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Social Bot Detection as a Temporal Logic Model Checking Problem

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 13039)

Abstract

Software-controlled bots, also called social bots, are computer programs that act like human users on social media platforms. Recent work on detection of social bots is dominated by machine learning approaches. In this paper we explore bot detection as a model checking problem. We introduce Temporal Network Logic (TNL) which we use to specify social networks where agents can post and follow each other. In this logic we formalize different types of social bot behavior. These are formulas that are satisfied in a model of a network with bots. We provide a simple algorithm to extract a logical model from a real-life social network. We show that we can reduce TNL to a fragment of linear temporal logic with past and use this to establish the computational efficiency of model checking for social bot detection.

Keywords

  • Bot detection
  • Model checking
  • Temporal logic
  • Social network logic

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Fig. 1.

Notes

  1. 1.

    For more information on the separation property, we advice the reader to turn to [16].

  2. 2.

    One could alternatively allow for a different setting in which we expand or shrink the set A of agents itself. This would require an extension of the framework and translation to PLTL would be more difficult, as was also mentioned by one of the reviewers.

  3. 3.

    This formula can also be expanded such that the agent follows a lot of other agents in some boundedly many steps and then eventually unfollows them, as was suggested by one of the reviewers.

  4. 4.

    By the Gabbay theorem, any PLTL formula can be written as an LTL formula [13, 28], however some properties can be more succinctly expressed in PLTL.

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Correspondence to Mina Young Pedersen .

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Pedersen, M.Y., Slavkovik, M., Smets, S. (2021). Social Bot Detection as a Temporal Logic Model Checking Problem. In: Ghosh, S., Icard, T. (eds) Logic, Rationality, and Interaction. LORI 2021. Lecture Notes in Computer Science(), vol 13039. Springer, Cham. https://doi.org/10.1007/978-3-030-88708-7_13

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