  Self-exciting processes of Hawkes type have been used to model various
phenomena including earthquakes, neural activities, and views of online videos.
Studies of temporal networks have revealed that sequences of social interevent
times for individuals are highly bursty. We examine some basic properties of
event sequences generated by the Hawkes self-exciting process to show that it
generates bursty interevent times for a wide parameter range. Then, we fit the
model to the data of conversation sequences recorded in company offices in
Japan. In this way, we can estimate relative magnitudes of the self excitement,
its temporal decay, and the base event rate independent of the self excitation.
These variables highly depend on individuals. We also point out that the Hawkes
model has an important limitation that the correlation in the interevent times
and the burstiness cannot be independently modulated.
