Online geolocalized emotion across US cities during the COVID crisis: Universality, policy response, and connection with local mobility

TL;DR

This study analyzes 13 million geotagged tweets across 49 US cities during COVID-19 to uncover universal online emotional responses, their relation to policies, and their predictive power for local mobility, revealing consistent sentiment trends despite policy variability.

Contribution

It demonstrates the universality of online emotional responses to COVID-19 across US cities and links these sentiments to mobility and policy impacts, offering new insights for crisis management.

Findings

Universal sentiment trends across cities over time.

Local sentiments strongly predict city mobility.

Weak correlation between sentiments and local COVID-19 cases/deaths.

Abstract

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.