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Reverse Prevention Sampling for Misinformation Mitigation in Social Networks

Authors Michael Simpson, Venkatesh Srinivasan, Alex Thomo

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  • 18 pages

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Author Details

Michael Simpson
  • Department of Computer Science, University of Victoria, Canada
Venkatesh Srinivasan
  • Department of Computer Science, University of Victoria, Canada
Alex Thomo
  • Department of Computer Science, University of Victoria, Canada

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Michael Simpson, Venkatesh Srinivasan, and Alex Thomo. Reverse Prevention Sampling for Misinformation Mitigation in Social Networks. In 23rd International Conference on Database Theory (ICDT 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 155, pp. 24:1-24:18, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020)


In this work, we consider misinformation propagating through a social network and study the problem of its prevention. In this problem, a "bad" campaign starts propagating from a set of seed nodes in the network and we use the notion of a limiting (or "good") campaign to counteract the effect of misinformation. The goal is to identify a set of k users that need to be convinced to adopt the limiting campaign so as to minimize the number of people that adopt the "bad" campaign at the end of both propagation processes. This work presents RPS (Reverse Prevention Sampling), an algorithm that provides a scalable solution to the misinformation prevention problem. Our theoretical analysis shows that RPS runs in O((k + l)(n + m)(1/(1 - γ)) log n / ε²) expected time and returns a (1 - 1/e - ε)-approximate solution with at least 1 - n^{-l} probability (where γ is a typically small network parameter and l is a confidence parameter). The time complexity of RPS substantially improves upon the previously best-known algorithms that run in time Ω(m n k ⋅ POLY(ε^{-1})). We experimentally evaluate RPS on large datasets and show that it outperforms the state-of-the-art solution by several orders of magnitude in terms of running time. This demonstrates that misinformation prevention can be made practical while still offering strong theoretical guarantees.

Subject Classification

ACM Subject Classification
  • Theory of computation → Graph algorithms analysis
  • Theory of computation → Approximation algorithms analysis
  • Graph Algorithms
  • Social Networks
  • Misinformation Prevention


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