Fake news detection using an ensemble learning model based on Self-Adaptive Harmony Search algorithms
•The appropriate features of news are analyzed for training models.•The ensemble learning model for fake news detection is proposed in the paper.•The weights of the ensemble learning model are optimized in the paper.•The cross-domain intractability issue is investigated in the paper. In general, the...
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| Published in: | Expert systems with applications Vol. 159; p. 113584 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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30.11.2020
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | •The appropriate features of news are analyzed for training models.•The ensemble learning model for fake news detection is proposed in the paper.•The weights of the ensemble learning model are optimized in the paper.•The cross-domain intractability issue is investigated in the paper.
In general, the features of fake news are almost the same as those of real news, so it is not easy to identify them. In this paper, we propose a fake news detection system using a deep learning model. First, news articles are preprocessed and analyzed based on different training models. Then, an ensemble learning model combining four different models called embedding LSTM, depth LSTM, LIWC CNN, and N-gram CNN is proposed for fake news detection. Besides, to achieve higher accuracy in fake news detection, the optimized weights of the ensemble learning model are determined using the Self-Adaptive Harmony Search (SAHS) algorithm. In the experiments, we verify that the proposed model is superior to the state-of-the-art methods, with the highest accuracy of 99.4%. Furthermore, we also investigate the cross-domain intractability issue and achieve the highest accuracy of 72.3%. Finally, we believe there is still room for improving the ensemble learning model in addressing the cross-domain intractability issue. |
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| AbstractList | •The appropriate features of news are analyzed for training models.•The ensemble learning model for fake news detection is proposed in the paper.•The weights of the ensemble learning model are optimized in the paper.•The cross-domain intractability issue is investigated in the paper.
In general, the features of fake news are almost the same as those of real news, so it is not easy to identify them. In this paper, we propose a fake news detection system using a deep learning model. First, news articles are preprocessed and analyzed based on different training models. Then, an ensemble learning model combining four different models called embedding LSTM, depth LSTM, LIWC CNN, and N-gram CNN is proposed for fake news detection. Besides, to achieve higher accuracy in fake news detection, the optimized weights of the ensemble learning model are determined using the Self-Adaptive Harmony Search (SAHS) algorithm. In the experiments, we verify that the proposed model is superior to the state-of-the-art methods, with the highest accuracy of 99.4%. Furthermore, we also investigate the cross-domain intractability issue and achieve the highest accuracy of 72.3%. Finally, we believe there is still room for improving the ensemble learning model in addressing the cross-domain intractability issue. In general, the features of fake news are almost the same as those of real news, so it is not easy to identify them. In this paper, we propose a fake news detection system using a deep learning model. First, news articles are preprocessed and analyzed based on different training models. Then, an ensemble learning model combining four different models called embedding LSTM, depth LSTM, LIWC CNN, and N-gram CNN is proposed for fake news detection. Besides, to achieve higher accuracy in fake news detection, the optimized weights of the ensemble learning model are determined using the Self-Adaptive Harmony Search (SAHS) algorithm. In the experiments, we verify that the proposed model is superior to the state-of-the-art methods, with the highest accuracy of 99.4%. Furthermore, we also investigate the cross-domain intractability issue and achieve the highest accuracy of 72.3%. Finally, we believe there is still room for improving the ensemble learning model in addressing the cross-domain intractability issue. |
| ArticleNumber | 113584 |
| Author | Chen, Po-Hong Huang, Yin-Fu |
| Author_xml | – sequence: 1 givenname: Yin-Fu orcidid: 0000-0001-6665-0135 surname: Huang fullname: Huang, Yin-Fu email: huangyf@yuntech.edu.tw – sequence: 2 givenname: Po-Hong surname: Chen fullname: Chen, Po-Hong |
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| Snippet | •The appropriate features of news are analyzed for training models.•The ensemble learning model for fake news detection is proposed in the paper.•The weights... In general, the features of fake news are almost the same as those of real news, so it is not easy to identify them. In this paper, we propose a fake news... |
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| Title | Fake news detection using an ensemble learning model based on Self-Adaptive Harmony Search algorithms |
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