Computer Games: Learning, Meaning and Method (London Knowledge Lab, January 2007)

1. Making e-friends and influencing
people in Second Life
Aleks Krotoski
University of Surrey
SPERI

3. What I’ll talk about
• Interpersonal relationships in cyberspace
• How I measure relationships in Second
Life
• How relationships are defined

4. Before I get ahead of myself
• The differences between online and offline:
– Anonymity
– Physical appearance
– Physical proximity
– Greater transience (more weak ties)
– Absence of social cues
• So how can we expect community to grow?

5. Online community I
• In traditional definitions of “community”, there’d be no
such thing in cyberspace
– Tied to place
– To misquote AOL ads, how can you fall for someone you’ve
never met?
• But we know that’s not true
– Chatrooms, forums, MySpace, Craig’s List, London Memorial
• These virtual worlds are the places which the online
communities are tied to

6. Online Communities I (cont)
• Transient and formal communities
– London Memorial in the virtual world 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. Online community II
• Form for the same reasons offline communities do:
– Make friends, provide motivation, offer support, meet like-minded others
• Whatever role trust plays in offline communities, it plays in
online communities because these interactions are human-bound
• What we know about online relationships
– Proximity and frequency of contact
– Similarity
– Self-presentation
– Reciprocity & self-disclosure
– Consistency
• Perpetuity: don’t mess with the orc if you’ve already PO’d the governor.

8. Trust in virtual communities I: we’re all
in it together
• Returning to Anonymity
– Perceived similarity (levelling the
playing field)
– No social cues, so lots of
uncertainty
– Expectations of openness and
honesty engenders a culture of
mutual sharing
• Relevant Social
Psychological dimension of
trust
– Similarity of goals and values
– Expectations of future interaction

9. Trust in virtual worlds III: Rep (cont)
• 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)
• And speaking of social networking applications, the
same principles work in-world too
• Finally, 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

10. How measure friendships?
Social Network Analysis
…studies social
relationships as a series
of interconnected
webs.
…focuses on inter-
relationships rather
than individuals’
attributes

11. 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?

13. N (respondents) =
33
N (total network)
Results
= 650

14. Picking apart communication network
closeness
• But what does it
mean in Second
Life if someone
in this
community is
rated “close” or
“distant”?

15. 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.

16. 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.

17. In Sum
• Closeness has implications for influence and persuasion,
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

18. Thank you!
Aleks Krotoski (Mynci Gorky)
A.Krotoski@surrey.ac.uk