Reverse Prevention Sampling for Misinformation Mitigation in Social Networks

TL;DR

This paper introduces RPS, a scalable algorithm for misinformation mitigation in social networks, which efficiently identifies key users to minimize misinformation spread with strong theoretical guarantees and practical performance.

Contribution

The paper presents RPS, a novel algorithm with improved time complexity and theoretical guarantees for misinformation mitigation, enabling practical large-scale application.

Findings

RPS outperforms previous algorithms in runtime by several orders of magnitude.

RPS achieves a (1 - 1/e - ε)-approximate solution with high probability.

Experimental results confirm RPS's scalability and effectiveness on large datasets.

Abstract

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 \emph{RPS} (Reverse Prevention Sampling), an algorithm that provides a scalable solution to the misinformation mitigation problem. Our theoretical analysis shows that \emph{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 \emph{RPS} substantially improves upon the previously best-known algorithms that run in time $\Omega(m n k \cdot POLY(\epsilon^{-1}))$. We experimentally evaluate \emph{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.