Social Synchrony: Predicting Mimicry of User Actions in Online Social Media

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.