ClaimVerif: A real-time claim verification system using the web and fact databases

Shi Zhi, Yicheng Sun, Jiayi Liu, Chao Zhang, Jiawei Han

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

Abstract

Our society is increasingly digitalized. Every day, a tremendous amount of information is being created, shared, and digested through all kinds of cyber channels. Although people can easily acquire information from various sources (social media, news articles, etc.) the truthfulness of most received information remains unverified. In many real-life scenarios, false information has become the de facto cause that leads to detrimental decision makings, and techniques that can automatically filter false information are highly demanded. However, verifying whether a piece of information is trustworthy is difficult because: (1) selecting candidate snippets for fact checking is nontrivial; and (2) detecting supporting evidences i.e. stances, suffers from the difficulty of measuring the similarity between claims and related evidences. We build ClaimVerif, a claim verification system that not only provides credibility assessment for any user-given query claim, but also rationales the assessment results with supporting evidences. ClaimVerif can automatically select the stances from millions of documents and employs two-step training to justify the opinions of the stances. Furthermore, combined with the credibility of stances sources, ClaimVerif degrades the score of stances from untrustworthy sources and alleviates the negative effects from rumor spreaders. Our empirical evaluations show that ClaimVerif achieves both high accuracy and efficiency in different claim verification tasks. It can be highly useful in practical applications by providing multidimension analysis for the suspicious statements, including the stances, opinions, source credibility and estimated judgements.

Original languageEnglish (US)
Title of host publicationCIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages2555-2558
Number of pages4
ISBN (Electronic)9781450349185
DOIs
StatePublished - Nov 6 2017
Event26th ACM International Conference on Information and Knowledge Management, CIKM 2017 - Singapore, Singapore
Duration: Nov 6 2017Nov 10 2017

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings
VolumePart F131841

Other

Other26th ACM International Conference on Information and Knowledge Management, CIKM 2017
CountrySingapore
CitySingapore
Period11/6/1711/10/17

ASJC Scopus subject areas

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

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