@inproceedings{lichouri-etal-2023-usthb,
title = "{USTHB} at {A}r{AIE}val{'}23 Shared Task: Disinformation Detection System based on Linguistic Feature Concatenation",
author = "Lichouri, Mohamed and
Lounnas, Khaled and
Zitouni, Aicha and
Latrache, Houda and
Djeradi, Rachida",
editor = "Sawaf, Hassan and
El-Beltagy, Samhaa and
Zaghouani, Wajdi and
Magdy, Walid and
Abdelali, Ahmed and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Habash, Nizar and
Khalifa, Salam and
Keleg, Amr and
Haddad, Hatem and
Zitouni, Imed and
Mrini, Khalil and
Almatham, Rawan",
booktitle = "Proceedings of ArabicNLP 2023",
month = dec,
year = "2023",
address = "Singapore (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.arabicnlp-1.47",
doi = "10.18653/v1/2023.arabicnlp-1.47",
pages = "508--512",
abstract = "In this research paper, we undertake a comprehensive examination of several pivotal factors that impact the performance of Arabic Disinformation Detection in the ArAIEval{'}2023 shared task. Our exploration encompasses the influence of surface preprocessing, morphological preprocessing, the FastText vector model, and the weighted fusion of TF-IDF features. To carry out classification tasks, we employ the Linear Support Vector Classification (LSVC) model. In the evaluation phase, our system showcases significant results, achieving an F$_1$ micro score of 76.70{\%} and 50.46{\%} for binary and multiple classification scenarios, respectively. These accomplishments closely correspond to the average F$_1$ micro scores achieved by other systems submitted for the second subtask, standing at 77.96{\%} and 64.85{\%} for binary and multiple classification scenarios, respectively.",
}
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<abstract>In this research paper, we undertake a comprehensive examination of several pivotal factors that impact the performance of Arabic Disinformation Detection in the ArAIEval’2023 shared task. Our exploration encompasses the influence of surface preprocessing, morphological preprocessing, the FastText vector model, and the weighted fusion of TF-IDF features. To carry out classification tasks, we employ the Linear Support Vector Classification (LSVC) model. In the evaluation phase, our system showcases significant results, achieving an F₁ micro score of 76.70% and 50.46% for binary and multiple classification scenarios, respectively. These accomplishments closely correspond to the average F₁ micro scores achieved by other systems submitted for the second subtask, standing at 77.96% and 64.85% for binary and multiple classification scenarios, respectively.</abstract>
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%0 Conference Proceedings
%T USTHB at ArAIEval’23 Shared Task: Disinformation Detection System based on Linguistic Feature Concatenation
%A Lichouri, Mohamed
%A Lounnas, Khaled
%A Zitouni, Aicha
%A Latrache, Houda
%A Djeradi, Rachida
%Y Sawaf, Hassan
%Y El-Beltagy, Samhaa
%Y Zaghouani, Wajdi
%Y Magdy, Walid
%Y Abdelali, Ahmed
%Y Tomeh, Nadi
%Y Abu Farha, Ibrahim
%Y Habash, Nizar
%Y Khalifa, Salam
%Y Keleg, Amr
%Y Haddad, Hatem
%Y Zitouni, Imed
%Y Mrini, Khalil
%Y Almatham, Rawan
%S Proceedings of ArabicNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore (Hybrid)
%F lichouri-etal-2023-usthb
%X In this research paper, we undertake a comprehensive examination of several pivotal factors that impact the performance of Arabic Disinformation Detection in the ArAIEval’2023 shared task. Our exploration encompasses the influence of surface preprocessing, morphological preprocessing, the FastText vector model, and the weighted fusion of TF-IDF features. To carry out classification tasks, we employ the Linear Support Vector Classification (LSVC) model. In the evaluation phase, our system showcases significant results, achieving an F₁ micro score of 76.70% and 50.46% for binary and multiple classification scenarios, respectively. These accomplishments closely correspond to the average F₁ micro scores achieved by other systems submitted for the second subtask, standing at 77.96% and 64.85% for binary and multiple classification scenarios, respectively.
%R 10.18653/v1/2023.arabicnlp-1.47
%U https://aclanthology.org/2023.arabicnlp-1.47
%U https://doi.org/10.18653/v1/2023.arabicnlp-1.47
%P 508-512
Markdown (Informal)
[USTHB at ArAIEval’23 Shared Task: Disinformation Detection System based on Linguistic Feature Concatenation](https://aclanthology.org/2023.arabicnlp-1.47) (Lichouri et al., ArabicNLP-WS 2023)
ACL