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There is a large amount of interest in understanding users of social media inorder to predict their behavior in this space. Despite this interest, userpredictability in social media is not well-understood. To examine thisquestion, we consider a network of fifteen thousand users on Twitter over aseven week period. We apply two contrasting modeling paradigms: computationalmechanics and echo state networks. Both methods attempt to model the behaviorof users on the basis of their past behavior. We demonstrate that the behaviorof users on Twitter can be well-modeled as processes with self-feedback. Wefind that the two modeling approaches perform very similarly for most users,but that they differ in performance on a small subset of the users. Byexploring the properties of these performance-differentiated users, wehighlight the challenges faced in applying predictive models to dynamic socialdata.

Understanding the Predictive Power of Computational Mechanics and Echo State Networks in Social Media