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exBAKE: Automatic Fake News Detection Model Based on Bidirectional Encoder Representations from Transformers (BERT) - 2019

Research Area:  Machine Learning

Abstract:

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.

Author(s) Name:  Heejung Jwa, D. Oh, Kinam Park, Jang Kang, Hueiseok Lim

Journal name:  Applied Sciences

Conferrence name:  

Publisher name:  MDPI

DOI:  10.3390/app9194062

Volume Information: