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Re-ranking Web Search Results for Better Fact-Checking: A Preliminary Study

Published:17 October 2018Publication History

ABSTRACT

Even though Web search engines play an important role in finding documents relevant to user queries, there is little to no attention given to how they perform in terms of usefulness for fact-checking claims. In this paper, we introduce a new research problem that addresses the ability of fact-checking systems to distinguish Web search results that are useful in discovering the veracity of claims from the ones that are not.We also propose a re-ranking method to improve ranking of search results for fact-checking. To evaluate our proposed method, we conducted a preliminary study for which we have developed a test collection that includes 22 claims and 20 manually-annotated Web search results for each. Our experiments show that the proposed method outperforms the baseline represented by the original ranking of search results. The contributions this improvement brings to real-world applications is two-fold: it will help human fact-checkers find useful documents for their task faster, and it will help automated fact-checking systems by pointing out which documents are useful and which are not.

References

  1. David Corney, Dyaa Albakour, Miguel Martinez, and Samir Moussa. 2016. What do a Million News Articles Look like?. In Proceedings of the First International Workshop on Recent Trends in News Information Retrieval co-located with 38th European Conference on Information Retrieval (ECIR 2016), Padua, Italy, March 20, 2016. 42--47.Google ScholarGoogle Scholar
  2. William Ferreira and Andreas Vlachos. 2016. Emergent: a novel data-set for stance classification. In Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: Human language technologies. 1163--1168.Google ScholarGoogle ScholarCross RefCross Ref
  3. Georgi Karadzhov, Preslav Nakov, Lluis Marquez, Alberto Barron-Cedeno, and Ivan Koychev. 2017. Fully Automated Fact Checking Using External Sources. arXiv preprint arXiv:1710.00341 (2017).Google ScholarGoogle Scholar
  4. Christina Lioma, Birger Larsen, Wei Lu, and Yong Huang. 2016. A study of factuality, objectivity and relevance: three desiderata in large-scale information retrieval?. In Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies. ACM, 107--117. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Christina Lioma, Jakob Grue Simonsen, and Birger Larsen. 2017. Evaluation Measures for Relevance and Credibility in Ranked Lists. In Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval. ACM, 91--98. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Christopher D. Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014. The Stanford CoreNLP Natural Language Processing Toolkit. In Association for Computational Linguistics (ACL) System Demonstrations . 55--60.Google ScholarGoogle Scholar
  7. An T Nguyen1 Aditya Kharosekar Matthew and Lease1 Byron C Wallace. 2018. An Interpretable Joint Graphical Model for Fact-Checking from Crowds. (2018).Google ScholarGoogle Scholar
  8. Ndapandula Nakashole and Tom M Mitchell. 2014. Language-aware truth assessment of fact candidates. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vol. 1. 1009--1019.Google ScholarGoogle ScholarCross RefCross Ref
  9. Nikolaos Pappas and Andrei Popescu-Belis. 2013. Sentiment Analysis of User Comments for One-Class Collaborative Filtering Over TED Talks. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval (SIGIR '13). 773--776. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. William Yang Wang. 2017. "Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Vol. 2. 422--426.Google ScholarGoogle ScholarCross RefCross Ref

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            cover image ACM Conferences
            CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management
            October 2018
            2362 pages
            ISBN:9781450360142
            DOI:10.1145/3269206

            Copyright © 2018 ACM

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            New York, NY, United States

            Publication History

            • Published: 17 October 2018

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            CIKM '18 Paper Acceptance Rate147of826submissions,18%Overall Acceptance Rate1,861of8,427submissions,22%

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