Predicting the Big 5 personality traits from digital footprints on social media: A meta-analysis

Abstract

The growing use of social media among Internet users produces a vast and new source of user generated ecological data, such as textual posts and images, which can be collected for research purposes. The increasing convergence between social and computer sciences has led researchers to develop automated methods to extract and analyze these digital footprints to predict personality traits. These social media-based predictions can then be used for a variety of purposes, including tailoring online services to improve user experience, enhance recommender systems, and as a possible screening and implementation tool for public health. In this paper, we conduct a series of meta-analyses to determine the predictive power of digital footprints collected from social media over Big 5 personality traits. Further, we investigate the impact of different types of digital footprints on prediction accuracy. Results of analyses show that the predictive power of digital footprints over personality traits is in line with the standard “correlational upper-limit” for behavior to predict personality, with correlations ranging from 0.29 (Agreeableness) to 0.40 (Extraversion). Overall, our findings indicate that accuracy of predictions is consistent across Big 5 traits, and that accuracy improves when analyses include demographics and multiple types of digital footprints.