This thesis studies social influence from the perspective of users' characteristics. The importance of users' characteristics in word-of-mouth applications has been emphasized in economics and marketing fields. We model a category of users called mavens where their unique characteristics nominate them to be the preferable seeds in viral marketing applications. In addition, we develop some methods to learn their characteristics based on a real dataset. We also illustrate the ways to maximize information flow through mavens in social networks. Our experiments show that our model can successfully detect mavens as well as fulfill significant roles in maximizing the information flow in a social network where mavens considerably outperform general influential users for influence maximization. The results verify the compatibility of our model with real marketing applications.