Twitter Visualization

List Visualizations

So these have been on a bit of a back burner lately, with the end of the semester and associated craziness. However I had a suggestion from Treena that’s been sitting in my inbox for a while. She suggested I try @erinblaskie‘s lists, here’s hoping they show more of what I think this visualization will be useful for bringing out (lists that represent actual mini-communities, rather than just grouping people you follow).

First up: metinreallife. After I graphed this for the first time, I removed an outlier who was following/followed by a ton of people, causing every other point to clump together at the bottom left of the axes. removing it improved things somewhat, as you can see below. It’s noticeable that the more engaged people in the list (in terms of conversations) are not those with the most followers. You can get to the interactive version by clicking on the image (for any of the graphs below).

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Next: interesting. There were no conversations in this, though, so I decided not to graph it.

Third: askerinlive. Again, there were few conversations in this, so I didn’t graph it.

I’m really looking for lists that represent communities, and perhaps a better way to go about this is to look for lists with more people following them. Erin’s most followed list is one for Ottawa, but that has 500 people in it. Intuitively, I’m looking for lists with a good ratio of people following them to the people in the list.

Let’s try geekylikeme: Following 155, followers 18. This one is better, but the outliers make it really hard to read. I wonder if it’s better to do it by ratio of followers/following plotted against number of mentions. I’d like to try this on a logarithmic scale, but ManyEyes does not support it. Really, I want more control over the graph which does not appear to be supported.

90195dd8-e357-11de-8ec3-000255111976 Blog_this_captionThe last list I’m going to try is entrepreneurship, Following 107, followers 14. Again, the points with more influence within the list (more conversation) are clustered in the bottom left corner.

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What have I learned from this?

  • Outliers are rarely the most influential in a list. Interaction is probably limited by followers/following – when very popular interaction will be low proportionally out of necessity.
  • I’m not looking for lists of celebrities (or wannabe celebrities), I’m looking for lists that represent communities. Thus the GGDOttawa list is the best I’ve found so far.
  • ManyEyes does not give me all the functionality I want, for example logarithmic scales, and it’s hard to remove outlying data-points (lots of clicks). Going to try Google Widgets next.