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Background <p>Online aggressive behavior (OAB) is a growing public health concern among young adults. However, most studies focus on isolated risk factors rather than their complex interplay. This study aimed to identify hierarchical risk profiles and map the systemic architecture of OAB among Chinese college students.</p> Methods <p>A cross-sectional survey was conducted with 5,431 college students in China. We integrated Classification Tree Analysis (CTA) using the CHAID algorithm to identify high-risk subgroups and Network Analysis (Ising model) to map the conditional dependencies among 15 OAB items and 7 key risk factors.</p> Results <p>The overall prevalence of OAB was 56.8%. CTA identified video game engagement as the primary stratifying variable (<i>χ²</i> = 280.24, <i>p</i> &lt; 0.001), revealing a clear risk gradient: competitive gaming (64.5%) &gt; non-competitive gaming (60.5%) &gt; no gaming (35.7%). Alcohol use and internet use further stratified these groups. The highest-risk profile (89.0% prevalence) was identified among non-competitive gamers who used alcohol and reported poor academic performance. The network analysis (95 edges; density = 0.411) identified competitive gaming as the central hub (highest Strength) and “Hacking to steal identities” as a highly influential node. “Insulting others in games” emerged as the critical bridge node (highest Bridge Strength). All network indices showed high stability (CS-coefficient ≥ 0.75).</p> Conclusions <p>OAB is highly prevalent among Chinese college students and its risk is structured hierarchically, with competitive gaming acting as a key high-risk context. Public health strategies could benefit from moving beyond universal approaches to targeted interventions aimed at specific high-risk profiles and disrupting central nodes and bridging behaviors within the OAB system.</p>

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Identifying risk profiles and systemic architecture of online aggressive behavior among college students: a large-scale cross-sectional study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月21日

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  • Lijun Zhu,
  • Yan Chen,
  • Mengyun Huang,
  • Zhengmei Fang,
  • Yurui Zhou,
  • Jiajing Zhao,
  • Yichen Li,
  • Yejun Xie,
  • Long Hua,
  • Liying Wen,
  • Weiwei Chang,
  • Yuelong Jin,
  • Yingshui Yao

摘要

Background

Online aggressive behavior (OAB) is a growing public health concern among young adults. However, most studies focus on isolated risk factors rather than their complex interplay. This study aimed to identify hierarchical risk profiles and map the systemic architecture of OAB among Chinese college students.

Methods

A cross-sectional survey was conducted with 5,431 college students in China. We integrated Classification Tree Analysis (CTA) using the CHAID algorithm to identify high-risk subgroups and Network Analysis (Ising model) to map the conditional dependencies among 15 OAB items and 7 key risk factors.

Results

The overall prevalence of OAB was 56.8%. CTA identified video game engagement as the primary stratifying variable (χ² = 280.24, p < 0.001), revealing a clear risk gradient: competitive gaming (64.5%) > non-competitive gaming (60.5%) > no gaming (35.7%). Alcohol use and internet use further stratified these groups. The highest-risk profile (89.0% prevalence) was identified among non-competitive gamers who used alcohol and reported poor academic performance. The network analysis (95 edges; density = 0.411) identified competitive gaming as the central hub (highest Strength) and “Hacking to steal identities” as a highly influential node. “Insulting others in games” emerged as the critical bridge node (highest Bridge Strength). All network indices showed high stability (CS-coefficient ≥ 0.75).

Conclusions

OAB is highly prevalent among Chinese college students and its risk is structured hierarchically, with competitive gaming acting as a key high-risk context. Public health strategies could benefit from moving beyond universal approaches to targeted interventions aimed at specific high-risk profiles and disrupting central nodes and bridging behaviors within the OAB system.