Social Synchrony: Predicting Mimicry of User Actions in 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 Clapping in an Auditorium
@ IEEE SocialCom 2009 September 6, 2009 2 Biological Oscillators
@ IEEE SocialCom 2009 September 6, 2009 3 Movement of herds of animals
@ IEEE SocialCom 2009 September 6, 2009 4 Today’s Online Social Media…
Digg LiveJournal Twitter MetaFilter Reddit Blogger Orkut Flickr MySpace YouTube @ IEEE SocialCom 2009 September 6, 2009 5 What causes users on a social media mimic each other with respect to a certain action?
@ IEEE SocialCom 2009 September 6, 2009 6 Some practical examples of large-scale mimicry…
Ref. Mashable, Twitter Blog
@ IEEE SocialCom 2009 September 6, 2009 7 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 September 6, 2009 8 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 September 6, 2009 9 Ref. Watts 2003, Leskovec et al 2007
The distinction with information cascades…
@ IEEE SocialCom 2009 September 6, 2009 10 A news reporter A political analyst A company
Who could benefit from this research?
@ IEEE SocialCom 2009 September 6, 2009 11 Potential applications of this research…
What have been the sales of the new Nikon D3000 SLR?
@ IEEE SocialCom 2009 September 6, 2009 12 Potential applications of this research…
Who is the best person in my social network to broadcast the news of my party to everyone?
@ IEEE SocialCom 2009 September 6, 2009 13 Potential applications of this research…
What has been Yahoo!’s stock prices post-Bing deal?
@ IEEE SocialCom 2009 September 6, 2009 14 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 September 6, 2009 15 Mathematical Framework
@ IEEE SocialCom 2009 September 6, 2009 16 Main Idea
. Socially-aware and unaware states.
. Learning – for each user in the social network, we need to predict her probability of actions at 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 September 6, 2009 17 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 September 6, 2009 18 Estimation
PAAPASAPSA ujuj,| ,1,1 ,CCC uj ujujuj ,,,1,1 | , , uj ujuj , | ,1,1 , uj Suj,
PASPSS u, j| u , j u , j | u , j 1 ,C u , j 1 , Suj,
where,
Au,j= action of user u at time slice j Estimate user context Cu,j-1= context of user u at time slice j-1 Su,j= state of user u at time slice j Estimate probability of Multinomial density of states user state given over the contextual attributes context with a Dirichlet prior Estimate probability of user action given the A continuous Hidden Markov Model where the actions are state the emissions
@ IEEE SocialCom 2009 September 6, 2009 19 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 September 6, 2009 20 Experimental Results
@ IEEE SocialCom 2009 September 6, 2009 21 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 September 6, 2009 22 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
Topics Our Method B1 B2 B3 B4 US Elections 0.19 0.67 0.52 0.38 0.35 World News 0.11 0.41 0.36 0.29 0.28 Olympics 0.19 0.54 0.49 0.44 0.41 Comedy 0.13 0.46 0.4 0.31 0.27 Celebrity 0.12 0.49 0.36 0.29 0.22 Tennis 0.15 0.53 0.41 0.32 0.27
@ IEEE SocialCom 2009 September 6, 2009 23 Summary…
@ IEEE SocialCom 2009 September 6, 2009 24 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 September 6, 2009 25 Questions? [email protected] Thanks!
@ IEEE SocialCom 2009 September 6, 2009 26