Topic and role discovery in social networks Export

@inproceedings{citeulike:344452, 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 direction-sensitive messages sent between entities. The model builds on Latent Dirichlet Allocation (LDA) 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, A. and Corrada-Emmanuel, A. and Wang, X.}, booktitle = {ijcai.org}, citeulike-article-id = {344452}, citeulike-linkout-0 = {http://www.cs.umass.edu/~mccallum/papers/art-ijcai05.pdf}, comment = {Domain: email (enron + private) Task: discovering topics, clustering to find social roles, summarizing, similar roles (automated coreference system, expert-finding, message recommendation) Method: Author-Recipient-Topic (ART) resp. Role-Author-Recipient-Topic (RART). Based on Bayesian Networks. Predecessors: Latent Dirichlet Allocation (Blei, Ng, Jordan, 2003), Author-Model (McCallum 99), Author-Topic-Model (Rosen-Zvi, Griffith, Steyvers, Smyth, 04) Motto: "distinguish author and recipient of a message"}, keywords = {mustread, socialnets}, posted-at = {2005-10-07 14:52:47}, priority = {0}, title = {Topic and role discovery in social networks}, url = {http://www.cs.umass.edu/~mccallum/papers/art-ijcai05.pdf}, year = {2005} }

TY - CONF ID - citeulike:344452 L3 - citeulike-article-id:344452 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 direction-sensitive messages sent between entities. The model builds on Latent Dirichlet Allocation (LDA) 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 - Topic and role discovery in social networks T2 - ijcai.org KW - mustread KW - socialnets N1 - Domain: email (enron + private) Task: discovering topics, clustering to find social roles, summarizing, similar roles (automated coreference system, expert-finding, message recommendation) Method: Author-Recipient-Topic (ART) resp. Role-Author-Recipient-Topic (RART). Based on Bayesian Networks. Predecessors: Latent Dirichlet Allocation (Blei, Ng, Jordan, 2003), Author-Model (McCallum 99), Author-Topic-Model (Rosen-Zvi, Griffith, Steyvers, Smyth, 04) Motto: "distinguish author and recipient of a message" AU - Mccallum, A AU - Corrada-Emmanuel, A AU - Wang, X PY - 2005/// UR - http://www.cs.umass.edu/~mccallum/papers/art-ijcai05.pdf ER -