On Misinformation Containment in Online Social Networks

TL;DR

This paper analyzes the complex problem of limiting misinformation spread in online social networks with multiple competing cascades, introducing a formal model, complexity results, cascade priorities, and effective algorithms validated by experiments.

Contribution

It presents the first formal model for multi-cascade diffusion with cascade priority, analyzes the problem's computational hardness, and proposes novel algorithms with experimental validation.

Findings

The misinformation containment problem is computationally hard to approximate.

Several realistic cascade priority models are introduced.

Proposed algorithms show promising experimental performance.

Abstract

The widespread online misinformation could cause public panic and serious economic damages. The misinformation containment problem aims at limiting the spread of misinformation in online social networks by launching competing campaigns. Motivated by realistic scenarios, we present the first analysis of the misinformation containment problem for the case when an arbitrary number of cascades are allowed. This paper makes four contributions. First, we provide a formal model for multi-cascade diffusion and introduce an important concept called as cascade priority. Second, we show that the misinformation containment problem cannot be approximated within a factor of $\Omega(2^{\log^{1-\epsilon}n^4})$ in polynomial time unless $NP \subseteq DTIME(n^{\polylog{n}})$. Third, we introduce several types of cascade priority that are frequently seen in real social networks. Finally, we design novel algorithms for solving the misinformation containment problem. The effectiveness of the proposed algorithm is supported by encouraging experimental results.