1. Social Synchrony: PredictingMimicry of User Actionsin Online Social Media Munmun De Choudhury1, Hari Sundaram1, Ajita John2 and Dorée Duncan Seligmann2 1 School of Arts, Media and Engineering, Arizona State University 2Avaya Labs Research, NJ

2. August 25, 2009 2 Clapping in an Auditorium @ IEEE SocialCom 2009

3. August 25, 2009 3 Biological Oscillators @ IEEE SocialCom 2009

4. August 25, 2009 4 Movement of herds of animals @ IEEE SocialCom 2009

5. August 25, 2009 5 Today’s Online Social Media… Slashdot Facebook Engadget LiveJournal Digg Twitter MetaFilter Flickr Reddit Orkut Blogger YouTube MySpace @ IEEE SocialCom 2009

6. August 25, 2009 6 What causes users on a social media mimic each other with respect to a certain action? @ IEEE SocialCom 2009

7. August 25, 2009 7 Some practical examples of large-scale mimicry… Ref. Mashable, Twitter Blog @ IEEE SocialCom 2009

8. August 25, 2009 8 Some practical examples of large-scale mimicry… 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). @ IEEE SocialCom 2009

9. August 25, 2009 9 Defining Social Synchrony… Social synchrony is a temporal phenomenon occurring in social networks which is characterized by: a certain topic an agreed upon action a set of seed users involved in performing the action at a certain point in time, and 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. @ IEEE SocialCom 2009

10. August 25, 2009 10 Ref. Watts 2003, Leskovecet al 2007 The distinction with information cascades… August 25, 2009 10 @ IEEE SocialCom 2009

11. August 25, 2009 11 A news reporter A political analyst A company Who could benefit from this research? August 25, 2009 11 @ IEEE SocialCom 2009

12. August 25, 2009 12 Potential applications of this research… What have been the sales of the new Nikon D3000 SLR? August 25, 2009 12 @ IEEE SocialCom 2009

13. August 25, 2009 13 Potential applications of this research… Who is the best person in my social network to broadcast the news of my party to everyone? August 25, 2009 13 @ IEEE SocialCom 2009

14. August 25, 2009 14 Potential applications of this research… What has been Yahoo!’s stock prices post-Bing deal? August 25, 2009 14 @ IEEE SocialCom 2009

15. August 25, 2009 15 Our Contributions Goal: a framework for predicting social synchrony in online social media over a period of time into the future. Approach: Operational definition of social synchrony. Learning – a dynamic Bayesian representation of user actions based on latent states and contextual variables. Evolution – evolve the social network size and the user models over a set of future time slices to predict social synchrony. Excellent results on a large dataset from the popular news-sharing social media Digg. @ IEEE SocialCom 2009

16. August 25, 2009 16 Mathematical Framework August 25, 2009 16 @ IEEE SocialCom 2009

17. August 25, 2009 17 Main Idea Socially-aware and unaware states. Learning– for each user in the social network, we need to predict her probability of actionsat each future time slice. Evolution –synchrony in a social network (a) is likely to involve sustained participation; and (b) persists over a period of time. Evolve network Evolve user models Predict synchrony @ IEEE SocialCom 2009

18. August 25, 2009 18 The Learning Framework A user’s intent to perform an action depends upon her state. 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). @ IEEE SocialCom 2009

19. August 25, 2009 19 Estimation where, Au,j= action of user u at time slice j Cu,j-1= context of user u at time slice j-1 Su,j= state of user u at time slice j Estimate user context Estimate probability of user state given context Multinomial density of states over the contextual attributes with a Dirichlet prior Estimate probability of user action given the state A continuous Hidden Markov Model where the actions are the emissions @ IEEE SocialCom 2009

20. August 25, 2009 20 The Evolution Framework Why? Online learning methods (e.g. incremental SVM Regression) that incrementally train and predict a value at each time slice, are not helpful. Synchrony needs to be predicted over a set of future time slices. Method: Estimating network size Evolving user models Choosing users based on high probability of comments / replies Predicting synchrony @ IEEE SocialCom 2009

21. August 25, 2009 21 Experimental Results August 25, 2009 21 @ IEEE SocialCom 2009

22. August 25, 2009 22 Experiments on Prediction Digg dataset August, September 2008 21,919 users, 187,277 stories, 7,622,678 diggs, 687,616 comments and 477,320 replies. Six sample topics – four inherently observed to have synchrony. @ IEEE SocialCom 2009

23. August 25, 2009 23 Comparative Empirical Study Baseline methods: B1: temporal trend learning method of user actions B2: a linear regressor based method over users’ comments and replies B3: SIR (susceptible-infected-removed) epidemiological model B4: a threshold based model of global cascades Error in Prediction of user actions over a future period of time @ IEEE SocialCom 2009

24. August 25, 2009 24 Summary… August 25, 2009 24 @ IEEE SocialCom 2009

25. August 25, 2009 25 Conclusions Summary: Synchrony - large-scale mimicry of actions of users over a short period of time, on a topic, given a seed user set. Modeling and predicting social synchrony: Learning framework, evolution framework DBN representation of user actions – context, latent states Extensive empirical studies on a large dataset from Digg. Future Work: Diffusion rates of information that are observed to be involved in social synchrony. User homophily and emergence of synchrony. @ IEEE SocialCom 2009

26. August 25, 2009 26 Questions? Munmun.Dechoudhury@asu.edu Thanks! August 25, 2009 26 August 25, 2009 26 @ IEEE SocialCom 2009