How good are you at sizing someone up? Perhaps not as good as a computer, according to researchers at the University of Cambridge and Stanford University.
By analysing someone's likes on Facebook, statistical modeling software could characterise a person's basic personality with an accuracy rivaling that of a spouse or close family member, according to the researchers.
Analysing such digital footprints could help computer programs to interact with people in more meaningful ways, according to the scientists, whose findings were published Monday in the Proceedings of the National Academy of Sciences.
Although big data systems already help to analyse human behavior, particularly around buying habits, today's statistical modeling techniques tend to be very narrow in scope. "It is difficult to find the meaning behind these predictions," said Michal Kosinski, a Stanford researcher and co-author of the study.
"The work we're doing is helping to interpret those predictions. It allows you to put the meaning to the prediction," he said.
Kosinski acknowledged that humans "are extremely good at predicting personality traits," but computers could be even better.
The researchers' work is a follow-up to a March 2013 study which showed that personality traits can be determined with surprising accuracy by analyzing Facebook likes.
A "like" is Facebook's jargon for showing approval for an item, such as a photo or article, posted on the social networking service.
With enough likes to analyse, computers can infer basic personality traits, the earlier study concluded. The researchers now wanted to see if computers could size people up more accurately than humans.
They sampled Facebook pages from 86,220 volunteers, many of whom also filled out a 100 question personality survey focused on five major psychological traits: openness, conscientiousness, extraversion, agreeableness, and neuroticism.
A few rounds of machine learning were used to associate the traits with additional Facebook likes. For instance, those liking "Salvador Dali" or "meditation" appeared to possess a high degree of openness.
To judge the effectiveness of the computer algorithms, researchers gave questionnaires to friends and relatives of some participants. The survey results and computerized assessments were then compared with the self-assessments from the subjects.
With just 10 likes, the computer would know someone as well as a work colleague. With more than 70, it would get to the level of a friend or roommate, and with more than 300 to the level of a spouse or close relative.
The study is notable because of its large sample size, said Jennifer Golbeck a computer scientist at the University of Maryland, College Park and the director of the University of Maryland Human-Computer Interaction Lab. Golbeck was not involved in the study, though she is one of a growing number of researchers studying how to predict personality traits through online footprints.
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