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Social Computing / IEEE International Conference on Privacy, Security, Risk and Trust, 2010 IEEE International Conference on (2013)
Alexandria, VA, USA USA
Sept. 8, 2013 to Sept. 14, 2013
pp: 102-107
Recent work has attempted to capture the behavior of users on social media by modeling them as computational units processing information. We propose to extend this perspective by explicitly examining the predictive power of such a view. We consider a network of fifteen thousand users on Twitter over a seven week period. To evaluate the predictability of the users, we apply two contrasting modeling paradigms: computational mechanics and echo state networks. Computational mechanics seeks to construct the simplest model with the maximal predictive capability, while echo state networks relax from very complicated dynamics until predictive capability is reached. We demonstrate that the behavior of users on Twitter can be well-modeled as processes with self-feedback and compare the performance of models built with both the statistical and neural paradigms.
social dynamics, prediction, social behavior modeling

D. Darmon, J. Sylvester, M. Girvan and W. Rand, "Predictability of User Behavior in Social Media: Bottom-Up v. Top-Down Modeling," 2013 International Conference on Social Computing (SocialCom)(SOCIALCOM), Alexandria, VA, USA, 2013, pp. 102-107.
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