用无监督学习发现社交媒体使用与心理健康的隐藏关联模式
Uncovering Latent Patterns in Social Media Usage and Mental Health: A Clustering-Based Approach Using Unsupervised Machine Learning
- 通过聚类分析用户行为与心理状态，识别出6种不同使用模式
- 发现每日使用时长与焦虑水平存在0.28相关性，睡眠质量最差组使用超4小时
- 适合心理学、数据科学交叉研究者参考
社交媒体的普及引发了对其心理影响的广泛关注，尤其在焦虑、抑郁、孤独感和睡眠质量等指标方面。尽管已有研究探讨其关联性，但极少利用无监督机器学习对用户行为与心理特征进行分群，难以揭示多样化群体中的风险画像。本研究基于551名参与者在线调查数据，采用KNN插补处理缺失值，对性别等5类类别变量进行独热编码，并通过四分位距与Z-score检测异常值。运用优化为6个簇的K均值聚类（肘部法则与轮廓系数0.32），结合主成分分析降维可视化，结果表明社交媒体使用时长与焦虑呈0.28正相关，睡眠质量最差群体日均使用超4小时，且各维度间存在显著相关性。
原文摘要 · Abstract (English)
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.
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