1. <urban mining>

2. U C L <who am i>

3. U C L daniele quercia

4. U C L

5. U C L

6. U C L

7. U C L

10. computational social science

11. U C L </who am i>

12. <urban mining>

13. s ocial contacts + social events + privacy

14. 1. recommend social contacts

15. How? Using mobility data

17. keep bluetooth on & record who’s around

18. upload records on registered profile

19. get people you may know

21. 2. recommend social events

22. mobility data + …

23. … + listings of social events

24. On input of area of residence : 1. popular events 2. geographically close 3. popular in area of residence

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

27. Lesson 1: geographically close isn’t the best ;-)

29. Lesson 2: popular in area rocks ;-)

31. Lesson 3: geographical patterns matter ;-)

32. geographical patterns matter geographically close isn’t the best ‘ popular in area’ rocks

33. [ICDM’10]

35. pricing billboards

37. share it all [by Facebook CEO]

38. share it all [by Facebook CEO] share nothing [by Wisdom of the Granmother’s Foundation]

39. share it all [by Facebook CEO] share nothing [by Wisdom of the Granmother’s Foundation] share fake data [by Us]

40. counting phones

41. counting phones + privacy

42. Idea:

43. true location + + “99” fake locations

44. true location + + “99” fake locations true location + + “99” fake locations true location + + “99” fake locations

45. #phones?

46. (random) fake locations

47. Tube passengers in London & car drivers in Zurich: accurately estimate #people

48. Tube passengers in London & car drivers in Zurich: accurately estimate #people (18% error with 99% fake locations)

49. (random) fake locations

50. (random) fake locations Evade paying taxes? Don’t answer! Flip a coin instead

51. Yes! [truth]

52. true location + + “99” fake locations true location + + “99” fake locations true location + + “99” fake locations

53. promoting location privacy… one lie at a time

54. O nline & Offline Worlds @danielequercia

55. social media language personality social media

56. social media language personality social media we can build monitoring tools

57. r =.350 word count r =.365 MaxEnt predicting socioeconomic well-being with twitter

58. predicting personality with twitter

59. predicting personality with twitter YES, we can!

60. predicting personality with twitter YES, we can! And only using followers, following, listed!

61. informal networks in offices

62. social interactions & space

63. “ Who talks to whom”

64. Network

65. t ools for change

66. Now: Auralist (music recommender) Next: ‘Nudge’ people for serendipity tools for change

67. @danielequercia