identity: physical, cyber, future

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

Identity: Physical, Cyber, Future 1

So ‘identity’; what do we mean?


So ‘identity’: what do we mean?


My

Identity

Shared Identity

Abstracted Identity

Persistent information Never changes

More detailed Information

More prone to change

Groups and demographics Interests and tastes


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


My

Identity

Shared Identity

Abstracted Identity

Cyber

My username/handle Where I say I live

My connections

My stated interests My behaviour


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


Development = conflicts

Development happens through

stages

How do users’ identities develop within cyber systems over time?



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(b) Out-degree

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(c) Lexical

Figure 2. Entropies of lifetime-stage distributions formed from users’in-degrees, out-degrees and lexical terms.

by computing the cross-entropy of one probability distri-bution with respect to another distribution from an lifecycleperiod, and then selecting the distribution that minimisescross-entropy. Assuming we have a probability distribution(P ) formed from a given lifecycle period ([t, t0]), and aprobability distribution (Q) from an earlier lifecycle period,then we define the cross-entropy between the distributionsas follows:

H(P,Q) = �X

x

p(x) log q(x) (5)

In the same vein as the earlier entropy analysis, wederived the period cross-entropy for each platform’s usersthroughout their lifecycles and then derived the mean cross-entropy for the 20 lifecycle periods. Figure 3 presents thecross-entropies derived for the different platforms and userproperties. We observe that for each distribution and eachplatform cross-entropies reduce throughout users’ lifecycles,suggesting that users do not tend to exhibit behaviour thathas not been seen previously. For instance, for the in-degreedistribution the cross-entropy gauges the extent to whichthe users who contact a given user at a given lifecyclestage differ from those who have contacted him previously,where a larger value indicates greater divergence. We findthat consistently across the platforms, users are contactedby people who have contacted them before and that fewernovel users appear. The same is also true for the out-degreedistributions: users contact fewer new people than they didbefore. This is symptomatic of community platforms wheredespite new users arriving within the platform, users formsub-communities in which they interact and communicatewith the same individuals. Figure 3(c) also demonstrates thatusers tend to reuse language over time and thus produce agradually decaying cross-entropy curve.

3) Community Contrasts (Community Cross-Entropy):For the third inspection of user lifecycles and how userproperties change, we examined how users compare withthe platform in which they are interacting over the sametime interval. We used the in-degree, out-degree and termdistributions and compared them with the same distributionsderived globally over the same time periods. For the globalprobability distributions we used the same means as for

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(b) Out-degree

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(c) Lexical

Figure 3. Cross-entropies derived from comparing users’ in-degree, out-degree and lexical term distributions with previous lifecycle periods. Wesee a consistent reduction in the cross-entropies over time.

forming user-specific distributions, but rather than using theset of posts that a given user had authored (Pui

) to derivethe probability distribution, we instead used all posts. Forinstance, for the global in-degree distribution we used thefrequencies of received messages for all users. Given thediscrete probability distribution of a user from a time interval(P[t,t0]), and the global probability distribution over the sametime interval (Q[t,t0]), we derived the cross-entropy as abovebetween the distributions. (H(P[t,t0], Q[t,t0])).

As before we derived the community cross-entropy foreach platform’s users over their lifetimes and then calculatedthe mean community cross-entropy for the lifecycle periods.Figure 4 presents the plots of the cross-entropies for the in-degree, out-degree and term distributions over the lifecycleperiods. We find that for all platforms the community cross-entropy of users’ in-degree increases over time indicatingthat a given user tends to diverge in his properties fromusers of the platform. For instance, for the community cross-entropy of the in-degree distribution the divergence towardslater parts of the lifecycle indicates that users who reply to agiven user differ from the repliers in the entire community.This complements cross-period findings from above wherewe see a reduction in cross entropy, thus suggesting thatusers form sub-communities in which interaction is consis-tently performed within (i.e. reduction in new users joining).We find a similar effect for the out-degree of the userswhere divergence from the community is evident towardsthe latter stages of users’ lifecycles. The term distributiondemonstrates differing effects however: for Facebook andSAP we find that the community cross-entropy reducesinitially before rising again towards the end of the lifecycle,while for Server Fault there is a clear increase in communitycross-entropy towards the latter portions of users’ lifecyclessuggesting that the language used by the users actually tendsto diverge from that of the community in a linear manner.This effect is consistent with the findings of Danescu et al.[2] where users adapt their language to the community tobegin with, before then diverging towards the end.

Identity properties @ time t

Identity properties @ time t of the social system (norms)

Dissimilarity between the properties


Out-degree distribution of users:

Greater dissimilarity

Time in the system

Converge towards social norms, before transitioning away

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Lexical distribution of users:

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What is happening here?!1!

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Identity achievement: divergence from norms Foreclosure: 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)


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”


¨  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


Questions?

identity: physical, cyber, future

Technology