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        <title>Toward Discourse-Aware Models for Multilingual Fake News Detection</title>
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    <name type="personal">
        <namePart type="given">Francielle</namePart>
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        <namePart type="given">Fabrício</namePart>
        <namePart type="family">Benevenuto</namePart>
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            <title>Proceedings of the Student Research Workshop Associated with RANLP 2021</title>
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            <namePart type="given">Souhila</namePart>
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            <namePart type="family">Mihaylova</namePart>
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            <namePart type="family">Nikolova-Koleva</namePart>
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    <abstract>Statements that are intentionally misstated (or manipulated) are of considerable interest to researchers, government, security, and financial systems. According to deception literature, there are reliable cues for detecting deception and the belief that liars give off cues that may indicate their deception is near-universal. Therefore, given that deceiving actions require advanced cognitive development that honesty simply does not require, as well as people’s cognitive mechanisms have promising guidance for deception detection, in this Ph.D. ongoing research, we propose to examine discourse structure patterns in multilingual deceptive news corpora using the Rhetorical Structure Theory framework. Considering that our work is the first to exploit multilingual discourse-aware strategies for fake news detection, the research community currently lacks multilingual deceptive annotated corpora. Accordingly, this paper describes the current progress in this thesis, including (i) the construction of the first multilingual deceptive corpus, which was annotated by specialists according to the Rhetorical Structure Theory framework, and (ii) the introduction of two new proposed rhetorical relations: INTERJECTION and IMPERATIVE, which we assume to be relevant for the fake news detection task.</abstract>
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        <url>https://aclanthology.org/2021.ranlp-srw.29</url>
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        <date>2021-09</date>
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%0 Conference Proceedings
%T Toward Discourse-Aware Models for Multilingual Fake News Detection
%A Vargas, Francielle
%A Benevenuto, Fabrício
%A Pardo, Thiago
%Y Djabri, Souhila
%Y Gimadi, Dinara
%Y Mihaylova, Tsvetomila
%Y Nikolova-Koleva, Ivelina
%S Proceedings of the Student Research Workshop Associated with RANLP 2021
%D 2021
%8 September
%I INCOMA Ltd.
%C Online
%F vargas-etal-2021-toward-discourse
%X Statements that are intentionally misstated (or manipulated) are of considerable interest to researchers, government, security, and financial systems. According to deception literature, there are reliable cues for detecting deception and the belief that liars give off cues that may indicate their deception is near-universal. Therefore, given that deceiving actions require advanced cognitive development that honesty simply does not require, as well as people’s cognitive mechanisms have promising guidance for deception detection, in this Ph.D. ongoing research, we propose to examine discourse structure patterns in multilingual deceptive news corpora using the Rhetorical Structure Theory framework. Considering that our work is the first to exploit multilingual discourse-aware strategies for fake news detection, the research community currently lacks multilingual deceptive annotated corpora. Accordingly, this paper describes the current progress in this thesis, including (i) the construction of the first multilingual deceptive corpus, which was annotated by specialists according to the Rhetorical Structure Theory framework, and (ii) the introduction of two new proposed rhetorical relations: INTERJECTION and IMPERATIVE, which we assume to be relevant for the fake news detection task.
%U https://aclanthology.org/2021.ranlp-srw.29
%P 210-218

Markdown (Informal)

[Toward Discourse-Aware Models for Multilingual Fake News Detection](https://aclanthology.org/2021.ranlp-srw.29) (Vargas et al., RANLP 2021)

• Toward Discourse-Aware Models for Multilingual Fake News Detection (Vargas et al., RANLP 2021)

ACL

• Francielle Vargas, Fabrício Benevenuto, and Thiago Pardo. 2021. Toward Discourse-Aware Models for Multilingual Fake News Detection. In Proceedings of the Student Research Workshop Associated with RANLP 2021, pages 210–218, Online. INCOMA Ltd..