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

2. How does content spread in blogs? What kinds of content are shared?
Motivation
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
How does content spread in blogs?
What kinds of content are shared?

3. Our goal
Characterize how 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?

4. Part1. Measurement methodology
Part2. Analysis of network properties
Part3. Analysis of spreading patterns

5. 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
Limitations
Comments and blogrolls missing
Some blogs only post summaries
Only used dataset with numbered ‘tiers’

6. Step1: Top 15 blog sites # blogs # posts posts/blog Language 390,812
1,217,757
3.1
English
321,730
1,161,103
3.6
Chinese
254,225
1,666,165
6.6
72,376
1,127,383
15.6
Japanese
66,598
2,120,474
31.8 …
Total
1,196,412
8,794,983
7.4
English

7. Step2: Extracting HTML links
Links to media content
Links to other blogs

8. Part1. Measurement methodology
Part2. Analysis of network properties
Part3. Analysis of spreading patterns

9. Directed network of 85,013 nodes and 129,079 edges
Network of blogs
Directed network of 85,013 nodes and 129,079 edges A B

10. Network structure 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
[ 73% of blogs in the largest connected component ]

11. Network structure is more sparse than social networks
Density = Ratio of observed links, out of all possible links
Network structure is more sparse than social networks

12. 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

13. Part1. Measurement methodology
Part2. Analysis of network properties
Part3. Analysis of spreading patterns

14. Spreading of media content
What types of content are shared?
How quickly does information spread?
media

15. Types of content shared
Popular sharing of user-generated content
Rank
Website
# posts 1
youtube.com
206,803 2
photobucket.com
140,194 3
flickr.com
135,327 4
imageshack.us
41,997 5
amazon.com
36,379 6
nytimes.com
33,801 7
twitter.com
30,572 8
technorati.com
27,583 9
tinypic.com
23,899 10
bbc.co.uk
20,893

16. Popularity of YouTube videos
Video popularity follows a power-law distribution:
Very large diffusion processes exist
Preferential attachment may drive linking

17. Popular video categories
We downloaded metadata of top 10,000 videos
Music most popular
Category
% of links
% of videos
Music
27.2
19.6
Taken down
19.9
22.3
News & Politics
18.4
23.5
Comedy
9.7
8.9
Entertainment
8.0
8.8
Film & Animation
3.8
4.7
People & Blogs
2.5
2.9
Science & Technology
2.4
1.5
Pets & Animals
1.7
1.2
Education
1.4
0.9
Still spread!
Keen on politics

18. Time lag in the spread of videos
Median video age
2 days
72 days
125 days
357 days
Flash floods
Ripples

19. Example spreading pattern
Other
Blogs linking the same video are connected
= Diffusion through the blogosphere
McCain’s political campaign linked by 79 blogs

20. 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

21. Part1. Measurement methodology
Part2. Analysis of network properties
Part3. Analysis of spreading patterns

22. 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