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Real-Time Social Network Data Mining for Predicting the Path for a Disaster

Saloni Jain 1, Brett Adams Duncan1, Yanqing Zhang 1, Ning Zhong 2, and Zejin Ding 3
1. Department of Computer Science, Georgia State University, Atlanta, USA
2. Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi-City, Japan
3. Hewlett-Packard Company, 5555 Windward Pkwy, Alpharetta, USA

Abstract—Traditional communication channels like news channels are not able to provide spontaneous information about disasters unlike social networks, namely, Twitter. This work proposes a framework by mining real-time disaster data from Twitter to predict the path; a disaster like a tornado will take. The users of Twitter act as the sensors, which provide useful information about the disaster by posting first-hand experience, warnings or location of a disaster. The steps involved in the framework are – data collection, data preprocessing, geo-location tagging data filtering and extrapolation of the disaster curve for prediction of susceptible locations. The framework is validated by analyzing the past events using regression with the government warnings. This framework has the potential to be developed into a full-fledged system to provide instantaneous warnings to people about disasters via news channels or broadcasts.

Index Terms—data mining, disaster computing, real-time disaster prediction, regression

Cite: Saloni Jain, Brett Adams Duncan,  Yanqing Zhang, Ning Zhong, and Zejin Ding, "Real-Time Social Network Data Mining for Predicting the Path for a Disaster," Vol. 7, No. 2, pp. 81-87, May, 2016. doi: 10.12720/jait.7.2.81-87