publication . Article . 2007

Topic and Role Discovery in Social Networks with Experiments on Enron and Academic Email

Andrew McCallum; Xuerui Wang; Andres Corrada-Emmanuel;
Open Access
  • Published: 13 Oct 2007 Journal: Journal of Artificial Intelligence Research, volume 30, pages 249-272 (eissn: 1076-9757, Copyright policy)
  • Publisher: AI Access Foundation
Previous work in social network analysis (SNA) has modeled the existence of links from one entity to another, but not the attributes such as 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 and gives lower perplexity on previously unseen messages. We also present the Role-Author-Recipient-Topic (RART) model, an extension to ART that explicitly represents people's roles.
Persistent Identifiers
free text keywords: Artificial Intelligence, Communication source, Extension (predicate logic), Social network analysis, Computer science, Latent Dirichlet allocation, symbols.namesake, symbols, World Wide Web, Key (cryptography), Perplexity
Related Organizations
Any information missing or wrong?Report an Issue