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Extracting Non-Situational Information from Twitter During Disaster Events

Extracting Non-Situational Information from Twitter During Disaster Events

Poonam Sarda, Ranu Lal Chouhan
Copyright: © 2017 |Volume: 19 |Issue: 1 |Pages: 9
ISSN: 1548-7717|EISSN: 1548-7725|EISBN13: 9781522510994|DOI: 10.4018/jcit.2017010102
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MLA

Sarda, Poonam, and Ranu Lal Chouhan. "Extracting Non-Situational Information from Twitter During Disaster Events." JCIT vol.19, no.1 2017: pp.15-23. http://doi.org/10.4018/jcit.2017010102

APA

Sarda, P. & Chouhan, R. L. (2017). Extracting Non-Situational Information from Twitter During Disaster Events. Journal of Cases on Information Technology (JCIT), 19(1), 15-23. http://doi.org/10.4018/jcit.2017010102

Chicago

Sarda, Poonam, and Ranu Lal Chouhan. "Extracting Non-Situational Information from Twitter During Disaster Events," Journal of Cases on Information Technology (JCIT) 19, no.1: 15-23. http://doi.org/10.4018/jcit.2017010102

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Abstract

Micro blogging sites have become important forums for discussion during disaster events in which Twitter has become one of the important source of real time information. Millions of tweets are posted during disasters, which include not only information about the present situation or relief efforts, but also the emotions or opinion of the masses. Much research has been conducted on extracting situational information from tweets during disaster. However, according to current knowledge, there has not been any prior attempt to study the non-situational tweets posted during disasters, such as those which express the emotions/opinions of the people, political and governmental views, raising charities and event analysis. In this study, the authors characterized the non-situational tweets posted during recent disaster events, the Nepal Earthquake and the Gurudaspur Terrorist attack. They developed a classifier to categorize various types of non-situational tweets into a set of fine-grained classes utilizing state-of-the-art machine learning technique. This system also helps in filtering out communal tweets which can make worst the situation by disrupting communal harmony during certain disaster events.

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