“Thoughts”, because I don’t have time to do this right now, (although it shouldn’t take that long to pull together? Maybe half a day, at most?) and also to give a glimpse into to the sort of thinking I’d do walking the dog, in between having an initial idea about something to hack together, and actually doing it…
So here’s the premise: what sort of network exists within the OU on Twitter?
Stuff I’d need – a list of all the usernames of people active in the OU on Twitter; Liam is aggregating some on PlanetOU, I think?, and I seem to remember I’ve linked to an IET aggregation before.
Stuff to do (“drafting the algorithm”):
– for each username, pull down the list of the people they follow (and the people who follow them?);
– clean each list so it only contains the names of OU folks (we’re gonna start with a first order knowledge flow network, only looking at links within the OU).
– for each person, p_i, with followers F_ij, create pairs username(p_i)->username(F_ij); or maybe build a matrix: M(i,j)=1 if p_j follows p_i??
– imagine two sorts of visualisation: one, an undirected network graph (using Graphviz) that only shows links where following is reciprocated (A follows B AND B follows A); secondly, a directed graph visualisation, where the link simply represents “follows”.
Why bother? Because we want to look at how people are connected, and see if there are any natural clusters (this might be most evident in the reciprocal link case?) cf. the author clusters evident in looking at ORO co-authorship stuff. Does the network diagram give an inkling as to how knowledge might flow round the OU? Are there distinct clusters/small worlds connected to other distinct clusters by one or two individuals (I’m guessing people like Martin who follows everyone who follows him?). Are there “supernodes” in the network that can be used to get a message out to different groups?
Re: the matrix view: I need to read up on matrices… maybe there’s something we can do to identify clusters in there?
Now if only I had a few hours spare…