Social Software: What, Why and How

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Social SoftwareWhat, Why and How?

• Künstliche Intelligenz II
• 21.10.2008

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History

• 1940s Vannevar Bush developed Memex concept,
  essentially a personal computer, considered to be
  a supplement to a persons memory, and wrote
  about mesh of associative trails running through
  them encyclopedias and other documents what
  we call hypertext today
• The chemist, struggling with the synthesis of an
  organic compound, has all the chemical literature
  before him in his laboratory, with trails
  following the analogies of compounds, and side
  trails to their physical and chemical behavior
  And his trails do not fade. Several years later,
  his talk with a friend turns to the queer ways in
  which a people resist innovations, even of vital
  interest"

3
1960s

• ARPANET, commercial time-sharing systems, and
  ultimately developed the Internet
• Development of an early hypermedia system by ARPA
  researcher, Doug Englebart, inspired by Bush's
  vision
• First successful implementation of hypertext
  (though term not created until later)
• Also mouse and first on-screen teleconference
  developed

4
1970s 80s

• Office automation products
• Electronic Information Exchange System first
  major development of collaboration software,
  similar functions to an early bulletin board
  system
• Groupware defined as "intentional group processes
  plus software to support them." Peter and Trudy
  Johnson-Lenz
• Computer-Supported Collaborative (or Cooperative)
  Work in mid-1980s

5
1990s and 2000s

• Groupware into common use but more for group
  tools such as Lotus Notes and Microsoft Exchange
  Server and Outlook rather than tools that allow
  groups to establish
• First uses of Social Software as a term but
  inconsistently applied

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Now What is It?

• Many people have differing opinions about what a
  good definition is ?
• Generally, software allowing group interactions
• Features
• Support for conversational interaction between
  individuals or groups
• Support for social feedback
• Support for social networks

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What?

• David Weinberger (quoted in 2) First, I consider
  social software actually to be emergent social
  software. That narrows the field to software that
  enables groups to form and organize
  themselves....
• Clay Shirky3 any software that supports group
  communications
• Wikipedia4 Social software enables people to
  rendezvous, connect or collaborate through
  computer-mediated communication and to form
  online communities.

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Early Types

• Sociable media has been around since the
  beginning of the Internet.5
• Email, especially with cc option
• Bulletin Board Systems
• Usenet
• Chatrooms
• MUDs4 (Multi-User Dungeon or Domain or Dimension)
  online computer game
• MOOs4 (MUD object oriented) text-based online
  virtual reality systems with multiple users
  connected at the same time

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Current Types

• Email
• Used for basic one-to-one or one-to-many
  communications
• Most common and well-known form of communication
• Chat
• Duo or group communication
• Used widely to facilitate group discussions in
  online courses
• May be used to facilitate group discussions for
  face-to-face classes
• Also common and well-known
• Useful as it normally provides a transcript for
  later use

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Instant Messaging

• Several systems available (AOL, ICQ etc.)
• Common for social interactions, less common
  currently in classes than chat
• Student learning groups and professors may use it
  for quick communications
• Can also now be useful for groups and some
  systems archive

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Wikis

• Collaborative website that is easy to update
• Means quick in Hawaiian quick to update
  webpages
• Free software, web-based

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What is a Wiki?

• Best known wiki Wikipedia
• Uses open source wiki software (MediaWiki)
• Typical wiki
• Anyone can edit any entry many contributors
• Log in to make changes
• Discussion page for all content

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(No Transcript)
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Features of Wikis

• Easy to update no HTML needed
• Collaborative can identify who made what changes
• Version control can restore older versions and
  see history of changes
• Discussion feature can discuss a page in a
  discussion area

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Anatomy of a Wiki
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Wikis in the Workplace

• Exposure to wiki technology and content is good
  for students resumes
• Used in many offices for team projects
  collaboration on anything from general project
  management to specifics like problem-solving
  product-design difficulties
• Growing belief in collaboration tools as
  effective contributor to innovation and growth
• Remember Employers value teamwork wikis are
  all about teamwork.

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Wikipedia Credibility

• Wikipedia vs. Encyclopedia Britannica in Nature,
  Dec. 2005
• Did Wikipedia really win?
• BBC article about the Nature research
• C Net news article about Britannica's response

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Should Students Cite Wikipedia?

• No clear answer it depends
• Often a great place to start or get background
  (Like most encyclopedias)
• Can be useful for current/emerging technologies
  or new research e.g. downgrading of Pluto to a
  dwarf planet
• Not something that should regularly appear in
  bibliographies (Like most encyclopedias)

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Citizendium

• Wikipedia written by experts and authorities.
• Created as a result of a dispute between two
  Wikipedia founders
• Also created because of Wikipedias credentialism
  problems
• Looks physically like Wikipedia
• _at_8400 articles as of October08 (over 1 million
  on Wikipedia)
• Still about collaboration but no anonymity
• More expensive to maintain and grow than
  Wikipedia.

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Creating Wikis in the Classroom

• Easy to set up a group project
• History can see/assess who is participating
• Built-in discussion area encourages
  participation
• Faculty projects for e.g. course mapping,
  policy documents, etc.
• Group work repositories can view content after
  course has finished
• Group work presented on a wiki is easily shared
  with everyone in the class

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Issues to be Aware of

• Anyone can write to it be careful of abuse
• Privacy when student work is online lock down
  with password
• Need to plan the navigation ahead of time

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Roll your own Wiki

• Mediawiki http//www.mediawiki.org/wiki/MediaWiki
  (need server, same software as Wikipedia)
• PBwiki http//pbwiki.com/
• Wetpaint http//www.wetpaint.com/
• Wikispaces http//www.wikispaces.com/
• SeedWiki http//www.seedwiki.com/
• Zoho Wiki http//wiki.zoho.com

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Blogs

• Short for weblog
• Many say they began as online calendars Example
  NCSAs Whats New
• Blogs as personal journals began in late 1990s
• Surge of blogs in early 2000 thanks to new
  software that makes them easy to publish

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Blogs Features Content
Author Blogger Entry Post

• Dated/time-stamped posts in reverse chronological
  order, emphasis on currency
• Often allow for comments by readers
• Often include links, photographs, audio clips,
  etc. that interest blogger
• Commonly refer to or post from other blogs

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• Title of blog

• Address of blog

• Link that interests blogger

• Refers to another blog
• Date / time stamp
• Comment feature

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Blogs post accessibility

• Calendar of posts--highlights date if post exists
• Permalinks--each post has permanent address
• Archives--depends on frequency of posts
• Additional features specific to blog--e.g.
  documents, glossary, recipe archive, etc.

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• Title of post

• Photograph as part of post

• Calendar of posts

• Link to a previous post (permalink)

• Special features

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Blogs topics
Blogs can be about anything politics, sex,
baseball, haiku, car repair. There are blogs
about blogs. --Lev Grossman, Time

• Blog topics can be specific or general
• Many topics and in numerous fields
• Personal, political, academic, legal, business
• Even best of blogs sites
• Example Technorati
• http//technorati.com/

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Effects
Bloggers are having an effect on traditional
journalism, doggedly fact-checking newspaper and
TV reports. --Mike Wendland, Detroit Free Press

• Source of news for many
• Many newspaper journalists have blogs
• Bloggers treated as press during political party
  conventions
• Blogs as form of citizen journalism
• Example CBS/George Bush National Guard story
• Commercial uses influence
• Example Album sales increase due to blog
  exposure
• Example Jonathan Schwartz of Sun Microsystems

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Public blogs issues to consider

• Audience purpose--making it worth reading
• Content--who will be responsible for it?
• Currency
• Maintenance
• Participation--is public allowed to comment?
• Software hosting--free and low cost available

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Why create an internal blog?

• Encourages communication
• Ease of posting/commenting reduces technology
  barriers
• Opens communication from and to all staff from
  the bottom up
• Encourages sharing and discussion between
  departments
• Can be a place for collaboration on projects or
  events

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Internal blogs continued

• Manages information
• Offers central place for staff info/library news
• Presentation of posts/comments more readable than
  e-mail
• Archiving and search features superior to those
  of e-mail
• Content organized and accessible in a variety of
  ways

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Blogs are...

• Having a strong impact on the way that many
  people experience information
• Easy to create maintain, thanks to software
• As varied in content presentation as web sites
• Can play an important part in communication at
  your library--either externally or internally

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Social Software Characteristics

• Successful pieces of Social Software tend to
  have
• Identity
• Presence
• Relationships
• Conversations
• Groups
• Reputation
• Sharing

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The Science of Social Networks
or, how I almost know a lot of famous people

• Kentaro Toyama
• Microsoft Research India
• Indian Institute of Science September 19, 2005

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Outline

• Small Worlds
• Random Graphs
• Alpha and Beta
• Power Laws
• Six Degrees of Separation

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Outline

• Small Worlds
• Random Graphs
• Alpha and Beta
• Power Laws
• Six Degrees of Separation

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Trying to make friends
Kentaro
39
Trying to make friends
Bash
Microsoft
Kentaro
40
Trying to make friends
Bash
Microsoft
Asha
Kentaro
Ranjeet
41
Trying to make friends
Bash
Microsoft
Asha
Kentaro
Ranjeet
Sharad
Yale
New York City
Ranjeet and I already had a friend in common!
42
I didnt have to worry
Bash
Kentaro
Sharad
Anandan
Venkie
Karishma
Maithreyi
Soumya
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Its a small world after all!
Bash
Kentaro
Ranjeet
Sharad
Prof. McDermott
Anandan
Prof. Sastry
Prof. Veni
Prof. Kannan
Prof. Balki
Venkie
Ravis Father
Karishma
Ravi
Pres. Kalam
Prof. Prahalad
Pawan
Maithreyi
Prof. Jhunjhunwala
Aishwarya
Soumya
PM Manmohan Singh
Dr. Isher Judge Ahluwalia
Amitabh Bachchan
Dr. Montek Singh Ahluwalia
Nandana Sen
Prof. Amartya Sen
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Society as a Graph
People are represented as nodes.
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Society as a Graph
People are represented as nodes. Relationships
are represented as edges. (Relationships may be
acquaintanceship, friendship, co-authorship,
etc.)
46
Society as a Graph
People are represented as nodes. Relationships
are represented as edges. (Relationships may be
acquaintanceship, friendship, co-authorship,
etc.) Allows analysis using tools of
mathematical graph theory
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The Kevin Bacon Game

• Invented by Albright College students in 1994
• Craig Fass, Brian Turtle, Mike Ginelly
• Goal Connect any actor to Kevin Bacon, by
  linking actors who have acted in the same movie.
• Oracle of Bacon website uses Internet Movie
  Database (IMDB.com) to find shortest link between
  any two actors
• http//oracleofbacon.org/

Boxed version of the Kevin Bacon Game
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The Kevin Bacon Game
An Example

• Kevin Bacon

Mystic River (2003)
Tim Robbins
Code 46 (2003)
Om Puri
Yuva (2004)
Rani Mukherjee
Black (2005)
Amitabh Bachchan
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The Kevin Bacon Game

• Total of actors in database 550,000
• Average path length to Kevin 2.79
• Actor closest to center Rod Steiger (2.53)
• Rank of Kevin, in closeness to center 876th
• Most actors are within three links of each other!

Center of Hollywood?
50
Not Quite the Kevin Bacon Game

• Kevin Bacon

Cavedweller (2004)
Aidan Quinn
Looking for Richard (1996)
Kevin Spacey
Bringing Down the House (2004)
Ben Mezrich
Roommates in college (1991)
Kentaro Toyama
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Erdos Number

• Number of links required to connect scholars to
  Erdos, via co-authorship of papers
• Erdos wrote 1500 papers with 507 co-authors.
• Jerry Grossmans (Oakland Univ.) website allows
  mathematicians to compute their Erdos numbers
• http//www.oakland.edu/enp/
• Connecting path lengths, among mathematicians
  only
• average is 4.65
• maximum is 13

Paul Erdos (1913-1996)
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Erdos Number
An Example

• Paul Erdos

Alon, N., P. Erdos, D. Gunderson and M. Molloy
(2002). On a Ramsey-type Problem. J. Graph Th.
40, 120-129.
Mike Molloy
Achlioptas, D. and M. Molloy (1999). Almost All
Graphs with 2.522 n Edges are not 3-Colourable.
Electronic J. Comb. (6), R29.
Dimitris Achlioptas
Achlioptas, D., F. McSherry and B. Schoelkopf.
Sampling Techniques for Kernel Methods. NIPS
2001, pages 335-342.
Bernard Schoelkopf
Romdhani, S., P. Torr, B. Schoelkopf, and A.
Blake (2001). Computationally efficient face
detection. In Proc. Intl. Conf. Computer Vision,
pp. 695-700.
Andrew Blake
Toyama, K. and A. Blake (2002). Probabilistic
tracking with exemplars in a metric space.
International Journal of Computer Vision.
48(1)9-19.
Kentaro Toyama
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Outline

• Small Worlds
• Random Graphs
• Alpha and Beta
• Power Laws
• Six Degrees of Separation

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Random Graphs
N 12
Erdos and Renyi (1959)
p 0.0 k 0

• N nodes
• A pair of nodes has probability p of being
  connected.
• Average degree, k pN
• What interesting things can be said for different
  values of p or k ?
• (that are true as N ? 8)

p 0.09 k 1
p 1.0 k ½N2
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Random Graphs
Erdos and Renyi (1959)
p 0.0 k 0
p 0.09 k 1
p 0.045 k 0.5
Lets look at
p 1.0 k ½N2
Size of the largest connected cluster
Diameter (maximum path length between nodes) of
the largest cluster
Average path length between nodes (if a path
exists)
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Random Graphs
Erdos and Renyi (1959)
p 0.0 k 0
p 0.09 k 1
p 1.0 k ½N2
p 0.045 k 0.5
Size of largest component
1
5
11
12
Diameter of largest component
4
0
7
1
Average path length between nodes
0.0
2.0
1.0
4.2
57
Random Graphs
Erdos and Renyi (1959)
Percentage of nodes in largest component Diameter
of largest component (not to scale)

• If k lt 1
• small, isolated clusters
• small diameters
• short path lengths
• At k 1
• a giant component appears
• diameter peaks
• path lengths are high
• For k gt 1
• almost all nodes connected
• diameter shrinks
• path lengths shorten

1.0
0
1.0
k
phase transition
58
Random Graphs
Erdos and Renyi (1959)

• What does this mean?
• If connections between people can be modeled as a
  random graph, then
• Because the average person easily knows more than
  one person (k gtgt 1),
• We live in a small world where within a few
  links, we are connected to anyone in the world.
• Erdos and Renyi showed that average
• path length between connected nodes is

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Random Graphs
Erdos and Renyi (1959)

• What does this mean?
• If connections between people can be modeled as a
  random graph, then
• Because the average person easily knows more than
  one person (k gtgt 1),
• We live in a small world where within a few
  links, we are connected to anyone in the world.
• Erdos and Renyi computed average
• path length between connected nodes to be

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Outline

• Small Worlds
• Random Graphs
• Alpha and Beta
• Power Laws
• Six Degrees of Separation

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The Alpha Model
Watts (1999)

• The people you know arent randomly chosen.
• People tend to get to know those who are two
  links away (Rapoport , 1957).
• The real world exhibits a lot of clustering.

The Personal Map by MSR Redmonds Social
Computing Group
Same Anatol Rapoport, known for TIT FOR TAT!
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The Alpha Model
Watts (1999)

• a model Add edges to nodes, as in random
  graphs, but makes links more likely when two
  nodes have a common friend.
• For a range of a values
• The world is small (average path length is
  short), and
• Groups tend to form (high clustering
  coefficient).

Probability of linkage as a function of number of
mutual friends (a is 0 in upper left, 1 in
diagonal, and 8 in bottom right curves.)
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The Alpha Model
Watts (1999)

• a model Add edges to nodes, as in random
  graphs, but makes links more likely when two
  nodes have a common friend.
• For a range of a values
• The world is small (average path length is
  short), and
• Groups tend to form (high clustering
  coefficient).

a
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The Beta Model
Watts and Strogatz (1998)
b 0
b 0.125
b 1
People know others at random. Not clustered, but
small world
People know their neighbors, and a few distant
people. Clustered and small world
People know their neighbors. Clustered,
but not a small world
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The Beta Model
Jonathan Donner
Kentaro Toyama
Watts and Strogatz (1998)
Nobuyuki Hanaki

• First five random links reduce the average path
  length of the network by half, regardless of N!
• Both a and b models reproduce short-path results
  of random graphs, but also allow for clustering.
• Small-world phenomena occur at threshold between
  order and chaos.

Clustering coefficient / Normalized path length
Clustering coefficient (C) and average path
length (L) plotted against b
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Outline

• Small Worlds
• Random Graphs
• Alpha and Beta
• Power Laws
• Six Degrees of Separation

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Power Laws
Albert and Barabasi (1999)

• Whats the degree (number of edges) distribution
  over a graph, for real-world graphs?
• Random-graph model results in Poisson
  distribution.
• But, many real-world networks exhibit a power-law
  distribution.

Degree distribution of a random graph, N 10,000
p 0.0015 k 15. (Curve is a Poisson curve,
for comparison.)
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Power Laws
Albert and Barabasi (1999)

• Whats the degree (number of edges) distribution
  over a graph, for real-world graphs?
• Random-graph model results in Poisson
  distribution.
• But, many real-world networks exhibit a power-law
  distribution.

Typical shape of a power-law distribution.
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Power Laws
Albert and Barabasi (1999)

• Power-law distributions are straight lines in
  log-log space.
• How should random graphs be generated to create a
  power-law distribution of node degrees?
• Hint
• Paretos Law Wealth distribution follows a
  power law.

Power laws in real networks (a) WWW
hyperlinks (b) co-starring in movies (c)
co-authorship of physicists (d) co-authorship of
neuroscientists
Same Velfredo Pareto, who defined Pareto
optimality in game theory.
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Power Laws
Albert and Barabasi (1999)

• The rich get richer!
• Power-law distribution of node distribution
  arises if
• Number of nodes grow
• Edges are added in proportion to the number of
  edges a node already has.
• Additional variable fitness coefficient allows
  for some nodes to grow faster than others.

Map of the Internet poster
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Outline

• Small Worlds
• Random Graphs
• Alpha and Beta
• Power Laws
• Searchable Networks
• Six Degrees of Separation

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Six Degrees of Separation
Milgram (1967)

• The experiment
• Random people from Nebraska were to send a letter
  (via intermediaries) to a stock broker in Boston.
• Could only send to someone with whom they were on
  a first-name basis.
• Among the letters that found the target, the
  average number of links was six.

Stanley Milgram (1933-1984)
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Applications of Network Theory

• World Wide Web and hyperlink structure
• The Internet and router connectivity
• Collaborations among
• Movie actors
• Scientists and mathematicians
• Cellular networks in biology
• Food webs in ecology
• Phone call patterns
• Word co-occurrence in text
• Neural network connectivity of flatworms
• Conformational states in protein folding

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References

• 2Stowe Boyd. 2003. Are You Ready for Social
  Software?http//www.darwinmag.com/read/050103/soc
  ial.html
• 3Shirky, Clay. 2003. Social Software and the
  Politics of Groups. http//www.shirky.com/writings
  /group_politics.html
• 4Wikipedia. http//en.wikipedia.org/wiki/Main_Page
  
• 5Danah Boyd. 2004. Autistic Social Software.
  Supernova Conference 2004. http//www.danah.org/pa
  pers/Supernova2004.html

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Credits
Albert, Reka and A.-L. Barabasi. Statistical
mechanics of complex networks. Reviews of Modern
Physics, 74(1)47-94. (2002) Barabasi,
Albert-Laszlo. Linked. Plume Publishing.
(2003) Kleinberg, Jon M. Navigation in a small
world. Science, 406845. (2000) Watts, Duncan.
Six Degrees The Science of a Connected Age. W.
W. Norton Co. (2003)