In the most comprehensive population surveys, mental health is only broadly captured through questionnaires asking about “mentally unhealthy days” or feelings of “sadness.” Further, population mental health estimates are predominantly consolidated to yearly estimates at the state level, which is considerably coarser than the best estimates of physical health. Through the large-scale analysis of social media, robust estimation of population mental health is feasible at finer resolutions. In this study, we created a pipeline that used ~1 billion Tweets from 2 million geo-located users to estimate mental health levels and changes for depression and anxiety, the two leading mental health conditions. Language-based mental health assessments (LBMHAs) had substantially higher levels of reliability across space and time than available survey measures. This work presents reliable assessments of depression and anxiety down to the county-weeks level. Where surveys were available, we found moderate to strong associations between the LBMHAs and survey scores for multiple levels of granularity, from the national level down to weekly county measurements (fixed effects β = 0.34 to 1.82; p < 0.001). LBMHAs demonstrated temporal validity, showing clear absolute increases after a list of major societal events (+23% absolute change for depression assessments). LBMHAs showed improved external validity, evidenced by stronger correlations with measures of health and socioeconomic status than population surveys. This study shows that the careful aggregation of social media data yields spatiotemporal estimates of population mental health that exceed the granularity achievable by existing population surveys, and does so with generally greater reliability and validity.

在最全面的人口调查中，心理健康状况只是通过询问“精神不健康的日子”或“悲伤”的感觉的问卷来广​​泛了解。此外，人口心理健康估计主要合并为州一级的年度估计，这比身体健康的最佳估计要粗糙得多。通过对社交媒体的大规模分析，可以以更精细的分辨率对人口心理健康状况进行稳健估计。在这项研究中，我们创建了一个管道，使用来自 200 万地理位置用户的约 10 亿条推文来估计心理健康水平以及抑郁和焦虑这两种主要心理健康状况的变化。基于语言的心理健康评估（LBMHA）在空间和时间上的可靠性远远高于现有的调查措施。这项工作提供了对县周级别的抑郁和焦虑的可靠评估。在进行调查的情况下，我们发现 LBMHA 与多个粒度级别的调查得分之间存在中等到强的关联，从国家级别到每周县测量（固定效应β  =  0.34至1.82；p  < 0.001）。 LBMHA 表现出时间有效性，在一系列重大社会事件后显示出明显的绝对增加（抑郁症评估的绝对变化+23%）。 LBMHA 显示出更高的外部效度，这通过与健康和社会经济状况指标的相关性比人口调查更强来证明。这项研究表明，社交媒体数据的仔细汇总可以对人口心理健康状况进行时空估计，超出了现有人口调查所能达到的粒度，并且通常具有更高的可靠性和有效性。