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Aaron Parecki

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  • Bayesian Twitter Client

    September 21, 2010

    Summary

    Gathering signal from the noise. Classify tweets automatically by learning what the user is interested in.

    Criteria for Positive Input

    • Clicking on a link in a tweet
    • Replying to a tweet
    • Favoriting a tweet

    Inputs to Bayesian Filter

    • Username of the tweeter
    • Number of people mentioned in the tweet
    • Number of links in the tweet
    • Is the tweet a RT?
    • Number of letters in the tweet other than links
    • Expand the links so that words in the URL are counted

    Other ranking factors

    • Search for "congrats" or "so happy for you" to find significant changes in people's lives. suggested by caseorganic
    • Look for an unusual volume of replies in your timeline to specific people to see what's grabbing your friends' attentions.
    Tue, Sep 21, 2010 11:13am -08:00
    1 mention

    Other Mentions

    • www.scoop.it
      Sat, Feb 1, 2014 6:35am -08:00
Posted in /articles

Hi, I'm Aaron Parecki, Director of Identity Standards at Okta, and co-founder of IndieWebCamp. I maintain oauth.net, write and consult about OAuth, and participate in the OAuth Working Group at the IETF. I also help people learn about video production and livestreaming. (detailed bio)

I've been tracking my location since 2008 and I wrote 100 songs in 100 days. I've spoken at conferences around the world about owning your data, OAuth, quantified self, and explained why R is a vowel. Read more.

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