talk of the city http://tinyurl.com/cctxbzo tracking emotions in the city http://tinyurl.com/7uvjasy

1. Londoners and Social Media:
Track Community “Happiness” + Target Ads
@danielequercia

2. <who am i>

3. daniele quercia

10. offline & online

11. <goal>

12. social media
language personality
social media

13. social media
<why>

14. social media

15. Pop press pundits (Archbishop England&Walses)
social media
“Social-networking sites “dehumanize” community life”

16. social media

17. social media
1
Q&A

18. social media
2
Q&A

19. social media
3
Q&A

20. CS Researchers:
“Twitter is NOT media
social a social network but a news media”

21. Pop press pundits (Archbishop England&Wales):
social media
“Social-networking sites “dehumanize” community life”
CS Researchers:
“Twitter is NOT a social network but a news media”

22. Pop press pundits (Archbishop England&Wales)
social media
“Social-networking sites “dehumanize” community life”
CS Researchers:
“Twitter is NOT a social network but a news media”
er” ;-)
g to diff
“I be

23. social media
language personality
social media

24. community deprivation  well-being  use of words
?

25. community deprivation  well-being  use of words

26. community deprivation  well-being  use of words

27. Goal
community deprivation  well-being  use of words
1 collect profiles & geo-reference them
2 classify sentiment of profiles
3 match sentiment with (census) deprivation

28. 1 collect profiles & geo-reference them
3 seeds: newspaper accounts
250K profiles in London (31.5M tweets)
1,323 in London neighborhoods  573 in 51 neighborhoods

29. 2 classify sentiment of profiles
Word Count vs. Maximum Entropy

30. Word Count

31. social media
language personality

32. social media
language personality

33. social media
language personality

34. Max Entropy
Training?
Upon 300K tweets with smiley and frowny faces  

35. Word Count vs. Max Entropy

36. Word Count vs. Max Entropy

37. 3 match sentiment with (census) deprivation
Index of Multiple Deprivation

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

39. [CSCW’12] Tracking Gross Community Happiness from Tweets

40. Going beyond sentiment …
Look at the subject matter of tweets!

41. Extract topics from tweets. Easiest way?
Matching Keywords

42. Extract topics from tweets. Easiest way?
Matching Keywords

43. Dictionary of keywords?
A machine learning model?
Training?

44. Use machine learning model (no training required)

45. Latent Dirichlet Allocation (LDA)

48. read profiles & define topics
create virtual bins (latent topics)
assign words to a bin (@ random)
for each bin:
select pair of words
if co-occur more than chance:
keep them in the bin
else:
put them into another bin (@ random)

49. read profiles & define topics
create virtual bins (latent topics)
assign words to a bin (@ random)
for each bin:
select pair of words
Facebook
if co-occur more than chance:
Twitter
keep them in the bin
else:
put them into another bin (@ random)

50. read profiles & define topics
create virtual bins (latent topics)
assign words to a bin (@ random)
for each bin:
select pair of words
Facebook
social
if co-occur more than chance:
Twitter
keep them in the bin
else:
econometrics
put them into another bin (@ random)

51. read profiles & define topics
create virtual bins (latent topics)
assign words to a bin (@ random)
for each bin:
select pair of words
Facebook
social
if co-occur more than chance:
Twitter
keep them in the bin
else:
econometrics
put them into another bin (@ random)

52. Latent Dirichlet Allocation (LDA)

53. Latent Dirichlet Allocation (LDA)

54. social media
environment
sports
health wedding parties
Spanish/Portuguese
celebrity gossips

55. Support Vector Regression
IMD <- SVR(topics)
accuracy: 8.14 in [13.12,46.88]

56. Some areas have very few profiles!
residents +

57. Some areas have very few profiles!
residents + visitors

58. Analyze geo-referenced tweets
(not only residents but also visitors)

61. Linear Regression
R2=.49 (49% of IMD variability explained)

62. So what?

63. Theoretical Implications

64. Practical Implications

65. Ads and the City:
Considering Geographic Distance Goes a Long Way

67. Problem Statement:
Given a venue (new bar/restaurant), suggests guests

68. Problem Statement:
Given a venue (new bar/restaurant), suggests guests

69. Problem Statement:
Given a venue (new bar/restaurant), suggests guests

70. Web ≠ people move!

71. Web ≠ people move!

72. On people mobility (from the literature):
1) likes might matter
2) distance matters
3) “power users” are special

73. On people mobility (from the literature):
1) likes might matter
2) distance matters
3) “power users” are special

74. On people mobility (from the literature):
1) likes might matter
2) distance matters
3) “power users” are special

75. The extent one is a power user ;)

78. HIGH α  travel farther

79. HIGH α  travel farther

82. 1) Naïve Bayesian
2) Bayesian
3) Linear Regression (learn weights)

83. (2)

84. (2)

85. (2)

86. (2)

87. (2)

88. (2)

89. (2)

90. Future (well, current & you could help)

91. 1 complex buildings

92. “Who talks to whom”

93. Network

94. 2 tools for topical & sentiment analysis

95. 3

96. 3

97. 1 Complex Buildings
2 Tools for topical & sentiment analysis
3 urbanopticon.org

98. @danielequercia