Event classification and location prediction from tweets during disasters

Jyoti Prakash Singh, Yogesh K. Dwivedi*, Nripendra P. Rana, Abhinav Kumar, Kawaljeet Kaur Kapoor

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Number of pages21
JournalAnnals of Operations Research
Early online date19 May 2017
DOIs
Publication statusE-pub ahead of print - 19 May 2017

Bibliographical note

© 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.

Keywords

  • Disaster management
  • Geo-tagging
  • Location inference
  • Social media
  • Twitter

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