Previous work in social network analysis (SNA) has modeled the existence of links from one entity
to another, but not the language content or topics on those links. We present the Author-Recipient-Topic
(ART) model for social network analysis, which learns topic distributions based on the the directionsensitive
messages sent between entities. The model builds on Latent Dirichlet Allocation and the
Author-Topic (AT) model, adding the key attribute that distribution over topics is conditioned distinctly
on both the sender and recipient—steering the discovery of topics according to the relationships between
people. We give results on both the Enron email corpus and a researcher’s email archive, providing evidence
not only that clearly relevant topics are discovered, but that the ART model better predicts people’s
roles.
%0 Report
%1 citeulike:344908
%A Mccallum, Andrew
%A Corrada-Emmanuel, Andres
%A Wang, Xuerui
%B Technical Report UM-CS-2004-096
%D 2004
%K email network role social topic
%T The Author-Recipient-Topic Model for
Topic and Role Discovery in Social Networks:
Experiments with Enron and Academic Email
%U http://www.cs.umass.edu/~mccallum/papers/art04tr.pdf
%X Previous work in social network analysis (SNA) has modeled the existence of links from one entity
to another, but not the language content or topics on those links. We present the Author-Recipient-Topic
(ART) model for social network analysis, which learns topic distributions based on the the directionsensitive
messages sent between entities. The model builds on Latent Dirichlet Allocation and the
Author-Topic (AT) model, adding the key attribute that distribution over topics is conditioned distinctly
on both the sender and recipient—steering the discovery of topics according to the relationships between
people. We give results on both the Enron email corpus and a researcher’s email archive, providing evidence
not only that clearly relevant topics are discovered, but that the ART model better predicts people’s
roles.
@techreport{citeulike:344908,
abstract = {Previous work in social network analysis (SNA) has modeled the existence of links from one entity
to another, but not the language content or topics on those links. We present the Author-Recipient-Topic
(ART) model for social network analysis, which learns topic distributions based on the the directionsensitive
messages sent between entities. The model builds on Latent Dirichlet Allocation and the
Author-Topic (AT) model, adding the key attribute that distribution over topics is conditioned distinctly
on both the sender and recipient—steering the discovery of topics according to the relationships between
people. We give results on both the Enron email corpus and a researcher’s email archive, providing evidence
not only that clearly relevant topics are discovered, but that the ART model better predicts people’s
roles.},
added-at = {2007-08-15T12:03:47.000+0200},
author = {Mccallum, Andrew and Corrada-Emmanuel, Andres and Wang, Xuerui},
biburl = {https://www.bibsonomy.org/bibtex/276dbff075af5bee9b76ae13d81da66cf/wnpxrz},
booktitle = {Technical Report UM-CS-2004-096},
citeulike-article-id = {344908},
comment = {Long Version of http://www.citeulike.org/user/ldietz/article/344452},
interhash = {e4524a120b9dc7a27ec8b4bea91fa171},
intrahash = {76dbff075af5bee9b76ae13d81da66cf},
keywords = {email network role social topic},
month = {December},
priority = {0},
timestamp = {2007-08-15T12:03:47.000+0200},
title = {The Author-Recipient-Topic Model for
Topic and Role Discovery in Social Networks:
Experiments with Enron and Academic Email},
url = {http://www.cs.umass.edu/~mccallum/papers/art04tr.pdf},
year = 2004
}