Information propagation in the flickr social network
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TU Berlin Deutsche Telekom Lab. Meeyoung Cha [email protected] Max Planck Institute for Software Systems With Alan Mislove and Krishna Gummadi . Information propagation in the Flickr social network . Diffusion of innovations.

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Information propagation in the flickr social network l.jpg

TU Berlin Deutsche Telekom Lab

Meeyoung Cha

[email protected]

Max Planck Institute for Software Systems

With Alan Mislove and Krishna Gummadi

Information propagationin the Flickrsocial network


Diffusion of innovations l.jpg
Diffusion of innovations

  • How, why, and at what rate new ideas and technology spread through cultures? [Rogers 1950s]

time


Milgram s experiment l.jpg
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


Slide4 l.jpg

Flickr

YouTube

LiveJournal

Friendster

Orkut

Cyworld

Facebook

MySpace

Delicious

Online social networks bring unique opportunityfor understanding information spreading


Online social networks osn l.jpg
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


Information flow mechanisms l.jpg
Information flow mechanisms

  • Featuring (front page, hotlists)

  • External links

  • Search results

  • Links between content

  • Online social links or word-of-mouth


Two key questions l.jpg
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?


Key challenge gathering the data l.jpg
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?


Methodology l.jpg
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!


Slide10 l.jpg

Part2. How widely dopictures spread?

Part3. How quickly dopictures spread?

Part1. Measurementmethodology


Topological distribution of popularity l.jpg
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?


Test of local and global popularity l.jpg
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


Hotlist from 2 hop neighborhood l.jpg
Hotlist from 2-hop neighborhood

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


Hotlists from 3 4 hop neighborhood l.jpg
Hotlists from 3-4 hop neighborhood

Top-100 photos from 4-hop neighborhoodsoverlap largely with global top-100 photos


Distance from uploaders to fans l.jpg
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


Network structure of fans l.jpg
Network structure of fans

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


Summary of spatial spreading patterns l.jpg
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


Slide18 l.jpg

Part2. How widely dopictures spread?

Part3. How quickly dopictures spread?

Part1. Measurementmethodology


Temporal evolution of photo popularity l.jpg
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


Pattern 1 steady growth l.jpg
Pattern 1: steady-growth

  • Gain new fans at a relatively constant rate

London cycling by lomokev

Linear pattern cannot be explained by existing theories


Pattern 2 growth spike l.jpg
Pattern 2: growth-spike

  • Sudden increase in fans over a short time period

One would. by antimethod


Pattern 3 dormant l.jpg
Pattern 3: dormant

  • Unknown to many users or stop gaining fans

Velcro being pulled apart by Trazy


Pattern across all popular photos l.jpg
Pattern across all popular photos

  • 5,346 photos (>1 year & >100 fans)

Characteristic growth in first few days and constant growth


Pattern across a 2 year period l.jpg
Pattern across a 2-year period

  • 798 photos (>2 years & >100 fans)

Even popular photos spread slowly throughout the network


Slide25 l.jpg

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


Conclusion l.jpg
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



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