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

Article


Nanath, K., Balasubramanian, S., Shukla, V., Islam, N. and Kaitheri, S. 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. https://doi.org/10.1016/j.techfore.2022.121560
TypeArticle
TitleDeveloping a mental health index using a machine learning approach: assessing the impact of mobility and lockdown during the COVID-19 pandemic
AuthorsNanath, K., Balasubramanian, S., Shukla, V., Islam, N. and Kaitheri, S.
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.]

KeywordsMental health index; Mobility; Lockdown; Machine learning approach; Twitter; COVID-19 pandemic
LanguageEnglish
PublisherElsevier
JournalTechnological Forecasting and Social Change
ISSN0040-1625
Electronic1873-5509
Publication dates
Online14 Feb 2022
Print01 May 2022
Publication process dates
Deposited07 Mar 2022
Accepted05 Feb 2022
Submitted03 May 2021
Output statusPublished
Publisher's version
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Copyright Statement

© 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/

Digital Object Identifier (DOI)https://doi.org/10.1016/j.techfore.2022.121560
Web of Science identifierWOS:000778411300004
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