When considering a social media corpus, we often have access to structural information about how messages are flowing between people or organizations. This information is particularly useful when the linguistic evidence is sparse, incomplete, or of dubious quality. In this paper we construct a simple model to leverage the structure of Twitter data to help determine the set of languages each user is fluent in. Our results demonstrate that imposing several intuitive constraints leads to improvements in performance and stability. We release the first annotated data set for exploring this task, and discuss how our approach may be extended to other applications.