Influence inflation in online social networks

Abstract

Online marketing exploits social influence to trigger chain-like cascades. However, recent practices actively employ agents to collaboratively inflate the spreading of influences. Through supporting structures, they help each other with false feedback and signals to attract other users in the spreading process and thus alter the spontaneous social dynamics. In this paper, we proposed a modeling framework to explain the mechanism of such operations and characterize the spreading dynamics. Model analytics and numerical simulations both showed a lifting in overall spreading influence. As empirical evidence, experiments on a large Weibo network revealed well-structured advertising groups that prominently amplified the influences of promoted commercials via meticulous cooperation in a core-peripheral structure. The inflation effect also brings new considerations into influence maximization problems. Based on our models, we solved the problem of maximizing inflated influence by optimizing the selection of agents under KKT conditions and their supporting structure using its submodular property.