Jeff Jarvis is right when he makes the point that those with the most followers may not be the most influential; but he misses the fact that some people might still be more influential than others:
Jeff Jarvis, The Hunt For The Elusive Influencer
[…] trying to find the big influencer with big audience is really just old mass marketing in a cheap dress. Old mass marketing (go with the largest numbers … and breasts) isn’t economical; neither, it turns out, is marketing to just one or a few powerful people — the mythical influencer. That brings us to a new hybrid to mass marketing, which is what I think Watts is suggesting: Target many people who at least have some friends who’ll hear them. (Disclosure: This was a key insight in the development of the company 33Across that made me invest in it.)
Or to put this question in the current argot: Is there more influence in the tail than in the head? If you talk to 100k people who talk to 10 people each, do you get more bang than talking to one person who has 1m followers? (Watts did also say that a combination of mass and tail marketing is effective.)
Just because the most popular people are not the most influential does not mean that no one is influential. Jarvis seems to fall back to a position that there are no influencers:
So the message spreads not because of who spoke it but because the message is worth spreading. What makes us spread it? First, again, we spread it if it resonates and it is relevance; it has value to us and we think it will have value to others. Second, trust or authority is a factor. If I see Clay Shirky or Jay Rosen or Kevin Marks tell me to click on a link I’m more likely to do so because I respect them and trust their judgment and I’ve found in the past that clicking on their links tends to be worth the effort. They give me ROC (return on click). But if I followed Miss Kardashian (I don’t) and she told me to click on a link, I’d be less likely to, both because I don’t put her in the same intellectual corral as my other friends and have no relationship with her and because I have seen that clicking on her links gives me lousy ROC. Is trust or authority or experience influence? In a small circle of actual friends, I don’t think so. And in any case, having only a small circle of friends isn’t the one-stop-shopping influence marketers are seeking.
So abandon the hunt, marketers. You’re not going to bag the influencer. She doesn’t exist (well, one did but she quit her TV show).
The flaw in his argument is that popularity is not the only way to weight nodes in a social network. Jarvis mentions authority, but doesn’t go very far with his analysis. However, I think authority is a red herring, too. It is looking at the transmission of messages int he context of an individual’s value judgments, as if we decide what to be influenced by, day by day.
But influence is actually a sort of dark matter: a force that surrounds us without us really being aware of it, like gravity.
In recent posts, I have explored these ideas at some length (see It’s Betweenness That Matters, Not Your Eigenvalue: The Dark Matter Of Influence and Social Scenes: The Invisible Calculus Of Culture), so I will simply reprise some of the recent research about social influence and what it means to us, as individuals.
The number of followers a person has is an indicator of a sort of connectedness in a social network, but it is not a good predictor of influence. Even when you weight the value of each link based on the rank of each person connecting to someone, like Kim Kardasian, it doesn’t line up with influencing others. That weighted measure is — to use technical terms for a second — called the eigenvalue, and while it is a measure of ‘centrality’ — a degree of connectedness in the network — centrality isn’t the measure of centrality that best aligns with influence.
Another form of centrality is to look at where an individual sits in the network relative to subnetworks. For example, a person who has solid connections in the tech community and a number of deep connections in the art world is likely to act as a bridge between those communities, and carry new ideas from art to tech and vice versa. This is called ‘betweenness’. To the degree that people that traverse different social scenes are rare, and if these communities benefit from this cross-pollination, then such bridgers will have an inordinate influence of both communities. But it is insufficient to simply measure the number of social scenes that an individual touches, just as it is inadequate to simply count links to a page to determine its page rank: you have to weight the links by the ranking of the pages that link. That’s the core of Google’s PageRank algorithm. However, in the case of this sort of bridging across social scenes, the individual’s betweenness has to be calculated based on the sum of the betweenness of all those that she connects to. In this way, one person’s betweenness is a function of the betweenness of all of her connections.
This seems intuitive: people who have many connections into diverse social scenes will act as the conduit for ideas to spread. And if I am connected to many others who are likewise connected to diverse social scenes, then I am even more likely to spread ideas: I am a better idea vector to the extent that I have more of these kinds of connections: more betweenness.
The conclusion here is that betweenness is a good predictor of influence, because influence is strongly linked with exposing people to new ideas, trends, or culture, while eigenvalues are not. The most popular person in a social scene may not be the one speanding a lot of time in other social scenes; on the contrary.
And the final piece of the puzzle is that we are all embedded in social scenes that are larger than we know. For example, I am connected to hundreds of friends, who are influenced by tens of thousands of their friends, some of which I may know, and more of which I could encounter. However, the friends of my friends are influenced by the third closure, the social scene of millions of people, a scene so large I cannot possibly know all those involved.
Recent research has shown that it is this social scene scale that influences our weight, our health, our smoking habits. This dark matter — the third closure — influences us like an atmosphere: we don’t notice it, but it is filling our lungs, and pressing against our skin. We meet some friends who mention a new sort of club music, and a few hours later you hear some on your favorite radio station. The next day, at a friends’ house, she’s playing the same band on her stereo, and that night you hear it again at your favorite bar. That’s because in your corner of the galaxy, some number of people with high betweenness, floating around in the third closure, dragged this new music into the tech scene from the art scene, and turned a bunch of people onto it, and a year later it’s on the pop radio channels.
So, people need to bone up a little on network research to get the differences between different sorts of centrality, and to unthread popularity from influence, mathematically and anthropologically. Just because popularity isn’t a good predictor of influence doesn’t mean nothing is.
Betweenness and the dark matter of the third closure are the keys to understanding — and potentially directing — how influence flows though social networks. There’s a lot to research yet, but these will likely be the starting points of influence science going forward.