A really quick post this one to add to exploit a couple of select hashtags and get people thinking about whether this approach is useful for anything…
The two hashtags are #alpsp and #jisclms, both things to do with academic libraries, publishing. I’ve plotted a couple of graphs (using Gephi) for each.
Firstly, the inner structure of the hashtag community, showing interconnectedness, node size proportional to the number of hshtaggers follower an individual, colour/hat proportional to the number of hashtaggers the person is following. In this case, large red means the individual follows and is followed by a large number of the other hashtaggers. Small red means the individual is following lots of the hashtaggers but not following many of them, small blue means the person has little connectedness with any of the hashtaggers, and large blue means lots of hashtaggers are following the individual but not many are following back.
And secondly, the twitterati graph (via @scottbw;-) where node size is proportional to the total number of followers and heat the total number of friends. In this case large red means lots of friends and followers overall, large blue is lots of followers but few friends, small red is lots of friends and few followers.
So here are the graphs for #jisclms; firstly the inner hashtag community graph:
And here are the graphs for #alpsp – again, hashtag community first:
And then the twitterati graph:
For starters, what sort of interaction do the publishers seem to have with the rest of the #alpsp community?!;-) Can folk be well connected in a hashtag community and insignificant in the twitterati stakes (and if so, what might that mean? The person has just started on twitter and they’re starting within that community?) And so on…