AI- based Prediction of Misinformation Virality Before Wide Dissemination using Attention-based Multi-modal

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 12 | 03 | Page :
    By

    Shokooh Khandan,

  • Deniz Beyazgul,

  1. AI Specialist, Department of computing and mathematics, Manchester Metropolitan University, Manchester, England
  2. Reseach Associate, Department of computing and mathematics, Manchester Metropolitan University, Manchester, England

Abstract

Misinformation on social media has emerged as a critical global challenge, impacting public health, democratic institutions, and societal trust. While existing research has largely concentrated on detecting misinformation after it begins circulating, predicting its virality before wide dissemination remains an underexplored area, limited work addresses predicting its virality before wide dissemination. This paper presents a conceptual framework using attention-based multi-modal deep learning models to estimate the virality of misinformation posts at the point of publication. By integrating textual, visual, and metadata features with attention mechanisms, the proposed system aims to identify potentially viral misinformation proactively. Although this work is theoretical, it lays the groundwork for future implementation and evaluation of such systems, highlighting key components, potential challenges, and research directions. We argue that pre-dissemination prediction represents a paradigm shift from reactive moderation to proactive mitigation, which could help reduce the societal harm caused by viral falsehoods.

Keywords: Misinformation virality, multi-modal learning, attention mechanisms, CNNs, Data Availability

How to cite this article:
Shokooh Khandan, Deniz Beyazgul. AI- based Prediction of Misinformation Virality Before Wide Dissemination using Attention-based Multi-modal. Journal of Mobile Computing, Communications & Mobile Networks. 2025; 12(03):-.
How to cite this URL:
Shokooh Khandan, Deniz Beyazgul. AI- based Prediction of Misinformation Virality Before Wide Dissemination using Attention-based Multi-modal. Journal of Mobile Computing, Communications & Mobile Networks. 2025; 12(03):-. Available from: https://journals.stmjournals.com/jomccmn/article=2025/view=234937


References

  1. Shu K, Sliva A, Wang S, Tang J, Liu H. Fake news detection on social media: A data mining perspective. ACM SIGKDD explorations newsletter. 2017 Sep 1;19(1):22-36.
  2. Zhou X, Zafarani R. A survey of fake news: Fundamental theories, detection methods, and opportunities. ACM Computing Surveys (CSUR). 2020 Sep 28;53(5):1-40.
  3. Jin Z, Cao J, Guo H, Zhang Y, Luo J. Multimodal fusion with recurrent neural networks for rumor detection on microblogs. InProceedings of the 25th ACM international conference on Multimedia 2017 Oct 19 (pp. 795-816).
  4. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention is all you need. Advances in neural information processing systems. 2017;30.
  5. Khattar D, Goud JS, Gupta M, Varma V. Mvae: Multimodal variational autoencoder for fake news detection. InThe world wide web conference 2019 May 13 (pp. 2915-2921).
  6. Vosoughi S, Roy D, Aral S. The spread of true and false news online. science. 2018 Mar 9;359(6380):1146-51.
  7. Cheng J, Adamic L, Dow PA, Kleinberg JM, Leskovec J. Can cascades be predicted?. InProceedings of the 23rd international conference on World wide web 2014 Apr 7 (pp. 925-936).
  8. Del Vicario M, Bessi A, Zollo F, Petroni F, Scala A, Caldarelli G, Stanley HE, Quattrociocchi W. The spreading of misinformation online. Proceedings of the national academy of Sciences. 2016 Jan 19;113(3):554-9.
  9. Ma J, Gao W, Wong KF. Rumor detection on twitter with tree-structured recursive neural networks. Association for Computational Linguistics. 2018-7, 1980: 1989.
  10. Ma J, Gao W, Wong KF. Detect rumors in microblog posts using propagation structure via kernel learning. Association for Computational Linguistics. 2017-8, 708: 717.
  11. Xu W, Wu J, Liu Q, Wu S, Wang L. Evidence-aware fake news detection with graph neural networks. InProceedings of the ACM web conference 2022 2022 Apr 25 (pp. 2501-2510).
Ahead of Print Subscription Original Research
Volume 12
03
Received 05/08/2025
Accepted 29/09/2025
Published 27/12/2025
Publication Time 144 Days
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