Archive for the ‘twitter’ Category

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A 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

doesfollow

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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Posted in hack, twitter | 6 Comments »

Should we ask employees to tweet client stories?

Friday, May 15th, 2009

wall of spam

Here’s an interesting ethical question: is it OK to ask employees to share company and client news through their personal social networks?

Here’s a hypothetical example. An agency has just launched a new ad campaign and posted the TV spot on YouTube. Is it OK to send an all-hands email asking people to share the link on Twitter and Facebook?

Let’s take it a little further. Is it OK to ask them to sign into YouTube using their personal accounts, and rate the video? It seems harmless enough, doesn’t it? You’re not telling them how they should rate it, after all.

But what if you asked them to leave comments? Any normal agency or client side social media policy will tell them that they have to disclose their relationship with the makers of the video. And you wouldn’t really want a whole bunch of comments that start “Hi, I work for the agency that made this ad and I think it’s really great,” would you? What makes the two things different?
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Posted in opinion, twitter | 10 Comments »

Methodology and thoughts behind those PR Week Twitter stats

Thursday, February 26th, 2009

There’s a school of thought that says that what’s important in social media is to attempt to create debate, not consensus.

Cat Among The Pigeons

Peter Hay from PR Week and I appear to have been rather successful in this. This morning, PR Week published an article, Twitter has suddenly exploded. Almost immediately, Twitter (or at least our particular neighbourhood of Twitter) suddenly exploded.

One or two people were rather scathing: suggesting that the stats demonstrated that Peter and I didn’t understand the “essence of Twitter” or that they were “obviously flawed”, or that we had “redefined shallow”.

Indeed (horror of horrors) some people even went so far to suggest that Porter Novelli had ginned up the results to put us at the top. In fact, in PR week’s list, we came second. But no doubt this was a Machiavellian ploy — it’s details like those, Pooh Bah would say, that “give artistic verisimilitude to an otherwise bald and unconvincing narrative.”

I joke, but I can completely understand people’s strong feelings about this; PR Week was torn between a desire to cover our approach (and give credit where appropriate) and a need to keep the article readable and relevant to the greater proportion of their readers.

I’d like to share our methodology with you all so that you can repeat our experiments, should you so wish. After that, I’ll talk about the methodology that we were originally going to follow,

Tomorrow (once it’s had a chance to blow over), I’ll post some quick thoughts on the whole storm-in-a-Tweetcup thing.

Methodology

We used Michael Litman’s (@litmanlive) list of UK Media Tweeple. This was based on original work by Stephen Davies (@stedavies) but has been wikified so that agencies can (should they so choose) keep their information up to date.

Lots of people on the list were pretty borderline — there are in-house teams and vendors there, as well as agencies with a significantly broader remit than simply “PR”. I am a relative newcomer to the world of PR, and was more than happy to let PR Week define who is PR and who isn’t, but we erred on the generous side. We are Social, for example, made the cut to be on the research list.

Had we had the time, I’d have sent a note out over Twitter asking everyone to update their entries. Time, however was not on our side, and I didn’t even get around to hinting at what I was doing until the evening of the 23rd.
PR Week Twitter Stats Yahoo! Pipe
By then though it was already clear that I had a large job on my hands; there were almost 350 people on the list. On the whole, the UK PR community should be proud of how quickly it has reacted to the whole “Twitter thing”.

I took the list, published it as a Google Spreadsheet and — using a Yahoo! Pipe that I adapted for the purpose, queried the Twitter API for the summary data on each account on that list.

Twitter gives you all sorts of interesting information, but what we were grabbed were the following:

  • Date joined Twitter
  • Number of Friends
  • Number of Followers, and
  • Number of Updates.

That allowed us to create this spreadsheet, from which the stats mentioned in the PR Week article were taken.

Again, Porter Novelli took no part in the editorial decisions (although they seem pretty straightforward.) You will recall that Peter and Gemma were writing for a general readership, not for the Twitterverse!

Methodology we’d like to have used

Those of you who’ve read my blog before will know that my real interest in Twitter is more complex than the previous methodology would suggest. When Peter and I first discussed the exercise on Monday we had been hoping to do something more along the lines of the network analysis that we’ve been fiddling with at Porter Novelli.

Here are some points to bear in mind.

First of all, not all followers are created equal. If I have only ten followers, but they each have a thousand followers, that may mean I have more opportunity-to-influence than if I had a hundred followers with only ten followers each.

More to the point, the fewer people those ten people follow themselves, the more influence I wield within their networks (if I am one of only ten people they follow between them, I will have greater share-of-voice than if I am merely one of ten thousand.)

Secondly, the followers whom I don’t share with the rest of the network count for more than those who follow several (or many) of my peers. The more “exclusive” my follower-base, the greater my control over on the flow of information within the overall network, and the greater my value to the network.

I’ve been doing some work looking at unduplicated reach among twitter networks. For example, looking at Porter Novelli’s own global Twitter footprint, it was interesting to see how many of our contacts were duplicated.

So what Peter and I really wanted to do was to use some of these techniques on the PR Week data set. For those of you with a mathematical (or social network analytical) bent, we were going to run some eigenvector shizzle on the whole bizzle. Oh — and look at unduplicated reach for the various companies on the list.

What went wrong?

It was always an ambitious project. The 344 people who were under analysis had a fairly daunting 95K followers between them. The Twitter API lets you make 100 requests an hour, and each request returns data on up to 100 followers. Even if we were to assume that everyone had followers in nice tidy multiples of 100 (they don’t) then it would have taken 9.5 hours to download the data using one Twitter login.

The trick of course, is to use more than one login. Tim Hoang (@timhoang) and I quickly registered 50 temporary accounts to power the API requests. Twitter’s terms have historically been quite relaxed about this sort of thing, and we’ve always been very careful to try and stay within the spirit of those terms.

But.

Twitter has been hit lately by a bunch of bad things (like spam bots and pyramid schemes), and they’re tightening up their defenses. This past weekend, they’ve tightened up a lot, and things that used to be fine just aren’t.

We managed to collect information on only around 60K followers out of the 95K. This was too large a margin of error to correct (although we made several attempts to do so).

So — we had to abandon our grand plans, and revert to the simple counts approach (as detailed above.) This won’t stop us trying to improve our processes, but we’ll need to talk to Twitter about that.

Some thoughts

Kate Hartley from Carrot Communications (who sits with me on the PRCA’s Digital Working Group) joked that it’s strange how PR people create research-for-news-stories for their own clients on a daily basis, but are miffed when their own techniques are used against them. At one level, I agree with her — I think that some people are probably disappointed that they aren’t the ones with their names on the research.

But there’s more to worry about than that. Here are my thoughts.

  1. For God’s sake get over yourselves! We’re talking about Twitter here, not the economy. Worry about something important, why don’t you? I still can’t get over the fact that — when a pilot managed land an airplane on a river, the story we all tell each other is “how it broke on Twitter.” What — the story’s not about a man who magically landed a f*cking plane on a f*cking river? Are we really getting this right?
  2. How influential you are on Twitter is not a real thing. It doesn’t really matter how many Twitter friends you have (although I’ve now got heaps, thank you very much!) Context is everything. My boss, who runs Porter Novelli’s EMEA network and sits on our Executive Committee is on Twitter. She is more influential than I, and will continue to be, no matter how many Twitter followers I accrue.

    Twitter is just one channel through which exercise your influence. Don’t give up on your blogs, your Facebook pages, your Amazon reviews, or your Last.fm playlists or your IM friend lists, for God’s sake. But remember, it’s who you are, and your relationships that matter; your “context”, and not your “counts.”

  3. The really interesting question isn’t “who are the Twitterati” or twitter influencers. I’m interested in the Twitter thing mainly because I want to see how well it reflects real life. After today, I’d probably say that it doesn’t very well, wouldn’t you?

Be warned — I may just follow this research up with some research on “how many phone numbers PR people have on their mobile phones.”

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Posted in pipes, porter novelli, twitter | 21 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:

Twixdagen's map of Twittering Swedish politicians - click to visit the original post

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.)

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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.)

Calculated Party Affinity US Congress

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.

Calculated Party Affinity US Congress

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.)

congress guesswork incorrect labels

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!”

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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.

Actual factions among Westminster MPs on Twitter

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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Posted in measurement, networks, research, twitter | 10 Comments »

Republicans vs. Democrats: Pareto charts of unduplicated Twitter reach

Sunday, February 8th, 2009

A couple of days ago I did a little more analysis on Republican and Democratic Congresspeople on Twitter. Pareto chart showing unduplicated reach for US congressTowards the end of the post, I realized that the unduplicated reach pareto chart that I’d built would only make sense if the US were a one-party state (or to be fair, if both parties had a single issue that they were united in wanting to promote.)

So — wanting to make this a little more representative — I went back and produced two charts; one showing Republican unduplicated reach (which follows a typical 80:20 distribution)…

Pareto chart showing unduplicated reach of Republican Twitterers in the US Congress
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Posted in twitter | 3 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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Posted in networks, twitter | 9 Comments »

Pareto Novelli — Some Q&As

Sunday, February 1st, 2009

A recent post about some Pareto analysis of the Porter Novelli Twitter sample , “Porter Novelli Twitter folk – the 80/20 rule”, stirred up a little bit of interest on Twitter — and made me think again about what I’m doing and why. Partly because those conversations were off-blog (and I’d like to capture the answers I gave somewhere more permanent) and partly because I’ve now had time to think of better answers I thought I’d set them down here.

First, a little background. This Q&A is the sixth post in an impromptu series about the Twitter people where I work (Porter Novelli, the international public relations agency.) By now you might think that I’d be tired of this stuff, but you’d have another think coming. Here’s a quick list to bring you up to date.

  1. Map of Porter Novelli people on Twitter on 17th Jan 2008
  2. Map of Porter Novelli people on Twitter on 20th Jan 2008
  3. Introducing the Porter Novelli magic Twitter friend maker (beta)
  4. Porter Novelli Twitter folk ranked by number of followers
  5. Porter Novelli Twitter folk – the 80/20 rule

Looking at this, you might also think I clearly had nothing better to do than analyze Porter Novelli people and their Twittering ways. In fact, as an experimental data set, I couldn’t really ask for anything much better. It’s sufficiently large (more than 200 people), international (I’ve counted more than 10 countries — and I’m sure there are more), and I have some real-world access to all of the people in the sample, which means I can compare my findings with some hard data.

That said, the experiment is more about learning about how we can analyze Twitter networks — about discovering how representative they are as a word-of-mouth (WOM) channel for example, and what they can tell us about other kinds of social network, or about finding new ways to analyze such data sets — than it is about answering any specific questions. So I’ve not got any carefully mapped-out research plan. Instead I follow paths that strike me as interesting, or possible, or that are suggested to me by friends and readers.

Question 1

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Posted in porter novelli, twitter | 7 Comments »

Porter Novelli Twitter folk – the 80/20 rule

Thursday, January 29th, 2009

Last weekend I posted a chart of Porter Novelli Twitter folk and their followers. If you read it, you’ll recall that I was dissatisfied by what it implied about the collective reach of Porter Novelli twitterers.The pareto chart should look more like this
Well, thanks to a long-ish train journey to Bolton and back, I was able to fudge a little perl script together to look through the data to find and remove everything other than the first instance of a follower. Let’s make that a little clearer. Let’s say that we’re looking at three Twitter people, Alice, Bob, and Carol. The first thing to do is to see who follows them:

alice bob carol
bob
carol
dave
xerxes
yasmine
zeus
alice
carol
edward
william
xerxes
yasmine
zeus
alice
bob
frank
william
xerxes

Now we need to rank them in order of “who has the most followers” (also known as “popularity” as it happens). Here I’ve done that from left to right. Bob has the most followers and Carol the fewest.

bob alice carol
alice
carol
edward
william
xerxes
yasmine
zeus
bob
carol
dave
xerxes
yasmine
zeus
alice
bob
frank
william
xerxes

And finally we go through from left to right removing all followers who have already shown up on someone else’s list.

bob alice carol
alice
carol
edward
william
xerxes
yasmine
zeus
bob
carol
dave
xerxes
yasmine
zeus
alice
bob
frank
william
xerxes

Bob, being at the top of the list gets to keep all his followers which may seem unfair. But it’s not unfair if the question we’re trying to answer is “how do I reach as many people as possible by speaking to as few people as possible?” That is, I’m looking for reach (marketing people often express themselves in terms of “reach” — or the number of people who are exposed to a message — and “frequency” — or the number of times the average person is exposed to that message.)

Looking at the example above, we can see that Alice really delivers an incremental benefit of two new people, and Carol only reaches one new person. That gives us a much better idea of how valuable the most popular person (Bob) really is.

Applying this to the Porter Novelli data set

Clearly it would be extraordinarily boring to perform the process described above for the 205 people in the Porter Novelli data set that I want to analyse. But the analysis script that I wrote (with plenty of help from the perl monks) goes through exactly these steps. It’s a pretty straightforward job, ranking and deduping. Here’s what we get.

Pareto chart showing unduplicated reach among Porter Novelli Twitter Users

This makes much more sense than the last run. According to the Pareto principle, roughly 80% of the effects should come from 20% of the causes. Here we see that 20% of the Porter Novelli Twitter users (marked in black) account for slightly more than 80% of the reach (marked in red.) It’s pretty much a text-book example. Things are as they should be, I suppose.

More to the point, we can now assign appropriate value to coverage at the head of the graph. This is of great value when thinking about our media planning and engagement

By the way — if you’d like a copy of either the Twitter follower API query engine (it’s a well-behaved command-line thing that was developed by the excellent Joachim Larsen) or the slightly shonky perl script that I wrote on the train, you have only to ask: I’ll be pleased to share. Send me a tweet at @mediaczar and I’ll send you the scripts.

Posted in porter novelli, twitter | 5 Comments »

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