An Automated Method of Topic-Coding Legislative Speech Over Time with Application to the 105th-108th U.S. Senate

In this paper, we describe a method for statistical learning from speech documents that we apply to the Congressional Record in order to gain new insight into the dynamics of the political agenda. Prior eorts to evaluate the attention of elected representatives across topic areas have been expensive manual coding exercises and are generally circumscribed along one or more features of detail: limited time periods, high levels of temporal aggregation, coarse topical categories, and so on. Conversely, the Congressional Record has scarcely been used for such analyses, largely because it contains too much information to absorb. We describe here a method for inferring, through the patterns of word choice in each speech and the dynamics of word choice patterns across time, (a) what the topics of speeches are, and (b) the probability that attention will be paid to any given topic or set of topics over time. We use the model to examine the agenda in the United States Senate from 1997-2004, a database of over 70 thousand documents containing over 70 million words. We estimate the model for 42 topics and provide evidence that we can reveal speech topics that are both distinctive and inter-related in substantively meaningful ways. We demonstrate further that the dynamics our model gives us leverage into important questions about the dynamics of the political agenda.

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