I’m not sure how, now, but earlier today I came across the first part of a two part NPR article on In London, A Case Study In Opinionated Press, looking at “ideology in the media”, and how the British print media at least tend to be associated with a particular political affiliation.
This reminded me in part of something I read in Click*, by Bill Tancer over the Christmas break (don’t worry – I didn’t pay even the full discounted price; it was remaindered for a couple of quid in an end-of-line bookshop in Cheltenham…probably still is…) relating to the flow of traffic between different overtly politically affiliated blogs in the US. One chart show traffic flows between blogs based on some work done by Matthew Hindman (Political Traffic, June 2007). Another cited Hitwise statistics about upstream and downstream traffic to/from certain news media sites.
* (The book itself is essentially a distillation of observations drawn from Hitwise stats. If nothing else, it prompted me to wonder (again) about the extent to which academic researchers make use of the big data generated on the web, as well as market research data for segmenting (large) sets of (social science) research data. (If you’re concerned about invasion of privacy online, at least in terms of what marketing profiles are being built around your behaviour, are you similarly concerned about what trad direct mail marketers know about you?!;-))
One of the things I’d idly considered whilst reading the book was what a map of the UK media might look like. The OU has a couple of Hitwise seats, I think, though I’m probably not allowed anywhere near them; Alexa offers a certain amount of data (I’ve no idea how reliable it is, though!), though I’m not sure about it’s provision of an API and any license conditions around the use of the data:
The interesting sites are likely to start appearing further down the long tail…
Data I do have access to is social graph data on Twitter. The Tweetminster folk maintain a variety of relevant lists collating MPs by party affiliation, journalists by media organisation, government departments and so on, so it was easy enough to grab this data and check out who’s following whom. (I really need to clean the data to provide the option of allowing only active twitterers to be displayed…)
Here are a few idle snapshots generated using Gephi, in the preview view; consider it a tease…
Nodes coloured and grouped by affiliation (party, media organisation, etc). This initial layout shows the groupings (I don’t remember what size is? Child nodes I think? (i.e. the number of actual tweeps in that category)) The actual layout was achieved by using a force directed layout (which is sensitive to the number of links between sets of nodes) on the individual people nodes, and then grouping them by category.
The next chart shows the individual twitterer nodes, coloured by party, under the force atlas layout; a link exists between A and B if A follows B.:
We see that nodes with similar affiliation are, on the whole, closer together; which is to say, folk in a particular party or organisation follow each other like crazy, and then follow some other folk too;-)
To explore the structure in a little more detail, I sized the nodes using betweenness centrality, which is related to how structurally important a node is in connecting different parts of the overall graph:
Out of interest, I also ran the modularity statistic over the network to see whether there were any natural forming clusters; because the whole network is quite highly interlinked, only three main clusters were identified:
– what looks largely like a Labour clump:
– a clump of folk associated with (or tracking) government shenanigans, maybe?
– I did toy with calling this cluster “hangers-on around the political scene”;-)
Okay – enough for now; if you want the data, then you have to demonstrate what impact it’ll get me;-) Or you can make a donation to a charity of my choice. Or you can just grab it yourself (I’ve blogged how enough times;-)
PS I really need to sort an effective colour palette out…
PPS on the wishlist – find a way of looking at edges going from nodes in one group to another group; I think the Gephi MASK operator may do this but i don’t have a clue how it works… A newt function to take two lists and just plot connections between members of the separate lists could be quite handy though, so I’ll save that up for my next train journey:-)
DOH! it’s obvious – just use s UNION filter and get the required attribute filtered nodes:
PPPS just by the by, I found a way of grabbing friends and followers data from protected Tweeps. My understanding of the Twitter API was that this required: a) authentication; b) the requesting party to be a friend of the protected party, but I don’t require any of that. I’m not sure if the data I’m getting is current, or whether it’s a bit stale, but it’s more than I think I should be able to access via the Twitter API?! I’m not going to blog the how-to, though… figure it out yourself;-)