Fully automated fact checking using external sources

Georgi Karadzhov, Preslav Nakov, Lluis Marques, Alberto Barron, Ivan Koychev

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

18 Citations (Scopus)

Abstract

Given the constantly growing proliferation of false claims online in recent years, there has been also a growing research interest in automatically distinguishing false rumors from factually true claims. Here, we propose a general-purpose framework for fully-automatic fact checking using external sources, tapping the potential of the entire Web as a knowledge source to confirm or reject a claim. Our framework uses a deep neural network with LSTM text encoding to combine semantic kernels with task-specific embeddings that encode a claim together with pieces of potentially-relevant text fragments from the Web, taking the source reliability into account. The evaluation results show good performance on two different tasks and datasets: (i) rumor detection and (if) fact checking of the answers to a question in community question answering forums.

Original languageEnglish
Title of host publicationInternational Conference on Recent Advances in Natural Language Processing
Subtitle of host publicationMeet Deep Learning, RANLP 2017 - Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages344-353
Number of pages10
Volume2017-September
ISBN (Electronic)9789544520489
DOIs
Publication statusPublished - 1 Jan 2017
Event11th International Conference on Recent Advances in Natural Language Processing, RANLP 2017 - Varna, Bulgaria
Duration: 2 Sep 20178 Sep 2017

Other

Other11th International Conference on Recent Advances in Natural Language Processing, RANLP 2017
CountryBulgaria
CityVarna
Period2/9/178/9/17

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Software
  • Electrical and Electronic Engineering

Cite this

Karadzhov, G., Nakov, P., Marques, L., Barron, A., & Koychev, I. (2017). Fully automated fact checking using external sources. In International Conference on Recent Advances in Natural Language Processing: Meet Deep Learning, RANLP 2017 - Proceedings (Vol. 2017-September, pp. 344-353). Association for Computational Linguistics (ACL). https://doi.org/10.26615/978-954-452-049-6-046