Targeting Item–level Nuances Leads to Small but Robust Improvements in Personality Prediction from Digital Footprints

Abstract

In the past decade, researchers have demonstrated that personality can be accurately predicted from digital footprint data, including Facebook likes, tweets, blog posts, pictures, and transaction records. Such computer–based predictions from digital footprints can complement—and in some circumstances even replace—traditional self–report measures, which suffer from well–known response biases and are difficult to scale. However, these previous studies have focused on the prediction of aggregate trait scores (i.e. a person's extroversion score), which may obscure prediction–relevant information at theoretical levels of the personality hierarchy beneath the Big 5 traits. Specifically, new research has demonstrated that personality may be better represented by so–called personality nuances—item–level representations of personality—and that utilizing these nuances can improve predictive performance. The present work examines the hypothesis that personality predictions from digital footprint data can be improved by first predicting personality nuances and subsequently aggregating to scores, rather than predicting trait scores outright. To examine this hypothesis, we employed least absolute shrinkage and selection operator regression and random forest models to predict both items and traits using out–of–sample cross–validation. In nine out of 10 cases across the two modelling approaches, nuance–based models improved the prediction of personality over the trait–based approaches to a small, but meaningful degree (4.25% or 1.69% on average, depending on method). Implications for personality prediction and personality nuances are discussed. © 2020 European Association of Personality Psychology