Image Credit: Michal Kosinski, David Stillwell, and Thore Graepel

According to Zurich’s Das Magazine, which profiled Kosinski in late 2016, “with a mere ten ‘likes’ as input his model could appraise a person’s character better than an average coworker. With seventy, it could ‘know’ a subject better than a friend; with 150 likes, better than their parents. With 300 likes, Kosinski’s machine could predict a subject’s behavior better than their partner. With even more likes it could exceed what a person thinks they know about themselves.”

Not long afterward, Kosinski was approached by Aleksandr Kogan, a fellow Cambridge professor in the psychology department, about licensing his model to SCL Elections, a company that claimed its specialty lay in manipulating elections. The offer would have meant a significant payout for Kosinki’s lab. Still, he declined, worried about the firm’s intentions and the downstream effects it could have.

It had taken Kosinski and his colleagues years to develop that model, but with his methods and findings now out in the world, there was little to stop SCL Elections from replicating them. It would seem they did just that.

According to a Guardian investigation, in early 2014, just a few months after Kosinski declined their offer, SCL partnered with Kogan instead. As a part of their relationship, Kogan paid Amazon Mechanical Turk workers $1 each to take the OCEAN quiz. There was just one catch: To take the quiz, users were required to provide access to all of their Facebook data. They were told the data would be used for research. The job was reported to Amazon for violating the platform’s Terms of Service. What many of the Turks likely didn’t realize: According to documents reviewed by The Guardian, “Kogan also captured the same data for each person’s unwitting friends.”

The data gathered from Kogan’s study went on to birth Cambridge Analytica, which spun out of SCL Elections soon after. The name, metaphorically at least, was a nod to Kogan’s work — and a dig at Kosinski.

But that early trove of user data was just the beginning — just the seed Analytica needed to build its own model for analyzing users personalities without having to rely on the lengthy OCEAN test.

After a successful proof of concept and backed by wealthy conservative investors, Analytica went on a data shopping spree for the ages, snapping up data about your shopping habits, land ownership, where you attend church, what stores you visit, what magazines you subscribe to — all of which is for sale from a range of data brokers and third party organizations selling information about you. Analytica aggregated this data with voter roles, publicly available online data — including Facebook likes — and put it all into its predictive personality model.

“Your behavior is driven by your personality and actually the more you can understand about people’s personality as psychological drivers, the more you can actually start to really tap in to why and how they make their decisions,” Nix explained to Bloomberg’s Sasha Issenburg. “We call this behavioral microtargeting and this is really our secret sauce, if you like. This is what we’re bringing to America.”

Image Credit: Cambridge Analytica

Using those dossiers, or psychographic profiles as Analytica calls them, Cambridge Analytica not only identifies which voters are most likely to swing for their causes or candidates; they use that information to predict and then change their future behavior.

As Vice reported recently, Kosinski and a colleague are now working on a new set of research, yet to be published, that addresses the effectiveness of these methods. Their early findings: Using personality targeting, Facebook posts can attract up to 63 percent more clicks and 1,400 more conversions.

Scout reached out to Cambridge Analytica with a detailed list of questions about their communications tactics, but the company declined to answer any questions or to comment on any of their tactics.

But researchers across the technology and media ecosystem who have been following Cambridge Analytica’s political messaging activities have unearthed an expansive, adaptive online network that automates the manipulation of voters at a scale never before seen in political messaging.

“They [the Trump campaign] were using 40-50,000 different variants of ad every day that were continuously measuring responses and then adapting and evolving based on that response,” Martin Moore, director of Kings College’s Centre for the Study of Media, Communication and Power, told The Guardian in early December. “It’s all done completely opaquely and they can spend as much money as they like on particular locations because you can focus on a five-mile radius.”

Where traditional pollsters might ask a person outright how they plan to vote, Analytica relies not on what they say but what they do, tracking their online movements and interests and serving up multivariate ads designed to change a person’s behavior by preying on individual personality traits.

“For example,” Nix wrote in an op-ed last year about Analytica’s work on the Cruz campaign, ”our issues model identified that there was a small pocket of voters in Iowa who felt strongly that citizens should be required by law to show photo ID at polling stations.”

“Leveraging our other data models, we were able to advise the campaign on how to approach this issue with specific individuals based on their unique profiles in order to use this relatively niche issue as a political pressure point to motivate them to go out and vote for Cruz. For people in the ‘Temperamental’ personality group, who tend to dislike commitment, messaging on the issue should take the line that showing your ID to vote is ‘as easy as buying a case of beer’. Whereas the right message for people in the ‘Stoic Traditionalist’ group, who have strongly held conventional views, is that showing your ID in order to vote is simply part of the privilege of living in a democracy.”

For Analytica, the feedback is instant and the response automated: Did this specific swing voter in Pennsylvania click on the ad attacking Clinton’s negligence over her email server? Yes? Serve her more content that emphasizes failures of personal responsibility. No? The automated script will try a different headline, perhaps one that plays on a different personality trait — say the voter’s tendency to be agreeable toward authority figures. Perhaps: “Top Intelligence Officials Agree: Clinton’s Emails Jeopardized National Security.”

Much of this is done through Facebook dark posts, which are only visible to those being targeted.

Based on users’ response to these posts, Cambridge Analytica was able to identify which of Trump’s messages were resonating and where. That information was also used to shape Trump’s campaign travel schedule. If 73 percent of targeted voters in Kent County, Mich. clicked on one of three articles about bringing back jobs? Schedule a Trump rally in Grand Rapids that focuses on economic recovery.

Political analysts in the Clinton campaign, who were basing their tactics on traditional polling methods, laughed when Trump scheduled campaign events in the so-called blue wall — a group of states that includes Michigan, Pennsylvania, and Wisconsin and has traditionally fallen to Democrats. But Cambridge Analytica saw they had an opening based on measured engagement with their Facebook posts. It was the small margins in Michigan, Pennsylvania and Wisconsin that won Trump the election.

Image Credit: Ali Zifan/Wikimedia Commons

Dark posts were also used to depress voter turnout among key groups of democratic voters. “In this election, dark posts were used to try to suppress the African-American vote,” wrote journalist and Open Society fellow McKenzie Funk in a New York Times editorial. “According to Bloomberg, the Trump campaign sent ads reminding certain selected black voters of Hillary Clinton’s infamous ‘super predator’ line. It targeted Miami’s Little Haiti neighborhood with messages about the Clinton Foundation’s troubles in Haiti after the 2010 earthquake.’”

Because dark posts are only visible to the targeted users, there’s no way for anyone outside of Analytica or the Trump campaign to track the content of these ads. In this case, there was no SEC oversight, no public scrutiny of Trump’s attack ads. Just the rapid-eye-movement of millions of individual users scanning their Facebook feeds.

In the weeks leading up to a final vote, a campaign could launch a $10-100 million dark post campaign targeting just a few million voters in swing districts and no one would know. This may be where future ‘black-swan’ election upsets are born.

Image Credit: Jonathan Albright

“The sites in the fake news and hyper-biased #MCM network,” Albright writes, “have a very small ‘node’ size — this means they are linking out heavily to mainstream media, social networks, and informational resources (most of which are in the ‘center’ of the network), but not many sites in their peer group are sending links back.”