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The War on Attention Poverty: Measuring Twitter Authority

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The War on Attention Poverty: Measuring Twitter Authority …

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.

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  • 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/