  We construct a novel database containing hundreds of thousands geotagged
messages related to the COVID-19 pandemic sent on Twitter. We create a daily
index of social distancing -- at the state level -- to capture social
distancing beliefs by analyzing the number of tweets containing keywords such
as "stay home", "stay safe", "wear mask", "wash hands" and "social distancing".
We find that an increase in the Twitter index of social distancing on day t-1
is associated with a decrease in mobility on day t. We also find that state
orders, an increase in the number of COVID cases, precipitation and temperature
contribute to reducing human mobility. Republican states are also less likely
to enforce social distancing. Beliefs shared on social networks could both
reveal the behavior of individuals and influence the behavior of others. Our
findings suggest that policy makers can use geotagged Twitter data -- in
conjunction with mobility data -- to better understand individual voluntary
social distancing actions.
