1. © 2004 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice Implicit Structure and Dynamics of Blogspace Lada Adamic Accelerating Change 2004 (joint work with: Eytan Adar, Li Zhang, and Rajan Lukose)

2. 2 Blogs and the digital experience Use: −Record real-world and virtual experiences −Easy to record and discuss things “seen” on the net Structure: blog-to-blog linking Use + Structure −Great to track “memes”: ideas spreading in the blogosphere like an epidemic

3. 3 Our interest Macroscopic patterns of blog epidemics −How does the popularity of a topic evolve over time? Microscopic patterns of blog epidemics −Implicit & Explicit −Who is getting information from whom? Ranking algorithms that take advantage of infection patterns

4. 4 Tracking Blogs Blogdex: Earliest example −Lets you see which blogs (and when) linked to a site −Others emerged with similar/related functionality Can find epidemic profiles (popularity over time) Our question: do different types of information have different epidemic profiles

5. 5 For Example… Popularity Time Slashdot Effect BoingBoing Effect

6. 6 Clusters reflect different epidemic profiles Slashdot huge surge followed by sharp drop (slashdot- effect) Major News – front page More delayed death (broader interest)

7. 7 Clusters Products, etc. Sustained over a period of time Major-news site (editorial content) – back of the paper

8. 8 Microscale Example: Giant Microbes

9. 9 Microscale Dynamics What do we need track specific epidemics? −Timings −Graphs b1b1 b1b1 Time of infection t0t0 t1t1 b2b2 b2b2 b3b3 b3b3

10. 10 Microscale Dynamics Challenges −Root may be unknown −Multiple possible paths −Uncrawled space, alternate media (email, voice) −No links b1b1 b1b1 Time of infection t0t0 t1t1 b2b2 b2b2 b3b3 b3b3 ? ? bnbn bnbn

11. 11 Microscale Dynamics who is getting info from whom Explicit blog to blog links (easy) −Via links are even better Implicit/Inferred transfer (harder) −Use ML algorithm for link inference problem Support Vector Machine (SVM) Logistic Regression −What we can use Full text Blogs in common Links in common History of infection

12. 12 Visualization Zoomgraph tool −Using GraphViz (by AT&T) layouts Simple algorithm −If single, explicit link exists, draw it −Otherwise use ML algorithm Pick the most likely explicit link Pick the most likely possible link Tool lets you zoom around space, control threshold, link types, etc.

13. 13 Giant Microbes epidemic visualization via link explicit link inferred linkblog

14. 14 iRank “Practical” uses of inferred epidemic information −Can use a simpler inference (timing) Finding good sources −Invisible authorities b1b1 b1b1 b2b2 b2b2 b3b3 b3b3 b4b4 b4b4 b5b5 b5b5 bnbn bnbn … True source Popular site

15. 15 iRank Algorithm Draw a weighted edge for all pairs of blogs that cite the same URL higher weight for mentions closer together run PageRank control for ‘spam’ Time of infection t0t0 t1t1

16. 16 Do Bloggers Kill Kittens? Friday morning Wired writes:Wired "Warning: Blogs Can Be Infectious.” Shortly thereafter Slashdot posts:Slashdot "Bloggers' Plagiarism Scientifically Proven" Which is picked up by Metafilter asMetafilter "A good amount of bloggers are outright thieves."

17. 17 Research at the Information Dynamics Lab at HP: http://www.hpl.hp.com/research/idl ladamic@hpl.hp.com