autobiography, mobile social life-logging and the transition from ephemeral to archival society

Auto-biography, Mobile Social Life-Logging, and the transition

from ephemeral to archival society

Marc A. SmithChief Social Scientist

TelligentMarc.Smith@telligent.com

Studying Society in a Digital World – Princeton UniversityApril 24th, 2009

Patterns are left behind

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Many organizations are adopting social media

• Use of these tools creates data sets that map their internal social network structure as an accidental by-product.

• Studying these data is sets is a focus of growing interest.

• Research projects like SenseCam are now becoming products and services like nTag, Spotme, Fire Eagle, and Google Latitude while devices like iPhone and G1 are weaving location into every application.

Information wants to be copied

Bits exist along a gradient from private to public.

But in practice they only move in one direction.

Strong links between

people and content…

…are as strong as the weakest link

Cryptography weakens over time

• Eventually, private bits, even when

encrypted, become public because the

march of computing power makes their

encryption increasingly trivial to

break.

No one expects privacy to be perfect in the physical world.

Unintended cascades

Taking a photo or updating a status message can now set off a series of unpredictable events.

Additional sensors will collect medical data to improve our health and safety, as early adopters in the "Quantified Self" movement make clear.

Continuous data collection

Microsoft Research, Cambridge, UK: “SenseCam”

When my phone notices your phone

a new set of mobile social software applications

become possible that capture data about other people

as they beacon their identifies to one another.

InteractionistSociology

• Central tenet– Focus on the active effort of

accomplishing interaction

• Phenomena of interest– Presentation of self – Claims to membership– Juggling multiple (conflicting) roles– Frontstage/Backstage – Strategic interaction– Managing one’s own and others’ “face”

• Methods– Ethnography and participant observation

(Goffman, 1959; Hall, 1990)

Innovations in the interaction order:

45,000 years ago: Speech, body adornment10,000 years ago: Amphitheater 5,000 years ago: Maps

150 years ago: Clock time

-2 years from now: machines with social awareness

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Sensors, Routes, Community

SpotMe: Wireless device for meetings and events

Community Aspects: A Sociological Revolution?

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Trace Encounters: http://www.traceencounters.org/

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• Central tenet – Social structure emerges from

the aggregate of relationships (ties)

among members of a population

• Phenomena of interest– Emergence of cliques and clusters

from patterns of relationships

– Centrality (core), periphery (isolates),

betweenness

• Methods– Surveys, interviews, observations, log file

analysis, computational analysis of matrices

(Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001)

Source: Richards, W. (1986). The NEGOPY network analysis program. Burnaby, BC: Department of Communication, Simon Fraser

University. pp.7-16

Social NetworkTheory

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Patterns of connection may uniquely identify

De-anonymizing Social Networks Arvind Narayanan & Vitaly Shmatikovhttp://33bits.org/2009/03/19/de-anonymizing-social-networks/

Abstract:

Operators of online social networks are increasingly sharing potentially sensitive information about users and their relationships with advertisers, application developers, and data-mining researchers. Privacy is typically protected by anonymization, i.e., removing names, addresses, etc.We present a framework for analyzing privacy and anonymity in social networks and develop a new re-identification algorithm targeting anonymized social-network graphs. To demonstrate its effectiveness on real-world networks, we show that a third of the users who can be verified to have accounts on both Twitter, a popular microblogging service, and Flickr, an online photo-sharing site, can be re-identified in the anonymous Twitter graph with only a 12% error rate. Our de-anonymization algorithm is based purely on the network topology, does not require creation of a large number of dummy “sybil” nodes, is robust to noise and all existing defenses, and works even when the overlap between the target network and the adversary’s auxiliary information is small.

• Answer person– Outward ties to local isolates

– Relative absence of triangles

– Few intense ties

• Reply Magnet– Ties from local isolates often

inward only

– Sparse, few triangles

– Few intense ties

Distinguishing attributes:

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Distinguishing attributes:

• Answer person– Outward ties to local isolates

– Relative absence of triangles

– Few intense ties

• Discussion person– Ties from local isolates often

inward only

– Dense, many triangles

– Numerous intense ties

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NodeXL

NetworkOverviewDiscoveryExploration

For

http://www.codeplex.com/nodexl

Tag Ecologies I

Adamic et al. WWW 2008

Result: lives that are more publicly displayed than ever before.

Add potential improvements in audio and facial recognition and a new world of continuous

observation and publication emerges.

Some benefits, like those displayed by the Google Flu tracking system, illustrate the potential for

insight from aggregated sensor data.

More exploitative applications are also likely.

Auto-biography, Mobile Social Life-Logging, and the transition

from ephemeral to archival society

Marc A. SmithChief Social Scientist

TelligentMarc.Smith@telligent.com

Studying Society in a Digital World – Princeton UniversityApril 24th, 2009