Abstract
Health preservation is one of the urgent priorities for any group of people. There is a lot of research currently underway on diagnosing and monitoring health using data from social media. In this paper, the problem of the automatic classification of users of the Russian-language social network VK.com in terms of whether they lead a healthy lifestyle is considered. To solve this problem, various types of information was collected from user profiles: text, numerical and graphic data. The users then took a lifestyle and health survey. The results of this survey were used in order to split the users into groups according to the degree of adherence to a healthy lifestyle. The survey results were used to train various binary classifiers. The best results (about 0.76 F1-score) in our experiment were shown by a model that was trained on combined features (images from users public “walls”, as well as N-gram features compiled from text from the users public “walls”). These results were achieved using the following machine learning models “multilayer perceptron”, “naive Bayesian classifier” and “k nearest neighbours”.
The reported study was funded by RFBR according to the research project 18-29-22041.
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Stepanov, D., Smirnov, A., Ivanov, E., Smirnov, I., Stankevich, M., Danina, M. (2022). Detection of Health-Preserving Behavior Among VK.com Users Based on the Analysis of Graphic, Text and Numerical Data. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 296. Springer, Cham. https://doi.org/10.1007/978-3-030-82199-9_39
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