Fake Detect: A Deep Learning Ensemble Model for Fake News Detection
Pervasive usage and the development of social media networks have provided the platform for the fake news to spread fast among people. Fake news often misleads people and creates wrong society perceptions. The spread of low-quality news in social media has negatively affected individuals and society...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5557784 |
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| _version_ | 1832567859730972672 |
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| author | Nida Aslam Irfan Ullah Khan Farah Salem Alotaibi Lama Abdulaziz Aldaej Asma Khaled Aldubaikil |
| author_facet | Nida Aslam Irfan Ullah Khan Farah Salem Alotaibi Lama Abdulaziz Aldaej Asma Khaled Aldubaikil |
| author_sort | Nida Aslam |
| collection | DOAJ |
| description | Pervasive usage and the development of social media networks have provided the platform for the fake news to spread fast among people. Fake news often misleads people and creates wrong society perceptions. The spread of low-quality news in social media has negatively affected individuals and society. In this study, we proposed an ensemble-based deep learning model to classify news as fake or real using LIAR dataset. Due to the nature of the dataset attributes, two deep learning models were used. For the textual attribute “statement,” Bi-LSTM-GRU-dense deep learning model was used, while for the remaining attributes, dense deep learning model was used. Experimental results showed that the proposed study achieved an accuracy of 0.898, recall of 0.916, precision of 0.913, and F-score of 0.914, respectively, using only statement attribute. Moreover, the outcome of the proposed models is remarkable when compared with that of the previous studies for fake news detection using LIAR dataset. |
| format | Article |
| id | doaj-art-4600c53af5da48d9ae02ca7a63b9bdb2 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-4600c53af5da48d9ae02ca7a63b9bdb22025-02-03T01:00:18ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55577845557784Fake Detect: A Deep Learning Ensemble Model for Fake News DetectionNida Aslam0Irfan Ullah Khan1Farah Salem Alotaibi2Lama Abdulaziz Aldaej3Asma Khaled Aldubaikil4Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi ArabiaPervasive usage and the development of social media networks have provided the platform for the fake news to spread fast among people. Fake news often misleads people and creates wrong society perceptions. The spread of low-quality news in social media has negatively affected individuals and society. In this study, we proposed an ensemble-based deep learning model to classify news as fake or real using LIAR dataset. Due to the nature of the dataset attributes, two deep learning models were used. For the textual attribute “statement,” Bi-LSTM-GRU-dense deep learning model was used, while for the remaining attributes, dense deep learning model was used. Experimental results showed that the proposed study achieved an accuracy of 0.898, recall of 0.916, precision of 0.913, and F-score of 0.914, respectively, using only statement attribute. Moreover, the outcome of the proposed models is remarkable when compared with that of the previous studies for fake news detection using LIAR dataset.http://dx.doi.org/10.1155/2021/5557784 |
| spellingShingle | Nida Aslam Irfan Ullah Khan Farah Salem Alotaibi Lama Abdulaziz Aldaej Asma Khaled Aldubaikil Fake Detect: A Deep Learning Ensemble Model for Fake News Detection Complexity |
| title | Fake Detect: A Deep Learning Ensemble Model for Fake News Detection |
| title_full | Fake Detect: A Deep Learning Ensemble Model for Fake News Detection |
| title_fullStr | Fake Detect: A Deep Learning Ensemble Model for Fake News Detection |
| title_full_unstemmed | Fake Detect: A Deep Learning Ensemble Model for Fake News Detection |
| title_short | Fake Detect: A Deep Learning Ensemble Model for Fake News Detection |
| title_sort | fake detect a deep learning ensemble model for fake news detection |
| url | http://dx.doi.org/10.1155/2021/5557784 |
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