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International Conference on Network and System Security

NSS 2021: Network and System Security pp 100–115Cite as

Preventing Fake News Propagation in Social Networks Using a Context Trust-Based Security Model

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Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 13041))

Abstract

Online Social Networks (OSN) security issues have been extensively researched in the past decade. Information is posted and shared by individuals and organizations in social networks in huge quantities. One of the most important non-resolved topics is the Fake News propagation problem. Fake news propagates because of several reasons, one of which is non-trustworthy users. These users, some with malicious intentions, and some with low social media awareness, are the ones actually spreading misleading information. As this occurs, other users, that are valid reliable users, are exposed to false 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 as a basis for the purpose of prevention of Fake News. We add context awareness and user profiling by analyzing the user’s activity in the network (posts, shares, etc.), and then use Machine Learning to detect these problematic users by analyzing data items that are fake or misleading. This addition creates a much more accurate picture of OSN users and their data and helps revealing the sources of the Fake News propagation and can prevent it. These aspects of the model create a strong reliable OSN data infrastructure.

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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

  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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Corresponding author

Correspondence to Nadav Voloch .

Editor information

Editors and Affiliations

  1. Fudan University, Shanghai, China

    Min Yang

  2. James Cook University, Townsville, QLD, Australia

    Chao Chen

  3. Nanyang Technological University, Singapore, Singapore

    Yang Liu

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Cite this paper

Voloch, N., Gudes, E., Gal-Oz, N. (2021). Preventing Fake News Propagation in Social Networks Using a Context Trust-Based Security Model. In: Yang, M., Chen, C., Liu, Y. (eds) Network and System Security. NSS 2021. Lecture Notes in Computer Science(), vol 13041. Springer, Cham. https://doi.org/10.1007/978-3-030-92708-0_6

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  • DOI: https://doi.org/10.1007/978-3-030-92708-0_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-92707-3

  • Online ISBN: 978-3-030-92708-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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