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

David Darmon, Jared Sylvester, Michelle Girvan, William Rand
2013 arXiv   pre-print
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
more » ... . 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.
arXiv:1306.6111v2 fatcat:om3zpwzgm5fcnhhjxd2t7lmohu