50 signals used to compute your Klout score

Posted: August 3rd, 2011 | Author: | Filed under: Klout | 3 Comments »

In my ongoing quest to deconstruct Klout, I’ve decided to begin to tackle the question “How is my Klout computed?” by looking at what signals make up an individual’s score. Klout CEO Joe Fernandez stated that his company’s score is computed using at least 50 signals. This post is my best guess at those 50 variables. Below I have a breakdown of putative signal by source (Twitter, Facebook, LinkedIn, etc.)


  2. following – followers
  3. total RT
  4. weighted total RT
  5. unique RT
  6. weighted unique RT
  7. RT/tweet
  8. @mentions
  9. weighted @mentions
  10. unique @mentioners
  11. weighted unique @mentioners
  12. @mentions/tweet
  13. weighted @mentions/tweet


  1. friends
  2. total likes
  3. likes/post
  4. total comments
  5. comments/post


  1. recommenders
  4. connections

The astute among you will notice that only 22 signals are mentioned above, however, this fails to account for time, one critical aspect of Klout. Below I have a plot of my Network Influence subscore of my Klout score from a few days ago.

You will notice a big drop towards the beginning of the plot. This occurred exactly one month after my initial Klout post that was tweeted by Robert Scoble. That was the point at which my Klout began to increase significantly (it has since decreased significantly). If we include a each of the scores above over all time and the past month, that yields 44 signals. In addition, the phrase “In the past 90 days” appears in the new Klout UI (pictured above), so I don’t think its a huge leap to infer that each signal is also used over a 90 day period as well, yielding 66 signals. Finally, Klout now allows you to connect your Foursquare and Youtube accounts, so I assume they are tracking friends, checkins, comments, mayorships, Youtube thumbs up, Youtube comments, etc., yielding an ever larger signal total.

I don’t actually think that all of these “raw” signals are being used directly to calculate scores, that would be naive. I’m sure scores/totals are transformed (perhaps log), then normalized on a scale from 0 to 100, similar to the Klout score. I also think its likely that several of the individual signals listed above are multiplied or otherwise combined to create composite signals. As an example, its impressive if you are retweeted often OR if you receive many @ mentions, however its super impressive/kloutastic if you are retweeted often AND receive many @ mentions. A composite signal may capture that interplay.

In closing, I think someone with some time and access to the Klout api, could use these signals to reconstruct the Klout algorithm. If you’d like to try, shoot me an email and I’d be happy to help in my spare time.

3 Comments on “50 signals used to compute your Klout score”

  1. 1 Sanford said at 2:06 pm on August 4th, 2011:

    Let me simply say – I love your feature extraction efforts. Suffice it to say, I would agree that you are on the right track.

  2. 2 Dejapalanormal said at 3:21 am on August 7th, 2011:

    Following your followers could improve your Klout score? I thought having 10 times more followers than persons you are following would be better for your Klout score.
    Thanks for the article 😉

  3. 3 Alex Braunstein said at 7:19 am on August 7th, 2011:

    I simply said that these signals are used to compute your Klout score, not that they are used as positive or negative signals. Having a similar number of followers and people you are following is actually a signal for a spam account in my opinion (if the number is high enough).

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