Misinformation Containment in OSNs leveraging Community Structure

With the emergence of Online Social Networks (OSNs) as a major platform of communication, its abuse to spread misinformation has become a major threat to our society. In this paper, we study the misinformation containment problem in OSN. Given a snapshot of the OSN with a set of misinformed nodes, and a budget in terms of maximum number of seed nodes, the problem is to select the seed nodes, referred here as the beacon nodes, to plant the correct information, to minimize and eventually eradicate the misinformation at the earliest. We leverage the community structure of the OSN to select the beacon nodes, prioritizing the Community Boundary Nodes. To the best of our knowledge, this is the first work to exploit the topology of the OSN to combat misinformation spread. A modified form of Independent Cascade Model is followed to study the adversarial propagation of both misinformation and the correct information. Simulation on real data set shows that the proposed algorithm outperforms earlier algorithm [1] significantly in terms of maximum (average) infected time and the point of decline.