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The work process of specialists in protection from information consists of many time-consuming tasks, including data collection, datasets formation, and data manual labelling. In this paper, we attempted to help such specialists with a two-model approach based on the iterative online training of binary classifiers. This approach is used for inappropriate information detection and applied on text posts from the VKontakte social network. The first model is used to detect text posts that are corresponding to the selected topic and is trained on the data that is labelled positively and negatively by experts as well as random text data. The second model is used to improve the accuracy of the first model and is trained only on the data that is labelled by the experts. The novelty of the approach lies in the constantly growing dataset, while the classifiers training process takes place during the operator’s work. The approach works …