autobiography, mobile social life-logging and the transition from ephemeral to archival society
DESCRIPTION
Presentation to the "Studying Society in a Digital World" conference at the Princeton University Center for Information Technology Policy.TRANSCRIPT
Auto-biography, Mobile Social Life-Logging, and the transition
from ephemeral to archival society
Marc A. SmithChief Social Scientist
Studying Society in a Digital World – Princeton UniversityApril 24th, 2009
Patterns are left behind
2
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.
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
17
Sensors, Routes, Community
SpotMe: Wireless device for meetings and events
Community Aspects: A Sociological Revolution?
21
• 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
27
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
Studying Society in a Digital World – Princeton UniversityApril 24th, 2009