A post on the Guardian Datablog earlier today took a dataset collected by the Tweetminster folk and graphed the sorts of thing that journalists tweet about ( Journalists on Twitter: how do Britain’s news organisations tweet?).
Tweetminster maintains separate lists of tweeting journalists for several different media groups, so it was easy to grab the names on each list, use the Twitter API to pull down the names of people followed by each person on the list, and then graph the friend connections between folk on the lists. The result shows that the hacks are follow each other quite closely:
Nodes are coloured by media group/Tweetminster list, and sized by PageRank, as calculated over the network using the Gephi PageRank statistic.
The force directed layout shows how folk within individual media groups tend to follow each other more intensely than they do people from other groups, but that said, inter-group following is still high. The major players across the media tweeps as a whole seem to be @arusbridger, @r4today, @skynews, @paulwaugh and @BBCLauraK.
I can generate an SVG version of the chart, and post a copy of the raw Gephi GDF data file, if anyone’s interested…
PS if you’re interested in trying out Gephi for yourself, you can download it from gephi.org. One of the easiest ways in is to explore your Facebook network
PPS for details on how the above was put together, here’s a related approach:
Trying to find useful things to do with emerging technologies in open education
Doodlings Around the Data Driven Journalism Round Table Event Hashtag Community.
For a slightly different view over the UK political Twittersphere, see Sketching the Structure of the UK Political Media Twittersphere. And for the House and Senate in the US: Sketching Connections Between US House and Senate Tweeps
OUseful.Info, the blog... 
I’m sure this is awfully clever and all.
But what, actually, is its value? What does it tell us of real significance? Where is the value in bothering to create it?
That journalists on Twitter are tribal and tend to follow other journalists they know and work with can’t – surely – rank as ‘news’.
… can it?
The point of this exercise was to provide take on the structure of the communication system that is UK political journalists on Twitter. It shows who of the journalists many of the other journalists think are influential, in terms of worthy of being followed. The force directed layout also points out the obvious – that journalists in one organisation tend to be highly connected to folk from their own organisation.
I’ve also used technique to plot how folk using the same hashtag, or sharing the same link, are connected by friend relations, which can be an aid to discovering people (and hence resources, via the links they share) with a certain amount of “influence” in a particular subject area. If we look at folk using the hashtag and their friends who don’t, the technique can also illustrate the extent to which a conversation e.g. around a hashtag is going on in an echochamber.
I suspect the reason that it was widely tweeted was that lots of journalists (with large follower numbers) tweeted it because they were on it… ;-) Hmm… when I get a chance, I think I’ll graph the connections between folk who did tweet it…
Hey Tony,
I’ve stumbled upon your visualisation on guardian.uk. I took the liberty to create something similar for the american journalists on twitter. I’ve took those that can be found on http://muckrack.com/sources.
Here is a nice visualisation of their friends follower connections. Node size is by directed page rank and color by organisation:
http://zoom.it/7601
You can find more info on the set on my page dedicated to research on twitter:
https://sites.google.com/site/twitterresearch09/articles/journalistslikejournalists
Hi Thomas
Great stuff; next thing on my todo list was to look at the *all* the people the journalists follow, to see if they cluster too, or whether the journalists follow a diverse set of people. (I’m not sure how big this network gets?). The other thing is to look at the distribution of followers, but I suspect that network will be hundreds of thousands of nodes, which my machine may choke on! I’m not sure how well Gephi scales once you get past tens of thousands of nodes?)
Something else on my to do list to is grab the links (and maybe hashtags?) shared by folk on the lists and then treat these as “documents” so that they can be visualised by Gource using its Custom Log Format (e.g. http://pyevolve.sourceforge.net/wordpress/?p=1423 ) I’m not sure if this will work, but it’s an interesting experiment for a spare few hours…;-) Gource typically visualises users who check code files into a repository. So I wonder, if we treat sharing a link as if it were a file checkin, we should be able to visualise who’s sharing the same links? If we can also colour the individual journalists by media group, we get to see the extent to which they share different links. (When visualising the links, we need to account for different shortenings of the same link, so we probably need to resolve them?)
Something else I’ve explored is to look at who’s tweeting the links a journalist has tweeted (if they’re shortening links as a logged in bit.ly user). So for example, this image: http://www.flickr.com/photos/psychemedia/5437539168/in/photostream shows connections between people who tweeted one or more of 15 links that also happened to be recently shortened on bitly by @charlesarthur. This used the bit.ly api to pull the most recent links @charlesarthur had shortened, and then the Backtype API to see who had tweeted each of those links. (See https://blog.ouseful.info/2011/02/03/visualising-ad-hoc-tweeted-link-communities-via-backtype/ for a quick discussion about Backtype.)
PS I’ve yet to tap into the firehose, so will pick up on your howtos:-)
el autor, Tony Hirst, la tendencia es que los colegas de un mismo grupo se sigan mutuamente. Sin embargo, también hay