exBAKE: Automatic fake news detection model based on Bidirectional Encoder Representations from Transformers (BERT)

Heejung Jwa, Dongsuk Oh, Kinam Park, Jang Mook Kang, Heuiseok Lim

Research output: Contribution to journalArticle


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.

Original languageEnglish
Article number4062
JournalApplied Sciences (Switzerland)
Issue number19
Publication statusPublished - 2019 Oct 1



  • Deep learning
  • Fake information
  • Fake news
  • Fake news challenge
  • Fake news classification
  • Fake news detect

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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