Wired hacking okcupid

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He decided to go for both, setting up two profiles and optimising one for group A, and one for group B. McKinlay text-mined the two clusters to learn what interested them; teaching was popular, so he wrote a bio that emphasised his work as a maths professor.

The important part, though, would be the survey. He'd pick out the questions that were most popular with both clusters, and fill out his answers honestly -- he didn't want to build his future relationship on lies. But he'd let his computer figure out how much importance to assign each question, using a machine-learning algorithm called adaptive boosting to derive the best weightings.

He created two profiles, one with a photo of him rock climbing and the other of him playing guitar at a gig. Sex or love? Answer: love. But for the younger A cluster, he followed his computer's direction and rated the question "very important". For the B cluster, it was "mandatory".

When the last question was answered and ranked, he ran a search on OkCupid for women in Los Angeles sorted by match percentage. At the top: a page of women matched at 99 per cent. Ten thousand women scrolled by, from all over Los Angeles, and he was still in the 90s. He needed one more step to get noticed. OkCupid members are notified when someone views their pages, so he wrote a program to visit the pages of his top-rated matches, cycling by age: a thousand year-old women on Monday, a thousand year-old women on Tuesday, looping back through when he reached year-olds two weeks later.

Women reciprocated by visiting his profiles, some a day. And messages began to roll in. The maths portion of McKinlay's search was done. One thing remained. He'd have to leave his cubicle and take his research into the field. He'd have to go on dates. Sheila was a web designer from the A-list of young artist types.

By the end of his date with Sheila, it was clear to both that the attraction wasn't there. He went on his second date the next day -- a blog editor from the B cluster. He'd planned a walk around Echo Park Lake, but found it was being dredged. She'd been reading Proust and feeling down about her life. Date three was also drawn from the B group. He met Alison at a bar in Koreatown. She was a screenwriting student with a tattoo of a Fibonacci spiral across her shoulder.

McKinlay got drunk on Korean beer and woke up in his cubicle the next day suffering a painful hangover. He sent Alison a follow-up message on OkCupid, but she didn't write back. Dating with his computer-endowed profiles was a completely different game.

He could ignore messages consisting of bad one-liners. He responded to the ones that showed a sense of humour or displayed something interesting in their bios. Back when he was the pursuer, he'd swapped three to five messages to get a single date.

Now he'd send just one reply: "You seem really cool. Want to meet? By date 20, he noticed latent variables emerging. D student at the time, McKinlay felt that their math was lacking, so he invented his own formula instead , Wired magazine reported.

Specifically, the numbers whiz created several fake profiles in order to collect data on women across the country and then created a special algorithm to hack the system. He suddenly found himself matched with thousands of women between the ages of 25 and 42 in Los Angeles, all with 90 percent and above compatibility. His system had worked. Check out their entire story, including details on McKinlay's proposal, in the video above.

Even for a mathematician, McKinlay is unusual. Raised in a Boston suburb, he graduated from Middlebury College in with a degree in Chinese.

In August of that year he took a part-time job in New York translating Chinese into English for a company on the 91st floor of the north tower of the World Trade Center. The towers fell five weeks later. McKinlay wasn't due at the office until 2 o'clock that day.

He was asleep when the first plane hit the north tower at am. The experience kindled his interest in applied math, ultimately inspiring him to earn a master's and then a PhD in the field.

Now he'd do the same for love. First he'd need data. While his dissertation work continued to run on the side, he set up 12 fake OkCupid accounts and wrote a Python script to manage them. To find the survey answers, he had to do a bit of extra sleuthing. OkCupid lets users see the responses of others, but only to questions they've answered themselves.

McKinlay watched with satisfaction as his bots purred along. Then, after about a thousand profiles were collected, he hit his first roadblock. OkCupid has a system in place to prevent exactly this kind of data harvesting: It can spot rapid-fire use easily.

One by one, his bots started getting banned. He turned to his friend Sam Torrisi, a neuroscientist who'd recently taught McKinlay music theory in exchange for advanced math lessons.

Torrisi was also on OkCupid, and he agreed to install spyware on his computer to monitor his use of the site. With the data in hand, McKinlay programmed his bots to simulate Torrisi's click-rates and typing speed.

He brought in a second computer from home and plugged it into the math department's broadband line so it could run uninterrupted 24 hours a day. After three weeks he'd harvested 6 million questions and answers from 20, women all over the country.

McKinlay's dissertation was relegated to a side project as he dove into the data. He was already sleeping in his cubicle most nights. Now he gave up his apartment entirely and moved into the dingy beige cell, laying a thin mattress across his desk when it was time to sleep. For McKinlay's plan to work, he'd have to find a pattern in the survey data—a way to roughly group the women according to their similarities.

The breakthrough came when he coded up a modified Bell Labs algorithm called K-Modes. First used in to analyze diseased soybean crops, it takes categorical data and clumps it like the colored wax swimming in a Lava Lamp. With some fine-tuning he could adjust the viscosity of the results, thinning it into a slick or coagulating it into a single, solid glob.

He played with the dial and found a natural resting point where the 20, women clumped into seven statistically distinct clusters based on their questions and answers. He retasked his bots to gather another sample: 5, women in Los Angeles and San Francisco who'd logged on to OkCupid in the past month. Another pass through K-Modes confirmed that they clustered in a similar way.

His statistical sampling had worked. Now he just had to decide which cluster best suited him. He checked out some profiles from each.

One cluster was too young, two were too old, another was too Christian. But he lingered over a cluster dominated by women in their mid-twenties who looked like indie types, musicians and artists. This was the golden cluster. The haystack in which he'd find his needle. Somewhere within, he'd find true love. Actually, a neighboring cluster looked pretty cool too—slightly older women who held professional creative jobs, like editors and designers. He decided to go for both.

He'd set up two profiles and optimize one for the A group and one for the B group. The service, which has more than a million downloads on Google Play and claims five million users overall, had exposed all photos on the site, including those marked as "private," to the open internet. The issue came from a misconfigured Amazon Web Services data repository, a common mistake that has led to all sorts of deeply problematic data exposures.

Other user information, including location data, was exposed as well due to the mistake. And anyone could have intercepted all of that data, because the Jack'd application was set up to retrieve photos from the cloud system over an unencrypted connection.

The company fixed the bug on February 7, but Ars reports that it took a year from when a security researcher initially disclosed the situation to Jack'd. Beyond these types of systemic security issues, criminals have also increasingly been using dating apps and other social media platforms to carry out "romance scams," in which a criminal pretends to form a bond with targets so they can eventually convince the victim to send them money.

A data analysis from the Federal Trade Commission released on Tuesday, found that romance scams were way up in , resulting in 21, complaints to the FTC in , up from 8, complains in The same factors that make dating sites an appealing target for hackers also make them useful for romance scams: It's easier to assess and approach people on a site that are already meant for sharing information with strangers.