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Satirical News Detection with Semantic Feature Extraction and Game-Theoretic Rough Sets

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Foundations of Intelligent Systems (ISMIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12117))

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Abstract

Satirical news detection is an important yet challenging task to prevent spread of misinformation. Many feature based and end-to-end neural nets based satirical news detection systems have been proposed and delivered promising results. Existing approaches explore comprehensive word features from satirical news articles, but lack semantic metrics using word vectors for tweet form satirical news. Moreover, the vagueness of satire and news parody determines that a news tweet can hardly be classified with a binary decision, that is, satirical or legitimate. To address these issues, we collect satirical and legitimate news tweets, and propose a semantic feature based approach. Features are extracted by exploring inconsistencies in phrases, entities, and between main and relative clauses. We apply game-theoretic rough set model to detect satirical news, in which probabilistic thresholds are derived by game equilibrium and repetition learning mechanism. Experimental results on the collected dataset show the robustness and improvement of the proposed approach compared with Pawlak rough set model and SVM.

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Authors and Affiliations

  1. School of Computer Science and Engineering, California State University, San Bernardino, San Bernardino, CA, USA

    Yue Zhou & Yan Zhang

  2. Department of Computer Science, University of Regina, Regina, SK, Canada

    JingTao Yao

Authors
  1. Yue Zhou
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  2. Yan Zhang
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  3. JingTao Yao
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Corresponding author

Correspondence to Yan Zhang .

Editor information

Editors and Affiliations

  1. Graz University of Technology, Graz, Austria

    Denis Helic

  2. University of Klagenfurt, Klagenfurt, Austria

    Gerhard Leitner

  3. Graz University of Technology, Graz, Austria

    Martin Stettinger

  4. Graz University of Technology, Graz, Austria

    Alexander Felfernig

  5. University of North Carolina at Charlotte, Charlotte, NC, USA

    Zbigniew W. Raś

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Zhou, Y., Zhang, Y., Yao, J. (2020). Satirical News Detection with Semantic Feature Extraction and Game-Theoretic Rough Sets. In: Helic, D., Leitner, G., Stettinger, M., Felfernig, A., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2020. Lecture Notes in Computer Science(), vol 12117. Springer, Cham. https://doi.org/10.1007/978-3-030-59491-6_12

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  • DOI: https://doi.org/10.1007/978-3-030-59491-6_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59490-9

  • Online ISBN: 978-3-030-59491-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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