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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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Social Networks in Virtual Worlds Social Networks in Virtual Worlds Presentation Transcript

  • 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 ?
  • 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)
    • 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