How Facebook ‘Likes’ Predict Race, Religion And Sexual Orientation

Posted by on June 9, 2018 in Internet, Media & Arts, Social Media with 0 Comments

 (Photo: Legal Loop)

By Ben Tinker | CNN

It’s true: Actions really do speak louder than words, even when you don’t want them to.

Last month, a former employee of data firm Cambridge Analytica went public with allegations that tens of millions of Facebook users’ data were captured and (mis)used in an attempt to influence them during the 2016 US presidential election.
The whistleblower, Christopher Wylie, explained to CNN how an app the company developed could pull data not only from its users’ profiles but from their entire friend networks.
“If we got one person to download the app, it would pull, you know, 200, 300 records, and that would scale really quickly,” Wylie said.
All of the data, we now know, were harvested without Facebook users’ knowledge. For its part, Facebook Inc. admits that it knew about the company’s access to the data but alleges that it was misused.
“Facebook granted permission for the app,” Wylie said. “They knew what the app was doing. They just didn’t necessarily know what it was for.”
But it wasn’t just the information people willingly volunteered in their profiles that Cambridge Analytica was after. It was also what they “liked” — such things as music, movies, foods and books. Turns out, it speaks volumes.

Just ‘like’ that

“Facebook is a really amazing place to give data and not really think about the data you’re giving, because you’re just sharing it with family and friends, right? But you’re actually sharing it with every company that utilizes Facebook,” said Timothy Summers, director of innovation, entrepreneurship and engagement at the University of Maryland’s College of Information Studies.
These companies, according to a 2013 study by computational psychologist and big data scientist Michal Kosinski and others, found that Facebook likes “can be used to automatically and accurately predict a range of highly sensitive personal attributes.”
Kosinski’s algorithm was able to predict whether a person was black or white with 95% accuracy, male or female with 93% accuracy, gay or straight with 88% accuracy and Democrat or Republican with 85% accuracy.

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