Brain Pickings

Love in the Age of Data: How One Woman Hacked Her Way to Happily Ever After

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Reverse-engineering the algorithms of romance, one picky data point at a time.

The question of how love works has bedeviled writers and scientists for centuries. But how do the dynamics of romance differ in the age of online dating? In Data, A Love Story: How I Gamed Online Dating to Meet My Match (public library; UK), digital strategist and journalist Amy Webb — one of the smartest people I know — takes us on her unexpected journey to true love, in which she sets out to “game the system, using math, data, and loopholes” to find the man of her dreams. If it sounds predictable and contrived, rest assured it’s anything but.

Amy writes in the introduction:

I realized that we’ve all been going about finding our matches the wrong way. Whether we’re dating in the real world or online, we’re relying too much now on hope and happenstance. And these days, algorithms, too. We don’t allow ourselves to think about what we really want in a partner, an then we don’t sell ourselves in order to get it.

After a series of bad dates following a major heartbreak, mathematically-driven Amy decided to take a quantitative approach to the playing field and started systematically recording various data points about her dates, revealing some important correlations. After one particularly bad date, she decided to formalize the exercise and wrote down everything that was important to her in a mate — from intellectual overlap to acceptable amount of body hair — eventually coming up with 72 attributes that she was going to demand in any future date. She then broke down these attributes into two tiers and developed a scoring system, assigning specific points to each. For 700 out of a maximum possible 1800, she’d agree to have an email exchange; for 900, she’d go on a date; for 1,500, she’d consider a long-term relationship.

But this, she soon realized, was only half the equation — it only illuminated what she was looking for in a mate. So Amy took the obvious data-driven next step: She set up 10 fake dating profiles, posing as 10 men with high scores on her rating system, and set about using the site as each of these different archetypes. She interacted with a total of 96 women, systematically noting their behaviors and responses, from the way they constructed their profiles to the language they used in interactions to how long they took in responding to messages, reverse-engineering what makes a successful, popular female profile that attracts the very kind of man Amy was looking for.

This allowed her to create a “super profile,” her very own custom “algorithm” of love. Once she looked at her data and set up a real profile for herself, it was a matter of time until she met Brian, fell in love, got married, and started a family — your ordinary happily-ever-after fairy tale ending, with an extraordinary side of quantitative and qualitative magic.

Amy writes:

Think about the way you’ve set up your Facebook profile. And if you don’t use Facebook, instead think about how you’ve described yourself to new people you’ve met recently. You list your favorite foods, bands, books. You talk about cities you want to visit. These aren’t meaningful data points; they’re stylized nuggets of information meant to personify ourselves in a formulaic way to others. A Facebook profile is in many ways an outfit we wear and the accessories and cologne we put with it: we’re hoping to project a particular image in order to socialize with (or avoid, in some cases) a particular group of people.

Dating sites and the algorithms they advertise purport to sort through our personalities, wants, and desires in order to connect us with our best possible matches. Which means that we’ve outsourced not just an introduction , but the consideration of whether or not that man or woman is really our ideal. We’re putting our blind trust in a system that’s meant to do the heavy lifting or figuring out what it is that we really want out of a mate, and what will truly make us happy. This job is being processed using information that we, ourselves, have entered into a computer system. Bad data in equals bad data out. Algorithms that dating sites have spent millions of dollars to refine aren’t necessarily bad. They’re just not as good as we want them to be, because they’re computing our half-truths and aspirational wishes.

One of the possible reasons for this imperfection, Amy points out, is a misalignment of motives. Dating sites make their money either through advertising or through subscriptions, and in either case they benefit from your coming back to the site again and again, spending as much time as possible looking for — but not finding — a mate. (If this sounds cynical, it isn’t any more so than the fundamental reality of the internet itself — it’s the same misalignment of publishers’ financial motives and the audience’s best interest that’s responsible for the web’s infestation of slideshows, pagination, and other vacant content aimed at maximizing pageviews while minimizing your reading experience and enjoyment of the content.)

Though some of the findings are dishearteningly prehistoric, reinforcing gender stereotypes and sexual archetypes — men prefer blondes and are turned off by powerful women, curly women are better off straightening their hair, and using light language bordering on the inane helps women attract more dates — the overall experiment offers some fascinating, and often counterintuitive, modern-day anthropological insights.

In the appendix, Amy shares some of the findings she arrived at in analyzing what makes a successful profile:

  • Use aspirational language; keep it positive and optimistic.
  • Sounding “writerly” doesn’t work in your favor.
  • Don’t recycle your resume on your dating profile.
  • Lead with your hobbies and activities.
  • Stay away from foreign words.
  • Keep your profile pithy, between 90 and 100 words — or about three sentences.
  • Use humor, but beware that sarcasm doesn’t translate well online and tends to come off as anger or aloofness.
  • Don’t talk about your job, especially if what you do is difficult to explain.

Sample Data, A Love Story with Amy’s entertaining, enlightening, infinitely heartening TEDx talk:

Ultimately, the point of Data, A Love Story isn’t to colonize romance by validating the rites of a Universal System where, in order to attain some regressive ideal of love, women dumb themselves down; rather, it is to demonstrate that it’s possible, with the right amount of intelligence, both technical, in reverse-engineering the system’s inner workings, and emotional, in being unafraid to want what one wants, to hack the system — any system — to serve one’s own ideal of love.

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