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

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 misinfo...

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Autores principales: Cheng, Mingxi, Yin, Chenzhong, Nazarian, Shahin, Bogdan, Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8128875/
https://www.ncbi.nlm.nih.gov/pubmed/34001937
http://dx.doi.org/10.1038/s41598-021-89202-7
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author Cheng, Mingxi
Yin, Chenzhong
Nazarian, Shahin
Bogdan, Paul
author_facet Cheng, Mingxi
Yin, Chenzhong
Nazarian, Shahin
Bogdan, Paul
author_sort Cheng, Mingxi
collection PubMed
description 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.
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spelling pubmed-81288752021-05-19 Deciphering the laws of social network-transcendent COVID-19 misinformation dynamics and implications for combating misinformation phenomena Cheng, Mingxi Yin, Chenzhong Nazarian, Shahin Bogdan, Paul Sci Rep Article 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. Nature Publishing Group UK 2021-05-17 /pmc/articles/PMC8128875/ /pubmed/34001937 http://dx.doi.org/10.1038/s41598-021-89202-7 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Cheng, Mingxi
Yin, Chenzhong
Nazarian, Shahin
Bogdan, Paul
Deciphering the laws of social network-transcendent COVID-19 misinformation dynamics and implications for combating misinformation phenomena
title 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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8128875/
https://www.ncbi.nlm.nih.gov/pubmed/34001937
http://dx.doi.org/10.1038/s41598-021-89202-7
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