Facebook’s Like button practically begs to be clicked. It’s easy to “like” an innocuous post that pops in up in your News Feed, or send some Like Love to a band, political or social organization, or the latest silly meme floating about the social network. But even the most privacy conscious Facebook user can be giving away the personal farm by tossing their tacit support at something.
Researchers from the Psychometrics Centre at the University of Cambridge in the United Kingdom analyzed the Facebook behaviors of 58,000 volunteers, in particular their Likes. Using that data in conjunction with a demographic profile and voluntary participation in a number of psychometric exams, the researchers were able to accurately predict a number of personal traits, from sexual orientation, political beliefs, race, and more.
The data tosses another log onto the fire that is the privacy debate about how online personal data is shared, and how it should be protected. Sites that offer online services such as Facebook and Google already capture reams of personal information in order to serve targeted ads on websites and fuel marketing campaigns. In extreme cases, hackers and criminals who trade identities, payment card data and other personally identifiable information online can also make use of this data to target companies, government agencies, manufacturers, the military, defense industrial base and other sensitive industries. The consequences can range anywhere from identity theft, to a wiped out online bank account, to the loss of sensitive intellectual property or military secrets.
“The widespread availability of extensive records of individual behavior, together with the desire to learn more about customers and citizens, presents serious challenges related to privacy and data ownership,” wrote Michal Kosinski, David Stillwell, and Thore Graepel in their paper, “Private traits and attributes are predictable from digital records of human behavior.” “People may choose not to reveal certain pieces of information about their lives, such as their sexual orientation or age, and yet this information might be predicted in a statistical sense from other aspects of their lives that they do reveal.”
The Cambridge researchers were able to build an app they called myPersonality that ran the Likes data through a linear regression test against demographic data provided in order to compare the results. In 88 percent of cases the application was able to correctly predict sexual orientation in men. It also had 95 percent accuracy in predicting race, and 85 percent accuracy in predicting political leanings.
Personal data is clearly big business, and even something as relatively innocent as a Facebook Like can put an individual’s privacy and personal safety at risk. Not long ago, Target used customer data to predict pregnancies among its customers in order to send them targeted advertising for vitamins and maternity clothing. While ingenious from a marketing standpoint, inadvertently revealing the pregnancy of an unmarried couple, as the report suggests, could be dangerous in some cultures where it is not permitted.
“As this example shows, predicting personal information to improve products, services and targeting can also lead to dangerous invasions of privacy,” the report said.
Facebook is in a unique position because its users are its principal product. The company sells advertising based on user behavior on the site and has built tools and services that help them further parse data. For example, Facebook’s new Graph Search feature, for example enables users to narrowly define searches on the social media site. In other words, you can use the tool to find individuals who work at a particular company, in a specific city, who have certain job titles and a common recreational interest. Again, depending on your motivation, the data retrieved can be fairly innocuous. But some security experts are nervous that identity thieves or state-sponsored hackers now have another weapon in their arsenal to construct targeted attacks against companies and other sensitive organizations.
“The predictability of individual attributes from digital records of behavior may have considerable negative implications, because it can easily be applied to large numbers of people without obtaining their individual consent and without them noticing,” the report said. “Commercial companies, governmental institutions, or even one’s Facebook friends could use software to infer attributes such as intelligence, sexual orientation, or political views that an individual may not have intended to share. One can imagine situations in which such predictions, even if incorrect, could pose a threat to an individual’s well-being, freedom, or even life. Importantly, given the ever-increasing amount of digital traces people leave behind, it becomes difficult for individuals to control which of their attributes are being revealed.”