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Sub-event discovery and retrieval during natural hazards on social media data

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Abstract

Social media sites contain a considerable amount of data for natural calamities events, such as earthquakes, snowstorms, mud-rock flows. With the increasing amount of social media data, an important task is to discover and retrieve sub-events over time. Especially in emergency situations, rescue and relief activities can be enhanced by identifying and retrieving sub-events of a natural hazard event. However, the existing event detection techniques in news-related reports cannot effectively work for social media data due to the unstructured of social network data. In this paper, we propose a new natural hazard sub-events discovery model SED (Sub-Events Discovery), which adopts multifarious features to detect sub-events. Moreover, in order to retrieve the sub-events over a specific event, we introduce a novel SER (Sub-Event Retrieval) algorithm from time-stamped social media data. Our novel approach SER makes use of automatically obtained messages from external search engines in the entire process. For purpose of determining the periodical convergence time for natural hazard event, our method provides online sub-events retrieval and sub-events discovery to meet the further needs. Next the improved estimation standards with timestamp are utilized in our experiments to verify the effectiveness and efficiency of SED model and SER algorithm.

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Acknowledgments

The authors thank the anonymous reviewers for their insightful and constructive comments. This research work is supported in part by the National Natural Science Foundation of China “Research on High-order Collaboration, Real-time and Temporal Characteristics in Automatic Test of Safety-critical Systems” (NO.61300007), Self-conducted Exploratory Research Program from State Key Laboratory for Software Development Environment in China (NO.SKLSDE-2013ZX-11), Social Service Project in National Earthquake Response Support Service “International Rescue and Disposition System against Strong Earthquakes” (NO.SJZX-B11).

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  1. State Key Lab of Software Development Environment, Beijing University of Aeronautics and Astronautics, Beijing, China

    Qunhui Wu, Shilong Ma & Yunzhen Liu

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  1. Qunhui Wu
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  2. Shilong Ma
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  3. Yunzhen Liu
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Correspondence to Qunhui Wu.

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Wu, Q., Ma, S. & Liu, Y. Sub-event discovery and retrieval during natural hazards on social media data. World Wide Web 19, 277–297 (2016). https://doi.org/10.1007/s11280-015-0359-8

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