The Social Life of Second Life: An analysis of the networks of a virtual world
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The Social Life of Second Life: An analysis of the networks of a virtual world

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for the Networks Network at University of Surrey, 27 April 2007

for the Networks Network at University of Surrey, 27 April 2007

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The Social Life of Second Life: An analysis of the networks of a virtual world The Social Life of Second Life: An analysis of the networks of a virtual world Presentation Transcript

  • The Social Life of Second Life An analysis of the Social Networks of a virtual world Aleks Krotoski Networks Network, 27 April 2007
  • Research Questions
    • What are the underlying social psychological phenomena which contribute to the effectiveness of a method like SNA?
    • What do network definitions mean in social psychological terms?
    • What social psychological knowledge might contribute to the greater predictive quality of the method?
    • What are the relative contributions of each on a measure of social influence?
  • The context
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  • 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 ?
  • The Importance of Being Pseudononymous
    • Anonymity
      • Users demonstrate enhanced self-awareness when online, and elaborate on the CONTENT rather than peripheral persuasive cues
      • Immersion as a mediator
        • people are more likely to focus on peripheral cues, like perceived trustworthiness, likeability, credibility and expertise of a source in an environment like Second Life (e.g., Blascovich & Yee, 2005)
  • The Importance of Being Pseudononymous
    • Most psychological internet research suggests that anonymity increases deindividuating behaviour
      • From Zimbardo’s nuns to strangers on a train
      • Online, conformity and compliance behaviours have been theorised to be a result of similar processes (e.g., SIDE)
      • As have less agreeable behaviours (e.g., flaming, griefing)
  • The Importance of Being Pseudononymous
    • However, most of the online influence research has been conducted in experimental situations , with simulated e-groups
    • What about the norms ?
      • Hierarchies, rules, practices, rituals
    • What about online identity ?
  • Three studies
    • Study 1 (completed June 2006)
      • To assess the best sociometric criteria for collecting relational data in Second Life
      • To assess the social psychological definition of the social network concepts “closeness” and “distance”
    • Study 2 (completed April 2007)
      • To extend Study 1 and to replicate the findings on a larger scale
      • To assess the individual contributions of social network and social psychological factors on a measure of social influence
  • Three Studies
    • Study 3 (data collection May 2007)
      • To measure the SL network at the individual (Friends) and group (Groups) level
      • To follow the diffusion of an innovation through SL at the individual and group level
      • To assess the effect central avatars have on diffusion
      • To ascertain any mediating effects group membership has on adoption
  • Study 1: Method
    • Online survey
      • Demographics
      • SN name generator (Calling Cards)
      • SNTrust and SNCommunication scales
      • Social Psychological items
  • Study 1: Results
    • N (respondents) = 33
    • N (actors) = 650
    • N (arcs) = 1734
    • Average neighbours: 2.32 (SD=11.10; min = 1, max = 331)
  •  
    • SNC scale offers a more robust measure of the effect of the social network on influence, by controlling for potential confluence with the social psychological measures.
    • The Social Network Communication Scale is a more discrete measure of a “social network” in this environment, whilst retaining reliable network measures
    • Instances of communication are the only way for Residents to develop interpersonal trust online
  • Picking apart the “communication” network closeness assumption
    • But what does it mean – psychologically - if someone in Second Life is rated “close” or “distant” with communication criteria?
    • Multi-Level Modelling (models)
  • Results: Single explanatory variable (General Communication)
    • The predictive power of the estimate of the value of this measure of General Trust is positively enhanced when we know how often two people communicate in general.
    *N=538; **N=539; σ 2 e : variance accounted for between avatars; T p<0.000, df=2 0.347 (0.023) 0.408 (0.027) 0.408 (0.027) 0.531 (0.035) 0.543 (0.035) σ 2 e 1086.919 T (1141.021) 0.271 (0.055) 0.035 (0.125) Domain-Specific Trust 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
  • Results: Multiple explanatory variables (General Trust)
    • Greatest improvement to the fit of a model occurs when offline communication scores are added to the single-variable public communication model
    • Adding online private communication to the online public communication model renders the weight of online public communication insignificant, so this model is rejected.
    Loglikelihood (fixed model LL) σ 2 e β2 (Std. Error) β1 (Std. Error) β 0 (Std. Error) Explanatory Variable 0.328 (0.046) 0.291 (0.051) 0.375 (0.074) N=539; σ 2 e : variance accounted for between avatars; T p<0.000, df=3; *model rejected on basis of ill-fit 0.314 (0.021) 0.332 (0.022) 0.394 (0.026) 1038.486 T (1115.396) 0.345 (0.057) 0.052 (0.087) Online private and offline communication 1057.941 T (1224.182) 0.399 (0.051) 0.059 (0.085) Online public + offline communication 1144.879 T (1224.182) 0.104 (0.057) 0.065 (0.121) Online public + online private communication
  • Discussion (Study 1)
    • Consistent with Latan é ’s research
      • people who are in greater communication have greater social impact
    • Provides empirical evidence for Garton et al (1997), Correll (1995).
    • What about position? What about structure?
  • Study 2: Method
    • Online survey
      • Demographics
      • SN name generator (Friends)
      • SNCommunication scale
      • Social Psychological items
      • Measure A to assess perceived trust towards central and peripheral avatars about general risk behaviours in Second Life .
      • Measure B which measures baseline attitudes about taking part in Second Life -specific risk activity.
  • Study 2: Results
    • N (respondents) = 750
    • N (actors) = 6767
    • N (arcs) = 9595
    • Average neighbours (in-degree) = 1.42 (SD= 1.09; min = 0, max = 17)
    • Components
      • 62 strong components > 2
      • 7 strong components > 5
    • Top 46 in-degree (8-17)
  • Density = 0.082
  • Position and SP 6653.37 0.534 (0.017) 0.190 (0.022) 0.803 (0.026) Prototypicality (N=2685) 0.483 (0.016) 0.530 (0.018) 0.532 (0.018) 0.531 (0.018) 0.420 (0.013) σ 2 e 6400.24 0.312 (0.020) 0.797 (0.026) Social Comparison (N=2685) 6460.3 0.240 (0.021) 0.805 (0.025) Trust about privacy (N=2649) 6510.62 0.215 (0.021) 0.809 (0.026) Trust about sex (N=2649) 6560.19 0.191 (0.021) 0.802 (0.026) Trust about business (N=2651) 6162.35 0.432 (0.020) 0.773 (0.023) Social network trust (N=2786) Loglikelihood (fixed model LL) β (Std. Error) β 0 (Std. Error) y
  • Opinion leaders
    • More likely to be sources of information because have access to broader info pool
    • Their adoption is likely to spread through local networks because
      • Trusted
      • Viewed as prototypical
      • Sources of others’ social comparison
  • Opinion Leader demographics
    • Older than average
    • More likely to be female than average
    • Greater than average time spent in community
    • Greater number of hours spent in SL per week
    • Concerned with anti-social behaviour
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  • Limitations
    • Self-reported experiences of 750 actors (ego-centric)
    • Partial network
    • Analytic strategy emphasises greater connectivity
  • Study 3 Adoption data for new technological innovation (Voice)
  • Voice in Second Life
    • Contentious issue
    • Relevant to entire virtual world
      • Who uses?
      • How long?
      • Where?
      • What type?
    • Descriptive
    • Community Building and Protection
  • Questions? Aleks Krotoski [email_address]