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

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 soc...
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Autor*in:	Jaber Valinejad [verfasserIn] Zhen Guo [verfasserIn] Jin-Hee Cho [verfasserIn] Ing-Ray Chen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

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

Übergeordnetes Werk:	In: Journal of Social Computing - Tsinghua University Press, 2022, 3(2022), 4, Seite 322-344
Übergeordnetes Werk:	volume:3 ; year:2022 ; number:4 ; pages:322-344

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.23919/JSC.2022.0019

Katalog-ID:	DOAJ087794292

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author	Jaber Valinejad
spellingShingle	Jaber Valinejad misc QA75.5-76.95 misc H1-99 misc community resilience misc social computing misc data science misc fake news misc social media misc urban computing misc computational social science misc machine learning misc Electronic computers. Computer science misc Social sciences (General) Measuring Community Resilience During the COVID-19 Based on Community Wellbeing and Resource Distribution
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title	Measuring Community Resilience During the COVID-19 Based on Community Wellbeing and Resource Distribution
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title_sort	measuring community resilience during the covid-19 based on community wellbeing and resource distribution
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title_auth	Measuring Community Resilience During the COVID-19 Based on Community Wellbeing and Resource Distribution
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.
abstractGer	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.
abstract_unstemmed	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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title_short	Measuring Community Resilience During the COVID-19 Based on Community Wellbeing and Resource Distribution
url	https://doi.org/10.23919/JSC.2022.0019 https://doaj.org/article/20dc769658ea415aa2cb8d90147b8839 https://www.sciopen.com/article/10.23919/JSC.2022.0019 https://doaj.org/toc/2688-5255
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up_date	2024-07-03T14:00:57.083Z
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