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

Piyush Katariya, Vedika Gupta, Rohan Arora, Adarsh Kumar, Shreya Dhingra, Qin Xin, Jude Hemanth

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.
Original languageEnglish
Pages (from-to)286-309
Number of pages24
JournalInternational Journal of Web Information Systems
Issue number5/6
Publication statusPublished - 12 Dec 2022


  • natural language processing
  • fake news
  • machine learning
  • gated recurrent unit
  • bidirectorial LSTM (bi-LSTM)
  • hyperparameters
  • fine tuning


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