Massively Multi-Learner Conference (The Higher Education Academy, University of Ulster, March 2007)

1. Social Networks in virtual worlds
Aleks Krotoski
University of Surrey

2. 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

3. 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

4. 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?

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

6. 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

7. Places of collaboration
Neualtenburg: an experiment in collective democracy

8. Places of friendship

9. 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

10. • 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

11. 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

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

13. 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

14. 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

15. 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

16. 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?

17. 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?

18. N=675

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

20. The micro-network: influence
• Density
• Position
• Role
• Direction

21. Results: Single explanatory variable
(General Communication)
y β0 (Std. β (Std. σ2 e Loglikelihood
Error) (fixed model LL)
Error)
Prototypicality 0.026 0.305 0.543 1292.354T
(0.101) (0.066) (0.035) (1335.299)
Credibility -0.093 0.519 0.531 1272.354T
(0.102) (0.071) (0.035) (1404.954)
Social Comparison -0.098 0.399 0.408 987.966T
(0.118) (0.064) (0.027) (1132.416)
General Trust -0.135 0.645 0.408 1114.31T
(0.098) (0.064) (0.027) (1345.777)
*N=538; **N=539; σ2e: variance accounted for between avatars; Tp<0.000, df=2
• The greatest prediction comes from general trust followed by
credibility, which is not surprising, as this is proposed in Sherif’s
(1981) contact hypothesis.

22. Single explanatory variable:
General Trust & SNC categories
Explanatory Variable β0 (Std. β (Std. σ2 e Loglikelihood
Error) Error) (fixed model LL)
Online Public 0.085 (0.093) 0.370 0.476 1124.182T
Communication (0.052) (0.031) (1345.777)
Online Private 0.070 (0.094) 0.442 (0.062) 0.407 1115.396T
Communication (0.027) (1345.777)
Offline 0.070 (0.090) 0.459 0.427 1159.681T
Communication (0.047) (0.028) (1345.777)
N=539; σ2e: variance accounted for between avatars; Tp<0.000, df=2
• 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.

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

24. 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.

25. Thank you!
E: A.Krotoski@surrey.ac.uk
W: http://www.toaskid.com
SL: Social Simulation Research Lab, Hyperborea