fragmentation of identity through structural holes in email contacts

danah boyd, Jeff Potter, Fernanda Viegas . Sociable Media, MIT Media Lab . Sunbelt . 17 February 2002

Fragmentation of identity through structural holes

in email contacts

danah boyd, Jeff Potter, Fernanda Viegas(Sociable Media, MIT Media Lab)


Research QuestionsHow does social network structure

impact individual construction of identity?

How is this behavior made explicit online?

How can this be observed within the context of email?


Construction of Individual Identity

Interrelated ideas of identity– Social identity: public presentation of self– Internal identity: private view of self

Fragmentation vs. Multi-Faceted Identity– Fragmentation: conflicting internal identity– Multi-faceted: coherent internal identity,

fragmented social identity


Managing Faceted SelvesDifferentiated presentation changed according to

context– How? Fashion, language, location/context, people – Why? Privacy, social appropriateness, reputation

differentiation– Who? Dependent on self-monitoring habits,

marginalization, fear of retributionFragmented social network (e.g., work, clubs,

family, …) – Separate social circles provide for segmentation of

presentation


Identity onlineConfusion of context

– Ease of moving between multiple contexts

– Data aggregated across “locations”Email address serves as context

– Allows for privacy and faceted behavior


Relating Network Structure

Structural holes & bridges (Burt)– Maximize & control information flow

Simmelian ties (Krackhardt)– In public settings, personally

constraining by restricting appropriate behavior – aggregate of all associations

Control of network structure– Minimize uncontrolled personal

information flow


Structuring social networks via email

Recognizing the power of multiple recipients– Copy/paste phenomenon to appear

personal or contextual– Slight content alternations for context– Making others aware of audience


Ego-Centric VisualizationVisualization tool to observe social

networks embedded in email– Focused on structure

Analyzed “Mike’s” email habits– 5 years worth of complete data– Maintains multiple email addresses for

different contexts– (Dis)advantages of using one person’s

behaviors


Introducing MikeSocial characteristics:

– 24-year old, gay-identified, white male– Born in northern CA, attended Yale (art &

computer science)– Friends & jobs in: Boston, SF, Chicago, NYC– Uses many forms of media to stay connected

Mike’s primary social communities:– Family, high school friends– Undergraduate friends – Gay men in/outside Boston, in NYC– Boston, Texas, California work colleagues


Mike’s dataset80,941 messages

– 1.03 average recipients per msg15,537 unique people

– 7,250 people w/ 2,618 knowledge ties (excluding listservs)

– 662,078 ties between all respondents (using only messages with <50 recipients; otherwise, 11.7 million)

– 226 trusted ties; 23 reciprocal


Defining ConnectivityKnowledge ties

– If A sends a message to B, A ‘knows’ B– B does not necessarily know A

Awareness ties– If B receives a message from A -> B is ‘aware’ of A– If B and C both receive a message from A -> B and C

are ‘aware’ of each otherTrusted ties

– If A sends a message to B and blind carbon copies (BCC’s) D -> A ‘knows’ and ‘trusts’ D

– (D has the ability to respond and reveal that A included people without B’s awareness)


Visualizations


Visualizations: Overview

Goal is to allow one to quickly see how Mike’s network is connected and view structural holes

- Methodology- Spring/Wire explanation- View of entire world- Close-up views of network


Visualizations: Methodology

Basic spring/node algorithm used to place nodes in optimal location

- annealing algorithms don’t work with 15,000 nodes

Colors are used to indicate the relationship to the person- based on which of Mike’s email address the person uses

- most common address used


Visualizations: Spring/Node (1/2)

Basic spring algorithm used to place nodes

-Ties act as springs, pulling connected nodes closer together

-Nodes act like magnets and repel each other


Visualizations: Spring/Node (2/2)

All nodes start out at random location, spring algorithm is run several hundred iterations

This (eventually) results in connected nodes being nearby and non-connected being far away


Visualization: Entire World (1/2)

Color key for all images


Social ImplicationsUsing one person’s email, we can

observe the social networks of hundreds of people - what are the implications of this?


Thoughts moving forwardMore detailed analysis

– Use visualizations to have ethnographic conversation with Mike

Extend to multiple users– Visual comparison valuable

Allow for interactivity– More detailed analysis of ego-centric graphs

Learn more from social network analysts


http://smg.media.mit.edu/projects/SocialFragments/

fragmentation of identity through structural holes in email contacts

Documents

social appropriateness

mikes network

separate social circles

aware of aif b

aawareness tiesif b

fragmentation of identity

coherent internal identity

context of email