中国大学生社交媒体成瘾、心理健康与学业倦怠关系的网络分析

Network Analysis of the Associations between Social Media Addiction, Mental Health, and Academic Burnout among Chinese College Students

摘要: 目的：随着互联网社交媒体应用的快速普及，其对大学生心理健康的影响日益凸显。本文旨在探讨社交媒体成瘾与心理健康问题(焦虑、抑郁、压力)及学业倦怠之间的网络特征与核心症状，期望为数字时代的心理干预与学业支持提供依据。方法：采用横断面设计，以某军医大学本科生与博士生为对象(n = 432)，测量抑郁–焦虑–压力量表(DASS)、学业倦怠量表以及Bergen社交媒体成瘾量表(BSMAS)。使用R语言进行网络分析，基于高斯图模型(Gaussian Graphical Model, GGM)并采用LASSO收缩与EBICglasso稀疏化估计网络结构，计算节点中心性与桥接期望影响(Bridge Expected Influence, BEI)并利用标准重抽样程序评估网络模型的稳定性和估计精度。结果：桥接预期影响显示，“抑郁”在网络中稳定地处于最核心位置，作为连接社交媒体成瘾、学业倦怠与心理压力的关键桥接节点。社交媒体成瘾与学业倦怠及心理健康问题总体呈显著正相关；其中，过度使用社交媒体可能削弱注意资源与学业投入，进而诱发或加剧学业倦怠，并强化焦虑与抑郁体验。结论：社交媒体成瘾对大学生心理健康与学业表现具有显著不良影响。本研究揭示了三者之间的复杂关联结构与核心桥接节点，为制定针对性的减桥–断链式干预提供了科学依据，以帮助学生降低对社交媒体的依赖，恢复健康的学习与生活状态。

Abstract: Objective: With the rapid proliferation of internet-based social media applications, their impact on college students’ mental health has become increasingly salient. This study maps the network characteristics and core symptoms linking social media addiction with mental health problems (anxiety, depression, stress) and academic burnout, to inform targeted psychological interventions and academic support in the digital era. Methods: A cross-sectional survey was administered to undergraduates and doctoral students at Air Force Medical University (n = 432). Participants completed the Depression Anxiety Stress Scales (DASS), the Academic Burnout Inventory, and the Bergen Social Media Addiction Scale (BSMAS). Network analysis in R estimated a Gaussian Graphical Model regularized via LASSO with EBICglasso. Node centrality and Bridge Expected Influence (BEI) were computed, with stability and accuracy assessed using standard resampling procedures. Results: BEI indicated that Depression was the most central and stable bridge node connecting social media addiction, academic burnout, and psychological stress. Social media addiction showed significant positive associations with both academic burnout and adverse mental health outcomes. Excessive social media use appears to divert attentional resources and diminish academic engagement, thereby fostering academic burnout and amplifying anxiety and depressive symptoms. Conclusions: Social media addiction exerts a substantial detrimental influence on college students’ mental health and academic functioning. By delineating the complex network structure and identifying bridge nodes, the study provides a methodological basis for bridge-reducing/chain-disrupting interventions to help students reduce dependence on social media and restore healthy learning and living patterns.