Assessment of the main features of the model of information distribution in social networks

Social networks provide a fairly wide range of data that allows one way or another to evaluate the effect of the dissemination of information. Unfortunately, not all data is publicly available, but even with the help of those that are open, it is sometimes possible, indirectly, to estimate the necessary parameters. Social networks can be used to develop new technologies and innovations. For example, social media research can help improve machine learning algorithms, develop new data analysis techniques, and create new social applications. Studying the processes of information dissemination in social networks is an important task in the modern information society. Such a study allows us to identify patterns and principles that guide `users when disseminating information on social networks. Network analysis, statistical methods, and machine learning are commonly used to conduct such research. This article presents the results of a study that describes methods for determining key model parameters necessary for analyzing and predicting the spread of information in social networks. The authors propose an approach based on the analysis of statistical data on user behavior in social networks, as well as on the analysis of the network structure of the social network. The article describes the process of assessing the main features of the model, including the mathematical methods used to analyze data and model information dissemination. The authors also present experimental results that show that the proposed approach allows one to achieve high accuracy in determining the model parameters. Finally, the article discusses potential applications of model parameterization, including more effectively developing social media marketing and advertising strategies, and for analyzing the impact of information on public opinion and decision making. Determining the basic parameters of the information dissemination model can also be useful for developing more accurate and effective algorithms for detecting and combating fake news on social networks. Assessing the key features of the information dissemination model helps determine the most effective methods of communicating information and improving its dissemination.