Scalable Misinformation Mitigation in Social Networks Using Reverse Sampling

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)(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(mnk.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.