Detection of depression features with user data from social network using neural network

The article studies the problem of identifying signs of depression based on user data from social networks using machine learning methods and network analysis. The study includes the development of a model for detecting users with signs of depression, which relies on text analysis of their social network posts and profile metadata. Neural networks were used as algorithms in the study, showing high classification accuracy. Network analysis was implemented to examine the influence of users with signs of depression and it shows that such users have low centrality and do not form dense clusters, indicating their social isolation. The hypothesis of depression spreading through social connections was not confirmed, suggesting minimal impact of depressive users on others. The research results can be utilized to develop systems for early detection of depression. Special attention is given to the study's limitations, including the use of data from a single social network and the complexity of processing textual data. The article proposes directions for further research aimed at expanding methods for analyzing the spread of depressive behavior in social networks.

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