The Author-Recipient-Topic Model for Topic and Role Discovery in Social Networks: Experiments with Enron and Academic Email Export

@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.}, author = {Mccallum, Andrew and Corrada-Emmanuel, Andres and Wang, Xuerui}, booktitle = {Technical Report UM-CS-2004-096}, citeulike-article-id = {344908}, citeulike-linkout-0 = {http://www.cs.umass.edu/~mccallum/papers/art04tr.pdf}, comment = {Long Version of http://www.citeulike.org/user/ldietz/article/344452}, day = {11}, keywords = {science, socialnets}, month = {December}, posted-at = {2005-10-07 16:48:42}, priority = {0}, 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} }

TY - RPRT ID - citeulike:344908 L3 - citeulike-article-id:344908 N2 - 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. TI - The Author-Recipient-Topic Model for Topic and Role Discovery in Social Networks: Experiments with Enron and Academic Email T2 - Technical Report UM-CS-2004-096 KW - science KW - socialnets N1 - Long Version of http://www.citeulike.org/user/ldietz/article/344452 AU - Mccallum, Andrew AU - Corrada-Emmanuel, Andres AU - Wang, Xuerui PY - 2004/12/11/ UR - http://www.cs.umass.edu/~mccallum/papers/art04tr.pdf ER -