Stemming the flow of information in a social network

Abstract

Social media has changed the way people interact with each other and has contributed greatly towards bringing people together. It has become an ideal platform for people to share their opinions. However, due to the volatility of social networks, a negative campaign or a rumor can go viral resulting in severe impact to the community. In this paper, we aim to solve this problem of stemming the flow of a negative campaign in a network by observing only parts of the network. Given a negative campaign and information about the status of its spread through a few candidate nodes, our algorithm estimates the information flow in the network and based on this estimated flow, finds a set of nodes which would be instrumental in stemming the information flow. The proposed algorithm is tested on real-world networks and its effectiveness is compared against other existing works.