The War on Attention Poverty: Measuring Twitter Authority


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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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The War on Attention Poverty: Measuring Twitter Authority

  1. The War on Attention Poverty: Measuring Twitter Authority Daniel Tunkelang Google
  2. Disclaimers <ul><li>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. </li></ul>
  5. Executive Summary Authority requires scarcity.
  6. Overview <ul><li>Aboutness and Authority
  7. Social Networks 101
  8. Measuring Twitter Authority
  9. TunkRank </li></ul>
  10. Aboutness and Authority
  11. Information Retrieval: Pre-Web
  12. Information Retrieval: Web
  13. How Authority Matters for IR <ul><li>Promoting official content
  14. Demoting spam
  15. Ranking everything in between </li></ul>
  16. Social Networking Sites <ul><li>2003: goes live
  17. 2010: claims 400M+ users
  18. Global Alexa Top 30 also include: </li></ul>
  19. Social Networks = Information Feeds
  20. Social Information Overload!
  21. What's a Friend?
  22. Bands of Reduced Attention
  23. Asymmetric Follower Model
  24. Follower Count as Status
  25. Follower Count as Authority? twitter-we-need-search-by-authority.html
  26. Buy Followers...on eBay!
  27. Exploit Norm of Reciprocity <ul><li>72% of users ....follow at least 80% of their followers
  28. 80% of users... ...have at least 80% of their friends as followers </li></ul>TwitterRank: finding topic-sensitive influential twitterers. [Weng et al, WSDM 2010]
  29. Do Actions Speak Louder? <ul><li>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] </li></ul><ul><li>Dan Zarrella's ReTweetability Metric: </li></ul>
  30. Gaming Retweet Count <ul><li>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] </li></ul>
  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
  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 <ul><li>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. </li></ul>
  42. TunkRank Pros and Cons <ul><li>Based entirely on follower graph. </li><ul><ul><li>Ignores retweets, etc.
  43. Resists manipulation. </li></ul></ul><li>Uniformly distributes attention among followers. </li><ul><ul><li>Distribution is probably a power law.
  44. But “fake follow” data is hidden.
  45. Bug or a feature? </li></ul></ul></ul>
  46. Press <ul><li> twitter-followers/
  47. twitters-spambots-ask-google/ </li></ul>
  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] <ul><li>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 </li></ul>
  54. Room for Improvement <ul><li>Still can be gamed through fake users.
  55. Multiply by follow cost?
  56. Consider user actions?
  57. Topic-sensitivity?
  58. Non-uniform distribution? </li></ul>Tradeoff of simplicity vs. realism.
  59. Conclusion <ul><li>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. </li></ul>
  63. Thank you! ...and thanks to Jason Adams for developing and maintaining the site! Questions? Email: [email_address] Twitter: @dtunkelang Blog: