Event classification and location prediction from tweets during disasters


Social media is a platform to express one’s view in real time. This real time nature of social media makes it an attractive tool for disaster management, as both victims and officials can put their problems and solutions at the same place in real time. We investigate the Twitter post in a flood related disaster and propose an algorithm to identify victims asking for help. The developed system takes tweets as inputs and categorizes them into high or low priority tweets. User location of high priority tweets with no location information is predicted based on historical locations of the users using the Markov model. The system is working well, with its classification accuracy of 81%, and location prediction accuracy of 87%. The present system can be extended for use in other natural disaster situations, such as earthquake, tsunami, etc., as well as man-made disasters such as riots, terrorist attacks etc. The present system is first of its kind, aimed at helping victims during disasters based on their tweets.

Publication DOI: https://doi.org/10.1007/s10479-017-2522-3
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Uncontrolled Keywords: Disaster management,Geo-tagging,Location inference,Social media,Twitter,Decision Sciences(all),Management Science and Operations Research
Publication ISSN: 1572-9338
Last Modified: 08 Dec 2023 11:09
Date Deposited: 02 Jul 2019 15:01
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://link.sp ... 0479-017-2522-3 (Publisher URL)
PURE Output Type: Article
Published Date: 2019-12
Published Online Date: 2017-05-19
Accepted Date: 2017-05-01
Authors: Singh, Jyoti Prakash
Dwivedi, Yogesh K.
Rana, Nripendra P.
Kumar, Abhinav
Kapoor, Kawaljeet Kaur (ORCID Profile 0000-0001-9524-905X)



Version: Published Version

License: Creative Commons Attribution

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