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Heuristic Gradient Optimization Approach to Controlling Susceptibility to Manipulation in Online Social Networks

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13831)


Manipulation through inferential attacks in online social networks (OSN) can be achieved by learning the user’s interests through their network and their interactions with the network. Since some users have a higher propensity for disclosure than others, a one-size-fits-all technique for limiting manipulation proves insufficient. In this work, we propose a model that allows the user to adjust their online persona to limit their susceptibility to manipulation based on their preferred disclosure threshold. Our experiment, using real-world data provides a way to measure manipulation gained from a single tweet. We then proffer solutions that show that manipulation gain derived as a result of participating in OSNs can be minimized and adjusted to meet the user’s needs and expectations, giving at least some measure of control to the user.


  • Manipulation
  • Gradient Optimization
  • Social Network Analysis

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This publication is based upon work supported by the National Science Foundation under Grants OIA-2148878 and CMMI-1952206, and by the NPRP grant #12C-33905-SP-165 from the Qatar National Research Fund (a member of Qatar Foundation). The findings achieved herein are solely the responsibility of the authors.

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Correspondence to Abiola Osho .

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Osho, A., Wei, S., Amariucai, G. (2023). Heuristic Gradient Optimization Approach to Controlling Susceptibility to Manipulation in Online Social Networks. In: Dinh, T.N., Li, M. (eds) Computational Data and Social Networks . CSoNet 2022. Lecture Notes in Computer Science, vol 13831. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26302-6

  • Online ISBN: 978-3-031-26303-3

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