What the Internet Really Knows About YOU: A digital media expert argues that your private life is exposed online, no matter how cautious you think you're being.

• Author: Tufekci, Zeynep
• Position: OPINION

People concerned about privacy often try to be "careful" online. They stay off social media, or if they're on it, they post cautiously. They don't share information about their religious beliefs, personal life, health status, or political views. They think they're protecting themselves.

But they are wrong. Because of technological advances and the sheer amount of data now available about billions of other people, discretion no longer suffices to protect your privacy. Computer algorithms and network analyses can . now infer, with a sufficiently high degree of accuracy, a wide range of things about you that you may have never disclosed, including your moods, your political beliefs, and your health.

There is no longer such a thing as individually opting out of our privacy-compromised world.

Tracking Minds and Emotions

The idea of data inference is not new. Magazine subscriber lists have long been purchased by retailers, charities, and politicians because they provide useful hints about people's views. For example, a subscriber to The Wall Street Journal is more likely to be a Republican voter than is a subscriber to Rolling Stone.

But today's technology works at a far higher level. In 2017, for example, the newspaper The Australian published an article revealing that Facebook had told advertisers that it could predict when younger users were feeling "insecure," "worthless," or otherwise in need of a "confidence boost." Facebook was apparently able to draw these inferences by monitoring photos, posts, and other social media data. (The company denied letting advertisers target people based on those characteristics, but it's almost certainly true that it has that capacity.)

Today's computational inference does not merely check to see if Facebook users posted phrases like "I'm depressed" or "I feel terrible." The technology is more sophisticated: Machine-learning algorithms are fed huge amounts of data, and the computer program categorizes who is most likely to become depressed. Consider another example. In 2017, researchers, armed with data from more than 40,000 Instagram photos, used machine-learning tools to identify signs of depression in a group of 166 Instagram users. Their computer models turned out to be better predictors of depression than humans who rated whether photos were happy or sad.

Used honorably, computational inference can be a wonderful thing. Predicting depression before the onset of clinical symptoms would be a boon for...