Open Access

ARTICLE

Possible Classifications of Social Network Addiction: A Latent Profile Analysis of Chinese College Students

Lin Luo^1,2,*, Junfeng Yuan¹, Yanling Wang¹, Rui Zhu¹, Huilin Xu¹, Siyuan Bi¹, Zhongge Zhang¹

1 School of Physical Education, Guizhou Normal University, Guiyang, 550025, China
2 Key Laboratory of Brain Function and Brain Disease Prevention and Treatment of Guizhou Province, Guiyang, 550025, China

* Corresponding Author: Lin Luo. Email:

(This article belongs to the Special Issue: Latent Profile Analysis in Mental Health Research: Exploring Heterogeneity through Person Centric Approach)

International Journal of Mental Health Promotion 2025, 27(6), 863-876. https://doi.org/10.32604/ijmhp.2025.064385

Received 13 February 2025; Accepted 21 May 2025; Issue published 30 June 2025

Abstract

Objectives: Social Network Addiction (SNA) is becoming increasingly prevalent among college students; however, there remains a lack of consensus regarding the measurement tools and their optimal cutoff score. This study aims to validate the 21-item Social Network Addiction Scale-Chinese (SNAS-C) in its Chinese version and to determine its optimal cutoff score for identifying potential SNA cases within the college student population. Methods: A cross-sectional survey was conducted, recruiting 3387 college students. Latent profile analysis (LPA) and receiver operating characteristic (ROC) curve analysis were employed to establish the optimal cutoff score for the validated 21-item SNAS-C. Results: Three profile models were selected based on multiple statistical criteria, classifying participants into low-risk, moderate-risk, and high-risk groups. The highest-risk group was defined as “positive” for SNA, while the remaining groups were considered “negative”, serving as the reference standard for ROC analysis. The optimal cutoff score was determined to be 72 (sensitivity: 98.2%, specificity: 96.86%), with an overall classification accuracy of 97.0%. The “positive” group reported significantly higher frequency of social network usage, greater digital media dependence scores, and a higher incidence of network addiction. Conclusion: This study identified the optimal cutoff score for the SNAS-C as ≥72, demonstrating high sensitivity, specificity, and diagnostic accuracy. This threshold effectively distinguishes between high-risk and low-risk SNA.

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Keywords

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Supplementary Material

Supplementary Material File

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