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.

中文翻译：

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COVID-19 大流行期间澳大利亚心理健康信号的城市区域差异：基于 Twitter 数据和机器学习模型的研究

本研究建立了一个新的经验框架，使用机器学习技术来衡量大流行期间澳大利亚公众心理健康信号的城市区域差异，并检查心理健康、社区人口和社会经济概况、健康风险和医疗保健之间的相互关系使用权。我们的研究结果表明，省会城市公众的心理健康信号好于偏远地区。首府城市的负面心理健康信号与较低的收入水平、更拥挤的生活空间、较低的医疗保健可及性以及更多的医疗保健获取困难有关。