social synchrony: predicting mimicry of user actions in ...september 6, 2009 15 our contributions...

Social Synchrony: Predicting Mimicry of UserActions in Online Social Media

Munmun De Choudhury1, Hari Sundaram1,Ajita John2 and Dorée Duncan Seligmann2

1 School of Arts, Media and Engineering, Arizona State University2Avaya Labs Research, NJ

September 6, 2009 2

Clapping in an Auditorium

@ IEEE SocialCom 2009

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Biological Oscillators


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Movement of herds of animals


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Today’s Online Social Media…

Facebook

Slashdot

Engadget

Flickr

LiveJournalDigg

YouTubeBlogger

MetaFilterReddit

MySpaceOrkut

Twitter


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What causes users on a social media mimic each other with respect to a certain action?


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Some practical examples of large-scale mimicry…

Ref. Mashable, Twitter Blog


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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).

Some practical examples of large-scale mimicry…


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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.


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The distinction with information cascades…


Ref. Watts 2003, Leskovec et al 2007


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A news reporter A political analyst A company

Who could benefit from this research?



What have been the sales of the new Nikon D3000 SLR?


Potential applications of this research…



Who is the best person in my social network to broadcast the news of my party to everyone?





What has been Yahoo!’s stock prices post-Bing deal?





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.



Mathematical Framework

September 6, 2009 16@ IEEE SocialCom 2009


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



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).



Estimation

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Estimate user context

Estimate probability of user state given context

Estimate probability of user action given the state

A continuous Hidden Markov Model where the actions are the emissions

Multinomial density of states over the contextual attributes with a Dirichlet prior

where,Au,j= action of user u at time slice jCu,j-1= context of user u at time slice j-1Su,j= state of user u at time slice j



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



Experimental Results



Experiments on Prediction

Digg dataset• August, September 2008

• 21,919 users, 187,277 stories, 7,622,678 diggs, 687,616comments and 477,320 replies.

• Six sample topics – four inherently observed to have synchrony.



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

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

Error in Prediction of user actions over a future period of time



Summary…



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.


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Thanks!

Questions?


social synchrony: predicting mimicry of user actions in ...september 6, 2009 15 our contributions...

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