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Article

exBAKE: Automatic Fake News Detection Model Based on Bidirectional Encoder Representations from Transformers (BERT)

1
Department of Computer Science and Engineering, Korea University, Seoul 02841, Korea
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Human-Inspired AI & Computing Research Center, Korea University, Seoul 02841, Korea
3
Creative Information & Computer Institute, Korea University, Seoul 02841, Korea
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Department of Convergence Contents, Global Cyber University, Seoul 02841, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(19), 4062; https://doi.org/10.3390/app9194062
Received: 4 September 2019 / Revised: 20 September 2019 / Accepted: 24 September 2019 / Published: 28 September 2019
(This article belongs to the Section Computing and Artificial Intelligence)
News currently spreads rapidly through the internet. Because fake news stories are designed to attract readers, they tend to spread faster. For most readers, detecting fake news can be challenging and such readers usually end up believing that the fake news story is fact. Because fake news can be socially problematic, a model that automatically detects such fake news is required. In this paper, we focus on data-driven automatic fake news detection methods. We first apply the Bidirectional Encoder Representations from Transformers model (BERT) model to detect fake news by analyzing the relationship between the headline and the body text of news. To further improve performance, additional news data are gathered and used to pre-train this model. We determine that the deep-contextualizing nature of BERT is best suited for this task and improves the 0.14 F-score over older state-of-the-art models. View Full-Text
Keywords: fake news; fake information; fake news detect; fake news challenge; fake news classification; deep learning fake news; fake information; fake news detect; fake news challenge; fake news classification; deep learning
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MDPI and ACS Style

Jwa, H.; Oh, D.; Park, K.; Kang, J.M.; Lim, H. exBAKE: Automatic Fake News Detection Model Based on Bidirectional Encoder Representations from Transformers (BERT). Appl. Sci. 2019, 9, 4062. https://doi.org/10.3390/app9194062

AMA Style

Jwa H, Oh D, Park K, Kang JM, Lim H. exBAKE: Automatic Fake News Detection Model Based on Bidirectional Encoder Representations from Transformers (BERT). Applied Sciences. 2019; 9(19):4062. https://doi.org/10.3390/app9194062

Chicago/Turabian Style

Jwa, Heejung; Oh, Dongsuk; Park, Kinam; Kang, Jang M.; Lim, Heuiseok. 2019. "exBAKE: Automatic Fake News Detection Model Based on Bidirectional Encoder Representations from Transformers (BERT)" Appl. Sci. 9, no. 19: 4062. https://doi.org/10.3390/app9194062

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