Summary of “Cambridge Analytica: how did it turn clicks into votes?”

How do 87m records scraped from Facebook become an advertising campaign that could help swing an election? What does gathering that much data actually involve? And what does that data tell us about ourselves?
For those 87 million people probably wondering what was actually done with their data, I went back to Christopher Wylie, the ex-Cambridge Analytica employee who blew the whistle on the company’s problematic operations in the Observer.
According to Wylie, all you need to know is a little bit about data science, a little bit about bored rich women, and a little bit about human psychology…. Step one, he says, over the phone as he scrambles to catch a train: “When you’re building an algorithm, you first need to create a training set.” That is: no matter what you want to use fancy data science to discover, you first need to gather the old-fashioned way.
The “Training set” refers to that data in its entirety: the Facebook likes, the personality tests, and everything else you want to learn from.
Facebook data, which lies at the heart of the Cambridge Analytica story, is a fairly plentiful resource in the data science world – and certainly was back in 2014, when Wylie first started working in this area.
In order to be paid for their survey, users were required to log in to the site, and approve access to the survey app developed by Dr Aleksandr Kogan, the Cambridge University academic whose research into personality profiling using Facebook likes provided the perfect access for the Robert Mercer-funded Cambridge Analytica to quickly get in on the field.
Where the psychological profile is the target variable, the Facebook data is the “Feature set”: the information a data scientist has on everyone else, which they need to use in order to accurately predict the features they really want to know.
How Cambridge Analytica turned Facebook likes into votes.

The orginal article.