CONF zhang-rr-05-51b/IDIAP Modeling Interactions from Email Communication Gatica-Perez, Daniel Zhang, Dong Bengio, Samy EXTERNAL https://publications.idiap.ch/attachments/reports/2006/zhang-icme06.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/zhang-rr-05-51 Related documents Proc. IEEE International Conference on Multimedia & Expo (ICME,',','), 2006 2006 IDIAP-RR 05-51 Email plays an important role as a medium for the spread of information, ideas, and influence among its users. We present a framework to learn topic-based interactions between pairs of email users, i.e., the extent to which the email topic dynamics of one user are likely to be affected by the others. The proposed framework is built on the influence model and the probabilistic latent semantic analysis (PLSA) language model. This paper makes two contributions. First, we model interactions between email users using the semantic content of email body, instead of email header. Second, our framework models not only email topic dynamics of individual email users, but also the interactions within a group of individuals. Experiments on the Enron email corpus show some interesting results that are potentially useful to discover the hierarchy of the Enron organization. We also present an email visualization and retrieval system which could not only search for relevant emails, but also for the relevant email users. REPORT zhang-rr-05-51/IDIAP Modeling Interactions from Email Communication Zhang, Dong Gatica-Perez, Daniel Roy, Deb Bengio, Samy EXTERNAL https://publications.idiap.ch/attachments/reports/2005/rr-05-51.pdf PUBLIC Idiap-RR-51-2005 2005 IDIAP Martigny, Switzerland Published in ``Proc. IEEE International Conference on Multimedia & Expo (ICME,',','), 2006'' Email plays an important role as a medium for the spread of information, ideas, and influence among its users. We present a framework to learn topic-based interactions between pairs of email users, i.e., the extent to which the email topic dynamics of one user are likely to be affected by the others. The proposed framework is built on the influence model and the probabilistic latent semantic analysis (PLSA) language model. This paper makes two contributions. First, we model interactions between email users using the semantic content of email body, instead of email header. Second, our framework models not only email topic dynamics of individual email users, but also the interactions within a group of individuals. Experiments on the Enron email corpus show some interesting results that are potentially useful to discover the hierarchy of the Enron organization. We also present an email visualization and retrieval system which could not only search for relevant emails, but also for the relevant email users.