Since more and more users participate in various social networking services, the volume of streaming data is considerably increasing. It is necessary to find out valuable messages from huge data archived every moment. This paper investigates the problem of detecting informative messages in Twitter, and proposes effective methods to solve the problem based on User History. Most of the sheer information in tweets has a common defect which is the fact that it is affected by influence of User level within the Twitter network. Our key idea is to leverage each user's history observed from a large scale dataset as features to determine whether a new message is informative or not, compared to their previous messages. This allows us to normalize influence of individual user on tweets and to estimate the probability of informativeness. Experimental results on a real Twitter data show that our method can effectively improve the performance on identifying informative tweets.