myDeaconmyhand
First man to get a team of horses up Bear Mountain
http://motherboard.vice.com/read/big-data-cambridge-analytica-brexit-trump
“We are thrilled that our revolutionary approach to data-driven communication has played such an integral part in President-elect Trump’s extraordinary win,” Alexander James Ashburner Nix was quoted as saying. Nix is British, 41 years old, and CEO of Cambridge Analytica... His company wasn't just integral to Trump’s online campaign, but to the UK's Brexit campaign as well."
How dangerous is big data?
Anyone who has not spent the last five years living on another planet will be familiar with the term Big Data. Big Data means, in essence, that everything we do, both on and offline, leaves digital traces. Every purchase we make with our cards, every search we type into Google, every movement we make when our mobile phone is in our pocket, every “like” is stored. Especially every “like.” For a long time, it was not entirely clear what use this data could have—except, perhaps, that we might find ads for high blood pressure remedies just after we’ve Googled “reduce blood pressure.”
On November 9, it became clear that maybe much more is possible. The company behind Trump’s online campaign—the same company that had worked for Leave.EU in the very early stages of its "Brexit" campaign—was a Big Data company: Cambridge Analytica.
"To understand the outcome of the election—and how political communication might work in the future—we need to begin with a strange incident at Cambridge University in 2014, at Kosinski’s Psychometrics Center.
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 is 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.
Michal Kosinski was a student in Warsaw when his life took a new direction in 2008. He was accepted by Cambridge University to do his PhD at the Psychometrics Centre, one of the oldest institutions of this kind worldwide. Kosinski joined fellow student David Stillwell (now a lecturer at Judge Business School at the University of Cambridge) about a year after Stillwell had launched a little Facebook application in the days when the platform had not yet become the behemoth it is today. Their MyPersonality app enabled users to fill out different psychometric questionnaires, including a handful of psychological questions from the Big Five personality questionnaire (“I panic easily,“ “I contradict others”). Based on the evaluation, users received a “personality profile”—individual Big Five values—and could opt-in to share their Facebook profile data with the researchers.
Followers of Lady Gaga were most probably extroverts, while those who “liked” philosophy tended to be introverts.
Kosinski had expected a few dozen college friends to fill in the questionnaire, but before long, hundreds, thousands, then millions of people had revealed their innermost convictions. Suddenly, the two doctoral candidates owned the largest dataset combining psychometric scores with Facebook profiles ever to be collected.
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.
Remarkably reliable deductions could be drawn from simple online actions. For example, men who “liked” the cosmetics brand MAC were slightly more likely to be gay; one of the best indicators for heterosexuality was “liking” Wu-Tang Clan. Followers of Lady Gaga were most probably extroverts, while those who “liked” philosophy tended to be introverts. While each piece of such information is too weak to produce a reliable prediction, when tens, hundreds, or thousands of individual data points are combined, the resulting predictions become really accurate.
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 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 it was not just about “likes” or even Facebook: Kosinski and his team could now ascribe Big Five values based purely on how many profile pictures a person has on Facebook, or how many contacts they have (a good indicator of extraversion). 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...
"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.“...
"...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...
...“Pretty much every message that Trump put out was data-driven,” Alexander Nix remembers. On the day of the third presidential debate between Trump and Clinton, Trump’s team tested 175,000 different ad variations for his arguments, in order to find the right versions above all via Facebook. 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.”"
“We are thrilled that our revolutionary approach to data-driven communication has played such an integral part in President-elect Trump’s extraordinary win,” Alexander James Ashburner Nix was quoted as saying. Nix is British, 41 years old, and CEO of Cambridge Analytica... His company wasn't just integral to Trump’s online campaign, but to the UK's Brexit campaign as well."
How dangerous is big data?
Anyone who has not spent the last five years living on another planet will be familiar with the term Big Data. Big Data means, in essence, that everything we do, both on and offline, leaves digital traces. Every purchase we make with our cards, every search we type into Google, every movement we make when our mobile phone is in our pocket, every “like” is stored. Especially every “like.” For a long time, it was not entirely clear what use this data could have—except, perhaps, that we might find ads for high blood pressure remedies just after we’ve Googled “reduce blood pressure.”
On November 9, it became clear that maybe much more is possible. The company behind Trump’s online campaign—the same company that had worked for Leave.EU in the very early stages of its "Brexit" campaign—was a Big Data company: Cambridge Analytica.
"To understand the outcome of the election—and how political communication might work in the future—we need to begin with a strange incident at Cambridge University in 2014, at Kosinski’s Psychometrics Center.
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 is 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.
Michal Kosinski was a student in Warsaw when his life took a new direction in 2008. He was accepted by Cambridge University to do his PhD at the Psychometrics Centre, one of the oldest institutions of this kind worldwide. Kosinski joined fellow student David Stillwell (now a lecturer at Judge Business School at the University of Cambridge) about a year after Stillwell had launched a little Facebook application in the days when the platform had not yet become the behemoth it is today. Their MyPersonality app enabled users to fill out different psychometric questionnaires, including a handful of psychological questions from the Big Five personality questionnaire (“I panic easily,“ “I contradict others”). Based on the evaluation, users received a “personality profile”—individual Big Five values—and could opt-in to share their Facebook profile data with the researchers.
Followers of Lady Gaga were most probably extroverts, while those who “liked” philosophy tended to be introverts.
Kosinski had expected a few dozen college friends to fill in the questionnaire, but before long, hundreds, thousands, then millions of people had revealed their innermost convictions. Suddenly, the two doctoral candidates owned the largest dataset combining psychometric scores with Facebook profiles ever to be collected.
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.
Remarkably reliable deductions could be drawn from simple online actions. For example, men who “liked” the cosmetics brand MAC were slightly more likely to be gay; one of the best indicators for heterosexuality was “liking” Wu-Tang Clan. Followers of Lady Gaga were most probably extroverts, while those who “liked” philosophy tended to be introverts. While each piece of such information is too weak to produce a reliable prediction, when tens, hundreds, or thousands of individual data points are combined, the resulting predictions become really accurate.
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 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 it was not just about “likes” or even Facebook: Kosinski and his team could now ascribe Big Five values based purely on how many profile pictures a person has on Facebook, or how many contacts they have (a good indicator of extraversion). 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...
"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.“...
"...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...
...“Pretty much every message that Trump put out was data-driven,” Alexander Nix remembers. On the day of the third presidential debate between Trump and Clinton, Trump’s team tested 175,000 different ad variations for his arguments, in order to find the right versions above all via Facebook. 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.”"
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