基于大规模多人在线游戏的通信日志和社交网络数据来预测社会焦虑障碍:使用图形神经网络
Kenji Yokotani1, Masanori Takano2, Nobuhito Abe3
1Graduate School of Technology, Industrial and Social Sciences, Tokushima University, Tokushima, Japan.
Psychiatry and clinical neurosciences
|February 26, 2025
概括
这项研究使用来自大规模多人在线游戏 (MMOG) 的通信日志来预测社会焦虑障碍 (SAD). 研究结果表明,MMOG数据可以帮助早期检测SAD,即使避免面对面的评估.
科学领域:
- 数字精神病学数字精神病学
- 计算社会科学 计算社会科学
- 心理健康信息学心理健康信息学
背景情况:
- 社会焦虑障碍 (SAD) 通常导致避免面对面的评估,延迟诊断和治疗.
- 早期发现SAD对于有效的干预和改善患者结果至关重要.
- 利用数字足迹提供了一种新的方法来识别患有SAD的个体,否则可能不会被诊断出来.
研究的目的:
- 预测社交焦虑障碍 (SAD) 的存在,使用来自大规模多人在线游戏 (MMOG) 的通信日志和社交网络数据.
- 探索游戏中的行为和互动作为SAD指标的潜力.
- 开发一个预测模型,用于在数字环境中早期检测SAD.
主要方法:
- 利用了819个MMOG用户的数据,包括通信日志和社交网络信息.
- 使用利博维茨社会焦虑量表 (LSAS-J) 进行SAD分类 (≥60 LSAS-J得分).
- 应用了Graph SAGE图形神经网络模型,根据用户交互和网络结构来预测SAD.
主要成果:
- 患有SAD的个体在MMOG中表现出不同的行为模式,包括社会退缩和较少的社会联系.
- SAD 预测模型获得了 0.717 的 F1 评分,证明了显著的预测准确性.
- 关键指标包括更少的游戏朋友,减少在别人的虚拟空间中花费的时间,以及更低的访问时间.
结论:
- MMOG通信日志和社交网络数据包含了SAD特征的人际回避行为有价值的指标.
- 这些数字足迹可以作为潜在的数字生物标志物,用于早期检测社会焦虑障碍.
- 这种方法为识别和支持避开传统诊断设置的SAD个体提供了一个有希望的途径.
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