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A deep neural network-based approach for fake news detection in regional language

Piyush Katariya (Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India)
Vedika Gupta (Jindal Global Business School, O.P. Jindal Global University, Sonipat, India)
Rohan Arora (Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India)
Adarsh Kumar (Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India)
Shreya Dhingra (Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India)
Qin Xin (Faculty of Science and Technology, University of Faroe Islands, Vestarabrygga, Faroe Islands)
Jude Hemanth (Department of Electronics and Communication Engineering, Karunya University, Coimbatore, India)

International Journal of Web Information Systems

ISSN: 1744-0084

Article publication date: 27 July 2022

Issue publication date: 12 December 2022

70

Abstract

Purpose

The current natural language processing algorithms are still lacking in judgment criteria, and these approaches often require deep knowledge of political or social contexts. Seeing the damage done by the spreading of fake news in various sectors have attracted the attention of several low-level regional communities. However, such methods are widely developed for English language and low-resource languages remain unfocused. This study aims to provide analysis of Hindi fake news and develop a referral system with advanced techniques to identify fake news in Hindi.

Design/methodology/approach

The technique deployed in this model uses bidirectional long short-term memory (B-LSTM) as compared with other models like naïve bayes, logistic regression, random forest, support vector machine, decision tree classifier, kth nearest neighbor, gated recurrent unit and long short-term models.

Findings

The deep learning model such as B-LSTM yields an accuracy of 95.01%.

Originality/value

This study anticipates that this model will be a beneficial resource for building technologies to prevent the spreading of fake news and contribute to research with low resource languages.

Keywords

Citation

Katariya, P., Gupta, V., Arora, R., Kumar, A., Dhingra, S., Xin, Q. and Hemanth, J. (2022), "A deep neural network-based approach for fake news detection in regional language", International Journal of Web Information Systems, Vol. 18 No. 5/6, pp. 286-309. https://doi.org/10.1108/IJWIS-02-2022-0036

Publisher

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Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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