UPDATE: I have discovered that I was a bit too quick with the results listed below. In the process where I manually (step 2) removed a cluster of non-blogs (NY Times, CNN, Time, Advertising Age and other large media properties) who are also often mentioned in a PR/Blog context I accidently also removed a handful of very influential blogs including (at least) Shell Holtz, Bulldog Reporter, Hyku and Todd Andrlik. I was simply not careful enough when I cut out the big media (or non-blog) cluster.
I have therefore decided to redo the analysis from scratch, so a new version should be posted in 2 weeks or so.
In my previous post
on this topic I promised to measure the influence of blogs discussing “PR” and “blogs” using citation analysis.
The list appears below but first a few comments on how it was done and how to read it.
How it was done:
1. Using a topical crawl of the Internet, blog posts that discussed the topic of “PR” and “Blogs” in the same article were collected along with blog posts that were sufficiently referenced in this context. (Meaning: If you discuss the topic of “PR” and “Blogs” or being discussed in that context, then you are a candidate).
2. Some blogs, that appeared to be very closely related, were consolidated and some blogs/websites were manually removed because they were not deemed relevant to the context.
3. The posts were analysed for references/citations between them. The citations were extracted and turned into a massive system of simultaneous equations that were solved to provide influence.
4. The influence was normalised to a scale between 1 and 100.
(Many more details can be found in the articles referenced in the Part I of this post).
As influence is a relative measure you read the table like this:
Micropersuasion has (roughly) twice the influence of B.L. Ochman when the topic is “PR and Blogs”; or Top Rank Blog has (roughly) half the influence of Constantin Basturea.
The influence is topical so it is only a good measure when the topic is PR and Blogs. If the topic was, say “PR, Blogs and Measurement” the number (and indeed the ranking) could be expected to be different.
A few comments to the list:
Micropersuasion is clearly in a league of its own. No question about that.
However, Edelman have a large network of employees who run good blogs and while I don’t think they have a deliberate strategy of over-referencing each other, no one can blame the network members for being more familiar with the other Edelman blogs and therefore referencing them a bit more than they might otherwise had done.
You may, if you inspect the list, find bloggers whose position you disagree with. You may think this analysis overrates or underrates them.
If you feel a blog is rated higher on this list than you would expect, it is likely because you are assessing their popularity rather than their influence.
A blog you feel is rated to high is often rated higher because it is read (and referenced) by other bloggers who have above average influence. You could say that such “over-influential” blogs “punch above their (popularity) weight”. Blogs like Into PR, The Blog Herald and Marketing Vox are examples of blogs that are somewhat more influential than their popularity should lead everyone to anticipate.
In the recent weeks a number of blog posts dealing with measuring influence have stirred up quite a debate.Jeff Jarvis
and Steve Rubel
aired their thoughts on the issue.
David Brain and Jonny Bentwood from Edelman’s London office published
something termed “Social Media Index” where they propose a brand new method for measuring on-line influence.
Most of my feelings after having read the article were (far better) summed up by Jennifer Mattern
in her comment
The article offers no research, no references to prior research, no logical reasoning for its claims and proposes no way of testing the model.
Can you imagine if the CEO and the Head of Research of a large investment bank published a new options pricing model without actual research and without mentioning the Black–Scholes
model? Of course not. So why in PR?
Actually the very notion that influence is not related to a topic is comical. As I understand it, the article proposes that someone has the same influence regardless of the topic discussed. In other words, TechCrunch (for example) is as influential on the topic of fly fishing or jogging as it is on Silicon Valley gossip. I don’t think that’s the case. We know both from intuition and from research that influence is topical.
At best, the Social Media Index is an indication of popularity. But popularity is not influence. Those who are very popular often have good influence but you don’t need to be very popular to have a lot of influence.
When you measure popularity, all “votes” count the same. However, when measuring influence each “vote” counts with the weight of all the votes leading to the voter.
Take a look at the figure below.
Person A is clearly more popular than person B. However, those who listen to person A do not, to any large extent, go on to influence others.
Quite differently so for B. Those who listen to person B they go on to influence others, who go on to influence others, and so on. The aggregated impact (or influence) of B is in this case bigger than A.
The figure (above) indicates why models that only take the first layer into account will come up short when trying to explain the overall outcome.
In pre-Internet times measuring popularity (or “reach” or “circulation”) was a relatively good proxy for influence because those who read a particular news paper or watched a TV programme did mostly not go on to influence anyone outside their close social circle.
But because of the Internet this has all changed and the social event horizon
is now defined by language rather than physical distance. Both direct and indirect influence must therefore be taken into account when trying to measure influence correctly.
Relative influence correlates better to outcome than relative popularity simply because influence takes the indirect effect into account.
So how to measure influence?
Well, it’s been done for a long time. In the academic community “citation analysis” has been used for decades to measure the influence of academic journals, articles, scholars and universities.
The principle is quite simple: You collect all references made between articles about a particular topic. The references are transformed into a large set of simultaneous equations that, when solved, provides the relative influence of each journal. Thomson Scientific
is probably the leading provider for the academic community.
However, the original science for measuring influence in a linked network was developed by Wassily Leontief
who devised something called the Input-Output Analysis
. It was originally (and still is) used to measure how sectors of the economy directly and indirectly influenced each other.
Leontief won the 1973 Nobel Prize
in Economics for this specific work.
Now, you may doubt that knowledge gained in pre-Internet times more than 60 years ago can provide any useful input to explaining how to measure online influence.
But in much the same way as engineers at NASA draw on Isaac Newton’s work
, those who shape the on-line world draw on Leontief’s.
In 1965 Random House published a book by W. H. Miernyk titled “The elements of input-output analysis”. The book deals, as the title says, extensively with Leontief’s work.
This book is cited as a source in the article “Measuring the relative standing of disciplinary journals” by P. Doreian (1988).
Doreian’s article then goes on to be cited as a source by Jon M. Kleinberg in his article “Authoritative Sources in a Hyperlinked Environment” (1998).
Then, same year, an article, which you may have heard of, cites Kleinberg’s:
Lawrence Page, Sergey Brin, Rajeev Motwani, Terry Winograd. The PageRank Citation Ranking: Bringing Order to the Web.
There you have it: From Leontief to Google in 4 easy steps. (There may be shorter/more paths).
Ok, end of part 1.
In part 2: The list of the 80 or so most influential PR blogs measured using citation analysis including their relative influence. Stay tuned.
Independent has a story
today about Nintendo quadrupling their profits in the most recent quarter; mostly due to Wii.
If you look at how the sentiment for a number of brands in the games console space has developed over the last 90 days you can see why Nintendo is doing well. (See below)
The Word-of-Mouth about Wii is just so much more positive than that of the competing brands.
Xbox followed Wii for the first 60 days, but couldn’t keep up. The others were left in the dust.
The sentiment above is adjusted for the topical influence of each voice to give a more correct representation.
See a previous post on Wii and positive sentiment here.
interesting example of how influence has developed over time comes from looking at the blog of Iain Dale
. Mr. Dale is a prominent political commentator whose blog has gained quite a following.
The graph below shows the influence of Mr. Dale’s blog when the topic is “David Cameron”. The baseline is again the influence of the BBC (same context).
We can see that the Influence of Mr. Dale’s blog in February was roughly 8% of that of the BBC.Since then his relative influence (compared with the BBC’s) has grown steadily – until the end of June. Here it took a bit of drop.
However, it is likely that the drop was attributable to a surge in the general influence of the BBC on the topic of David Cameron.
Using the contextual influence of The Guardian as a baseline for Mr. Dale’s blog reveals that he is still doing well. (See below).Benchmarking influence aside it is actually interesting to see how much influence a blog can carry on a major topic. Mr. Dale’s influence on the debate on David Cameron currently stands at about 15% of that of the BBC and just under 60% of that of The Guardian.
Measuring how the influence of a stakeholder develops over time is a topic of great interest to many of our clients.
Some clients are interested in understanding the effects of a marketing/PR effort. Others may be interested in identifying new rising stars among the influencers.
However, benchmarking influence over time can sometimes be a bit tricky. Because influence is relative we have to benchmark the influence of a stakeholder relative to one or more other stakeholders.
Using InfluenceMonitor I took a look at how the BBC
’s influence on the topic of climate change has developed.
As a baseline I used the influence of IPCC
, the Intergovernmental Panel on Climate Change.
Figure 1 (below) shows the relative influence of the BBC as a percentage of that of the IPPC since March 1st this year.
The BBC used to be more influential than IPCC on this issue, but around the end of April its influence dropped below that of IPCC. We can see this as the relative influence drops below 100%. Notice, however, the spike in BBC’s influence at the very end of the graph. The increase in BBC’s influence is a good illustration of how a heavily discussed article can dramatically increase influence.
On the 10th of July BBC published an article
about a new study that (apparently) concludes that there is no link between the cosmic rays from the sun and global warming. (See partial screen shot below).
As can be seen from the graph below, this article caused a spike in the number of references and inbound links to the BBC, in the context of climate change, over the following days.
I am trying to fill 2 new openings – one for a Media Analyst and one for an ASP.NET/SQL programmer.
Please pass it on if you know someone who might be interested.
The full descriptions are here http://www.onalytica.com/jobs.aspx
No agencies, please.