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Social Synchrony: PredictingMimicry of User Actionsin Online Social Media,[object Object],Munmun De Choudhury1, Hari Sundaram1,,[object Object],Ajita John2 and Dorée Duncan Seligmann2,[object Object],1 School of Arts, Media and Engineering, Arizona State University,[object Object],                                                    2Avaya Labs Research, NJ,[object Object]
August 25, 2009,[object Object],2,[object Object],Clapping in an Auditorium,[object Object],@ IEEE SocialCom 2009,[object Object]
August 25, 2009,[object Object],3,[object Object],Biological Oscillators,[object Object],@ IEEE SocialCom 2009,[object Object]
August 25, 2009,[object Object],4,[object Object],Movement of herds of animals,[object Object],@ IEEE SocialCom 2009,[object Object]
August 25, 2009,[object Object],5,[object Object],Today’s Online Social Media…,[object Object],Slashdot,[object Object],Facebook,[object Object],Engadget,[object Object],LiveJournal,[object Object],Digg,[object Object],Twitter,[object Object],MetaFilter,[object Object],Flickr,[object Object],Reddit,[object Object],Orkut,[object Object],Blogger,[object Object],YouTube,[object Object],MySpace,[object Object],@ IEEE SocialCom 2009,[object Object]
August 25, 2009,[object Object],6,[object Object],What causes users on a social media mimic each other with respect to a certain action?,[object Object],@ IEEE SocialCom 2009,[object Object]
August 25, 2009,[object Object],7,[object Object],Some practical examples of large-scale mimicry…,[object Object],Ref. Mashable, Twitter Blog,[object Object],@ IEEE SocialCom 2009,[object Object]
August 25, 2009,[object Object],8,[object Object],Some practical examples of large-scale mimicry…,[object Object],Topic ‘Olympics’ is observed to have several old users continually involved in the action of digging stories, as well as there are large number of new users joining in the course of time (Sept 3-Sept 13). ,[object Object],@ IEEE SocialCom 2009,[object Object]
August 25, 2009,[object Object],9,[object Object],Defining Social Synchrony…,[object Object],Social synchrony is a temporal phenomenon occurring in social networks which is characterized by:,[object Object],a certain topic,[object Object],an agreed upon action,[object Object],a set of seed users involved in performing the action at a certain point in time, and,[object Object],large numbers of continuing old users as well as new users getting involved over a period of time in the future, following the actions of the seed set. ,[object Object],@ IEEE SocialCom 2009,[object Object]
August 25, 2009,[object Object],10,[object Object],Ref. Watts 2003, Leskovecet al 2007,[object Object],The distinction with information cascades…,[object Object],August 25, 2009,[object Object],10,[object Object],@ IEEE SocialCom 2009,[object Object]
August 25, 2009,[object Object],11,[object Object],A news reporter,[object Object],A political analyst,[object Object],A company,[object Object],Who could benefit from this research?,[object Object],August 25, 2009,[object Object],11,[object Object],@ IEEE SocialCom 2009,[object Object]
August 25, 2009,[object Object],12,[object Object],Potential applications of this research…,[object Object],What have been the sales of the new Nikon D3000 SLR?,[object Object],August 25, 2009,[object Object],12,[object Object],@ IEEE SocialCom 2009,[object Object]
August 25, 2009,[object Object],13,[object Object],Potential applications of this research…,[object Object],Who is the best person in my social network to broadcast the news of my party to everyone?,[object Object],August 25, 2009,[object Object],13,[object Object],@ IEEE SocialCom 2009,[object Object]
August 25, 2009,[object Object],14,[object Object],Potential applications of this research…,[object Object],What has been Yahoo!’s stock prices post-Bing deal?,[object Object],August 25, 2009,[object Object],14,[object Object],@ IEEE SocialCom 2009,[object Object]
August 25, 2009,[object Object],15,[object Object],Our Contributions,[object Object],Goal:,[object Object],a framework for predicting social synchrony in online social media over a period of time into the future. ,[object Object],Approach:,[object Object],Operational definition of social synchrony.,[object Object],Learning – a dynamic Bayesian representation of user actions based on latent states and contextual variables.,[object Object],Evolution – evolve the social network size and the user models over a set of future time slices to predict social synchrony.,[object Object],Excellent results on a large dataset from the popular news-sharing social media Digg. ,[object Object],@ IEEE SocialCom 2009,[object Object]
August 25, 2009,[object Object],16,[object Object],Mathematical Framework,[object Object],August 25, 2009,[object Object],16,[object Object],@ IEEE SocialCom 2009,[object Object]
August 25, 2009,[object Object],17,[object Object],Main Idea,[object Object],Socially-aware and unaware states.,[object Object],Learning– for each user in the social network, we need to predict her probability of actionsat each future time slice.,[object Object],Evolution –synchrony in a social network (a) is likely to involve sustained participation; and (b) persists over a period of time. ,[object Object],Evolve network,[object Object],Evolve user models,[object Object],Predict synchrony,[object Object],@ IEEE SocialCom 2009,[object Object]
August 25, 2009,[object Object],18,[object Object],The Learning Framework,[object Object],A user’s intent to perform an action depends upon her state.,[object Object],The user state in turn is affected by the user context (e.g. actions of the neighboring contacts, coupling with seed users and / or the user’s communication over the topic).,[object Object],@ IEEE SocialCom 2009,[object Object]
August 25, 2009,[object Object],19,[object Object],Estimation,[object Object],where,,[object Object],Au,j= action of user u at time slice j,[object Object],Cu,j-1= context of user u at time slice j-1,[object Object],Su,j= state of user u at time slice j,[object Object],Estimate user context,[object Object],Estimate probability of user state given context,[object Object],Multinomial density of states over the contextual attributes with a Dirichlet prior,[object Object],Estimate probability of user action given the state,[object Object],A continuous Hidden Markov Model where the actions are the emissions,[object Object],@ IEEE SocialCom 2009,[object Object]
August 25, 2009,[object Object],20,[object Object],The Evolution Framework,[object Object],Why?,[object Object],Online learning methods (e.g. incremental SVM Regression) that incrementally train and predict a value at each time slice, are not helpful.,[object Object],Synchrony needs to be predicted over a set of future time slices.,[object Object],Method:,[object Object],Estimating network size,[object Object],Evolving user models,[object Object],Choosing users based on high probability of comments / replies,[object Object],Predicting synchrony,[object Object],@ IEEE SocialCom 2009,[object Object]
August 25, 2009,[object Object],21,[object Object],Experimental Results,[object Object],August 25, 2009,[object Object],21,[object Object],@ IEEE SocialCom 2009,[object Object]
August 25, 2009,[object Object],22,[object Object],Experiments on Prediction,[object Object],Digg dataset,[object Object],August, September 2008 ,[object Object],21,919 users, 187,277 stories, 7,622,678 diggs, 687,616 comments and 477,320 replies.,[object Object],Six sample topics – four inherently observed to have synchrony.,[object Object],@ IEEE SocialCom 2009,[object Object]
August 25, 2009,[object Object],23,[object Object],Comparative Empirical Study,[object Object],Baseline methods:,[object Object],B1: temporal trend learning method of user actions ,[object Object],B2: a linear regressor based method over users’ comments and replies,[object Object],B3: SIR (susceptible-infected-removed) epidemiological model ,[object Object],B4: a threshold based model of global cascades,[object Object],Error in Prediction of user actions over a future period of time,[object Object],@ IEEE SocialCom 2009,[object Object]
August 25, 2009,[object Object],24,[object Object],Summary…,[object Object],August 25, 2009,[object Object],24,[object Object],@ IEEE SocialCom 2009,[object Object]
August 25, 2009,[object Object],25,[object Object],Conclusions,[object Object],Summary:,[object Object],Synchrony - large-scale mimicry of actions of users over a short period of time, on a topic, given a seed user set.,[object Object],Modeling and predicting social synchrony:,[object Object],Learning framework, evolution framework,[object Object],DBN representation of user actions – context, latent states ,[object Object],Extensive empirical studies on a large dataset from Digg. ,[object Object],Future Work:,[object Object],Diffusion rates of information that are observed to be involved in social synchrony.,[object Object],User homophily and emergence of synchrony.,[object Object],@ IEEE SocialCom 2009,[object Object]
August 25, 2009,[object Object],26,[object Object],Questions?,[object Object],Munmun.Dechoudhury@asu.edu,[object Object],Thanks!,[object Object],August 25, 2009,[object Object],26,[object Object],August 25, 2009,[object Object],26,[object Object],@ IEEE SocialCom 2009,[object Object]

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