MavenRank: Identifying influential members of the US senate using lexical centrality

Anthony Fader, Dragomir Radev, Michael H. Crespin, Burt L. Monroe, Kevin M. Quinn, Michael Colaresi

Research output: Contribution to conferencePaper

8 Scopus citations

Abstract

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 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 legislation.

Original languageEnglish (US)
Pages658-666
Number of pages9
StatePublished - Dec 1 2007
Event2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2007 - Prague, Czech Republic
Duration: Jun 28 2007Jun 28 2007

Other

Other2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2007
CountryCzech Republic
CityPrague
Period6/28/076/28/07

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

Cite this

Fader, A., Radev, D., Crespin, M. H., Monroe, B. L., Quinn, K. M., & Colaresi, M. (2007). MavenRank: Identifying influential members of the US senate using lexical centrality. 658-666. Paper presented at 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2007, Prague, Czech Republic.