studying social selection vs social influence in virtual ... · assumption of stochastic...
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Chang Heon Lee
June 13, 2013
Studying Social Selection vs Social Influence in Virtual Financial Communities
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Dynamics of Networks and Behavior
Selection vs Influence
Studying Selection vs Influence
Stochastic Actor-oriented Model
Advice Network
Data
Estimation Results
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Outline
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Dynamics of Networks and Behavior
Social network dynamics depends on individual behavioral characteristics. Homophily vs. Heterophily
But individuals’ behavior can depend on the network. Assimilation vs. Differentiation
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Selection vs Influence
Selection Individuals make changes to their social ties as a result
of the behavior or characteristics of the ego, the alter, and the dyad.
Influence Individuals’ behavior changes as a function of
interaction with alters.
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Studying Selection vs Influence
How can we separate cause and effect?
Net(tn) Structural Effects
Behavioral Effects
Net(tn+1)
Beh(tn) Beh(tn+1 )
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Stochastic Actor-oriented Model
Assumption of stochastic actor-oriented models
Network actors drive the process: individual decisions; • decisions about network selection or termination • decisions about own behavior
Longitudinal versions of Exponential Random Graph Model (ERGM)
Assumption: network change driven by change in tie variables
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Stochastic Actor-oriented Model-cont.
( , )i k ikkf x s x
Network micro step
individual decisions options - change tie variable to one other actor - or change nothing
maximize an objective function with respect to the network configuration.
The probability that actor i changes his ties variable with j is
1
exp( ( , ( 젨? )( , )
exp( ( , ( 젨? )
iij n
i
k
f x i jp x
f x i j
↝
↝
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Stochastic Actor-oriented Model-cont.
Model Parameters
Estimated from observed data
Stochastic simulation models •Markov Chain Monte Carlo(MCMC) algorithm •Approximate the solution of the Method of Moment
Parameter Estimation
Choose statistics
Obtain parameters such that the expected values of the statistics are equal to the observed values
Expected values are approximated as the averages over a lot of simulated network
Observed values are calculated from the dataset (target values)
↝
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Organizational scholars have framed the advice network in terms of information transmission, knowledge transfer, and joint problem solving.
Simply, stock message board sites include;
Advice Network in Virtual Financial Community
I
K
J
I
K
J
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Data
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Snowball Sample from the largest Australian VFCs
The network consists of 707 active users.
Panel Data
The network data is divided into three successive two-month periods.
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Estimation Results
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[Representation of Selection and Influence Effects]
Baseline Network Structure
Contribution Behavior Changes in Individual Contribution Behavior
Changes in Peer Network
Time 1 Time 2 Structural Effects
Behavioral Tendencies
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Results(1): Structural Effects
Effects Parameter
Estimate Standard Error p-value
Endogenous Effects
Out-degree -2.798 0.287 <.001***
Reciprocity 2.753 0.135 <.001***
Popularity-Alter 0.487 0.073 <.001***
Activity-Alter -0.084 0.022 <.001***
In-In Degree Assortativity 0.057 0.015 <.001***
Out -Out Degree Assortativity -0.035 0.017 <0.05*
In-Out Degree Assortativity 0.085 0.02 0.42
Out-In Degree Assortativity 0.005 0.011 <.001***
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*p <0.05; **p <0.01; ***p <0.001
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Results(2): Selection
Effects Parameter
Estimate
Standard
Error p-value
Contribution Quantity Effects
Contribution Quantity-Alter 0.328 0.127 <.001**
Contribution Quantity -Ego -0.352 0.201 0.080
Contribution Quantity -Similarity 0.443 0.467 0.949
*p <0.05; **p <0.01; ***p <0.001
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Results(2): Selection
Contribution behavior as antecedent to advice network structure
Individuals who are salient in terms of contribution quantity are more likely to be sought as an advice partner for repeated advice exchanges over time.
People don’t select similar others when seeking information.
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Results(3): Influence
Effects Parameter
Estimate
Standard
Error p-value
Rate Function
Rate of Contribution Quantity Change 1 192.130 17.352 <.001***
Rate of Contribution Quantity Change 2 158.051 14.952 <.001***
Rate of Contribution Quantity Change 3 175.543 15.093 <.001***
Linear Shape 0.1312 0.2834 0.463
Quadratic Shape -0.0103 0.0155 0.665
Contribution Quantity- In-degree 0.0096 0.0028 <.001***
Contribution Quantity- Out-degree -0.0094 0.0124 0.758
Contribution Quantity Total Similarity 0.4754 0.2210 <0.05*
*p <0.05; **p <0.01; ***p <0.001
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Results(3): Influence
Contribution behavior as outcome of advice network structure
The greater the number of incoming advice ties to an individual, the higher the quantity of contributions he makes to the community over time. Thus, individuals adjust their level of contribution quantity as a result of their advice tie formation.
But, individuals contribution in terms of the number of postings are likely to become similar to that of other partners.
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Conclusions
Influence rather than Selection
Individual adjust their level of contribution to that
their peers (patterns of assimilation).
There is no patterns of homophily.