Quantifying Information Flow During Emergencies

被引:0
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作者
Liang Gao
Chaoming Song
Ziyou Gao
Albert-László Barabási
James P. Bagrow
Dashun Wang
机构
[1] Systems Science Institute,Department of Physics
[2] State Key Laboratory of Rail Traffic Control and Safety and MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology,Department of Physics
[3] Beijing Jiaotong University,Department of Medicine
[4] Center for Complex Network Research,Department of Engineering Sciences and Applied Mathematics
[5] Northeastern University,Department of Mathematics and Statistics
[6] University of Miami,undefined
[7] Center for Cancer Systems Biology,undefined
[8] Dana-Farber Cancer Institute,undefined
[9] Brigham and Women's Hospital,undefined
[10] Harvard Medical School,undefined
[11] Northwestern Institute on Complex Systems,undefined
[12] Northwestern University,undefined
[13] Vermont Advanced Computing Center,undefined
[14] Complex Systems Center,undefined
[15] University of Vermont,undefined
[16] IBM Thomas J. Watson Research Center,undefined
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摘要
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.
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