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Social Networks in Virtual Worlds

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presented 22 March 2007 at Massively Multi-Learner, The Higher Education Academy

presented 22 March 2007 at Massively Multi-Learner, The Higher Education Academy

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    • 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)
      • 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
    • 22. 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
    • 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: [email_address]
      • W: http://www.toaskid.com
      • SL: Social Simulation Research Lab, Hyperborea

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