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

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.

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