Recently, lots of micro-blog message sharing applications have emerged on the web. Users can publish short messages freely and get notified by the subscriptions instantly. Prominent examples include Twitter, Facebook's statuses, and Sina Weibo in China. The Micro-blog platform becomes a useful service for real time information creation and propagation. However, these messages' short length and dynamic characters have posed great challenges for effective content understanding. Additionally, the noise and fragments make it difficult to discover the temporal propagation trail to explore development of micro-blog messages. In this paper, we propose a provenance model to capture connections between micro-blog messages. Provenance refers to data origin identification and transformation logging, demonstrating of great value in recent database and workflow systems. To cope with the real time micro-message deluge, we utilize a novel message grouping approach to encode and maintain the provenance information. Furthermore, we adopt a summary index and several adaptive pruning strategies to implement efficient provenance updating. Based on the index, our provenance solution can support rich query retrieval and intuitive message tracking for effective message organization. Experiments conducted on a real dataset verify the effectiveness and efficiency of our approach.