User-generated content (UGC) systems such as Twitter, Facebook, and YouTube are quickly becoming the dominant form of information exchange on the web: shifting informational power from media conglomerates to individual users. Understanding the popularity trends in UGC content has proven problematic as traditional content popularity techniques (e.g. those developed for television) are not suited for the disparate origins and ephemeral lifecycle of UGC. Contentbased trend detection with UGC systems has been an intensely growing field of research in recent years, yet surprisingly, there is no single method or approach that can be used to track and compare trends in user posts across multiple UGC sources. Therefore, in this work, we develop a standard system for detecting emerging trends in user posts for UGC that contains some form of textual data. We demonstrate the use and implementation of this system through a case study with approximately 2 million YouTube video posts. Furthermore, to help facilitate future comparative studies in UGC trend analysis, we have made this system open-source and straightforward to integrate with various UGC systems