Fagon: Fake news detection model using grammatical transformation on deep neural network

Youngkyung Seo, Seong Soo Han, You Boo Jeon, Chang Sung Jeong

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


As technology advances, the amount of fake news is increasing more and more by various reasons such as political issues and advertisement exaggeration. However, there have been very few research works on fake news detection, especially which uses grammatical transformation on deep neural network. In this paper, we shall present a new Fake News Detection Model, called FAGON(Fake news detection model using Grammatical transformation On deep Neural network) which determines efficiently if the proposition is true or not for the given article by learning grammatical transformation on neural network. Especially, our model focuses the Korean language. It consists of two modules: sentence generator and classification. The former generates multiple sentences which have the same meaning as the proposition, but with different grammar by training the grammatical transformation. The latter classifies the proposition as true or false by training with vectors generated from each sentence of the article and the multiple sentences obtained from the former model respectively. We shall show that our model is designed to detect fake news effectively by exploiting various grammatical transformation and proper classification structure.

Original languageEnglish
Pages (from-to)4958-4970
Number of pages13
JournalKSII Transactions on Internet and Information Systems
Issue number10
Publication statusPublished - 2019 Oct 30

Bibliographical note

Publisher Copyright:
Copyright © 2019 KSII.


  • Deep neural network
  • Fake news detection
  • Grammatical transformation

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications


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