Talk of the City: Londoners and Social Media

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talk of the city
http://tinyurl.com/cctxbzo

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

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  • Talk of the City: Londoners and Social Media

    1. 1. Londoners and Social Media:Track Community “Happiness” + Target Ads@danielequercia
    2. 2. <who am i>
    3. 3. daniele quercia
    4. 4. offline & online
    5. 5. <goal>
    6. 6. social media language personality social media
    7. 7. social media <why>
    8. 8. social media
    9. 9. Pop press pundits (Archbishop England&Walses) social media“Social-networking sites “dehumanize” community life”
    10. 10. social media
    11. 11. social media 1 Q&A
    12. 12. social media 2 Q&A
    13. 13. social media 3 Q&A
    14. 14. CS Researchers:“Twitter is NOT media social a social network but a news media”
    15. 15. 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”
    16. 16. 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
    17. 17. social media language personality social media
    18. 18. community deprivation  well-being  use of words ?
    19. 19. community deprivation  well-being  use of words
    20. 20. community deprivation  well-being  use of words
    21. 21. Goalcommunity deprivation  well-being  use of words1 collect profiles & geo-reference them2 classify sentiment of profiles3 match sentiment with (census) deprivation
    22. 22. 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
    23. 23. 2 classify sentiment of profiles Word Count vs. Maximum Entropy
    24. 24. Word Count
    25. 25. social media language personality
    26. 26. social media language personality
    27. 27. social media language personality
    28. 28. Max EntropyTraining?Upon 300K tweets with smiley and frowny faces  
    29. 29. Word Count vs. Max Entropy
    30. 30. Word Count vs. Max Entropy
    31. 31. 3 match sentiment with (census) deprivation Index of Multiple Deprivation
    32. 32. predicting socioeconomic well-being with twitter r=.350 word count r=.365 MaxEnt
    33. 33. [CSCW’12] Tracking Gross Community Happiness from Tweets
    34. 34. Going beyond sentiment … Look at the subject matter of tweets!
    35. 35. Extract topics from tweets. Easiest way?Matching Keywords
    36. 36. Extract topics from tweets. Easiest way?Matching Keywords
    37. 37. Dictionary of keywords?A machine learning model?Training?
    38. 38. Use machine learning model (no training required)
    39. 39. Latent Dirichlet Allocation (LDA)
    40. 40. read profiles & define topicscreate 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)
    41. 41. read profiles & define topicscreate 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)
    42. 42. read profiles & define topicscreate 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)
    43. 43. read profiles & define topicscreate 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)
    44. 44. Latent Dirichlet Allocation (LDA)
    45. 45. Latent Dirichlet Allocation (LDA)
    46. 46. social mediaenvironmentsportshealth wedding parties Spanish/Portuguese celebrity gossips
    47. 47. Support Vector Regression IMD <- SVR(topics) accuracy: 8.14 in [13.12,46.88]
    48. 48. Some areas have very few profiles! residents +
    49. 49. Some areas have very few profiles! residents + visitors
    50. 50. Analyze geo-referenced tweets(not only residents but also visitors)
    51. 51. Linear Regression R2=.49 (49% of IMD variability explained)
    52. 52. So what?
    53. 53. Theoretical Implications
    54. 54. Practical Implications
    55. 55. Ads and the City:Considering Geographic Distance Goes a Long Way
    56. 56. Problem Statement: Given a venue (new bar/restaurant), suggests guests
    57. 57. Problem Statement: Given a venue (new bar/restaurant), suggests guests
    58. 58. Problem Statement: Given a venue (new bar/restaurant), suggests guests
    59. 59. Web ≠ people move!
    60. 60. Web ≠ people move!
    61. 61. On people mobility (from the literature): 1) likes might matter 2) distance matters 3) “power users” are special
    62. 62. On people mobility (from the literature): 1) likes might matter 2) distance matters 3) “power users” are special
    63. 63. On people mobility (from the literature): 1) likes might matter 2) distance matters 3) “power users” are special
    64. 64. The extent one is a power user ;)
    65. 65. HIGH α  travel farther
    66. 66. HIGH α  travel farther
    67. 67. 1) Naïve Bayesian2) Bayesian3) Linear Regression (learn weights)
    68. 68. (2)
    69. 69. (2)
    70. 70. (2)
    71. 71. (2)
    72. 72. (2)
    73. 73. (2)
    74. 74. (2)
    75. 75. Future (well, current & you could help)
    76. 76. 1 complex buildings
    77. 77. “Who talks to whom”
    78. 78. Network
    79. 79. 2 tools for topical & sentiment analysis
    80. 80. 3
    81. 81. 3
    82. 82. 1 Complex Buildings2 Tools for topical & sentiment analysis3 urbanopticon.org
    83. 83. @danielequercia

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