Extraction, characterization and utility of prototypical communication groups in the blogosphere
ACM Transactions on Information Systems (TOIS)
Hierarchical role classification based on social behavior analysis
Proceedings of the 8th International Conference on Advances in Mobile Computing and Multimedia
Diversity dynamics in online networks
Proceedings of the 23rd ACM conference on Hypertext and social media
Preferential attachment in online networks: measurement and explanations
Proceedings of the 5th Annual ACM Web Science Conference
The self-feeding process: a unifying model for communication dynamics in the web
Proceedings of the 22nd international conference on World Wide Web
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We propose a computational framework to predict synchronyof action in online social media. Synchrony is a temporalsocial network phenomenon in which a large number of usersare observed to mimic a certain action over a period of timewith sustained participation from early users.Understanding social synchrony can be helpful in identifyingsuitable time periods of viral marketing. Our method consistsof two parts – the learning framework and the evolutionframework. In the learning framework, we develop a DBNbased representation that includes an understanding of usercontext to predict the probability of user actions over a set oftime slices into the future. In the evolution framework, weevolve the social network and the user models over a set offuture time slices to predict social synchrony. Extensiveexperiments on a large dataset crawled from the popularsocial media site Digg (comprising ~7M diggs) show thatour model yields low error (15.2+4.3%) in predicting useractions during periods with and without synchrony.Comparison with baseline methods indicates that our methodshows significant improvement in predicting user actions.