Making e-friends and influencing
    people in Second Life
         Aleks Krotoski
        University of Surrey
          ...
What I’ll talk about


• Interpersonal relationships in cyberspace
• How I measure relationships in Second
  Life
• How re...
Before I get ahead of myself
• The differences between online and offline:
  –   Anonymity
  –   Physical appearance
  –  ...
Online community I
• In traditional definitions of “community”, there’d be no
  such thing in cyberspace
   – Tied to plac...
Online Communities I (cont)




• Transient and formal communities
   – London Memorial in the virtual world Second Life
 ...
Online community II
• Form for the same reasons offline communities do:
    – Make friends, provide motivation, offer supp...
Trust in virtual communities I: we’re all
                in it together
• Returning to Anonymity
   – Perceived similarit...
Trust in virtual worlds III: Rep (cont)
• Trust is based upon
   – past experience…
   – …which is either based upon funct...
How measure friendships?
           Social Network Analysis

…studies social
relationships as a series
of interconnected
w...
Asking personal questions
• Surveys
  – Who do you know?
     • Who do you communicate with?
     • Who do you trust?
  – ...
N (respondents) =
  33
N (total network)
                    Results
  = 650
Picking apart communication network
                 closeness

• But what does it
  mean in Second
  Life if someone
  in...
Results: Single explanatory variable
       (General Communication)
     y                                          β0 (St...
Single explanatory variable:
       General Trust & SNC categories
        Explanatory Variable               β0 (Std.    ...
In Sum
• Closeness has implications for influence and persuasion,
  even in the virtual environment
• Virtual communities ...
Thank you!
Aleks Krotoski (Mynci Gorky)
  A.Krotoski@surrey.ac.uk
Making E Friends And Influencing People In Second Life
Making E Friends And Influencing People In Second Life
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Making E Friends And Influencing People In Second Life

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

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Making E Friends And Influencing People In Second Life

  1. 1. Making e-friends and influencing people in Second Life Aleks Krotoski University of Surrey SPERI
  2. 2. What I’ll talk about • Interpersonal relationships in cyberspace • How I measure relationships in Second Life • How relationships are defined
  3. 3. 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?
  4. 4. 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
  5. 5. 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
  6. 6. 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.
  7. 7. 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
  8. 8. 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
  9. 9. How measure friendships? Social Network Analysis …studies social relationships as a series of interconnected webs. …focuses on inter- relationships rather than individuals’ attributes
  10. 10. 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?
  11. 11. N (respondents) = 33 N (total network) Results = 650
  12. 12. Picking apart communication network closeness • But what does it mean in Second Life if someone in this community is rated “close” or “distant”?
  13. 13. 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.
  14. 14. 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.
  15. 15. 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
  16. 16. Thank you! Aleks Krotoski (Mynci Gorky) A.Krotoski@surrey.ac.uk
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