Journal of Social Computing 2022, 3(4): 322-344 https://doi.org/10.23919/JSC.2022.0019

Open Access | Issue | Published: 31 December 2022

Measuring Community Resilience During the COVID-19 Based on Community Wellbeing and Resource Distribution

Show Author's Information Hide Author's Information Jaber Valinejad^¹(

), Zhen Guo^², Jin-Hee Cho^², Ing-Ray Chen^²

1Data and System Science Lab, Harvard Medical School, Harvard University, Cambridge, MA 01451, USA

2Department of Computer Science, Virginia Tech, National Capital Region Campus, Falls Church, VA 22043, USA

Keywords:

machine learning, social media, computational social science, social computing, data science, community resilience, fake news, urban computing

Cite this article:

Valinejad J, Guo Z, Cho J-H, et al. Measuring Community Resilience During the COVID-19 Based on Community Wellbeing and Resource Distribution. Journal of Social Computing, 2022, 3(4): 322-344. https://doi.org/10.23919/JSC.2022.0019

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Abstract

The COVID-19 pandemic has severely harmed every aspect of our daily lives, resulting in a slew of social problems. Therefore, it is critical to accurately assess the current state of community functionality and resilience under this pandemic for successful recovery. To this end, various types of social sensing tools, such as tweeting and publicly released news, have been employed to understand individuals’ and communities’ thoughts, behaviors, and attitudes during the COVID-19 pandemic. However, some portions of the released news are fake and can easily mislead the community to respond improperly to disasters like COVID-19. This paper aims to assess the correlation between various news and tweets collected during the COVID-19 pandemic on community functionality and resilience. We use fact-checking organizations to classify news as real, mixed, or fake, and machine learning algorithms to classify tweets as real or fake to measure and compare community resilience (CR). Based on the news articles and tweets collected, we quantify CR based on two key factors, community wellbeing and resource distribution, where resource distribution is assessed by the level of economic resilience and community capital. Based on the estimates of these two factors, we quantify CR from both news articles and tweets and analyze the extent to which CR measured from the news articles can reflect the actual state of CR measured from tweets. To improve the operationalization and sociological significance of this work, we use dimension reduction techniques to integrate the dimensions.

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Measuring Community Resilience During the COVID-19 Based on Community Wellbeing and Resource Distribution

Show Author's information Hide Author's Information Jaber Valinejad^¹(

), Zhen Guo^², Jin-Hee Cho^², Ing-Ray Chen^²

1Data and System Science Lab, Harvard Medical School, Harvard University, Cambridge, MA 01451, USA

2Department of Computer Science, Virginia Tech, National Capital Region Campus, Falls Church, VA 22043, USA

Abstract

Keywords: machine learning, social media, computational social science, social computing, data science, community resilience, fake news, urban computing

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Received: 16 September 2022

Revised: 14 January 2023

Accepted: 15 January 2023

Published: 31 December 2022

Issue date: December 2022

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).