Mathematical Model of Clustering of Informational Messages with Indicators of Activity for the Information Content by Tone and Areas of Society Activity

Abstract

The mathematical model of clustering of information messages has been further developed, which is based on the frequency analysis of their tonality using Natural Language Processing methodologies with the support of large language models; OLAP visualization of clustering results and is distinguished by an established system of indicators of information content activity by areas of society  activity with hierarchical compression of incoming Big Data arrays, which determines the database model for their storage. This provides an improvement to the analysis of information messages in global information networks by taking into account many factors in the areas of society activity.

The main idea and goal of the mathematical model for clustering information messages is to implement a sequence of preparation stages for detecting critical activity of the information content in global media. In practice, this is the establishment of a list and the determination of indicator values that measure content activity in primary messages, followed by their transformation into a time series – a systematized dataset. In the conditions of high density of the flow of occurrence, dynamics of development, and transformation of information content, a Big Data structure of information messages is taken into account. Therefore, the clustering model, apart from division by informational features, should provide the hierarchical compression of incoming Big Data arrays.

Research objective: development of a mathematical model of clustering information messages with indicators of information content activity by tone and spheres of activity of society. Research subject: methods of clustering information messages. Research object: process of clustering information messages.