Statistical Models for Exploring Individual Email Communication Behavior

Nicholas Navaroli, Christopher DuBois, Padhraic Smyth
Proceedings of the Asian Conference on Machine Learning, PMLR 25:317-332, 2012.

Abstract

As digital communication devices play an increasingly prominent role in our daily lives, the ability to analyze and understand our communication patterns becomes more important. In this paper, we investigate a latent variable modeling approach for extracting information from individual email histories, focusing in particular on understanding how an individual communicates over time with recipients in their social network. The proposed model consists of latent groups of recipients, each of which is associated with a piecewise-constant Poisson rate over time. Inference of group memberships, temporal changepoints, and rate parameters is carried out via Markov Chain Monte Carlo (MCMC) methods. We illustrate the utility of the model by applying it to both simulated and real-world email data sets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v25-navaroli12, title = {Statistical Models for Exploring Individual Email Communication Behavior}, author = {Navaroli, Nicholas and DuBois, Christopher and Smyth, Padhraic}, booktitle = {Proceedings of the Asian Conference on Machine Learning}, pages = {317--332}, year = {2012}, editor = {Hoi, Steven C. H. and Buntine, Wray}, volume = {25}, series = {Proceedings of Machine Learning Research}, address = {Singapore Management University, Singapore}, month = {04--06 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v25/navaroli12/navaroli12.pdf}, url = {https://proceedings.mlr.press/v25/navaroli12.html}, abstract = {As digital communication devices play an increasingly prominent role in our daily lives, the ability to analyze and understand our communication patterns becomes more important. In this paper, we investigate a latent variable modeling approach for extracting information from individual email histories, focusing in particular on understanding how an individual communicates over time with recipients in their social network. The proposed model consists of latent groups of recipients, each of which is associated with a piecewise-constant Poisson rate over time. Inference of group memberships, temporal changepoints, and rate parameters is carried out via Markov Chain Monte Carlo (MCMC) methods. We illustrate the utility of the model by applying it to both simulated and real-world email data sets.} }
Endnote
%0 Conference Paper %T Statistical Models for Exploring Individual Email Communication Behavior %A Nicholas Navaroli %A Christopher DuBois %A Padhraic Smyth %B Proceedings of the Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2012 %E Steven C. H. Hoi %E Wray Buntine %F pmlr-v25-navaroli12 %I PMLR %P 317--332 %U https://proceedings.mlr.press/v25/navaroli12.html %V 25 %X As digital communication devices play an increasingly prominent role in our daily lives, the ability to analyze and understand our communication patterns becomes more important. In this paper, we investigate a latent variable modeling approach for extracting information from individual email histories, focusing in particular on understanding how an individual communicates over time with recipients in their social network. The proposed model consists of latent groups of recipients, each of which is associated with a piecewise-constant Poisson rate over time. Inference of group memberships, temporal changepoints, and rate parameters is carried out via Markov Chain Monte Carlo (MCMC) methods. We illustrate the utility of the model by applying it to both simulated and real-world email data sets.
RIS
TY - CPAPER TI - Statistical Models for Exploring Individual Email Communication Behavior AU - Nicholas Navaroli AU - Christopher DuBois AU - Padhraic Smyth BT - Proceedings of the Asian Conference on Machine Learning DA - 2012/11/17 ED - Steven C. H. Hoi ED - Wray Buntine ID - pmlr-v25-navaroli12 PB - PMLR DP - Proceedings of Machine Learning Research VL - 25 SP - 317 EP - 332 L1 - http://proceedings.mlr.press/v25/navaroli12/navaroli12.pdf UR - https://proceedings.mlr.press/v25/navaroli12.html AB - As digital communication devices play an increasingly prominent role in our daily lives, the ability to analyze and understand our communication patterns becomes more important. In this paper, we investigate a latent variable modeling approach for extracting information from individual email histories, focusing in particular on understanding how an individual communicates over time with recipients in their social network. The proposed model consists of latent groups of recipients, each of which is associated with a piecewise-constant Poisson rate over time. Inference of group memberships, temporal changepoints, and rate parameters is carried out via Markov Chain Monte Carlo (MCMC) methods. We illustrate the utility of the model by applying it to both simulated and real-world email data sets. ER -
APA
Navaroli, N., DuBois, C. & Smyth, P.. (2012). Statistical Models for Exploring Individual Email Communication Behavior. Proceedings of the Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 25:317-332 Available from https://proceedings.mlr.press/v25/navaroli12.html.

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