Graph-based Fake News Detection using a Summarization Technique

Gihwan Kim, Youngjoong Ko


Abstract
Nowadays, fake news is spreading in various ways, and this fake information is causing a lot of social damages. Thus the need to detect fake information is increasing to prevent the damages caused by fake news. In this paper, we propose a novel graph-based fake news detection method using a summarization technique that uses only the document internal information. Our proposed method represents the relationship between all sentences using a graph and the reflection rate of contextual information among sentences is computed by using an attention mechanism. In addition, we improve the performance of fake news detection by utilizing summary information as an important subject of the document. The experimental results demonstrate that our method achieves high accuracy, 91.04%, that is 8.85%p better than the previous method.
Anthology ID:
2021.eacl-main.287
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Editors:
Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3276–3280
Language:
URL:
https://aclanthology.org/2021.eacl-main.287
DOI:
10.18653/v1/2021.eacl-main.287
Bibkey:
Cite (ACL):
Gihwan Kim and Youngjoong Ko. 2021. Graph-based Fake News Detection using a Summarization Technique. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 3276–3280, Online. Association for Computational Linguistics.
Cite (Informal):
Graph-based Fake News Detection using a Summarization Technique (Kim & Ko, EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.287.pdf