Archive for the ‘research’ Category
« Older EntriesThinking 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 »
Creating blog seed lists for research
Tuesday, February 10th, 2009
Colleagues and regular readers will know that we’ve been working on an “online influencer mapping” tool called Rufus. Those of you who’ve had a chance to use Rufus will know that it requires a seed list of URLs to get started. Creating this seed list can be automated in one or two ways, but one of the fastest, most effective, and most sensible ways to build a seed list is still to do it by hand.
We’ve got one or two other processes that also require us to build a seed list. No doubt other people do too — lots of web research is quite data hungry. So — because I’ve found myself telling a few of my Porter Novelli colleagues how we go about the process, I thought I’d share it here, in the interests of:
- having somewhere to point people in future,
- general good-heartedness: I’ve learned a lot from people in the past, and I like to give stuff back, and
- getting feedback and tips from people about how they might go about the same process.
Oh – and while these methods should work in any language, please bear in mind that I tend to think and work in English. I’d appreciate feedback on how best to localize these methods.
Building a seed list: 5 easy methods
With all these methods, there’s no substitute for checking out the blog. I don’t ask people to read the blog (that comes at a different stage of the process altogether) but you should at least click through and see what you’re dealing with. In fact, method 3 rather relies on you visiting the blogs you’re researching.
1. Look for someone who has already done your research for you
Start by being optimistic. Generally you’ll find that someone else has created a list of the “top ten” (or however many) blogs in the niche that interests you. Take a look at Brendan’s regularly updated PR Friendly Index for example. If you’re searching for English language blogs then you could do worse than start by looking at Guy Kawasaki’s Alltop. But simply Googling for lists of blogs or blog charts should get you a long way.
This is generally a source of fairly high-quality data. One thing to watch out for, though, are search engine spamming link farms, and shady “Make Money Online” (MMO) directories. You’ll learn to recognize these soon enough, but as long as you’re visiting all the blogs you’re putting on your seed list you should be alright.
2. Do a tag search on delicious
I picked up this technique from Anthony Mayfield, who showed me that by searching on the delicious social bookmarking site for the tags “xxx” and “cool” and/or “inspiration” you could find sites about “xxx” that people thought were cool. Knowing what your digital trendsetters think is cool is one hell of an insight.
For our purposes though, we’re looking for cool blogs. So (1) click the “Explore Tags” tab on the home page, and then (2) type your keyword and the word “blog” into the search box. Couldn’t be simpler?
Well — actually it could be simpler. You can query the delicious database when you type the URL into the address bar of your browser like this:
http://delicious.com/tag/blog+keyword
Where “keyword” is the word you’re looking for.
When you get the results, check the ones that (a) have the right kind of title (if you’re looking for French blogs, look for French titles for example), (b) have the right kind of tags and description and (c) have been bookmarked most often
If there’s a better local language social bookmarking site, I’d use that whenever possible. For example, Mister Wong is a good one for German language sites.
A quick note: social news sites like Digg and Reddit, and “serendipity browsers” like StumbleUpon tend not to work so well in my experience.
This method also owes a lot to Marshall Kirkpatrick. You might like to try out the Yahoo! Pipe that I built based on the process that Marshall documents.
3. Look for blog rolls
On every blog you visit during the research process, look for the blog roll — and check the likely-looking links. See if they’re useful or useless. Quite often you’ll find that someone who has an interest in widgets will also read and link to blogs that cover widgets. That, after all, is the principle on which Rufus works wrote small. So we reckon it’s a pretty good approach.
4. Ask your Twitter followers
Seriously — this works. Well — it worked for me and my team from around +100 followers onwards. I’d be interested in others’ experience.
5. Call someone
Get hold of someone who knows about the subject and phone them up or get them on IM. Category experts are an excellent source of low-volume but high-quality information. It’s time consuming, but can work well if you have the right contacts. Journalist friends might be a great source of blog lists.
I’ve purposefully left this one till last; I think it’s a good rule of thumb to do your desk research before picking up the phone. That way you can ask intelligent questions instead of damn fool ones.
Using a text editor
I try to keep two lists running all the time that I’m working; a scratchpad list of blogs I have yet to visit and the seed list itself. Because I’m on a Mac, I use the excellent BBEdit (there’s a free version called TextWrangler which will be just as good for most people.) If — as is more probable — you’re on a Windows machine, you might like to try the very powerful but slightly less pretty Notepad++. But if you just want to use Excel, though, that’s fine, too.
Tags: bbedit, bloggers, blogs, delicious, digg, notepad++, reddit, research, seed list, textwrangler
Posted in research | 4 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.
(more…)
Tags: congress, mapping, network analysis, twitter, visualization
Posted in networks, twitter | 9 Comments »











