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CSSS Seminar: A Network Model for Dynamic Textual Communications with Application to Government Email Corpora

Posted: 4/18/2017 (Local Events)

Abstract: In this paper, we introduce the interaction-partitioned topic model (IPTM)—a probabilistic model of who communicates with whom about what, and when. Broadly speaking, the IPTM partitions time-stamped textual communications, such as emails, according to both the network dynamics that they reflect and their content. To define the IPTM, we integrate a dynamic version of the exponential random graph model—a generative model for ties that tend toward structural features such as triangles—and latent Dirichlet allocation—a generative model for topic-based content. The IPTM assigns each topic to an “interaction pattern”—a generative process for ties that is governed by a set of dynamic network features. Each communication is then modeled as a
mixture of topics and their corresponding interaction patterns. We use the IPTM to analyze emails sent between department managers in two county governments in North Carolina; one of these email corpora covers the Outer Banks during the time period surrounding Hurricane Sandy. Via this application, we demonstrate that the IPTM is effective at predicting and explaining continuous-time textual communications.

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Date: 04/19/2017

Time: 12:30 - 1:30 PM

Location: University of Washington, Savery 409