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Discovery of Blog Communities based on Mutual Awareness

This research explores how blog communities form based on mutual awareness among bloggers and presents an analytical framework for community extraction using performance metrics and experimental results.

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Discovery of Blog Communities based on Mutual Awareness

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  1. Discovery of Blog Communities based on Mutual Awareness Yu-Ru Lin, Hari Sundaram, YunChi, Jun Tatemura, Belle Tseng [3rd Annual Workshop on the Weblogging Ecosystems] Advisor: Dr. Koh Jia-Ling Reporter: Che-Wei, Liang Date: 2008/06/19

  2. Outline • Introduction • Analytical framework • Community formation • Extracting blog communities • Performance metrics • Experiment

  3. Introduction • Blog • Become prominent social media • Enable users to publish content quickly • Blog communities are different from traditional web communities • Formed based on mutual awareness

  4. Introduction • Mutual awareness • Individual bloggers become aware of each other’s presence through interaction (e.g., Comments, trackback). • No blogger is aware of others’ s actions Not communities!

  5. Introduction • New approach to community extraction Two steps: (a) Analysis of mutual awareness from bloggers’ actions. (b) Ranking-based community extraction from mutual awareness.

  6. Analytical framework (1/3) • Community emerges through individual bloggers’ behavior • Individual bloggers are aware of each other’s presence through interaction (Bi-directional property) • Community needs to sustain over time

  7. Analytical framework (2/3) • Properties of blogs 1. Temporal dynamics: Blogs represent easily editable content. 2. Event Locality:A typical blog entry is time sensitive. 3. Link SemanticsA hyperlink can have different semantics. 4. Community CentricPeople that interested in each others’ content

  8. Analytical framework (3/3) • Community extraction problem • Distinct from traditional ranking problems on the web. • Key difference is the semantics of the hyperlinked structure. Hub Authority

  9. Community Formation • Acts of bloggers in the blogosphere 1. Surf/read 2. Create entries 3. Comment 4. Change blogroll

  10. Extracting blog communities • Extracting blog communities 1. Computing Mutual Awareness 2. Ranking-Based Clustering Method

  11. Computing Mutual Awareness (1/7) • Mutual awareness is affected by • Type of action • Number of actions for each type • When the action occurred • Mutual awareness depends on sustained actions

  12. Computing Mutual Awareness (2/7) • Graph G = (V, E), nodes = bloggers, edge = connection of nodes, wij= weight on edges is a function of mutual awareness • Mutual awareness matrix M • A weighted linear combination of action matrices

  13. Computing Mutual Awareness (3/7) • For each action type k at time t, compute Temporal action matrix Xk,t • Each entry xij,k,t of matrix represents the number of times the kth action ak was performed by blogger i on blogger j. • e.g. Blogger i leaves a comment on blogger j’s entry.

  14. Computing Mutual Awareness (4/7) C action type 1:yellow action type 2: blue action type 3:red X1,t =X2,t =

  15. Computing Mutual Awareness (5/7) • Effect of actions to mutual awareness diminish gradually. (λkisdecayingfactorfortheactiontypek)

  16. Computing Mutual Awareness (6/7) • Two specific types of actions (a) create an entry-to-entry link (mij=0 if mij < λm) (b) send a trackback (r denotes how likely the trackback receiver is to be aware of the trackback sender by the action of sending a trackback)

  17. Computing Mutual Awareness (7/7) • Fusion of Actions • Assume actions ak in the action set are independent of each other.

  18. Ranking-Based Clustering Method • Start from highly ranked blogs to extract dense subgraphs that include popular bloggers 1. Choose seeds based on PageRank (rt is a vector of ranking score r, α is damping factor) 2. Find community members associated with seeds 3. Iterate 1 and 2 to discover N communities Exclude blogs in the existing communities

  19. Performance Metrics • Coverage • measures the fraction of edges that are intra-community • Higher coverage have higher quality • Conductance • Small conductance has higher equality

  20. Performance Metrics • Interest Coefficient • Measure how much a community member is interested in his or her assigned community. • Interest coefficient of an individual blogger m toward an assigned cluster Ck • Interest coefficient of a cluster Ck

  21. Performance Metrics • Sustainability • Average sustainability for a set C of k extracted communities

  22. Experimental Results • NEC Blog Dataset • 127,467 entries for a period of 25 consecutive weeks • 584 seed blogs • 40,877 links • 2,898 trackback links

  23. Experimental Results • Compare the communities extracted by using two different features • Baseline adjacency matrix (wij = # entry to entry links) • Mutual awareness matrix (wij = mutual awareness score)

  24. Experimental Results • Experimental design

  25. Experimental Results

  26. Experimental Results • Sustainability of communities

  27. Experimental Results • Use content of blog entries to validate communities red: trackback link blue: entry-to entry links

  28. Experimental Results

  29. Experimental Results • WWW 2006 Blog Workshop Dataset • 8.37 million entries • 1.43 million blog sites (Narrow down to 122K)

  30. Experimental Results

  31. Experimental Results

  32. Experimental Results

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