Correlation analysis and prediction of personality traits using graphic data collections

Abstract

The questions of building mechanisms for identifying patterns and building modern tools for analyzing data from social networks are considered. It is proposed to apply modern methods of web pages’ automatic analysis, testing hypotheses about the presence of correlation links, automatic classification of graphic information using the apparatus of artificial neural networks. The presence of correlation between personality traits of the "Big Five" is investigated. Strong fluctuations in the values of personality traits were revealed depending on various types for groups of people. The problem of predicting the personality traits of the Internet user by the images posted by him is investigated, artificial neural networks are used as a tool. Two series of experiments were carried out, in the first series, a convolutional neural network, trained on the images and results of the NEO-FFI questionnaire, was used to predict personality traits. The sequential use of convolution and subsampling in the convolution network leads to the so-called increase in the level of features: if the first layer extracts local features from the image, then subsequent layers extract common features that are called high-order features. In the second series of experiments, this type of artificial neural network was used to extract high-level features, which were then used to train a direct distribution network that performs forecasting. Thus, the more layers are used, the more features associated with personality traits are extracted from the images. For processing arrays of graphic information, the “Microsoft Cognitive Toolkit library” and the Nvidia Geforce GTX 1080 Ti graphics accelerator were used. The results of the experiments revealed those personality traits that are most correlated with the images posted by Internet users.