Longitudinal Adolescent Social Media Addiction and Mental Health Symptom Networks

A longitudinal dataset of 1,240 adolescents surveyed in 2023 and 2024, measuring social media addiction, anxiety, and depression symptoms. The data includes responses to the Bergen Social Media Addiction Scale (BSMAS), Patient Health Questionnaire–9 (PHQ–9), and Generalized Anxiety Disorder–7 (GAD–7) at two time points. The dataset was created by Wenxin Xu and shared under a CC-BY-4.0 license.

Use Cases

• Analyzing the temporal stability of symptom networks based on correlation metrics between two time points.
• Identifying core symptoms of social media addiction based on Expected Influence centrality scores.
• Detecting bridge symptoms linking social media addiction to anxiety and depression using Bridge Expected Influence.
• Comparing network structures of symptom comorbidity between different mental health constructs.

Strengths

• Longitudinal design with 1,240 adolescent participants surveyed at two time points (2023 and 2024).
• Includes validated clinical scales (BSMAS, PHQ-9, GAD-7) for measuring three distinct mental health constructs.
• Network analysis results show strong temporal stability with high correlations (r = .699 – .973) reported for edge weights and centrality measures.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The sample is predominantly female (1,061 females vs. 179 males), which may affect generalizability.

Provenance

Source

figshare

Collection Method

Survey data collected from adolescents using standardized clinical questionnaires.

Time Range

2023 to 2024

Freshness

Last updated 2026-04-22 05:46:01; freshness should be verified.

Geography

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