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FAGON: Fake News Detection Model Using Grammatical Transformation on Deep Neural Network
  • KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

FAGON: Fake News Detection Model Using Grammatical Transformation on Deep Neural Network

Vol. 13, No. 10, October 30, 2019
10.3837/tiis.2019.10.008, Download Paper (Free):

Abstract

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.


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Cite this article

[IEEE Style]
Y. Seo, S. Han, Y. Jeon and C. Jeong, "FAGON: Fake News Detection Model Using Grammatical Transformation on Deep Neural Network," KSII Transactions on Internet and Information Systems, vol. 13, no. 10, pp. 4958-4970, 2019. DOI: 10.3837/tiis.2019.10.008.

[ACM Style]
Youngkyung Seo, Seong-Soo Han, You-Boo Jeon, and Chang-Sung Jeong. 2019. FAGON: Fake News Detection Model Using Grammatical Transformation on Deep Neural Network. KSII Transactions on Internet and Information Systems, 13, 10, (2019), 4958-4970. DOI: 10.3837/tiis.2019.10.008.