem, sleep quality, family support, and academic engagement. The Emotionally Distressed subtype exhibited significantly higher depression, anxiety, stress, and internet addiction severity. The Impulsive–Sensation Seeking subtype showed significantly elevated impulsivity and night-time internet use. The Socially Withdrawn subtype was characterized by significantly higher loneliness and poorer peer functioning. The Relatively Adaptive subtype demonstrated significantly lower psychological symptoms and internet addiction severity alongside higher protective factors.
Conclusion: The findings demonstrate that adolescents with internet addiction are psychologically heterogeneous and can be meaningfully classified into distinct latent subtypes with differing risk and protective profiles.
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