Social media has become ubiquitous. Tweets and other user-generated content have become so abundant that better tools for information organization are needed in order to fully exploit their potential richness. ”Social cu- ration” has recently emerged as a promising new frame- work for organizing and adding value to social media, complementing the traditional methods of algorithmic search and aggregation. For example, web services like Togetter and Storify empower users to collect and or- ganize tweets to form stories that are pertinent, mem- orable, and easy to read. While social curation services are gaining popularity, little academic research has stud- ied the phenomenon. In this work, we perform one of the first analysis of a large corpus of social curation data. We seek to understand why and how people cu- rate tweets. We also propose an machine learning sys- tem that suggests new tweets, increasing the curator’s productivity and breadth of perspective.