Developing a mental health index using a machine learning approach: Assessing the impact of mobility and lockdown during the COVID-19 pandemic

Nanath, Krishnadas ORCID logoORCID: https://orcid.org/0000-0002-3515-9084, Balasubramanian, Sreejith ORCID logoORCID: https://orcid.org/0000-0002-0475-7305, Shukla, Vinaya ORCID logoORCID: https://orcid.org/0000-0002-2546-4931, Islam, Nazrul ORCID logoORCID: https://orcid.org/0000-0003-0515-1134 and Kaitheri, Supriya (2022) Developing a mental health index using a machine learning approach: Assessing the impact of mobility and lockdown during the COVID-19 pandemic. Technological Forecasting and Social Change, 178 , 121560. pp. 1-14. ISSN 0040-1625 [Article] (doi:10.1016/j.techfore.2022.121560)

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Abstract

Governments worldwide have implemented stringent restrictions to curtail the spread of the COVID-19 pandemic. Although beneficial to physical health, these preventive measures could have a profound detrimental effect on the mental health of the population. This study focuses on the impact of lockdowns and mobility restrictions on mental health during the COVID-19 pandemic. We first develop a novel mental health index based on the analysis of data from over three million global tweets using the Microsoft Azure machine learning approach. The computed mental health index scores are then regressed with the lockdown strictness index and Google mobility index using fixed-effects ordinary least squares (OLS) regression. The results reveal that the reduction in workplace mobility, reduction in retail and recreational mobility, and increase in residential mobility (confinement to the residence) have harmed mental health. However, restrictions on mobility to parks, grocery stores, and pharmacy outlets were found to have no significant impact. The proposed mental health index provides a path for theoretical and empirical mental health studies using social media. [Abstract copyright: © 2022 Elsevier Inc. All rights reserved.]

Item Type: Article
Keywords (uncontrolled): Mobility, Lockdown, COVID-19 pandemic, Mental health index, Twitter, Machine learning approach
Research Areas: A. > Business School
A. > Business School > Leadership, Work and Organisations
A. > School of Science and Technology
Item ID: 34840
Notes on copyright: © 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license https://creativecommons.org/licenses/by/4.0/
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Depositing User: Jisc Publications Router
Date Deposited: 07 Mar 2022 10:09
Last Modified: 29 Jun 2022 09:51
URI: https://eprints.mdx.ac.uk/id/eprint/34840

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