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DMLData Mining Lab
A Computa1onal Framework for Social Capital in Online Communi1es
Ma#hew Smithh#p://m.smithworx.com
Department of Computer Scienceh#p://dml.cs.byu.edu
Brigham Young University
PhD DissertaDon Defense, April 2011
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IntroducDon
Online communi+es increasingly important
Prevalent shi6 in how people discover informa+on
Large social networks are dynamic and complex
Rich social network data is becoming available
Social capital within these networks is poorly understood
Computa(onal Social Science
2
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Social Capital Framework
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Social Capital
The value of social networks. More specifically,
Bonding similar people together (Putnam)
Bridging diverse people together (Putnam)
Access to and use of resources embedded in social networks (Lin)
4
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Social Capital
Rela(onships
Affini(es
Social Resources
5
Social Capital
Burt, Portes
Putnam, Coleman
Lin, Bourdieu
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RelaDonships
Explicit Link
Direct knowledge, interac1on, or communica1on
Ex. behavioral interacDon, evaluaDon of one by another (e.g., friends, web links), formal (e.g., co-‐worker), biological (e.g., sibling)
Implicit Link
Inherent similari1es or affini1es
Ex. a#ributes, hobbies, interests, background, etc.6
A B
BA
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Explicit Social Network (ESN)
7
C
DE
B
A
I am a friend to C and A
I am a friend to B
I am a friend to A and D
I am an acquaintance to EI am a friend to D and an
acquaintance to A
F I am a friend to no one
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Implicit Affinity Network (IAN)
8
C
DE
B
A
I’m studying computers
I’m studying sociology and computers
I’m studying sociology
We are entrepreneurs studying accoun1ngF
Smith, M., Giraud-‐Carrier, C. and Judkins, B. (2007). Implicit Affinity Networks. In Proceedings of the Seventeenth Annual Workshop on InformaDon Technologies and Systems, 1-‐6.
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Hybrid Network
9
C
DE
B
A
F
bonding
poten1al bonding
bridging
poten1al bridging (between F and A)
Smith, M., Giraud-‐Carrier, C., and Purser, N. (2009). Implicit Affinity Networks and Social Capital. InformaDon Technology and Management, 10(2-‐3):123-‐134, September.
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RelaDonships
Directed
Strength
Ini,aliza,on: Behavioral interac+on, evalua+on of one person by another, formal rela+onships, biological rela+onships; base on affini+es
Dynamic: interac+ons, +me decay
10
ji
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AffiniDes
The set of aJributes exposed by individual j
The affinity strength between individuals i and j
11
Jaccard Index(set similarity)
S ijIAN
ji
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Resources
Social resource: a specific asset, material or symbolic, available through social connec+ons within a network
Characteris,cs: (e.g., exhaus+ble, returnable, quan+fiable, durable)
Value: assigned by the individual (o`en dependent on characterisDcs)
Possessed or Sought
12
i j
r
abc
abc
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Social Resource Examples
Material Goods
land, houses, car, and money
Symbolic Goods
educa+on, memberships in clubs, honorific degrees, nobility or organiza+onal +tles, family name, reputa+on, or fame
From: Lin 2001, Social Capital: A Theory of Social Structure and Ac,on 13
A
C
D
B
E
F
G
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Access to Resources
14
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Access to Resources
15
iv r = .4
.9
= .2
.4
.8
.5
.4
k
j
i
r
S ijESN
rr
Who has access
to this resource?
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Access to Resources
16
iv r = .4
.9
= .2
.4
.8
.5
.4
k
j
i
r
S ijESN
rr
Only individual k
has access
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Social Capital ComputaDon
Resources available to individual j from individual i
Individual Social capital
17
Context Vector
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Bonding and Bridging Social Capital
Bonding and Bridging
Individual Network
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Bonding and Bridging Social Capital
Bonding and Bridging
Individual Network
19
Note: used as surrogate when
resources are not available
“It is a very interes<ng implementa<on of the s
ocial capital
ideas, especially bridging and bon
ding. I have long thought
that the social capital research fie
ld would be immensely
aided if some of it could be formalized in terms of network
theory, and this is the best job of
that that I've seen so far.”
-‐ Robert Putnam (personal corres
pondence)
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InteracDons
Interac(on: a purposeful exchange between individuals
How is it perceived? (+, -‐, or neutral)
Is a resource involved?
Who to interact with?
20
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InteracDons
Generalized form
Instan+ated examples (determined by j’s goals)
21
Get sought a`er resources
as quickly as possible
Seek resources in a
priority order
Seek resources from who
has many and is most
similar to you
Resources sought by jResources possessed by i
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Dynamic InteracDon FuncDon
22
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NetLogoImplementa1on
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Simula1on Community & defectors
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PublicaDons (Framework)
Implicit Affinity Networks (published in Proceedings of the 17th Annual Workshop on Informa=on Technologies and Systems)
Implicit Affinity Networks and Social Capital (published in Informa=on Technology and Management -‐ Journal)
Measuring and Reasoning about Social Capital (submiTed to Social Networks, 2011)
25
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Case Studies
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Blogosphere
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Blogosphere Experiment
Focus
Blogger communi+es centered around topics
Started with Scoble’s blog
Data
13 million blog en+res, 38K+ blogs
July 2006 -‐ July 2007 (1 year)
28
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Hybrid Network -‐ Blogosphere
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Twi-er
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Twi#er Experiment
Leverages the Framework to empirically test:
“Bonding is more likely to occur than bridging” -‐ Lin 01
“Closure is the most obvious force” -‐ Burt 05
Homophily principle: “Birds of a feather flock together”
Following users with whom the most affini=es are shared (i.e., aJemp=ng to bond) produces more follow-‐backs (i.e., bonding) than other following strategies.
Smith-‐Lovin 87 “Similarity begets friendship”
31
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Results
32
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Follow-‐backs Retained
33
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Language Acquisi8on
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Experimental Setup
Study abroad in Japan
204 par+cipants
Spent an average of 8.4 months in Japan, taking 13.2 hours per week of Japanese language courses in 38 language programs across 22 different ci+es
Language improvement measured (pre-‐test and post-‐test)
Offline community
35
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Experiment Details
Study abroad social interac+on ques+onnaire (SASIQ)
Friends and topics about which they spoke were listed
Bonding and bridging scores were computed
36
par+cipant friends
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Results
37
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PublicaDons (Case Studies)
Social Capital in the Blogosphere: A Case Study (published in Papers from the AAAI Spring Symposium on Social Informa=on Processing)
Bonding vs. Bridging Social Capital: A Case Study in TwiJer (published in Interna=onal Symposium on Social Intelligence and Networking)
Iden+fying Health-‐Related Topics on TwiJer (published at Interna=onal Conference on Social Compu=ng, Behavioral-‐Cultural Modeling, & Predic=on)
Social Capital and Language Acquisi+on during Study Abroad (published in The 33rd Annual Conference of the Cogni=ve Science Society)
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Conclusion
Case Study Contribu8ons
Blogosphere -‐ Highlighted poten8al bonding/bridging
Twi-er -‐ Tested and confirmed principle of homophily
Public Health -‐ Increased understanding of tobacco-‐related behavior in Twi-er
Language Acquisi8on -‐ Bridging rela8onships achieve higher levels of language improvement
39
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Conclusion
Computa(onal Framework for Social Capital
Unifies mul8ple defini8ons of social capital into general framework
Uses rela8onships, affini8es, and resources
Hybrid networks -‐ dis8nguishing poten8al and realized social capital
Bonding and bridging metrics vary independently (Putnam)
Supports access to and mobiliza=on of resources
No8on of interac=on formalized
Offers bridge between resource-‐sharing sites and social networks
40
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Comparison of SCFto Popular Applica1ons
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Future Work
Perform addi8onal resource simula8ons with mul8ple types of individuals within the network (e.g., producers and consumers, altruists and free-‐riders).
Map prac8cal selec8on func8ons to specific tasks (e.g., finding a job, obtaining support, learning a new skill).
U8lize prior social exchange theory to seed reasonable hypotheses to then be empirically tested within the framework. Perhaps, suitable individual-‐level exchange func8ons could be developed.
Design a range of rela8onship strength upda8ng func8ons that allow external feedback (e.g., holidays, weather, crisis) to affect changes.
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Future Work
Explore the economics of automa8cally adjus8ng resource values based on the supply and demand within the network.
Test addi8onal well-‐developed theories of the social sciences to confirm validity within the context of specific online communi8es (e.g., can bonding social capital on Twi-er be leveraged to lose weight?)
Observe and track socially connected communi8es that ac8vely exchange resources.
Many of these ideas are finding their way into local startups (e.g., kalood, stubtopia)
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