The Collaborative Organization of Knowledge D. Spinellis and P. Louridas Strong Regularities in Online Peer Production D. Wilkinson

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The Collaborative Organization of KnowledgeD.
Spinellis and P. LouridasStrong Regularities in
Online Peer ProductionD. Wilkinson
Harvard University

• Ziyad Aljarboua
• Monday, November 10, 2008

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Intro - Wikipedia

• Free multilingual encyclopedia launched in 2001
• Operated by the non-profit Wikimedia Foundation
• Contains 2,610,291 articles in English and 10
  million in total
• 236 active language editions
• Content written by volunteers

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Intro - Wikipedia

• Developed by Jimmy Wales and Larry Sanger
• Times 2006 list of the worlds most influential
  people
• Largest and most popular general reference work
  on the internet. Wikipedia

Source Wikipedia
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Intro - Wikipedia

• No formal peer-review and changes take effect
  immediately
• New articles are created by registered users but
  can be edited by anyone
• Redistribution, creation of derivative works and
  commercial use of content is permitted
• 25,000 to 60,000 page request per second
• 50 of traffic to Wikipedia comes from Google

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Intro - Wikipedia
Source Wikipedia
Wikipedia contributors by country
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Intro - Wikipedia
Source Wikipedia
Article Count from Jan, 2001 to Sep 2007
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Wikipedia - Concerns

• Michael Scott from the office"Wikipedia is the
  best thing ever. Anyone in the world can write
  anything they want about any subject, so you know
  you are getting the best possible information".
• Quality of articles undermined
• Bias Content reflects contributors interest

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Wikipedia - vandalism
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The Collaborative Organization of Knowledge

• Attempts to study Wikipedias growth how human
  knowledge is recorded and organized through an
  open collaborative process (in Wikipedia)
• Examines relationship between existing and
  referenced nonexistent articles
• How existing entries foster development of new
  entries?

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The Collaborative Organization of Knowledge

• Examines the recorded evolutionary development of
  Wikipedia's structure through article revisions
  and contributions
• Motivation Wikipedias coverage has not declined
  while its scope sharply increased.

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Growth

• Technologies and open participation policy behind
  rapid growth
• Edit with no prior authorization
• Edit history for all pages
• Watchlist for users to alerts them for changes in
  their selected pages
• Ability to revert changes if page is vandalized
• Ability to lock entries against revisions
• Easiness to link to other articles
• Categorizing articles using markup tags

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The Study

• Study processed all material on Wikipedia as of
  February of 2006 (485GB worth of xml documents)
• examined all recorded changes (28.2 million
  revisions on 1.9 million pages) and how entries
  were created and linked

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General findings

• Reverting is returning page to previous version
  most of the time to undo vandalism
• 4 of article revisions were reverts
• Average time to revert a vandalized page is 13
  hours
• 11 of pages that were reverted at least once had
  been vandalized at least once
• Most reverted and revised George W. Bush with
  28,000 revisions (29,300 reverts and vandalism)
• 2,441 entries (0.13) locked
• 20 of articles were stubs

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Conclusion 1

• Creation of new Wikipedia entries is not a random
  process but is related to the references to
  nonexistent articles
• what drives Wikipedia growth is the inclusion of
  red links, ie references to articles that do not
  exist yet. Wikipedia

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Conclusion 1
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Conclusion 1
Mena number of references to a nonexistent
article raised exponentially until the article
was created. Once article is created, mean rises
linearly or levels.
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Inflationary/deflationary hypothesis

• Inflationary hypothesis number of links to
  nonexistent articles increase at a higher rate
  than that of the new article creation
• Wikipedia is located in a midpoint between the
  two scenarios (thin coverage vs. decline in
  growth rate)

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Wikipedia growth
Incomplete include nonexistent articles and stubs
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Wikipedia growth

• Between 2003 and 2006, number of entries
  increased from 140,000 to 1.4 million and ration
  of complete/incomplete remained roughly the same
• Growth of Wikipedia partly attributed to
  splitting of articles (depth in articles
  translate into breadth)
• Rate of article creation vs rate of knowledge
  expansion ?

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Wikipedia content

• Process of adding new articles that depends on
  current nonexistent referenced articles leads to
  content balance
• Articles are more likely to be written because
  they are popular (have many references leading to
  them) that because contributor is interested
• Are not most references originating from an
  articles will link to an article similar in
  subject? (assumes knowledge is a fully connected
  graph)

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Finding 1

• Process of referencing an nonexistent article and
  subsequent definition of that article seemed to
  be a collaborative effort.
• The person who referenced a nonexistent article
  and the person who started the referenced article
  was the same in only 3 of the cases
• Wikipedia growth is limited by number of
  contributors not individual contributors!

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Conclusion 2

• Wikipedia is a scale-free network

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Scale-Free Network

• Degree of a node number of connections to other
  nodes
• Degree distribution probability distribution of
  degrees over entire network
• For degree j
• P(j) nodes with degree j / nodes
• Fraction of nodes with degree j to all nodes

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Scale-Free Network

• A network where degree distribution follows a
  power law
• i.e. degree distribution approaches 1/js as j
  increases
• Fraction of nodes with degree j decreases as j
  (number of connections) increases

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Scale-Free network
Source Wikipedia
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Building the network

• Models explaining why Wikipedia is scale-free
• Power laws result of an optimization process
• Power laws result of growth model (preferential
  attachment model)
• Simple network
• Wikipedia
• Expected reference

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Building the network
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• Strong Regularities in Online Peer ProductionD.
  Wilkinson

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Introduction

• Open source software development, blogs, wikis,
  social networks
• Some of most visited website and continue to
  grow
• Online peer production share common macroscopic
  properties?

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Objective

• Describe strong macroscopic regularities in
  peoples contributions to PPS (distribution of
  user participation and activity per topic)
• Examine basic dynamical rules guiding evolution
  of PPS
• Why distribution of levels of user participation
  is power law?
• Not a psychological analysis of contributors

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Methodology

• Examines 4 different PPS Wikipedia, Bugzilla,
  Digg, Essembly
• Data analyzed are exhaustive involves all users
  and contributions

System Time span Users Topics contributions
Wikipedia 6y, 10m 5.07M 1.5M 50M
Bugzilla 6y, 7m 111K 357k 3.08M
Digg 3y 1.05M 3.57M 105M
Essembly 1y, 4m 12.04K 24.9K 1.31M
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PPSs

• Wikipedia
• Essembly social network for individuals to
  discuss and vote on political matters and
  organize to take action
• Bugzilla bug-tracking system where developers
  report and collaborate to fix bugs
• Digg news aggregator

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User Participation

• Power law distribution few dedicated members
  account for most activity
• Focus on inactive users (generality)
• of Inactive
• Wikipedia 71 of editors
• Bugzilla 95 of commentors
• Digg 61 of voters 56 of submitters
• Essembly 83 of voters 53 submitters
• Inactive
• Digg Essembly 3 months
• Wikipedia bugzilla 6 months

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User Participation
Essembly Votes Digg Votes
Essembly Resolves Bugzilla comments
Wikipedia edits Digg submissions
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User Contributions

• Power law exponent is strongly related to the
  systems barrier to contribution (cost of
  contributions)
• Both active and inactive users have distribution
  of contributions that follows a power law

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Participation Momentum

• When people stop participating?

• Momentum associated with users participation
• Probability of stop is inversely proportional to
  of contributions

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Participation Momentum
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Exponent Significance

• Probability to contribute proportional to
  contribution cost (exponent)
• Power law exponent reflects cost to make a
  contribution

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User Participation

• Distribution of count of all users
  (activeinactive) also follows power law but with
  smaller exponent

Inactive users
All users
Inactive users
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Activity per topic

• contributions/topic. (edits/article)
• Popular topics attract more users ? more edits.
• Results
• Distribution of contributions/topic is lognormal
• Lognormal mean and variance depend linearly on
  time for topics where novelty decay is not a
  factor
• Contributions to a topic increases its visibility
  and popularity.

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Activity per Topic

• Contributions ? popularity ? more contributions
  (multiplicative reinforcement mechanism)

Wikipedia Essembly
Digg
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Activity per Topic
Number of articles
Number of resolves
Log(number of edits)
Log(number of votes)
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Activity per Topic

• Variance and mean depend linearly on age (t) of
  topic

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Popularity factor interface design

• Digg vs. Essembly vs. Wikipedia
• Small number of topics attracts vast majority of
  contributions (long-tail log dist. plots)

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Discussion

• How size of a group coactively working together
  affect results?

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Sources

• Wikipedia
• D. Spinellis and P. Louridas, The Collaborative
  Organization of Knowledge
• D. Wilkinson, Strong Regularities in Online Peer
  Production