Analysis of lockdown perception in the United States during the COVID-19 pandemic
Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Turin, Italy
2 Department of Mechanical and Aerospace Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, USA
3 Center for Urban Science and Progress, Tandon School of Engineering, New York University, Brooklyn, NY, USA
4 Department of Biomedical Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, USA
5 Office of Innovation, Tandon School of Engineering, New York University, Brooklyn, NY, USA
Accepted: 29 July 2021
Published online: 1 September 2021
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.
© The Author(s) 2021
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