Open AccessThe subject of research is a binary classification method of social network users based on the data analysis they have placed. Relevance of the task to gain information about a person by examining the content of his/her pages in social networks is exemplified. The most common approach to its solution is a visual browsing. The order of the regional authority in our country illustrates that its using in school education is needed. The article shows restrictions on the visual browsing of pupil’s pages in social networks as a tool for the teacher and the school psychologist and justifies that a process of social network users’ data analysis should be automated. Explores publications, which describe such data acquisition, processing, and analysis methods and considers their advantages and disadvantages. The article also gives arguments to support a proposal to study the classification method of social network users. One such method is credit scoring, which is used in banks and credit institutions to assess the solvency of clients. Based on the high efficiency of the method there is a proposal for significant expansion of its using in other areas of society. The possibility to use logistic regression as the mathematical apparatus of the proposed method of binary classification has been justified. Such an approach enables taking into account the different types of data extracted from social networks. Among them: the personal user data, information about hobbies, friends, graphic and text information, behaviour characteristics. The article describes a number of existing methods of data transformation that can be applied to solve the problem. An experiment of binary gender-based classification of social network users is described. A logistic model obtained for this example includes multiple logical variables obtained by transforming the user surnames. This experiment confirms the feasibility of the proposed method. Further work is to define a system of criteria and characteristics derived from social networks. This will allow applying the method for classification according to various criteria.