Using a supervised machine learning algorithm for detecting faking good in a personality self-report

We developed a supervised machine learning classifier to identify faking good by analyzing item response patterns of a Big Five personality self-report. We used a between-subject design, dividing participants (N = 548) into two groups and manipulated their faking behavior via instructions given prior to administering the self-report. We implemented a simple classifier based on the Lie scale's cutoff score and several machine learning models fitted either to the personality scale scores or to the items response patterns. Results shown that the best machine learning classifier—based on the XGBoost algorithm and fitted to the item responses—was better at detecting faked profiles than the Lie scale classifier.