Abstract

During the writing of the article, the complex dynamics of forecasting public opinion based on the implementation of narratives in social networks were analyzed using Bass's innovation diffusion model. Recognizing the important role of narratives in the formation of collective views provides a basis for using Bass's innovation diffusion model to predict and understand changes in public attitudes during the introduction and implementation of narratives in social networks. On the basis of the above, the research task was set: improvement of the Bass innovation diffusion model for predicting changes in public opinion in social networks, which will directly contribute to the development of quality content by determined forces and means to create favorable conditions during the use of troops (forces) and in peacetime due to entering the coefficient of coverage of the target audience. During the writing of the article, theoretical methods were applied, namely, the analysis of research and publications on the topic of development in relation to the use of diffusion of innovations, which describes the process of how innovations are accepted by the population, the analysis of statistical data of public opinion, comparisons to identify development trends in the consumption of information in social networks, as well as synthesis to achieve the goal of the study. The article presents a mathematical apparatus that incorporates the Bass diffusion model into network behavior for modeling the spread of narratives and predicting changes in public opinion. For the first time, an approach to simulation modeling of the introduction and implementation of a narrative in a social network based on the Bass diffusion model was substantiated, which made it possible to investigate the consumer behavior of social network users under the influence of information and interpersonal communication in a social network. Integrating concepts from Bass's model, such as innovation adoption and imitative behavior, the proposed model aims to predict the dynamics of public attitudes in response to the spread of narratives.

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