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. (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

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

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

46. people who listen.

47. people who follow them.

48. = Audience

49. Audience

53. audience : a common understanding of who’s listening

54. audience : a common understanding of who’s listening to you

55. vs.

57. I know my audienceI know some numbers

58. Evidence of understanding of audience on Twitter: @replies RTs

59. latest redesign? supports a better model of audience

61. > you see everyone’s updates now

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

67. Analyzing influence

68. finding the influencers

69. What makes one person more influential?

70. How many people listen?

72. It’s who listens, not how many.

73. Analyzing influence

74. I’m friends with Shaq!! Analyzing influence

75. I’m famou s I’m friends with Shaq!! Analyzing influence

76. I’m famou s I’m friends with Shaq!! I’m speci al Analyzing influence

77. Connector Maven

78. Analyzing influence: The Twitter example

79. Tim “Importance” of Tim is determined by the importance of the people who follow Tim. Analyzing influence: The Twitter example

80. 8 = importance / # of outgoing connections Analyzing influence: The Twitter example

81. 8 + 8 / 3 Analyzing influence: The Twitter example = importance / # of outgoing connections = 8 / 3

82. 3 = 8/3 + 2/12 + 1/6 = 3 + 8/3 + 2/12 + 1/6 Analyzing influence: The Twitter example

83. 3 = 8/3 + 2/12 + 1/6 + 8/3 + 2/12 + 1/6 Analyzing influence: The Twitter example + 3/2

84. 3 = 8/3 + 2/12 + 1/6 + 8/3 + 2/12 + 1/6 Analyzing influence: The Twitter example + 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. 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

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.