Citation

Moussa AM, Abdou S, Elsayed KM, Rashwan M, Asif A, Khatoon S, Alshamari MA. PLoS One 2024; 19(4): e0301255.

Copyright

(Copyright © 2024, Public Library of Science)

DOI

10.1371/journal.pone.0301255

PMID

38574077

PMCID

PMC10994337

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.

Language: en