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Implicit Structure and Dynamics of Blogspace. Lada Adamic Accelerating Change 2004 (joint work with: Eytan Adar, Li Zhang, and Rajan Lukose). Blogs and the digital experience. Use: Record real-world and virtual experiences Easy to record and discuss things “seen” on the net - PowerPoint PPT Presentation
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© 2004 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice
Implicit Structure and Dynamics of Blogspace
Lada AdamicAccelerating Change 2004
(joint work with: Eytan Adar, Li Zhang, and Rajan Lukose)
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
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
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
For Example…
Pop
ula
rity
Time
Slashdot EffectSlashdot Effect
BoingBoing EffectBoingBoing Effect
6
Clusters reflect different epidemic profiles
5 10 15 200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
day
% o
f hits
5 10 15 200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
day
% o
f hits
Slashdot huge surge followed by sharp drop
(slashdot-effect)
Major News – front page
More delayed death (broader interest)
7
Clusters
5 10 15 200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
day
% o
f hits
5 10 15 200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
day
% o
f hits
Products, etc.
Sustained over a period of time
Major-news site (editorial content) – back of the paper
8
Microscale Example: Giant Microbes
9
Microscale Dynamics• What do we need track specific epidemics?
− Timings− Graphs
b1b1
Time of infectiont0 t1
b2b2
b3b3
10
Microscale Dynamics
• Challenges− Root may be unknown− Multiple possible paths− Uncrawled space, alternate media (email, voice)− No links
b1b1
Time of infectiont0 t1
b2b2
b3b3
??
bnbn
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
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
Giant Microbes epidemic visualization
via link explicit link inferred link blog
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iRank• “Practical” uses of inferred epidemic
information− Can use a simpler inference (timing)
• Finding good sources− Invisible authorities b1b1
b2b2
b3b3 b4b4 b5b5 bnbn…
True source
Popular site
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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 infectiont0 t1
16
Do Bloggers Kill Kittens?
Friday morning Wired writes:
"Warning: Blogs Can Be Infectious.”
Shortly thereafter Slashdot posts:
"Bloggers' Plagiarism Scientifically Proven"
Which is picked up by Metafilter as "A good amount of bloggers are outright thieves."
17
Research at the Information Dynamics Lab at HP:
http://www.hpl.hp.com/research/idl
