Tastes, ties, and time: A new social network dataset using Facebook.com Lewis, K., Kaufman, J., Gonzalez, M., Wimmer, A. &...
Paper overview <ul><li>Introduction of a new public datased retrieved from a popular SNS
Features of this dataset
Descriptive findings from this dataset (actually early findings...) </li></ul>
Background <ul><li>Research dataset retrieved from Facebook.com
Why Facebook.com? </li><ul><li>Several articles published on Facebook, accepted as a “standard”
Has profile creation and network articulation as primary community tasks (Lampe et al., 2007)
Facebook communities generally correspond to existing offline network membership (Lampe et al., 2007)
Provides users with standardized profile templates </li></ul></ul>
State of the art <ul><li>Several articles published on Facebook, for example: </li><ul><li>Identity Management: Multiple P...
A Familiar Face(book): Profile Elements as Signals in an Online Social Network (Lampe et al., 2007) </li></ul></ul>
State of the art Past research has tended to draw upon only a very small portion of the wealth of data available on Facebo...
Data collection Using an official roster provided by a college, profile and network data provided by one cohort of college...
Complete network data
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Review of "Tastes, ties, and time: A new social network dataset using Facebook.com"

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Review of "Tastes, ties, and time: A new social network dataset using Facebook.com"

  1. 1. Tastes, ties, and time: A new social network dataset using Facebook.com Lewis, K., Kaufman, J., Gonzalez, M., Wimmer, A. & Christakis, N. Tastes, ties, and time: A new social network dataset using facebook.com. Social Networks In Press, Accepted Manuscript (2008). URL http://dx.doi.org/10.1016/j.socnet.2008.07.002
  2. 2. Paper overview <ul><li>Introduction of a new public datased retrieved from a popular SNS
  3. 3. Features of this dataset
  4. 4. Descriptive findings from this dataset (actually early findings...) </li></ul>
  5. 5. Background <ul><li>Research dataset retrieved from Facebook.com
  6. 6. Why Facebook.com? </li><ul><li>Several articles published on Facebook, accepted as a “standard”
  7. 7. Has profile creation and network articulation as primary community tasks (Lampe et al., 2007)
  8. 8. Facebook communities generally correspond to existing offline network membership (Lampe et al., 2007)
  9. 9. Provides users with standardized profile templates </li></ul></ul>
  10. 10. State of the art <ul><li>Several articles published on Facebook, for example: </li><ul><li>Identity Management: Multiple Presentations of Self in Facebook (DiMicco et al., 2007)
  11. 11. A Familiar Face(book): Profile Elements as Signals in an Online Social Network (Lampe et al., 2007) </li></ul></ul>
  12. 12. State of the art Past research has tended to draw upon only a very small portion of the wealth of data available on Facebook. Most focused only on profile data, ignoring network ties between users: no study made use of data on user tastes
  13. 13. Data collection Using an official roster provided by a college, profile and network data provided by one cohort of college students were downloaded. College provided additional data on those students: link between online profile and college housing record. Five defining features of the dataset: <ul><ul><li>Natural research instrument
  14. 14. Complete network data
  15. 15. Longitudinal data (analysis replied for 4 years)
  16. 16. Data on multiple social relationships (three measures of relationships)
  17. 17. Cultural data (Facebook profiles contains open-ended spaces for respondents -> tastes!) </li></ul></ul>
  18. 18. Three measures of relationships Network size (degree centrality) histograms for Facebook friends, picture friends, and housing groupmates. Note : students with no photo albums (zero outgoing ties) are omitted from outgoing picture friend histogram.
  19. 19. Dataset All explanatory variables are inferred or directly drawn from students' Facebook profiles (e.g. Sex, Home Town, …) Race/ethnicity and socioeconomic status (SES) retrieved with more elaborate procedures (photo albums, socioeconomic national data, ..)
  20. 20. Population demographics <ul><li>Diversity of population allows to make comparison across subgroups </li></ul>
  21. 21. Methodology Network properties as size, density and betweenness centrality calculated using UCINET Network ethno-racial diversity calculated using IQV (index of qualitative variation). It measures the heterogeneity of ego’s network independent of the race/ethnicity of ego
  22. 22. Methods Used OLS regression (multivariables regression) to see how gender, race/ethnicity, SES, and online activity are associated with our network variables of interest
  23. 23. Culture Compiled three spreadsheets of data (on for each kind of taste) with the data retrieved from students' Facebook profile linking the student to their cultural preferences Calculated the proportion of taste overlap for every possible dyads
  24. 24. The shape of cultural proclivities <ul><li>OLS regression coefficients for shared tastes in movies, music and books
  25. 25. Note : p -values determined by MRQAP. All coefficients reported in percentages (%).
  26. 26. * p < .05; ** p < .01; *** p ≤.001. </li></ul>
  27. 27. Conclusion <ul><li>Introduced a new public database available here: http://dvn.iq.harvard.edu/dvn/dv/t3
  28. 28. Starting point to future social network research involving cultural preferences as well as social, socioeconomic and demographic aspect. </li></ul>

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