Analysis of lockdown perception in the United States during the COVID-19 pandemic

Francesco Vincenzo Surano, Maurizio Porfiri, Alessandro Rizzo

Research output: Contribution to journalArticlepeer-review

Abstract

Containment measures have been applied throughout the world to halt the COVID-19 pandemic. In the United States, several forms of lockdown have been adopted in different parts of the country, leading to heterogeneous epidemiological, social, and economic effects. Here, we present a spatio-temporal analysis of a Twitter dataset comprising 1.3 million geo-localized Tweets about lockdown, from January to May 2020. Through sentiment analysis, we classified Tweets as expressing positive or negative emotions about lockdown, demonstrating a change in perception during the course of the pandemic modulated by socio-economic factors. A transfer entropy analysis of the time series of Tweets unveiled that the emotions in different parts of the country did not evolve independently. Rather, they were mediated by spatial interactions, which were also related to socio-ecomomic factors and, arguably, to political orientations. This study constitutes a first, necessary step toward isolating the mechanisms underlying the acceptance of public health interventions from highly resolved online datasets.

Original languageEnglish (US)
JournalEuropean Physical Journal: Special Topics
DOIs
StateAccepted/In press - 2021

ASJC Scopus subject areas

  • Materials Science(all)
  • Physics and Astronomy(all)
  • Physical and Theoretical Chemistry

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