Pre-Trained Language Model Ensemble for Arabic Fake News Detection
Fake news detection (FND) remains a challenge due to its vast and varied sources, especially on social media platforms. While numerous attempts have been made by academia and the industry to develop fake news detection systems, research on Arabic content remains limited. This study investigates tran...
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MDPI AG
2024-09-01
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| Series: | Mathematics |
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| author | Lama Al-Zahrani Maha Al-Yahya |
| author_facet | Lama Al-Zahrani Maha Al-Yahya |
| author_sort | Lama Al-Zahrani |
| collection | DOAJ |
| description | Fake news detection (FND) remains a challenge due to its vast and varied sources, especially on social media platforms. While numerous attempts have been made by academia and the industry to develop fake news detection systems, research on Arabic content remains limited. This study investigates transformer-based language models for Arabic FND. While transformer-based models have shown promising performance in various natural language processing tasks, they often struggle with tasks involving complex linguistic patterns and cultural contexts, resulting in unreliable performance and misclassification problems. To overcome these challenges, we investigated an ensemble of transformer-based models. We experimented with five Arabic transformer models: AraBERT, MARBERT, AraELECTRA, AraGPT2, and ARBERT. Various ensemble approaches, including a weighted-average ensemble, hard voting, and soft voting, were evaluated to determine the most effective techniques for boosting learning models and improving prediction accuracies. The results of this study demonstrate the effectiveness of ensemble models in significantly boosting the baseline model performance. An important finding is that ensemble models achieved excellent performance on the Arabic Multisource Fake News Detection (AMFND) dataset, reaching an F1 score of 94% using weighted averages. Moreover, changing the number of models in the ensemble has a slight effect on the performance. These key findings contribute to the advancement of fake news detection in Arabic, offering valuable insights for both academia and the industry |
| format | Article |
| id | doaj-art-1c9e8fb7a76247dca5b5d36138af3fab |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-1c9e8fb7a76247dca5b5d36138af3fab2025-08-20T01:55:38ZengMDPI AGMathematics2227-73902024-09-011218294110.3390/math12182941Pre-Trained Language Model Ensemble for Arabic Fake News DetectionLama Al-Zahrani0Maha Al-Yahya1Information Technology Department, College of Computer and Information Sciences, King Saud University, P.O. Box 145111, Riyadh 4545, Saudi ArabiaInformation Technology Department, College of Computer and Information Sciences, King Saud University, P.O. Box 145111, Riyadh 4545, Saudi ArabiaFake news detection (FND) remains a challenge due to its vast and varied sources, especially on social media platforms. While numerous attempts have been made by academia and the industry to develop fake news detection systems, research on Arabic content remains limited. This study investigates transformer-based language models for Arabic FND. While transformer-based models have shown promising performance in various natural language processing tasks, they often struggle with tasks involving complex linguistic patterns and cultural contexts, resulting in unreliable performance and misclassification problems. To overcome these challenges, we investigated an ensemble of transformer-based models. We experimented with five Arabic transformer models: AraBERT, MARBERT, AraELECTRA, AraGPT2, and ARBERT. Various ensemble approaches, including a weighted-average ensemble, hard voting, and soft voting, were evaluated to determine the most effective techniques for boosting learning models and improving prediction accuracies. The results of this study demonstrate the effectiveness of ensemble models in significantly boosting the baseline model performance. An important finding is that ensemble models achieved excellent performance on the Arabic Multisource Fake News Detection (AMFND) dataset, reaching an F1 score of 94% using weighted averages. Moreover, changing the number of models in the ensemble has a slight effect on the performance. These key findings contribute to the advancement of fake news detection in Arabic, offering valuable insights for both academia and the industryhttps://www.mdpi.com/2227-7390/12/18/2941fake news detectionArabiclearning ensembleLLMAraBERTMARBERT |
| spellingShingle | Lama Al-Zahrani Maha Al-Yahya Pre-Trained Language Model Ensemble for Arabic Fake News Detection Mathematics fake news detection Arabic learning ensemble LLM AraBERT MARBERT |
| title | Pre-Trained Language Model Ensemble for Arabic Fake News Detection |
| title_full | Pre-Trained Language Model Ensemble for Arabic Fake News Detection |
| title_fullStr | Pre-Trained Language Model Ensemble for Arabic Fake News Detection |
| title_full_unstemmed | Pre-Trained Language Model Ensemble for Arabic Fake News Detection |
| title_short | Pre-Trained Language Model Ensemble for Arabic Fake News Detection |
| title_sort | pre trained language model ensemble for arabic fake news detection |
| topic | fake news detection Arabic learning ensemble LLM AraBERT MARBERT |
| url | https://www.mdpi.com/2227-7390/12/18/2941 |
| work_keys_str_mv | AT lamaalzahrani pretrainedlanguagemodelensembleforarabicfakenewsdetection AT mahaalyahya pretrainedlanguagemodelensembleforarabicfakenewsdetection |