Public healthcare is a big priority for society. The ability to diagnose and monitor various aspects of public health through social networks is one of the new problems that are of interest to researchers. In this paper, we consider the task of automatically classifying people who lead a healthy lifestyle and users who do not lead a healthy lifestyle by processing text messages and other profile information from the Russian-speaking social network VKontakte. We describe the process of extracting relevant data from user profiles for our dataset. We evaluate several machine learning methods and report experimental results. The best performance in our experiments was achieved by the model that was trained on a combination of N-gram features retrieved from user original posts and reposts. © Springer Nature Switzerland AG 2020.