M}aven{R}ank: Identifying Influential Members of the {US} Senate Using Lexical Centrality

We introduce a technique for identifying the most salient participants in a discussion. Our method, MavenRank is based on lexical cen- trality: a random walk is performed on a graph in which each node is a participant in the discussion and an edge links two partici- pants who use similar rhetoric. As a test, we used MavenRank to identify the most influ- ential members of the US Senate using data from the US Congressional Record and used committee ranking to evaluate the output. Our results show that MavenRank scores are largely driven by committee status in most topics, but can capture speaker centrality in topics where speeches are used to indicate ideological position instead of influence leg- islation.