For the last few years, we have been studying existing social media sites and created new ones in the context of London.

1. Urban*: Crowdsourcing for the good of London
@danielequercia
Yahoo! Labs, Barcelona

2. daniele quercia

3. offline & online

4. offline & online

5. Facebook+

6. census deprivation + data

7. So what?

8. Situation

(Its already 75% in the USA)

9. Situation

By 2025 another 1.2 billion living in urban areas

10. Situation

Cities in developing countries: 5M new inhabitants each month

11. Problem
Inequality! Timely allocation of
scarce resources

12. census deprivation + londoners on twitter

13. 1 census deprivation + sentiment

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

15. 3 match sentiment with (census) deprivation
2 classify sentiment of profiles
1 collect profiles & geo-reference them

16. 250K profiles in London (31.5M tweets)
3 seeds: newspaper accounts
1 collect profiles & geo-reference them
1,323 in London neighborhoods  573 in 51 neighborhoods

17. social medialanguage personality

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

19. 2 census deprivation + topics

20. social media
environment
sports
health Royal wedding
Spanish/Portuguese
celebrity gossips
Talk of the City [ICWSM’12]

21. 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)
Facebook
Twitter

22. 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)
Facebook
Twitter
social
econometrics

23. 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)
Facebook
Twitter
social
econometrics

24. Latent Dirichlet Allocation (LDA)

25. Latent Dirichlet Allocation (LDA)

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

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

28. 3 census deprivation + tube trips

32. 4 census deprivation + “mental maps”
Psychological Maps 2.0 [WWW’13]

33. draw a map

39. WEIRD trap!
Few hundreds of WEIRDos
White,Educated,Industrialized,Rich, and Democratic undergraduates

40. WWW

42. launched few months ago > 2K players

43. Regions

44. Regions

45. Boroughs

46. Boroughs

47. Londoners vs. UK vs. World

48. Vibility vs. Exposure

49. Visibility & Social Deprivation

50. 5 Beyond visibility…
UrbanGems.org
To quantify “fuzzy” concepts

52. (a) (b) (c) (d)
(e) (f) (g) (h)
Figure 5: Visual Wordsfor Beauty (top row) and Ugly (bottom row).
and guardrails. The red dots on top-ranked pictures
hose on bottom-ranked ones mean two different things
ormer reflect positive(e.g., happy) visual words, while
Picture Quality Bias. Photos might not necessarily
what they aresupposed to show (representativeurban s
in each neighborhood), and somepictures might beof

53. Research?
This work is at intersection of two emerging fields:
a) computational aesthetic
b) computational geo-cultural modeling

54. unleashing the potential of mobile data
vs0

55. @danielequercia