Mining influencers using information flows in social streams

Abstract

The problem of discovering information flow trends in social networks has become increasingly relevant due to the increasing amount of content in online social networks, and its relevance as a tool for research into the content trends analysis in the network. An important part of this analysis is to determine the key patterns of flow in the underlying network. Almost all the work in this area has focused on fixed models of the network structure, and edge-based transmission between nodes. In this article, we propose a fully content-centered model of flow analysis in networks, in which the analysis is based on actual content transmissions in the underlying social stream, rather than a static model of transmission on the edges. First, we introduce the problem of influence analysis in the context of information flow in networks. We then propose a novel algorithm InFlowMine to discover the information flow patterns in the network and demonstrate the effectiveness of the discovered information flows using an influence mining application. This application illustrates the flexibility and effectiveness of our information flow model to find topic- or network-specific influencers, or their combinations. We empirically show that our information flow mining approach is effective and efficient than the existing methods on a number of different measures.