Socioeconomic factors analysis for COVID-19 US reopening sentiment with Twitter and census data

Md Mokhlesur Rahman; G G Md Nawaz Ali; Xue Jun Li; Jim Samuel; Kamal Chandra Paul; Peter H J Chong; Michael Yakubov

doi:10.1016/j.heliyon.2021.e06200

Socioeconomic factors analysis for COVID-19 US reopening sentiment with Twitter and census data

Heliyon. 2021 Feb;7(2):e06200. doi: 10.1016/j.heliyon.2021.e06200. Epub 2021 Feb 6.

Authors

Md Mokhlesur Rahman^{1

2}, G G Md Nawaz Ali³, Xue Jun Li⁴, Jim Samuel³, Kamal Chandra Paul¹, Peter H J Chong⁴, Michael Yakubov⁵

Affiliations

¹ University of North Carolina at Charlotte, NC 28223, USA.
² Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh.
³ University of Charleston, WV 25304, USA.
⁴ Auckland University of Technology, Auckland 1010, New Zealand.
⁵ Wilmington University, DE 19720, USA.

Abstract

Investigating 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.

Keywords: Binary logit model; COVID-19; Census; Coronavirus; Reopen; Sentiment analysis; Twitter.