Socioeconomic Factors Analysis for COVID-19 US Reopening Sentiment With Twitter and Census Data

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aut.relation.issue2en_NZ
aut.relation.journalHeliyonen_NZ
aut.relation.volume7en_NZ
aut.researcherLi, Xuejun
dc.contributor.authorRahman, MMen_NZ
dc.contributor.authorAli, GGMNen_NZ
dc.contributor.authorLi, Xen_NZ
dc.contributor.authorSamuel, Jen_NZ
dc.contributor.authorPaul, KCen_NZ
dc.contributor.authorChong, PHJen_NZ
dc.contributor.authorYakubov, Men_NZ
dc.date.accessioned2021-03-11T23:20:05Z
dc.date.available2021-03-11T23:20:05Z
dc.date.copyright2021en_NZ
dc.date.issued2021en_NZ
dc.description.abstractInvestigating and classifying sentiments of social media users (e.g., positive, negative) towards an item, situation, and system are very popular among researchers. However, they rarely discuss the underlying socioeconomic factor associations for such sentiments. This study attempts to explore the factors associated with positive and negative sentiments of the people about reopening the economy, in the United States (US) amidst the COVID-19 global crisis. It takes into consideration the situational uncertainties (i.e., changes in work and travel patterns due to lockdown policies), economic downturn and associated trauma, and emotional factors such as depression. To understand the sentiment of the people about the reopening economy, Twitter data was collected, representing the 50 States of the US and Washington D.C, the capital city of the US. State-wide socioeconomic characteristics of the people (e.g., education, income, family size, and employment status), built environment data (e.g., population density), and the number of COVID-19 related cases were collected and integrated with Twitter data to perform the analysis. A binary logit model was used to identify the factors that influence people toward a positive or negative sentiment. The results from the logit model demonstrate that family households, people with low education levels, people in the labor force, low-income people, and people with higher house rent are more interested in reopening the economy. In contrast, households with a high number of family members and high income are less interested in reopening the economy. The accuracy of the model is reasonable (i.e., the model can correctly classify 56.18% of the sentiments). The Pearson chi-squared test indicates that this model has high goodness-of-fit. This study provides clear insights for public and corporate policymakers on potential areas to allocate resources, and directional guidance on potential policy options they can undertake to improve socioeconomic conditions, to mitigate the impact of pandemic in the current situation, and in the future as well.en_NZ
dc.identifier.citationHeliyon, e06200.
dc.identifier.doi10.1016/j.heliyon.2021.e06200en_NZ
dc.identifier.issn2405-8440en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/14046
dc.languageengen_NZ
dc.publisherElsevier
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S2405844021003054?via%3Dihub
dc.rights© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectBinary logit modelen_NZ
dc.subjectCOVID-19en_NZ
dc.subjectCensusen_NZ
dc.subjectCoronavirusen_NZ
dc.subjectReopenen_NZ
dc.subjectSentiment analysisen_NZ
dc.subjectTwitteren_NZ
dc.titleSocioeconomic Factors Analysis for COVID-19 US Reopening Sentiment With Twitter and Census Dataen_NZ
dc.typeJournal Article
pubs.elements-id398118
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies/School of Engineering, Computer & Mathematical Sciences
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies/PBRF ECMS
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