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Australasian Conference on Data Mining

AusDM 2021: Data Mining pp 165–179Cite as

SOMPS-Net: Attention Based Social Graph Framework for Early Detection of Fake Health News

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Part of the Communications in Computer and Information Science book series (CCIS,volume 1504)

Abstract

Fake news is fabricated information that is presented as genuine, with intention to deceive the reader. Recently, the magnitude of people relying on social media for news consumption has increased significantly. Owing to this rapid increase, the adverse effects of misinformation affect a wider audience. On account of the increased vulnerability of people to such deceptive fake news, a reliable technique to detect misinformation at its early stages is imperative. Hence, the authors propose a novel graph-based framework SOcial graph with Multi-head attention and Publisher information and news Statistics Network (SOMPS-Net) (https://github.com/PrasannaKumaran/SOMPS-Net-Social-graph-framework-for-fake-health-news-detection) comprising of two components – Social Interaction Graph (SIG) and Publisher and News Statistics (PNS). The posited model is experimented on the HealthStory dataset and generalizes across diverse medical topics including Cancer, Alzheimer’s, Obstetrics, and Nutrition. SOMPS-Net significantly outperformed other state-of-the-art graph-based models experimented on HealthStory by 17.1%. Further, experiments on early detection demonstrated that SOMPS-Net predicted fake news articles with 79% certainty within just 8 h of its broadcast. Thus the contributions of this work lay down the foundation for capturing fake health news across multiple medical topics at its early stages.

Keywords

  • Fake health news
  • Early detection
  • Social network
  • Graph neural networks
  • Multi-head attention

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Author information

Authors and Affiliations

  1. SSN College of Engineering, Chennai, India

    Prasannakumaran Dhanasekaran & Saikrishnan Sankar

  2. Solarillion Foundation, Chennai, India

    Harish Srinivasan & Vineeth Vijayaraghavan

  3. College of Engineering Guindy, Anna University, Chennai, India

    S. Sowmiya Sree & I. Sri Gayathri Devi

Authors
  1. Prasannakumaran Dhanasekaran
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  2. Harish Srinivasan
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  3. S. Sowmiya Sree
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  4. I. Sri Gayathri Devi
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  5. Saikrishnan Sankar
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  6. Vineeth Vijayaraghavan
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Corresponding author

Correspondence to Prasannakumaran Dhanasekaran .

Editor information

Editors and Affiliations

  1. Queensland University of Technology, Brisbane, QLD, Australia

    Prof. Yue Xu

  2. Western Sydney University, Parramatta, NSW, Australia

    Rosalind Wang

  3. University of Queensland, Herston, Australia

    Anton Lord

  4. RMIT University, Melbourne, VIC, Australia

    Prof. Dr. Yee Ling Boo

  5. Queensland University of Technology, Brisbane, QLD, Australia

    Richi Nayak

  6. Data61, CSIRO, Canberra, ACT, Australia

    Yanchang Zhao

  7. Australian National University, Canberra, ACT, Australia

    Graham Williams

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Dhanasekaran, P., Srinivasan, H., Sree, S.S., Devi, I.S.G., Sankar, S., Vijayaraghavan, V. (2021). SOMPS-Net: Attention Based Social Graph Framework for Early Detection of Fake Health News. In: , et al. Data Mining. AusDM 2021. Communications in Computer and Information Science, vol 1504. Springer, Singapore. https://doi.org/10.1007/978-981-16-8531-6_12

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  • DOI: https://doi.org/10.1007/978-981-16-8531-6_12

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