A method for detecting sources of disinformation and inauthentic behavior of chat users in social networks has been developed. The developed model is based on the analysis of text information using modern machine learning algorithms, in particular classifiers (SVM, Naive Bayes, decision trees, etc.) and clustering methods to identify structural relationships between news and users. Considerable attention is paid to the collection and balancing of datasets, as well as the visualization of networks to assess the spread of fake news. The results of the experiments showed a high level of classification accuracy, with the best indicators in Naive Bayes, which confirms the potential of the proposed approach for automatic monitoring and counteraction to disinformation in social networks.
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