2004 hewlett-packard development company, l.p. the information contained herein is subject to change...
DESCRIPTION
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 patternsTRANSCRIPT
© 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)
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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
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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
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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
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For Example…
Popularity
Time
Slashdot Effect
BoingBoing Effect
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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 pageMore delayed death (broader interest)
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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
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Microscale Example: Giant Microbes
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Microscale Dynamics• What do we need track specific epidemics?
− Timings− Graphs
b1
Time of infectiont0 t1
b2
b3
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Microscale Dynamics• Challenges
− Root may be unknown− Multiple possible paths− Uncrawled space, alternate media (email, voice)− No links
b1
Time of infectiont0 t1
b2
b3
??
bn
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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
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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.
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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 b1
b2
b3 b4 b5 bn…
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
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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."
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Research at the Information Dynamics Lab at HP:
http://www.hpl.hp.com/research/idl