implicit structure and dynamics of blogspace
DESCRIPTION
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 PresentationTRANSCRIPT
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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)
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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…
Pop
ula
rity
Time
Slashdot EffectSlashdot Effect
BoingBoing EffectBoingBoing Effect
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Clusters reflect different epidemic profiles
5 10 15 200
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% o
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Slashdot huge surge followed by sharp drop
(slashdot-effect)
Major News – front page
More delayed death (broader interest)
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Clusters
5 10 15 200
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f hits
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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
b1b1
Time of infectiont0 t1
b2b2
b3b3
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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
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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 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
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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