the ties that bind: social network principles in online communities
DESCRIPTION
The ties that bind: Social network principles in online communities. Alan Fco . Diaz Hernandez Prof. Dr. Eduard Heindl. Content. First part -Keywords -Introduction -Background -Network theory and social capital. Second part -Slashdot -Model and hypothesis -User conduct - PowerPoint PPT PresentationTRANSCRIPT
The ties that bind: Social network principles in online communities.
Alan Fco. Diaz Hernandez
Prof. Dr. Eduard Heindl
ContentFirst part
-Keywords-Introduction -Background-Network theory and
social capital
Second part -Slashdot -Model and hypothesis -User conduct-Hypothesis 1,2,3,4 -Research design and
data-Results-Conclusions
What is a SOCIAL NETWORK?
Keywords. Online communities.
Social Capital.
Structural Holes.
Reputation Systems .
Web 2.0
Ronald Stuart Burt
Introduction. Web 2.0 (Wikipedia, Facebook, Slashdot).
The client is faceless.
Online social networks had become a parallel world to many people.
Social network theory's. Online social
networks.
BrokerageClosure.
Background.
Can a online social network which is not much more than a network be considered an organization?
Aristoteles.Granovetter.
Ouchi.
Network theory and social capital.
Social Network Social capital
Online Social networks. ex. TWITTER
Network Theory.
Burt Theory of social capital in network by focusing on the presence or absence of structural holes.
BROKERAGE vs. CLOSURE
CLOSURE vs. BROKERAGE.
Studies (Brokerage). Burt The social capital of French and American
managers.
Zaheer y bell Benefiting from network position: firm capabilities, structural holes, and performance.
Studies (Closure). Ashleight y Nandhakumar Trust and
technologies.
So which one it´s better????
Closure Brokerage
Second partStudy for the site Slashdot.
Site which provides news of technology founded in 1997.
How it works?
What´s “KARMA”.
2002Online social network.
Model and hypotheses The relationship between network structure
and social capital.
Social capital KARMA Brokerage High between ness/low
constraint.
Closure Low between ness/High constraint.
Users conduct Constraint Between-ness
Research design and data. 6000 users with over 200,000
relationships.
Standard regression of several variables like: comments, friend ratio, foe ratio and karma.
Using UCINET.
Results.
Results. Respond Hypothesis 1.
Results. Respond Hypothesis 2.
Results. Respond Hypothesis 3.
Results. Respond Hypothesis 4.
Conclusion Structural Holes have an important role
in a social network.
Brokerage lower levels of karma. Closure higher levels of karma.
Based on advertising.
Conclusion
High Karma Lower Karma
Questions?
Results
How is karma generated?.
Hypothesis 1.A.-Most participants of the site will exhibit both
low between-ness and low constraint.
B.-There will be more participants with high constraint measures than with high between-ness measures.
C.-There will be few individuals who score highly in both constraint and between-ness.
Hypothesis 2.A.-High between-ness and high constraint are
individually associated with high social capital.
B.-High between-ness and high constraint are jointly associated with high social capital.
C.-High constraint is more associated with high social capital than is high between-ness.
Hypothesis 3.A.-Between-ness is inversely related to
participation intensity.
B.-Constraint is directly related to participation intensity.
C.-Network investment moderates the relationship between both between-ness and constraint and social capital.
Hypothesis 4.A.-Positive outcomes from between-ness are
more significant to those with high social capital.
B.-Positive outcomes from constraint are more significant to those with low social capital.