Presentation to the "Studying Society in a Digital World" conference at the Princeton University Center for Information Technology Policy.

1. Auto-biography, Mobile Social
Life-Logging, and the transition
from ephemeral to archival
society
Marc A. Smith
Chief Social Scientist
Telligent
Marc.Smith@telligent.com
Studying Society in a Digital World – Princeton University
April 24th, 2009

2. Patterns are left behind
2

3. 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.

4. Information wants to be copied

5. Bits exist along a gradient
from private to public.
But in practice they only move in one direction.

6. Strong links
between
people and
content…

7. …are as str
as the weakest

8. 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.

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

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

11. Additional sensors will collect medical data to
improve our health and safety, as early adopters
in the quot;Quantified Selfquot; movement make clear.

13. Continuous data collection
Microsoft Research,
Cambridge, UK:
“SenseCam”

14. 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.

15. Interactionist
Sociology
• 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)

16. Innovations in the interaction order:
45,000 years ago: Speech, body adornment
10,000 years ago: Amphitheater
5,000 years ago: Maps
150 years ago: Clock time
-2 years from now: machines with
social awareness

17. 17

18. Sensors, Routes, Community
Community Aspects: A Sociological Revolution?
SpotMe: Wireless device for meetings and events

19. 19

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

21. 21

23. Social Network
Theory
• 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), Source: Richards,
betweenness W. (1986). The
NEGOPY network
• Methods analysis program.
Burnaby, BC:
– Surveys, interviews, observations, log file Department of
analysis, computational analysis of Communication,
matrices Simon Fraser
University. pp.7-16
(Hampton &Wellman, 1999; Paolillo,
2001; Wellman, 2001)

24. 24

25. Patterns of connection
may uniquely identify
De-anonymizing Social Networks
Arvind Narayanan & Vitaly Shmatikov
http://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.

26. Distinguishing attributes:
• 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
26

27. 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
27

28. http://w
NodeXL
Network
Overview
Discovery
Exploration

30. Tag Ecologies I
Adamic et al. WWW 2008

32. 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.

33. Auto-biography, Mobile Social
Life-Logging, and the transition
from ephemeral to archival
society
Marc A. Smith
Chief Social Scientist
Telligent
Marc.Smith@telligent.com
Studying Society in a Digital World – Princeton University
April 24th, 2009