Cambridge University psychologist shows your computer knows you better than your family do

There really is a link between your Facebook posts and your personality

Dr David Stillwell is Deputy Director of The Psychometrics Centre at the University of Cambridge. He is also Lecturer in Big Data Analytics and Quantitative Social Science at the Cambridge Judge Business School.

Dr Stillwell’s research has shown that analysis of your Facebook ‘Likes’ could give better insight into your personality than asking your friends and family, and only your partner knows you slightly better.

He research was shared in an article posted on The Conversation, which follows.

Privacy campaigners this week applauded Facebook’s decision to block big UK insurance firm Admiral from using young people’s social media data to help set their car insurance premiums. But this is just the start of a debate over the use of social media information for such purposes. Setting aside privacy issues for a moment, there is a very valid social reason for doing this. In fact, it could benefit countless numbers of people.

Admiral wanted to use young customers’ Facebook conversations and “likes” (with their permission) to assess whether they were low-risk drivers and entitled to discounts of up to £150. But Facebook quickly announced that this was against their terms of use, thwarting Admiral’s plan.

Whatever side you take on this issue, it’s important to understand the science behind Admiral’s plan and behind similar plans sure to come from companies large and small. Indeed, my research suggests that using social media data to make such predictions could be very accurate.

In 2015, the average Facebook user had liked 225 things, from films to politicians, as well as statements such as “I like stepping on crunchy leaves”.

My colleagues and I collected data from 6m Facebook users through an opt-in survey that measured their personality and gave them feedback on their results. We then measured how well their Facebook activity could predict their personality using a number between 0 and 1. The higher the number, the stronger the correlation.

When we used 60,000 users’ “likes” to predict their self-reported psychological traits, we found that the correlation between “Likes” and personality was 0.56. To put that in perspective, if you ask someone’s work colleague to predict their personality the accuracy is 0.27, friends can predict at 0.45, family at 0.50 and even someone’s spouse can only predict at 0.58. In other words, the computer knows you almost as well as your husband or wife –- and better than almost everyone else.

Privacy campaigners this week applauded Facebook’s decision to block big UK insurance firm Admiral from using young people’s social media data to help set their car insurance premiums. But this is just the start of a debate over the use of social media information for such purposes. Setting aside privacy issues for a moment, there is a very valid social reason for doing this. In fact, it could benefit countless numbers of people.

Admiral wanted to use young customers’ Facebook conversations and “likes” (with their permission) to assess whether they were low-risk drivers and entitled to discounts of up to £150. But Facebook quickly announced that this was against their terms of use, thwarting Admiral’s plan.

Whatever side you take on this issue, it’s important to understand the science behind Admiral’s plan and behind similar plans sure to come from companies large and small. Indeed, my research suggests that using social media data to make such predictions could be very accurate.

In 2015, the average Facebook user had liked 225 things, from films to politicians, as well as statements such as “I like stepping on crunchy leaves”.

My colleagues and I collected data from 6m Facebook users through an opt-in survey that measured their personality and gave them feedback on their results. We then measured how well their Facebook activity could predict their personality using a number between 0 and 1. The higher the number, the stronger the correlation.

When we used 60,000 users’ “likes” to predict their self-reported psychological traits, we found that the correlation between “Likes” and personality was 0.56. To put that in perspective, if you ask someone’s work colleague to predict their personality the accuracy is 0.27, friends can predict at 0.45, family at 0.50 and even someone’s spouse can only predict at 0.58. In other words, the computer knows you almost as well as your husband or wife –- and better than almost everyone else.