Millions of people use social networks everyday to talk about a variety of subjects, publish opinions and share information. Understanding this data to infer user's topical interests is a challenging problem with applications in various data-powered products. In this paper, we present 'LASTA' (Large Scale Topic Assignment), a full production system used at Klout, Inc., which mines topical interests from five social networks and assigns over 10,000 topics to hundreds of millions of users on a daily basis. The system continuously collects streams of user data and is reactive to fresh information, updating topics for users as interests shift. LASTA generates over 50 distinct features derived from signals such as user generated posts and profiles, user reactions such as comments and retweets, user attributions such as lists, tags and endorsements, as well as signals based on social graph connections. We show that using this diverse set of features leads to a better representation of a user's topical interests as compared to using only generated text or only graph based features. We also show that using cross-network information for a user leads to a more complete and accurate understanding of the user's topics, as compared to using any single network. We evaluate LASTA's topic assignment system on an internal labeled corpus of 32,264 user-topic labels generated from real users.