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

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dc.contributor.author Banujan, K.
dc.contributor.author Kumara, B.T.G.S.
dc.contributor.author Incheon Paik
dc.date.accessioned 2018-08-15T07:27:45Z
dc.date.available 2018-08-15T07:27:45Z
dc.date.issued 2018
dc.identifier.citation Banujan,K. , Kumara,B.T.G.S. and Incheon Paik (2018). Social media mining for post-disaster management - A case study on Twitter and news. International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka. p.134. en_US
dc.identifier.uri http://repository.kln.ac.lk/handle/123456789/19018
dc.description.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. en_US
dc.language.iso en en_US
dc.publisher International Research Conference on Smart Computing and Systems Engineering - SCSE 2018 en_US
dc.subject API en_US
dc.subject Data mining en_US
dc.subject Disaster management en_US
dc.subject Social media en_US
dc.subject Twitter en_US
dc.title Social media mining for post-disaster management - A case study on Twitter and news en_US
dc.type Article en_US


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