Geomob London September 2011

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Geomob London September 2011

  1. 1. <urban mining>
  2. 2. U C L <who am i>
  3. 3. U C L daniele quercia
  4. 4. U C L
  5. 5. U C L
  6. 6. U C L
  7. 7. U C L
  8. 10. computational social science
  9. 11. U C L </who am i>
  10. 12. <urban mining>
  11. 13. s ocial contacts + social events + privacy
  12. 14. 1. recommend social contacts
  13. 15. How? Using mobility data
  14. 17. keep bluetooth on & record who’s around
  15. 18. upload records on registered profile
  16. 19. get people you may know
  17. 21. 2. recommend social events
  18. 22. mobility data + …
  19. 23. … + listings of social events
  20. 24. On input of area of residence : 1. popular events 2. geographically close 3. popular in area of residence
  21. 25. On input of area of residence : 1. popular events 2. geographically close 3. popular in area of residence 4. TF-IDF 5. K-N Locations 6. K-N Events
  22. 27. Lesson 1: geographically close isn’t the best ;-)
  23. 29. Lesson 2: popular in area rocks ;-)
  24. 31. Lesson 3: geographical patterns matter ;-)
  25. 32. geographical patterns matter geographically close isn’t the best ‘ popular in area’ rocks
  26. 33. [ICDM’10]
  27. 35. pricing billboards
  28. 37. share it all [by Facebook CEO]
  29. 38. share it all [by Facebook CEO] share nothing [by Wisdom of the Granmother’s Foundation]
  30. 39. share it all [by Facebook CEO] share nothing [by Wisdom of the Granmother’s Foundation] share fake data [by Us]
  31. 40. counting phones
  32. 41. counting phones + privacy
  33. 42. Idea:
  34. 43. true location + + “99” fake locations
  35. 44. true location + + “99” fake locations true location + + “99” fake locations true location + + “99” fake locations
  36. 45. #phones?
  37. 46. (random) fake locations
  38. 47. Tube passengers in London & car drivers in Zurich: accurately estimate #people
  39. 48. Tube passengers in London & car drivers in Zurich: accurately estimate #people (18% error with 99% fake locations)
  40. 49. (random) fake locations
  41. 50. (random) fake locations Evade paying taxes? Don’t answer! Flip a coin instead
  42. 51. Yes! [truth]
  43. 52. true location + + “99” fake locations true location + + “99” fake locations true location + + “99” fake locations
  44. 53. promoting location privacy… one lie at a time
  45. 54. O nline & Offline Worlds @danielequercia
  46. 55. social media language personality social media
  47. 56. social media language personality social media we can build monitoring tools
  48. 57. r =.350 word count r =.365 MaxEnt predicting socioeconomic well-being with twitter
  49. 58. predicting personality with twitter
  50. 59. predicting personality with twitter YES, we can!
  51. 60. predicting personality with twitter YES, we can! And only using followers, following, listed!
  52. 61. informal networks in offices
  53. 62. social interactions & space
  54. 63. “ Who talks to whom”
  55. 64. Network
  56. 65. t ools for change
  57. 66. Now: Auralist (music recommender) Next: ‘Nudge’ people for serendipity tools for change
  58. 67. @danielequercia

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