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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Bibliographic Details
Published in:Expert systems with applications Vol. 159; p. 113584
Main Authors: Huang, Yin-Fu, Chen, Po-Hong
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 30.11.2020
Elsevier BV
Subjects:
Accuracy
Adaptive algorithms
Deep learning
Domains
Ensemble learning
Fake news
Harmony search algorithm
Machine learning
Natural language processing
News
Search algorithms
Deep learning
Natural language processing
Fake news
Harmony search algorithm
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:•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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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2020.113584