Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

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

A deep neural network-based approach for fake news detection in regional language 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/approachThe 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.FindingsThe deep learning model such as B-LSTM yields an accuracy of 95.01%.Originality/valueThis 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Web Information Systems Emerald Publishing

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

Loading next page...
 
/lp/emerald-publishing/a-deep-neural-network-based-approach-for-fake-news-detection-in-c20vGsVc8L

References (38)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1744-0084
eISSN
1744-0084
DOI
10.1108/ijwis-02-2022-0036
Publisher site
See Article on Publisher Site

Abstract

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/approachThe 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.FindingsThe deep learning model such as B-LSTM yields an accuracy of 95.01%.Originality/valueThis 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.

Journal

International Journal of Web Information SystemsEmerald Publishing

Published: Dec 12, 2022

Keywords: Natural language processing; Fake news; Machine learning; Gated recurrent unit; Bidirectional LSTM (bi-LSTM); Hyperparameters; Fine tuning

There are no references for this article.