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News
April 30, 2026
HSE Researchers Compile Scientific Database for Studying Childrens Eating Habits
The database created at HSE University can serve as a foundation for studying children’s eating habits. This is outlined in the study ‘The Influence of Age, Gender, and Social-Role Factors on Children’s Compliance with Age-Based Nutritional Norms: An Experimental Study Using the Dish-I-Wish Web Application.’ The work has been carried out as part of the HSE Basic Research Programme and was presented at the XXVI April International Academic Conference named after Evgeny Yasin.
April 30, 2026
New Foresight Centre Study Identifies the Most Destructive Global Trends for Humankind
A team of researchers from the HSE International Research and Educational Foresight Centre has examined how global trends affect the quality of human life—from life expectancy to professional fulfilment. The findings of the study titled ‘Human Capital Transformation under the Influence of Global Trends’ were published in Foresight.
April 28, 2026
Scientists Develop Algorithm for Accurate Financial Time Series Forecasting
Researchers at the HSE Faculty of Computer Science benchmarked more than 200,000 model configurations for predicting financial asset prices and realised volatility, showing that performance can be improved by filtering out noise at specific frequencies in advance. This technique increased accuracy in 65% of cases. The authors also developed their own algorithm, which achieves accuracy comparable to that of the best models while requiring less computational power. The study has been published in Applied Soft Computing.

 

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An approach to automation of user's profile analysis

P. 461–467.
Budin E., Smirnova K., Suvorova A., Tulupyeva T.

Information from users’ profiles on social networking sites is an important data source for analysis of the users’ psychological characteristics. Texts, video and audio files, images, public pages can be easily accessible and analyzed. We consider the ways of estimating the users’ psychological characteristics on the base of his or her profile in the social network VKontakte. We compare different machine learning models for the analysis of user's texts, such as linear regression, decision trees, random forest, support vector machine with linear, radial and sigmoidal kernels. Also we discussed the possible further stages of research including the sentiment analysis for better text description, the analysis of profile photo, and, finally, ways of combining all steps for estimating psychological characteristics of social networks users.

Language: English
Full text
DOI
Keywords: online social networksmachine learningclassificationautomated text analysis

In book

Digital Transformation and Global Society: 4th International Conference, DTGS 2019, St. Petersburg, Russia, June 19–21, 2019, Revised Selected Papers
Cham: Springer, 2019.
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