Playing around with looking at the structure of my own Twitter friends network (see recent previous posts) by using the Gephi modularity statistic to partition (or cluster) my Twitter network depending on the strengths of connections between members of that network, it struck me that I could take a similar approach to exploring the structure of the relations between the members of a Twitter list. So I grabbed the members of the PLENK2010 list (which I had automatically created by mining the Twapperkeeper archive of posts tagged with PLENK2010, and then adding frequent hashtaggers to the list), grabbed all their friends lists, and had a poke around the friends connections between the list members.
The Gephi modularity tool identified three medium sized clusters, one large cluster, and several smaller ones. Looking at the three middle sized clusters, let’s see who’s in each cluster, where they’re from (from their Twitter location info) and what their interests are (from their Twitter bio field).
Here’s the first cluster:
Here’s the second cluster:
And here’s the third:
Not surprisingly, it seems as if geography still plays a role in defining networks…
There was also a large cluster identified in the original pass:
Here’s what they’re interested in:
And here’s where they’re from:
Here’s what happens if we partition that large cluster by running the modularity tool over just the members of this cluster again:
Do they make any sort of sense…?
So is this:
If it’s useful – why? What can we do with this information?