12 Pages Posted: 28 Jun 2013 Last revised: 6 Sep 2015
Date Written: June 26, 2013
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.
Keywords: prediction, social behavior modeling, social dynamics
Suggested Citation: Suggested Citation
Darmon, David and Sylvester, Jared and Girvan, Michelle and Rand, William, Understanding the Predictive Power of Computational Mechanics and Echo State Networks in Social Media (June 26, 2013). Available at SSRN: https://ssrn.com/abstract=2285537 or http://dx.doi.org/10.2139/ssrn.2285537