Everything Is Obvious (14 page)

As many people immediately pointed out, this conclusion was based entirely on computer simulations. And as I’ve already mentioned, these simulations were highly simplified versions of reality, and made a large number of assumptions, any of which could have been wrong. Computer simulations are useful tools that can generate great insight. But in the end they are more like thought experiments than real experiments, and as such are better suited to provoking new questions than to answering them. So if we really want to know whether particular individuals are capable of stimulating the diffusion of ideas, information, and influence—and if these influencers exist, which attributes distinguish them from ordinary people—then we need to run experiments in the real world. But studying the relationship between individual influence and large-scale impact in the real world is easier said than done.

The main problem is that you need an enormous amount of data, and most of it is very hard to collect. Just demonstrating that one person has influenced another is difficult enough. And if you wanted to make the connection to how they influence larger populations, you need to gather similar information for whole chains of influence, in which one person influences another who in turn influences another, and so on. Pretty soon, you’re talking about thousands or even millions of relationships, just to track how a single piece of information was spread. And ideally you would want to study many such cases. It’s an over-whelming
amount of data to test what seems to be a relatively straightforward claim—that some people matter more than others—but there’s no getting around it. It also helps explain why diffusion research, as it is known, has remained such a myth-laden business for so long: when it’s impossible to prove anything, everyone is free to propose whatever plausible story they like. There’s no way to decide who is right.

As with experiments like Music Lab, however, the Internet is starting to change this picture in important ways. A handful of recent studies have begun to explore diffusion in social networks on a scale that would have been unimaginable just a decade ago. Blog postings diffuse among networks of bloggers. Fan pages diffuse among networks of friends on Facebook. Special capabilities called “gestures” diffuse among players on the online game Second Life. And premium voice services have been shown to diffuse among networks of IM buddies.
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Inspired by these studies, my Yahoo! colleagues Jake Hofman and Winter Mason and I, along with Eytan Bakshy, a talented graduate student at the University of Michigan, decided to look for the diffusion of information in the largest communication network we could get our hands on: Twitter. In the process, we would look for influencers.
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In many respects, Twitter is ideally suited to this objective. Unlike Facebook, say, where people connect to one another for a multitude of reasons, the whole point of Twitter is to broadcast information to other people—your “followers”—who have explicitly indicated that they want to hear from you. Getting people to pay attention to you—influencing them, in other words—is what Twitter is all about. Second, Twitter is remarkably diverse. Many users are regular people whose followers are mostly friends interested in hearing from them. But many of the most followed users on Twitter
are public figures, including bloggers, journalists, celebrities (Ashton Kutcher, Shaquille O’Neal, Oprah), media organizations such as CNN, and even government agencies and nonprofits (the Obama administration, No. 10 Downing Street, the World Economic Forum). This diversity is helpful because it allowed us to compare the influence of all manner of would-be influencers—ordinary people all the way up to Oprah and Ashton—in a consistent way.

Finally, although many tweets are mundane updates (“Having coffee at Starbucks on Broadway! It’s a beautiful day!!”), many of them refer either to other online content, like breaking news stories and funny videos, or to other things in the world, like books, movies, and so on, about which Twitter users wish to express their opinions. And because the format of Twitter forces users to keep every message to no more than 140 characters, users often make use of “URL shorteners,” such as bit.ly, to replace the long, messy URL of the original website with something like http://bit.ly/beRKJo. The nice thing about these shortened URLs is that they effectively assign a unique code to every piece of content broadcast on Twitter. Thus when a user wishes to “retweet” something, it’s possible to see whom it came from originally, and thereby trace chains of diffusion across the follower graph.

In total, we tracked more than 74 million of these diffusion chains initiated by more than 1.6 million users, over a two-month interval in late 2009. For each event, we counted how many times the URL in question was retweeted—first by the original “seed” user’s immediate followers, then by their followers, and their followers’ followers, and so on—thereby tracing out the full “cascade” of retweets triggered by each original tweet. As the figure on
this page
shows, some of these cascades were broad and shallow, while others were narrow
and deep. Others still were very large, with complex structure, starting out small and trickling along before gaining momentum somewhere else in the network. Most of all, however, we found that the vast majority of attempted cascades—roughly 98 percent of the total—didn’t actually spread at all.

Cascades on Twitter

This result is important because, as I’ll discuss in more detail in the next chapter, if you want to understand why some things “go viral”—those occasional YouTube videos that attract millions of downloads, or funny messages that circulate
wildly through e-mail or on Facebook—it’s a mistake to consider only the rare few that actually succeed. In most settings, unfortunately, it is only possible to study the “successes” for the simple reason that nobody bothers to keep track of all the failures, which have a tendency to get swept under the rug. On Twitter, however, we can keep track of every single event, no matter how small, thereby enabling us to learn who is influential, how much more influential than average they really are, and whether or not it is possible to tell the differences between individuals in a way that could potentially be exploited.

The way we went about this exercise was to imitate what a hypothetical marketer might try to do—that is, using everything known about the attributes and past performance of a million or so individuals, to predict how influential each of them will be in the future. Based on these predictions, the marketer could then “sponsor” some group of individuals to tweet whatever information it is trying to disseminate, thereby generating a series of cascades. The better the marketer can predict how large a cascade any particular individual can trigger, the more efficiently it can allocate its budget for sponsored tweets. Actually running such an experiment is still extremely difficult in practice, so we instead did our best to approximate it using the data we had already collected. Specifically, we divided our data in two, artificially setting the first month of our time period as our “history” and the second half as the “future.” We then fed all our “historical” data into a statistical model, including how many followers each user had, how many others they were following, how frequently they tweeted, when they had joined, and how successful they had been at triggering cascades during this period. Finally, we used the model to “predict” how influential
each user would be in our “future” data and checked the model’s performance against what actually transpired.

In a nutshell, what we found was that individual-level predictions are extremely noisy. Even though it was the case that on average, individuals with many followers who had been successful at triggering cascades of retweets in the past were more likely to be successful in the future, individual cases fluctuated wildly at random. Just as with the
Mona Lisa
, for every individual who exhibited the attributes of a successful influencer, there were many other users with indistinguishable attributes who were not successful. Nor did this uncertainty arise simply because we weren’t able to measure the right attributes—in reality we had more data than any marketer would normally have—or to measure them accurately. Rather, the problem was that, like the simulations above, much of what drives successful diffusion depends on factors outside the control of the individual seeds. What this result suggests, in other words, is that marketing strategies that focus on targeting a few “special” individuals are bound to be unreliable. Like responsible financial managers, therefore, marketers should adopt a “portfolio” approach, targeting a large number of potential influencers and harnessing their average effect, thereby effectively reducing the individual-level randomness.
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Although promising in theory, a portfolio approach also raises a new issue, of cost effectiveness. To illustrate the point, consider a recent story in the
New York Times
that claimed that Kim Kardashian, the reality TV actress, was getting paid $10,000 per tweet by various sponsors who wanted her to mention their products. Kardashian at the time had well over a million followers, so it seems plausible that paying someone like her would generate more attention than paying some
ordinary person with only a few hundred followers. But how did they come up with that particular figure? Ordinary people, that is, might be prepared to tweet about their products for much less than $10,000. Assuming, therefore, that more visible individuals “cost” more than less visible ones, should marketers be targeting a relatively small number of more influential, more expensive, individuals or a larger number of less influential, less expensive individuals? Better yet, how should one strike the optimal balance?
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Ultimately, the answer to this question will depend on the specifics of how much different Twitter users would charge prospective marketers to sponsor their tweets—if indeed, they would agree to such an arrangement at all. Nevertheless, as a speculative exercise, we tested a range of plausible assumptions, each corresponding to a different hypothetical “influencer-based” marketing campaign, and measured their return on investment using the same statistical model as before. What we found was surprising even to us: Even though the Kim Kardashians of the world were indeed more influential than average, they were so much more expensive that they did not provide the best value for the money. Rather, it was what we called ordinary influencers, meaning individuals who exhibit average or even less-than-average influence, who often proved to be the most cost-effective means to disseminate information.

Before you rush out to short stock in Kim Kardashian, I should emphasize that 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.