Anant Pradhan PET: A Statistical Model for Popular Events Tracking in Social Communities Cindy Xide Lin, Bo Zhao, Qiaozhu Mei, Jiawei Han (UIUC)

Introduction Challenge: Tracking the evolution of a popular topic 2

Introduction Observing and tracking: – Popular events – Topics that evolve over time Existing approaches focus on: – Burstiness – Evolution of networks – Ignore interplay between textual topics and network structures. 3

Propose a novel statistical method (PET) that: – Models the popularity of events over time – Considers burstiness of user interest – Information diffusion on the network structure – Evolution of textual topics Introduction 4

Gibbs Random Field used to model: – Influence of historical status – Dependency relationships in the graph Topic Model: – designed to explain the generation of text data Interplay by regularizing each other. 5

Problem Definition Set of vertices: V k Set of edges: E k Network Stream: G = {G 1, G 2, · · ·, G T } Snapshot of network: G k = {V k, E k } Document Stream: D = {D 1,D 2, · · ·, D T } Topic: θ Event: Θ E = {θ E 0, θ E 1, θ E 2,· · ·, θ E T } Interest: H k = {h k (1), h k (2), · · ·, h k (N)} 6

Problem Definition Event-related information in a social community: – An observed stream of network structures – An observed stream of text documents – A latent stream of topics about the event – A latent stream of interests 7

The General Model Task is cast as the inference of previous H k and Θ k : P(H k,Θ k |G k, D k, H k−1 ) Assumption 1: Current interest status H k is independent of the document collection D k Assumption 2: Current topic model θ k is independent of the network structure G k and the previous interest status H k−1 8

From the assumptions: P(H k,Θ k |G k,D k,H k−1 ) = P(H k |G k,H k−1 ) · P(Θ k |H k,D k ) The General Model Interest Model Topic Model 9

The Interest Model Modelled as a Gibbs Random Field on the network G k Uses specially designed potential functions Uses weighting scheme motivated by real world networks 10

The Topic Model Models historical interest status and relationships on the network. Allows the topics and popularity of the events to mutually influence each other over time. P(Θ k |H k,D k ) ∝ P(D k |H k,Θ k ) P(Θ k |H k ) 11

Connection to Existing Models Special cases of PET under certain conditions. The State Automation Model: – When the network effect is omitted The Contagion Model – When the topic effect is omitted 12

Complexity Analysis PLSA (Probabilistic Latent Semantic Analysis): O((N +M)mt) PET: O(NMmT) N documents involving t topics with M words, m rounds and time T. Reasonable. 13

Experiments JonK: State automation model. First Baseline. Cont: The contagion model. Second Baseline. PET- : PET minus network structures. BOM: Box Office Earning. Gold Standard for movie-related events. GInt: Google Insight. Gold Standard for news related events. 14

Experiments Twitter – 5000 users – 1,438,826 tweets – From Oct 2009 to Jan 2010 – Events: 2 movies (Avatar, Twilight) 2 news events (Tiger Woods affair, Copenhagen Climate Conference) 15

Setup: λ T : Interest model. Weight for historical info. λ A : Interest model. Weight for structural info. μ E : Topic model. λ T = 1 λ A = 3 μ E = 1 Experiments 16

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Result Analysis PET has the best performance. Cont has the worst performance. JonK generally performs well, but less accurate than PET. 19

Network Diffusion Analysis Cont can’t tell the difference between interest levels. Both PET and PET– are able to catch the rising trend of popularity. PET is still superior. 20

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Events Analysis on DBLP For popular events, PET generates: – More accurate trends – smoother diffusion – meaningful content evolution 23

Future Work Apply this model to track evolution of ideas, scientific innovation. Real-time event search system.

Conclusion A novel approach. Experimental evidence is convincing. Complexity might be a reason of concern.

Thank you. Questions?