Nitoring systems and prioritize sources that distribute destructive content material in social networks. At the exact same time, inside the approach of establishing an method to ranking information sources in social networks, the basis for evaluation is discrete options, including the amount of supply messages, the number of comments, plus the quantity of “like” and “dislike” marks from the audience of social networks. The novelty in the proposed method is the fact that the developed model of malicious information along with a set of algorithms for analyzing and evaluating information and facts sources supply a ranking of sources by priority, considering the number of messages containing destructive content that is certainly produced by the supply and feedbacks from the audience, without the need of taking into account the connection amongst objects within the social network. It could drastically lessen resource and time expenses within the evaluation method. It truly is essential to note that the aim of the proposed method was to prioritize the malicious messages as outlined by their importance in accordance with the impact on the audience. The content analysis along with the quite recognition in the presence in the malicious content material have been out of the scope of this investigation. It was assumed that all of the messages in the input dataset for the approach had a similar quantity of malicious info. The distinction between messages lied only in their audience and in the activity of this audience. The paper is structured as follows. The second section presents an analysis of relevant studies. The third section describes the proposed approach, represented by the created model of malicious info plus a set of algorithms for ranking information and facts sources in social networks. The fourth section presents the results from the experiments and shows the applicability of your proposed method. The fourth section also includes an assessment with the strategy in addition to a discussion. The fifth section concludes the paper. The dataset for conducting the analysis and experiments was obtained from the Russian social network VK by connecting to an open API and preprocessed (depersonalized) for the possibility of open use for scientific purposes. two. Background The first studies on countering the spread of destructive content have been carried out by scientists following the initial improvement of social networks, from 1995000. FifteenInformation 2021, 12,three ofworks referring to the resource had been published in the Google Academy [7] Class-mates and twenty-eight in SixDegrees. Together with the advent of new platforms, the amount of research in the field of social network evaluation is developing exponentially. In 1990, Social Network Evaluation (SNA) was the prerogative of such sciences as sociology and Pirenperone Cancer political science. For example, the collection of operates [8] includes papers devoted