Forecasting Changes in States in Social Networks and Sentiment Security Using the Principles of Percolation Theory and Stochastic Dynamics

This paper proposes a novel method of forecasting changes in social network systems based on applying percolation theory and a stochastic dynamics approach. New results from computer modeling the influence of a social network density on its percolation threshold are discussed. Percolation thresholds are calculated for various network densities and can be used in models describing the stochastic dynamics of a system's transition from one state to another. For example, with the help of the developed models, it is possible to forecast the change in time of the share of people with certain opinions and views depending on the network density, percolation threshold, etc. The stochastic model represented in this paper presents the possibility of a jump-like transition of moods (states) of people in a social network during a very short period of time without any external influences, which is defined by the system's self-organisation features and the memory of its nodes about the previous states.