  As the COVID-19 pandemic began to sweep across the US it elicited a wide
spectrum of responses, both online and offline, across the population. To aid
the development of effective spatially targeted interventions in the midst of
this turmoil, it is important to understand the geolocalization of these online
emotional responses, as well as their association with offline behavioral
responses. Here, we analyze around 13 million geotagged tweets in 49 cities
across the US from the first few months of the pandemic to assess regional
dependence in online sentiments with respect to a few major topics, and how
these sentiments correlate with policy development and human mobility.
Surprisingly, we observe universal trends in overall and topic-based sentiments
across cities over the time period studied, with variability primarily seen
only in the immediate impact of federal guidelines and local lockdown policies.
We also find that these local sentiments are highly correlated with and
predictive of city-level mobility, while the correlations between sentiments
and local cases and deaths are relatively weak. Our findings point to
widespread commonalities in the online public emotional responses to COVID
across the US, both temporally and relative to offline indicators, in contrast
with the high variability seen in early local containment policies. This study
also provides new insights into the use of social media data in crisis
management by integrating offline data to gain an in-depth understanding of
public emotional responses, policy development, and local mobility.
