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

Authors

Andrew McCallum, University of Massachusetts - Amherst
Andrés Corrada-Emmanuel, University of Massachusetts - Amherst
Xuerui Wang, University of Massachusetts - Amherst

Publication Date

2005

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.

Comments

This paper was harvested from CiteSeer

Recommended Citation

McCallum, Andrew; Corrada-Emmanuel, Andrés; and Wang, Xuerui, "The Author-Recipient-Topic Model for Topic and Role Discovery in Social Networks: Experiments with Enron and Academic Email" (2005). Computer Science Department Faculty Publication Series. 44.
Retrieved from https://scholarworks.umass.edu/cs_faculty_pubs/44