Final published version
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Enhanced Arabic disaster data classification using domain adaptation
AU - Moussa, Abdullah M.
AU - Abdou, Sherif
AU - Elsayed, Khaled M.
AU - Rashwan, Mohsen
AU - Asif, Amna
AU - Khatoon, Shaheen
AU - Alshamari, Majed A.
A2 - Elbassuoni, Shady
PY - 2024/4/4
Y1 - 2024/4/4
N2 - 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.
AB - 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.
U2 - 10.1371/journal.pone.0301255
DO - 10.1371/journal.pone.0301255
M3 - Journal article
VL - 19
JO - PLoS One
JF - PLoS One
SN - 1932-6203
IS - 4
M1 - e0301255
ER -