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Identifying Malicious Behavior in Multi-party Bipolar Argumentation Debates

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Book cover Multi-Agent Systems and Agreement Technologies (EUMAS 2015, AT 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9571))

Abstract

Lately, several works have analyzed potential uses of argumentation in multi-party debates. Usually, the focus of such works is the computation of a collectively “correct” outcome, a challenging task even when the debate’s users truthfully express their beliefs. This work focuses on debates where some users may exhibit specific types of “malicious” behavior: they may lie (by making statements they do not believe to hold) and they may hide valuable information (by not making relevant statements they believe to hold). Our approach is the following: firstly, we define “user attributes” which capture different aspects of a user’s behavior in a debate (how active, how opinionated and how classifiable a user has been); then, we build and test experimentally hypotheses that, from the values of these attributes, can predict whether a user has lied and/or hidden valuable information.

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Acknowledgments

This research has been supported by the EU project DesMOLD (FP7/2007-2013-314581).

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Authors and Affiliations

  1. Department of Computing, Imperial College London, London, UK

    Dionysios Kontarinis & Francesca Toni

Authors
  1. Dionysios Kontarinis
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  2. Francesca Toni
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Corresponding author

Correspondence to Dionysios Kontarinis .

Editor information

Editors and Affiliations

  1. University of Edinburgh, Edinburgh, United Kingdom

    Michael Rovatsos

  2. Department of Digital Systems, University of Piraeus, Piraeus, Greece

    George Vouros

  3. Technical University of Valencia, Valencia, Spain

    Vicente Julian

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Kontarinis, D., Toni, F. (2016). Identifying Malicious Behavior in Multi-party Bipolar Argumentation Debates. In: Rovatsos, M., Vouros, G., Julian, V. (eds) Multi-Agent Systems and Agreement Technologies. EUMAS AT 2015 2015. Lecture Notes in Computer Science(), vol 9571. Springer, Cham. https://doi.org/10.1007/978-3-319-33509-4_21

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  • DOI: https://doi.org/10.1007/978-3-319-33509-4_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33508-7

  • Online ISBN: 978-3-319-33509-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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