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International Symposium on Cyber Security, Cryptology, and Machine Learning

CSCML 2022: Cyber Security, Cryptology, and Machine Learning pp 180–188Cite as

Fake News Detection in Social Networks Using Machine Learning and Trust

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13301))

Abstract

Fake news propagation is a major challenge for Online Social Networks (OSN) security, which is not yet resolved. Fake news propagates because of several reasons, one of which is non-trustworthy users. Non-trustworthy users are those who spread misleading information either for malicious intentions or innocently as they lack social media awareness. As a result, they expose their sub networks to false or inaccurate information.

In our previous research we have devised a comprehensive Trust-based model that can handle this problem from the user Trust aspect. The model involves Access Control for the direct circle of friends and Flow Control for the friends’ networks. In this paper we use this model and extend it for the purpose of preventing Fake News. We analyze user’s activity in the network (posts, shares, etc.) to learn their contexts. Using Machine Learning methods on data items that are fake or misleading, we detect suspicious users. This addition facilitates a much more accurate mapping of OSN users and their data which enables the identification of the Fake News propagation source. The extended model can be used to create a strong and reliable data infrastructure for OSN.

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Author information

Authors and Affiliations

  1. Ben-Gurion University of the Negev, P.O.B. 653, 8410501, Beer-Sheva, Israel

    Nadav Voloch, Ehud Gudes, Rotem Mitrany, Ofri Shani & Maayan Shoel

  2. Sapir Academic College, 79165, Sderot, Israel

    Nurit Gal-Oz

Authors
  1. Nadav Voloch
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  2. Ehud Gudes
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  3. Nurit Gal-Oz
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  4. Rotem Mitrany
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  5. Ofri Shani
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  6. Maayan Shoel
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Corresponding author

Correspondence to Nadav Voloch .

Editor information

Editors and Affiliations

  1. Ben-Gurion University of the Negev, Be’er Sheva, Israel

    Shlomi Dolev

  2. University of Maryland, College Park, MD, USA

    Jonathan Katz

  3. Ben-Gurion University of the Negev, Be’er Sheva, Israel

    Amnon Meisels

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Cite this paper

Voloch, N., Gudes, E., Gal-Oz, N., Mitrany, R., Shani, O., Shoel, M. (2022). Fake News Detection in Social Networks Using Machine Learning and Trust. In: Dolev, S., Katz, J., Meisels, A. (eds) Cyber Security, Cryptology, and Machine Learning. CSCML 2022. Lecture Notes in Computer Science, vol 13301. Springer, Cham. https://doi.org/10.1007/978-3-031-07689-3_14

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  • DOI: https://doi.org/10.1007/978-3-031-07689-3_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07688-6

  • Online ISBN: 978-3-031-07689-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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