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Incorporating Relational Knowledge in Explainable Fake News Detection

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Book cover Advances in Knowledge Discovery and Data Mining (PAKDD 2021)

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Abstract

The greater public has become aware of the rising prevalence of untrustworthy information in online media. Extensive adaptive detection methods have been proposed for mitigating the adverse effect of fake news. Computational methods for detecting fake news based on the news content have several limitations, such as: 1) Encoding semantics from original texts is limited to the structure of the language in the text, making both bag-of-words and embedding-based features deceptive in the representation of a fake news, and 2) Explainable methods often neglect relational contexts in fake news detection. In this paper, we design a knowledge graph enhanced framework for effectively detecting fake news while providing relational explanation. We first build a credential-based multi-relation knowledge graph by extracting entity relation tuples from our training data and then apply a compositional graph convolutional network to learn the node and relation embeddings accordingly. The pre-trained graph embeddings are then incorporated into a graph convolutional network for fake news detection. Through extensive experiments on three real-world datasets, we demonstrate the proposed knowledge graph enhanced framework has significant improvement in terms of fake news detection as well as structured explainability.

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Notes

  1. 1.

    https://github.com/huggingface/neuralcoref.

  2. 2.

    https://spacy.io/.

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Acknowledgements

This work is supported in part by the US National Science Foundation under grants 1948432, 1763620 and 1948374.

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Authors and Affiliations

  1. Stevens Institute of Technology, Hoboken, NJ, USA

    Kun Wu & Yue Ning

  2. University of Louisiana at Lafayette, Lafayette, LA, USA

    Xu Yuan

Authors
  1. Kun Wu
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  2. Xu Yuan
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  3. Yue Ning
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Corresponding author

Correspondence to Kun Wu .

Editor information

Editors and Affiliations

  1. IIIT, Hyderabad, Hyderabad, India

    Kamal Karlapalem

  2. Chinese University of Hong Kong, Shatin, Hong Kong

    Hong Cheng

  3. Virginia Tech, Arlington, VA, USA

    Naren Ramakrishnan

  4. Jawaharlal Nehru University, New Delhi, India

    R. K. Agrawal

  5. IIIT Hyderabad, Hyderabad, India

    P. Krishna Reddy

  6. University of Minnesota, Minneapolis, MN, USA

    Jaideep Srivastava

  7. IIIT Delhi, New Delhi, India

    Tanmoy Chakraborty

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Wu, K., Yuan, X., Ning, Y. (2021). Incorporating Relational Knowledge in Explainable Fake News Detection. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12714. Springer, Cham. https://doi.org/10.1007/978-3-030-75768-7_32

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  • DOI: https://doi.org/10.1007/978-3-030-75768-7_32

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  • Online ISBN: 978-3-030-75768-7

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