Skip to main content

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

  • 340 Accesses

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


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) ( 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.


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

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-981-16-8531-6_12
  • Chapter length: 15 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   64.99
Price excludes VAT (USA)
  • ISBN: 978-981-16-8531-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   84.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.


  1. Badawy, A., Ferrara, E., Lerman, K.: Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign, p. 258–265. IEEE Press (2018)

    Google Scholar 

  2. Dylan de Beer, M.M.: Approaches to identify fake news: a systematic literature review. ACM Trans. Comput. Syst. 32(2) (2020).

  3. Bhutani, B., Rastogi, N., Sehgal, P., Purwar, A.: Fake news detection using sentiment analysis. In: 2019 Twelfth International Conference on Contemporary Computing (IC3), pp. 1–5 (2019).

  4. Chandra, S., Mishra, P., Yannakoudakis, H., Nimishakavi, M., Saeidi, M., Shutova, E.: Graph-based modeling of online communities for fake news detection. CoRR abs/2008.06274 (2020).

  5. Dai, E., Sun, Y., Wang, S.: Ginger cannot cure cancer: battling fake health news with a comprehensive data repository. arXiv preprint arXiv:2002.00837 (2020)

  6. Gabielkov, M., Ramachandran, A., Chaintreau, A., Legout, A.: Social clicks: what and who gets read on twitter? ACM SIGMETRICS Perform. Eval. Rev. 44, 179–192 (2016).

  7. Gu, L., Kropotov, V., Yarochkin, F.: The fake news machine: how propagandists abuse the internet and manipulate the public. Trend Micro. 5, 1–85 (2017)

    Google Scholar 

  8. Hogg, M.A.: Chapter 5 Social Identity Theory: pp. 112–138. Stanford University Press (2020).

  9. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (ICLR) (2017)

    Google Scholar 

  10. Liao, H., Liu, Q., Shu, K., Xie, X.: Incorporating user-comment graph for fake news detection. CoRR abs/2011.01579 (2020).

  11. Liu, Y., Wu, Y.F.B.: FNED: a deep network for fake news early detection on social media. ACM Trans. Inf. Syst. 38(3) (2020).

  12. Lu, Y.J., Li, C.T.: GCAN: graph-aware co-attention networks for explainable fake news detection on social media (2020)

    Google Scholar 

  13. Pan, J.Z., Pavlova, S., Li, C., Li, N., Li, Y., Liu, J.: Content based fake news detection using knowledge graphs. In: Vrandečić, D., et al. (eds.) ISWC 2018. LNCS, vol. 11136, pp. 669–683. Springer, Cham (2018).

  14. Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014).

  15. Qi, P., Cao, J., Yang, T., Guo, J., Li, J.: Exploiting multi-domain visual information for fake news detection. In: 2019 IEEE International Conference on Data Mining (ICDM), pp. 518–527 (2019).

  16. Rath, B., Morales, X., Srivastava, J.: SCARLET: explainable attention based graph neural network for fake news spreader prediction. In: PAKDD (2021)

    Google Scholar 

  17. Rubin, V., Conroy, N., Chen, Y., Cornwell, S.: Fake news or truth? Using satirical cues to detect potentially misleading news. In: Proceedings of the Second Workshop on Computational Approaches to Deception Detection, pp. 7–17. Association for Computational Linguistics, San Diego, California, June 2016.,

  18. Salge, C.: Is that social bot behaving unethically? Commun. ACM 60, 29–31 (2017).

  19. Schuster, M., Paliwal, K.: Bidirectional recurrent neural networks. IEEE Trans. Sign. Process. 45(11), 2673–2681 (1997).

    CrossRef  Google Scholar 

  20. Shao, C., Ciampaglia, G., Varol, O., Flammini, A., Menczer, F., Yang, K.C.: The spread of low-credibility content by social bots. Nat. Commun. 9 (2018).

  21. Shu, K., Mahudeswaran, D., Liu, H.: FakeNewsTracker: a tool for fake news collection, detection, and visualization. Comput. Math. Organ. Theory 25, 60–71 (2019)

    CrossRef  Google Scholar 

  22. Shu, K., Wang, S., Liu, H.: Beyond news contents: the role of social context for fake news detection. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 312–320. WSDM 2019. Association for Computing Machinery, New York, NY, USA (2019).

  23. Shu, K., Zhou, X., Wang, S., Zafarani, R., Liu, H.: The role of user profiles for fake news detection. In: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 436–439. ASONAM 2019. Association for Computing Machinery, New York, NY, USA (2019).

  24. Thackeray, R., Crookston, B., West, J.: Correlates of health-related social media use among adults. J. Med. Internet Res. 15, e21 (2013).

  25. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)

    Google Scholar 

  26. Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018).,

  27. Wu, L., Liu, H.: Tracing fake-news footprints: characterizing social media messages by how they propagate. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 637–645. WSDM 2018. Association for Computing Machinery, New York, NY, USA (2018).

  28. Wynne, H.E., Wint, Z.Z.: Content based fake news detection using n-gram models. In: Proceedings of the 21st International Conference on Information Integration and Web-Based Applications & amp; Services, pp. 669–673. iiWAS2019. Association for Computing Machinery, New York, NY, USA (2019).

  29. Zhao, Z., et al.: Fake news propagate differently from real news even at early stages of spreading. EPJ Data Sci. 9 (2018).

  30. Zhou, X., Wu, J., Zafarani, R.: \(\sf SAFE\): similarity-aware multi-modal fake news detection. In: Lauw, H.W., Wong, R.C.-W., Ntoulas, A., Lim, E.-P., Ng, S.-K., Pan, S.J. (eds.) PAKDD 2020. LNCS (LNAI), vol. 12085, pp. 354–367. Springer, Cham (2020).

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Prasannakumaran Dhanasekaran .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

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.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-8530-9

  • Online ISBN: 978-981-16-8531-6

  • eBook Packages: Computer ScienceComputer Science (R0)