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By Root 1049 0

we didn’t actually run the experiment that we imagined. Even though we were studying data from the real world, not a computer simulation, our statistical models still made a lot of assumptions. Assuming, for example, that our hypothetical marketer could persuade a few thousand ordinary influencers to tweet about their product, it is not at all obvious that their followers would respond as favorably as they do to normal tweets. As anyone whose friend has tried to sell them on Amway products would know, there is something a little icky about a sales message embedded in a personal communication. People who follow Kim Kardashian, however, might have no such concerns; thus she may be far more effective in real life than our study could determine. Or perhaps our measure of influence—the number of retweets—was the wrong measure. We measured retweets because that’s what we could measure, and that was definitely better than nothing. But presumably what you really care about is how many people click through to a story, or donate money to a charitable cause, or buy your product. Possibly Kardashian followers act on her tweets even when they don’t retweet them to their friends—in which case, once again, we would have underestimated her influence.

Then again, we may not have. In the end, we simply don’t know who is influential or what influencers, however defined, can accomplish. Until it is possible to measure influence with respect to some outcome that we actually care about, and until someone runs the real-world experiments that can measure the influence of different individuals, every result—including ours—ought to be taken with a grain of salt. Nevertheless, the findings I have discussed—from the small-world experiment, from the simulation studies of influence spreading on networks, and from the Twitter study—ought to raise some serious doubts about claims like the law of the few that explain social epidemics as the work of a tiny minority of special people.

It’s not even clear, in fact, that social epidemics are the right way to think about social change to begin with. Although our Twitter study found that epidemic-like events do occur, we also found that they are incredibly rare. Of 74 million events in our data, only a few dozen generated even a thousand retweets, and only one or two got to ten thousand. In a network of tens of millions of users, ten thousand retweets doesn’t seem like that big a number, but what our data showed is that even that is almost impossible to achieve. For practical purposes, therefore, it may be better to forget about the large cascades altogether and instead try to generate lots of small ones. And for that purpose, ordinary influencers may work just fine. They don’t accomplish anything dramatic, so you may need a lot of them, but in harnessing many such individuals, you can also average out much of the randomness, generating a consistently positive effect.

Finally, and quite apart from any specific findings, these studies help us to see a major shortcoming of commonsense thinking. It is ironic in a way that the law of the few is portrayed as a counterintuitive idea because in fact we’re so used to thinking in terms of special people that the claim that a few special people do the bulk of the work is actually extremely natural. We think that by acknowledging the importance of interpersonal influence and social networks, we have somehow moved beyond the circular claim from the previous chapter that “X happened because that’s what people wanted.” But when we try to imagine how a complex network of millions of people is connected—or worse still, how influence propagates through it—our intuition is immediately defeated. By effectively concentrating all the agency into the hands of a few individuals, “special people” arguments like the law of the few reduce the problem of understanding how network structure affects outcomes to the much simpler problem of understanding what it is that motivates the special people. As with all commonsense explanations, it sounds reasonable and it might be right. But in