Jeff Jarvis on The Hunt For The Elusive Influencer

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.

5 April 2010 23 Permalink

It’s Betweenness That Matters, Not Your Eigenvalue: The Dark Matter Of Influence

Let me explain, before you think I have been gargling alphabet soup.

Recent research suggests that the most important people in social networks, relative to actually transmitting ideas, viruses, or moods, might not be the folks with the most followers, but instead might be people that are connected to a large number of individuals through shorter paths than others have.

- ARVIX blog, Best Connected Individuals Are Not the Most Influential Spreaders in Social Networks

The study of social networks has thrown up more than a few surprises over the years. It’s easy to imagine that because the links that form between various individuals in a society are not governed by any overarching rules, they must have a random structure. So the discovery in the 1980s that social networks are very different came as something of a surprise. In a social network, most nodes are not linked to each other but can easily be reached by a small number of steps. This is the so-called small worlds network.

Today, there’s another surprise in store for network connoisseurs courtesy of Maksim Kitsak at Boston University and various buddies. One of the important observations from these networks is that certain individuals are much better connected than others. These so-called hubs ought to play a correspondingly greater role in the way information and viruses spread through society.

In fact, no small effort has gone into identifying these individuals and exploiting them to either spread information more effectively or prevent them from spreading disease.

The importance of hubs may have been overstated, say Kitsak and pals. “In contrast to common belief, the most influential spreaders in a social network do not correspond to the best connected people or to the most central people,” they say.

At first glance this seems somewhat counter-intuitive but on reflection it makes perfect sense. Kitsak and co point out that there are various scenarios in which well connected hubs have little influence over the spread of information. “For example, if a hub exists at the end of a branch at the periphery of a network, it will have a minimal impact in the spreading process through the core of the network.”

By contrast, “a less connected person who is strategically placed in the core of the network will have a significant effect that leads to dissemination through a large fraction of the population.”

The question then is how to find these influential individuals. Kitsak and co say that the way to do this is to study a quantity called the network’s “k-shell decomposition”. That sounds complicated but it isn’t: a k-shell is simply a network pruned down to the nodes with more than k neighbours. Individuals in the highest k-shells are the most influential spreaders.

(via @karllong)

In network theory, these two cases are both example of centrality: ways of assigning values to individual nodes in a network based on how each node relates to the others.

The most connected people in a social network — those with the highest number of incoming and outgoing connections — have high eigenvalues. These eigenvalues can be calculated — like Google’s PageRank algorithm — by weighting the value of each connection based on the eigenvalue of the originator.

It’s not who you know, it’s where you know.

But this research suggests that a different way to measure the centrality might be more useful in determining how much throw weight a person actually has. 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.

related articles

• Location determines social network influence, CCNY-led team finds (scienceblog.com)
• Proximity to Core Determines Social Network Influence (chrisabraham.com)

3 February 2010 35 Permalink

Social Scenes: The Invisible Calculus Of Culture

Today, in New York, I heard Clay Shirky talk two times — midday at the Betaworks monthly Brown Bag Lunch, and this evening at the New York Tech Meetup — on the same topic. He is extrapolating in very interesting ways from the research of social scientists Nicholas Christakis and James Fowler on the social dimension buried in the data of the Framingham Heart Study.

In a nutshell, it turns out that the activities of the ‘third neighborhood’ influence you in ways you may be completely unaware of.  These are people that you do not know, but are (dis)connected to you by two removes: the friends of your friends’ friends. Christakis and Fowler found that obesity, smoking, and many other medical factors strongly correlated with the prevalence of corresponding activities in these large social scenes:

- Clive Thompson, Are Your Friends Making You Fat?

Christakis knew about the Framingham Heart Study and arranged a visit to the town to learn more. The study seemed promising: he knew it had been underway for more than 50 years and had followed more than 15,000 people, spanning three generations, so in theory, at least, it could offer a crucial moving picture. But how to track social connections? During his visit, Christakis asked one of the coordinators of the study how she and her colleagues were able to stay in contact with so many people for so long. What happened if a family moved away? The woman reached under her desk and pulled out a green sheet. It was a form that staff members used to collect information from every participant each time they came in to be examined — and it asked them to list all their family and at least one of their friends. “They asked you, ‘Who is your spouse, who are your children, who are your parents, who are your siblings, where do they live, who is your doctor, where do you work, where do you live, who is a close friend who would know where to find you in four years if we can’t find you?” Christakis said. “And they were writing all this stuff down.” He felt a jolt of excitement: he and Fowler could use these thousands of green forms to manually reconstruct the social ties of Framingham — who knew whom, going back decades.

Over the next few years, Christakis and Fowler managed a team that painstakingly sifted through the records. When they were done, they had a map of how 5,124 subjects were connected, tracing a web of 53,228 ties between friends and family and work colleagues. Next they analyzed the data, beginning with tracking patterns of how and when Framingham residents became obese. Soon they had created an animated diagram of the entire social network, with each resident represented on their computer screens as a dot that grew bigger or smaller as he or she gained or lost weight over 32 years, from 1971 to 2003. When they ran the animation, they could see that obesity broke out in clusters. People weren’t just getting fatter randomly. Groups of people would become obese together, while other groupings would remain slender or even lose weight.

And the social effect appeared to be quite powerful. When a Framingham resident became obese, his or her friends were 57 percent more likely to become obese, too. Even more astonishing to Christakis and Fowler was the fact that the effect didn’t stop there. In fact, it appeared to skip links. A Framingham resident was roughly 20 percent more likely to become obese if the friend of a friend became obese — even if the connecting friend didn’t put on a single pound. Indeed, a person’s risk of obesity went up about 10 percent even if a friend of a friend of a friend gained weight.

“People are connected, and so their health is connected,” Christakis and Fowler concluded when they summarized their findings in a July 2007 article in The New England Journal of Medicine, the first time the prestigious journal published a study of how social networks affect health. Or as Christakis and Fowler put it in “Connected,” their coming book on their findings: “You may not know him personally, but your friend’s husband’s co-worker can make you fat. And your sister’s friend’s boyfriend can make you thin.

Obesity was only the beginning. Over the next year, the sociologist and the political scientist continued to analyze the Framingham data, finding more and more examples of contagious behavior. Smoking, they discovered, also appeared to spread socially — in fact, a friend taking up smoking increased your chance of lighting up by 36 percent, and if you had a three-degrees-removed friend who started smoking, you were 11 percent more likely to do the same. Drinking spread socially, as did happiness and even loneliness. And in each case one’s individual influence stretched out three degrees before it faded out. They termed this the “three degrees of influence” rule about human behavior: We are tied not just to those around us, but to others in a web that stretches farther than we know.

This research brings to mind the obsrvation of Blaise Pascal, “The heart has its reasons that the mind knows not.” It appears that negative behaviors like overeating and smoking are in some hidden way transmitted through our social networks, even when we are not in contact with those others who are influencing us. Likewise, it turns out that happiness is spread in a similarly diffuse and oblique fashion.

Getting back to Clay: he wonders what this means for the way that modern social tools work, like Twitter, for example.

In social tools, we are each the center of our own universe, and we are connected to our friends (who are each the center of their own universes, too). We are aware that our friends have friends we don’t know (or are aware of in the most insubstantial of ways). And these friends of friends likewise have friends, which are unknown to us as well.

But despite their anonymity and distance from us, they are influencing us, as Christakis and Fowler showed. But out tools, like Twitter, don’t allow us to deal with this mass of people — which is likely to be on the order of a million people, plus or minus — in any way at all. It is not addressible, or searchable, or filterable. I can’t find out what TV my social scene is watching, or what music they like, or how they voted in the last election.

Shirky points out that it is easy to find out what my friends are doing, or what the world as a whole is doing, but what the world is doing is fairly ‘bland’ as he puts it. The world’s combined interests lead to the dropping out of all the odd and eclectic, and you are left with Lady Gaga and Obama. BIg surprise.

But my social scene — the group that actually influences my thinking, moods, and buying behavior — in completely untapped and untappable by out tools today.

However, its clear that it could be tapped: just as in the Framingham Heart Study. It’s possible (and not even very technically challening) to create the swirling, dynamic, and ever-changing opinions and activities of your one million closest ‘friends’, only a few hundred that you know well and perhaps a few thousand that you ‘know of’. We are all surrounded by ‘dark matter’, the next ring in the social cosmology out past those you know and the friends of those you know: a million people exerting an invisible influence on those that influence those that influence you. If that group is down on smoking, you will be getting social cues to not smoke. If they are crazy about Korean food, you will be served kim chi at dinner parties. If they are into country and western music, you will find yourself shopping for cowboy boots with your cousin.

Shirky clearly states that he doesn’t know where this will lead, even if he is right. But I think that it is obvious that we would like to explicit see and measure the influences in our unverse (each in their own overlapping universes), so on a personal level this may be a tremendous adjunct to the filtering, amplifying, and serendipity that we all want social tools to help us with. And perhaps just as much as a possible driver of technical experimentation in this sector, companies would like to know how influence is channeled and how it impacts individuals. The underbelly of this is exactly that: that marketers would like to tap into this social juju, and influence us through social ties that we can’t even touch directly.

But it is always the brightest light that casts the darkest shadow.

It comes as no surprise that there is value — and power — in identifying the wellspring of our desires and the foundation of our apsirations. Social scenes may turn out to be the crux of this transitive and reflexive influence that we exchange in ten thousand ways, every day. If it turns out that our place in the world — our position in an invisble sphere of one million almost friends of ours — defines strongly who we are, what we love, and who we hate, would we be surprised? Not me.

This may be just the face of tribalism, proven through scientific observation. I am choosing to use the term ‘social scene’ though, because tribalism has so many connotations and associations that could take us off the track. Also, it was Brian Eno that coined the term ‘scenius’ to represent the positive side of a social scene:

via Kevin Kelly, quoting Brian Eno

Scenius stands for the intelligence and the intuition of a whole cultural scene. It is the communal form of the concept of the genius.

We are a result of the accumulated sum of influences that are being tallied behind our backs, and behind the backs of all those that we know. Apparently, we are impacted by a hidden calculus in which we are the integral of the specturm of influences on all those we hold dear.

The Bantu people have a saying “Through people we become human,” and ever aspect of our identity and psychology is shaped by the cultural milieu in which we are part. I have said for years, “I am made greater by the sum of my connections, and so are my connections,’ alluding in a recursive way to these hidden network dynamics. And of course we want better tools to bring these indistinct and indirect forces into high relief. Clay is right about that.

2 February 2010 1 Permalink