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CMU ML Lunch (April 14): Inferring Political Ideologies from Text

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Published on Apr 14, 2014

Speaker: Yanchuan Sim
School of Computer Science, CMU

Abstract
In this talk, I will present some of my recent work with my collaborators on building models for inferring political ideologies from text. Given political candidate speeches from the 2008 and 2012 US elections, we seek to measure their ideological positioning. To accomplish this, we infer ideological cues from a corpus of political writings annotated with known ideologies. We then represent the speeches of U.S. presidential candidates as sequences of cues and lags (filler distinguished only by its length in words). We apply a domain-informed Bayesian HMM to infer the proportions of ideologies each candidate uses in each campaign. The results are validated against a set of preregistered, domain expert authored hypotheses. I will also present some preliminary results on our work with briefs and data from the US Supreme Court, studying the latent behaviors of amicus brief filers from a utility maximizing perspective.

For more talks, visit http://www.cs.cmu.edu/~learning/

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