ArticleThe European physical journal. Special topics2022
Analysis of lockdown perception in the United States during the COVID-19 pandemic.
Article in The European physical journal. Special topics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Pandemic risk-related behaviour change in England from June 2020 to March 2022: the cross-sectional REACT-1 study among over 2 million people.BMJ public health · 2025Article
- Understanding the role of media in the formation of public sentiment towards the police.Communications psychology · 2024Article
- Analysis of the evolving factors of social media users' emotions and behaviors: a longitudinal study from China's COVID-19 opening policy period.BMC public health · 2023Article
- Staying Home, Tweeting Hope: Mixed Methods Study of Twitter Sentiment Geographical Index During US Stay-At-Home Orders.Journal of medical Internet research · 2023Article
- Comprehensive sentimental analysis of tweets towards COVID-19 in Pakistan: a study on governmental preventive measures.PeerJ. Computer science · 2023Article
- Using geospatial social media data for infectious disease studies: a systematic review.International journal of digital earth · 2023Article
- A mechanistic model for airborne and direct human-to-human transmission of COVID-19: effect of mitigation strategies and immigration of infectious persons.The European physical journal. Special topics · 2022Article
- Complex urban systems: a living lab to understand urban processes and solve complex urban problems.The European physical journal. Special topics · 2022Article
- A proposed fractional dynamic system and Monte Carlo-based back analysis for simulating the spreading profile of COVID-19.The European physical journal. Special topics · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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