Abstract
Social isolation and loneliness, which have been increasing in recent years strongly contribute toward suicide rates. Although social isolation and loneliness are not currently recorded within the US National Violent Death Reporting System's (NVDRS) structured variables, natural language processing (NLP) techniques can be used to identify these constructs in law enforcement and coroner medical examiner narratives. Using topic modeling to generate lexicon development and supervised learning classifiers, we developed high-quality classifiers (average F1: .86, accuracy: .82). Evaluating over 300,000 suicides from 2002 to 2020, we identified 1,198 mentioning chronic social isolation. Decedents had higher odds of chronic social isolation classification if they were men (OR = 1.44; CI: 1.24, 1.69, p<.0001), gay (OR = 3.68; 1.97, 6.33, p<.0001), or were divorced (OR = 3.34; 2.68, 4.19, p<.0001). We found significant predictors for other social isolation topics of recent or impending divorce, child custody loss, eviction or recent move, and break-up. Our methods can improve surveillance and prevention of social isolation and loneliness in the United States.
Abstract (translated)
近年来，社会孤立和孤独感的增加显著推动了自杀率的增长。尽管美国国家暴力死亡报告系统（NVDRS）当前并未在其结构化变量中记录这些因素，但自然语言处理（NLP）技术可以用来在执法部门和验尸官的描述文档中识别这些特征。通过主题建模生成词典开发和监督学习分类器，我们成功创建了高质量的分类器（平均F1分数为0.86，准确率为0.82）。评估从2002年到2020年的超过30万起自杀案例后，我们发现有1,198起案例提及慢性社会孤立。死者若为男性，则被归类于慢性社会孤立的风险更高（OR = 1.44；CI：1.24, 1.69，p<.0001），同性恋者风险也较高（OR = 3.68；1.97, 6.33，p<.0001），离婚者同样具有更高的风险（OR = 3.34；2.68, 4.19，p<.0001）。我们还发现近期或即将面临的离婚、失去子女监护权、被驱逐或近期内搬迁以及分手等事件是其他社会孤立主题的重要预测因素。我们的方法可以改进美国对社会孤立和孤独感的监测与预防措施。
URL
https://arxiv.org/abs/2506.15030
