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meeting ID: 937 6054 7545

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Abstract:

Estimating political positions of lawmakers has a long tradition in political science and usually lawmakers’ votes are used to quantify their political positions. But lawmakers also give speeches or press statements. In this work we present a time varying text based ideal point model (TV-TBIP) which allows to study political positions of lawmakers in a completely unsupervised way. In doing so, our model combines the class of topic models with ideal point models in a time-dynamic setting.

Our model is inspired by the idea of political framing, so that specific words or terms used when discussing a topic can convey political messages.

The insights of our model are twofold: Firstly, it allows to detect how political discussion of certain topics has changed over time, and secondly it estimates ideological positions of lawmakers on a party level. Using only the texts of Senate speeches, our model identifies US-senators along an interpretable progressive-to-moderate spectrum.

We apply our model to nearly 40 years of US Senate house discussions between 1981 and 2017.