Analysis of the Relationship between the Users Personality Traits and the Images They Post on Social Media

Abstract

Due to the dramatic rise in the popularity of the social media, the problem of analyzing the interconnections of digital traces and the psychological characteristics of the user become urgent. The development of deep learning models has ensured the automatic image analysis and feature extraction. In this study, we propose a clustering model for digital traces of users based on VGG16 deep learning model and a K-means clustering algorithm. We use this model for predictive analytics of digital footprints in the form of arbitrary images. The scientific novelty of the research lies in the originality of the proposed methodology. This technique firstly consists in directly using the compressed representation obtained by the encoder instead of object tags. And secondly, we propose a method for obtaining representations of clusters, based on the weighting of users belonging to a given cluster. To study the correlations of the user’s personality characteristics and their posted images, dataset of BIG Five traits of VK social network users was used. According to the results of the expert assessment of a specialist psychologist, the presented relations between the clusters content and the personality characteristics of the users were determined. Namely, the “faces” cluster is characterized by a high value of consciousness, the “cats” cluster is characteristic of introverted users, and low agreeableness was associated with the “landscapes” cluster. Based on the obtained similarity of cluster representations, the personality traits of users related to these clusters were found similar, and the possibility to combine these clusters was proposed.