GCAN: Graph-aware co-attention networks for explainable fake news detection on social media

Yi Ju Lu, Cheng Te Li

研究成果: Conference contribution

246 引文 斯高帕斯(Scopus)

摘要

This paper solves the fake news detection problem under a more realistic scenario on social media. Given the source short-text tweet and the corresponding sequence of retweet users without text comments, we aim at predicting whether the source tweet is fake or not, and generating explanation by highlighting the evidences on suspicious retweeters and the words they concern. We develop a novel neural network-based model, Graph-aware Co-Attention Networks (GCAN), to achieve the goal. Extensive experiments conducted on real tweet datasets exhibit that GCAN can significantly outperform state-of-the-art methods by 16% in accuracy on average. In addition, the case studies also show that GCAN can produce reasonable explanations.

原文English
主出版物標題ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
發行者Association for Computational Linguistics (ACL)
頁面505-514
頁數10
ISBN(電子)9781952148255
出版狀態Published - 2020
事件58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Virtual, Online, United States
持續時間: 2020 7月 52020 7月 10

出版系列

名字Proceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN(列印)0736-587X

Conference

Conference58th Annual Meeting of the Association for Computational Linguistics, ACL 2020
國家/地區United States
城市Virtual, Online
期間20-07-0520-07-10

All Science Journal Classification (ASJC) codes

  • 電腦科學應用
  • 語言和語言學
  • 語言與語言學

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