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

Saloni Jain, Brett Adams Duncan, Yanqing Zhang, Ning Zhong, Zejin Ding
<span title="">2016</span> <i title="Engineering and Technology Publishing"> <a target="_blank" rel="noopener" href="" style="color: black;">Journal of Advances in Information Technology</a> </i> &nbsp;
Traditional communication channels like news channels are not able to provide spontaneous information about disasters unlike social networks namely, Twitter. The present research 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
more &raquo; ... mework are -data collection, data preprocessing, geolocating the tweets, data filtering and extrapolation of the disaster curve for prediction of susceptible locations. The framework is validated by analyzing the past events. This framework has the potential to be developed into a full-fledged system to predict and warn people about disasters. The warnings can be sent to news channels or broadcasted for pro-active action.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.12720/jait.7.2.81-87</a> <a target="_blank" rel="external noopener" href="">fatcat:vvyjz42z6rgqznfilw3fjeigk4</a> </span>
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