Very interesting new article published about interpreting private traits from online behavior entitlted: Private traits and attributes are predictable from digital records of human behavior by Michal Kosinskia,1, David Stillwella, and Thore Graepel.
Here’s the abstract:
We show that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes including: sexual orienta- tion, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental sepa- ration, age, and gender. The analysis presented is based on a dataset of over 58,000 volunteers who provided their Facebook Likes, detailed demographic profiles, and the results of several psychomet- ric tests. The proposed model uses dimensionality reduction for preprocessing the Likes data, which are then entered into logistic/ linear regression to predict individual psychodemographic profiles from Likes. The model correctly discriminates between homosexual and heterosexual men in 88% of cases, African Americans and Caucasian Americans in 95% of cases, and between Democrat and Republican in 85% of cases. For the personality trait “Openness,” prediction accuracy is close to the test–retest accuracy of a standard personality test. We give examples of associations between attri- butes and Likes and discuss implications for online personalization and privacy.
I’m afraid that our social communication is not only revealing, but strangely static. It is so hard to remove things that have been posted – a lesson our kids and teens need to learn early in their online careers!