Facebook Could Know You Better Than Your Friends

Using Facebook Likes a computer is able to judge people's personality better than friends and family, according to a new study published in the journal PNAS.

With enough Likes, only a person's spouse was able to rival the computer in assessing his or her personality. Researchers at the University of Cambridge and Stanford University have shown that machines are able to understand people better than was previously thought. In the future, computers could become emotionally intelligent and socially skilled machines.

In an analysis of just ten Likes, a computer was better able to predict a person's personality than a work colleague. The computer beat a subject's friend or roommate with 70 Likes, a family member such as a parent or sibling with 150 Likes, and a spouse with 300 Likes. An average Facebook member has some 227 Likes and this number is growing steadily; therefore, computers may be poised to know us better than are closest human companions.

For this study, the researchers used a sample of 86,220 Facebook volunteers who completed a questionnaire on the myPersonality app and provided access to their Likes.

Self-reported scores were provided for the five big psychological traits openness, conscientiousness, extraversion, agreeableness and neuroticism. Using these scores, the researchers were able to equate certain traits with particular Likes. For instance, liking 'meditation' showed a high degree of openness. The subjects could invite their friends and family to judge their psychological traits. A total of 17,622 participants were judged by one friend or family member, and 14,410 were judged by two.

The researchers said computers have a couple of advantages that make them analyse people's personality with such accuracy: the abilty to retain and access vast volumes of data and the ability to analyse it using algorithms. Big data, coupled with machine learning, provides a level of accuracy that the human mind achieves with great difficulty, the researchers said.

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