Research Area:  Machine Learning
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.
Automatic Fake News Detection
Bidirectional Encoder Representations From Transformers (Bert)
Author(s) Name:  Heejung Jwa, D. Oh, Kinam Park, Jang Kang, Hueiseok Lim
Journal name:  Applied Sciences
Publisher name:  MDPI
Paper Link:   https://www.mdpi.com/2076-3417/9/19/4062