Chen Jianyong, Su Ting, Dong Junqiang, Li Yuzhi, Feng Ju, Chen Yingxiu, Liu Gu
School of Psychology, Zhejiang Normal University, Jinhua, China.
Intelligent Laboratory of Child and Adolescent Mental Health and Crisis Intervention of Zhejiang Province, Zhejiang Normal University, Jinhua, China.
Front Psychiatry. 2025 Jan 13;15:1386845. doi: 10.3389/fpsyt.2024.1386845. eCollection 2024.
While the constitutive features of problematic social media use (PSMU) have been formulated, there has been a lack of studies in the field examining the structure of relationships among PSMU components.
This study employed network analytic methods to investigate the connectivity among PSMU components in a large sample of 1,136 college student social media users ( = 19.69, = 1.60). Components of PSMU were assessed by the Bergen Social Media Addiction Scale (BSMAS) derived from a components model of addiction. We computed two types of network models, Gaussian graphical models (GGMs) to examine network structure and influential nodes and directed acyclic graphs (DAGs) to identify the probabilistic dependencies among components.
Relapse component consistently emerged as a central node in the GGMs and as a parent node of other components in the DAGs. Relapse and tolerance components exhibited strong mutual connections and were linked to the most vital edges within the networks. Additionally, conflict and mood modification nodes occupied more central positions within the PSMU network for the low-BSMAS-score subgroup compared with the high-BSMAS-score subgroup.
Our findings shed new light on the complex architecture of PSMU and its potential implications for tailored interventions to relieve PSMU.
虽然问题性社交媒体使用(PSMU)的构成特征已被明确,但该领域缺乏对PSMU各组成部分之间关系结构的研究。
本研究采用网络分析方法,对1136名大学生社交媒体用户(平均年龄=19.69岁,标准差=1.60)的大样本进行PSMU各组成部分之间连通性的调查。PSMU的组成部分通过基于成瘾成分模型的卑尔根社交媒体成瘾量表(BSMAS)进行评估。我们计算了两种类型的网络模型,即用于检查网络结构和有影响力节点的高斯图形模型(GGM),以及用于识别各组成部分之间概率依赖性的有向无环图(DAG)。
复发成分在GGM中始终作为中心节点出现,在DAG中作为其他成分的父节点出现。复发和耐受性成分表现出强烈的相互联系,并与网络中最重要的边相连。此外,与高BSMAS分数亚组相比,冲突和情绪调节节点在低BSMAS分数亚组的PSMU网络中占据更中心的位置。
我们的研究结果为PSMU的复杂结构及其对缓解PSMU的定制干预措施的潜在影响提供了新的见解。