Abstract
Social Internet of things (SIoT) has obtained significant attention to address the computational intelligence for handling emergencies which can be sensed through the Internet of smart social things. In recent years, humans act as a social sensor to disseminate the information via microblogs in Online Social Networks (OSNs) such as Twitter, Weibo. At times, the reaction to certain microblogging prompts thousands of people to rethink and impulsively react on that which can handle humanity unrest situations during earthquakes, floods, Tsunamis, etc. In this paper, the streaming microblog tweets are investigated to detect the disaster events for a specified time and location. Firstly, the real-time Twitter streaming data is sensed via Apache Flume service agent. Secondly, the tweets are preprocessed and filtered tweets are clustered using the microblog-DBSCAN algorithm. The tweets belonging to various sliding window from diversified geographic event locations during disasters are aggregated. In addition, the crowdsourced photographs are added with geo-tags (location) during the period of analysis stood up as supplementary evidence to detect an event which acts as crisis recovery. The dynamic hive queries are executed to filter location-level tweets for the analysis. In contrast to conventional approaches which mainly focus the microblog textual content, we incorporate significant metadata features, namely photographs and its geo-tag, to precisely identify the events in real time.
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Bhuvaneswari, A., Valliyammai, C. (2019). Social IoT-Enabled Emergency Event Detection Framework Using Geo-Tagged Microblogs and Crowdsourced Photographs. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 814. Springer, Singapore. https://doi.org/10.1007/978-981-13-1501-5_13
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DOI: https://doi.org/10.1007/978-981-13-1501-5_13
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