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Examining Linguistic Biases in Telegram with a game theoretic analysis *

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.
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https://hal.archives-ouvertes.fr/hal-03328712
Contributor : Nicholas Asher Connect in order to contact the contributor
Submitted on : Monday, August 30, 2021 - 11:49:16 AM
Last modification on : Tuesday, October 19, 2021 - 2:23:41 PM

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  • HAL Id : hal-03328712, version 1

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Sviatlana Höhn, Nicholas Asher, Sjouke Mauw. Examining Linguistic Biases in Telegram with a game theoretic analysis *. 3rd Multidisciplinary International Symposium on Disinformation in Open Online Media (MISDOOM 2021), Oxford Internet Institute, Sep 2021, Oxford (virtual), United Kingdom. pp.1-15. ⟨hal-03328712⟩

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