Deciphering the laws of social network-transcendent COVID-19 misinformation dynamics and implications for combating misinformation phenomena

Abstract The global rise of COVID-19 health risk has triggered the related misinformation infodemic. We present the first analysis of COVID-19 misinformation networks and determine few of its implications. Firstly, we analyze the spread trends of COVID-19 misinformation and discover that the COVID-1...

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Autores principales:	Mingxi Cheng, Chenzhong Yin, Shahin Nazarian, Paul Bogdan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/56c58c5066294d77b18850688ce00f8b
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spelling	oai:doaj.org-article:56c58c5066294d77b18850688ce00f8b2021-12-02T15:53:01ZDeciphering the laws of social network-transcendent COVID-19 misinformation dynamics and implications for combating misinformation phenomena10.1038/s41598-021-89202-72045-2322https://doaj.org/article/56c58c5066294d77b18850688ce00f8b2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89202-7https://doaj.org/toc/2045-2322Abstract The global rise of COVID-19 health risk has triggered the related misinformation infodemic. We present the first analysis of COVID-19 misinformation networks and determine few of its implications. Firstly, we analyze the spread trends of COVID-19 misinformation and discover that the COVID-19 misinformation statistics are well fitted by a log-normal distribution. Secondly, we form misinformation networks by taking individual misinformation as a node and similarity between misinformation nodes as links, and we decipher the laws of COVID-19 misinformation network evolution: (1) We discover that misinformation evolves to optimize the network information transfer over time with the sacrifice of robustness. (2) We demonstrate the co-existence of fit get richer and rich get richer phenomena in misinformation networks. (3) We show that a misinformation network evolution with node deletion mechanism captures well the public attention shift on social media. Lastly, we present a network science inspired deep learning framework to accurately predict which Twitter posts are likely to become central nodes (i.e., high centrality) in a misinformation network from only one sentence without the need to know the whole network topology. With the network analysis and the central node prediction, we propose that if we correctly suppress certain central nodes in the misinformation network, the information transfer of network would be severely impacted.Mingxi ChengChenzhong YinShahin NazarianPaul BogdanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mingxi Cheng Chenzhong Yin Shahin Nazarian Paul Bogdan Deciphering the laws of social network-transcendent COVID-19 misinformation dynamics and implications for combating misinformation phenomena
description	Abstract The global rise of COVID-19 health risk has triggered the related misinformation infodemic. We present the first analysis of COVID-19 misinformation networks and determine few of its implications. Firstly, we analyze the spread trends of COVID-19 misinformation and discover that the COVID-19 misinformation statistics are well fitted by a log-normal distribution. Secondly, we form misinformation networks by taking individual misinformation as a node and similarity between misinformation nodes as links, and we decipher the laws of COVID-19 misinformation network evolution: (1) We discover that misinformation evolves to optimize the network information transfer over time with the sacrifice of robustness. (2) We demonstrate the co-existence of fit get richer and rich get richer phenomena in misinformation networks. (3) We show that a misinformation network evolution with node deletion mechanism captures well the public attention shift on social media. Lastly, we present a network science inspired deep learning framework to accurately predict which Twitter posts are likely to become central nodes (i.e., high centrality) in a misinformation network from only one sentence without the need to know the whole network topology. With the network analysis and the central node prediction, we propose that if we correctly suppress certain central nodes in the misinformation network, the information transfer of network would be severely impacted.
format	article
author	Mingxi Cheng Chenzhong Yin Shahin Nazarian Paul Bogdan
author_facet	Mingxi Cheng Chenzhong Yin Shahin Nazarian Paul Bogdan
author_sort	Mingxi Cheng
title	Deciphering the laws of social network-transcendent COVID-19 misinformation dynamics and implications for combating misinformation phenomena
title_short	Deciphering the laws of social network-transcendent COVID-19 misinformation dynamics and implications for combating misinformation phenomena
title_full	Deciphering the laws of social network-transcendent COVID-19 misinformation dynamics and implications for combating misinformation phenomena
title_fullStr	Deciphering the laws of social network-transcendent COVID-19 misinformation dynamics and implications for combating misinformation phenomena
title_full_unstemmed	Deciphering the laws of social network-transcendent COVID-19 misinformation dynamics and implications for combating misinformation phenomena
title_sort	deciphering the laws of social network-transcendent covid-19 misinformation dynamics and implications for combating misinformation phenomena
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/56c58c5066294d77b18850688ce00f8b
work_keys_str_mv	AT mingxicheng decipheringthelawsofsocialnetworktranscendentcovid19misinformationdynamicsandimplicationsforcombatingmisinformationphenomena AT chenzhongyin decipheringthelawsofsocialnetworktranscendentcovid19misinformationdynamicsandimplicationsforcombatingmisinformationphenomena AT shahinnazarian decipheringthelawsofsocialnetworktranscendentcovid19misinformationdynamicsandimplicationsforcombatingmisinformationphenomena AT paulbogdan decipheringthelawsofsocialnetworktranscendentcovid19misinformationdynamicsandimplicationsforcombatingmisinformationphenomena
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Deciphering the laws of social network-transcendent COVID-19 misinformation dynamics and implications for combating misinformation phenomena

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