If Facebook were a country, a conceit that founder Mark Zuckerberg has entertained in public, its 900 million members would make it the third largest in the world.

It would far outstrip any regime past or present in how intimately it records the lives of its citizens. Private conversations, family photos, and records of road trips, births, marriages, and deaths all stream into the company’s servers and lodge there. Facebook has collected the most extensive data set ever assembled on human social behavior. Some of your personal information is probably part of it.

And yet, even as Facebook has embedded itself into modern life, it hasn’t actually done that much with what it knows about us. Now that the company has gone public, the pressure to develop new sources of profit (see “The Facebook Fallacy”) is likely to force it to do more with its hoard of information. That stash of data looms like an oversize shadow over what today is a modest online advertising business, worrying privacy-conscious Web users (see “Few Privacy Regulations Inhibit Facebook”) and rivals such as Google. Everyone has a feeling that this unprecedented resource will yield something big, but nobody knows quite what.

Heading Facebook’s effort to figure out what can be learned from all our data is Cameron Marlow, a tall 35-year-old who until recently sat a few feet away from ­Zuckerberg. The group Marlow runs has escaped the public attention that dogs Facebook’s founders and the more headline-grabbing features of its business. Known internally as the Data Science Team, it is a kind of Bell Labs for the social-networking age. The group has 12 researchers—but is expected to double in size this year. They apply math, programming skills, and social science to mine our data for insights that they hope will advance Facebook’s business and social science at large. Whereas other analysts at the company focus on information related to specific online activities, Marlow’s team can swim in practically the entire ocean of personal data that Facebook maintains. Of all the people at Facebook, perhaps even including the company’s leaders, these researchers have the best chance of discovering what can really be learned when so much personal information is compiled in one place.

Facebook has all this information because it has found ingenious ways to collect data as people socialize. Users fill out profiles with their age, gender, and e-mail address; some people also give additional details, such as their relationship status and mobile-phone number. A redesign last fall introduced profile pages in the form of time lines that invite people to add historical information such as places they have lived and worked. Messages and photos shared on the site are often tagged with a precise location, and in the last two years Facebook has begun to track activity elsewhere on the Internet, using an addictive invention called the “Like” button. It appears on apps and websites outside Facebook and allows people to indicate with a click that they are interested in a brand, product, or piece of digital content. Since last fall, Facebook has also been able to collect data on users’ online lives beyond its borders automatically: in certain apps or websites, when users listen to a song or read a news article, the information is passed along to Facebook, even if no one clicks “Like.” Within the feature’s first five months, Facebook catalogued more than five billion instances of people listening to songs online. Combine that kind of information with a map of the social connections Facebook’s users make on the site, and you have an incredibly rich record of their lives and interactions.

“This is the first time the world has seen this scale and quality of data about human communication,” Marlow says with a characteristically serious gaze before breaking into a smile at the thought of what he can do with the data. For one thing, Marlow is confident that exploring this resource will revolutionize the scientific understanding of why people behave as they do. His team can also help Facebook influence our social behavior for its own benefit and that of its advertisers. This work may even help Facebook invent entirely new ways to make money.

Contagious Information

Marlow eschews the collegiate programmer style of Zuckerberg and many others at Facebook, wearing a dress shirt with his jeans rather than a hoodie or T-shirt. Meeting me shortly before the company’s initial public offering in May, in a conference room adorned with a six-foot caricature of his boss’s dog spray-painted on its glass wall, he comes across more like a young professor than a student. He might have become one had he not realized early in his career that Web companies would yield the juiciest data about human interactions.

In 2001, undertaking a PhD at MIT’s Media Lab, Marlow created a site called Blogdex that automatically listed the most “contagious” information spreading on weblogs. Although it was just a research project, it soon became so popular that Marlow’s servers crashed. Launched just as blogs were exploding into the popular consciousness and becoming so numerous that Web users felt overwhelmed with information, it prefigured later aggregator sites such as Digg and Reddit. But Marlow didn’t build it just to help Web users track what was popular online. Blogdex was intended as a scientific instrument to uncover the social networks forming on the Web and study how they spread ideas. Marlow went on to Yahoo’s research labs to study online socializing for two years. In 2007 he joined Facebook, which he considers the world’s most powerful instrument for studying human society. “For the first time,” Marlow says, “we have a microscope that not only lets us examine social behavior at a very fine level that we’ve never been able to see before but allows us to run experiments that millions of users are exposed to.”

Marlow’s team works with managers across Facebook to find patterns that they might make use of. For instance, they study how a new feature spreads among the social network’s users. They have helped Facebook identify users you may know but haven’t “friended,” and recognize those you may want to designate mere “acquaintances” in order to make their updates less prominent. Yet the group is an odd fit inside a company where software engineers are rock stars who live by the mantra “Move fast and break things.” Lunch with the data team has the feel of a grad-student gathering at a top school; the typical member of the group joined fresh from a PhD or junior academic position and prefers to talk about advancing social science than about Facebook as a product or company. Several members of the team have training in sociology or social psychology, while others began in computer science and started using it to study human behavior. They are free to use some of their time, and Facebook’s data, to probe the basic patterns and motivations of human behavior and to publish the results in academic journals—much as Bell Labs researchers advanced both AT&T’s technologies and the study of fundamental physics.

It may seem strange that an eight-year-old company without a proven business model bothers to support a team with such an academic bent, but ­Marlow says it makes sense. “The biggest challenges Facebook has to solve are the same challenges that social science has,” he says. Those challenges include understanding why some ideas or fashions spread from a few individuals to become universal and others don’t, or to what extent a person’s future actions are a product of past communication with friends. Publishing results and collaborating with university researchers will lead to findings that help Facebook improve its products, he adds.

For one example of how Facebook can serve as a proxy for examining society at large, consider a recent study of the notion that any person on the globe is just six degrees of separation from any other. The best-known real-world study, in 1967, involved a few hundred people trying to send postcards to a particular Boston stockholder. Facebook’s version, conducted in collaboration with researchers from the University of Milan, involved the entire social network as of May 2011, which amounted to more than 10 percent of the world’s population. Analyzing the 69 billion friend connections among those 721 million people showed that the world is smaller than we thought: four intermediary friends are usually enough to introduce anyone to a random stranger. “When considering another person in the world, a friend of your friend knows a friend of their friend, on average,” the technical paper pithily concluded. That result may not extend to everyone on the planet, but there’s good reason to believe that it and other findings from the Data Science Team are true to life outside Facebook. Last year the Pew Research Center’s Internet & American Life Project found that 93 percent of Facebook friends had met in person. One of Marlow’s researchers has developed a way to calculate a country’s “gross national happiness” from its Facebook activity by logging the occurrence of words and phrases that signal positive or negative emotion. Gross national happiness fluctuates in a way that suggests the measure is accurate: it jumps during holidays and dips when popular public figures die. After a major earthquake in Chile in February 2010, the country’s score plummeted and took many months to return to normal. That event seemed to make the country as a whole more sympathetic when Japan suffered its own big earthquake and subsequent tsunami in March 2011; while Chile’s gross national happiness dipped, the figure didn’t waver in any other countries tracked (Japan wasn’t among them). Adam Kramer, who created the index, says he intended it to show that Facebook’s data could provide cheap and accurate ways to track social trends—methods that could be useful to economists and other researchers.

Other work published by the group has more obvious utility for Facebook’s basic strategy, which involves encouraging us to make the site central to our lives and then using what it learns to sell ads. An early study looked at what types of updates from friends encourage newcomers to the network to add their own contributions. Right before Valentine’s Day this year a blog post from the Data Science Team listed the songs most popular with people who had recently signaled on Facebook that they had entered or left a relationship. It was a hint of the type of correlation that could help Facebook make useful predictions about users’ behavior—knowledge that could help it make better guesses about which ads you might be more or less open to at any given time. Perhaps people who have just left a relationship might be interested in an album of ballads, or perhaps no company should associate its brand with the flood of emotion attending the death of a friend. The most valuable online ads today are those displayed alongside certain Web searches, because the searchers are expressing precisely what they want. This is one reason why Google’s revenue is 10 times Facebook’s. But Facebook might eventually be able to guess what people want or don’t want even before they realize it.

Recently the Data Science Team has begun to use its unique position to experiment with the way Facebook works, tweaking the site—the way scientists might prod an ant’s nest—to see how users react. Eytan Bakshy, who joined Facebook last year after collaborating with Marlow as a PhD student at the University of Michigan, wanted to learn whether our actions on Facebook are mainly influenced by those of our close friends, who are likely to have similar tastes. That would shed light on the theory that our Facebook friends create an “echo chamber” that amplifies news and opinions we have already heard about. So he messed with how Facebook operated for a quarter of a billion users. Over a seven-week period, the 76 million links that those users shared with each other were logged. Then, on 219 million randomly chosen occasions, Facebook prevented someone from seeing a link shared by a friend. Hiding links this way created a control group so that Bakshy could assess how often people end up promoting the same links because they have similar information sources and interests.

He found that our close friends strongly sway which information we share, but overall their impact is dwarfed by the collective influence of numerous more distant contacts—what sociologists call “weak ties.” It is our diverse collection of weak ties that most powerfully determines what information we’re exposed to.

That study provides strong evidence against the idea that social networking creates harmful “filter bubbles,” to use activist Eli Pariser’s term for the effects of tuning the information we receive to match our expectations. But the study also reveals the power Facebook has. “If [Facebook’s] News Feed is the thing that everyone sees and it controls how information is disseminated, it’s controlling how information is revealed to society, and it’s something we need to pay very close attention to,” Marlow says. He points out that his team helps Facebook understand what it is doing to society and publishes its findings to fulfill a public duty to transparency. Another recent study, which investigated which types of Facebook activity cause people to feel a greater sense of support from their friends, falls into the same category.

But Marlow speaks as an employee of a company that will prosper largely by catering to advertisers who want to control the flow of information between its users. And indeed, Bakshy is working with managers outside the Data Science Team to extract advertising-related findings from the results of experiments on social influence. “Advertisers and brands are a part of this network as well, so giving them some insight into how people are sharing the content they are producing is a very core part of the business model,” says Marlow.

Facebook told prospective investors before its IPO that people are 50 percent more likely to remember ads on the site if they’re visibly endorsed by a friend. Figuring out how influence works could make ads even more memorable or help Facebook find ways to induce more people to share or click on its ads.

Social Engineering

Marlow says his team wants to divine the rules of online social life to understand what’s going on inside Facebook, not to develop ways to manipulate it. “Our goal is not to change the pattern of communication in society,” he says. “Our goal is to understand it so we can adapt our platform to give people the experience that they want.” But some of his team’s work and the attitudes of Facebook’s leaders show that the company is not above using its platform to tweak users’ behavior. Unlike academic social scientists, Facebook’s employees have a short path from an idea to an experiment on hundreds of millions of people.

In April, influenced in part by conversations over dinner with his med-student girlfriend (now his wife), Zuckerberg decided that he should use social influence within Facebook to increase organ donor registrations. Users were given an opportunity to click a box on their Timeline pages to signal that they were registered donors, which triggered a notification to their friends. The new feature started a cascade of social pressure, and organ donor enrollment increased by a factor of 23 across 44 states.

Marlow’s team is in the process of publishing results from the last U.S. midterm election that show another striking example of Facebook’s potential to direct its users’ influence on one another. Since 2008, the company has offered a way for users to signal that they have voted; Facebook promotes that to their friends with a note to say that they should be sure to vote, too. Marlow says that in the 2010 election his group matched voter registration logs with the data to see which of the Facebook users who got nudges actually went to the polls. (He stresses that the researchers worked with cryptographically “anonymized” data and could not match specific users with their voting records.)

This is just the beginning. By learning more about how small changes on Facebook can alter users’ behavior outside the site, the company eventually “could allow others to make use of Facebook in the same way,” says Marlow. If the American Heart Association wanted to encourage healthy eating, for example, it might be able to refer to a playbook of Facebook social engineering. “We want to be a platform that others can use to initiate change,” he says.

Advertisers, too, would be eager to know in greater detail what could make a campaign on Facebook affect people’s actions in the outside world, even though they realize there are limits to how firmly human beings can be steered. “It’s not clear to me that social science will ever be an engineering science in a way that building bridges is,” says Duncan Watts, who works on computational social science at Microsoft’s recently opened New York research lab and previously worked alongside Marlow at Yahoo’s labs. “Nevertheless, if you have enough data, you can make predictions that are better than simply random guessing, and that’s really lucrative.”

Doubling Data

Like other social-Web companies, such as Twitter, Facebook has never attained the reputation for technical innovation enjoyed by such Internet pioneers as Google. If Silicon Valley were a high school, the search company would be the quiet math genius who didn’t excel socially but invented something indispensable. Facebook would be the annoying kid who started a club with such social momentum that people had to join whether they wanted to or not. In reality, Facebook employs hordes of talented software engineers (many poached from Google and other math-genius companies) to build and maintain its irresistible club. The technology built to support the Data Science Team’s efforts is particularly innovative. The scale at which Facebook operates has led it to invent hardware and software that are the envy of other companies trying to adapt to the world of “big data.”

In a kind of passing of the technological baton, Facebook built its data storage system by expanding the power of open-source software called Hadoop, which was inspired by work at Google and built at Yahoo. Hadoop can tame seemingly impossible computational tasks—like working on all the data Facebook’s users have entrusted to it—by spreading them across many machines inside a data center. But Hadoop wasn’t built with data science in mind, and using it for that purpose requires specialized, unwieldy programming. Facebook’s engineers solved that problem with the invention of Hive, open-source software that’s now independent of Facebook and used by many other companies. Hive acts as a translation service, making it possible to query vast Hadoop data stores using relatively simple code. To cut down on computational demands, it can request random samples of an entire data set, a feature that’s invaluable for companies swamped by data. Much of Facebook’s data resides in one Hadoop store more than 100 petabytes (a million gigabytes) in size, says Sameet Agarwal, a director of engineering at Facebook who works on data infrastructure, and the quantity is growing exponentially. “Over the last few years we have more than doubled in size every year,” he says. That means his team must constantly build more efficient systems.

All this has given Facebook a unique level of expertise, says Jeff Hammerbacher, Marlow’s predecessor at Facebook, who initiated the company’s effort to develop its own data storage and analysis technology. (He left Facebook in 2008 to found Cloudera, which develops Hadoop-based systems to manage large collections of data.) Most large businesses have paid established software companies such as Oracle a lot of money for data analysis and storage. But now, big companies are trying to understand how Facebook handles its enormous information trove on open-source systems, says Hammerbacher. “I recently spent the day at Fidelity helping them understand how the ‘data scientist’ role at Facebook was conceived … and I’ve had the same discussion at countless other firms,” he says.

As executives in every industry try to exploit the opportunities in “big data,” the intense interest in Facebook’s data technology suggests that its ad business may be just an offshoot of something much more valuable. The tools and techniques the company has developed to handle large volumes of information could become a product in their own right.

Mining for Gold

Facebook needs new sources of income to meet investors’ expectations. Even after its disappointing IPO, it has a staggeringly high price-to-earnings ratio that can’t be justified by the barrage of cheap ads the site now displays. Facebook’s new campus in Menlo Park, California, previously inhabited by Sun Microsystems, makes that pressure tangible. The company’s 3,500 employees rattle around in enough space for 6,600. I walked past expanses of empty desks in one building; another, next door, was completely uninhabited. A vacant lot waited nearby, presumably until someone invents a use of our data that will justify the expense of developing the space.

One potential use would be simply to sell insights mined from the information. DJ Patil, data scientist in residence with the venture capital firm Greylock Partners and previously leader of LinkedIn’s data science team, believes Facebook could take inspiration from Gil Elbaz, the inventor of Google’s AdSense ad business, which provides over a quarter of Google’s revenue. He has moved on from advertising and now runs a fast-growing startup, Factual, that charges businesses to access large, carefully curated collections of data ranging from restaurant locations to celebrity body-mass indexes, which the company collects from free public sources and by buying private data sets. Factual cleans up data and makes the result available over the Internet as an on-demand knowledge store to be tapped by software, not humans. Customers use it to fill in the gaps in their own data and make smarter apps or services; for example, Facebook itself uses Factual for information about business locations. Patil points out that Facebook could become a data source in its own right, selling access to information compiled from the actions of its users. Such information, he says, could be the basis for almost any kind of business, such as online dating or charts of popular music. Assuming Facebook can take this step without upsetting users and regulators, it could be lucrative. An online store wishing to target its promotions, for example, could pay to use Facebook as a source of knowledge about which brands are most popular in which places, or how the popularity of certain products changes through the year.

Hammerbacher agrees that Facebook could sell its data science and points to its currently free Insights service for advertisers and website owners, which shows how their content is being shared on Facebook. That could become much more useful to businesses if Facebook added data obtained when its “Like” button tracks activity all over the Web, or demographic data or information about what people read on the site. There’s precedent for offering such analytics for a fee: at the end of 2011 Google started charging $150,000 annually for a premium version of a service that analyzes a business’s Web traffic.

Back at Facebook, Marlow isn’t the one who makes decisions about what the company charges for, even if his work will shape them. Whatever happens, he says, the primary goal of his team is to support the well-being of the people who provide Facebook with their data, using it to make the service smarter. Along the way, he says, he and his colleagues will advance humanity’s understanding of itself. That echoes Zuckerberg’s often doubted but seemingly genuine belief that Facebook’s job is to improve how the world communicates. Just don’t ask yet exactly what that will entail. “It’s hard to predict where we’ll go, because we’re at the very early stages of this science,” says ­Marlow. “The number of potential things that we could ask of Facebook’s data is enormous.”

Tom Simonite is Technology Review’s senior IT editor.