Abstract: Alternative methods of collecting and processing data from open sources of information that form a common understanding of situations and trends in business development in target markets for goods and services have been developed. Recommendations for the improvement of analytical technologies for processing information arrays from open or widely available sources of information in the system of marketing research of enterprises are offered. Effective ways to assess the quality and reliability of data from open or widely available sources of information are being studied and tested. Improving approaches to integration into modern marketing information systems of modern content analysis tools, which organically provides constructive synergy of applied methods and tools of neuro-fuzzy modeling and clustering of information arrays, statistical analysis of information units, identification of psycho-emotional number of potential and actual consumers. The author’s variant of the algorithm of the organization of qualitative and effective processing of data from open sources in the system of information marketing support of business management is offered. It is shown that the great variability of the modern information environment (data, content) creates significant prerequisites and significant combinatorial opportunities for generating distorted information in different ways, as well as the dissemination of the latter. Possibilities for detecting inaccurate information (fakes) in the information field of a particular product market have been worked out. Comparison of the results of different models based on the confusion matrix showed that two of the four learning models, namely the neural network and the «random forest» model, did well enough to assess the reliability messages.