  Background. After a year and half and over 4 million deaths, the COVID-19
pandemic continues to be widespread, and its related topics continue to
dominate the global media. Although COVID-19 diagnoses have been well
monitored, neither the impacts of the disease on human behavior and social
dynamics nor the effectiveness of policy interventions aimed at its containment
are fully understood. Monitoring the spatial and temporal patterns of behavior,
social dynamics and policy - and then their interrelations - can provide
critical information for preparatory action and effective response. Methods.
Here we present an open-source dataset of 1.92 million keyword-selected Twitter
posts, updated weekly from January 2020 to present, along with a dynamic
dashboard showing totals at national and subnational administrative divisions.
Results. The dashboard presents 100% of the geotagged tweets that contain
keywords or hashtags related COVID-19. We validated our inclusion criteria
using a machine learning-based text classifier and found that 88% of the
selected tweets were correctly labeled as related to COVID-19. With this
information we tested the correlation between tweets and covid diagnosis from
January 1, 2020 through December 31, 2020 and see a decreasing correlation
across time. Conclusions. With emerging COVID variants but ongoing vaccine
hesitancy and resistance, this dataset could be used by researchers to study
numerous aspects of COVID-19 and provide valuable insights for preparing future
pandemics.
