Blog Community Discovery and Evolution

Transcription

1 Blog Community Discovery and Evolution Mutual Awareness, Interactions and Community Stories Yu-Ru Lin, Hari Sundaram, Yun Chi, Junichi Tatemura and Belle Tseng What do people feel about Hurricane Katrina? What do people think about global warming? What is the best school district in Manhattan? How do teenagers like the movie transformer? amateurs expert reviewers Jun Jul Aug Sep semi-professional shooters well-heeled Intelligence 2007 April 22,

2 Research Scope Goal: extract query-sensitive communities and their dynamics from blog networks What is a community? How does a community form? How does a community change? Approach: Observation: mutual awareness Community discovery using iterative clustering Community dynamics via temporal correlation Result: communities for query of hurricane Intelligence 2007 April 22, Talk Outline Motivation and Goal Related Work Community Discovery Community Dynamics Experiments Summary and Conclusions 2

3 Related Work prior work our work Online social network dynamics Burstyevolution of links [Kumar 2005] Dynamic community formation [Backstrom 2006] micro level (individual communities) structural and thematic changes Normalized cut [Shi 2000] Graph clustering Kernel k-means [Dhillon 2005] Interactive spectral clustering [Kanna 2004] clustering criteria: symmetric social distance Community evolution Quantify the social group evolution [Palla 2007] [Falkowski 2006] community correlation based on member interaction Motivation and Goal Related Work Community Discovery Community Dynamics Experiments Summary and Conclusions Mutual Awareness Community Formation Social Distance from Random Walk Extraction Intelligence 2007 April 22,

4 What is a community? Notions of community Virtual / online community [Rheingold 2000]: social aggregations that emerge from the Net when enough people carry on those public discussion long enough, with sufficient human feeling Virtual settlement [Jones 1997]: Interactivity, communicators, virtual common-public-place, sustained membership Sense of community [McMillan 1996]: spirit, trust, trade and art Sense of community among blogs [Blanchard 2004] Focus theory [Feld 1981] Mutual awareness [Dourish 2001]: presence and awareness Communities emerge due to mutual awareness Principle Insight: Mutual Awareness Lisa me 4

5 Community Formation Mutual awareness expansion Lisa transitivity reciprocity frequency Community: me a group of people interacting with each other more closely than with others = mutually observable actions Social Distance me? Lisa Original six degrees of separation experiment [Travers and Milgram 1969] E Expected symmetric social distance 5

6 Social Distance from Random Walk me u transition matrix P=D -1 W W: mutually observable interactions D: digonalmatrix with d ii = j w ij j Lisa? P ij i v τ u v hitting time: expected hops from uto v commute time: τ u v = τ u v + τ u v expected hops from uto v and vto u k 1 τ vol( W ) ( φ ( u) φ ( v)) u v i i i= 2λi Laplancian matrix L=D(I-P)=D-W vol(w)= i,j w ij λ k : the k-th smallest eigenvalue φ k : the k-th smallest eigenvector 2 Ref. [Chung 2000] Extraction Algorithm a set of bloggers S Criteria: expected symmetric social distance S = arg max ω( SV, ) τu v, S V u S v V \ S V S\V weighting for balanced splits Note: the k -thlargest eigenvectors of Pis equivalent to the k-th smallest eigenvectors of L 6

7 Motivation and Goal Related Work Community Discovery Community Dynamics Experiments Summary and Conclusions Interaction based representation Interaction Correlation Evolutionary Intelligence 2007 April 22, Interaction based representation A B? community behave differently due to members interaction members play different roles in a community A B' interaction matrix for community A [ P] ij, if i A x( Ai ;, j) = 0, otherwise N bloggers 7

8 Interaction Correlation A? B' histogram intersection interaction correlation between community A and B s( AB, ') = N N i= 1 j= 1 N N i= 1 j= 1 ( x Ai j x B i j ) min ( ;, ), ( ';, ) ( x Ai j x B i j ) max ( ;, ), ( ';, ) Evolutionary Patterns (c) split time t t+1 Post(Ci) Prior(Cj) Cj C i Cj Prior(C j ) = argmaxs(c i,c j ) Post(C i ) = argmaxs(c i,c j ) interaction correlation 8

9 Motivation and Goal Related Work Community Discovery Community Dynamics Experiments Summary and Conclusions Experimental setup Evaluation Metrics Comparison with Baseline Methods Intelligence 2007 April 22, Experimental setup Dataset: Real world blogs 407 blogs during 63 consecutive weeks (July 10, 2005 September 23, 2006) 0.27M entries, 0.15M entry-entry links Query-sensitive graph Picked keywords related to four significant events: katrina, london bomb, ipod nano, zotob worm 9

10 Evaluation Metrics cohesiveness consistency time C E w E o C i C j m ij m j E w Eb b E b E b E p ij = m ij /m j b conductance= min( E b + E w, E b + E ) entropy o L E 1 w coverage= H( j) = pij logp E + E logl i = 1 Ideal community extraction: low conductance, high coverage, low entropy L: number of communities ij Comparison with Baseline Methods KKM: kernel k-means SPEC: normalized cut ICC: iterative conductance cutting MAE outperforms baseline methods: lower conductance, higher coverage, lower entropy and relatively low variation 10

11 Stories ipod nano Hurricane Katrina Computer worm London bombing Hurricane Katrina communities extracted at week 5 right wing left wing left wing political communities right wing technical communities 8/23 Hurricane Katrina forms 8/29 Leevefailure in New Orleans 9/17 Hurricane Rita forms node size: community size node shade: query relevancy 11

12 London Bombing political communities technical communities ipod nano fan communities 12

13 Motivation and Goal Related Work Community Discovery Community Dynamics Experiments Summary and Intelligence 2007 April 22, Summary Queries involved costly decisions require contemplation on multiple viewpoints Community discovery: mutual awareness Extract communities using symmetric social distance Community dynamics: temporal correlation Extract evolutionary patterns using histogram intersection between interaction matrices Results: Outperforms baseline community detection methods Insightful results for community evolution E 13

14 Conclusions Combining social aspect with graph theory help us discover meaningful communities Tracking community evolution reveals complex picture of multiple viewpoints, which is important for decision making Future work: A unified framework that considers membership consistency and evolutionary relationship An approach for discovering emergent communities and supporting community awareness Validating community analysis through user actions and ethnography study Questions? Yu-Ru.Lin@asu.edu Thanks! 14