Jordan Boyd-Graber

Big Data Analysis with Topic Models: Human Interaction, Streaming Computation, and Social Science Applications
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 scale to large datasets; and how to detect framing and spin using these techniques. After introducing topic models, I will argue why traditional measures of topic model quality – borrowed from machine learning – are inconsistent with how topic models are actually used. In response, I will 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 computational and statistical limits to existing approaches and how streaming topic models, with an "infinite vocabulary", can be applied to real-world online datasets. Finally, I’ll discuss ongoing collaborations with political scientists to use these techniques to detect spin and framing in political and online interactions.