Friday, April 15, 2016

When Dating Algorithms Can Watch You Blush

Let’s get the basics over with,” W said to M when they met on a 4-minute speed date. “What are you studying?”

“Uh, I’m studying econ and poli sci. How about you?”

“I’m journalism and English literature.”

“OK, cool.”


They talked about where they were from (she hailed from Iowa, he from New Jersey), life in a small town, and the transition to college. An eavesdropper would have been hard-pressed to detect a romantic spark in this banal back-and-forth. Yet when researchers, who had recorded the exchange, ran it through a language-analysis program, it revealed what W and M confirmed to be true: They were hitting it off.

The researchers weren’t interested in what the daters discussed, or even whether they seemed to share personality traits, backgrounds, or interests. Instead, they were searching for subtle similarities in how they structured their sentences—specifically, how often they used function words such as it, that, but, about, never, and lots. This synchronicity, known as “language style matching,” or LSM, happens unconsciously. But the researchers found it to be a good predictor of mutual affection: An analysis of conversations involving 80 speed daters showed that couples with high LSM scores were three times as likely as those with low scores to want to see each other again.

It’s not just speech patterns that can encode chemistry. Other studies suggest that when two people unknowingly coordinate nonverbal cues, such as hand gestures, eye gaze, and posture, they’re more apt to like and understand each other. These findings raise a tantalizing question: Could a computer know whom we’re falling for before we do?

Picture this: You’re home from work for the evening. You curl up on the couch, steel your nerves, maybe pour yourself a glass of wine, and open the dating app on your phone. Then for 30 minutes or so, you commit to a succession of brief video dates with other users who satisfy a basic set of criteria, such as gender, age, and location. Meanwhile, using speech- and image-recognition technologies, the app tracks both your and your dates’ words, gestures, expressions, even heartbeats.

Afterward, you rate your dates. And so does the app’s artificial intelligence, which can recognize signs of compatibility (or incompatibility) that you might have missed. At the end of the night, the app tells you which prospects are worth a second look. Over time, the AI might even learn (via follow-up experiments) which combination of signals predicts the happiest relationships, or the most enduring.

Welcome to the vision of Eli Finkel. A professor of psychology and management at Northwestern University and a co-author of the LSM study, Finkel is a prominent critic of popular dating sites such as eHarmony and Chemistry, which claim to possess a formula that can connect you with your soul mate. Finkel’s beef with these sites, he says, isn’t that they “use math to get you dates,” as OKCupid puts it. It’s that they go about it all wrong. As a result, Finkel argues, their matching algorithms likely foretell love no better than chance.

The problem, he explains, is that they rely on information about individuals who have never met—namely, self-reported personality traits and preferences. Decades of relationship research show that romantic success hinges more on how two people interact than on who they are or what they believe they want in a partner. Attraction, scientists tell us, is created and kindled in the glances we exchange, the laughs we share, and the other myriad ways our brains and bodies respond to one another.

Which is why, according to Finkel, we’ll never predict love simply by browsing photographs and curated profiles, or by answering questionnaires. “So the question is: Is there a new way to leverage the Internet to enhance matchmaking, so that when you get face to face with a person, the odds that you’ll be compatible with that person are higher than they would be otherwise?”

by Julia M. Klein, Nautilus |  Read more:
Image: Jesse Chan-Norris / Flickr