Scalable Misinformation Mitigation in Social Networks Using Reverse Sampling

Abstract

Abstract We consider misinformation propagating through a social network and study the problem of its prevention. The goal is to identify a set of $k$ users that need to be convinced to adopt a limiting campaign so as to minimize the number of people that end up adopting the misinformation. This work presents Reverse Prevention Sampling (RPS), an algorithm that provides a scalable solution to the misinformation mitigation problem. Our theoretical analysis shows that RPS runs in $O((k + l)(n + m)(\frac{1}{1 - \gamma }) \log n / \epsilon ^2 )$ expected time and returns a $(1 - 1/e - \epsilon )$-approximate solution with at least $1 - n^{-l}$ probability (where $\gamma $ 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 $\Omega (m n k \cdot POLY(\epsilon ^{-1}))$. 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 mitigation can be made practical while still offering strong theoretical guarantees.