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Examining Linguistic Biases in Telegram with a Game Theoretic Analysis

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Disinformation in Open Online Media (MISDOOM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12887))

Abstract

Selective formulations and selective reporting of facts in political news are deliberately used to create particular identities of different political sides. This becomes evident in media dialogue reporting about political conflicts. In contrast to most NLP-based studies of linguistic bias, we engage critically with its nature, aiming at a later de-biasing or at least raising awareness about linguistic bias in political news. We found inspiration in conversation analysis (CA), membership categorisation analysis (MCA) and a game-theoretic approach to discourse called epistemic message exchange (ME) games. We identified three types of bias: selective reports about facts, selective formulations when reporting about the same facts, and different histories built up by the differences in the first two. We extend the epistemic ME games model with findings from a qualitative study.

We thank the ANR PRCI grant SLANT, the Luxembourgish National Research Fund, INTER-SLANT 13320890 and the 3IA Institute ANITI funded by the ANR-19-PI3A-0004 grant for research support.

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Notes

  1. 1.

    Available at https://github.com/sviatlanahoehn/BelElect.

  2. 2.

    https://en.wikipedia.org/wiki/Square_of_Changes.

  3. 3.

    Using the definitions of first order beliefs, \(S\), the set of strategies, and types, [5] define higher order beliefs, beliefs that players or the Jury have about the beliefs of other players (and the Jury) and fill out the epistemic picture of our players.

  4. 4.

    For a definition of independence see [5].

  5. 5.

    Otryad Militsyi Osobogo Nasnacheniya, En.: Special police detachment.

  6. 6.

    Lebedev, Executive Secretary of the CIS.

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Author information

Authors and Affiliations

  1. DCS/SnT, University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Sviatlana Höhn & Sjouke Mauw

  2. IRIT, Toulouse, France

    Nicholas Asher

Authors
  1. Sviatlana Höhn
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  2. Nicholas Asher
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  3. Sjouke Mauw
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Corresponding author

Correspondence to Sviatlana Höhn .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Jonathan Bright

  2. Utrecht University, Utrecht, The Netherlands

    Anastasia Giachanou

  3. University of Leeds, Leeds, UK

    Viktoria Spaiser

  4. Boise State University, Boise, ID, USA

    Francesca Spezzano

  5. University of Oxford, Oxford, UK

    Anna George

  6. University of Oxford, Oxford, UK

    Alexandra Pavliuc

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Höhn, S., Asher, N., Mauw, S. (2021). Examining Linguistic Biases in Telegram with a Game Theoretic Analysis. In: Bright, J., Giachanou, A., Spaiser, V., Spezzano, F., George, A., Pavliuc, A. (eds) Disinformation in Open Online Media. MISDOOM 2021. Lecture Notes in Computer Science(), vol 12887. Springer, Cham. https://doi.org/10.1007/978-3-030-87031-7_2

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  • DOI: https://doi.org/10.1007/978-3-030-87031-7_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87030-0

  • Online ISBN: 978-3-030-87031-7

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