Information propagation in the Flickr social network

1. TU Berlin Deutsche Telekom Lab Meeyoung Cha mcha@mpi-sws.mpg.de Max Planck Institute for Software Systems With Alan Mislove and Krishna Gummadi Information propagationin the Flickrsocial network

2. Diffusion of innovations • How, why, and at what rate new ideas and technology spread through cultures? [Rogers 1950s] time

3. Milgram’s Experiment • Through which social links information flow? • 160 people in NebraskaPass letters to a colleague socially close to themTowards a particular stockbroker in Boston [1960s] • It took Six hops to deliver each letter

4. Flickr YouTube LiveJournal Friendster Orkut Cyworld Facebook MySpace Delicious Online social networks bring unique opportunityfor understanding information spreading

5. Online social networks (OSN) • OSN websites are popular • Used for a variety of information propagation purposes • Viral marketing, political campaign, content sharing, launch of movie trailers, product promotions, etc. • In 2007, $12 billion spent on advertisements in OSNs

6. Information flow mechanisms • Featuring (front page, hotlists) • External links • Search results • Links between content • Online social links or word-of-mouth

7. Two key questions • Word-of-mouth expected to spread content widely and quickly throughout the network • 1. How widely does information propagate in social network? Do popular content reach different parts of the network? • 2. How quickly does information spread through the social network? How long does it take for people to find content?

8. Key challenge: Gathering the data • Flickr: Founded in 2004, acquired by Yahoo! in 2005 • The largest photo sharing site • User activities • Make friends • Upload and tag photos • Comment on photos • Mark photos as favorites How does information propagate in Flickr?

9. Methodology • Crawled a substantial fraction of Flickr social network • 2.5M users and 33M friend links(in its largest weakly connected component) Repeated the crawls for 104 consecutive days • Gathered Flickr users’ favorite-marked pictures • 34M bookmarks of 11M distinct photos Largest OSN data analyzed for information flow to date!

10. Part2. How widely dopictures spread? Part3. How quickly dopictures spread? Part1. Measurementmethodology

11. Topological distribution of popularity • Popularity measured as the number of fans or favorite-marks • Three questions • 1. Are globally popular pictures also popular locally? • 2. What is the distance from uploaders of photos to fans? • 3. What does the network of fans look like?

12. Test of local and global popularity • 250 random users • Find top-100 photos (hotlist) within a k-hop neighborhood • Compare local hotlist with global hotlist • Degree of overlap reflects topological correlation in popularity

13. Hotlist from 2-hop neighborhood Photos popular in a local neighborhood (2-hop) different from globally popular ones

14. Hotlists from 3-4 hop neighborhood Top-100 photos from 4-hop neighborhoodsoverlap largely with global top-100 photos

15. Distance from uploaders to fans • Very popular pictures with more than 500 fans 46% fans1-hop away 45% fans2-hop away 9% fans3-hop away uploader High content locality for even popular photos

16. Network structure of fans 72% of pictures (>100 fans) with fans connecting each otherindicating a strong topological correlation

17. Summary of spatial spreading patterns • Strong correlation between topology and content popularity • Difference in local and global hotlists • Concentration of fans around uploaders • Most fans forming a single connected component → Even popular photos do not spread widely in the network

18. Part2. How widely dopictures spread? Part3. How quickly dopictures spread? Part1. Measurementmethodology

19. Temporal evolution of photo popularity • Goal is to understand how quickly pictures obtain fans over time - focus on long-term trends for popular photos • Case study on three popularity growth patterns • Long-term trends in popularity growth

20. Pattern 1: steady-growth • Gain new fans at a relatively constant rate London cycling by lomokev Linear pattern cannot be explained by existing theories

21. Pattern 2: growth-spike • Sudden increase in fans over a short time period One would. by antimethod

22. Pattern 3: dormant • Unknown to many users or stop gaining fans Velcro being pulled apart by Trazy

23. Pattern across all popular photos • 5,346 photos (>1 year & >100 fans) Characteristic growth in first few days and constant growth

24. Pattern across a 2-year period • 798 photos (>2 years & >100 fans) Even popular photos spread slowly throughout the network

25. Summary of temporal growth patterns • Photos become popular with very different patterns • Key patterns: steady-growth, growth-spike, dormant • Contrary to popular expectations about viral marketing, even popular pictures gain fans very slowly

26. Conclusion • Largest scale analysis to investigate the role of OSN in information propagation using real traces • Even popular pictures do not spread widely and quickly • Data analysis shows different patterns from the common expectations about viral marketing • Calls for the better design of social network features that enable full viral speed as suggested in theory

27. http://socialnetworks.mpi-sws.org/