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Keywords:

  • information retrieval;
  • credibility;
  • NLP;
  • trustworthy;
  • dependency tree;
  • content trust;
  • natural language processing

Abstract

People encounter more information than they can possibly use every day. But all information is not necessarily of equal value. In many cases, certain information appears to be better, or more trustworthy, than other information. And the challenge that most people then face is to judge which information is more credible. In this paper we propose a new problem called Corroboration Trust, which studies how to find credible news events by seeking more than one source to verify information on a given topic. We design an evidence-based corroboration trust algorithm called TrustNewsFinder, which utilizes the relationships between news articles and related evidence information (person, location, time and keywords about the news). A news article is trustworthy if it provides many pieces of trustworthy evidence, and a piece of evidence is likely to be true if it is provided by many trustworthy news articles. Our experiments show that TrustNewsFinder successfully finds true events among conflicting information and identifies trustworthy news better than the popular search engines. Copyright © 2009 John Wiley & Sons, Ltd.