Natural Disaster Based on Social Data

Social media becomes a communicating channel during a natural disaster for detecting disaster events because people share their opinions, feelings, activity during the disaster through the Twitter. Twitter is not simply a platform for broadcasting information, but one of informational interaction. So, we use this platform for mining various disaster relevant tweets during a natural disaster. We examine more than 4,500 tweets during crisis moment. In this paper, we propose a classifier for classifying the disaster phases using social data and identify these types of phases. We use KNN, a machine learning classification algorithm for classifying the disaster relevant tweets. By knowing different phases of a disaster, response teams can detect where disaster will happen; the medical enterprise can be prepared to mitigate the damage after disaster and neighborhood area may also be alert to face the disaster. We classify the disaster-related tweets into three phases that are: pre (preparedness before the disaster event), on (during disaster event), post (impact and recovery after the disaster).We also take the geolocation with latitude and longitude of the disaster event for visualizing it using an earth map which can be useful to emergency response teams and also increase social awareness of the disaster.