Uncovering Latent Patterns in Social Media Usage and Mental Health: A Clustering-Based Approach Using Unsupervised Machine Learning

2026-04-27

AI Summary

This study addresses a critical gap in existing research by conducting an unsupervised clustering of social media users based on multidimensional behavioral and psychological indicators—including anxiety, depression, loneliness, and sleep quality—to uncover heterogeneous risk profiles. Leveraging data from 551 participants, the analysis integrates K-nearest neighbors imputation, outlier detection, PCA for dimensionality reduction, and an optimized K-means clustering approach validated via the elbow method and silhouette score. The pipeline identifies six distinct user subgroups with significant psychological differences (silhouette coefficient = 0.32). Notably, the findings reveal a significant positive correlation between social media usage duration and anxiety levels (r = 0.28), offering both empirical evidence and methodological innovation to inform targeted mental health interventions.

Technology Category

Data Mining & Knowledge Management: Anomaly/Outlier DetectionMachine Learning: ClusteringApplication Domains: Humanities & Computational Social Science

Application Category

Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web textUser Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social WebSocial Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

The widespread adoption of social media has heightened interest in its psychological effects, particularly on mental health indicators such as anxiety, depression, loneliness, and sleep quality, as these platforms increasingly influence social interactions and well-being. Although previous research has examined correlations between social media use and mental health, few studies have utilized unsupervised machine learning to segment users based on behavioral and psychological patterns, leaving a gap in identifying distinct risk profiles across diverse groups. This study seeks to address this by segmenting individuals according to their social media usage and psychological well-being, employing clustering to reveal hidden patterns and evaluate their mental health implications. Data from 551 participants, collected via an online survey, were preprocessed using KNN imputation for missing values, one-hot encoding for categorical variables like Gender with 5 unique values, and outlier detection via IQR and Z-score methods. K-Means clustering, optimized at 6 clusters using the Elbow Method and a Silhouette Score of 0.32, was applied, with PCA reducing 22 dimensions for visualization and a correlation heatmap highlighting relationships, such as a 0.28 correlation between social media hours and anxiety.

Problem

• social media usage
• mental health
• unsupervised machine learning
• user clustering
• latent patterns

Innovation

• unsupervised machine learning
• clustering
• mental health
• social media usage
• K-Means