Evaluation of tree-based ensemble algorithms for predicting the big five personality traits based on social media photos: Evidence from an Iranian sample

Machine learning has been widely used in psychological issues. In this study, Iranian Instagram users' photos were used to predict the big five personality traits with extra trees, gradient boosted trees and random forest. The big five personality traits of 142 users were measured using the short form of Goldberg's 50-item personality scale, and 30 color features including classified color histogram, color level mean, and color level median in RGB (Red, Green, and Blue) and HSV (Hue, Saturation, and Value) spaces were extracted from the 1348 photos. Predictive models were cross-validated, and the highest root mean square error and mean absolute error values for all models did not reach 0.16 and 0.13, respectively, which were related to the prediction of Extraversion. Overall, all models had good performance and generalizability in prediction. Results showed that each personality trait has a special type of color preference and photo posting. The features' importance was evaluated by the Gini method. Color histogram and HSV color space were the most important features in modeling. As a result, it can be said that the proposed methodology is effective in practice, but care must be taken with multicollinearity, accuracy, and integrity of social media data.