War on Attention Poverty

Posted: 14 July 2010 in Uncategorized
Tags: , ,

Daniel Tunkelang has posted his slides from his talk at AT&T Labs on TunkRank over at the Noisy Channel. Embedded below for your viewing pleasure:

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Comments
  1. Ram says:

    Hi Jason, I was wondering about some specifics in the TunkRank formula. For example how are p^{notice}, p^{retweet} computed. Since the computation is recursive, is the rank of a twitterer without any followers set to zero. Is this where the recursion ends? Details about such implementation specifics would be great. Thanks for your time.

    • Jason Adams says:

      I ignored p^{notice} originally since it wasn’t a part of the algorithm when Daniel posted it. I continue to leave it out, since it can be factored out. If you assume a uniform probability for all users, then the scores are all scaled accordingly. That said, I have plans to estimate these probabilities rather than just assuming uniform probabilities. p^{retweet} = 0.05, which was an estimate I came to after discussing it with several members of the community. That one is probably a little lower, like maybe 2-3%, and again, I will be introducing better estimates in the future.

  2. ram says:

    hey thanks, Jason. I see you are still working on p^notice. Anyways, I see an update on tunkrank.com that shows top followers given a twitter id, that is those which contribute most attention. I was wondering about the algorithm/heuristic that is used to arrive at these twitter ids. I see these are different from most influential followers which can be easily elicited by comparing tunkranks of all followers.

  3. Jason Adams says:

    Those are the followers who contribute the most to your tunkrank score. So it’s your followers ordered (descending) by TunkRank(Y) / Following(Y).

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