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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

Published in: Technology

Social Networks in Virtual Worlds

  1. 1. Social Networks in virtual worlds Aleks Krotoski University of Surrey
  2. 2. Overview <ul><li>The Social Life of Virtual Worlds </li></ul><ul><ul><li>What does it mean to be “ close ”? </li></ul></ul><ul><li>Informal learning in virtual worlds </li></ul><ul><ul><li>Who teaches who what? </li></ul></ul><ul><li>Important Ethical Concerns </li></ul><ul><ul><li>In research and in general practice </li></ul></ul>
  3. 3. But before we get ahead of ourselves… <ul><li>The differences between online and offline : </li></ul><ul><ul><li>Anonymity </li></ul></ul><ul><ul><li>Physical appearance </li></ul></ul><ul><ul><li>Physical proximity </li></ul></ul><ul><ul><li>Greater transience (more weak ties) </li></ul></ul><ul><ul><li>Absence of social cues </li></ul></ul>
  4. 4. So how can the interactions in cyberspace be meaningful ? <ul><li>In traditional definitions of “community”, there’d be no such thing in cyberspace </li></ul><ul><ul><li>How can you develop meaningful relationships with people you’ve never met ? </li></ul></ul>
  5. 5. It’s been happening for years <ul><li>These virtual worlds are the places which the online communities are tied to </li></ul>
  6. 6. Places of ritual <ul><li>London Memorial in Second Life </li></ul><ul><ul><li>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 </li></ul></ul><ul><ul><li>Fewer than 10% claimed any British ties </li></ul></ul><ul><ul><li>Maker’s motivations were altruistic and purely community-driven </li></ul></ul>
  7. 7. Places of collaboration <ul><li>Neualtenburg: an experiment in collective democracy </li></ul>
  8. 8. Places of friendship
  9. 9. So how does it happen ? <ul><li>The same reasons offline community does: </li></ul><ul><ul><li>Make friends , offer support , meet like-minded others </li></ul></ul><ul><li>What we know about online relationships: </li></ul><ul><ul><li>Proximity and frequency of contact </li></ul></ul><ul><ul><li>Similarity </li></ul></ul><ul><ul><li>Self-presentation </li></ul></ul><ul><ul><li>Reciprocity & self-disclosure </li></ul></ul><ul><ul><li>Consistency </li></ul></ul>
  10. 10. <ul><li>Virtual worlds are designed for sociability – people must rely upon one another to survive and advance </li></ul><ul><li>Anonymity becomes Pseudonymity </li></ul><ul><li>Whatever role trust plays in offline communities, it plays in online communities because these interactions are human-bound </li></ul>
  11. 11. Social Learning Theory <ul><li>We learn from those around us </li></ul><ul><li>We learn from similar others </li></ul><ul><li>We adapt these learnings for our own goals </li></ul><ul><li>Social norms dictate acceptability </li></ul>
  12. 12. Social Capital <ul><li>We learn from those we trust </li></ul><ul><li>We learn who to trust through reputation </li></ul>
  13. 13. Building reputations <ul><li>Trust is based upon… </li></ul><ul><ul><li>past experience … </li></ul></ul><ul><ul><li>… which is either based upon functional goals or pre-existing social relationships… </li></ul></ul><ul><ul><li>… or some kind of disinterested third party (e.g., Craig’s List or MySpace) </li></ul></ul><ul><li>You Must Comply : </li></ul><ul><ul><li>A non-official policing force in a space where an official police is absent </li></ul></ul><ul><ul><li>The emphasis is on friendship and dedication to the group </li></ul></ul><ul><ul><li>Rejection is cruel </li></ul></ul>
  14. 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. 15. SNA offers… <ul><li>A measure of the social context , as defined by the actors within that context, rather than the researcher </li></ul><ul><li>Identification of key people for use as independent variables in social influence assessment </li></ul><ul><li>A map of the direction information will spread, including rate and possible barriers </li></ul>
  16. 16. SNA and friendship <ul><li>Who’s connected with whom? </li></ul><ul><li>How closely ? </li></ul><ul><li>How many people are they connected with? </li></ul><ul><li>Who else is connected to this many people? </li></ul>
  17. 17. Asking personal questions <ul><li>Surveys </li></ul><ul><ul><li>Who do you know ? </li></ul></ul><ul><ul><ul><li>Who do you communicate with? </li></ul></ul></ul><ul><ul><ul><li>Who do you trust? </li></ul></ul></ul><ul><ul><li>Define your relationship: </li></ul></ul><ul><ul><ul><li>Who’s trustworthy ? (Poortinga & Pidgeon, 2003; Cvetkovich (1999); Renn & Levine, 1991) </li></ul></ul></ul><ul><ul><ul><li>Who’s credible ? (Renn & Levine, 1991) </li></ul></ul></ul><ul><ul><ul><li>Who do you compare yourself with? (Lennox & Wolfe, 1984) </li></ul></ul></ul><ul><ul><ul><li>Who’s the most prototypical ? </li></ul></ul></ul>
  18. 18. N=675
  19. 19. <ul><li>This N=75 </li></ul><ul><li>But what does it mean if someone’s considered “close” or “distant”? </li></ul>
  20. 20. The micro-network: influence <ul><li>Density </li></ul><ul><li>Position </li></ul><ul><li>Role </li></ul><ul><li>Direction </li></ul>
  21. 21. Results: Single explanatory variable (General Communication) <ul><li>The greatest prediction comes from general trust followed by credibility, which is not surprising, as this is proposed in Sherif’s (1981) contact hypothesis. </li></ul>*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. 22. Single explanatory variable: General Trust & SNC categories <ul><li>Effect of interpersonal closeness on mode of communication (e.g., Garton et al , 1997) </li></ul><ul><li>Offline communication contributes the most to the estimate of General Trust. Online public communication contributes the least. </li></ul>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. 23. Spare a thought for ethics <ul><li>Be transparent </li></ul><ul><li>Give something back </li></ul><ul><li>Talk to anyone who asks </li></ul><ul><li>Follow ethics guidelines (AoIR, UNESCO and others) </li></ul>
  24. 24. In Sum <ul><li>Closeness has implications for social learning, even in the virtual environment </li></ul><ul><li>Virtual communities operate in very similar ways to other communities – both on and offline </li></ul><ul><li>They bring together distributed individuals based on common experience, motivations and reputation </li></ul><ul><li>This is particularly true for virtual world participants because of the explicit social design of the software </li></ul><ul><li>Trust varies according to communication medium </li></ul><ul><li>Trust is paramount </li></ul><ul><li>Don’t jeopardise that trust. </li></ul>
  25. 25. <ul><li>Thank you! </li></ul><ul><li>E: [email_address] </li></ul><ul><li>W: </li></ul><ul><li>SL: Social Simulation Research Lab, Hyperborea </li></ul>