TY - JOUR
T1 - Analysis of lockdown perception in the United States during the COVID-19 pandemic
AU - Surano, Francesco Vincenzo
AU - Porfiri, Maurizio
AU - Rizzo, Alessandro
N1 - Funding Information:
This work was partially supported by the Compagnia di San Paolo, Torino, Italy, within the “Joint Projects with Prestigious University” (FVS) and “Starting Grant” (AR) initiatives, and the National Science Foundation under grant number CMMI-2027990. FVS acknowledges the Dynamical Systems Laboratory at the New York University Tandon School of Engineering for hosting him during the preparation of this work.
Funding Information:
Open access funding provided by Politecnico di Torino within the CRUI-CARE Agreement.
Funding Information:
This work was partially supported by the Compagnia di San Paolo, Torino, Italy, within the “Joint Projects with Prestigious University” (FVS) and “Starting Grant” (AR) initiatives, and the National Science Foundation under grant number CMMI-2027990. FVS acknowledges the Dynamical Systems Laboratory at the New York University Tandon School of Engineering for hosting him during the preparation of this work.
Publisher Copyright:
© 2021, The Author(s).
PY - 2022/7
Y1 - 2022/7
N2 - 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.
AB - 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.
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U2 - 10.1140/epjs/s11734-021-00265-z
DO - 10.1140/epjs/s11734-021-00265-z
M3 - Article
C2 - 34490058
AN - SCOPUS:85114035399
SN - 1951-6355
VL - 231
SP - 1625
EP - 1633
JO - European Physical Journal: Special Topics
JF - European Physical Journal: Special Topics
IS - 9
ER -