The document describes an agent-based computer simulation that models online social network formation and privacy behaviors, finding that two stable configurations emerge - many small isolated social networks or a single large network, and that privacy settings evolve based on connectivity and openness over time, potentially leading to periods where privacy is more important again after initial openness.

1. Simulating online privacy
Simulating online privacy
Ethno-computational insights
Paola Tubaro1 Antonio A. Casilli2
1 University of Greenwich, London
2 TELECOM ParisTech and EHESS, Paris
Sunbelt XXXII, 18 March 2012

2. Simulating online privacy
Introduction
The privacy challenge in social media
Periodic privacy incidents on FB;
Alleged tendency to renounce
privacy for an open, connected
existence;
Mark Zuckerberg: ”Public is the
new social norm”;
Are we approaching the “End of
Privacy” as we know it?
The notion of publicness from
Jürgen Habermas. . . to Jeff Jarvis.

3. Simulating online privacy
Introduction
Preliminary ethnographic evidence
FB ethnography
Small data approach;
Experiment: create two profiles;
Invite 100 contacts to become
friends;
One of the two profiles discloses,
the other is a control profile;
FB friends provide feedback on how
to enrich and develop profile
(comments, messages, likes, shares,
etc.);
Compare the two profiles over 50
days. A.A. Casilli (2010) Les liaisons numériques. Vers une nouvelle sociabilité ? Paris, Seuil.

4. Simulating online privacy
Introduction
Preliminary ethnographic evidence
The importance of disclosure on FB
Compare social graphs of two
profiles;
Personal network of actual profile
continues to grow in size and
displays a distinctive balance
between social cohesion (bonding)
and social connectedness
(bridging);
Disclosure is crucial: does this
necessarily validate the
‘End-of-privacy’ hypothesis?
A.A. Casilli (2010) Les liaisons numériques. Vers une nouvelle sociabilité ? Paris, Seuil.

5. Simulating online privacy
Introduction
Theoretical framework
Problematizing privacy
In fact, online interactions complexify
the very notion of privacy;
Traditional notion based on metaphor
of concentric circles of intimacy;
Mono-directional notion (Brandeis): a
core of sensitive data to be protected.
⇒ This notion no longer seems well
adapted to interactions in a networked
society.

6. Simulating online privacy
Introduction
Theoretical framework
Privacy as a multi-directional, dynamic process
Online privacy better described
through multi-directional notion
of privacy as regulation
(Altman);
Brunswik’s lens model:
Individuals send signals to, and
receive feedback from, the
environment.
⇒ Self-disclosure accompanies
adaptation to signals from the
(social) environment over time.

7. Simulating online privacy
Introduction
Research question
Research question
In a social system with:
Formation of personal networks through bonding and bridging;
Disclosure needed to form ties;
Adaptation to signals from the environment through a
feedback process;
What will be the final configuration of the system, in terms of
degree of disclosure?

8. Simulating online privacy
Methods
Agent-based computer simulation
Generate socially consistent scenarios
on a computer;
Compare their outcomes;
To detect and assess variables coming
into play within specific social
processes;
To identify sufficient conditions for a
macro phenomenon to emerge from
the interaction of micro behaviours.
An aid to perform a thought
experiment.

9. Simulating online privacy
Methods
The logic of an agent-based model
Generate an artificial population of
agents in an environment;
Endow them with basic rules of
behaviour;
Let them interact for a certain time
and step aside;
Observe outcomes at the system
level at the end.

10. Simulating online privacy
Methods
Our Simulation model
Programmed and run on NetLogo (Wilensky 1999);
Tie formation rules allowing for both bonding and bridging;
Two embedded notions of privacy:
Gradual self-disclosure and adaptation to one’s personal
network, through a feedback process;
Binary on/off visibility settings.

11. Simulating online privacy
Methods
Model Interface

12. Simulating online privacy
Results
Resulting system configurations
Figure: Stable configurations (20,000 time steps): (1) Small subnets, (2) Supernet.

13. Simulating online privacy
Results
Two solutions emerge
Many small subnets where contents are locked to contexts
⇒ “Elective communities” scenario.
Supernet where all contents are shared by all individuals,
regardless of context
⇒ Is this the “End-of-Privacy” scenario?

14. Simulating online privacy
Results
Effects of varying parameters
Figure: Number and size of nets, varying with connectedness and openness to diversity.

15. Simulating online privacy
Results
Evolution of privacy on/off settings
Figure: Average privacy settings, varying with connectedness and openness to diversity over time. It is when
individuals grow more and more connected, and share more and more contents, that privacy becomes relevant again.

16. Simulating online privacy
Results
For further reflection
The supposed “End of Privacy”
scenario is in fact more complex
than expected;
Tendency to greater openness is
not linear and may give rise to
counter-tendencies;
Possibility of cyclical patterns:
FB zeroes out privacy settings,
users retune them. Bakshy E., I. Rosenn, C. Marlow, L. Adamic (2012) The Role of Social Networks in Information Diffusion,
http://arxiv.org/abs/1201.4145.

17. Simulating online privacy
Results
Acknowledgements
We acknowledge Fondation CIGREF (ISD Programme 2011) for support.
Paola Tubaro, p.tubaro@gre.ac.uk
Antonio A. Casilli, casilli@telecom-paristech.fr