Urban-regional disparities in mental health signals in Australia during the COVID-19 pandemic: a study via Twitter data and machine learning models

Siqin Wang, Mengxi Zhang, Xiao Huang, Tao Hu, Zhenlong Li, Qian Chayn Sun, Yan Liu

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

This study establishes a novel empirical framework using machine learning techniques to measure the urban-regional disparity of the public's mental health signals in Australia during the pandemic, and to examine the interrelationships amongst mental health, demographic and socioeconomic profiles of neighbourhoods, health risks and healthcare access. Our results show that the public's mental health signals in capital cities were better than those in regional areas. The negative mental health signals in capital cities are associated with a lower level of income, more crowded living space, a lower level of healthcare availability and more difficulties in healthcare access.

Original languageEnglish (US)
Pages (from-to)663-682
Number of pages20
JournalCambridge Journal of Regions, Economy and Society
Volume15
Issue number3
DOIs
StatePublished - Nov 1 2022

All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Sociology and Political Science
  • Economics and Econometrics

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