ArticleHeliyon2021
Socioeconomic factors analysis for COVID-19 US reopening sentiment with Twitter and census data.
Article in Heliyon, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 64 citations in OpenAlex.
- Public sentiment dynamics in policy transitions: a sentiment analysis based on Weibo data.Frontiers in public health · 2025Article
- 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
- Pandemic vulnerability index of US cities: A hybrid knowledge-based and data-driven approach.Sustainable cities and society · 2023Article
- A global portrait of expressed mental health signals towards COVID-19 in social media space.International journal of applied earth observation and geoinformation : ITC journal · 2023Article
- Enhanced sentiment analysis regarding COVID-19 news from global channels.Journal of computational social science · 2023Article
- A study on the emotional and attitudinal behaviors of social media users under the sudden reopening policy of the Chinese government.Frontiers in public health · 2023Article
- Using Twitter Data to Estimate the Prevalence of Symptoms of Mental Disorders in the United States During the COVID-19 Pandemic: Ecological Cohort Study.JMIR formative research · 2022Article
- Associations between COVID-19 Pandemic, Lockdown Measures and Human Mobility: Longitudinal Evidence from 86 Countries.International journal of environmental research and public health · 2022Article
- Public sentiments toward COVID-19 vaccines in South African cities: An analysis of Twitter posts.Frontiers in public health · 2022Article
- Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review.SN computer science · 2022Review
- Nowcasting unemployment rate during the COVID-19 pandemic using Twitter data: The case of South Africa.Frontiers in public health · 2022Article
- The impact of COVID-19 on tertiary educational institutions and students in Bangladesh.Heliyon · 2022Article
- The Turing Teacher: Identifying core attributes for AI learning in K-12.Frontiers in artificial intelligence · 2022Article
- Analysis of lockdown perception in the United States during the COVID-19 pandemic.The European physical journal. Special topics · 2022Article
- Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network.Scientific reports · 2021Article
- Resumption of Cosmetic Surgery During COVID - Experience of a Specialised Cosmetic Surgery Day-case Hospital.Journal of plastic, reconstructive & aesthetic surgery : JPRAS · 2021Article
- Spatial association of mobility and COVID-19 infection rate in the USA: A county-level study using mobile phone location data.Journal of transport & health · 2021Article
- Public Perceptions of COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics.Healthcare (Basel, Switzerland) · 2021Article
- Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review.IEEE access : practical innovations, open solutions · 2021Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.