SocialCom 2009 - Social Synchrony

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SocialCom 2009 - Social Synchrony

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

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