The document presents an analysis of measuring authority on Twitter through a proposed method called 'tunkrank,' which aims to address the issue of attention scarcity in an information-rich world. It discusses the influence dynamics and the importance of follower relationships while highlighting challenges such as spam and data manipulation. The conclusion emphasizes the need for effective measures to manage social information overload, positioning tunkrank as a viable solution.

1. The War on Attention Poverty: Measuring Twitter Authority Daniel Tunkelang Google http://www.wvculture.org/history/thisdayinwvhistory/0424.html

2. Disclaimers Much of the material in this presentation is work done prior to my employment at Google.

3. Google is not, to the best of my knowledge, using TunkRank.

4. Any opinions expressed are my own, and do not represent Google's official positions.

5. Executive Summary Authority requires scarcity. http://www.southparkstudios.com/ http://en.wikipedia.org/wiki/Diamond

6. Overview Aboutness and Authority

7. Social Networks 101

8. Measuring Twitter Authority

9. TunkRank

10. Aboutness and Authority http://www.ncgenealogy.org/blogs/ngs2009/2009_04_01_archive.html http://www.clker.com/clipart-2406.html

11. Information Retrieval: Pre-Web http://archimedes.fas.harvard.edu/presentations/2002-03-09/img13.html

12. Information Retrieval: Web http://blogoscoped.com/archive/2007-01-11-n25.html

13. How Authority Matters for IR Promoting official content

14. Demoting spam

15. Ranking everything in between http://whitehouse.org/

16. Social Networking Sites 2003: goes live

17. 2010: claims 400M+ users

18. Global Alexa Top 30 also include:

19. Social Networks = Information Feeds

20. Social Information Overload! http://loiclemeur.com/english/2007/06/im-overload.html

21. What's a Friend?

22. Bands of Reduced Attention http://bhc3.wordpress.com/2009/02/25/the-serendipity-of-attention/

23. Asymmetric Follower Model http://www.engineeringdaily.net/brain-game-weighing-24-coins/

24. Follower Count as Status http://www.southparkstudios.com/

25. Follower Count as Authority? http://loiclemeur.com/english/2008/12/ twitter-we-need-search-by-authority.html http://twithority.com/

26. Buy Followers...on eBay!

27. Exploit Norm of Reciprocity 72% of users ....follow at least 80% of their followers

28. 80% of users... ...have at least 80% of their friends as followers TwitterRank: finding topic-sensitive influential twitterers. [Weng et al, WSDM 2010]

29. Do Actions Speak Louder? influence = “potential of an action of a user to initiate a further action by another user” The Influentials: New Approaches for Analyzing Influence on Twitter [Leavitt et al, 2009] Dan Zarrella's ReTweetability Metric:

30. Gaming Retweet Count Create two users. Tweet. Retweet. Repeat.

31. Retweet counts are low: less than 2% of tweets State of the Twittersphere [Zarrella, June 2009]

32. Twitter “cyborgs” already produce retweet spam Twitter Cyborgs [Mowbray and Andrade, 2010]

33. Actions can be (and are) Faked

34. What Should We Measure? “in an information-rich world, the wealth of information means... a scarcity of whatever it is that information consumes... the attention of its recipients.” Designing Organizations for an Information-Rich World [Herbert Simon, 1971]

35. Introducing...TunkRank!

36. Demo http://tunkrank.com/

37. Retweet Decision Model

38. Simple Recurrence Measures expected propagation of tweet from X p notice = total attention user devotes to Twitter p retweet = probability that user retweets Note Following(Y) in denominator!

39. Discourages Exploiting Reciprocity Indiscriminate followers who follow many users make low contributions to TunkRank.

40. Consistent with idea that influence correlates to high follower-friend ratio.

41. But TunkRank only considers user's followers, not user's friends.

42. TunkRank Pros and Cons Based entirely on follower graph. Ignores retweets, etc.

43. Resists manipulation. Uniformly distributes attention among followers. Distribution is probably a power law.

44. But “fake follow” data is hidden.

45. Bug or a feature?

46. Press http://techcrunch.com/2010/06/16/barackobama-techcrunch- twitter-followers/

47. http://blogs.forbes.com/firewall/2010/07/09/a-better-way-to-filter- twitters-spambots-ask-google/

48. Research TwitterRank: finding topic-sensitive influential twitterers. [Weng et al, 2010] Overcoming Spammers in Twitter – A Tale of Five Algorithms [Gayo-Avello and Brenes, 2010] Nepotistic Relationships in Twitter and their Impact on Rank Prestige Algorithms [Gayo-Avello, 2010]

49. Go TunkRank! [Gayo-Ayello, 2010] similar to PageRank but better vs. “cheating”

50. aggressive marketers almost indistinguishable from common users

51. spammers grab small amount of global available prestige

52. agrees with PageRank for top-ranked users

53. simple, induces plausible rankings, severely penalizes spammers compared to PageRank

54. Room for Improvement Still can be gamed through fake users.

55. Multiply by follow cost?

56. Consider user actions?

57. Topic-sensitivity?

58. Non-uniform distribution? Tradeoff of simplicity vs. realism. http://followcost.com/

59. Conclusion Web IR is unthinkable without modeling attention scarcity.

60. Social networks are new and increasingly important information feeds.

61. We need measures to mitigate social information overload.

62. TunkRank is a promising proof-of-concept.

63. Thank you! ...and thanks to Jason Adams for developing and maintaining the http://tunkrank.com site! Questions? Email: [email_address] Twitter: @dtunkelang Blog: http://thenoisychannel.com/