MavenRank: Identifying Influential Members of the US Senate Using Lexical Centrality

@inproceedings{Fader2007MavenRankII,
  title={MavenRank: Identifying Influential Members of the US Senate Using Lexical Centrality},
  author={Anthony Fader and Dragomir R. Radev and Michael H. Crespin and Burt L. Monroe and Kevin M. Quinn and Michael Colaresi},
  booktitle={EMNLP-CoNLL},
  year={2007}
}
We introduce a technique for identifying the most salient participants in a discussion. Our method, MavenRank is based on lexical centrality: a random walk is performed on a graph in which each node is a participant in the discussion and an edge links two participants who use similar rhetoric. As a test, we used MavenRank to identify the most influential members of the US Senate using data from the US Congressional Record and used committee ranking to evaluate the output. Our results show that… CONTINUE READING
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An automated method of topic-coding legislative speech over time with application to the 105th-108th U.S. senate

  • Kevin M. Quinn, Burt L. Monroe, Michael Colaresi, Michael H. Crespin, Dragomir R. Radev
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United states congressional speech corpus

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  • Department of Political Science, The Pennsylva-
  • 2006
3 Excerpts

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