Neural Network Algorithms and Methods for Monitoring the Psychological State of Society During Epidemics

Abstract

The article presents the issue of monitoring the sociopsychological state of society in the period of epidemics by means of neural network algorithms and methods. Publications of sociologists, psychologists, and philologists, who have created a number of methods for in-depth analysis of emotions and tonality of texts in the Internet media, including cognitive and interpretive decoding, are devoted to substantiating approaches and methods for studying the content of Internet content. The purpose of the study is to substantiate the methods and computer tools for studying the socio-psychological state of society in crisis situations, in particular epidemics, based on neural network technologies based on Internet resources. The article considers methodological approaches and particular methods of their computer implementation. It is shown that for the computer analysis of the psychological state of society in the context of the epidemic, it is necessary to adapt the methodology for designing neural network technologies, as well as systems for collecting and textual analysis of the content of electronic and Internet resources. An effective approach to creating such systems is embedding, which uses a dense vector representation of tokens in a multidimensional space, the dimension of which should be selected experimentally in the process of training and testing the developed artificial neural networks (ANN). For contextual neural network analysis, a multiclass-oriented ANN with regularization layers of the form “SpatialDropout1D”can be used. The neural network architecture can be built on fully connected layers with an activation function of the “ReLU” type. The scientific and applied significance of the results of neural network analysis based on Internet resources is the possibility of obtaining classified assessments and segmentation of target information about the psychological state of society during periods of epidemics. This information can be used to effectively counter information threats to society.