Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique
Recently, fake news has been widely spread through the Internet due to the increased use of social media for communication. Fake news has become a significant concern due to its harmful impact on individual attitudes and the community’s behavior. Researchers and social media service providers have c...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/18/6970 |
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| author | Abdullah Marish Ali Fuad A. Ghaleb Bander Ali Saleh Al-Rimy Fawaz Jaber Alsolami Asif Irshad Khan |
| author_facet | Abdullah Marish Ali Fuad A. Ghaleb Bander Ali Saleh Al-Rimy Fawaz Jaber Alsolami Asif Irshad Khan |
| author_sort | Abdullah Marish Ali |
| collection | DOAJ |
| description | Recently, fake news has been widely spread through the Internet due to the increased use of social media for communication. Fake news has become a significant concern due to its harmful impact on individual attitudes and the community’s behavior. Researchers and social media service providers have commonly utilized artificial intelligence techniques in the recent few years to rein in fake news propagation. However, fake news detection is challenging due to the use of political language and the high linguistic similarities between real and fake news. In addition, most news sentences are short, therefore finding valuable representative features that machine learning classifiers can use to distinguish between fake and authentic news is difficult because both false and legitimate news have comparable language traits. Existing fake news solutions suffer from low detection performance due to improper representation and model design. This study aims at improving the detection accuracy by proposing a deep ensemble fake news detection model using the sequential deep learning technique. The proposed model was constructed in three phases. In the first phase, features were extracted from news contents, preprocessed using natural language processing techniques, enriched using n-gram, and represented using the term frequency–inverse term frequency technique. In the second phase, an ensemble model based on deep learning was constructed as follows. Multiple binary classifiers were trained using sequential deep learning networks to extract the representative hidden features that could accurately classify news types. In the third phase, a multi-class classifier was constructed based on multilayer perceptron (MLP) and trained using the features extracted from the aggregated outputs of the deep learning-based binary classifiers for final classification. The two popular and well-known datasets (LIAR and ISOT) were used with different classifiers to benchmark the proposed model. Compared with the state-of-the-art models, which use deep contextualized representation with convolutional neural network (CNN), the proposed model shows significant improvements (2.41%) in the overall performance in terms of the F1score for the LIAR dataset, which is more challenging than other datasets. Meanwhile, the proposed model achieves 100% accuracy with ISOT. The study demonstrates that traditional features extracted from news content with proper model design outperform the existing models that were constructed based on text embedding techniques. |
| first_indexed | 2024-03-09T22:33:30Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T22:33:30Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1493c146446140bab2b877ec31b9a0d92023-11-23T18:52:20ZengMDPI AGSensors1424-82202022-09-012218697010.3390/s22186970Deep Ensemble Fake News Detection Model Using Sequential Deep Learning TechniqueAbdullah Marish Ali0Fuad A. Ghaleb1Bander Ali Saleh Al-Rimy2Fawaz Jaber Alsolami3Asif Irshad Khan4Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaFaculty of Engineering, School of Computing, Universiti Teknologi Malaysia, Johor Bahru 81310, MalaysiaFaculty of Engineering, School of Computing, Universiti Teknologi Malaysia, Johor Bahru 81310, MalaysiaDepartment of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaDepartment of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaRecently, fake news has been widely spread through the Internet due to the increased use of social media for communication. Fake news has become a significant concern due to its harmful impact on individual attitudes and the community’s behavior. Researchers and social media service providers have commonly utilized artificial intelligence techniques in the recent few years to rein in fake news propagation. However, fake news detection is challenging due to the use of political language and the high linguistic similarities between real and fake news. In addition, most news sentences are short, therefore finding valuable representative features that machine learning classifiers can use to distinguish between fake and authentic news is difficult because both false and legitimate news have comparable language traits. Existing fake news solutions suffer from low detection performance due to improper representation and model design. This study aims at improving the detection accuracy by proposing a deep ensemble fake news detection model using the sequential deep learning technique. The proposed model was constructed in three phases. In the first phase, features were extracted from news contents, preprocessed using natural language processing techniques, enriched using n-gram, and represented using the term frequency–inverse term frequency technique. In the second phase, an ensemble model based on deep learning was constructed as follows. Multiple binary classifiers were trained using sequential deep learning networks to extract the representative hidden features that could accurately classify news types. In the third phase, a multi-class classifier was constructed based on multilayer perceptron (MLP) and trained using the features extracted from the aggregated outputs of the deep learning-based binary classifiers for final classification. The two popular and well-known datasets (LIAR and ISOT) were used with different classifiers to benchmark the proposed model. Compared with the state-of-the-art models, which use deep contextualized representation with convolutional neural network (CNN), the proposed model shows significant improvements (2.41%) in the overall performance in terms of the F1score for the LIAR dataset, which is more challenging than other datasets. Meanwhile, the proposed model achieves 100% accuracy with ISOT. The study demonstrates that traditional features extracted from news content with proper model design outperform the existing models that were constructed based on text embedding techniques.https://www.mdpi.com/1424-8220/22/18/6970fake news detectionmisinformationtwo-stage classificationdeep learningensemble model |
| spellingShingle | Abdullah Marish Ali Fuad A. Ghaleb Bander Ali Saleh Al-Rimy Fawaz Jaber Alsolami Asif Irshad Khan Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique Sensors fake news detection misinformation two-stage classification deep learning ensemble model |
| title | Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique |
| title_full | Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique |
| title_fullStr | Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique |
| title_full_unstemmed | Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique |
| title_short | Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique |
| title_sort | deep ensemble fake news detection model using sequential deep learning technique |
| topic | fake news detection misinformation two-stage classification deep learning ensemble model |
| url | https://www.mdpi.com/1424-8220/22/18/6970 |
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