Abstract: Activity of users in Online Social Networks (OSN) leaves traces that reflect their personality characteristics. The study of these traces is important for a number of fields, such as a social science, psychology, OSN, marketing, and others. Despite a marked increase on research in personality prediction on the basis of online behavior the focus has been solely on individual personality traits largely neglecting relational facets of personality. This study aims to address this gap by providing a prediction model for an holistic personality profiling in OSNs that included socio-relational attachment orientations in combination with standard personality traits. Specifically, we first design a feature engineering methodology that extracts a wide range of features (accounting for behavior, language, and emotions) from OSN accounts of users. Then, we design a machine learning model that predicts scores for the psychological traits of the users based on the extracted features. The proposed model architecture is inspired by characteristics emerged from psychological theory, e.g., utilizing interrelations among personality facets, and leads to increased accuracy in comparison with the state of the art approaches. To demonstrate the usefulness of our approach, we apply our model to two datasets, one of random OSN users and one of organizational leaders, and compare their psychological profiles. Our findings show that the two groups can be clearly separated by only using their psychological profiles, which opens a promising direction for future research on OSN user characterization and classification.