<urban mining>
U   C   L <who am i>
U   C   L daniele quercia
U   C   L
U   C   L
U   C   L
U   C   L
 
 
computational social science
U   C   L </who am i>
<urban mining>
s ocial contacts + social events + privacy
1. recommend  social contacts
How? Using  mobility data
 
keep bluetooth on & record who’s around
upload records on registered profile
get people you may know
 
2. recommend social events
mobility data +  …
…  + listings of social events
On input of  area of residence :  1.  popular events  2.  geographically close 3.  popular in area of residence
On input of  area of residence :  1.  popular events  2.  geographically close 3.  popular in area of residence 4.  TF-IDF...
 
Lesson 1: geographically close isn’t the best ;-)
 
Lesson 2: popular in area rocks ;-)
 
Lesson 3: geographical patterns matter ;-)
geographical patterns matter geographically close isn’t the best  ‘ popular in area’ rocks
[ICDM’10]
 
pricing billboards
 
share it all [by Facebook CEO]
share it all [by Facebook CEO] share nothing [by Wisdom of the Granmother’s Foundation]
share it all [by Facebook CEO] share nothing [by Wisdom of the Granmother’s Foundation] share fake data [by Us]
counting phones
counting phones  + privacy
Idea:
true location +  + “99” fake locations
true location +  + “99” fake locations true location +  + “99” fake locations true location +  + “99” fake locations
#phones?
(random)  fake locations
Tube passengers in London & car drivers in Zurich:  accurately estimate #people
Tube passengers in London & car drivers in Zurich:  accurately estimate #people (18% error with 99% fake locations)
(random)  fake locations
(random)  fake locations Evade paying taxes? Don’t answer! Flip a coin instead
Yes! [truth]
true location +  + “99” fake locations true location +  + “99” fake locations true location +  + “99” fake locations
promoting location privacy… one lie at a time
O nline & Offline Worlds @danielequercia
social media  language personality social media
social media  language personality social media we can build monitoring tools
r =.350 word count r =.365 MaxEnt predicting   socioeconomic  well-being  with twitter
predicting personality with twitter
predicting personality with twitter YES, we can!
predicting personality with twitter YES, we can! And only using followers, following, listed!
informal networks in offices
social interactions & space
“ Who talks to whom”
Network
t ools for change
Now:  Auralist  (music recommender) Next:  ‘Nudge’ people for serendipity tools for  change
@danielequercia
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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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