Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/14717
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dc.contributor.authorSingh, JP-
dc.contributor.authorDwivedi, YK-
dc.contributor.authorRana, NP-
dc.contributor.authorKumar, A-
dc.contributor.authorKapoor, KK-
dc.date.accessioned2017-06-08T13:37:07Z-
dc.date.available2017-05-19-
dc.date.available2017-06-08T13:37:07Z-
dc.date.issued2017-
dc.identifier.citationAnnals of Operations Research, pp. 1 - 21, (2017)en_US
dc.identifier.issn0254-5330-
dc.identifier.issn1572-9338-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/14717-
dc.description.abstractSocial 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.en_US
dc.format.extent1 - 21-
dc.language.isoenen_US
dc.publisherSpringer Verlagen_US
dc.subjectDisaster managementen_US
dc.subjectLocation inferenceen_US
dc.subjectGeo-taggingen_US
dc.subjectTwitter Social mediaen_US
dc.titleEvent classification and location prediction from tweets during disastersen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1007/s10479-017-2522-3-
dc.relation.isPartOfAnnals of Operations Research-
pubs.publication-statusAccepted-
Appears in Collections:Brunel Business School Research Papers

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