Modeling Interactions from Email Communication

E-mail 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 E-mail users, i.e., the extent to which the E-mail 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 E-mail users using the semantic content of E-mail body, instead of E-mail header. Second, our framework models not only E-mail topic dynamics of individual E-mail users, but also the interactions within a group of individuals. Experiments on the Enron E-mail corpus show some interesting results that are potentially useful to discover the hierarchy of the Enron organization