Slide 1

Xintao Wu Jan 18, 2013

Retweeting Behavior and Spectral Graph Analysis in Social Media1Social Media Customer Analytics 2Network topology

namesexagediseasesalaryAdaF18cancer25kBobM25heart110kidSexageaddressIncome5FYNC25k3MYSC110kStructured profileRetweet sequenceUnstructured text (e.g., blog, tweet)Customer profileCustomer transactionInventoryProduct desc and reviewEntity resolutionPatternsTemporal/spatialScalabilityVisualizationSentimentPrivacy2OutlineExamining retweeting behavior to understand information propagationMulti-factor interaction analysis Coverage predictionBurst detectionSpectral graph analysisCommunity partitionFraud detection

3Multi-factor interaction analysis 4 For each following relationship , what factors affect the user As decision on whether to forward messages from B to A s followers?We examine users retweet behaviors by using various featuresPower ratio (A)Link structure (B)Location factor (C)Gender factor (D) We apply a fitted Log-linear model to capture and interpret interaction patterns among features A-D and retweet E.

4Interpreting interaction effect5

Interpretation exampleNeither gender nor location has any significant effect on retweeting solely.However, considering link structure, Females are more conservative and have a lower tendency to retweet messages from non-friend (especially female) users, but have a higher tendency to retweet messages from friends or superstars. Males are more open-minded and have a higher tendency to retweet messages from non-friend (especially female) users.66OutlineExamining retweeting behavior to understand information propagationMulti-factor interaction analysis Coverage predictionBurst detectionSpectral graph analysisCommunity partitionFraud detection

7Retweet Sequence Information dynamically flows through the network.8AliceBobCathyDavidEllenFredD1D2D3t1m1ARetweet SequenceInformation dynamically flows through a social network.9AliceBobCathyDavidEllenFredD1D2D3t1m1At2m2Bt1m1AFlow Through Tree StructureInformation dynamically flows through a social network.10AliceBobCathyDavidEllenFredD1D2D3t1m1At2m2Bt1m1At3m3D\t Bt1m1AFlow Through Tree StructureInformation dynamically flows through a social network.11AliceBobCathyDavidEllenFredD1D2D3t1m1At2m2Bt1m1At3m3D\t Bt1m1At4m4Ct1m1AWISE12 ChallengeSina Weibo# of user: 5,636,858# of tweets: 46,584,914# of retweets: 190,920,026

33 test messages each with 100 initial retweets composed by 27 users from 6 events

For each message, predictM1: the number of retweets in 30 daysM2: the number of possible-views in 30 days

12IdeaWe treat retweeting activities of each original message in the training data as a time series Each value corresponds to the number of times that the original message during time period tFor each message in the test data

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Known from 100 retweetsUse ARMA to predictPrediction Result

14Runner-up award (2nd place) on WISE 2012 Challenge Mining Track.Death of Steve JobsXiaomi ReleaseYao Jiaxin Murder Case Xiaomi ReleaseOutlineExamining retweeting behavior to understand information propagationMulti-factor interaction analysis Coverage predictionBurst detectionSpectral graph analysisCommunity partitionFraud detection

15Bursts16

Peak TimeDuration TimeWe found that some particular tweet would be heated and widely-retweeted continually even after a long time they had been posted, while most tweets would not be retweeted in a very short period of time after they were potsted.

81:8% of the tweets would not be retweeted any more after one day of their originally posted time;6:28% of the tweets would survive for two days; A few tweets would still be retweeted after 100 days.

16Topic 17Retweet vs. Time18Retweet vs. Time19Burst Analysis : UsersTop 100 users tend to have: shorter path length, shorter peak time, shorter duration time.20

Burst PredictionExtract featuresUser related including profile and history informationTweet-related including time series and retweet tree Run classifiersLogistic regressionRandom forestDecision treeNave bayesSVMKNNAchieve 83.2% accuracy

21OutlineExamining retweeting behavior to understand information propagationMulti-factor interaction analysis Coverage predictionBurst detectionSpectral graph analysisCommunity partitionFraud detection

22Spectral graph analysis

Spectral coordinate:

Polbook Network2323Accuracy of AdjClusterLap [Miller and Teng 1998]: Laplacian based Ncut [Shi and Malik, 2000]: Normalized cutHE [Wakita and Tsurumi, 2007]: Modularity based agglomerative clustering SpokEn [Prakash et al., 2010]: EigenSpoke

Accuracy: where :the i-th community produced by different algorithms

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Refer to IJCAI 11 for detailsOr add introduction of Lap NCut He SpokEn here instead of in the introduction section?Evaluation on Web spam challenge data

SPCTRA fraud detection

25GREEDY: based on outer-triangles [Shrivastava, ICDE, 2008]100-1000 times fasterRefer to ICDE11details.25AcknowledgmentsThis work was supported in part by U.S. National Science Foundation CNS-0831204 and CCF-1047621, and UNC Charlotte Chancellors Special Fund .

Thank You! Questions?

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