A common information need is to understand large, unstructured datasets: millions of e-mails during e-discovery, a decade worth of science correspondence, or a day's tweets.  In the last decade, topic models have become a common tool for navigating such datasets.  This talk investigates the foundational research that allows successful tools for these data exploration tasks: how to know when you have an effective model of the dataset; how to correct bad models; how to measure topic model effectiveness; and how to detect framing and spin using these techniques.  After introducing topic models, I argue why traditional measures of topic model quality--borrowed from machine learning--are inconsistent with how topic models are actually used.

In response, I describe interactive topic modeling, a technique that enables users to impart their insights and preferences to models in a principled, interactive way.  I will then address measuring topic model effectiveness in real-world tasks.  Finally, I'll discuss ongoing collaborations with political scientists to use these techniques to detect spin and framing in political and online interactions.