Modern linguistics witnesses a growing interest in the problem of profiling the authors of written texts. At the same time, advertising materials published by tutors have received little scholarly attention. The paper presents the results of studying such texts published on the VK social network. The experimental corpus comprises more than 30,000 tokens. Statistical regularities that demonstrate dependencies between various parameters of texts and tutors’ age are supplemented with Python-scatter plots in the Google Colab environment. The analysis of the collected data reveal correlations between age and specific textual features, such as word length and emoticon concentration. For instance, tutors aged 20 to 40 use emoticons more frequently compared to those in other age groups. Perspectives for future research include expanding the study to incorporate patterns related to the number of ‘Like’ marks on posts, which could provide insights into the popularity of specific advertising content. This approach may contribute to modeling an ‘ideal’ promotional text in the field of education.