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

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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.