On November 10th, Stephen Davies collected together a list of “UK PR people on Twitter” According to PostRank, this (and his earlier post, “UK Journalists on Twitter“) are the most popular posts on his blog.
Then a couple of days later, Stephen Waddington pushed that list through TwitterGrader to come up with his list of “Top 50 UK PR people by Twitter influence”
A couple of weeks ago, I was looking for a seed list with which I could test our “whitelist” and “canonify exception” rules on Rufus (the network analysis tool that Porter Novelli has been working on for the past six months.) This isn’t the right place to go into it, but to put it simply, the whitelist restricts the search to domains that are on the list (like a guest list), and the canonify exception list stops Rufus from chopping the subdomains or directories off the list (without this, a site like sethgodin.typepad.com would just show up as typepad.com or en.wikipedia.org/wiki/Social_network_analysis would show up as wikipedia.org. Rufus, by the way, is named after the George Carlin character in Bill & Ted’s Excellent Adventure.
My colleague, Tim Hoang used to work with Stephen W., so he sent him the image. Wadds then posted “the map on his blog“. My flickr page has never had so much activity.
Here’s the original graph:
Lots of people started drawing conclusions about the nature of PR, or the nature of Twitter from the graphs. There was lots of interesting speculation. Some people thought that this demonstrated how introverted the twitter crowd is. Others thought that it showed how introverted the PR/Social media crowd is. Others seemed to think that it didn’t matter.
But the truth is, Rufus isn’t the right tool for Social Network Analysis. Designed to look at how different blogs, forums and news sites contribute to the “conversation” around a particular topic, it’s not specialized enough to look closely at one site. But our interest was piqued. Among other things (and this isn’t false modesty) I never really believed that I belonged in the top 50. After all, I’ve been working in public relations for about eighteen months. I may be influential (get me!) outside the PR-sphere, but inside? Less so.
The Twitter Spider
By now we know just about enough with regards to social network analysis that we can throw up low-cost tools with only a very short planning stage. So that’s what we started to do. We built a twitter spider (which seems to be legitimate under Twitter’s terms and conditions) and started testing it. The spider is designed to find friends (i.e. people that a given Twitter user follows) and followers (vice versa). We’re only really using friend data in the rest of our analysis, but if you’re going to build one, you may as well build the other.
After some early setbacks:
We finally got it right. That’s when we ended up with the “cranberry hedgehog” map.
The hedgehog is actually a map of Twitter users who are followed by at least two of Waddington’s top 50. The more followers a user has, the larger their “cranberry”, and the closer they are to the centre of the map. What we see here is a very, very dense network. But it’s too hard to read. There’s too much peripheral data.
Analysis
At this stage, we thought it might be interesting to see who (other than themselves) the top 50 were following. Here’s the list of all those people who are followed by at least 10 of the top 50:
Nice to see Robin Grant, Jemima Kiss, and Mike Butcher leading the list. And – surprise surprise – wordy smugbucket Stephen Fry is there in joint second place.
Over the weekend, I wrote a script to process the data a little more (I’ll share it when I’ve tidied it up a little). Now we can look at links between the top 50 only. If Mr Waddington’s seedlist is really representative (bear with me here) then what this will show is their relative status (or prestige) as assessed by their peer group. It doesn’t matter whether a bunch of credulous know-nothings think they’re worth listening to; their peers (who one would assume know a lot more about the topic area) think they’re worth it.
So here it is: Waddington’s list of Top 50 UK PR people by Twitter influence ranked by degree centrality within their peer group.
Interesting? Well — there’s been a lot of movement. Here’s a comparison of where they rank among their group as opposed to among the whole twittersphere. Notable winners are @JedHallam and @Wadds who leap up into leader board. Notable losers are Jangles (Neville Hobson) and Mediaczar (me, of course). It is with great relief that I feel the mantle of greatness sliding from my shoulders.
Now, there’s a metric that we haven’t really used much (and I’m not sure how meaningful it is) but I thought I’d share it nonetheless to see what others thought. It’s the ratio of Friends to Followers — or how many of the top 50 a user follows compared to the number of the top 50 who follow them.
I’ve been a bit intrigued by this metric since I did some social network analysis (SNA) on Facebook late year and in the first quarter of this (you can see some of the results in this post about weight-loss groups on Facebook.)
What you should note in the chart below are the extreme outliers around the 400 to 460 mark (marked with a bright red circle).
I happen to know that one of these people is so widely admired that people seek to be their friend, and that another is an aggressive networker who strives to befriend vast swathes of the general population.
Because Facebook is an undirected network (i.e. if I’m your friend, you are ipso facto my friend) the data won’t tell you which is which. Looking at this, it seems obvious that the ratio between the people I ask to be my friend and the people who ask to be my friend should be a useful indicator in cases like this. When we’re dealing with websites (using Rufus) links only go one way. I may link to Wikipedia a lot, but Wikipedia is unlikely to link to me. That gradient is important.
So here – neatly marked in green and red are the domain-specific Friend-to-Follow ratios for the top 50.
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Fantastic analysis. I’m still trying to work out if I’m a big deal or in fact a sad individual who craves attention and approval from his peers.
Either way, thanks. Will give it more time to digest.
Stephen, from the data to hand (i.e. a slightly spurious seedlist, Twitter data etc.), I think the answer is you’re a big deal. What we don’t know of course is how well twitter data works as a proxy for “real world” influence.
I’ve got a vested interest in proving that the web is a good proxy for the real world; among other reasons I’d like to prove this because it will stop PR people wittering on about the problems of measuring effective communications. So – anything I say has to be taken with a pinch of salt.
Twitter, of course, is a much smaller data set than “the web.” But it’s one where we can measure all sorts of things. We can compare populations (I want to run the same analysis on your journalist data, for example; and then a comparison of a randomly selected set of twitterers.) We can see how often (and who) people refer to in their tweets (by eavesdropping for words that contain “@”)
It’s an immensely rich source of data. I don’t know whether this will become a tool for us further down the line (but I’ll release the various bits of code we’re using should anyone feel the need to tinker.)
I love a pretty graph!
But.. influence or chat? And how can you do that with Rufus?
(Keep up the excellent work and then open-source it, eh)
Tough reading at 23:47 but interesting nontheless.
Firstly apologies for jumping the gun with the initial graph but many of the private conversations I’ve had relating to it suggested that many PR people are getting, frankly, a bit bored with Twitter. I supposed I just saw what I wanted to see and ignored some of the inaccurate [but to some extent legitimate] haphazard analysis.
RE: Ian Delaney. I agree chatting/following is not entirely representative of influence
I follow both @jedhallam and @stuartbruce, I speak to Jed more, but no disrespect to Jed, I am aware that Stuart owns Wolfstar and for me he is more influential in terms of what i want out of Twitter.
What you have to remember is that social network analysis is one way at looking at influence and relies entirely on quantative data. You could probably argue that influence is perhaps more Qualitative. For me it is the only viable option anyone in comms for the foreseeable future for measuring success of a comms campaign, but there are others [readership figures, opportunities to see, advertising equivalent, eyeballs, karma, BUZZ!! etc]
Interesting [though complex-ish]Twitter analysis http://tinyurl.com/5ruc8d
Shared: Some Twitter Social Network Analysis | Mediaczar: Fascinating analysis. http://tinyurl.com/5ruc8d
Really interesting qualitative analysis, somewhat marred by almost keeling over when I found out that Top UK PR people follow me in reasonable numbers – don’t think realise I’m in the social media silo?….
Influence is the ability to change what someone does, thinks, or feels. Simply identifying people who do/think/feel something is not enough.
I’ll go into this in a better and more thorough post or presentation somewhere, but we know a little about the qualitative and behavioural aspects of influence, and a little about the quantitative. There are tools to assess both. And there are strategies to affect/act on all the points.
But it’s a complex business, and always has been. Focusing too deeply on something like Network Analysis alone won’t get us to where we want to be.
Also posted at http://andresvarela.posterous.com/
If one is looking for *influencers* then one must surely track the *influnces*? I might follow Stephen Fry on twitter but if he gives an opinion about Nokia or Microsoft that doesn’t mean I’d give the comment any more weight than I would if it came from someone else.
I’d suggest that one crude way to track influence in twitter would be to look at retweets, and as a case study I’d suggest tracking the transmission of Mat’s article/meme/retweet.
We can assume that the first person to twitter about this article is you. So from that point in time, who else sent out a tweet with its URL and the abbreviated variations: http://tinyurl.com/5ruc8d, bit.ly, budurl, eweri, hex.io and so on…
By looking at the timing of the postings you can get a reasonable idea of who influenced whom. It’s quite likely that there’ll be posts made by people completely unconnected within twitter, which would indicate that their influences come from elsewhere.
Also, you may find people who are connected, but in fact their ‘influence gradient’ (should I trademark that) runs in a counter-intuitive way: for example where an apparent ‘follower’ posts before the top 50 influencer they’re connected to, suggesting that they’re picking up on memes outside of twitter, before they’re being broadcast by a guru…like Mat.
[...] Some Twitter Social Network Analysis | Mediaczar Fascinating analysis. (tags: forblog socialmedia twitter microblogging pr mediaczar matmorrison) [...]
Anyone interested in this post might also like to check out Twinfluence, and their metrics.
What’s different about their approach? For one thing, they look at several more data points (which may make it more “accurate”). But they look at Twitter’s total universe as the data set; we’ve tried to limit the data set to a specialized subset.
Does anyone have any data on Twitter usage (total accounts/active users in past week?)
Fantastic analysis. Not only to see that I’m ranked alongside Guy Kawasaki in the list of the top 50 following but very interesting to see the quant data listed. What stats analysis package was used to create the cool maps?
Tom — we use a combination of our own tool (the graphing is based on the opensource JUNG java framework), NetDraw, and yEd.
We also use UCINET to do a lot of the number crunching. And I write bad perl scripts at the weekend to do some of the nitty gritty data processing and frequency analysis.
[...] 50 UK PR people by Twitter influence. As a result of the analysis he produced a list of people who are followed by at least 10 of the Top 50 UK PR Tweeple. Whilst scrolling through the list I suddenly saw my name there ‘TomChapman’. Based on [...]
[...] Some Twitter Social Network Analysis | Mediaczar – interesting data points from Mat Morrison over at Porter Novelli [...]
Some Twitter Social Network Analysis from @mediaczar http://bit.ly/nMEx
Easy 30 min gym session today. Interesting twitter analysis: http://tinyurl.com/5ruc8d
Interesting analysis – obviously got far too much time on your hands :)
In all seriousness, I find Twitter facinating, both in terms of human interaction, as well as how people are now trying to exploit it commercially.
Great article btw
and another Twitter Social Network Analysis here: http://tinyurl.com/5ruc8d
[...] Some Twitter Social Network Analysis My Experiences with Twitter Part 1 Scott Hanselman on How To Use Twitter Perl Script for Twitter Analysis [...]
I was just about to start writing an app with this sort of capability. I’m ultimately interested in information flow but this type of graph shows the backbone framework of the flow.
Truly fascinating field, if only I could get paid to learn more about it!
Btw I’m victusfate on twitter
Have you checked out http://www.keyhubs.com? This is a simple, online survey tool for mapping social networks in organizations.
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