Dataclysm: Love, Sex, Race, and Identity — What Our Online Lives Tell Us about Our Offline Selves

"Yes. Rudder is a mathematician and one of the co-founders of the dating site OkCupid. His book looks at what we can learn about human behaviour from analysing OkCupid data, as well as Big Data on social networks more broadly. Like his blog, it is full of graphs and charts that appeal to my inner math geek. For my purposes, the dating bits were the most fascinating, although sometimes disturbing. He looks at racial bias in people’s sexual preferences. He looks at age bias: a woman is most attractive to men of all ages in her early twenties, statistically speaking, so of course anyone over the age of 24 is not going to be thrilled to read that. That said, young women with the highest erotic capital, as it were, aren’t necessarily enjoying apps due to the paradox of choice. When you look at the evolution of matching algorithms, a lot of time was invested by traditional dating platforms in trying to predict compatibility. Most sites began by matching people based on their stated preferences, then eventually adjusted the algorithms for their revealed preferences, what they actually choose. Because it was searchable, people would put out criteria that were very specific. You know: ‘I want a non-smoker, who’s a Democrat…’ yada yada. But it turned out that the big issues you would think would predict compatibility—things like religion, politics—turned out not to matter. The three most predictive questions OkCupid found were indicators of a penchant for adventure: ‘Do you like scary movies?’, ‘Have you ever travelled alone?’ and ‘Would you like to ditch it all and go live on a sail boat?’ “So we have built our entire system on a criterion that ends up being unimportant” Apps essentially threw in the towel on matching based on preferences, letting users pick for themselves, almost exclusively on the basis of looks: 90% of people will swipe left or right based on the first photo. So we’ve been moving towards this system in which looks are the only selection criterion. But it turns out that looks matter far less than we think. Rudder recounts an experiment in which OkCupid sent 10,000 people on blind dates in 2013. Couples were paired randomly: you could be matched with anyone from a ‘1’ to a ‘10’, in terms of attractiveness. While the messaging activity on the site is quite linear—more attractive people get more responses—it turned out that once people sat down in person, looks had no impact on whether or not they had a good time. Women had a good time 75% of the time and men had a good time 85% of the time, regardless of relative attractiveness . So we have built our entire system—pretty much, now that other ways of meeting are getting harder and harder—on a criterion that ends up being unimportant. Which is insane when you think about it. Absolutely. There’s no accounting for chemistry from a picture. There is just so much more that can make someone attractive in person, from charm to humour to kindness. One thing that I learned from the cyberpsychologists I interviewed that I found disturbing is that texting can create a false sense of intimacy. In the absence of in-person cues, we tend to overshare to make up for what’s missing (thus sexting and the ubiquity of nudes). This ‘escalation effect’ can lead to disappointment when you end up meeting, or even be downright dangerous. The National Crime Agency reported a sixfold increase in first-date sexual assaults over five years, a spike they attribute to the growth of dating apps. Forty-one per cent of these assaults happened on first dates that started in a residence. So people are meeting strangers for the first time in their homes. At the beginning of online dating, people were very aware of stranger danger, but that caution seems to have gone out the window."

"I don’t know if it’s the future of social sciences. I don’t think the future of medical research is crowdsourced health but I think it’s one more important tool in the toolbox. So Christian Rudder was the chief scientist of a dating website, OK Cupid, and in the book he describes both what they learned about people’s preferences and also some of the interventions, some of the changes they made to the website, to see what effect they had on people’s dating behaviour. Actually, one of his examples is a histogram the distribution of the heights of males on OK Cupid, and he shows that it doesn’t fit the height distribution that the Centers for Disease Control collect. It’s shifted to the right, people report a height that’s about 2 inches or 5 centimetres greater than what you would expect in the general population. Many more people are 6 feet than what you would expect. We know that when people have an incentive not to tell the truth, they are likely not to tell the truth. We have opposite examples, as well, from France in 1844, where people could dodge the draft by being too short. It was estimated that about 2 per cent of people lied about their height to dodge the draft. So when the people have an incentive to lie they will lie. But, on average, what people report on search queries and on questions they ask on sites like Yahoo Answers, is remarkably accurate. If we look at the data where people report their heights, it matches beautifully to the known distribution of heights in the US. Of heights, of weights… “We can use a tool that’s usually used to sell you something to help you improve your health” Of course we cannot say about every individual, whether they are reflecting the truth. Some people don’t want to tell the truth, others don’t know the truth, you may not know your exact weight right now. But this kind of internet data correlates remarkably well with what we know from the physical measurements that have been taken of this population. When you’re anonymous on a search engine in your own room there is no reason for you to lie. It also leads to a burden on us: we don’t want to break this anonymity, we don’t want to identify our users when they had not intended for that to happen. Maintaining user privacy is an important aspect. We don’t want to break the anonymity of our users. But it raises a problem too. How do you actually find a group of anonymous people who share some condition that you’re interested in? We had to work very hard in order to be able to do these things together, on the one hand not identify the people who post their messages on Twitter or ask questions on Bing, on the other hand, finding a sequence of queries or single queries that would be typical of a person who has a mood disorder or a person who has anorexia. Mood disorders are interesting, partly because of the way that treatment works. If somebody has a mood disorder, they may get prescribed drugs. The drugs frequently have side effects. What sometimes happens is a patient will take the drugs, and if they work, the patient stops experiencing mood shifts. They think they are cured so they shouldn’t take the drugs because they are only giving side effects, and so they stop taking the drugs. Then they might experience a manic or a depressive episode. So we were interested in whether we could actually use internet data, whether we could look at the queries that people make on a search engine in order to identify when they were likely going to have a manic episode. And it turns out that we can probably do this. So imagine an application on your smartphone or on a computer which you download, you install, and it monitors your queries for you. When it identifies, in people who are at risk, that they might be going to have an event, it will tell them they might want to take some mitigating steps. So this is taking data collected from a large group of people and turning them into something very personal that a person can decide to use or not."