Finding influential neighbors to maximize information diffusion in twitter

Abstract

The problem of spreading information is a topic of considerable recent interest, but the traditional influence maximization problem is inadequate for a typical viral marketer who cannot access the entire network topology. To fix this flawed assumption that the marketer can control any arbitrary k nodes in a network, we have developed a decentralized version of the influential maximization problem by influencing k neighbors rather than arbitrary users in the entire network. We present several reasonable neighbor selection schemes and evaluate their performance with a real dataset collected from Twitter. Unlike previous studies using network topology alone or synthetic parameters, we use real propagation rate for each node calculated from the Twitter messages during the 2010 UK election campaign. Our experimental results show that information can be efficiently propagated in online social networks using neighbors with a high propagation rate rather than those with a high number of neighbors.