Utilization Strategy of User Engagements in Korean Fake News Detection

Myunghoon Kang, Jaehyung Seo, Chanjun Park, Heuiseok Lim

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Fake News (disinformation with malicious intent) has emerged as a major social problem. To address this issue, previous studies mainly utilized single information, the news content, to detect fake news. However, using only news content in training is insufficient. Moreover, most studies did not consider the propagation aspect of fake news as a training feature. Thus, in an attempt to incorporate the ability to learn representation based on textual information and social context, this study proposed a fake news detection algorithm that thoroughly utilizes user graph in Korean fake news and dataset construction methods. In addition, a training strategy was proposed for utilizing user graph in Korean fake news detection through comparative and ablation studies. The experimental results showed that K-FANG outperformed the baseline in detecting fake news. Moreover, user engagements were found to be useful for detecting fake news even if the data contained hate speech. Finally, the validity of using stance information by expanding its class and controlling the class imbalance issues was also verified. This study provided useful implications for utilizing user information in fake news detection.

Original languageEnglish
Pages (from-to)79516-79525
Number of pages10
JournalIEEE Access
Volume10
DOIs
Publication statusPublished - 2022

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • Deep learning
  • fake news
  • graph representation
  • natural language processing

ASJC Scopus subject areas

  • General Engineering
  • General Materials Science
  • General Computer Science

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