Archive for the ‘research’ Category
Newer Entries »Relationships between “top 50″ UK PR twitterers
Tuesday, December 16th, 2008
This is a 300dpi map of the top 50 PR twitterers (as per Stephen Waddington’s analysis) and the interrelationships between them.
To generate this:
We first crawled all the accounts for “friends” (accounts that they follow) and “followers” (accounts that follow them). This is a profligate use of resources because we were always going to throw away a massive load of that data. But it’s always more interesting to start with a large data set. You don’t know what you’re going to find.
Then I wrote a quick-and-dirty perl script to process the data looking only for those instances where one of the top 50 followed another.
Then we dropped everything into NetDraw (if you are at all interested in this stuff, you really should get hold of a copy and start reading around the subject.) We laid out the chart so that the people who have the most peer-group followers are in the centre of the chart – and to make it even more obvious, we sized their nodes according to the number of peer-group followers that they have.
So people on the peripheries (like me – mediaczar) are peripheral to the community, and those in the middle are central. Obvious, huh?
This chart already shows a massive difference between our analysis (as it progresses) and the raw data from Wadds’s list. There are some really good reasons for this, which I’ll go into on the blog.
Tags: citation analysis, influence, influence mapping, network analysis, networks, twitter
Posted in influence, networks, twitter | 4 Comments »
Map of top 50 UK PR twitter people and their followers
Saturday, December 13th, 2008
This is not a hedgehog in a cranberry field. It is a network map, but a particularly tightly-knit one.
Spurred on by some of the comments we’ve received about the Rufus map we made of the top 50 UK PR twitter people (as measured by Stephen Waddington) I thought it’d be a good idea to look at this in a bit more depth. Rufus isn’t really the right tool for looking at this kind of thing, so we’ve built something else to do it better. Looking at one site or service is a lot easier than looking at lots of sites — so this took hours, not months to create.
After a little debugging we were ready to test on a seedlist of 50. The crawl took about an hour to run.
This is a visualization of the data set we got (correct as at December 12, 2008) after very little processing.
The size of the blobs relates roughly to “how many people in the group follow you.”
We’ve removed anyone who is only followed by one person in the group. So everyone here is followed by at least two others (obviously.)
There are just too many people in this graph to show labels. And a lot of the top 50 people are hidden by other top 50 people. Maybe I should do a graph rotating in 3D. (Later, having tried this: if I had a SGI workstation, maybe I would.)
What I’ll do over the weekend is process the data files I’ve got (one’s got around 30K records, and the other 40K records) to see if we can tease a little more information out of them.
Then we’ll run this on other twitter communities, and random twitter seedlists to see how (if at all) the networks differ. Are PR people more introspective than the rest of the twitterverse?
This is a very high def image, so it will blow up nicely.
Tags: citation analysis, influence mapping, mapping, network analysis, networks, public relations, twitter
Posted in influence, networks, twitter | 2 Comments »
High network density in twitter UK PR community
Monday, December 8th, 2008
For this graph we took a list of the top 50 PR twitterers as measured by Stephen Waddington (Nov 2008). We added “twitter*” to the canonify exception list to identify individual twitterers (this isn’t perfect — the regex may need some tweaking) and further limited the crawl to domains that contained the word “twitter” using the Whitelist function.
Again – look at how dense the network is here.
Tags: network analysis, network graph, rufus, twitter
Posted in influence, networks, porter novelli, rufus | 1 Comment »
Reading RUFUS data with yEd
Monday, December 8th, 2008
Most of the time we use UCINET and NetDraw to analyze the data from Rufus. Rufus exports crawl data to a Pajek .net file by default. But we can also export GraphML and read the data into other tools that handle that format. This is a test we ran of this feature using yEd
It’s not working beautifully yet, but it is working.
Tags: graph, network analysis, rufus, yEd
Posted in influence, networks, porter novelli, rufus | 2 Comments »
A sneak peek at our online influence mapping tool
Thursday, December 4th, 2008
Porter Novelli has been working on its own “online influencer mapping” tool for about six months now. Recently, I’ve started posting screen grabs on our Flickr page to see what people think about it. I thought it was probably time to share some of the images here.
Version 3.5.4 (Always in Beta)
The project is named Rufus after the character George Carlin played in “Bill & Ted’s Excellent Adventure”.
For those of you who know how network analysis works and what it’s used for, this is revolutionary only in that it’s fast and accurate enough to use as an exploratory tool.
For those of you who have no idea what network analysis is or how it’s used in many, many situations, 2009 would be a really good year to start finding out.
For this graph (which took around 5 mins to generate), we took as a seed list the first 50 back links as generated by Yahoo Site Explorer (http://siteexplorer.search.yahoo.com/.) We’ve tested this up to 100 seeds, but there’s plenty of room to go further.
Tags: citation analysis, influence, influence mapping, network analysis, networks, porter novelli
Posted in influence, networks, porter novelli, rufus | Comments Off
What we can learn from the real evangelists?
Tuesday, July 15th, 2008
This is a description of Billy Graham crusades from an academic study I’ve been reading. I’m interested in how real evangelists work (after all, I use the term often enough when talking to colleagues and clients):
Counselors begin their work after the singing, testimonials, collection and Billy Graham’s sermon, which culminates in the altar call. At the moment of Graham’s invitation to “come forward to Christ.” counselors and choir members begin moving forward to an area usually in front of the speaker’s platform or rostrum. To a naive member of the audience or a television viewer, this movement creates an illusion of a spontaneous and mass response to the invitation. Having been assigned seating in strategic areas of the auditorium or arena and given instructions on the staggered time-sequencing for coming forward, the counselors move forward in such a fashion so as to create the illusion of individuals “flowing” into the center of the arena from all quarters, in a steady outpouring of individual decision. Unless an outsider or observer of these events has been instructed to look for the name tags and ribbons worn by those moving forward it is all too easy to infer from these appearances the “charismatic” impact of Graham and his invitation. These strategies promote the respectability of making a public commitment and represent methods calculated to manipulate the consent of the passive, the uncertain, the wary, and the indecisive.
(from: David L. Altheide and John M. Johnson, Counting Souls: A Study of Counseling at Evangelical Crusades, The Pacific Sociological Review, Vol. 20, No. 3, (Jul., 1977), pp. 323-348)
Momentum
A recent (and criticised) study by Tubemogul on the short shelf life of online video reminded me of some research into views on YouTube videos I did back in 2006. I only looked at about 130 random YouTube videos for the first 20 days of their life cycle, while TubeMogul’s methodology was somewhat more sound (they tracked more than 10K videos for around three months, among other things.)
Here’s the chart from my analysis: (more…)
Tags: billy graham, evangelism, influence, marian salzman, tubemogul, youtube
Posted in influence, opinion | 4 Comments »
Mapping the social graph of weight loss groups
Wednesday, July 2nd, 2008
These are the graphs from some research on weightloss groups on Facebook. I’ve processed the data so that:
1) the size of dot is related to "total number of friends" – this only works where a user’s friends are publicly visible – quite often they aren’t, and I haven’t checked to see what the incidence of this privacy setting is generally and specifically
2) all isolates (i.e. those users who have no (public) personal relationships within the group have been removed.
This is the network graph of relationships on a personal weight loss support group. A college student set this up to support her own goals. She told me: " For my group, I just started it out by inviting all of my friends and then some people joined the group who found it in a search, I think. I am amazed by the amount of support I receive from random people who encourage me to keep on going. There are some spammers on the group who are just there trying to sell stuff and that gets annoying, but I know I can’t avoid them."
This is the network graph of relationships on an unofficial weightwatchers group on Facebook. You can see that there are hardly any member-get-member relationships here. My friend Valery (who has a professorship in this sort of thing at Wharton) says:
"It’s very common that organizations and interest groups become foci for personal networks. In fact, I believe that joint activities are the prevalent mechanism of tie formation. "
But it doesn’t look like it here. Looks to me that – while people may form relationships around special interests – they don’t mirror these on Facebook. Say I suffer from Meniere’s Disease (apparently true) and I participate in a Meniere’s support forum (not true at present), I don’t necessarily make those people my Facebook friends…
Another example of the "not many personal relationships" graph for a weightloss support group on Facebook.
How do people get information on weight loss? After a few interviews, I think the answer is like this:
1) Influencers are "pull", rather than "push" resources (I’m thinking of going on a particular product, so I mention it casually to several friends to gauge consensus/temperature. One or more of them tell me "oh yes, I’ve heard of that", and one tells me "yes, My friend tried that, and lost 20lbs") This is not an active market. Most people won’t be evangelizing, and evangelizing behaviour may even appear suspicious.
2) That said, people trust strangers to an extraordinary degree. Friend-of-friend endorsement is readily accepted, as is the anonymous commentary on boards & groups. Bloggers are slightly less trustworthy, it seems – because most of them have an axe to grind.
OK — so this really isn’t v. scientific. But compare this to the map of green issue member-get-member activity and you’ll see a huge difference.
Tags: facebook, influence, mapping, networks, research, social media, weight loss
Posted in networks | 4 Comments »
Influence Mapping: The Maverick Cop Way (Part 2)
Wednesday, June 18th, 2008
The story so far: In the last episode of Influence Mapping: The Maverick Cop Way, we discussed a simple process for organizing what you know about influencers. We discussed briefly the decision making unit model we were using, our (very broad) definition of “influencer”, and showed how we can score them quickly for the three key variables reach, authority, and “ease-of-use”. At the end of the process, we found ourselves with something like this:
Now we’re going to go a little further, and show how we can map the relationships between the various stakeholders. This is the second and final post, and it may introduce a lot more that’s new to you. But stick with it – it might be worth it.
What you’ll need before you start
You’re going to need UCINET, a programme that analyses matrices and networks. It comes along with another programme from the same publishers (Analytictech) called NetDraw that draws networks. You’re going to want both. UCINET costs $250 for a corporate license, but the first 30 days are free. NetDraw is a free download.
They only work on Windows, but I’ve not experienced any real problems running them on a Mac using Parallels.
1. Create a matrix
Take your list of influencers (as per the table above), and add in the four key players from the decision making unit; the initiator, the decision maker, the purchaser and the end user.
Paste them down the left hand side of your table, and along the top edge, as in the illustration below. Excel’s Edit > Paste Special > Transpose command is useful, not to say essential, when you’re doing this. At the end of the process, you should have something that looks like this.
The rows show the influencers, the columns show those being influenced, the targets.
Because we don’t think that an influencer can exert influence themselves we drop a line of zeroes down the diagonal. This is more to help us navigate than anything else. Don’t feel you have to do this.
2. Fill in the matrix
Go through the rows one by one, deciding if a given influencer has any effect on the targets (the column headings). So for example, we know that the initiator influences the decision maker, and so on. But knowledge of the end user may well influence the decision maker, too. So – for example – when I’m choosing my dad a computer, I take into account the fact that he’s not so au fait with technology, and that if he can’t use it, he’s going to call me to ask for tech support. So I’d better choose something that will limit these calls.
At the end of the process, you’ll end up with something like this (although probably much bigger). It can take quite a lot of time to go through this process – this may be one of those times when you want to work with a partner to bounce the ideas around.
Tags: audience, influence, mapping, networks, planning, public relations, tool
Posted in how to, influence | 2 Comments »
Unravelling member-get-member activity
Tuesday, June 17th, 2008
This is a network map. You’ve probably seen something like this before. What’s quite interesting is that this is an undirected graph (that is, the links go both ways – if A knows B, B knows A, that sort of thing) but there’s some directed activity implied.
Groups on sites like Facebook and Bebo work by members recruiting other members, either actively (“join this group”) or passively (friends can see on their friend feed that other friends have joined up, and decide to get involved).
We’d expect to find that people have strong existing personal relationships within such groups.
We took one group (a green interest group) which has around 2,000 members. We selected this group because it is has a clear single-minded proposition,as well as a strong local element that means that people are more likely to know each other than on some of the more generalist boards.
There are more than 250K publicly available relationships inside and outside the group, and we looked at analyzing all of them.
It seems that there are a few highly connected people (people with between 395-400 friends) who sit at the centre of these things, and on whom the eventual success of the group depends. While they may be no more active than other users (and may even be less active), they are the hubs who link together the network.
In the maps you can clearly see three kinds of shape.
Long fingers show where users get users (usually one or two at a time). Fan shapes show one user who mobilizes many friends. These are clearly interesting to us. In the middle are “mares nests” where lots of people know each other.
This is a pretty straightforward Pareto-like distribution: 33% of the users are connected to 79% of the members, the remaining 66% only link to 22% of the users.
Here’s how we’re going about it.
1) Spider every member of a group, and their friends
This shows the members of a group (red) and all their publicly-visible friends. This is a much smaller group than the one in the graph above (these things grow exponentially, you understand)
Red dots are members, grey dots are their friends.
I’ve gone through and blurred out the individuals’ names. We probably shouldn’t have been collecting those anyway…
2) Look for relationships between members
Here we just use a little perl script to sort through the lines selecting only those relationships between two members. In essence we’re throwing away all the other relationships. Perl is good at this sort of thing. I found a trick over at the perlmonks site that shows you how to search an array much faster than just grepping (which took a while).
3) Drop the results into NetDraw
We’re using Analytech’s excellent/free network drawing application, NetDraw. You can tweak the Windows Metafile output in things like Illustrator.
Tags: mapping, networks
Posted in influence, networks | 4 Comments »
Influence Mapping: The Maverick Cop Way (Part 1)
Monday, June 9th, 2008
You know the maverick cop? The one who breaks all the rules, has to surrender his gun and badge somewhere in Act II, but nevertheless somehow gets the job done? His approach to solving problems is crude but effective (usually it involves shaking people down, which isn’t something we get to do a lot in the communications business.)
I don’t know that maverick cops are into influence mapping. I don’t know how many people really are, but it’s a big part of my work life. These days it seems I’m always being asked, “Mat, who is the most influential x?”, or, “Mat, who influences the online discussion on y?” or, “Mat, what influences people’s purchase decision-making when it comes to z?” These are all interesting questions, and bear a lot of thought and research and planning.
However, time isn’t something we all have a lot of these days, so right now I’m going to share a very quick-and-dirty method I’ve been working on; the research equivalent of holding a pimp upside down over a balcony.
Before we start, what’s wrong with this approach?
It builds on what we know, or what we think we know. Using it successfully will require all those assets that the maverick cop has in spades: a sharp brain honed by experience, a deep knowledge of the streets (well, your market), and an underworld intelligence network of pimps and hookers (in our case these are more likely to be clients and colleagues, of course.)
Axel Foley aside, maverick cops don’t always do so well out of their jurisdiction. This approach isn’t going to expose surprises or new information so well. It’s all about organizing what you know.
If you are a rookie cop, you’d better stick by the book. That’s all I’m saying. Or someone will bust you down to traffic duty before you know what’s hit you.
And what’s so good about it?
Well, it’s fast, for one thing. And it’s a process – which is another. Now if you add into that the fact that – when you have more accurate data, you can go back and plug it into the model without breaking it, but only making it better – then you’re onto a winner.
Tags: digital, influence, integration, mapping, planning, public relations, social media, tool
Posted in how to, influence | 1 Comment »
