7 years ago
Election Analytics Falacies: “Moneyball” Doesn’t Always Win

Originally published in CivicHall.org

For the past week or so, an article titled “The Data That Turned the World Upside Down” has been following me around like a bad headcold.

The article tells a compelling “whodunit” story of data scientists, engineers, and political communicators, all fighting for control of a new, weaponized form of online political propaganda. It leads readers to the conclusion that conservative data vendor Cambridge Analytica (CA) used Big Data and “psychometric targeting” (also called psychographic targeting) to propel Donald Trump to the White House.

I’ve written about Cambridge Analytica before (several times, in fact). I am on record as a loud CA skeptic. I have described them as the Theranos of political data: I think they have a tremendous marketing department, coupled with a team of research scientists who provide on virtually none of those marketing promises.

And though I tried my best to read the article with an open mind, I am still left with the same fundamental skepticism about this new brand of political data alchemy.*

To be clear, I am not questioning the underlying science of psychometric targeting. Psychometric targeting simply categorizes individuals according to the standard “big five” personality traits, then treats these categories as market segments for the delivery of targeted advertising. Targeted advertising based on psychometrics is conceptually quite simple and practically very complicated. And there is no evidence that Cambridge Analytica has solved the practical challenges of applying psychometrics to voter behavior.

Here is a list of what you would need in order to apply psychometrics to voter behavior:

1. A comprehensive file of psychographic data on American citizens. Alexander Nix, the CEO of Cambridge Analytica, told the authors that his company has “profiled the personality of every adult in the United States of America—220 million people.” But, in a statement after the original publication of the article, the company also claims that it does not use data from Facebook and hardly used psychographics at all. So it is unclear where these comprehensive files are supposed to have come from, or how robust they are.
2. A comprehensive national voter file, matched to this psychographic data. As Daniel Kreiss shows in his new book, Prototype Politics, the Republican voter file was still very much a work-in-progress during the 2016 election. Matching this data to CA’s purported psychographic file would be a hairy technical endeavor, involving heavy collaboration from other Republican vendors who instead have downplayed CA’s role and raised questions about its transparency.
3. A massive creative team to craft targeted messages for each of these audience segments. This is one of the (many) insights from Eitan Hersh’s 2015 book, Hacking the Electorate. The more segments a campaign creates within a voter universe, the more distinct messages that campaign has to develop, test, and refine. Even if Cambridge Analytica correctly assigned every American to one of its 32 psychographic categories AND linked those profiles to a national voter file, the data would only become useful if the Trump communications operation was crafting distinct messages for each of the categories. But we know for a fact that the Trump campaign had a bare-bones communications staff. If CA had been able to hand the Trump communications team a detailed psychographic assessment of every targeted voter, the practical response would have been a bit like Henry Ford’s old comment: “The customer can have any color he wants so long as it’s black.”

CONTINUE READING: Access the complete article in CivicHall.org, where it was originally published.

Author Bio:

Dave Karpf is an assistant professor in the George Washington University School of Media and Public Affairs. He conducts research on the internet and organized political advocacy, and is the author of The MoveOn Effect: The Unexpected Transformation of American Political Advocacy (2012, Oxford University Press). When he writes about civic technology, he tries to play the role of “helpful critic.” He mostly focuses on the places where our proposed technologies presume a more willing government or a more uniformly engaged public than we actually have.