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Strengthening Post-Disaster Management Activities by Rating Social Media Corpus

Strengthening Post-Disaster Management Activities by Rating Social Media Corpus

Banujan Kuhaneswaran, Banage T. G. S. Kumara, Incheon Paik
Copyright: © 2020 |Volume: 10 |Issue: 1 |Pages: 17
ISSN: 1947-3052|EISSN: 1947-3060|EISBN13: 9781799806790|DOI: 10.4018/IJSSOE.2020010103
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MLA

Kuhaneswaran, Banujan, et al. "Strengthening Post-Disaster Management Activities by Rating Social Media Corpus." IJSSOE vol.10, no.1 2020: pp.34-50. http://doi.org/10.4018/IJSSOE.2020010103

APA

Kuhaneswaran, B., Kumara, B. T., & Paik, I. (2020). Strengthening Post-Disaster Management Activities by Rating Social Media Corpus. International Journal of Systems and Service-Oriented Engineering (IJSSOE), 10(1), 34-50. http://doi.org/10.4018/IJSSOE.2020010103

Chicago

Kuhaneswaran, Banujan, Banage T. G. S. Kumara, and Incheon Paik. "Strengthening Post-Disaster Management Activities by Rating Social Media Corpus," International Journal of Systems and Service-Oriented Engineering (IJSSOE) 10, no.1: 34-50. http://doi.org/10.4018/IJSSOE.2020010103

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Abstract

In times of natural disasters such as floods, tsunamis, earthquakes, landslides, etc., people need information so that relief operations such as help can save many lives. The implications of using social media in post-disaster management are explored in the article. The approach has three main parts: (1) extraction, (2) classification, and (3) validation. The results prove that machine learning algorithms are highly reliable in elimination disaster non-related tweets and news posts. The authors strongly believe that their model is more reliable as they are validating the tweets using news posts by providing various ratings according to the trueness.

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