Join us for a talk by Dr. Will Lowe on why some computational models for measuring spatial policy preferences work and others don't. This is part of the Data Science Brown Bag series.
Abstract from speaker:
Everybody from the political scientists, to the computational linguists, to political parties and pundits would like to know how to infer people's policy preferences from what they say. Since the mid 2000s we've had a nice methods for doing that for one policy dimension. A little while ago I showed how to extend spatial voting models to work with text and extract more than one dimension. It was pretty cool. But then I broke it.
This talk is about how that model works. When it doesn't. Why it doesn't. And what we might conclude from all that. This should be of interest to people interested in (and perhaps even a little skeptical of) the very idea of spatial policy preferences, in 'text as data' as an approach to measurement, or in watching me break things.
Bring your own lunch bag! Light pastries and drinks will be available in case you forget to bring it.
—
The Data Science Brown Bag Series is an informal and interactive gathering where participants bring their own brown bag lunch and engage in discussions on research and insights the field of data and computational social science (light pastries and drinks will be available if you forget your lunch bag!).
The series provides a platform for data enthusiasts, researchers, and practitioners to share their experiences, best practices, and emerging methodologies and research in using data science to analyze and understand social and political phenomena. The brown bag talk series is for anyone interested in data science and social science to network, learn, and share ideas in a casual and friendly setting.
