A Hybrid Multitask Learning Framework with a Fire Hawk Optimizer for Arabic Fake News Detection
The exponential spread of news and posts related to the COVID-19 pandemic on social media platforms led to the emergence of the disinformation phenomenon. The phenomenon of spreading fake information and news creates significant concern for the public health and safety of the population. In this pap...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/11/2/258 |
| _version_ | 1797439080881979392 |
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| author | Mohamed Abd Elaziz Abdelghani Dahou Dina Ahmed Orabi Samah Alshathri Eman M. Soliman Ahmed A. Ewees |
| author_facet | Mohamed Abd Elaziz Abdelghani Dahou Dina Ahmed Orabi Samah Alshathri Eman M. Soliman Ahmed A. Ewees |
| author_sort | Mohamed Abd Elaziz |
| collection | DOAJ |
| description | The exponential spread of news and posts related to the COVID-19 pandemic on social media platforms led to the emergence of the disinformation phenomenon. The phenomenon of spreading fake information and news creates significant concern for the public health and safety of the population. In this paper, we propose a disinformation detection framework based on multi-task learning (MTL) and meta-heuristic algorithms in the context of the COVID-19 pandemic. The developed framework uses an MTL and a pre-trained transformer-based model to learn and extract contextual feature representations from Arabic social media posts. The extracted contextual representations are fed to an alternative feature selection technique which depends on modified version of the Fire Hawk Optimizer. The proposed framework, which aims to improve the disinformation detection rate, was evaluated on several datasets of Arabic social media posts. The experimental results show that the proposed framework can achieve accuracy of 59%. It obtained, at best, precision, recall, and F-measure of 53%, 71%, and 53%, respectively, on all datasets; and it outperformed the other algorithms in all measures. |
| first_indexed | 2024-03-09T11:47:36Z |
| format | Article |
| id | doaj.art-7897a51ccecd4bff8c67d17fddf4210f |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T11:47:36Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-7897a51ccecd4bff8c67d17fddf4210f2023-11-30T23:19:40ZengMDPI AGMathematics2227-73902023-01-0111225810.3390/math11020258A Hybrid Multitask Learning Framework with a Fire Hawk Optimizer for Arabic Fake News DetectionMohamed Abd Elaziz0Abdelghani Dahou1Dina Ahmed Orabi2Samah Alshathri3Eman M. Soliman4Ahmed A. Ewees5Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, EgyptMathematics and Computer Science Department, University of Ahmed DRAIA, Adrar 01000, AlgeriaFaculty of Media Production, Galala University, Suez 435611, EgyptDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaFaculty of Media Production, Galala University, Suez 435611, EgyptDepartment of Computer, Damietta University, Damietta 34517, EgyptThe exponential spread of news and posts related to the COVID-19 pandemic on social media platforms led to the emergence of the disinformation phenomenon. The phenomenon of spreading fake information and news creates significant concern for the public health and safety of the population. In this paper, we propose a disinformation detection framework based on multi-task learning (MTL) and meta-heuristic algorithms in the context of the COVID-19 pandemic. The developed framework uses an MTL and a pre-trained transformer-based model to learn and extract contextual feature representations from Arabic social media posts. The extracted contextual representations are fed to an alternative feature selection technique which depends on modified version of the Fire Hawk Optimizer. The proposed framework, which aims to improve the disinformation detection rate, was evaluated on several datasets of Arabic social media posts. The experimental results show that the proposed framework can achieve accuracy of 59%. It obtained, at best, precision, recall, and F-measure of 53%, 71%, and 53%, respectively, on all datasets; and it outperformed the other algorithms in all measures.https://www.mdpi.com/2227-7390/11/2/258social media platformsfake informationmulti-task learning (MTL)feature selectionFire Hawk Optimizer (FHO) |
| spellingShingle | Mohamed Abd Elaziz Abdelghani Dahou Dina Ahmed Orabi Samah Alshathri Eman M. Soliman Ahmed A. Ewees A Hybrid Multitask Learning Framework with a Fire Hawk Optimizer for Arabic Fake News Detection Mathematics social media platforms fake information multi-task learning (MTL) feature selection Fire Hawk Optimizer (FHO) |
| title | A Hybrid Multitask Learning Framework with a Fire Hawk Optimizer for Arabic Fake News Detection |
| title_full | A Hybrid Multitask Learning Framework with a Fire Hawk Optimizer for Arabic Fake News Detection |
| title_fullStr | A Hybrid Multitask Learning Framework with a Fire Hawk Optimizer for Arabic Fake News Detection |
| title_full_unstemmed | A Hybrid Multitask Learning Framework with a Fire Hawk Optimizer for Arabic Fake News Detection |
| title_short | A Hybrid Multitask Learning Framework with a Fire Hawk Optimizer for Arabic Fake News Detection |
| title_sort | hybrid multitask learning framework with a fire hawk optimizer for arabic fake news detection |
| topic | social media platforms fake information multi-task learning (MTL) feature selection Fire Hawk Optimizer (FHO) |
| url | https://www.mdpi.com/2227-7390/11/2/258 |
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