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Minimizing the misinformation spread in social networks

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  • Kübra Tanınmış
  • Necati Aras
  • İ. Kuban Altınel
  • Evren Güney

Abstract

The Influence Maximization Problem has been widely studied in recent years, due to rich application areas including marketing. It involves finding k nodes to trigger a spread such that the expected number of influenced nodes is maximized. The problem we address in this study is an extension of the reverse influence maximization problem, i.e., misinformation minimization problem where two players make decisions sequentially in the form of a Stackelberg game. The first player aims to minimize the spread of misinformation whereas the second player aims its maximization. Two algorithms, one greedy heuristic and one matheuristic, are proposed for the first player’s problem. In both of them, the second player’s problem is approximated by Sample Average Approximation, a well-known method for solving two-stage stochastic programming problems, that is augmented with a state-of-the-art algorithm developed for the influence maximization problem.

Suggested Citation

  • Kübra Tanınmış & Necati Aras & İ. Kuban Altınel & Evren Güney, 2020. "Minimizing the misinformation spread in social networks," IISE Transactions, Taylor & Francis Journals, vol. 52(8), pages 850-863, August.
    • Handle: RePEc:taf:uiiexx:v:52:y:2020:i:8:p:850-863
    DOI: 10.1080/24725854.2019.1680909
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    1. Tanınmış, Kübra & Aras, Necati & Altınel, İ. Kuban, 2022. "Improved x-space algorithm for min-max bilevel problems with an application to misinformation spread in social networks," European Journal of Operational Research, Elsevier, vol. 297(1), pages 40-52.

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