Event classification and location prediction from tweets during disasters. Singh, J. P., Dwivedi, Y. K., Rana, N. P., Kumar, A., & Kapoor, K. K. Annals of Operations Research, May, 2017. ![Event classification and location prediction from tweets during disasters [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{singh_event_2017,
title = {Event classification and location prediction from tweets during disasters},
issn = {0254-5330, 1572-9338},
url = {https://link.springer.com/article/10.1007/s10479-017-2522-3},
doi = {10.1007/s10479-017-2522-3},
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.},
language = {en},
urldate = {2018-03-20},
journal = {Annals of Operations Research},
author = {Singh, Jyoti Prakash and Dwivedi, Yogesh K. and Rana, Nripendra P. and Kumar, Abhinav and Kapoor, Kawaljeet Kaur},
month = may,
year = {2017},
keywords = {灾害中信息的分类},
pages = {1--21},
}
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