the analysis of influence in social network is drawing more and more attention. it can be applied in different areas such as political campaigns and marketing. in this work, the analysis of influence in twitter, based on users‟ profile statistics in a real-time scale, was studied and discussed. two methods of identifying influential users by given keyword in real-time are introduced. to understand the relationship between users‟ influence features and social states in real life, two influence measures were presented: local influence which the user has on his/her immediate set of contacts and global influence which the user has on the entire social network. this study describes in details these two metrics and shows their implementation for a real social network. our case study, using twitter, showed that the proposed model can create clusters of users in 2d space corresponding to their social standing, and can further be used to classify previously-unseen users into the correct classes with an f-measure of 0.82 which is significantly higher than benchmark algorithms. f-measure is often used for measuring the accuracy of the test for classification.