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Identity: Physical, Cyber, Future

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Invited talk at the Dagstuhl seminar on Physical-Cyber-Social Computing. 2nd October 2013.

Invited talk at the Dagstuhl seminar on Physical-Cyber-Social Computing. 2nd October 2013.

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  • 1. IDENTITY: PHYSICAL, CYBER, FUTURE MATTHEW ROWE LANCASTER UNIVERSITY, UK WWW.LANCASTER.AC.UK/STAFF/ROWEM @MROWEBOT Dagstuhl Seminar: Physical-Cyber-Social Computing – September/October 2013
  • 2. Identity: Physical, Cyber, Future 1 So ‘identity’; what do we mean?
  • 3. Identity: Physical, Cyber, Future 2 So ‘identity’: what do we mean?
  • 4. Identity: Physical, Cyber, Future 3 My Identity Shared Identity Abstracted Identity Persistent information Never changes More detailed Information More prone to change Groups and demographics Interests and tastes
  • 5. Identity: Physical, Cyber, Future 4 My Identity Shared Identity Abstracted Identity Physical My ‘real world’ name My parents and siblings Where I live My friends My neighbours My interests & hobbies Society memberships
  • 6. Identity: Physical, Cyber, Future 5 My Identity Shared Identity Abstracted Identity Cyber My username/handle Where I say I live My connections My stated interests My behaviour
  • 7. Identity: Physical, Cyber, Future 6 My Identity Shared Identity Abstracted Identity Cyber Intentional (ego) Existential My username/handle Where I say I live My connections My stated interests My behaviour
  • 8. Identity: Physical, Cyber, Future 7 Development = conflicts Development happens through stages
  • 9. Identity: Physical, Cyber, Future 8
  • 10. How do users’ identities develop within cyber systems over time? Identity: Physical, Cyber, Future 9
  • 11. Identity: Physical, Cyber, Future 10 by computing the cross-entropy of one probability distri- bution with respect to another distribution from an lifecycle period, and then selecting the distribution that minimises cross-entropy. Assuming we have a probability distribution (P) formed from a given lifecycle period ([t, t0 ]), and a probability distribution (Q) from an earlier lifecycle period, then we define the cross-entropy between the distributions as follows: H(P, Q) = X x p(x) log q(x) (5) In the same vein as the earlier entropy analysis, we derived the period cross-entropy for each platform’s users throughout their lifecycles and then derived the mean cross- entropy for the 20 lifecycle periods. Figure 3 presents the cross-entropies derived for the different platforms and user properties. We observe that for each distribution and each platform cross-entropies reduce throughout users’ lifecycles, suggesting that users do not tend to exhibit behaviour that has not been seen previously. For instance, for the in-degree see a consisten forming user set of posts the probabil instance, for frequencies discrete prob (P[t,t0]), and time interval between the As before each platform the mean co Figure 4 pre degree, out-d periods. We entropy of u Identity properties @ time t Identity properties @ time t of the social system (norms) Dissimilarity between the properties
  • 12. Identity: Physical, Cyber, Future 11 Out-degree distribution of users: Greater dissimilarity Time in the system Converge towards social norms, before transitioning away ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 2.03.04.05.0 Lifecycle Stages DistributionCrossEntropy 0 0.2 0.4 0.6 0.8 1
  • 13. Identity: Physical, Cyber, Future 12 Lexical distribution of users: ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 6.06.57.07.58.08.5 Lifecycle Stages DistributionCrossEntropy 0 0.2 0.4 0.6 0.8 1
  • 14. Identity: Physical, Cyber, Future 13 What is happening here?!1!
  • 15. ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 6.06.57.07.58.08.5 Lifecycle Stages DistributionCrossEntropy 0 0.2 0.4 0.6 0.8 1 Identity: Physical, Cyber, Future 14 Identity achievement: divergence from normsForeclosure: convergence on social norms Changing with Time: Modelling and Detecting User Lifecycle Periods in Online Community Platforms. M Rowe. To appear in the proceedings of the International Conference on Social Informatics. Kyoto, Japan. (2013) Mining User Lifecycles from Online Community Platforms and their Application to Churn Prediction. M Rowe. To appear in the proceedings of the International Conference on Data Mining. Dallas, US. (2013)
  • 16. Identity: Physical, Cyber, Future 15 My Identity Shared Identity Abstracted Identity Cyber Physical Future = lens blend •  Development being co-dependent between physical and cyber layers •  Transcendent identity (theories, recommendations, development) “All boundaries are conventions, waiting to be transcended”
  • 17. Identity: Physical, Cyber, Future 16 ¨  Informing social theory from cyber layer’s interpretation, and vice versa ¨  Behaviour diffusion through developmental stages ¨  Pre-empting physical decisions through understanding of the cyber lens ¨  Redefinition of cross-lens social norms
  • 18. Identity: Physical, Cyber, Future 17 Questions?

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