Modeling the Spread of Influence on the Blogosphere

1. Modeling the Spread of Influence on the Blogosphere Akshay Java, Pranam Kolari, Tim Finin, and Tim Oates UMBC Tech Report 04/12/06

2. Outline • What is influence? • Basic Influence Model • Influence models for the blogosphere • Results • Conclusions

3. What is Influence? Main Entry: in·flu·encePronunciation: 'in-"flü-&n(t)s, esp Southern in-'Function: nounEtymology: Middle English, from Middle French, from Medieval Latin influentia, from Latin influent-, influens, present participle of influere to flow in, from in- + fluere to flow -- more at FLUID1 a: an ethereal fluid held to flow from the stars and to affect the actions of humans b: an emanation of occult power held to derive from stars2: an emanation of spiritual or moral force3 a:the act or power of producing an effect without apparent exertion of force or direct exercise of commandb: corrupt interference with authority for personal gain4 : the power or capacity of causing an effect in indirect or intangible ways : SWAY5: one that exerts influence- under the influence: affected by alcohol : DRUNK <was arrested for driving under the influence> NOT This Kind of Influence! ;-)

4. Motivation • Influence models studied for cocitation graphs • David Kempe, Jon Kleinberg, Eva TardosMaximizing the Spread of Influence through a Social Network, KDD 2003 • Applies to blogs also. • Recent Examples: Startups, Microsoft Origami, Walmart,DoD • GOAL: Predict influential blogs • Target nodes to help achieve a “Tipping Point”* * The Tipping Point: Malcolm Gladwell

5. Influence on the Blogosphere Post was Influenced by NPR, eWeek

6. Influence Models for the Blogosphere Blog Graph Influence Graph 1/3 U 2 2 1 3 3 2/5 1/3 V 1/3 1 1 1 1/5 5 5 2/5 4 4 1/2 1/2 Wu,v = Cu,v / dv U links to V => U is Influenced by V

7. Basic Influence Models Influence Graph • Linear Threshold Model Σ bvw ≥ θv w is the active neighbor of v • Cascade Model Pvw- probability with which a node can activate each of its neighbors, independent of history. 1/3 Active 2 1 3 2/5 1/3 θv 1/3 1 1 1/5 5 2/5 Active 4 Inactive 1/2 1/2

8. Node Selection Heuristics • Inlinks • Easily spammed • Centrality • Expensive to compute for every large graphs • PageRank • Requires link information • However, is easy to compute • Greedy Heuristic • Computationally expensive • However performs better

9. Effect of Splogs on Node Selection(indegree vs pagerank) Almost 54% of the links were from splogs/failed to splogs/failed!

10. Effect of Splogs on Inlinks Tightly Knit Community of Splog

11. Influence Models(without splog detection) Number of nodes selected

12. Influence Models (After splog removal)

13. Influence Models(w.r.t. Technorati Ranks)

14. Conlusions • Influence models can be applied to blogs not just cocitation graphs • Splogs are a problem • Greedy heuristics work well, pagerank is an inexpensive approximation

15. Ideas for CIKM 06 • Good or bad influence? Associating sentiment with links. • Finding influential blogs for a topic. (SVM accuracy 75-85%) • Community structure of blogs.

16. Questions • Comments/ Feedback? • Thanks! • Acknowledgement: • Buzzmetrics/Blogpulse for the dataset.