Web-Informed-Augmented Fake News Detection Model Using Stacked Layers of Convolutional Neural Network and Deep Autoencoder

Abdullah Marish Ali, Fuad A. Ghaleb (), Mohammed Sultan Mohammed, Fawaz Jaber Alsolami and Asif Irshad Khan
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Abdullah Marish Ali: Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Fuad A. Ghaleb: Department of Computer Science, Faculty of Computing, Universiti Teknologi, Malaysia, Johor Bahru 81310, Malaysia
Mohammed Sultan Mohammed: Faculty of Electrical Engineering, Universiti Teknologi, Malaysia, Johor Bahru 81310, Malaysia
Fawaz Jaber Alsolami: Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Asif Irshad Khan: Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Mathematics, 2023, vol. 11, issue 9, 1-21

Abstract: Today, fake news is a growing concern due to its devastating impacts on communities. The rise of social media, which many users consider the main source of news, has exacerbated this issue because individuals can easily disseminate fake news more quickly and inexpensive with fewer checks and filters than traditional news media. Numerous approaches have been explored to automate the detection and prevent the spread of fake news. However, achieving accurate detection requires addressing two crucial aspects: obtaining the representative features of effective news and designing an appropriate model. Most of the existing solutions rely solely on content-based features that are insufficient and overlapping. Moreover, most of the models used for classification are constructed with the concept of a dense features vector unsuitable for short news sentences. To address this problem, this study proposed a Web-Informed-Augmented Fake News Detection Model using Stacked Layers of Convolutional Neural Network and Deep Autoencoder called ICNN-AEN-DM. The augmented information is gathered from web searches from trusted sources to either support or reject the claims in the news content. Then staked layers of CNN with a deep autoencoder were constructed to train a probabilistic deep learning-base classifier. The probabilistic outputs of the stacked layers were used to train decision-making by staking multilayer perceptron (MLP) layers to the probabilistic deep learning layers. The results based on extensive experiments challenging datasets show that the proposed model performs better than the related work models. It achieves 26.6% and 8% improvement in detection accuracy and overall detection performance, respectively. Such achievements are promising for reducing the negative impacts of fake news on communities.

Keywords: fake news detection; web-informed; augmented information; misinformation; two-stage classification; deep learning; stacked learning; CNN; deep autoencoder (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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