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        <title>Stance Detection in Facebook Posts of a German Right-wing Party</title>
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        <namePart type="given">Manfred</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics</title>
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            <namePart type="given">Michael</namePart>
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        <name type="personal">
            <namePart type="given">Nasrin</namePart>
            <namePart type="family">Mostafazadeh</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Nathanael</namePart>
            <namePart type="family">Chambers</namePart>
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        <name type="personal">
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    <abstract>We argue that in order to detect stance, not only the explicit attitudes of the stance holder towards the targets are crucial. It is the whole narrative the writer drafts that counts, including the way he hypostasizes the discourse referents: as benefactors or villains, as victims or beneficiaries. We exemplify the ability of our system to identify targets and detect the writer’s stance towards them on the basis of about 100 000 Facebook posts of a German right-wing party. A reader and writer model on top of our verb-based attitude extraction directly reveal stance conflicts.</abstract>
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%0 Conference Proceedings
%T Stance Detection in Facebook Posts of a German Right-wing Party
%A Klenner, Manfred
%A Tuggener, Don
%A Clematide, Simon
%Y Roth, Michael
%Y Mostafazadeh, Nasrin
%Y Chambers, Nathanael
%Y Louis, Annie
%S Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F klenner-etal-2017-stance
%X We argue that in order to detect stance, not only the explicit attitudes of the stance holder towards the targets are crucial. It is the whole narrative the writer drafts that counts, including the way he hypostasizes the discourse referents: as benefactors or villains, as victims or beneficiaries. We exemplify the ability of our system to identify targets and detect the writer’s stance towards them on the basis of about 100 000 Facebook posts of a German right-wing party. A reader and writer model on top of our verb-based attitude extraction directly reveal stance conflicts.
%R 10.18653/v1/W17-0904
%U https://aclanthology.org/W17-0904
%U https://doi.org/10.18653/v1/W17-0904
%P 31-40

Markdown (Informal)

[Stance Detection in Facebook Posts of a German Right-wing Party](https://aclanthology.org/W17-0904) (Klenner et al., LSDSem 2017)

• Stance Detection in Facebook Posts of a German Right-wing Party (Klenner et al., LSDSem 2017)

ACL

• Manfred Klenner, Don Tuggener, and Simon Clematide. 2017. Stance Detection in Facebook Posts of a German Right-wing Party. In Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics, pages 31–40, Valencia, Spain. Association for Computational Linguistics.