New research suggests that your personality could be easier to read than you think. Researchers at Cambridge University used algorithms to predict religion, political views, race and sexual orientation, based on what people liked and reposted on their Facebook profiles.

The study involved 58,000 American volunteers who took part in psychometric tests. The results from the tests were then compared to their ‘likes’ and other publicly shared Facebook information.

The research proven to be 95% accurate in distinguishing African-Americans from Caucasian-Americans, 88% correct for predicting male sexuality and 82% reliable for differentiating those of Islamic belief from Christians.

My first thoughts on this are that the pages ‘liked’ revealed these traits, but I then went on to discover that the results were based on less informative things. For instance, curly fries were said to be correlated with high intelligence, users who liked the brand ‘Hello Kitty’ were shown to be less emotionally stable and people who had liked ‘The Dark Knight’ tended to have fewer Facebook friends.

Something that interested me however, was what this research could mean for marketing and advertising. The information is particularly useful in guiding marketers in tailoring their advertising campaigns for their target audience.

Collecting data and learning more about your online audience in the digital world is highly valuable and now much easier. If marketers can cater to a customer’s interests, hobbies and online habits, their campaigns will be understandably more effective.

The news on Facebook demographics should ring a bell for Technical PRpeople and make us start thinking about how it could be used effectively in the long term. This will be particularly useful in a B2B market if when we see this type of big data analysis being conducted on other social media outlets such as Twitter and LinkedIn – which I predict will not be long.

When this happens, or indeed when the Facebook process becomes even more sophisticated, we might find ourselves able to tell who the process engineers on Twitter are, for example, based only on statistical likelihood.

You can read more on the research here.

Image courtesy of cooldesign at FreeDigitalPhotos.net

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