Fragmentation Of Identity

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  • We are computer scientists & designers (not social scientists) Our goals: Relate research in social networks to issues of digital identity representation Use visualization techniques to understand social characteristics of individuals Learn from social network analysts
  • Fragmentation Of Identity

    1. 1. Fragmentation of identity through structural holes in email contacts danah boyd, Jeff Potter, Fernanda Viegas (Sociable Media, MIT Media Lab)
    2. 2. Research Questions <ul><li>How does social network structure impact individual construction of identity? </li></ul><ul><li>How is this behavior made explicit online? </li></ul><ul><li>How can this be observed within the context of email? </li></ul>
    3. 3. Construction of Individual Identity <ul><li>Interrelated ideas of identity </li></ul><ul><ul><li>Social identity: public presentation of self </li></ul></ul><ul><ul><li>Internal identity: private view of self </li></ul></ul><ul><li>Fragmentation vs. Multi-Faceted Identity </li></ul><ul><ul><li>Fragmentation: conflicting internal identity </li></ul></ul><ul><ul><li>Multi-faceted: coherent internal identity, fragmented social identity </li></ul></ul>
    4. 4. Managing Faceted Selves <ul><li>Differentiated presentation changed according to context </li></ul><ul><ul><li>How? Fashion, language, location/context, people </li></ul></ul><ul><ul><li>Why? Privacy, social appropriateness, reputation differentiation </li></ul></ul><ul><ul><li>Who? Dependent on self-monitoring habits, marginalization, fear of retribution </li></ul></ul><ul><li>Fragmented social network (e.g., work, clubs, family, …) </li></ul><ul><ul><li>Separate social circles provide for segmentation of presentation </li></ul></ul>
    5. 5. Identity online <ul><li>Confusion of context </li></ul><ul><ul><li>Ease of moving between multiple contexts </li></ul></ul><ul><ul><li>Data aggregated across “locations” </li></ul></ul><ul><li>Email address serves as context </li></ul><ul><ul><li>Allows for privacy and faceted behavior </li></ul></ul>
    6. 6. Relating Network Structure <ul><li>Structural holes & bridges (Burt) </li></ul><ul><ul><li>Maximize & control information flow </li></ul></ul><ul><li>Simmelian ties (Krackhardt) </li></ul><ul><ul><li>In public settings, personally constraining by restricting appropriate behavior – aggregate of all associations </li></ul></ul><ul><li>Control of network structure </li></ul><ul><ul><li>Minimize uncontrolled personal information flow </li></ul></ul>
    7. 7. Structuring social networks via email <ul><li>Recognizing the power of multiple recipients </li></ul><ul><ul><li>Copy/paste phenomenon to appear personal or contextual </li></ul></ul><ul><ul><li>Slight content alternations for context </li></ul></ul><ul><ul><li>Making others aware of audience </li></ul></ul>
    8. 8. Ego-Centric Visualization <ul><li>Visualization tool to observe social networks embedded in email </li></ul><ul><ul><li>Focused on structure </li></ul></ul><ul><li>Analyzed “Mike’s” email habits </li></ul><ul><ul><li>5 years worth of complete data </li></ul></ul><ul><ul><li>Maintains multiple email addresses for different contexts </li></ul></ul><ul><ul><li>(Dis)advantages of using one person’s behaviors </li></ul></ul>
    9. 9. Introducing Mike <ul><li>Social characteristics: </li></ul><ul><ul><li>24-year old, gay-identified, white male </li></ul></ul><ul><ul><li>Born in northern CA, attended Yale (art & computer science) </li></ul></ul><ul><ul><li>Friends & jobs in: Boston, SF, Chicago, NYC </li></ul></ul><ul><ul><li>Uses many forms of media to stay connected </li></ul></ul><ul><li>Mike’s primary social communities: </li></ul><ul><ul><li>Family, high school friends </li></ul></ul><ul><ul><li>Undergraduate friends </li></ul></ul><ul><ul><li>Gay men in/outside Boston, in NYC </li></ul></ul><ul><ul><li>Boston, Texas, California work colleagues </li></ul></ul>
    10. 10. Mike’s dataset <ul><li>80,941 messages </li></ul><ul><ul><li>1.03 average recipients per msg </li></ul></ul><ul><li>15,537 unique people </li></ul><ul><ul><li>7,250 people w/ 2,618 knowledge ties (excluding listservs) </li></ul></ul><ul><ul><li>662,078 ties between all respondents (using only messages with <50 recipients; otherwise, 11.7 million) </li></ul></ul><ul><ul><li>226 trusted ties; 23 reciprocal </li></ul></ul>
    11. 11. Defining Connectivity <ul><li>Knowledge ties </li></ul><ul><ul><li>If A sends a message to B , A ‘knows’ B </li></ul></ul><ul><ul><li>B does not necessarily know A </li></ul></ul><ul><li>Awareness ties </li></ul><ul><ul><li>If B receives a message from A -> B is ‘aware’ of A </li></ul></ul><ul><ul><li>If B and C both receive a message from A -> B and C are ‘aware’ of each other </li></ul></ul><ul><li>Trusted ties </li></ul><ul><ul><li>If A sends a message to B and blind carbon copies (BCC’s) D -> A ‘knows’ and ‘trusts’ D </li></ul></ul><ul><ul><li>( D has the ability to respond and reveal that A included people without B ’s awareness) </li></ul></ul>
    12. 12. Visualizations
    13. 13. Visualizations: Overview <ul><li>Goal is to allow one to quickly see how Mike’s network is connected and view structural holes </li></ul><ul><li>Methodology </li></ul><ul><li>Spring/Wire explanation </li></ul><ul><li>View of entire world </li></ul><ul><li>Close-up views of network </li></ul>
    14. 14. Visualizations: Methodology <ul><li>Basic spring/node algorithm used to place nodes in optimal location </li></ul><ul><li>- annealing algorithms don’t work with 15,000 nodes </li></ul><ul><li>Colors are used to indicate the relationship to the person </li></ul><ul><li>- based on which of Mike’s email address the person uses </li></ul><ul><li>- most common address used </li></ul>
    15. 15. Visualizations: Spring/Node (1/2) <ul><li>Basic spring algorithm used to place nodes </li></ul><ul><li>-Ties act as springs, pulling connected nodes closer together </li></ul><ul><li>-Nodes act like magnets and repel each other </li></ul>
    16. 16. Visualizations: Spring/Node (2/2) <ul><li>All nodes start out at random location, spring algorithm is run several hundred iterations </li></ul><ul><li>This (eventually) results in connected nodes being nearby and non-connected being far away </li></ul>
    17. 17. Movie of Visualization <ul><li>watch movie </li></ul><ul><li>(using quicktime) </li></ul>
    18. 19. Visualization: Entire World (1/2) <ul><li>Color key for </li></ul><ul><li>all images </li></ul>
    19. 26. Social Implications <ul><li>Using one person’s email, we can observe the social networks of hundreds of people - what are the implications of this? </li></ul>
    20. 27. Thoughts moving forward <ul><li>More detailed analysis </li></ul><ul><ul><li>Use visualizations to have ethnographic conversation with Mike </li></ul></ul><ul><li>Extend to multiple users </li></ul><ul><ul><li>Visual comparison valuable </li></ul></ul><ul><li>Allow for interactivity </li></ul><ul><ul><li>More detailed analysis of ego-centric graphs </li></ul></ul><ul><li>Learn more from social network analysts </li></ul>
    21. 28. http://smg.media.mit.edu/projects/SocialFragments/

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