Social Networks in Virtual Worlds

Social Networks in virtual worlds Aleks Krotoski University of Surrey

Overview
The Social Life of Virtual Worlds
What does it mean to be “ close ”?
Informal learning in virtual worlds
Who teaches who what?
Important Ethical Concerns
In research and in general practice

But before we get ahead of ourselves…
The differences between online and offline :
Anonymity
Physical appearance
Physical proximity
Greater transience (more weak ties)
Absence of social cues

So how can the interactions in cyberspace be meaningful ?
In traditional definitions of “community”, there’d be no such thing in cyberspace
How can you develop meaningful relationships with people you’ve never met ?

It’s been happening for years
These virtual worlds are the places which the online communities are tied to

Places of ritual
London Memorial in Second Life
Between 12-1pm on 7 July 2005, over 150 Second Life residents visited. It was open for 7 days and racked up thousands of visitors
Fewer than 10% claimed any British ties
Maker’s motivations were altruistic and purely community-driven

Places of collaboration
Neualtenburg: an experiment in collective democracy

Places of friendship

So how does it happen ?
The same reasons offline community does:
Make friends , offer support , meet like-minded others
What we know about online relationships:
Proximity and frequency of contact
Similarity
Self-presentation
Reciprocity & self-disclosure
Consistency

Virtual worlds are designed for sociability – people must rely upon one another to survive and advance
Anonymity becomes Pseudonymity
Whatever role trust plays in offline communities, it plays in online communities because these interactions are human-bound

Social Learning Theory
We learn from those around us
We learn from similar others
We adapt these learnings for our own goals
Social norms dictate acceptability

Social Capital
We learn from those we trust
We learn who to trust through reputation

Building reputations
Trust is based upon…
past experience …
… which is either based upon functional goals or pre-existing social relationships…
… or some kind of disinterested third party (e.g., Craig’s List or MySpace)
You Must Comply :
A non-official policing force in a space where an official police is absent
The emphasis is on friendship and dedication to the group
Rejection is cruel

How the heck do you measure this? Social Network Analysis … studies social relationships as a series of interconnected webs. … focuses on inter-relationships rather than individuals’ attributes

SNA offers…
A measure of the social context , as defined by the actors within that context, rather than the researcher
Identification of key people for use as independent variables in social influence assessment
A map of the direction information will spread, including rate and possible barriers

SNA and friendship
Who’s connected with whom?
How closely ?
How many people are they connected with?
Who else is connected to this many people?

Asking personal questions
Surveys
Who do you know ?
Who do you communicate with?
Who do you trust?
Define your relationship:
Who’s trustworthy ? (Poortinga & Pidgeon, 2003; Cvetkovich (1999); Renn & Levine, 1991)
Who’s credible ? (Renn & Levine, 1991)
Who do you compare yourself with? (Lennox & Wolfe, 1984)
Who’s the most prototypical ?

This N=75
But what does it mean if someone’s considered “close” or “distant”?

The micro-network: influence
Density
Position
Role
Direction

Results: Single explanatory variable (General Communication)
The greatest prediction comes from general trust followed by credibility, which is not surprising, as this is proposed in Sherif’s (1981) contact hypothesis.
*N=538; **N=539; σ 2 e : variance accounted for between avatars; T p<0.000, df=2 0.408 (0.027) 0.408 (0.027) 0.531 (0.035) 0.543 (0.035) σ 2 e 1114.31 T (1345.777) 0.645 (0.064) -0.135 (0.098) General Trust 987.966 T (1132.416) 0.399 (0.064) -0.098 (0.118) Social Comparison 1272.354 T (1404.954) 0.519 (0.071) -0.093 (0.102) Credibility 1292.354 T (1335.299) 0.305 (0.066) 0.026 (0.101) Prototypicality Loglikelihood (fixed model LL) β (Std. Error) β 0 (Std. Error) y

Single explanatory variable: General Trust & SNC categories
Effect of interpersonal closeness on mode of communication (e.g., Garton et al , 1997)
Offline communication contributes the most to the estimate of General Trust. Online public communication contributes the least.
N=539; σ 2 e : variance accounted for between avatars; T p<0.000, df=2 0.427 (0.028) 0.407 (0.027) 0.476 (0.031) σ 2 e 1159.681 T (1345.777) 0.459 (0.047) 0.070 (0.090) Offline Communication 1115.396 T (1345.777) 0.442 (0.062) 0.070 (0.094) Online Private Communication 1124.182 T (1345.777) 0.370 (0.052) 0.085 (0.093) Online Public Communication Loglikelihood (fixed model LL) β (Std. Error) β 0 (Std. Error) Explanatory Variable

Spare a thought for ethics
Be transparent
Give something back
Talk to anyone who asks
Follow ethics guidelines (AoIR, UNESCO and others)

In Sum
Closeness has implications for social learning, even in the virtual environment
Virtual communities operate in very similar ways to other communities – both on and offline
They bring together distributed individuals based on common experience, motivations and reputation
This is particularly true for virtual world participants because of the explicit social design of the software
Trust varies according to communication medium
Trust is paramount
Don’t jeopardise that trust.

Thank you!
E: [email_address]
W: http://www.toaskid.com
SL: Social Simulation Research Lab, Hyperborea

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Social Networks in Virtual Worlds

by Aleks Krotoski

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