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        <title>Multi-Source Multi-Class Fake News Detection</title>
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    <name type="personal">
        <namePart type="given">Hamid</namePart>
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    <name type="personal">
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        <namePart type="family">Saba-Sadiya</namePart>
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        <dateIssued>2018-aug</dateIssued>
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    <abstract>Fake news spreading through media outlets poses a real threat to the trustworthiness of information and detecting fake news has attracted increasing attention in recent years. Fake news is typically written intentionally to mislead readers, which determines that fake news detection merely based on news content is tremendously challenging. Meanwhile, fake news could contain true evidence to mock true news and presents different degrees of fakeness, which further exacerbates the detection difficulty. On the other hand, the spread of fake news produces various types of data from different perspectives. These multiple sources provide rich contextual information about fake news and offer unprecedented opportunities for advanced fake news detection. In this paper, we study fake news detection with different degrees of fakeness by integrating multiple sources. In particular, we introduce approaches to combine information from multiple sources and to discriminate between different degrees of fakeness, and propose a Multi-source Multi-class Fake news Detection framework MMFD, which combines automated feature extraction, multi-source fusion and automated degrees of fakeness detection into a coherent and interpretable model. Experimental results on the real-world data demonstrate the effectiveness of the proposed framework and extensive experiments are further conducted to understand the working of the proposed framework.</abstract>
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        <url>https://aclanthology.org/C18-1131</url>
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%0 Conference Proceedings
%T Multi-Source Multi-Class Fake News Detection
%A Karimi, Hamid
%A Roy, Proteek
%A Saba-Sadiya, Sari
%A Tang, Jiliang
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 aug
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F karimi-etal-2018-multi
%X Fake news spreading through media outlets poses a real threat to the trustworthiness of information and detecting fake news has attracted increasing attention in recent years. Fake news is typically written intentionally to mislead readers, which determines that fake news detection merely based on news content is tremendously challenging. Meanwhile, fake news could contain true evidence to mock true news and presents different degrees of fakeness, which further exacerbates the detection difficulty. On the other hand, the spread of fake news produces various types of data from different perspectives. These multiple sources provide rich contextual information about fake news and offer unprecedented opportunities for advanced fake news detection. In this paper, we study fake news detection with different degrees of fakeness by integrating multiple sources. In particular, we introduce approaches to combine information from multiple sources and to discriminate between different degrees of fakeness, and propose a Multi-source Multi-class Fake news Detection framework MMFD, which combines automated feature extraction, multi-source fusion and automated degrees of fakeness detection into a coherent and interpretable model. Experimental results on the real-world data demonstrate the effectiveness of the proposed framework and extensive experiments are further conducted to understand the working of the proposed framework.
%U https://aclanthology.org/C18-1131
%P 1546-1557

Markdown (Informal)

[Multi-Source Multi-Class Fake News Detection](https://aclanthology.org/C18-1131) (Karimi et al., COLING 2018)

• Multi-Source Multi-Class Fake News Detection (Karimi et al., COLING 2018)

ACL

• Hamid Karimi, Proteek Roy, Sari Saba-Sadiya, and Jiliang Tang. 2018. Multi-Source Multi-Class Fake News Detection. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1546–1557, Santa Fe, New Mexico, USA. Association for Computational Linguistics.