打破沉默:利用社交互动数据,通过网络分析和机器学习在线识别高风险的自杀用户
Damien Lekkas1,2, Nicholas C Jacobson3,4,5,6
1Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, 46 Centerra Parkway, Suite 300, Office #313S, Lebanon, NH, 03766, USA. Damien.Lekkas.GR@dartmouth.edu.
Scientific reports
|August 21, 2024
概括
在线社交网络分析可以识别自杀思想和行为 (STB) 风险较高的个人. 网络特征如过渡性和密度有效地表明在线社区中自杀风险增加.
科学领域:
- 数字心理健康数字心理健康
- 计算社会科学 计算社会科学
- 网络科学 网络科学
背景情况:
- 自杀思想和行为 (STB) 是一个重要的公共卫生问题,经常受到污名和难以检测.
- 尽管存在审查制度,但在线环境为了解性病风险提供了独特的数据.
- 来自社交互动的数字标记可能会改善STB风险检测.
研究的目的:
- 开发和验证机器学习模型,用于在线自杀论坛中预测高风险用户 (HRU).
- 识别主要的自我中心网络特征,表明自杀风险增加.
- 探索社交互动动态作为STB的数字标记物的实用性.
主要方法:
- 在一个支持选择自杀的在线论坛上收集了来自192个人的网络数据,涵盖了超过320万次的互动.
- 设计了17个以自我为中心的网络特征,以量化社会互动和参与动态.
- 训练,验证和测试一个机器学习分类器来预测HRU状态,分析特征重要性.
主要成果:
- 该分类模型实现了0.73的测试AUC,表明有效预测HRU状态.
- 关键的预测特征包括过渡性,密度和内度中心性.
- 预测的HRU表现出明显的网络特性,包括不太频繁的参与和"小世界"网络结构.
结论:
- 在网络互动中基于网络的社会行为模式可以作为自杀风险增加的指标.
- 这项研究表明,分析STB研究未经审查的在线社区的社会动态有潜力.
- 这些发现支持将网络特征集成到未来的STB描述,预测和预防策略中.
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