2009 CharacterizingIndividualCommuni

(Malmgren et al., 2009) ⇒ R. Dean Malmgren, Jake M. Hofman, Luis A.N. Amaral, and Duncan J. Watts. (2009). “Characterizing Individual Communication Patterns.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557088

Subject Headings:

Notes

Categories and Subject Descriptors: G.3 Probability and Statistics: Markov processes, Stochastic processes, Time series analysis, Probabilistic Algorithms; H.4.3 Information systems applications: Communication applications — electronic mail; I.6.1 Simulations and Modeling: Simulation theory — Model Classification; J.4 Social and Behavioral Sciences: Sociology.
General Terms: Algorithms, Measurement

Cited By

Quotes

Author Keywords

Individual Characterization, Hidden Markov Model, Cascading Non-homogeneous Poisson Process, Forward-Backward Algorithm

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.

References

,

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 CharacterizingIndividualCommuni	R. Dean Malmgren Jake M. Hofman Duncan J. Watts Luis A.N. Amaral			Characterizing Individual Communication Patterns		KDD-2009 Proceedings		10.1145/1557019.1557088		2009