XFake: Explainable fake news detector with visualizations

Fan Yang, Mengnan Du, Eric D. Ragan, Shiva K. Pentyala, Hao Yuan, Shuiwang Ji, Sina Mohseni, Rhema Linder, Xia Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

66 Scopus citations

Abstract

In this demo paper, we present the XFake system, an explainable fake news detector that assists end-users to identify news credibility. To effectively detect and interpret the fakeness of news items, we jointly consider both attributes (e.g., speaker) and statements. Specifically, MIMIC, ATTN and PERT frameworks are designed, where MIMIC is built for attribute analysis, ATTN is for statement semantic analysis and PERT is for statement linguistic analysis. Beyond the explanations extracted from the designed frameworks, relevant supporting examples as well as visualization are further provided to facilitate the interpretation. Our implemented system is demonstrated on a real-world dataset crawled from PolitiFact1, where thousands of verified political news have been collected.

Original languageEnglish (US)
Title of host publicationThe Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019
PublisherAssociation for Computing Machinery, Inc
Pages3600-3604
Number of pages5
ISBN (Electronic)9781450366748
DOIs
StatePublished - May 13 2019
Externally publishedYes
Event2019 World Wide Web Conference, WWW 2019 - San Francisco, United States
Duration: May 13 2019May 17 2019

Publication series

NameThe Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019

Conference

Conference2019 World Wide Web Conference, WWW 2019
Country/TerritoryUnited States
CitySan Francisco
Period5/13/195/17/19

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

Keywords

  • Explainable models
  • Fake news detection
  • System visualization

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