Mapping Ratata: Who’s Hot?

I wanted to play around in Gephi a bit more after my previous post about visualizing my social network on Facebook. So for my second project I turned my eyes to Ratata, a Swedish blog community in Finland with just over 1200 bloggers. A friend of mine, Poppe (also on Ratata), has been talking about analyzing the Swedish blogosphere. I hope he doesn’t mind me “borrowing” the idea.

I have almost no prior programming experience, but for some time now I have been trying to learn more about screen scraping. Guided mostly by the Dan Nguyen’s brilliant tutorial on coding for journalist I have started to know my way around Ruby. Scraper wiki also provides good guidance for those of us who still mostly do copy-paste programming.

After two days of trial and error I managed to put together a script that extracts all the links to fellow Ratata blogs from all the 1207 blogs. That gave me a data set of almost 2000 connections (due to some technical issues I had to exclude a couple of blogs). I obviously wanted to find out who is most popular. That is, who gets the most in-links? This is the result (click for full scale pdf):

The size depends on the number of in-links. Karin, one of the founders of the blog community, is maybe not to surprisingly number one with 70 other Ratata bloggers linking to her, followed by Mysfabon (43) and Kisimyran (37).
You’ll also notice that the gap between the haves and the have-nots is big when comes to links. The core of the map is surrounded by a cloud of unconnected blogs (and shattered dreams of blogger fame perhaps?).

I’ve uploaded the Gephi file if you want to take a closer look at the dataset yourself.

Here is the complete top ten:

Blog Links 70 43 37 33 33 32 31 30 28 27

Project One: Visualizing Friendship

I looked around for tools for visualizing social networks yesterday and found two great things:

  • An application called Gephi.
  • A tutorial explaingin how to get started with Gephi.

In the tutorial Tony Hirst shows how easy it can be to visualize Facebook friendship. With a small Facebook app called netvizz you can easily download information about who knows who in your social network. You’ll get the data in a neat .gdf file that can turn in to a nice graph in a couple of seconds.

With a little bit of color my Facebook network vizualized like this (click to open full scale pdf):

The public interest in this visualization might not be enormous. However, it does say quite a bit about my life and different stages of it:

  • The big yellow bulb represents my Helsinki network, mostly friends from university.
  • The blue network are friends from my hometown, old school friends that is.
  • On the right, between the yellow and the blue network you see friends from my student nation, Vasa nation. That is friends that study in Helsinki (yellow connections), but also know people from around my home town (blue connections).
  • The green ones are people I know from sports (close to blue as that was something I did when I was younger).
  • The red network is for people I know from Åland.
  • The purple network for my Erasmus pals.

Considering that I didn’t know anything about network visualization 24 hours ago I am quite pleased with this result.