Enhanced Arabic disaster data classification using domain adaptation

Article


Moussa, A. M., Abdou, S., Elsayad, K. M., Rashwan, M., Asif, A., Khatoon, S. and Alshamari, M. A. 2024. Enhanced Arabic disaster data classification using domain adaptation. PLoS ONE. 19 (4), p. e0301255. https://doi.org/10.1371/journal.pone.0301255
AuthorsMoussa, A. M., Abdou, S., Elsayad, K. M., Rashwan, M., Asif, A., Khatoon, S. and Alshamari, M. A.
Abstract

Natural disasters, like pandemics and earthquakes, are some of the main causes of distress and casualties. Governmental crisis management processes are crucial when dealing with these types of problems. Social media platforms are among the main sources of information regarding current events and public opinion. So, they have been used extensively to aid disaster detection and prevention efforts. Therefore, there is always a need for better automatic systems that can detect and classify disaster data of social media. In this work, we propose enhanced Arabic disaster data classification models. The suggested models utilize domain adaptation to provide state-of-the-art accuracy. We used a standard dataset of Arabic disaster data collected from Twitter for testing the proposed models. Experimental results show that the provided models significantly outperform the previous state-of-the-art results.

JournalPLoS ONE
Journal citation19 (4), p. e0301255
ISSN1932-6203
Year2024
PublisherPublic Library of Science (PLoS)
Publisher's version
License
File Access Level
Anyone
Digital Object Identifier (DOI)https://doi.org/10.1371/journal.pone.0301255
Publication dates
Online04 Apr 2024
Publication process dates
Deposited01 May 2024
FunderSaudi Arabian Ministry of Education's Deputyship for Research and Innovation
Copyright holder© 2024, The Authors
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License: CC BY 4.0
File access level: Anyone

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