Social Networks in virtual worlds

Aleks KrotoskiUniversity 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

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 ritual

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?

N=675

• 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)

y β0 (Std. Error)

β (Std. Error)

σ2e

Loglikelihood (fixed model LL)

Prototypicality 0.026 (0.101)

0.305 (0.066)

0.543 (0.035)

1292.354T (1335.299)

Credibility -0.093 (0.102)

0.519 (0.071)

0.531 (0.035)

1272.354T

(1404.954)

Social Comparison -0.098 (0.118)

0.399 (0.064)

0.408 (0.027)

987.966T

(1132.416)

General Trust -0.135 (0.098)

0.645 (0.064)

0.408 (0.027)

1114.31T

(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.

Single explanatory variable: General Trust & SNC

categoriesExplanatory Variable

β0 (Std. Error)

β (Std. Error)

σ2e Loglikelihood

(fixed model LL)

Online Public Communication

0.085 (0.093)

0.370 (0.052)

0.476 (0.031)

1124.182T

(1345.777)

Online Private Communication

0.070 (0.094)

0.442 (0.062)

0.407 (0.027)

1115.396T

(1345.777)

Offline Communication

0.070 (0.090)

0.459 (0.047)

0.427 (0.028)

1159.681T

(1345.777)N=539; σ2

e: 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.

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: A.Krotoski@surrey.ac.ukW: http://www.toaskid.com

SL: Social Simulation Research Lab, Hyperborea