The Noisy Channel

 

API for TunkRank Scores

April 4th, 2009 · 2 Comments · General

I hope that most readers here have had a chance to try out TunkRank. TunkRank is an application Jason Adams built, in response to a challenge to implement a measure that takes a PageRank-like approach to measuring influence on Twitter.

To my delight:

  • TunkRank has become  an influential user on Twitter, with 47 followers, a Twitter Grader score in the  80th percentile, and a TunkRank score in the 83rd percentile.
  • The TunkRank page has a Google PageRank of 4–impressive for such a new site! For perspective, this blog has a PageRank of 5.
  • TunkRank has become more than a stand-alone site. It now offers an API so that people can use TunkRank scores in their own applications. Note that the raw TunkRank score (which is what the API gives you) are meaningful without the percentiles, since it models the expected number of users who will view a tweet by that user.

I’ve observed anecdotally that, when two users have similar numbers of followers, TunkRank favors the user who follows fewer users. That is particularly interesting, since the TunkRank measure only looks at the users who follow you, not the users whom you follow.

This hypothesis is consistent with my claim that users who follow a lot of other users generally participate in a culture of reciprocity (or, to put it less gently, an attention Ponzi scheme) that leads to their obtaining followers who themselves follow a lot of other users. A user’s follower-to-following ratio signals the likelihood that a user is to reciprocate if you follow him or her.

I suspect that the expectation of reciprocation is negatively correlates to a user’s TunkRank (and, in my view, influence), and that the best test for this hypothesis is to see if, holding follower count constant, the follower-to-following ratio correlates positively to TunkRank.

In any case, I’m excited about the progress, and again congratulate Jason for making this a reality.

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