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- W4200567350 abstract "Abstract The characteristics of human emergency behaviour under the emergency are a crucial scientific issue in basic emergency management research. The analysis of time dynamic aspects of human behaviour based on electronic footprint data provides a new method for quantitative investigation of this problem. Previous studies generally assumed that human behaviours were randomly distributed in time, but few studies studied the impact of emergencies and carried out prediction methods through social media data. Using mobile QQ space communication data, this paper from four kinds of emergencies and one kind of conventional event data, digging out the statistical characteristic on the time dimension of human communication behaviour, and in case of any emergencies, such as public security mode of evolution, to explore intrinsic emergency regularity of the impact of human communication behaviour model and further predict human behaviour characteristics. We found that the communication peaks accompanying an emergency are local in time, resulting in a communication avalanche that importantly engages eyewitness social networks. In mobile QQ space communication, the probability distribution of the interval time of the Posting behaviour sequence shows the statistical characteristics of power-law and approximate exponential tail. Compared with most of the typical Posting behaviour, the probability distribution of the interval time of the Posting behaviour sequence is higher. At the same time, the mnemonic is lower than most of the typical Posting behaviour, with a weak anti- mnemonic. These results are theoretically helpful in understanding the regularity of the impact of emergencies on human communication behaviour patterns and have potential application value in predicting the impact degree of crises and the analysis and classification of human social attributes." @default.
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- W4200567350 date "2021-12-22" @default.
- W4200567350 modified "2023-10-16" @default.
- W4200567350 title "Human Emergency Behaviour Characteristics in Large-scale Emergencies Based on Mobile Communication Data" @default.
- W4200567350 doi "https://doi.org/10.21203/rs.3.rs-1177241/v1" @default.
- W4200567350 hasPublicationYear "2021" @default.
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