exBAKE: Automatic Fake News Detection Model Based on Bidirectional Encoder Representations from Transformers (BERT)
- Authors
- Jwa, Heejung; Oh, Dongsuk; Park, Kinam; Kang, Jang Mook; Lim, Heuiseok
- Issue Date
- 10월-2019
- Publisher
- MDPI
- Keywords
- fake news; fake information; fake news detect; fake news challenge; fake news classification; deep learning
- Citation
- APPLIED SCIENCES-BASEL, v.9, no.19
- Indexed
- SCIE
SCOPUS
- Journal Title
- APPLIED SCIENCES-BASEL
- Volume
- 9
- Number
- 19
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/62652
- DOI
- 10.3390/app9194062
- ISSN
- 2076-3417
- 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.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - Graduate School > Department of Computer Science and Engineering > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.