Readers of any prominent OU bloggers will probably have noticed that we appear to have something of Twitter culture developing within the organisation (e.g. “Twitter, microblogging and living in the stream“). After posting a few Thoughts on Visualising the OU Twitter Network…, I couldn’t resist the urge to have a go at drawing the OpenU twittergraph at the end of last week (although I had hoped someone else on the lazyweb might take up the challenge…) and posted a few teaser images (using broken code – oops) via twitter.
Anyway, I tidied up the code a little, and managed to produce the following images, which I have to say are spectacularly uninteresting. The membership of the ‘OU twitter network’ was identified using a combination of searches on Twitter for “open.ac.uk” and “Open University”, coupled with personal knowledge. Which is to say, the membership list may well be incomplete.
The images are based on a graph that plots who follows whom. If B follows A, then B is a follower and A is followed. In the network graphs, an arrow goes from A to B if A is followed by B (so in the network graph, the arrows point to people who follow you. The graph was constructed by making calls to the Twitter API for the names of people an individual followed, for each member of the OU Twitter network. An edge appears in the graph if a person in the OU twitter network follows another person in the OU Twitter network. (One thing I haven’t looked at is to see whether there are individuals followed by a large number of OpenU twitterers who aren’t in the OpenU twitter network… which might be interesting…)
Wordle view showing who in the network has the most followers (the word size is proportional to the number of followers, so the bigger your name, the more people there are in the OU network that follow you). As Stuart predicted, this largely looks like a function of active time spent on Twitter.
We can compare this with a Many Eyes tag cloud showing how widely people follow other members of the OU network (the word size is proportional to the number of people in the OU network that the named individual follows – so the bigger your name, the more people in the OU network you follow).
Note that it may be interesting to scale this result according to the total number of people a user is following:
@A’s OU network following density= (number of people @A follows in OU Twitter network)/(total number of people @A follows)
Similarly, maybe we could also look at:
@A’s OU network follower density= (number of people in OU Twitter network following @A)/(total number of people following @A)
(In the tag clouds, the number of people following is less than the number of people followed; I think this is in part because I couldn’t pull down the names of who a person was following for people who have protected their tweets?)
Here’s another view of people who actively follow other members of the OU twitter network:
And who’s being followed?
These treemaps uncover another layer of information if we add a search…
So for example, who is Niall following/not following?
And who’s following Niall?
I’m not sure how useful a view of the OU Twittergraph is itself, though?
Maybe more interesting is to look at the connectivity between people who have sent each other an @message. So for example, here’s how Niall has been chatting to people in the OU twitter network (a link goes from A to B if @A sends a tweet to @B):
We can also compare the ‘active connectivity’ of several people in the OU Twitter network. For example, who is Martin talking to, (and who’s talking to Martin) compared with Niall’s conversations?
As to why am I picking on Niall…? Well, apart from making the point that by engaging in ‘public’ social networks, other people can look at what you’re doing, it’s partly because thinking about this post on ‘Twitter impact factors’ kept me up all night: Twitter – how interconnected are you?.
The above is all “very interesting”, of course, but I’m not sure how valuable it is, e.g. in helping us understand how knowledge might flow around the OU Twitter network? Maybe I need to go away and start looking at some of the social network analysis literature, as well as some of the other Twitter network analysis tools, such as Twinfluence (Thanks, @Eingang:-)
PS Non S. – Many Eyes may give you a way of embedding a Wordle tagcloud…?)
