SCH: Advancing Language-based Analyses of Social Media to Reliably Monitor Variation in Population

这项提案旨在解决公共卫生中的一个关键挑战：持续和细粒度的 人口心理健康的测量。目前，在最大的人口调查中，衡量 心理健康在时间上限于年度估计，在空间上限于以大都市为主的地区，在 范围仅限于关于“精神健康”或“抑郁”的单一问题。通过跨学科的工作，我们 建议推进基于语言的社交媒体分析方法，以衡量心理健康状况， 分年度，·在县一级，并将范围从抑郁扩大到焦虑、压力以及 保护性心理健康因素(如健康的社会关系)。这将为研究提供 社区具有更丰富、及时和本地化的人口心理健康状况。 社交媒体的语言已被证明是一种灵活的人口信息来源 行为、思想和感觉它具有很高的空间和时间分辨率，这意味着 研究和监测人群心理健康的潜力。然而，跟踪的方法 社交媒体上不同社区的心理状态没有考虑到空间因素 和时间上的混淆或充分利用数据的多层次结构(和样本量)。 拟议的工作将开发多层次的方法来控制空间相关性和社区 社会经济协方差，以增加统计能力和测量的准确性。增加的 POWER还将更好地支持流行病学的准实验设计，这些设计将与 这项基于推特的估计旨在跟踪政策和社会经济冲击对心理健康的影响。 这项提案中的工作可能会显著改变人口心理健康的研究和 能够应用和跟踪政策的效力，以改善公共健康，它将允许研究人员观察 每季度和县人口心理健康的时间变化，这提供了衡量 基础设施，观察对经济冲击和政策等自然实验的反应变化 干预措施。这将是可能的，几乎实时，而不是像目前的几年报告延迟 调查方法。正在进行的衡量将有助于确定最需要的领域，并可能有所帮助 确定资源分配的优先顺序。心理健康决定因素的改进准实验模型 可以为政策干预提供信息，正在进行的监测可以建立其有效性的证据，在TOM， 从长远来看，精神不健康给社会带来的负担可能会大幅减轻。

This proposal seeks to address a key challenge in public health : the ongoing and fine-grained measurement of population mental health. Currently, in the largest population surveys, measurement of mental health is limited in time to annual estimates, in space to predominantly metropolitan areas and in scope to single questions about "mental health" or "depression". Through interdisciplinary work, we propose to advance approaches to language-based analysis of social media to measure mental health, sub-annually, ·at the county level and broaden the scope beyond depression to anxiety, stress, as well as to protective mental health factors (such as healthy social relationships). This will provide the research community with a much richer, timely and localized picture of population mental health. The language of social media has been shown to be a flexible source of information about population behaviors, thoughts and feelings It is available with high spatial and temporal resolution, suggesting great potential for the study and monitoring of population mental health. However, approaches for tracking psychological states across communities on social media were not developed with consideration for spatial and temporal confounds or to fully leverage the multi-level structure (and sample sizes) of the data. Proposed work will develop multi-level methods to control for spatial correlation and community socioeconomic covariance to increase statistical power and the accuracy of measurement. The increased power will also better enable quasi-experimental designs from epidemiology which will be combined with the Twitter-based estimates to track the impact of policy and socioeconomic shocks on mental health. The work in this proposal could significantly transform both research in population mental health and the ability to apply and track the efficacy of policy to improve public health , It will allow researchers to observe temporal changes in population mental health quarterly and for counties, which provides the measurement infrastructure to observe changes in response to natural experiments such as economic shocks and policy interventions. This will be possible in near real-time, without the reporting lag of a few years as in current survey methodologies. The ongoing measurement will help identify areas of greatest need and may help prioritize resource allocation. The improved quasi-experimental modeling of mental health determinants may inform policy interventions, and the ongoing monitoring can establish evidence of their efficacy, In tum, the burden of mental unhealth on society may be substantively reduced in the long term.