Under the reference desk, on a shelf about the height of his knees, Castiel maintains an irregular but meticulously curated rotation of books. They have no particular relation to one another. They’re new, most of them, or so old they exist only in the library’s undigitized card catalog (last updated in 1982). He keeps this collection in a cast-off copy paper box with a lid, where his fellow reference librarians won’t see and ask questions about it. It includes both fiction and nonfiction, periodicals, and the occasional length of microfiche. Right now, it also contains a freshly baked banana muffin, carefully separated from old paper and cardstock by the three-ring binder that houses their printer manual.
However, the reference desk is slow this Wednesday, and librarians are known to be particularly nosy. Kevin is wordsmithing his thesis under the guise of working, and Charlie has been staring at her phone for the past hour; when he returns from a coffee run, Castiel is almost unsurprised to find her under his side of the desk, clearly searching for something.
“I smell baked goods,” she says accusingly as Castiel sets her mug on the counter above her head. The script on the side reads, SHE BLINDED ME WITH LIBRARY SCIENCE.
“You know the rules,” Kevin says from his assistant’s station across the way. “No food or drinks around the books—”
“Unless you’re sharing,” Charlie chimes in, slightly muffled. “Cough it up, Novak.”
“It’s not for me, or you,” Castiel says with dignity, pulling out his chair. “Yes, ma’am, may I help you?”
“Oh,” the woman on the other side of the counter says, eyeing Charlie’s position. Her toddler is trying to peer over the edge to see what his mother is looking at. “Could you tell me where the children’s books are?”
“Ooo, what’s in the box?” Charlie says, pulling it out.
“Be careful with that,” Castiel says. “The children’s books are on the lower level— I can take you to the preschooler’s section, if you like.”
“Oh, that would be nice,” the woman says nervously as Charlie pops up with the box in her hands.
“That’s private,” Castiel says to Charlie, and to the woman, “please, follow me.”
There was probably nothing else he could have said to better inflame their interest, and so of course when he comes back up the stairs both Charlie and Kevin have unpacked the box and strewn the contents over the entire reference desk, including microfiche, muffin, and easily thirty pounds of miscellaneous reading material.
“I don’t get it,” Charlie informs him.
“I really don’t get it,” Kevin affirms, holding up a recent land survey and photocopies of a series of newspaper clippings from the 1900s. “Is this for an outside project?”
“Of a sort,” Castiel says, pulling it firmly out of his grip.
“Um,” says a new voice, and Castiel looks up and sees Sam standing in front of the desk with a quizzical look on his face, eyes tracking over the mess spread across the desks.
“There you are,” Castiel says, and hands him the land surveys. “Topographical and soil type maps included. McHenry County is on Drummer Silty Clay Loam. And here are a few articles from the last time the… anomalies you mentioned were seen.” He picks up another book. “Here’s a new folklore compendium put out by the community college. It has a section devoted to Algonquin tribal myth.” Another, bigger book. “The book from Galloise County Public Library on Irish immigrant settlers. I need it back in five days.” A new issue of Popular Mechanic. “For Dean.” A sheaf of handwritten notes. “Isa Wells was in yesterday and I asked her about the… anomalies. She had a few things that might be helpful.” And at the very top of the stack, which now nearly reaches Sam’s chin, Castiel gently places the banana muffin. “For the road,” he says.
It’s a little hard to meet Sam’s eyes with Charlie and Kevin hovering with palpable interest just over his shoulder, but the man’s slow smile is worth it. “You know I’m going to have to eat this before I get in the car,” Sam says.
“They sell them at the end of the street if he wants his own,” Castiel says, which is as good as admitting that he’d gone and bought a muffin specifically to give to Sam, only for Sam and dear Lord. Oh, God.
But Sam smile just broadens, and he says, “I’ll let him know. See you soon, Cas,” with a soft undercurrent of something that leaves Castiel’s face hot, blinking dazedly after him as he turns and heads for check-out counter.
“Whoa,” Kevin says.
“Castiel,” Charlie breathes in delight.
“No,” Castiel says preemptively, still blushing, and settles the lid on the box again.
“Oh, he’s hot,” the drunk and disorderly in the back seat says, leaning over as much as she can to peer through the grate between the headrests. “What’s his name? I can’t read it from here.”
“It’s none of your business,” Castiel says, scanning through the file on the MDT. Dean Hendrix, 27 and an Illinois native, grins up at him from the screen with the same easy charm he’d layered on when Castiel had first walked up to his window. According to the DMV, his eyes are hazel, but they’d looked very green in Castiel’s flashlight. No history of moving violations, not even a parking ticket, and a criminal record that’s similarly clean. But he’d been going so far over the speed limit it was technically a felony, on a gravel road to boot, and Castiel’s in no mood to grant favors.
“At least tell me if he’s legal,” the woman whines. “If you throw him back here, I want to know how creepy I can be.”
“Ms. Masters, please,” Castiel mutters, eyes on his pen and ticketbook. “Contain yourself.”
“So— a little? A lot?”
Castiel privately decides he will not be bringing Dean back to the cruiser, even if the man is drinking directly out of a whiskey bottle when he goes back to hand him this ticket. He opens his door and the warm night air rushes in, heavy with the smell of rain.
Wind is driving the light rain in sheets, rippling and quiet on the road and the shoulders of Castiel’s clear raincoat. They’re far enough out in the country, in the tall corn and dense soy, that everything outside of Castiel’s headlights is a murky black. The shadows twine long and febrile against the wet ground. Dean gives him another smile when Castiel reaches the car, sprawled loose and relaxed across the front bucket seat. “Hi again, officer.”
“Mr. Hendrix,” Castiel says gravely, and his face falls a little.
“Man, really?”
“The speed limit in this zone is fifty-five,” Castiel says, and tears out the thin pink slip. “You’ll be required to show up in court, whether you plead guilty or innocent, and the court date is set for a month from now. I hope—”
“Hey.”
Dean Hendrix is still smiling but his body is still, his face gone fixed and rigid. His eyes are on his side mirror, slightly wider than they were before.
“Hey, officer. Can I tell you something?”
“What would that be?” Castiel says. There’s something about the way the man is staring at the mirror that makes him uncomfortable.
Dean slowly turns his head to look up at him. “There’s a reason I was speeding.”
“You told me,” Castiel says. “You were trying to get to a party. You’re late.”
“I lied,” Dean says, blinking hard.
“I assumed so, yes. You’re hardly the first person to lie about that.”
“The real reason is that I was trying to get away from something,” Dean says, face drawn, eyes fever-bright. “Something bad.”
Castiel’s unease is growing, and he shifts his weight back on one heel.
“Don’t look back,” Dean says.
Castiel stares at him. “Why?” he says, and then something moves out of the corner of his eye, from the direction of the cruiser behind them.
“Don’t,” Dean says urgently, his fingers twitching, and Castiel doesn’t dare take his eyes off of him. “Don’t look. We’re having a nice conversation about this fucking ticket. Everything’s fine. Is there someone in your car?”
“What?” Castiel asks, dry-mouthed, watching Dean’s hand clench on the side of the car, the other with a white-knuckled grip on his knee.
“Is there someone in your car with you?” Dean repeats, pitching his voice under the susurrus of the rain. “Do you remember where you picked them up?”
“I… she was drunk,” Castiel says, searching for the memory. Truck stop? Gas station? “It was close by.”
“I bet it was,” Dean says. “Just keep looking at me, alright?”
Castiel swallows, and settles a hand on his belt, closer to his gun. “What’s going on?”
“Nothing is going on. You’re giving me a ticket, and I’m trying to talk you out of it, and in a second you’re going to come around the front of the car. Maybe you saw something on the floor that made you suspicious. Maybe you want to make sure my headlights are both working.”
One of the cruiser’s doors slams shut.
“Shit,” Dean says, and brings up a sawed-off shotgun he’d been hiding behind his leg.
Castiel is diving away from the car as Dean brings the barrel up, and his entire focus is on the tall cornstalks pressing up to the sides of the narrow road and the cover they can offer. He’s not looking when the shotgun goes off, and he’s not looking when something shrieks, a raw shredded-metal sound that tears at his ears. The darkness around him is thrashing, roiling like a thunderstorm, and when the shotgun fires again it shudders with red edges.
He’s down on the wet gravel, panting, palms sore from the impact and water soaking into the knees of his pants. He doesn’t remember getting there, and flinches violently when he realizes that someone is standing next to him. He rolls and yanks his pistol out, blinking furiously in the rain, and Dean stops with his hands raised.
“Hey, it’s okay. It’s gone now.”
The darkness has gone flat and black, still like death. Dean has blood dripping down his forehead, all the way to his chin.
“You can put the gun away,” he says. “Really.”
Castiel just stares at him. “What,” he says. “What the hell.”
“Well, look at that,” Dean says. “Right on the first guess.”
“Oh, hello there,” she says, surprised but making sure her smile is extra big for the little boy who answers the door. “My name’s Donna! Are your parents home, by any chance?”
The boy just stares at her.
“Your mom? Dad?” she tries.
In the hallway leading into the house, a tow-headed girl sticks her head around a corner. “Oh my God,” she says. “Seriously?”
“Seriously,” says the boy, who’s looking at Donna like she’s a particularly underwhelming school pet.
“Um. Maybe we can talk inside—?” Donna starts, but the girl’s head disappears.
“Mom,” she hears, “I told you! Someone called the cops on you!”
“What?” An older, female voice.
“I said they called the cops!”
“Who did? Why?”
“I don’t know! They probably heard the screaming!”
“Anyway, our moms’re home,” the boy says, and leans back with his hand on the knob. “I guess you can come in, if you’re a police officer.”
He pulls the door all the way open, then trots away, leaving Donna deeply confused and stranded at the edge of the foyer rug.
“O-o-okay then,” she says to herself, slowly stepping forward, and then yelps in surprise as a door crashes open and a tiny woman in an apron appears, complete with murderous-looking hand trowel.
“I’m not keeping her chained up in the basement, or whatever that sick old man thinks!” she yells, shaking the trowel at Donna.
“You might as well be!” a second woman yells through the door. From the direction of the sound and stairs beyond the doorway, she probably is in the basement.
“I see,” Donna says weakly.
The first women whirls and says, “Jody! You are not helping!”
“He’s saving me. We’re going to run away together on his ugly-ass boat, and my next book will be a Jimmy Buffet tribute.”
“Nice try! You’re on contract for four more books with Hachette, including the one that I am supposed to be editing right now.”
“We’ll fake my murder, and frame you. It won’t be hard.”
“If you have time to come up with that, you have time to write! Write!”
“Ma’am,” Donna says, and tries not to quail when the tiny woman turns on her. “We, ah, received a noise complaint and—” The woman is swelling like a bullfrog, and Donna hastily pushes out, “And I’d appreciate the opportunity to speak with your partner. Before I go. If that’s okay?”
Before the tiny woman can explode, there are footsteps on the stairs, and Jody emerges to wrap her arms around her wife. From Donna’s perspective, this looks a bit like hugging a nuclear warhead.
“Linda. Hun. If you scare the nice police officer, who’s going to save us from burglars?”
“I have a gun,” the first woman, Linda, mutters.
“And a permit,” Jody adds hastily, looking up at Donna. “Listen, I am so sorry about this. Can I make you some coffee?”
“Ohmygodyourejodymills,” Donna squeaks out, because Black Heart is on her nightstand right this minute in hardcover because she just couldn’t wait that long. The solemn face from the dust jacket is now blinking at her surprise from the above her wife’s head.
“Why, yes,” Jody says, starting to smile. “Yes I am.”
“No,” Linda says. “You are an irresponsible and endlessly procrastinating—”
“Can I sign something for you?” Jody says, already moving around Linda towards her. “Come on in, I’ve got plenty of promo copies if you don’t. Let’s talk about your favorite. I’ll get a new pot going just for you.”
“Jody!”
“She’s a guest, we can’t just let her go empty-handed.“
“I don’t want to be any trouble,” Donna says, faint and getting fainter as Jody Mills, the crime writer of the century, puts her arm over her shoulders. Oofda hey.
“Oh yeah, baby,” Charlie says with a leer. “Come to mama, you’re so pretty—”
Jo chooses not to be offended that it’s the phone she’s talking to, a shiny newest generation iPhone that had somehow made its way out of the warden’s back pocket and down Jo’s workshirt. Charlie makes grabby hands and Jo passes it to her with an eyeroll.
“I’m going to do such bad things to you,” Charlie murmurs, hefting a hammer. “Oh, yes.”
“Do you want to be alone together?” Jo says, leaning back again the wall.
“Oh, honey, don’t be jealous,” Charlie says, already cracking open the casing with deft little taps. “You’re getting at least thirty minutes of head for this one, especially if I can get it hooked up before they call dinner.”
“A good grab, then?” Jo asks, a little strained because there are a bunch of girls right outside this closet who might hear her. Still pleased, though, because she’s had to get pretty damn close to Buckley’s hairy ass to get the thing.
Charlie grins at her, eyes bright and hair falling loose from her ponytail. “That, and I really like giving you head.”
“That’s fair,” Jo allows, and Charlie laughs while she eviscerates the phone.
The company behind Trump’s online ad campaign as well as Brexit Leave (leave.eu led by Nigel Farage) is Cambridge Analytica. It uses psychometrics to ‘measure’ tendencies among individuals and groups through their digital traces. Specifically what you like on Facebook.
Cambridge Analytica’s parent company – SCL or Strategic Communications Laboratories – uses psychometrics for psychological manipulation and behavioral change communication. The business structure conducts “political upheavals in developing countries; others had done work for NATO, developing methods for the psychological manipulation of the population in Afghanistan.”
For those who know me know that I work with analytics, algorithms and the intersection of technology and society (society in the holistic people-oriented sense, not cloistered enclaves). I’ve been talking about this for so long; how Big Data has played a massive role in emboldening the most unhinged forms of fascism in society and how Silicon Valley is one of the places to blame.
Of course, when a woman says something like this, the go-to reaction among tech folks is to label her hysterical and brush her doubts aside by telling her that she is too ‘emotional’ to discuss and analyze technology’s role in politics and governance.
Among those to mock my thoughts were upper class white men who hide their lack of empathy under the guise of ‘rationalism,’ middle class white men who buy waifu pillows on Amazon and yell at minorities online via anonymous Twitter accounts, and South/East Asian men who’ve made it big in SF and feel the need to parrot their white peers’ ways so as to avoid being called betas. It’s a pretty sad sight all in all.
But this is a truly terrifying read on how psychometrics and data-driven communications played an integral role in fascism and Trump’s victory through online marketing and micro-targeting using the O.C.E.A.N. method.
Many voices have claimed that the statisticians lost the election because their predictions were so off the mark. But what if statisticians in fact helped win the election—but only those who were using the new method? It is an irony of history that Trump, who often grumbled about scientific research, used a highly scientific approach in his campaign.
Psychometrics, sometimes also called psychographics, focuses on measuring psychological traits, such as personality. In the 1980s, two teams of psychologists developed a model that sought to assess human beings based on five personality traits, known as the “Big Five.” These are: openness (how open you are to new experiences?), conscientiousness (how much of a perfectionist are you?), extroversion (how sociable are you?), agreeableness (how considerate and cooperative you are?) and neuroticism (are you easily upset?). Based on these dimensions—they are also known as OCEAN, an acronym for openness, conscientiousness, extroversion, agreeableness, neuroticism—we can make a relatively accurate assessment of the kind of person in front of us. This includes their needs and fears, and how they are likely to behave. The “Big Five” has become the standard technique of psychometrics. But for a long time, the problem with this approach was data collection, because it involved filling out a complicated, highly personal questionnaire. Then came the Internet. And Facebook. And Kosinski.
…
The approach that Kosinski and his colleagues developed over the next few years was actually quite simple. First, they provided test subjects with a questionnaire in the form of an online quiz. From their responses, the psychologists calculated the personal Big Five values of respondents. Kosinski’s team then compared the results with all sorts of other online data from the subjects: what they “liked,” shared or posted on Facebook, or what gender, age, place of residence they specified, for example. This enabled the researchers to connect the dots and make correlations.
…
Kosinski and his team tirelessly refined their models. In 2012, Kosinski proved that on the basis of an average of 68 Facebook “likes” by a user, it was possible to predict their skin color (with 95 percent accuracy), their sexual orientation (88 percent accuracy), and their affiliation to the Democratic or Republican party (85 percent). But it didn’t stop there. Intelligence, religious affiliation, as well as alcohol, cigarette and drug use, could all be determined. From the data it was even possible to deduce whether someone’s parents were divorced.
The strength of their modeling was illustrated by how well it could predict a subject’s answers. Kosinski continued to work on the models incessantly: before long, he was able to evaluate a person better than the average work colleague, merely on the basis of ten Facebook “likes.” Seventy “likes” were enough to outdo what a person’s friends knew, 150 what their parents knew, and 300 “likes” what their partner knew. More “likes” could even surpass what a person thought they knew about themselves. On the day that Kosinski published these findings, he received two phone calls. The threat of a lawsuit and a job offer. Both from Facebook.
…
But we also reveal something about ourselves even when we’re not online. For example, the motion sensor on our phone reveals how quickly we move and how far we travel (this correlates with emotional instability). Our smartphone, Kosinski concluded, is a vast psychological questionnaire that we are constantly filling out, both consciously and unconsciously.
Above all, however—and this is key—it also works in reverse: not only can psychological profiles be created from your data, but your data can also be used the other way round to search for specific profiles: all anxious fathers, all angry introverts, for example—or maybe even all undecided Democrats? Essentially, what Kosinski had invented was sort of a people search engine. He started to recognize the potential—but also the inherent danger—of his work.
To him, the internet had always seemed like a gift from heaven. What he really wanted was to give something back, to share. Data can be copied, so why shouldn’t everyone benefit from it? It was the spirit of a whole generation, the beginning of a new era that transcended the limitations of the physical world. But what would happen, wondered Kosinski, if someone abused his people search engine to manipulate people? He began to add warnings to most of his scientific work. His approach, he warned, “could pose a threat to an individual’s well-being, freedom, or even life.” But no one seemed to grasp what he meant.
…
Finally, Kosinski remembers, Kogan revealed the name of the company: SCL, or Strategic Communication Laboratories. Kosinski Googled the company: “[We are] the premier election management agency,” says the company’s website. SCL provides marketing based on psychological modeling. One of its core focuses: Influencing elections. Influencing elections? Perturbed, Kosinski clicked through the pages. What kind of company was this? And what were these people planning?
What Kosinski did not know at the time: SCL is the parent of a group of companies. Who exactly owns SCL and its diverse branches is unclear, thanks to a convoluted corporate structure, the type seen in the UK Companies House, the Panama Papers, and the Delaware company registry. Some of the SCL offshoots have been involved in elections from Ukraine to Nigeria, helped the Nepalese monarch against the rebels, whereas others have developed methods to influence Eastern European and Afghan citizens for NATO. And, in 2013, SCL spun off a new company to participate in US elections: Cambridge Analytica.
…
Kosinski came to suspect that Kogan’s company might have reproduced the Facebook “Likes”-based Big Five measurement tool in order to sell it to this election-influencing firm. He immediately broke off contact with Kogan and informed the director of the institute, sparking a complicated conflict within the university. The institute was worried about its reputation. Aleksandr Kogan then moved to Singapore, married, and changed his name to Dr. Spectre. Michal Kosinski finished his PhD, got a job offer from Stanford and moved to the US.
All was quiet for about a year. Then, in November 2015, the more radical of the two Brexit campaigns, “Leave.EU,” supported by Nigel Farage, announced that it had commissioned a Big Data company to support its online campaign: Cambridge Analytica. The company’s core strength: innovative political marketing—microtargeting—by measuring people’s personality from their digital footprints, based on the OCEAN model.
…
After the Brexit result, friends and acquaintances wrote to him: Just look at what you’ve done. Everywhere he went, Kosinski had to explain that he had nothing to do with this company. (It remains unclear how deeply Cambridge Analytica was involved in the Brexit campaign. Cambridge Analytica would not discuss such questions.)
…
A few weeks earlier, Trump had tweeted, somewhat cryptically, “Soon you’ll be calling me Mr. Brexit.” Political observers had indeed noticed some striking similarities between Trump’s agenda and that of the right-wing Brexit movement. But few had noticed the connection with Trump’s recent hiring of a marketing company named Cambridge Analytica.
Up to this point, Trump’s digital campaign had consisted of more or less one person: Brad Parscale, a marketing entrepreneur and failed start-up founder who created a rudimentary website for Trump for $1,500. The 70-year-old Trump is not digitally savvy—there isn’t even a computer on his office desk. Trump doesn’t do emails, his personal assistant once revealed. She herself talked him into having a smartphone, from which he now tweets incessantly.
Hillary Clinton, on the other hand, relied heavily on the legacy of the first “social-media president,” Barack Obama. She had the address lists of the Democratic Party, worked with cutting-edge big data analysts from BlueLabs and received support from Google and DreamWorks. When it was announced in June 2016 that Trump had hired Cambridge Analytica, the establishment in Washington just turned up their noses. Foreign dudes in tailor-made suits who don’t understand the country and its people? Seriously?
…
“So how did he do this?” Up to now, explains Nix, election campaigns have been organized based on demographic concepts. “A really ridiculous idea. The idea that all women should receive the same message because of their gender—or all African Americans because of their race.” What Nix meant is that while other campaigners so far have relied on demographics, Cambridge Analytica was using psychometrics.
Though this might be true, Cambridge Analytica’s role within Cruz’s campaign isn’t undisputed. In December 2015 the Cruz team credited their rising success to psychological use of data and analytics. In Advertising Age, a political client said the embedded Cambridge staff was “like an extra wheel,” but found their core product, Cambridge’s voter data modeling, still “excellent.” The campaign would pay the company at least $5.8 million to help identify voters in the Iowa caucuses, which Cruz won, before dropping out of the race in May.
Nix clicks to the next slide: five different faces, each face corresponding to a personality profile. It is the Big Five or OCEAN Model. “At Cambridge,” he said, “we were able to form a model to predict the personality of every single adult in the United States of America.” The hall is captivated. According to Nix, the success of Cambridge Analytica’s marketing is based on a combination of three elements: behavioral science using the OCEAN Model, Big Data analysis, and ad targeting. Ad targeting is personalized advertising, aligned as accurately as possible to the personality of an individual consumer.
Nix candidly explains how his company does this. First, Cambridge Analytica buys personal data from a range of different sources, like land registries, automotive data, shopping data, bonus cards, club memberships, what magazines you read, what churches you attend. Nix displays the logos of globally active data brokers like Acxiom and Experian—in the US, almost all personal data is for sale. For example, if you want to know where Jewish women live, you can simply buy this information, phone numbers included. Now Cambridge Analytica aggregates this data with the electoral rolls of the Republican party and online data and calculates a Big Five personality profile. Digital footprints suddenly become real people with fears, needs, interests, and residential addresses.
The methodology looks quite similar to the one that Michal Kosinski once developed. Cambridge Analytica also uses, Nix told us, “surveys on social media” and Facebook data. And the company does exactly what Kosinski warned of: “We have profiled the personality of every adult in the United States of America—220 million people,” Nix boasts.
He opens the screenshot. “This is a data dashboard that we prepared for the Cruz campaign.” A digital control center appears. On the left are diagrams; on the right, a map of Iowa, where Cruz won a surprisingly large number of votes in the primary. And on the map, there are hundreds of thousands of small red and blue dots. Nix narrows down the criteria: “Republicans"—the blue dots disappear; “not yet convinced"—more dots disappear; “male”, and so on. Finally, only one name remains, including age, address, interests, personality and political inclination. How does Cambridge Analytica now target this person with an appropriate political message?
Nix shows how psychographically categorized voters can be differently addressed, based on the example of gun rights, the 2nd Amendment: “For a highly neurotic and conscientious audience the threat of a burglary—and the insurance policy of a gun.” An image on the left shows the hand of an intruder smashing a window. The right side shows a man and a child standing in a field at sunset, both holding guns, clearly shooting ducks: “Conversely, for a closed and agreeable audience. People who care about tradition, and habits, and family.”
Trump’s striking inconsistencies, his much-criticized fickleness, and the resulting array of contradictory messages, suddenly turned out to be his great asset: a different message for every voter. The notion that Trump acted like a perfectly opportunistic algorithm following audience reactions is something the mathematician Cathy O’Neil observed in August 2016.
…
The messages differed for the most part only in microscopic details, in order to target the recipients in the optimal psychological way: different headings, colors, captions, with a photo or video. This fine-tuning reaches all the way down to the smallest groups, Nix explained in an interview with us. “We can address villages or apartment blocks in a targeted way. Even individuals.”
In the Miami district of Little Haiti, for instance, Trump’s campaign provided inhabitants with news about the failure of the Clinton Foundation following the earthquake in Haiti, in order to keep them from voting for Hillary Clinton. This was one of the goals: to keep potential Clinton voters (which include wavering left-wingers, African-Americans, and young women) away from the ballot box, to “suppress” their vote, as one senior campaign official told Bloomberg in the weeks before the election. These “dark posts"—sponsored news-feed-style ads in Facebook timelines that can only be seen by users with specific profiles—included videos aimed at African-Americans in which Hillary Clinton refers to black men as predators, for example.
Nix finishes his lecture at the Concordia Summit by stating that traditional blanket advertising is dead. “My children will certainly never, ever understand this concept of mass communication.” And before leaving the stage, he announced that since Cruz had left the race, the company was helping one of the remaining presidential candidates.
Just how precisely the American population was being targeted by Trump’s digital troops at that moment was not visible, because they attacked less on mainstream TV and more with personalized messages on social media or digital TV. And while the Clinton team thought it was in the lead, based on demographic projections, Bloomberg journalist Sasha Issenberg was surprised to note on a visit to San Antonio—where Trump’s digital campaign was based—that a “second headquarters” was being created …
Whereas European privacy laws require a person to “opt in” to a release of data, those in the US permit data to be released unless a user “opts out.”
The measures were radical: From July 2016, Trump’s canvassers were provided with an app with which they could identify the political views and personality types of the inhabitants of a house. It was the same app provider used by Brexit campaigners. Trump’s people only rang at the doors of houses that the app rated as receptive to his messages. The canvassers came prepared with guidelines for conversations tailored to the personality type of the resident. In turn, the canvassers fed the reactions into the app, and the new data flowed back to the dashboards of the Trump campaign.
…
But to what extent did psychometric methods influence the outcome of the election? When asked, Cambridge Analytica was unwilling to provide any proof of the effectiveness of its campaign. And it is quite possible that the question is impossible to answer.
And yet there are clues: There is the fact of the surprising rise of Ted Cruz during the primaries. Also there was an increased number of voters in rural areas. There was the decline in the number of African-American early votes. The fact that Trump spent so little money may also be explained by the effectiveness of personality-based advertising. As does the fact that he invested far more in digital than TV campaigning compared to Hillary Clinton. Facebook proved to be the ultimate weapon and the best election campaigner, as Nix explained, and as comments by several core Trump campaigners demonstrate.
Many voices have claimed that the statisticians lost the election because their predictions were so off the mark. But what if statisticians in fact helped win the election—but only those who were using the new method? It is an irony of history that Trump, who often grumbled about scientific research, used a highly scientific approach in his campaign.
Another big winner is Cambridge Analytica. Its board member Steve Bannon, former executive chair of the right-wing online newspaper Breitbart News, has been appointed as Donald Trump’s senior counselor and chief strategist. Whilst Cambridge Analytica is not willing to comment on alleged ongoing talks with UK Prime Minister Theresa May, Alexander Nix claims that he is building up his client base worldwide, and that he has received inquiries from Switzerland, Germany, and Australia. His company is currently touring European conferences showcasing their success in the United States. This year three core countries of the EU are facing elections with resurgent populist parties: France, Holland and Germany. The electoral successes come at an opportune time, as the company is readying for a push into commercial advertising.
…
The world has been turned upside down. Great Britain is leaving the EU, Donald Trump is president of the United States of America. And in Stanford, Kosinski, who wanted to warn against the danger of using psychological targeting in a political setting, is once again receiving accusatory emails. “No,” says Kosinski, quietly and shaking his head. “This is not my fault. I did not build the bomb. I only showed that it exists.“
kototyph 