Finding influential neighbors to maximize information diffusion in twitter

The problem of spreading information is a topic of consid- erable recent interest, but the traditional influence maxi- mization problem is inadequate for a typical viral marketer who cannot access the entire network topology. To fix this awed assumption that the marketer can control any arbi- Trary k nodes in a network, we have developed a decentral- ized version of the influential maximization problem by influ- encing 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 net- work 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 ex- perimental 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.