The researcher whose work is at the center of the Facebook-Cambridge Analytica data analysis and political advertising uproar has revealed that his method worked much like the one Netflix uses to recommend movies.
In an email to me, Cambridge University scholar Aleksandr Kogan explained how his statistical model processed Facebook data for Cambridge Analytica. The accuracy he claims suggests it works about as well as established voter-targeting methods based on demographics like race, age and gender.
If confirmed, Kogan’s account would mean the digital modeling Cambridge Analytica used was hardly the virtual crystal ball a few have claimed. Yet the numbers Kogan provides also show what is – and isn’t – actually possible by combining personal data with machine learning for political ends.
Regarding one key public concern, though, Kogan’s numbers suggest that information on users’ personalities or “psychographics” was just a modest part of how the model targeted citizens. It was not a personality model strictly speaking, but rather one that boiled down demographics, social influences, personality and everything else into a big correlated lump. This soak-up-all-the-correlation-and-call-it-personality approach seems to have created a valuable campaign tool, even if the product being sold wasn’t quite as it was billed.