The War on Attention Poverty: Measuring Twitter Authority As social networks like Facebook and Twitter have grown in popularity, we've had ample opportunity to appreciate Herb Simon's admonition that "a wealth of information creates a poverty of attention". Since there is no way we can hope to follow all of the information being shared by our social networks, we need some filtering or ranking mechanism. A broad class of approaches involves determining which authors are the most authoritative or influential. There are already a variety of proposed authority measures, as well as research on their effectiveness. In this talk, I will review the various attempts that have been made to measure Twitter authority. In particular, I will discuss the work on TunkRank, a measure inspired by PageRank that explicitly models attention scarcity.

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

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

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

14. Demoting spam

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

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!

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

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!

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

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

44. But “fake follow” data is hidden.

45. Bug or a feature?

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]

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

55. Multiply by follow cost?

56. Consider user actions?

57. Topic-sensitivity?

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

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/