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How Cambridge Analytica’s Facebook Targeting Model Really Worked


Side note: I originally read this piece at, but since it was first published by The Conversation, that’s what I’m linking to.

Matthew Hindman:

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.

As noted in the article, Cambridge Analytica’s system is far from being a crystal ball. It seems like it’s even further from being a voodoo doll, allowing people be nudged towards CA’s clients’ ends. After all: Netflix can make reasonable guesses at the movies and TV shows you’re likely to enjoy. It can’t change your taste by strategically recommending particular media1.

If you’re interested in going deeper into this subject, I’d be inclined to recommend two places to look:

First: educational material on the type of machine learning concerned. Weeks 8 (specifically the lectures on Dimensionality Reduction) and 9 (specifically Recommender Systems) of Andrew Ng’s Machine Learning course on Coursera (non-affiliate link);

Second: a work of speculative fiction2. The novel Interface (Amazon US, Amazon UK) by Neal Stephenson and George Jewsbury, writing as Stephen Bury.

  1. I hope.
  2. Which is also a somewhat paranoid and eerily prescient political techno-thriller.