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    <titleInfo>
        <title>Automatic Detection of Fake News</title>
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    <name type="personal">
        <namePart type="given">Verónica</namePart>
        <namePart type="family">Pérez-Rosas</namePart>
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    <name type="personal">
        <namePart type="given">Bennett</namePart>
        <namePart type="family">Kleinberg</namePart>
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    <name type="personal">
        <namePart type="given">Alexandra</namePart>
        <namePart type="family">Lefevre</namePart>
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    <name type="personal">
        <namePart type="given">Rada</namePart>
        <namePart type="family">Mihalcea</namePart>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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            <namePart type="given">Emily</namePart>
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            <namePart type="family">Bender</namePart>
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            <namePart type="given">Leon</namePart>
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            <namePart type="given">Pierre</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content. In this paper, we focus on the automatic identification of fake content in online news. Our contribution is twofold. First, we introduce two novel datasets for the task of fake news detection, covering seven different news domains. We describe the collection, annotation, and validation process in detail and present several exploratory analyses on the identification of linguistic differences in fake and legitimate news content. Second, we conduct a set of learning experiments to build accurate fake news detectors, and show that we can achieve accuracies of up to 76%. In addition, we provide comparative analyses of the automatic and manual identification of fake news.</abstract>
    <identifier type="citekey">perez-rosas-etal-2018-automatic</identifier>
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        <url>https://aclanthology.org/C18-1287</url>
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        <date>2018-08</date>
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            <start>3391</start>
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%0 Conference Proceedings
%T Automatic Detection of Fake News
%A Pérez-Rosas, Verónica
%A Kleinberg, Bennett
%A Lefevre, Alexandra
%A Mihalcea, Rada
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F perez-rosas-etal-2018-automatic
%X The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content. In this paper, we focus on the automatic identification of fake content in online news. Our contribution is twofold. First, we introduce two novel datasets for the task of fake news detection, covering seven different news domains. We describe the collection, annotation, and validation process in detail and present several exploratory analyses on the identification of linguistic differences in fake and legitimate news content. Second, we conduct a set of learning experiments to build accurate fake news detectors, and show that we can achieve accuracies of up to 76%. In addition, we provide comparative analyses of the automatic and manual identification of fake news.
%U https://aclanthology.org/C18-1287
%P 3391-3401

Markdown (Informal)

[Automatic Detection of Fake News](https://aclanthology.org/C18-1287) (Pérez-Rosas et al., COLING 2018)

• Automatic Detection of Fake News (Pérez-Rosas et al., COLING 2018)

ACL

• Verónica Pérez-Rosas, Bennett Kleinberg, Alexandra Lefevre, and Rada Mihalcea. 2018. Automatic Detection of Fake News. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3391–3401, Santa Fe, New Mexico, USA. Association for Computational Linguistics.