Location inference on social media data for agile monitoring of public health crises: An application to opioid use and abuse during the Covid-19 pandemic

The Covid-19 pandemic has intersected with the opioid epidemic to create a unique public health crisis, with the health and economic consequences of the virus and associated lockdowns compounding pre-existing social and economic stressors associated with rising opioid and heroin use and abuse. In order to better understand these interlocking crises, we use social media data to extract qualitative and quantitative insights on the experiences of opioid users during the Covid-19 pandemic. In particular, we use an unsupervised learning approach to create a rich geolocated data source for public health surveillance and analysis. To do this we first infer the location of 26,000 Reddit users that participate in opiate-related sub-communities (subreddits) by combining named entity recognition, geocoding, density-based clustering, and heuristic methods. Our strategy achieves 63 percent accuracy at state-level location inference on a manually-annotated reference dataset. We then leverage the geospatial nature of our user cohort to answer policy-relevant questions about the impact of varying state-level policy approaches that balance economic versus health concerns during Covid-19. We find that state government strategies that prioritized economic reopening over curtailing the spread of the virus created a markedly different environment and outcomes for opioid users. Our results demonstrate that geospatial social media data can be used for agile monitoring of complex public health crises.