Predicting Psychological Traits from Digital Footprints

Michal Kosinski, Predicting Psychological Traits from Digital Footprints. A growing proportion of human activities such as social interactions, entertainment, and gathering information, are now mediated by online social networks. Such activities can be easily recorded, offering an unprecedented opportunity to study and assess psychological traits using actual – rather than self-reported – behavior. Our research shows that digital records of behavior, such as Tweets or Facebook Likes can be used to accurately measure a wide range of psychological traits. Such Big Data assessment has a number of advantages: it does not require participants’ active involvement; it can be easily and inexpensively
applied to large populations; and it is relatively immune to cheating or misrepresentation. Essentially, if the ethical and methodological challenges could be overcome, Big Data has the potential to revolutionize psychological assessment, marketing, recruitment, insurance and many other industries.

Michal Kosinski is the Assistant Professor in Organizational Behavior at the Graduate School of Business, Stanford University. After receiving his PhD in Psychology from the University of Cambridge (UK) in 2014, Kosinski spent a year as a Postdoctoral Scholar at the Computer Science Department at Stanford University. Kosinski’s research had a significant impact on both academia and the industry. His findings featured in The Economist, inspired two TED talks, and prompted a discussion in the EU Parliament. In 2013, Kosinski was listed among the 50 most influential people in Big Data by DataIQ and IBM, while three of his papers were placed among Altmetrics’ “Top 100 Papers That Most Caught the Public Imagination”.