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        <title>Fact Checking or Psycholinguistics: How to Distinguish Fake and True Claims?</title>
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    <name type="personal">
        <namePart type="given">Aleksander</namePart>
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    <name type="personal">
        <namePart type="given">Grzegorz</namePart>
        <namePart type="family">Wojdyga</namePart>
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    <name type="personal">
        <namePart type="given">Justyna</namePart>
        <namePart type="family">Sarzyńska-Wawer</namePart>
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            <title>Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)</title>
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            <namePart type="given">James</namePart>
            <namePart type="family">Thorne</namePart>
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        <name type="personal">
            <namePart type="given">Andreas</namePart>
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        <name type="personal">
            <namePart type="given">Christos</namePart>
            <namePart type="family">Christodoulopoulos</namePart>
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        <name type="personal">
            <namePart type="given">Arpit</namePart>
            <namePart type="family">Mittal</namePart>
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    <abstract>The goal of our paper is to compare psycholinguistic text features with fact checking approaches to distinguish lies from true statements. We examine both methods using data from a large ongoing study on deception and deception detection covering a mixture of factual and opinionated topics that polarize public opinion. We conclude that fact checking approaches based on Wikipedia are too limited for this task, as only a few percent of sentences from our study has enough evidence to become supported or refuted. Psycholinguistic features turn out to outperform both fact checking and human baselines, but the accuracy is not high. Overall, it appears that deception detection applicable to less-than-obvious topics is a difficult task and a problem to be solved.</abstract>
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%0 Conference Proceedings
%T Fact Checking or Psycholinguistics: How to Distinguish Fake and True Claims?
%A Wawer, Aleksander
%A Wojdyga, Grzegorz
%A Sarzyńska-Wawer, Justyna
%Y Thorne, James
%Y Vlachos, Andreas
%Y Cocarascu, Oana
%Y Christodoulopoulos, Christos
%Y Mittal, Arpit
%S Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F wawer-etal-2019-fact
%X The goal of our paper is to compare psycholinguistic text features with fact checking approaches to distinguish lies from true statements. We examine both methods using data from a large ongoing study on deception and deception detection covering a mixture of factual and opinionated topics that polarize public opinion. We conclude that fact checking approaches based on Wikipedia are too limited for this task, as only a few percent of sentences from our study has enough evidence to become supported or refuted. Psycholinguistic features turn out to outperform both fact checking and human baselines, but the accuracy is not high. Overall, it appears that deception detection applicable to less-than-obvious topics is a difficult task and a problem to be solved.
%R 10.18653/v1/D19-6602
%U https://aclanthology.org/D19-6602
%U https://doi.org/10.18653/v1/D19-6602
%P 7-12

Markdown (Informal)

[Fact Checking or Psycholinguistics: How to Distinguish Fake and True Claims?](https://aclanthology.org/D19-6602) (Wawer et al., 2019)

• Fact Checking or Psycholinguistics: How to Distinguish Fake and True Claims? (Wawer et al., 2019)

ACL

• Aleksander Wawer, Grzegorz Wojdyga, and Justyna Sarzyńska-Wawer. 2019. Fact Checking or Psycholinguistics: How to Distinguish Fake and True Claims?. In Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER), pages 7–12, Hong Kong, China. Association for Computational Linguistics.