Functional Modeling of Distributions of Substantive-Content Message Properties in the Information Background

This paper presents a novel methodology for modeling the distribution of substantive-content message properties in the information background. This study develops a toolkit to analyze and predict information dynamics by identifying key themes, evaluating their importance, and understanding their connections. The proposed approach is based on the concept of multimodality, where properties are characterized by peaks of varying intensity and frequency. Intensity and frequency components are modeled separately and combined into a unified probabilistic framework; model parameters (shape and internal-covariance coefficients) are searched within the range (0,1). The genetic search uses mutation of ±20% with probability 50% and normalization of the intensity scale ( = 1). Model quality is assessed by the Mean Absolute Error between ranked histogram bins (discretization coefficient DC defines the number of bins). Intensity, reflecting the depth and saturation of the information signal, is modeled using the Gamma distribution, while frequency, reflecting the number of occurrences, is represented by the multivariate normal distribution. A genetic algorithm is employed to identify the optimal parameters for these distributions. The methodology offers a more comprehensive understanding of information dynamics by considering both intensity and frequency, and effectively handles complex interdependencies between properties. It can be applied to various domains, including social media analysis, political communication, and marketing, providing valuable insights for decision-making.

Information Background Analysis, Intensity of Presence, Substantive-Content Message, Symbolic Data Constructs, Thematic Analysis

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