DISCOVERING DISASTER EVENTS FROM SOCIAL MEDIA STREAMS
Keywords:data analytics, disaster management, event detection, social network analysis
Natural and man-made disasters can both cause severe loss of lives and economic damages. Examples include earthquakes, floods, and road crashes. Nevertheless, to rapidly and accurately identify the latest status of a disaster event is undoubtedly one of the most difficult tasks for agencies in crisis management. In this work, we thus propose to monitor online data streams in social media for detecting and tracking real world events. Unlike conventional media, social media is advantageous because of its immediateness, huge data scale, and worldwide availability. Nevertheless, the messages generated by netizens could be incomplete, subjective, or even error prone. Only with an appropriately designated scheme, invaluable clues embedded in huge amounts of online messages can be discovered when carefully exploiting the information over content, temporal, and social dimensions. Specifically, we collect data from multiple social networks, conduct real-time analysis, and present interactive visualization. Experimental studies show that the proposed scheme is demonstrated to be feasible for agencies in practice.
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