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

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 conference › Paper › peer-review

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 language	English (US)
Pages	658-666
Number of pages	9
State	Published - 2007
Event	2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2007 - Prague, Czech Republic Duration: Jun 28 2007 → Jun 28 2007

Other

Other	2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2007
Country/Territory	Czech Republic
City	Prague
Period	6/28/07 → 6/28/07

All Science Journal Classification (ASJC) codes

Computational Theory and Mathematics
Computer Science Applications
Information Systems

Cite this

@conference{895890ec1ddf426b938add0a69646782,

title = "MavenRank: Identifying influential members of the US senate using lexical centrality",

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.",

author = "Anthony Fader and Dragomir Radev and Crespin, {Michael H.} and Monroe, {Burt L.} and Quinn, {Kevin M.} and Michael Colaresi",

year = "2007",

language = "English (US)",

pages = "658--666",

note = "2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2007 ; Conference date: 28-06-2007 Through 28-06-2007",

}

Fader, A, Radev, D, Crespin, MH, Monroe, BL, Quinn, KM & Colaresi, M 2007, 'MavenRank: Identifying influential members of the US senate using lexical centrality', Paper presented at 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2007, Prague, Czech Republic, 6/28/07 - 6/28/07 pp. 658-666.

MavenRank: Identifying influential members of the US senate using lexical centrality. / Fader, Anthony; Radev, Dragomir; Crespin, Michael H. et al.
2007. 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.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - MavenRank

T2 - 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2007

AU - Fader, Anthony

AU - Radev, Dragomir

AU - Crespin, Michael H.

AU - Monroe, Burt L.

AU - Quinn, Kevin M.

AU - Colaresi, Michael

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

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SP - 658

EP - 666

Y2 - 28 June 2007 through 28 June 2007

ER -

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

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All Science Journal Classification (ASJC) codes

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