  There is a large amount of interest in understanding users of social media in
order to predict their behavior in this space. Despite this interest, user
predictability in social media is not well-understood. To examine this
question, we consider a network of fifteen thousand users on Twitter over a
seven week period. We apply two contrasting modeling paradigms: computational
mechanics and echo state networks. Both methods attempt to model the behavior
of users on the basis of their past behavior. We demonstrate that the behavior
of users on Twitter can be well-modeled as processes with self-feedback. We
find that the two modeling approaches perform very similarly for most users,
but that they differ in performance on a small subset of the users. By
exploring the properties of these performance-differentiated users, we
highlight the challenges faced in applying predictive models to dynamic social
data.
