Strengthening Post-Disaster Management Activities by Rating Social Media Corpus

Banujan Kuhaneswaran, Banage T. G. S. Kumara and Incheon Paik
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Banujan Kuhaneswaran: Sabaragamuwa University of Sri Lanka, Sri Lanka
Banage T. G. S. Kumara: Sabaragamuwa University of Sri Lanka, Sri Lanka
Incheon Paik: University of Aizu, Japan

International Journal of Systems and Service-Oriented Engineering (IJSSOE), 2020, vol. 10, issue 1, 34-50

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.

Date: 2020
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