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Urban*: Crowdsourcing for the Good of London

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

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Urban*: Crowdsourcing for the Good of London

  1. 1. Urban*: Crowdsourcing for the good of London@danielequerciaYahoo! Labs, Barcelona
  2. 2. daniele quercia
  3. 3. offline & online
  4. 4. offline & online
  5. 5. Facebook+
  6. 6. census deprivation + data
  7. 7. So what?
  8. 8. Situation(Its already 75% in the USA)
  9. 9. SituationBy 2025 another 1.2 billion living in urban areas
  10. 10. SituationCities in developing countries: 5M new inhabitants each month
  11. 11. ProblemInequality! Timely allocation ofscarce resources
  12. 12. census deprivation + londoners on twitter
  13. 13. 1 census deprivation + sentiment
  14. 14. [CSCW’12] Tracking Gross Community Happiness from Tweets
  15. 15. 3 match sentiment with (census) deprivation2 classify sentiment of profiles1 collect profiles & geo-reference them
  16. 16. 250K profiles in London (31.5M tweets)3 seeds: newspaper accounts1 collect profiles & geo-reference them1,323 in London neighborhoods  573 in 51 neighborhoods
  17. 17. social medialanguage personality
  18. 18. r=.350 word count r=.365 MaxEntpredicting socioeconomic well-being with twitter
  19. 19. 2 census deprivation + topics
  20. 20. social mediaenvironmentsportshealth Royal weddingSpanish/Portuguesecelebrity gossipsTalk of the City [ICWSM’12]
  21. 21. read profiles & define topicscreate virtual bins (latent topics)assign words to a bin (@ random)for each bin:select pair of wordsif co-occur more than chance:keep them in the binelse:put them into another bin (@ random)FacebookTwitter
  22. 22. read profiles & define topicscreate virtual bins (latent topics)assign words to a bin (@ random)for each bin:select pair of wordsif co-occur more than chance:keep them in the binelse:put them into another bin (@ random)FacebookTwittersocialeconometrics
  23. 23. read profiles & define topicscreate virtual bins (latent topics)assign words to a bin (@ random)for each bin:select pair of wordsif co-occur more than chance:keep them in the binelse:put them into another bin (@ random)FacebookTwittersocialeconometrics
  24. 24. Latent Dirichlet Allocation (LDA)
  25. 25. Latent Dirichlet Allocation (LDA)
  26. 26. Analyze geo-referenced tweets(not only residents but also visitors)
  27. 27. Linear RegressionR2=.49 (49% of IMD variability explained)
  28. 28. 3 census deprivation + tube trips
  29. 29. 4 census deprivation + “mental maps”Psychological Maps 2.0 [WWW’13]
  30. 30. draw a map
  31. 31. WEIRD trap!Few hundreds of WEIRDosWhite,Educated,Industrialized,Rich, and Democratic undergraduates
  32. 32. WWW
  33. 33. launched few months ago > 2K players
  34. 34. Regions
  35. 35. Regions
  36. 36. Boroughs
  37. 37. Boroughs
  38. 38. Londoners vs. UK vs. World
  39. 39. Vibility vs. Exposure
  40. 40. Visibility & Social Deprivation
  41. 41. 5 Beyond visibility…UrbanGems.orgTo quantify “fuzzy” concepts
  42. 42. (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 pictureshose on bottom-ranked ones mean two different thingsormer reflect positive(e.g., happy) visual words, whilePicture Quality Bias. Photos might not necessarilywhat they aresupposed to show (representativeurban sin each neighborhood), and somepictures might beof
  43. 43. Research?This work is at intersection of two emerging fields:a) computational aestheticb) computational geo-cultural modeling
  44. 44. unleashing the potential of mobile datavs0
  45. 45. @danielequercia

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