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RETRACTED ARTICLE: Hybrid deep learning model for automatic fake news detection

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This article was retracted on 08 January 2024

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Abstract

With the fast advancement in digital news, fake news has already caused grave threats to the public’s actual judgment and credibility, in specific, with the wide use of social networking platforms, which provide a rich environment for the generation and dissemination of fake news. To cope with these challenges, several techniques were proposed to detect fake news, but still, there is an urgent need to propose an improved detection technique that provides a high level of detection performance in an automatic manner. Therefore, this article proposes a hybrid-improved deep learning model for automatic fake news detection. The proposed model adopts automatic data augmentation method, called Auxiliary Classifier Generative Adversarial Networks, to artificially synthesize new fake news samples, and then, hybridize the Convolutional Neural Network with the Recurrent Neural Networks to detect the fake news efficiently. The proposed model shows superior results against the state-of-the-art models as it provides 93.87% accuracy, 10.39% recall, 93.12% precision in detecting the fake news using Buzzfeed, FakeNewsNet and FakeNewsChallenges datasets.

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Notes

  1. https://www.kaggle.com/mdepak/fakenewsnet.

  2. https://www.kaggle.com/sohamohajeri/buzzfeed-news-analysis-and-classification.

  3. http://www.fakenewschallenge.org/

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Authors and Affiliations

  1. Electrical and Computer Engineering, İstanbul Altınbaş Üniversitesi, Istanbul, Turkey

    Othman A. Hanshal, Osman N. Ucan & Yousef K. Sanjalawe

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  1. Othman A. Hanshal
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  2. Osman N. Ucan
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  3. Yousef K. Sanjalawe
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Correspondence to Yousef K. Sanjalawe.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s13204-024-03001-w"

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Hanshal, O.A., Ucan, O.N. & Sanjalawe, Y.K. RETRACTED ARTICLE: Hybrid deep learning model for automatic fake news detection. Appl Nanosci 13, 2957–2967 (2023). https://doi.org/10.1007/s13204-021-02330-4

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