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TU Berlin Deutsche Telekom Lab. Flash Floods and Ripples:. Meeyoung Cha. Juan A. Navarro Max Planck Institute for Software Systems (MPI-SWS). Hamed Haddadi. The Spread of Media Content through the Blogosphere . ICWSM Data Challenge 2009. Motivation. How does content spread in blogs?

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the spread of media content through the blogosphere

TU Berlin Deutsche Telekom Lab

Flash Floods and Ripples:

Meeyoung Cha

Juan A. Navarro

Max Planck Institute for Software Systems (MPI-SWS)

Hamed Haddadi

The Spread of Media Content through the Blogosphere

ICWSM Data Challenge 2009

motivation
Motivation

How does content spread in blogs?

What kinds of content are shared?

  • Blogs play a significant role in today’s Internet culture
  • Blogs are used for information propagation purposes
    • Discuss political issues
    • Review new products and online contents
    • Form communities and special interest groups
  • Increasingly, media content is shared through blogs
our goal
Our goal
  • Characterizehow the structure of the blogosphere influences the patterns of content spreading
  • 1. Understand the structure of the blogosphere
    • Is the structure ideal for content dissemination?
  • 2. Understand the spreading patterns of content
    • What types of content spread?
    • How quickly does content spread?
spinn3r dataset
Spinn3r dataset
  • Extracted post URL, site, host, language, timestamps, etc.
    • Step1: Focus on top 15 blog domains
    • Step2: Scrape content to find embedded HTML links
    • Code available at http://www.mpi-sws.org/~jnavarro/tools/
  • Limitations
    • Comments and blogrolls missing
    • Some blogs only post summaries
    • Only used dataset with numbered ‘tiers’
step2 extracting html links
Step2: Extracting HTML links

Links tomedia content

Links toother blogs

network of blogs
Network of blogs

Directed network of 85,013 nodes and 129,079 edges

A

B

network structure
Network structure

[ 73% of blogs in the largest connected component ]

Average node degree 1.5

Power-law degree distribution

6% of links are reciprocal

35% of links cross blog domains

7% of links cross language boundaries

network structure 2
Network structure – 2

Network structure is more sparse than social networks

Density = Ratio of observed links, out of all possible links

insights for information propagation
Insights for information propagation
  • Sparse structure & power-law degree distribution
    • Clear preference for bloggers to particular topics or sources
    • Trend setters (high in-degree) and recommenders (high out-degree)
  • Potential factors that can limit spreading
      • Blog domains had no visible effect on linking
      • Language barriers inhibit the flow of information
spreading of media content
Spreading of media content

media

  • What types of content are shared?
  • How quickly does information spread?
types of content shared
Types of content shared

Popular sharing of user-generated content

popularity of youtube videos
Popularity of YouTube videos
  • Video popularity follows a power-law distribution:
  • Very large diffusion processes exist
  • Preferential attachment may drive linking
popular video categories
Popular video categories

Musicmost popular

Still spread!

Keen onpolitics

We downloaded metadata of top 10,000 videos

example spreading pattern
Example spreading pattern

Blogs linking the same video are connected

= Diffusion through the blogosphere

Other

McCain’s political campaignlinked by 79 blogs

insights from spreading patterns
Insights from spreading patterns
  • Videos in different genres spread with very different patterns
    • Flash floods: found quickly and spread rapidly
    • Ripples: took longer to spread, re-discovered years after upload
  • Diffusion through links in the blogosphere
    • 24% of videos had any spreading in the blog graph
    • Other spreading factors: featuring and search
conclusion
Conclusion
  • Identified spreading patterns and factors that limit spreading
  • Blogs serve as a medium to filter and spread media content
  • Potential implication: Recommendation systems can take into account and exploit different spreading patterns
  • Future work: spreading patterns of other types of content
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