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Crowdsourcing based social media data analysis of urban emergency events

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Abstract

An urban emergency event requires an immediate reaction or assistance for an emergency situation. With the popularity of the World Wide Web, the internet is becoming a major information provider and disseminator of emergency events and this is due to its real-time, open, and dynamic features. However, faced with the huge, disordered and continuous nature of web resources, it is impossible for people to efficiently recognize, collect and organize these events. In this paper, a crowdsourcing based burst computation algorithm of an urban emergency event is developed in order to convey information about the event clearly and to help particular social groups or governments to process events effectively. A definition of an urban emergency event is firstly introduced. This serves as the foundation for using web resources to compute the burst power of events on the web. Secondly, the different temporal features of web events are developed to provide the basic information for the proposed computation algorithm. Moreover, the burst power is presented to integrate the above temporal features of an event. Empirical experiments on real datasets show that the burst power can be used to analyze events.

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  1. www.google.com

  2. www.baidu.com

  3. Nlp.stanford.edu.com

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Acknowledgments

This work was supported in part by the National Science and Technology Major Project under Grant 2013ZX01033002-003, in part by the National High Technology Research and Development Program of China (863 Program) under Grant 2013AA014601, 2013AA014603, in part by National Key Technology Support Program under Grant 2012BAH07B01, in part by the National Science Foundation of China under Grant 61300202, 61300028, in part by the Project of the Ministry of Public Security under Grant 2014JSYJB009, in part by the China Postdoctoral Science Foundation under Grant 2014 M560085, the project of Shanghai Municipal Commission of Economy and Information under Grant 12GA-19, and in part by the Science Foundation of Shanghai under Grant 13ZR1452900.

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Authors and Affiliations

  1. The Third Research Institute of the Ministry of Public Security, Shanghai, China

    Zheng Xu, Yunhuai Liu & Lin Mei

  2. Tsinghua University, Beijing, China

    Zheng Xu

  3. Shanghai University, Shanghai, China

    Junyu Xuan

  4. East China University of Political Science and Law, Shanghai, China

    Haiyan Chen

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  1. Zheng Xu
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  2. Yunhuai Liu
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  3. Junyu Xuan
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  4. Haiyan Chen
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  5. Lin Mei
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Correspondence to Zheng Xu.

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Xu, Z., Liu, Y., Xuan, J. et al. Crowdsourcing based social media data analysis of urban emergency events. Multimed Tools Appl 76, 11567–11584 (2017). https://doi.org/10.1007/s11042-015-2731-1

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  • DOI: https://doi.org/10.1007/s11042-015-2731-1

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