Abstract
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.
Users
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