Posts Tagged ‘network analysis’
« Older EntriesA first stab at a perl script to create Twitter friend/follow matrices
Tuesday, July 14th, 2009
Geek alert: if the title of this post isn’t a dead giveaway I should tell you — unless you’re interested in APIs and badly-put-together bits of code — this probably isn’t for you.
I’ve recently found myself using a service provided by Damon Clinkscale called DoesFollow. All it does is answer the simple question “does twitter user A follow twitter user B?” Apart from a frill which lets you reverse the order of your question (“does twitter user B follow twitter user A?”) that’s all it does. You can even interrogate it from the address bar like this: http://doesfollow.com/barackobama/mediaczar
While I was thinking about how useful a service this is, I was suddenly struck by a moment of clarity. A lot of the research I’ve been doing could be simplified by something like this.
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Tags: kludge, network analysis, networks, perl, twitter
Posted in hack, twitter | 6 Comments »
The #interestingOPMLexperiment (stage 1)
Wednesday, July 1st, 2009
A couple of weeks ago, I asked a bunch of people to send me their OPML files (for those of you who aren’t aware, an OPML file is what tells your RSS reader what feeds you’ve subscribed to — it can act as a way of moving your subscriptions between readers.) Some of the more trusting among them agreed, and that gave me the raw material for the first bit of my experiment.
Some red herrings
Along the way I uncovered a couple of things that were interesting but not (entirely) relevant to the experiment.
- Some people are cagey about sharing their list of feeds: whether they consider it intellectual property, or whether they think that it may be too revealing, I don’t know.
- Lots of people said things like “oh — my RSS reader? Haven’t looked at that in a while. I get all my news off Twitter these days.”
Tags: #interestingOPMLexperiment, bloggers, citation analysis, influence, influence mapping, network analysis, opml, research, social media, visualization
Posted in influence, networks, research | 4 Comments »
Thinking differently about word-of-mouth
Tuesday, June 30th, 2009
The current approach to WOM is to try to stimulate positive WOM while addressing or countering negative WOM. A sort of “accentuate the positive, eliminate the negative and don’t mess with Mr In-Between” strategy.
But what if we could do it a different way?
This idea stems from a conversation I had back in February with Martin Kelly and Andy Cocker of Infectious Media. Since that time I’ve chatted it through a couple of times with various interesting people. It’s not properly thought through yet, but following a chat a couple of weeks ago with Ketchum London’s new Head of Digital, the excellent Fernando Rizo, I’ve decided to put the idea out into the public domain to gauge what (if any) interest there is and whether I should continue to work on it.
“Word of Mouth” is hard to do well
I’ve read lots of word of mouth marketing case studies (there’s a great list over at WOMMA) and it strikes me that WOM is hard to do well for a few reasons. I don’t want to go into these in too much detail, but here are a couple of the structural issues:
- Unless I’m a journalist, an A-list blogger or media personality or have some kind of platform, I probably have a very low reach.
Despite everything pointing towards personal contact being the best impetus for positive word of mouth, most word of mouth campaigns compensate for my low reach by trying to get me to self-service my relationship with the brand and the campaign.
- “Viral” distribution just doesn’t work the way most people seem to think it does; and this is particularly true when it comes to WOM.
While I’m quite likely to tell stories about my personal experience of a brand and fairly likely to tell stories that involve a mutual friend, I’m much less likely to tell stories about other friends’ experience, and not likely at all to tell stories about friends-of-friends.
Furthermore because of the ‘clumpiness’ of most people’s social graphs, geometric progression (the “I tell two people and they each tell two people and so on” effect) just doesn’t happen.
Homophily
One of the many reasons that WOM works is a thing called homophily — which roughly translates to “birds of a feather flock together”, or “you can tell a man by the company he keeps.”
I’ve written about examples of this before: for example, my analyses of twittering US Congresspersons and Westminster MPs which showed that one can predict with some reasonable degree of accuracy the political colouration of any given twitter account based on their mutual friends and follows (if you want to know more about the methodology, it’s worth reading Robert Hanneman’s chapter on cliques and subgroups.)
But there’s another side to the homophily coin; the social pressure to conform to the group’s norms.
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Tags: influence, influence mapping, marketing, network analysis, public relations
Posted in influence, networks | 8 Comments »
Swedish Politicians on Twitter
Sunday, February 22nd, 2009
Twixdagen does for Swedish politics what Tweetminster does for British. Hampus Brynolf (@hampusbrynolf) just sent me a link to this map he’s pulled together for their blog:
You’ll need to click through to his blog post to experience and interact with the map properly.
Hampus says that he used aiSee to generate an SVG file which could then be opened in Illustrator to “search and replace” on shapes, colors and lines (which explains the good-looking graph.)
Tags: aisee, mapping, network analysis, networks, politics, sweden
Posted in networks, twitter | 2 Comments »
Can we calculate party affiliation? (the US Congress Edition)
Friday, February 13th, 2009
Using nothing more than their public twitter relationships, is it possible to predict whether a US Congressperson is a Republican or a Democrat? The answer seems to be a guarded “yes” — our tools predict correctly 40/46 times (or around 87% of the cases.)
This post follows on from a post earlier today in which I asked, “can we calculate party affiliation?” The data set in the earlier post was gathered from the 16 members of the UK parliament who are on Twitter and the relationships between them.
Tweetcongress maintains a list of US congresspeople on Twitter. Today (February 13, 2009) there are 76 congresspeople on the service, but when I collected my data set of “who follows who” on February 3, 2009 there were only 65. Of these 65, fully 19 (29%) lived a life of noble isolation with regards the network — none of their peers linked to them, and they in turn linked to none of their peers. Removing these Miss Havishams from the data set leaves me with 46 twittering congresspeople who form a network.
Now as both social network analysis and Aesop would have it, “a man is known by the company he keeps.” What I mean by this is that given the partisan nature of politics, we should expect that Democrats will link to other Democrat twitterers more often than they link to Republican twitterers and vice versa. So that’s what NetDraw[1] , the software I’m using for most of this stuff, looks for, or more accurately:
To identify factions, NetDraw software iteratively searches for a distribution of nodes among a selected number of factions to minimise the number of connections between factions and to maximize the number of connections within factions.
Whatever. So I let NetDraw loose on the data, and here’s what it did.
I coloured the nodes red for Republican and blue for Democrats[2], labeled the nodes by party (for the sake of clarity, and for the hard-of-thinking, that’s “R” for Republican and “D” for Democrat) then counted all the nodes where label said one thing but colour another. There were six of these nodes; so NetDraw got the answer right 40⁄46 of the time (just about 87%.) This is less than the astonishing 93.75% accuracy we got with the Westminster twittering members of parliament in the previous post. Nevertheless I think we can safely say that it’s not a particularly integrated (or bipartisan) network if we can predict party affiliation with quite such success.
Here’s exactly the same map with the errant sheep re-labeled with their proper names so it’ll be easier to refer to them (if it helps, you can click on the image to view or download a larger version.)
You’ll see, I hope, that NetDraw has made a pretty good fist of the job. Where it has gone wrong on the whole is where the data clearly suggests something else. So Rep. Jared Polis for instance follows (and is followed by) no Democrat peers. Rep. Nancy Pelosi (D) and Sen. Richard Durbin (D) follow each other, but since Pelosi is followed by several Republicans and none of her other Democrat peers you can see why the algorithm has made the incorrect guess that the two of them are Republicans. Long-serving member Neil Abercrombie, as discussed in a previous post on US Congress Twitter folk, forms a bit of a bridge between the two parties, so despite his membership of the Congressional Progressive Caucus and liberal voting record, from the Twitter network point of view, his affiliation is somewhat ambiguous.
Sen. McCain follows none of his peers, and appears to inherit his incorrect attribution from Sen. Susan Collins. For the life of me, I can’t work out what makes it think that Sen. Susan Collins is a Democrat. She really isn’t, you know.
Note 1: NetDraw is a free program written by Steve Borgatti from the University of Kentucky. If you’re interested in playing around with this stuff, you’ll need to get yourself a copy.
Note 2: Actually, that’s not true. Despite a friend sharing the simple mnemonic that “‘Republicans’ and ‘red’ begin with the same letter,” I just can’t get it out of my English head that the Republicans should be blue and the Democrats red. As a result I waste precious minutes re-colouring these maps in Illustrator. It is worth pointing out that I also have problems with “left” and “right” on occasion — preferring instead the binary opposition “left” and “No! no! The other left, for God’s sake!”
Tags: congress, democrat, gop, jared polis, john mccain, mapping, nancy pelosi, Neil Abercrombie, network analysis, networks, republican, research, richard durbin, susan collins, twitter
Posted in networks, research, twitter | 1 Comment »
Can we calculate party affiliation? (The Westminster edition)
Friday, February 13th, 2009
This is a follow-up post to Why doesn’t the Tory MP have Twitter friends? — a report on some early research into the interrelationships between the few Westminster MPs who are on Twitter.
According to Tweetminster, the number of UK MPs on Twitter has doubled since this time last month. Where there were eight Twittering MPs, there are now sixteen. Here’s the map that shows who follows whom (the labels may be too small to read — if you want to see a larger image, click on the map.
I’ve coloured each node to show party affiliation; for those of you who are unfamiliar with British politics, Labour (our left-of-centre party) shows up in red, Conservatives (our right-of-centre party) in blue, and Liberal Democrats (what it says on the tin) in yellow.
The size of each node represents the individual’s “betweenness centrality” — a network analysis term that helps us place a value on individuals within a network. To give you a sense of what it means, the higher the betweenness centrality of an individual, the greater the impact when you take them out of the network. For those of you who work in large companies, it may be worth noting that senior management’s personal assistants generally have very high betweenness — something that is mostly remarked upon when they go on holiday (simultaneous translation: “take a vacation”.)
So far so good. By now, regular readers will probably be kissing their teeth and thinking “so what?” I’ve done a lot of these Twitter maps in the past and the novelty must be wearing off on you by now.
So here’s the thing. There are a few network analysis techniques that let one identify cliques and factions. What we’ve got here is a small set where we already know what people’s affiliations should be. How interesting, I thought, would it be to see how well the calculated result fits the real world data? Here’s what I found:
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Tags: Andy Reed, andyreedmp, celia barlow, celiabarlowmp, david kidney, David Lammy, davidkidney, davidlammymp, derek wyatt, edvaizey, edward vaizey, Grant Shapps, grantshapps, james plaskitt, jamesplaskitt, jgoldsworthy, Jo Swinson, joswinson, julia goldsworthy, Kerry McCarthy, kerrymp, lfeatherstone, Lynne Featherstone, mapping, members of parliament, michael fabricant, mike_fabricant, mp, network analysis, networks, research, sadiq khan, sadiqkhanmp, Tom Harris, Tom Watson, tomharris1964, tom_watson, twitter, visualization, westminster, wyattd
Posted in measurement, networks, research, twitter | 10 Comments »
Republicans still outperforming Democrats on TweetCongress
Wednesday, February 4th, 2009
Three weeks ago (and at the prompting of my colleague Eddie Garrett who heads up Porter Novelli DC’s digital team) I mapped out the interconnections between US Congress Tweeters. We’d been working on a Twitter crawler and it seemed like a good opportunity to test things out on a new data set.
This is a follow-up post. Once again it was prompted by a third party: Christie Findlay at Politics Magazine asked whether it would be OK to print a copy of one of the maps in their March edition. I’ve heard that three weeks are a long time in politics, so I thought I’d better run the crawl again just in case. Also I’ve got a new crawler that uses the proper Twitter API (I can see some of your eyes glazing over you know. Just skip ahead when that happens.) I’d tried it out on the Porter Novelli data set, but welcomed a chance to try it on something more meaty.
So yesterday morning before work I ran the crawl. I use the excellent Tweet Congress as my source of information about which congress people are on Twitter.
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Tags: congress, mapping, network analysis, twitter, visualization
Posted in networks, twitter | 9 Comments »
Map of Porter Novelli people on Twitter on 17th Jan 2008
Tuesday, January 20th, 2009
Marian Salzman (our Global CMO here at Porter Novelli) has had the inspired idea of getting people in the agency to tweet about the most exciting story this week (probably) — the inauguration of Barack Obama
You can see the results of the experiment on her blog.
I’m all for this, of course, for several reasons:
- It gets new people onto Twitter
- It helps us create a stronger network among Porter Novelli twitterers
- It means I can track who at the agency is on Twitter
Tags: Barack Obama, influence, influence mapping, mapping, network analysis, networks, porter novelli, social media, twitter, visualization
Posted in networks, porter novelli, twitter | 6 Comments »
Network map of US Congress twitterers
Tuesday, January 13th, 2009
This is a map of the current US congressmen and women who are currently on Twitter (you can click it to see a bigger map where you can read the names.) The direction of the arrows show who follows whom, and the size of the blobs indicates how “popular” a given congressperson is among their twittering peers (where “popular” means something like “is followed by many of their peers.”) Colours indicate party affiliation (for those of you who — like me — don’t live in the ‘States and who — like me — need reminding from time to time, the Democrats are the blue dots.)
Network of US Congress twitterers showing citation frequency. Click for bigger.
A cursory glance at this map shows a few things:
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Tags: Barack Obama, Hillary Clinton, John Culberson, mapping, Neil Abercrombie, network analysis, networks, twitter, united states congress, visualization
Posted in networks, twitter | 49 Comments »
