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

8 Comments on “Mapping Ratata: Who’s Hot?”

  1. Tommix says:

    Including, and would make the cloud more authentic. But still, very nice, good job.

  2. Kim Holmberg says:

    Interesting work. Do both the size of the node and the width of the links indicate number of links? Which algorithm did you use to visualize the data, to place the nodes?

    The gap between have’s and have-not’s is usually big on the web. The phenomenon has been called Matthew effect or that the rich get richer (older and larger sites receive even more links, partly because they have been around longer and they have more pages to link to). This phenomenon has also been called a power law, where only few cases receive the majority of connections.

    What you have done here is a nice piece of webometric research. For more info on webometrics you could have a look at e.g.

    • Jens Finnäs says:

      Thanks. I’m not sure I fully understand your question. The size of the nodes is determined by the numbers of links, yes. The width (distance between nodes?) – I think – is a bit distorted as I adjust the layout by node size (Layout > Force Atlas > Adjust by Sizes). Apart from that I just used preset settings in Gephi and visualized with Force Atlas (repulsion strength=600).

      One question though, do you have any ideas on how one could scrape the blogs on Peppar as well? Ratata was easy as they list all their blogs. But Peppar doesn’t do this. I thought about trying to get a list of all subdomains, but that turned out to be difficult if not impossble without DNS access.

  3. Anne says:

    Ååå vad fint. Jag fick en riktigt stor fin boll. 🙂

    Snyggt jobbat annars!

  4. dizi says:

    Oh this is cool stuff.
    I love these types of things, so abstract, but still not. Like a painting.
    Nice work!

  5. kisimyran says:

    I feel funny. I feel sort of… aroused. This is the coolest place I’ve ever run into myself. Awesome!

  6. […] I don’t know how many people were able to stumble through it. Though last week I noticed this recent trackback from dataist, a new “blog about data exploration” by Finnish journo Jens Finnäs. He writes that he […]

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