Microblogging as a new form of communication on Internet, has attracted the attention from researchers recently. Relying the real-time and conversational properties of microblogging, its users update their statuses and share experience within their the social network. Those characteristics also make microblogging an important tool for users to share or discuss real world events such as earth quake or sport game. In this paper, we propose a novel and flexible solution to detect and recognize real-time events from sport games based on analyzing the messages posted on microblogging services. We take Twitter as the experiment platform and collect a large-scale dataset of Twitter messages that are called tweets for 18 prominent sport games covering four types of sports in 2011. We also collect corresponding sport videos for those games. The proposed solution applies moving-threshold burst detection on the volume of tweets to detect highlights in sport games. A tf-idf-based weighting method is applied on the tweets within detected highlights for semantic extraction. According to the experiments we perform on the tweet and video datasets, we find that the proposed methods can achieve competent performance in sport event detection and recognition. Besides, our method can find non pre-defined tidbits that are difficult to detect in previous works.