Artificial intelligence

Abstract: The article defines the algorithm and details the sequential tasks for building an effective model of automated classification of events in the information space. On the eve and during the armed aggression of the Russian Federation against Ukraine, the consequences of external negative information influence were noticeable. Therefore, the organization and implementation of counteraction to such influence is urgent. An important component of this activity is the classification (clustering) of information events in the information space in order to further analyze them and form proposals for decision-making to counteract the negative information impact. Given the fact that in the global information space and, in particular, the information space of the state in the interests of counteracting such influence, it is necessary to constantly process a significant amount of information, so the task of improving the efficiency of this process is provided by automating its components. The algorithm of the automated classification process is based on a number of consecutive tasks, namely: data retrieval, preelection of messages ("rough" classification), saving pre-selected messages in the database, determining a set of indicators for automated classification of information events, pre-processing a single document (indexing), distribution of messages by criteria by categories ("accurate" classification), presentation of information in a convenient form (visualization), saving the results of classification in the database. The proposed material reveals the content of these tasks. The proposed algorithm will serve to automatically divide information events (messages) of different nature into categories (classes) in order to increase the efficiency of assessing the level of negative information impact on target audiences for timely (proactive) response to its manifestations.

Keywords: algorithm, automated classification, database, indexing, information events, negative information impact, terms

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