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

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

    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
      • Evidence of understanding of audience on Twitter: @replies RTs
    58. latest redesign? supports a better model of audience
    59. latest redesign? supports a better model of audience
    60. latest redesign? supports a better model of audience > you see everyone’s updates now
    61.  
    62. Feedback Letting people know their content matters
    63. Ownership Letting people know who contributed what
    64. Serendipity Letting people extend their networks without effort
    65.  
    66. Analyzing influence
    67. finding the influencers
    68. What makes one person more influential?
    69. How many people listen?
    70. How many people listen?
    71. It’s who listens , not how many.
    72. Analyzing influence
    73. Analyzing influence I’m friends with Shaq!!
    74. Analyzing influence I’m famous I’m friends with Shaq!!
    75. Analyzing influence I’m famous I’m friends with Shaq!! I’m special
    76. Analyzing influence Connector Maven
    77. Analyzing influence: The Twitter example
    78. Analyzing influence: The Twitter example Tim “ Importance” of Tim is determined by the importance of the people who follow Tim.
    79. Analyzing influence: The Twitter example 8 = importance / # of outgoing connections
    80. Analyzing influence: The Twitter example 8 + 8 / 3 + 8 / 3 + 8 / 3 = importance / # of outgoing connections = 8 / 3
    81. Analyzing influence: The Twitter example 3 = 8/3 + 2/12 + 1/6 = 3 + 8/3 + 2/12 + 1/6
    82. Analyzing influence: The Twitter example 3 = 8/3 + 2/12 + 1/6 + 8/3 + 2/12 + 1/6 + 3/2 + 3/2
    83. Analyzing influence: The Twitter example 3 = 8/3 + 2/12 + 1/6 + 8/3 + 2/12 + 1/6 + 3/2 + 3/2 and repeat!
    84. “ Honey, this looks familiar—”
    85. “ It’s PageRank, dear. You use it every day.”
    86. Analyze influence wherever people trade feedback
    87. Analyze influence wherever people exchange something
    88. Analyze influence wherever people exchange something asymmetry is your friend!
    89. Analyze influence wherever people exchange something asymmetry is your friend! the opposite may not be true.
    90. Follows try it on Twitter
    91. Favorites, Likes try it on Flickr
    92. Messages, Comments try it on Facebook
    93. Ratings try it on Amazon, Yelp
    94. So. Understanding Influence:
    95. Use it for ranking
    96. Use it for ranking
    97. Use it for sorting
    98. Use it for discovery
    99. Use it for reducing the noise
    100. Web 2.0 Expo San Francisco April 3, 2009 INFLUENCE Gregor Hochmuth dotgrex.com / @grex thanks.

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