Decomposing Behavioral Variability in Email Communication: Self-Excitation, Latent State-Switching, and Their Interaction in the Enron Corpus

Human email communication exhibits substantial temporal variability that resists simple characterization. We treat individual email activity as a stochastic system and ask how much behavioral variability is explained by each of three generative mechanisms: circadian and weekly periodicity, self-excitation from incoming messages, and latent work–rest state-switching. Using the Enron email corpus (58 users, 101,299 sent emails, 1998–2002), we fit a hierarchy of five models—inhomogeneous Poisson, lognormal renewal, Hawkes process, Poisson hidden Markov model (HMM), and a hybrid HMM-on-Hawkes-residuals—evaluated via strict 70/30 temporal train/test splits. All three pre-registered hypotheses pass: 83% of users exhibit significant self-excitation (α > 0; mean branching ratio 0.15), 84.5% show greater than 10% log-likelihood improvement under state-switching, and 62% retain residual state-switching structure after Hawkes filtering. Latent state-switching provides the largest predictive gains, while self-excitation is real but modest, and the two mechanisms capture complementary sources of variability. All code and derived data (timestamps and anonymized identifiers only) are publicly available for full reproducibility.