Rural‐Urban Differences in the Determinants of Subjective Well‐Being Among X/Twitter Users in the United States

ABSTRACT Twitter Sentiment Geographical Index (TSGI) has been proposed to complement traditional surveys to measure subjective well‐being (SWB) at the US county level. Our study aims to investigate determinants of TSGI‐measured SWB in rural and urban US counties. Using the Classification and Regression Tree, we identified phenotypes or county‐level characteristics associated with high SWB. Counties with newer homes were the top characteristic of high SWB in both urban and rural areas. Counties of the identical phenotypes tend to concentrate geographically, with the most favorable phenotypes clustered in the South. Random Forest analysis identified additional characteristics of high SWB, including higher population density in rural areas and lower real estate tax ratio in urban areas. Our results yield a comprehensive understanding of determinants of SWB at the local level, guiding evidence‐based policy decisions and community initiatives to improve well‐being in target populations.

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Publication Details

Journal
Population Space and Place
Published
2024-12-01
DOI
https://doi.org/10.1002/psp.2861
Citations
2
Primary Topic
Psychological Well-being and Life Satisfaction
Type
article
Field-Weighted Citation Impact
1.29
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article

Rural‐Urban Differences in the Determinants of Subjective Well‐Being Among X/Twitter Users in the United States

Wenting Jiang, Connor Y. H. Wu, Weichuan Dong, Mengxi Zhang
2 citations
Population Space and Place
Psychological Well-being and Life Satisfaction
1.29
article

Rural‐Urban Differences in the Determinants of Subjective Well‐Being Among X/Twitter Users in the United States

Wenting Jiang, Connor Y. H. Wu, Weichuan Dong, Mengxi Zhang
article en
2 citations

Abstract

ABSTRACT Twitter Sentiment Geographical Index (TSGI) has been proposed to complement traditional surveys to measure subjective well‐being (SWB) at the US county level. Our study aims to investigate determinants of TSGI‐measured SWB in rural and urban US counties. Using the Classification and Regression Tree, we identified phenotypes or county‐level characteristics associated with high SWB. Counties with newer homes were the top characteristic of high SWB in both urban and rural areas. Counties of the identical phenotypes tend to concentrate geographically, with the most favorable phenotypes clustered in the South. Random Forest analysis identified additional characteristics of high SWB, including higher population density in rural areas and lower real estate tax ratio in urban areas. Our results yield a comprehensive understanding of determinants of SWB at the local level, guiding evidence‐based policy decisions and community initiatives to improve well‐being in target populations.

Population Space and PlaceVol. 31(1)
Oklahoma State University Oklahoma City (US), Carilion Clinic (US), Case Western Reserve University (US), Virginia Tech (US)
Sustainable cities and communities
Openalex Percentile: Top 17%
Psychological Well-being and Life Satisfaction
1.29
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