The explosive use of social media, in information dissemination and communication, has also made it a popular platform for the spread of rumors. Rumors could be easily propagated and received by a large number of users in social media, resulting in catastrophic effects in the physical world in a very short period. It is a challenging task, if not impossible, to apply classical supervised learning methods to the early detection of rumors, since the labeling process is time-consuming and labor-intensive. Motivated by the fact that abundant label information of historical rumors is publicly available, in this paper, we propose to investigate whether knowledge learned from historical data could potentially help identify newly emerging rumors. In particular, since a disputed factual claim arouses certain reactions such as curiosity, skepticism, and astonishment, we identify and utilize patterns from prior labeled data to help reveal emergent rumors. Experimental results on real-world data sets demonstrate the effectiveness. Further experiments are conducted to show how much earlier it can detect an emerging rumor than traditional approaches.