The purpose of this paper is to show that a new type of information-theoretic learning method called “potential learning” can be used to detect and extract important tweets among a great number of redundant ones. In the experiment, we used a dataset of 10,000 tweets, among which there existed only a few important ones. The experimental results showed that the new method improved overall classification accuracy by correctly identifying the important tweets.