Characterizing Individual Communication Patterns

TL;DR

This paper introduces a simple, interpretable model of individual email communication patterns using a cascading non-homogeneous Poisson process, enabling analysis of variability and classification of user behavior across diverse datasets.

Contribution

It presents a novel, interpretable model for individual communication dynamics and demonstrates its effectiveness in classifying users and identifying behavioral types.

Findings

Communication patterns are consistent across different populations.

Individual behavior variability is less than population variability.

Model-based user classification is effective and interpretable.

Abstract

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 that at least some features of communication dynamics generalize beyond specific contexts. We also find that variability of individual behavior over time is significantly less than variability across the population, suggesting that individuals can be classified into persistent "types". We conclude that communication patterns may prove useful as an additional class of attribute data, complementing demographic and network data, for user classification and outlier detection--a point that we illustrate with an interpretable clustering of users based on their inferred model parameters.