Structured diffusion: Amplify your voice in online social networks

Marketing in a social network via structured groups outperforms spontaneous social conversations. It regulates the social interactions and thus the information pathways, which boosts overall impact. We start with a case study of a strongly-organized advertising network in Weibo, a popular microblogging service, which demonstrates that commercials promoted by this group are averagely twice effective as normal tweets. Then we present a modeling framework to characterize this structured diffusion paradigm. An analytical model using heterogeneous mean-field approximation was thus worked out to capture transmission dynamics. The transmission probability incorporates the inflation and propagation of node influences that explains why and how the structures within a campaign affect the spreading speed, scale, and impact. As empirical evidences, our model numerically simulates four typical structure settings on a large Twitter network. It confirms quantitatively that, hierarchical grouping excels the loosely or randomly coupled ones and classical marketing heuristics.