dEFEND: A system for explainable fake news detection

Limeng Cui, Kai Shu, Suhang Wang, Dongwon Lee, Huan Liu

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

Abstract

Despite recent advancements in computationally detecting fake news, we argue that a critical missing piece be the explainability of such detection-i.e., why a particular piece of news is detected as fake-and propose to exploit rich information in users' comments on social media to infer the authenticity of news. In this demo paper, we present our system for an explainable fake news detection called dEFEND, which can detect the authenticity of a piece of news while identifying user comments that can explain why the news is fake or real. Our solution develops a sentence-comment co-attention sub-network to exploit both news contents and user comments to jointly capture explainable top-k check-worthy sentences and user comments for fake news detection. The system is publicly accessible.

Original languageEnglish (US)
Title of host publicationCIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages2961-2964
Number of pages4
ISBN (Electronic)9781450369763
DOIs
StatePublished - Nov 3 2019
Event28th ACM International Conference on Information and Knowledge Management, CIKM 2019 - Beijing, China
Duration: Nov 3 2019Nov 7 2019

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference28th ACM International Conference on Information and Knowledge Management, CIKM 2019
CountryChina
CityBeijing
Period11/3/1911/7/19

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All Science Journal Classification (ASJC) codes

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

Cite this

Cui, L., Shu, K., Wang, S., Lee, D., & Liu, H. (2019). dEFEND: A system for explainable fake news detection. In CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 2961-2964). (International Conference on Information and Knowledge Management, Proceedings). Association for Computing Machinery. https://doi.org/10.1145/3357384.3357862