Abstract
Natural disasters happen at any time and at any place. Social media can provide an important mean for both people affected and emergency personnel in sharing and receiving relevant information as the disaster unfolds across the different phases of the disaster. Focusing on the phases of preparedness, response and recovery, certain information needs to be retrieved due to the critical mission of emergency personnel. Such information can be directed depending on the disaster phase towards warning citizens, saving lives, or reducing the disaster impact. In this paper, we present an analytical study on Twitter data for three recent major hurricane disasters covering the three main disaster phases of preparedness, response and recovery. Our goal is to identify relevant tweets that will carry important information for disaster phase discovery. To achieve our goal, we propose a cloud-based system framework focused on three main components of disaster relevance classification, disaster phase classification and knowledge extraction. The framework is general enough for the three main disaster phases and specific to a hurricane disaster. Our results show that relevant tweets from different disaster data sets spanning different disaster phases can be classified for relevancy with an accuracy around 0.86, and for disaster phase with an accuracy of 0.85, where key information for disaster management personnel can be extracted.
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Microsoft Azure Machine Learning Studio
https://azure.microsoft.com/en-us/services/machine-learning-studio/.
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Acknowledgements
Special thanks to Dr. Farnoush Banaei-Kashani, University of Colorado Denver. This work is supported by the Department of Education GAANN Program, Fellowship # P200A150283, focused on Big Data Science and Engineering.
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Khaleq, A.A., Ra, I. (2019). Twitter Analytics for Disaster Relevance and Disaster Phase Discovery. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Proceedings of the Future Technologies Conference (FTC) 2018. FTC 2018. Advances in Intelligent Systems and Computing, vol 880. Springer, Cham. https://doi.org/10.1007/978-3-030-02686-8_31
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