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Cross-Domain Fake News Detection on Social Media: A Context-Aware Adversarial Approach

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Frontiers in Fake Media Generation and Detection

Abstract

People nowadays increasingly consume information from social media due to its convenience and fast dissemination. However, social media also accelerate the propagation of disinformation and fake news, causing detrimental effects. Thus, detecting fake news is a critical task for benefiting individuals and society. However, it is a non-trivial task due to the expensive annotation cost and the diverse nature of news domains. Thus, it is important to exploit auxiliary information to help improve prediction performance. We develop a deep architecture to exploit both cross-domain knowledge transfer and within-domain joint modeling of news content, user comments, and user-news interactions for fake news detection. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed method, even with limited labeled data in the target domain.

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Notes

  1. 1.

    In this paper, we are interested in fake news as a sample medium for disinformation but we recognize that other media exist.

  2. 2.

    The code and partial data are available at https://www.dropbox.com/s/zax0guj0nb9hhmy/Fake

    %20News%20Classifier.zip?dl=0#.

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Author information

Authors and Affiliations

  1. Illinois Institute of Technology, Chicago, IL, USA

    Kai Shu

  2. Arizona State University, Phoenix, AZ, USA

    Ahmadreza Mosallanezhad & Huan Liu

Authors
  1. Kai Shu
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  2. Ahmadreza Mosallanezhad
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  3. Huan Liu
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Corresponding author

Correspondence to Kai Shu .

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Editors and Affiliations

  1. Cross Compass, Cross Labs, Tokyo, Japan

    Mahdi Khosravy

  2. Information and Society Research Division, National Institute of Informatics (NII), Tokyo, Japan

    Isao Echizen

  3. Graduate School of Engineering, Osaka University, Suita Osaka, Japan

    Noboru Babaguchi

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Shu, K., Mosallanezhad, A., Liu, H. (2022). Cross-Domain Fake News Detection on Social Media: A Context-Aware Adversarial Approach. In: Khosravy, M., Echizen, I., Babaguchi, N. (eds) Frontiers in Fake Media Generation and Detection. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981-19-1524-6_9

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