Characterizing individual communication patterns

The increasing availability of electronic communication data, such as that arising from e-mail exchange, presents social and information scientists with new possibilities for characterizing individual behavior and, by extension, identifying latent structure in human populations. Here, we propose a model of individual e-mail communication that is sufficiently rich to capture meaningful variability across individuals, while remaining simple enough to be interpretable. We show that the model, a cascading non-homogeneous Poisson process, can be formulated as a double-chain hidden Markov model, allowing us to use an efficient inference algorithm to estimate the model parameters from observed data. We then apply this model to two e-mail data sets consisting of 404 and 6,164 users, respectively, that were collected from two universities in different countries and years. We find that the resulting best-estimate parameter distributions for both data sets are surprisingly similar, indicating th...

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: R. Dean Malmgren, Jake M. Hofman, Luis A. N. Amaral, Duncan J. Watts
• Keywords: Data Mining, Electronic Communication Data, Individual E-mail Communication, Inferred Model Parameters, KDD 2009