Evidence map›Paper›PMID 33585707›Full record

ArticleHeliyon2021

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

Open access · goldAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
16.0field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 64 citations in OpenAlex.

  1. Article
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  4. 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 · 2023
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  19. Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review.IEEE access : practical innovations, open solutions · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 3 countries.

Md Mokhlesur RahmanUniversity of North Carolina at Charlotte, NC 28223, USA.
G G Md Nawaz AliUniversity of Charleston, WV 25304, USA.
Xue Jun LiAuckland University of Technology, Auckland 1010, New Zealand.
Jim SamuelUniversity of Charleston, WV 25304, USA.
Kamal Chandra PaulUniversity of North Carolina at Charlotte, NC 28223, USA.
Peter H J ChongAuckland University of Technology, Auckland 1010, New Zealand.
Michael YakubovWilmington University, DE 19720, USA.
Auckland University of Technology · NZUniversity of Charleston · USUniversity of North Carolina at Charlotte · USWilmington University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Binary logit modelCensusCoronavirusCOVID-19ReopenSentiment analysisTwitter

Identifiers

PMID33585707
PMCPMC7867397
OpenAlexW3127917150

What OpenQuestion holds

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Registered trials

None linked

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.