Mathew Ingram reports on the continuing efforts of various web services to decipher influence:
Mathew Ingram, The Race to Build a PageRank for the Social Web Continues
Both PeerIndex and Klout rank users based on data that comes from their Twitter, Facebook and LinkedIn accounts, although the two sites describe their rankings somewhat differently. Klout talks about overall “reach” and “amplification,” both of which are determined by looking at a user’s activity and how much impact it has on their social graph — whether their tweets are re-tweeted by others with influence, for example. PeerIndex says that it looks at a user’s activity in Twitter, Facebook and LinkedIn and then comes up with an authority rank for their expertise in eight topic areas, which it uses to create an influence “footprint” for each user.
As Klout and PeerIndex add more sources of reputation or influence data such as Quora to their rankings, the web moves closer to having a kind of Google PageRank for social activity, with all that implies. The big problem, as with Google search, is how to exclude the social equivalent of black-hat SEO and link spam, and how to determine what it is real influence and what is simply Justin Bieber-style popularity.
You are who you follow.
I continue to believe that these tools are motivated by a skewed model of what is going on online. It’s a sort of neo-classical economics viewpoint, where are individuals are considered as interchangeable, like replacement parts on an assembly line, or atoms banging into each other in a tea kettle of boiling water. And worse, they are considered in isolation, not as connected to others in a deep way.
We are social beings, and our social value is an emergent property of the network we are situated in, not a personality trait. We need to think about this issue in terms of position in the social network — who we follow and are followed by — not purely statistically.
Stowe Boyd, It’s Betweenness That Matters, Not Your Eigenvalue
Betweenness is a measure of how short are the chains that connects a person to the totality of the network. Like PageRank, betweenness is recursive: the people with the highest betweenness are likely to be connected to other people with high betweenness.
This means people are influential because they are connected to many influential people. But influence doesn’t seem directly linked to how many people you are connected to. It’s a function of being connected to others who have short chains to many other people with high betweenness. Or, looked at differently, betweenness is a measure of how many social circles, or social scenes, a person is connected to.
So, it’s not who you know, it’s where you know. It’s where you are situated in the network, and not just in the limited sense of how many immediate contacts you have.
The subtle, dark-matter mystery of social networks is that influence is oblique, and not easily determined by the sorts of tools we have today.
It is not your follower count, or who you follow, per se. But, instead, do you have short paths into other social scenes, both incoming and outgoing? That is the deep structure of being truly connected: bridging over different social scenes, acting as a conduit, a vector, a filter and amplifier for ideas good and bad, the best insights, and deadly viruses.
I maintain that the best predictor of an individual’s social value and the likelihood that their social activities will make an impact on others is who they are connected to, and in particular, who they follow. Following people from a diverse range of social scenes — different parts of the global social network — increases the likelihood that you will encounter new insights, new thoughts. And following more people who are connected themselves to diverse social scenes increases that likelihood. And that increases your social value to others.
You are who you follow.
Attempts to quantify our influence — our social value — without taking this into account are doomed to be a second order reflection of what is actually happening between us. It’s like trying to figure out why someone threw a rock into a stream by calculating the rock’s weight, trajectory, and the spread of the ripples. All very interesting, but it doesn’t get to why.