Affiliation:
1. Sabaragamuwa University of Sri Lanka, Sri Lanka
2. University of Aizu, Japan
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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