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Finding & Analyzing Influence (Gregor Hochmuth) - Web 2.0 Expo San Francisco -

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Session from Web 2.0 Expo San Francisco, April 3, 2009

Finding Influence: Design Patterns for Smarter Crowds

Who’s important and how do I know? Who has the scoop and how will I find out? In any of your network of connections, some people are more interesting to you than others. Influence is about applying that understanding at large scale to the content people share and knowing who’s interesting.

http://www.web2expo.com/webexsf2009/public/schedule/detail/7796

Published in: Technology
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Finding & Analyzing Influence (Gregor Hochmuth) - Web 2.0 Expo San Francisco -

  1. Web 2.0 Expo San Francisco April 3, 2009 INFLUENCE Gregor Hochmuth dotgrex.com / @grex Finding and applying
  2. Web 2.0 Expo San Francisco April 3, 2009 Gregor Hochmuth dotgrex.com * This is not a Google presentation. This is independent pondering prior to my current work at Google.
  3. We need new models for understanding what’s interesting
  4. We need new models for understanding what’s interesting (right now)
  5. While some have been busy building “recommender systems” for the last 20 years …
  6. … others just brought our best recommender system online:
  7. People we know
  8. People we trust
  9. People we #follow
  10. We don’t need machines anymore to tell us what’s interesting.
  11. Our friends do that now.
  12. The new problem is:
  13. Making machines understand what’s interesting
  14. Making machines understand who’s interesting
  15. Making machines understand who’s important
  16. Making machines understand who’s connected
  17. Making machines understand who’s safe to ignore
  18. Making machines understand INFLUENCE
  19. Understanding influence ↓ who’s interesting ↓ what’s interesting
  20. Understanding influence ↓ who’s interesting ↓ what’s interesting
  21. Understanding influence ↓ who’s interesting ↓ what’s interesting
  22. Understanding influence ↓ interesting people ↓ interesting content
  23. Understanding influence ↓ interesting people ↕ interesting content
  24. We need new models for understanding what’s interesting (right now)
  25. We need new models for understanding what’s interesting right now
  26. sort by: Most Recent
  27.  
  28.  
  29.  
  30.  
  31.  
  32. sort by: Most Recent
  33. sort by: Most Recent
  34.  
  35. sort by: Most Recent
  36. sort by: Most interesting
  37. sort by: Most timely
  38. sort by: Most influential
  39. Influencers have things to spread
  40. Influencers have things to spread.
  41. Influencers are everywhere.
  42. Influencers are everyday people.
  43. Influencers are in every social circle.
  44. But influencers need … people who listen
  45.  
  46. people who listen.
  47. people who follow them.
  48. = Audience
  49. Audience
  50. Audience
  51. Audience
  52. Audience
  53. audience : a common understanding of who’s listening
  54. audience : a common understanding of who’s listening
  55. vs.
  56. vs.
  57. vs. I know my audience I know some numbers
  58. <ul><li>Evidence of understanding of audience on Twitter: @replies RTs </li></ul>
  59. latest redesign? supports a better model of audience
  60. latest redesign? supports a better model of audience
  61. latest redesign? supports a better model of audience > you see everyone’s updates now
  62.  
  63. Feedback Letting people know their content matters
  64. Ownership Letting people know who contributed what
  65. Serendipity Letting people extend their networks without effort
  66.  
  67. Analyzing influence
  68. finding the influencers
  69. What makes one person more influential?
  70. How many people listen?
  71. How many people listen?
  72. It’s who listens , not how many.
  73. Analyzing influence
  74. Analyzing influence I’m friends with Shaq!!
  75. Analyzing influence I’m famous I’m friends with Shaq!!
  76. Analyzing influence I’m famous I’m friends with Shaq!! I’m special
  77. Analyzing influence Connector Maven
  78. Analyzing influence: The Twitter example
  79. Analyzing influence: The Twitter example Tim “ Importance” of Tim is determined by the importance of the people who follow Tim.
  80. Analyzing influence: The Twitter example 8 = importance / # of outgoing connections
  81. Analyzing influence: The Twitter example 8 + 8 / 3 + 8 / 3 + 8 / 3 = importance / # of outgoing connections = 8 / 3
  82. Analyzing influence: The Twitter example 3 = 8/3 + 2/12 + 1/6 = 3 + 8/3 + 2/12 + 1/6
  83. Analyzing influence: The Twitter example 3 = 8/3 + 2/12 + 1/6 + 8/3 + 2/12 + 1/6 + 3/2 + 3/2
  84. Analyzing influence: The Twitter example 3 = 8/3 + 2/12 + 1/6 + 8/3 + 2/12 + 1/6 + 3/2 + 3/2 and repeat!
  85. “ Honey, this looks familiar—”
  86. “ It’s PageRank, dear. You use it every day.”
  87. Analyze influence wherever people trade feedback
  88. Analyze influence wherever people exchange something
  89. Analyze influence wherever people exchange something asymmetry is your friend!
  90. Analyze influence wherever people exchange something asymmetry is your friend! the opposite may not be true.
  91. Follows try it on Twitter
  92. Favorites, Likes try it on Flickr
  93. Messages, Comments try it on Facebook
  94. Ratings try it on Amazon, Yelp
  95. So. Understanding Influence:
  96. Use it for ranking
  97. Use it for ranking
  98. Use it for sorting
  99. Use it for discovery
  100. Use it for reducing the noise
  101. Web 2.0 Expo San Francisco April 3, 2009 INFLUENCE Gregor Hochmuth dotgrex.com / @grex thanks.

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