Social media mining for post-disaster management - A case study on Twitter and news

Abstract

A natural disaster is a natural event which can cause damage to both lives and properties. Social media are capable of sharing information on a real-time basis. Post disaster management can be improved to a great extent if we mine the social media properly. After identifying the need and the possibility of solving that through social media, we chose Twitter to mine and News for validating the Twitter Posts. As a first stage, we fetch the Twitter posts and news posts from Twitter API and News API respectively, using predefined keywords relating to the disaster. Those posts were cleaned and the noise was reduced at the second stage. Then in the third stage, we get the disaster type and geolocation of the posts by using Named Entity Recognizer library API. As a final stage, we compared the Twitter datum with news datum to give the rating for the trueness of each Twitter post. Final integrated results show that the 80% of the Twitter posts obtained the rating of “3” and 15% obtained the rating of “2”. We believe that by using our model we can alert the organizations to do their disaster management activities. Our future development consists mainly of two folds. Firstly, we are planning to integrate the other social media to fetch the data, i.e. Instagram, YouTube, etc. Secondly, we are planning to integrate the weather data into the system in order to improve the precision and accuracy for finding the trueness of the disaster and location.