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Vancouver, Canada
Aug. 29, 2009 to Aug. 31, 2009
ISBN: 978-0-7695-3823-5
pp: 151-158
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.
social media, user actions, social synchrony, cascades, social networks, Digg
Munmun De Choudhury, Hari Sundaram, Ajita John, Dorée Duncan Seligmann, "Social Synchrony: Predicting Mimicry of User Actions in Online Social Media", CSE, 2009, 2013 IEEE 16th International Conference on Computational Science and Engineering, 2013 IEEE 16th International Conference on Computational Science and Engineering 2009, pp. 151-158, doi:10.1109/CSE.2009.439
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