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Deception detection: dependable or defective?

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Abstract

How do human beings tell the difference between truths and lies, and avoid being deceived? And is it possible for a machine to determine the veracity of any given statement or set of statements prior to incorporating such statements in a knowledge base, or to determine whether the deception even exists at the statement level? This paper reviews past research in deception and its detection to explore such questions. We focus in on various inconsistencies, contradictions, and other difficulties in recent deception research, and show how the nature of the deception largely dictates the methods that can be deployed effectively in detection by reference to several experiments on materials which can have a strongly deceptive framing.

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Notes

  1. Robin Sage (2013).

  2. Vijayan (2010).

  3. Section 2 is adapted from our publication in the 13th Conference of the European Chapter of the Association for Computational Linguistics specialised workshop on Computational Approaches to Deception Detection (Vartapetiance and Gillam 2012a). The EACL copyright agreement allows for publication of this material in other outlets.

  4. MuchMore Springer Bilingual Corpus, Available at: http://muchmore.dfki.de/resources1.htm.

  5. Dossier D (2013).

  6. http://www.perverted-justice.com/.

  7. Spinning the Election (Skillicorn and Little) http://research.cs.queensu.ca/home/skill/election/election.html.

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Acknowledgments

The authors gratefully acknowledge prior from EPSRC/JISC (EP/I034408/1), the UK’s Technology Strategy Board (TSB, 169201), and also the efforts of the PAN13 organizers in crafting and managing the tasks.

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Authors and Affiliations

  1. Department of Computing, Faculty of Engineering and Physical Sciences, University of Surrey, Surrey, UK

    Anna Vartapetiance & Lee Gillam

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  1. Anna Vartapetiance
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  2. Lee Gillam
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Correspondence to Anna Vartapetiance.

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This article is part of the Topical Collection on Uncovering Deception in Social Media.

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Vartapetiance, A., Gillam, L. Deception detection: dependable or defective?. Soc. Netw. Anal. Min. 4, 166 (2014). https://doi.org/10.1007/s13278-014-0166-8

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