Apr
28

Another Way to Classify Twitter Users

Recently, blogger Michael Klier came introduced a Greasemonkey script for identifying Twitter spammers based on the recommendations of a site called "The Twitter Blacklist." The site, which determines who is a Twitter spammer based on a "follow factor" ratio, has blocked 164 spammers to date. Michael’s script looks up the username of the visited Twitter profile, and, if the user is identified as a spammer, displays a hard to miss warning message at the topic of the screen:

twitter-blacklist

 

 

(image via CH!MER!C.de)

Inspired by this script, blogger Andreas Gohr decided to create a script of his own using Evan Prodromou’s new scale for deciding who to follow.

The scale Evan proposed is as follows:

  • 1:5 = twittercaster
  • 1:2 = notable
  • 1:1 = socially healthy
  • 2:1 = newbie or social climber
  • 5:1 = twitter spammer

This new script not only identifies Twitter spammers, but also classifies all Twitterers into categories based on their following to follower ratio, and displays their category in a colored bar at the top of the screen:

twitterclassify 

 

(image via splitbrain)

Considering Louis Gray’s recent classification brouhaha which categorized people as listeners, middle ground, conversationalists, or megaphones, it’s interesting to compare his findings with the classifications made by the script.

Below are Louis’s findings updated to include the new classification level as determined by this greasemonkey script:

Twitter’s Listeners (Ratio of Updates to Followers of Less than 1)

Twitter’s Middle Ground (Ratio of Updates to Followers of 1 to 2.0)

Twitter’s Conversationalists (Ratio of Updates to Followers of 2.0 to 5.0) 

Twitter’s Megaphones (Ratio of Updates to Followers of more than 5.0) 

What Does It Mean?

As you can tell, this represents a completely different way to categorize users – some of our megaphones become healthy and some of our listeners become twittercasters. But is this way any more helpful or accurate?

And then there is Dave Winer, who has his own method to determine who’s responsible for "Twitter spewage." But who is right?

I personally am using an auto-follow/auto-welcome script, something I turned on after Marshall posted the RWW team’s Twitter usernames in a recent post since I wanted to add the readers that were following me. That makes me socially healthy according to this script, but to be honest, if left to my own devices, I probably wouldn’t have added everyone that had added me…at least not right away. Scoble, too, had auto-followed people for quite some time, until he maxed out at 20,000. But here he sits at "socially healthy." I don’t know about you, but I wouldn’t call Scoble’s user of Twitter socially healthy!

What do you think?

(P.S. Thanks to Corvida for discovering this script!)

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