#Measuring in 2010 – Analyzing the Twitter Data of #Measure Twitterati

#Measure in 2010

Who influences the influencers?

#Measure Twitter Graph 2010

Click through the image for a high res picture . . . it is pretty big so it may take a second or two.

#Measure Twitterati

In preparation for publicizing the Web Analytics Association Spring Awards Gala I wanted to know who were the most influential members of the #measure Twitterverse. Fortunately, @minethatdata had great foresight to store the tweets tagged with #measure on Twapper Keeper.

@Minethatdata has been posting about the dynamics of the group for a while so I took a different tactic. That, and somehow improving upon the declartion that @MicheleHinojosa is the oxygen of the community seemed impossible.

Data Distribution

Tweets with the #measure hastag are distributed as shown in this graph, with just a selection of users producing most of the content:

Content is King

I used a scoring algorithm to value an increased variety of content, specifically Entropy as defined by:

This is applied to the content of all tweets from a user in 2010 tagged with #measure; entropy is a calculated metric which Google uses in part to value content on web pages.

Final Algorithm

The final algorithm turned out like this:

Value = Count of Tweets + Entropy – 0.5 * Count of Retweets

Subtracting a bit for retweeting to value original content slightly higher.


Interesting to me was the value of @ulyssez, who score very high with this algorithm. Get him to re-tweet your stuff to gain followers!

Also interesting are the groups outside the main group of information exchange, sometimes consisting primarily of vendors. If they are tweeting to sell to the #measure crowd, 2010 was anything but a success in that effort.


I did not take into account clicks on links, or tweets being retweeted which would improve the utility for sure.

Email Michael

Questions? Comments?

Interested in working with me?

Email me at mdh@michaeldhealy.com.

Technorati Tags: Graph Theory, Measure, Social Graph

This entry was posted in Analytics, Python, Social Media, Twitter and tagged , , . Bookmark the permalink.

6 Responses to #Measuring in 2010 – Analyzing the Twitter Data of #Measure Twitterati

  1. Pingback: Tweets that mention #Measuring in 2010 – Analyzing the Twitter Data of #Measure Twitterati | MichaelDHealy.com -- Topsy.com

  2. Interesting analysis. Could the huge impact of @ulyssez be correlated with the fact he follows twice as many people has people follow him? Although I don’t see that as a factor in your formula.

    • I don’t always reply to someone’s good Tweet content, but I often read and appreciate them. My goal is to create a measurement that would somehow return tweets that are just that.

      Followers were not even considered, the algorithm is exactly as I laid out. @Ulyssyz just tweeted with the #measure hashtag . . . A LOT! He also had a great deal of variety in his tweets; something that I consider an indicator of good, original content.

      I rewarded people who:

      Put out original content
      Put out content on different topics

  3. Pingback: Gilligan on Data by Tim Wilson » Blog Archive » Twitter User Analysis — Still Searching for the Perfect Answer

  4. Pingback: Explaining #Measure in 2010 – Limitations and Improvements of Twitter Content Valuation | MichaelDHealy.com

  5. It’s difficult to find experienced people on this subject, however,
    you sound like you know what you’re talking about! Thanks

Leave a Reply

Your email address will not be published. Required fields are marked *

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <strike> <strong>