Understanding the Predictive Power of Computational Mechanics and Echo State Networks in Social Media
This work addresses the challenge of user predictability in social media for researchers and practitioners, but it is incremental as it compares existing methods without introducing new ones.
The study tackled the problem of predicting user behavior on social media by applying computational mechanics and echo state networks to a dataset of 15,000 Twitter users over seven weeks, finding that both methods performed similarly for most users but differed for a small subset.
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.