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Fake News Classification Using a Fuzzy Convolutional Recurrent Neural Network

Dheeraj Kumar Dixit*, Amit Bhagat, Dharmendra Dangi
Department of Mathematics and Computer Applications, Maulana Azad National Institute of Technology (MANIT), Bhopal, Madhya Pradesh, India
* Corresponding Author: Dheeraj Kumar Dixit. Email:
(This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)

Computers, Materials & Continua 2022, 71(3), 5733-5750. https://doi.org/10.32604/cmc.2022.023628

Received 14 September 2021; Accepted 04 November 2021; Issue published 14 January 2022

Abstract

In recent years, social media platforms have gained immense popularity. As a result, there has been a tremendous increase in content on social media platforms. This content can be related to an individual's sentiments, thoughts, stories, advertisements, and news, among many other content types. With the recent increase in online content, the importance of identifying fake and real news has increased. Although, there is a lot of work present to detect fake news, a study on Fuzzy CRNN was not explored into this direction. In this work, a system is designed to classify fake and real news using fuzzy logic. The initial feature extraction process is done using a convolutional recurrent neural network (CRNN). After the extraction of features, word indexing is done with high dimensionality. Then, based on the indexing measures, the ranking process identifies whether news is fake or real. The fuzzy CRNN model is trained to yield outstanding results with 99.99 ± 0.01% accuracy. This work utilizes three different datasets (LIAR, LIAR-PLUS, and ISOT) to find the most accurate model.

Keywords

Fake news detection; text classification; convolution recurrent neural network; fuzzy convolutional recurrent neural networks

Cite This Article

D. Kumar Dixit, A. Bhagat and D. Dangi, "Fake news classification using a fuzzy convolutional recurrent neural network," Computers, Materials & Continua, vol. 71, no.3, pp. 5733–5750, 2022.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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