Quantifying Information Flow During Emergencies

Recent advances on human dynamics have focused on the normal patterns of human activities, with the quantitative understanding of human behavior under extreme events remaining a crucial missing chapter. This has a wide array of potential applications, ranging from emergency response and detection to...

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Main Authors:	Gao, Liang, Song, Chaoming, Gao, Ziyou, Barabási, Albert-László, Bagrow, James P., Wang, Dashun
Format:	Online
Language:	English
Published:	Nature Publishing Group 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3915310/

id	pubmed-3915310
recordtype	oai_dc
spelling	pubmed-39153102014-02-06 Quantifying Information Flow During Emergencies Gao, Liang Song, Chaoming Gao, Ziyou Barabási, Albert-László Bagrow, James P. Wang, Dashun Article Recent advances on human dynamics have focused on the normal patterns of human activities, with the quantitative understanding of human behavior under extreme events remaining a crucial missing chapter. This has a wide array of potential applications, ranging from emergency response and detection to traffic control and management. Previous studies have shown that human communications are both temporally and spatially localized following the onset of emergencies, indicating that social propagation is a primary means to propagate situational awareness. We study real anomalous events using country-wide mobile phone data, finding that information flow during emergencies is dominated by repeated communications. We further demonstrate that the observed communication patterns cannot be explained by inherent reciprocity in social networks, and are universal across different demographics. Nature Publishing Group 2014-02-06 /pmc/articles/PMC3915310/ /pubmed/24499738 http://dx.doi.org/10.1038/srep03997 Text en Copyright © 2014, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by-nc-nd/3.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Gao, Liang Song, Chaoming Gao, Ziyou Barabási, Albert-László Bagrow, James P. Wang, Dashun
spellingShingle	Gao, Liang Song, Chaoming Gao, Ziyou Barabási, Albert-László Bagrow, James P. Wang, Dashun Quantifying Information Flow During Emergencies
author_facet	Gao, Liang Song, Chaoming Gao, Ziyou Barabási, Albert-László Bagrow, James P. Wang, Dashun
author_sort	Gao, Liang
title	Quantifying Information Flow During Emergencies
title_short	Quantifying Information Flow During Emergencies
title_full	Quantifying Information Flow During Emergencies
title_fullStr	Quantifying Information Flow During Emergencies
title_full_unstemmed	Quantifying Information Flow During Emergencies
title_sort	quantifying information flow during emergencies
description	Recent advances on human dynamics have focused on the normal patterns of human activities, with the quantitative understanding of human behavior under extreme events remaining a crucial missing chapter. This has a wide array of potential applications, ranging from emergency response and detection to traffic control and management. Previous studies have shown that human communications are both temporally and spatially localized following the onset of emergencies, indicating that social propagation is a primary means to propagate situational awareness. We study real anomalous events using country-wide mobile phone data, finding that information flow during emergencies is dominated by repeated communications. We further demonstrate that the observed communication patterns cannot be explained by inherent reciprocity in social networks, and are universal across different demographics.
publisher	Nature Publishing Group
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3915310/
_version_	1612055507868057600

Quantifying Information Flow During Emergencies

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