This paper 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 developed and verified methods to learn their characteristics from a real dataset. Also, we illustrated ways to maximize information flow through mavens in social networks. Our experiments show that our model successfully detected mavens as well as fulfilled significant roles in maximizing the information flow in a social network comparing to the spread that was a result of traditional influencer users in influence maximization problem. These results showed the compatibility of our model with real marketing approaches.