Casilli lseisp110510 light

Studying Eating Disorders

ISP SSS - LSE

Lonfon, May 11th, 2010

Promesses et limites des SMAP Tubaro & AA Casilli

Studying Eating Disorders in the Social Web

New methods, new questions

Antonio A. Casilli, CEM, EHESS

• Edgar Morin Centre, School forAdvanced Studies in SocialSciences (EHESS, Paris)http://en.wikipedia.org/wiki/Edgar_Morin_Centre

• Interdisciplinary Institute for


• Interdisciplinary Institute forContemporary Anthropology(IIAC, Paris)

• Research topics: CMC andhealth - especially eatingbehaviors

• Social network approach toeating disorders (ANAMIAproject)

TOC

• Pro-ED online communities• Our approach• Methods


• Methods• Empirical data• Agent-based simulations• Conclusions

• Pro-ED online communities


• “Pro-ana”, “pro-mia”:controversial subculturesadvocating anorexia andbulimia nervosa on theweb 3

4

5

6

Search Volume Index

Ana_mia

Pro_ana


web• A “movement”? A “skill”?• How to study this social

phenomenon?• How to devise suitable

public health tools andcommunication policies?

0

1

2

3

Jan 2 2005

Mar 27 2005

Jun 19 2005

Sep 11 2005

Dec 4 2005

Feb 26 2006

May 21 2006

Aug 13 2006

Nov 5 2006

Jan 28 2007

Apr 22 2007

Jul 15 2007

Oct 7 2007

Dec 30 2007

Mar 23 2008

Jun 15 2008

Sep 7 2008

Nov 30 2008

Feb 22 2009

Search Volume Index

Pro_ana

Pro_mia

Evolution over four years of the search volume index for common ana-mia queries

(Source: Google Trends, March 12th 2009)

• Rough typology ofonline pro-EDwebsites:

1. Personal websites


1. Personal websites2. Online social

media/forums3. ‘Cloaked’ websites4. ‘Universities’

• ‘Thinspiration’, self-help, advice

László Gulyás 2002 On the Transition to Agent-Based Modeling: Implementation Strategies From Variables To Agents. Social Science Computer Review 20(4): 389-. 399. DOI: 10.1177/089443902237318

• Our approach


•The pro-ED population is:

•relatively small;

•vulnerable (health risk; underage);

•partly hidden;


•frequent migrations.

•Large quantitative surveys & webcrawling possible only to an extent

•Rely on smaller-scale, purposive samples for qualitative enquiry

• Ongoing project(ANAMIA) : a socialnetworks approach topro-ED sociability

• Social factors


• Social factorsinfluencing healthbehaviours

• Computer useinfluencing socialfactors

• Focus on online/offinepersonal networks

László Gulyás 2002 On the Transition to Agent-Based Modeling: Implementation Strategies From Variables To Agents. Social Science Computer Review 20(4): 389-. 399. DOI: 10.1177/089443902237318

• Methods


•A wide array of methods

•Online ethnographies

•Online experiments

•Social network analysis


•Social network analysis

•Web-based in-depth interviews

•Multi-agent simulations

•General framework: Ethnographically-informed social simulation (P Tubaro & AA Casilli BMS, 2010)

• Combining qualitative data and agent-based computersimulation:• enriches model with insight into actors behavior and motivations;• performs “thought experiments” to test consistency of theories;• replicates and generalizes findings from fieldwork;• supports cross-disciplinary validation of results.


• supports cross-disciplinary validation of results.

• Starting point: an actual social process;

• Qualitative sub-loop: formulate hypotheses, collect data,

• adjust categories, until a


• adjust categories, until a theory is produced;

• Design, build, code and de-bug an agent-based model;

• Generate simulated data and revise theory;

• This may direct back to the field (resample and re-start sub-loop).

• Empirical data


• Fieldwork with ana-mia subjects is currently at an early stage.

• Data collected so far are exploratory, and include:– At micro level: a qualitative study of network tie

formation on Facebook;– At macro level: a web cartography of the pro ED-sphere


– At macro level: a web cartography of the pro ED-sphere in France and UK

• Use of preliminary data to inform and validate a first simulation.

• Facebook experiment• Creation of social structures

(‘friendship’, ‘social capital’) through self-disclosure and adoption of cultural traits

• 50 days (Apr. 27th, - Jun. 15th, 09)


15th, 09) • Name generator and IOS to

select subjects• An actual and a control

profile send 50 friend requests

• Recognizable name and picture

• 15 reply (14 for control)

• “Friends” provide feedback on how to adapt profile– Comments– Private messages– Like– Share


– Share• “Friends” appreciate

disclosure• Actual profile contains more

personal information, images, interactive data

• Control data contains sparse information

•Pete Warden, Nicholas Christakis :«harvesters» and «dataminers»

•«Mark Zuckerberg has built hissocial networking empire on thebelief that "information wants to beshared", a particular philosophy ofinformation that directly impacts the


information that directly impacts thevalues built into the design ofFacebook, ranging from its userinterface, privacy policies, terms ofservice, and method of governance »(Michael Zimmer)•Social media is ethically puzzling

(Axelrod);


Starting point 11 05 09

Actual Profile Control Profile


Taste display 19 05 09

Actual Profile Control Profile


Personal information 22 05 09

Control ProfileActual Profile


Photo album online 13 06 09

Control ProfileActual Profile

•Insight from preliminary qualitative study is that online network formation may depend upon:

•Privacy settings, i.e. visibility of contents to others;


others;•Self-display, i.e. personal and cultural traits exhibited and that traits may change with network composition.

•The model aims to problematize these factors in simulated larger networks.

(Axelrod);

• Agent-based simulations


•We focus on the impact of:•tendency to conformism vs. dissonance in cultural traits;•preference for bonding vs. bridging in tie formation;•possibility to limit incoming ties through privacy protection.


ties through privacy protection.•We measure impact through:

•number and size of components;•homogeneity of traits within and between components;•evolution of privacy settings over time.

(Axelrod);


(Axelrod);

•At initialization, each actor is endowed with:•a vector (several dimensions) of traits;•a privacy setting (visible/invisible).

•Actors can be:•isolates;•connected;

•If connected:


•If connected:•they share most traits with their contacts;•but may dffier on one dimension;•this depends on the ‘Dissonance’ parameter

•We test the following values of parameters:

Paramètres Valeurs

Dissonance 0,01 0,03 0,08

Seuil de bonding 0,20 0,50 0,70

Privacy On Off

(Axelrod);

SETUP

Tick t+1

Pick up random A

Y

N

Is anom(A) < AnomThrshld?

Update z(Ai) ≠ µz (friendsi)


Break Tie

Y

Create Tie

Is µz(A)-µz (grp) < BBThrshd? Create Bridging Tie

N

Y

Create Bonding Tie

Is µz(A)-µz (grp) ≥ BBThrshd?

Y

Break Bonding Tie

Break Bridging Tie

N

SETUP = Z=[zA 1, zA 2, zA3] #ties value x(A) Value anom(A)

• Simulation results (20,000 ticks): three stable configurations


(1) Giant Component (2) Hegemony (3) Little Boxes

(Axelrod);


Number and size of components, varying Dissonance and Bonding Propensity, privacy protection on


Number and size of components, varying Dissonance and Bonding Propensity, privacy protection off

• Explain the effects of parameters– With lower propensity to bonding (=greater openness to– bridging), only one or few components emerge;– This effect is stronger with higher Dissonance;– With higher propensity to bonding, many small communities emerge;– In this case, differences in Dissonance have little impact;– With no privacy protection, these effects are slightly amplified, because


– With no privacy protection, these effects are slightly amplified, because more ties can be formed.

• Explain changes in privacy over time– Agents restrict access only when a giant component appears;– This is the only case in which average privacy increases;– Otherwise, average privacy diminishes until there are no more isolates,

then is stable.


Average Privacy over time, varying Dissonance and Bonding propensity

Structural signatures of learning dynamics


1. Inprinting 2. No learning

3. Mixed situation (no learning & continuous learning)

•Retrodictive validation •Web cartography of the pro ED-sphere in France and UK•Close to configuration 2 ‘Hegemony’


‘Hegemony’•Large component: personal pages and blogs of teenagers and young adults, strongly pro-ana;•Smaller components: different age groups and social positioning (cultural variance)•Homogeneity within components, heterogeneity between them.

French pro-ED sphere (by Dr. Manuel Boutet)

•Retrodictive validation •Web cartography of the pro ED-sphere in France and UK•Close to configuration 2 ‘Hegemony’


‘Hegemony’•Large component: personal pages and blogs of teenagers and young adults, strongly pro-ana;•Smaller components: different age groups and social positioning (cultural variance)•Homogeneity within components, heterogeneity between them.

British pro-ED sphere (by Dr. Manuel Boutet)

• Conclusions


• Preliminary results support the claim that agent-based models can complement analyses based on small qualitative fieldworks

• Combine insight into social phenomenon and


• Combine insight into social phenomenon and generalization of results

• These methods are particularly useful with sensitive and hidden populations.

• These methods are particularly open to cross-disciplinary validation

Thank you!


Contact me at:antonio [dot] casilli [at] ehess [dot] frFind this presentation on my research blog: http://www.bodyspacesociety.euFollow me on Twitter: http://www.twitter.com/bodyspacesoc

Education

Casilli lseisp110510 light