Predicting Emerging Social Conventions in Online Social Networks

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Predicting Emerging Social Conventions in Online Social Networks. Farshad Kooti * Winter Mason Krishna Gummadi * Meeyoung Cha MPI-SWS * Stevens Institute of Technology KAIST . Metric. Imperial. Linguistic conventions. Hello. Hey. Aloha. Hows it going. - PowerPoint PPT Presentation

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The Emergence of Conventions in Online Social NetworksPredicting Emerging Social Conventions in Online Social Networks Farshad Kooti* Winter MasonKrishna Gummadi* Meeyoung ChaMPI-SWS* Stevens Institute of Technology KAISTCIKM 20121Prediction of Emerging Social Conventions in OSNs- Farshad KootiImperialMetric22Linguistic conventionsPrediction of Emerging Social Conventions in OSNs- Farshad KootiHeyAlohaHows it goingHello33The retweeting conventionQuoting another user while citing the original author4Prediction of Emerging Social Conventions in OSNs- Farshad KootiBobAliceRT @Bob:CIKM startedCIKM startedRT @Bob:CIKM started4Why retweeting convention? Information-sharing channels are explicit in Twitter Specific to Twitter: exposures within the community Contained in Twitter, hence capturing all usagesPrediction of Emerging Social Conventions in OSNs- Farshad Kooti55Twitter datasetUsed near-complete data from 03-2006 to 09-200954 million users1.9 billion tweets1.7 billion follow linksFollow links are a snapshot of the network in 2009Prediction of Emerging Social Conventions in OSNs- Farshad Kooti66The retweeting variationsSearched for syntax token @usernameAdopter refers to a user using the variation at least onceVariation# of adopters# of retweetsRT1,836 K53,221 Kvia751 K5367 KRetweeting50 K296 KRetweet36 K110 KHT8 K22 KR/T5 K28 K3 K18 KTotal 2,059 K59,065 KPrediction of Emerging Social Conventions in OSNs- Farshad Kooti77Our study of retweeting conventionCharacterizing the emergence [ICWSM12, best paper award]Predicting the adoption process[this work, CIKM 2012]Prediction of Emerging Social Conventions in OSNs- Farshad Kooti88Defining prediction problemSuppose we are given a social network with records of users, their interactions, and times of adoptions. However, information about which variation was adopted by user u at time t is hidden. How reliably we can infer that user u has adopted variation v at time t? Prediction of Emerging Social Conventions in OSNs- Farshad Kooti99Prediction of Emerging Social Conventions in OSNs- Farshad Kooti10RT or via or ...?RT @john: tweettweet (RT @joe)via @jane: tweet2,053 TWEETS1,738 FOLLOWING1,581 FOLLOWERSBob10Motivation & ProblemFeatures impacting adoptionPredictive power & results 11Feature categoriesPrediction of Emerging Social Conventions in OSNs- Farshad Kooti12PersonalSocialGlobal12feature: # of followersPrediction of Emerging Social Conventions in OSNs- Farshad Kooti13Personal13featuresPrediction of Emerging Social Conventions in OSNs- Farshad Kooti14# of exposures# of adopter friendsSocial14feature: # of adopter friendsPrediction of Emerging Social Conventions in OSNs- Farshad Kooti15Social15feature: adoption datePrediction of Emerging Social Conventions in OSNs- Farshad Kooti16Global16All the considered features# of followers and friends, # of posted tweets and URLs, join date, geo-location# of exposures, # of adopter friendsTime of adoptionPrediction of Emerging Social Conventions in OSNs- Farshad Kooti17GlobalSocialPersonal17Motivation & ProblemFeatures impacting adoptionPredictive power & results 18Measuring the predictive power of featuresWe calculate Information Gain (IG) of each feature, which shows the predictive powerIG: change in entropy (measure of uncertainty) because of the given featureIG(Variation, feat.) = H(Variation) - H(Variation|feat.)Prediction of Emerging Social Conventions in OSNs- Farshad Kooti1919Predictive power of features: resultsPrediction of Emerging Social Conventions in OSNs- Farshad Kooti20RankFeatureType1DateGlobal2# of exposures to RTSocial3# of posted URLsPersonal4# of exposures to viaSocial5Join date of adopterPersonal6# of posted tweetsPersonal7# of RT adopter friendsSocialFindings:# of exposures has more predictive power than # of adopter friendsGeography is not important20Prediction methodologyUsing different ML classifiers: Bayesian models, boosting, decision trees, etc.Bagging yields the best resultFeature selection techniques to find best subset of features (excluded 8 features)Prediction of Emerging Social Conventions in OSNs- Farshad Kooti2121Prediction accuracyVariationAccuracyPrecisionRecallRT71.272.868.1via72.652.166.6Retweeting98.043.190.5Retweet98.534.380.1HT99.750.584.9R/T99.819.081.5recycle icon99.935.982.3Weighted average72.665.769.8Prediction of Emerging Social Conventions in OSNs- Farshad Kooti2222Dealing with unbalanced classesProblem:Most of the adoptions (68%) are RTA simple classifier of always predicting the most used variation performs goodSolution:Take the same number of cases from two groups (baseline: 50%)Prediction of Emerging Social Conventions in OSNs- Farshad Kooti2323Prediction accuracy from balanced dataVariationAccuracyPrecisionRecallRT61.360.763.1via60.760.660.1Retweeting59.158.961.8Retweet56.956.656.6HT82.382.881.5R/T77.377.077.2recycle icon81.583.180.2Weighted average61.060.761.5Prediction of Emerging Social Conventions in OSNs- Farshad Kooti2424Stronger definitionsPrediction of Emerging Social Conventions in OSNs- Farshad Kooti2525SummaryPredicting adoption of social conventionsInvestigated impact of various factorsGlobal feature trumps social and personal featuresThe number of exposures had more predictive power than number of adopter friendsUsing the features from network is not enough for a prediction with high accuracyPrediction of Emerging Social Conventions in OSNs- Farshad Kooti2626Thank you!Prediction of Emerging Social Conventions in OSNs- Farshad Kooti2727