Twitter and Online News analytics for Enhancing Post-Natural Disaster Management Activities

A natural disaster is a natural event which can cause damage to both lives and properties. The detection of natural disasters is a significant and non-trivial problem. Social media (SM) is a powerful resource to improve the management of disaster situations. Post-disaster management can be improved to a great extent if we mine the SM properly because SM is capable of real-time nature of sharing the information. In this paper, we proposed an approach to enhance post-natural disaster management activities by identifying the correct location and disaster type. As the first step, we fetch the twitter posts using predefined keywords relating to the disaster from Twitter API. Those posts were cleaned and the noise was reduced at the second stage. Then in the third stage, we get the geolocation and disaster type. Named Entity Recognizer library and Google Maps Geocoding API was used for getting the geolocation. We did the same three stages for news which was fetched from News API. As a final stage, we compared the twitter datum with news datum to give the rating for the trueness of each Twitter post. “More accurate” rating was obtained for 24% of the posts. 15% and 13% of the posts showed “Moderately accurate” and “Less accurate” rating respectively. “No correlation” was obtained for 48% of the posts. The precision of 85% for Twitter posts filtering and 92% for News posts filtering were obtained when compared to the posts manually. We strongly believe that using this model we can alert the organizations to do their disaster management activities in a timely manner. We are planning to extend our work with the weather data and as well as with other social media to provide more scaled ratings.