Forecasting the depression with user data from Russian-language social network

The article explores the possibilities of applying semantic analysis of user posts on the social network VKontakte for monitoring and predicting depression. It emphasizes the seriousness of the depression issue, its negative impact on health and society, and the relevance of early diagnosis and assistance. The study also justifies the necessity and prospects of analyzing data from Russian-language social networks to prevent the development of depression among users. The article examines the analysis of textual data and the use of logistic regression to classify users based on the presence of depression. The study's results show high model accuracy using logistic regression, demonstrating the potential for automating the processes of identifying and supporting users suffering from depression in the online environment based on user information from social networks. The significance of this method is also highlighted, along with its practical usefulness for personalized interventions, its advantages, and its development prospects, including the use of neural network methods and the analysis of data dynamics. Despite the results achieved, there is a need for further work on the model, including the study of other machine learning methods and taking into account changes in the user’s mental state over time. The development of depression prediction methods based on social network data, as proposed in the article, is an important direction that can make a significant contribution to psychology, healthcare, and information technology.

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