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News
July 9, 2026
HSE Economists Use Search Queries to Forecast Birth Rates
Researchers from the HSE Faculty of Economic Sciences have shown that the accuracy of birth rate forecasts for Russia can be improved by almost 50% by incorporating the dynamics of online search queries related to pregnancy and childbirth into forecasting models. In the best-performing models, the forecasting error fell from 4.6% to 3.2%. The findings have been published in Populations and Economics.
July 8, 2026
HSE Researchers Discover Who Eats Out in Russia-And Why
Around one-third of Russians (31.3%) rarely eat out or buy ready-made meals. The core group of active consumers—those who eat out or purchase prepared food almost every day or several times a week—accounts for only about 9% of the population. These are the findings of a study conducted by the HSE Institute for Social Policy. According to the researchers eating out is no longer a marker of high social status in Russia.
July 8, 2026
HSE University and RREDA Join Forces to Support 2026 Renewable Energy of the Planet Competition
HSE University and the Russia Renewable Energy Development Association (RREDA) have signed a partnership and information cooperation agreement to support Renewable Energy of the Planet—2026, a national competition with international participation for students and early-career researchers. Applications are open on the competition's website until September 20, 2026.

 

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Взаимосвязь сетевых характеристик и субъектности сетевых сообществ в социальной сети Твиттер

Вопросы кибербезопасности. 2020. Т. 37. № 3. С. 40–57.
Chepovskiy A., Воронин А. Н., Ковалева Ю. В.

The purpose of the study: analysis of the graph of interacting objects of social networks based on the selection of implicit communities, assessment of the subjectivity of the selected communities and comparison of the network characteristics of communities and various indicators of their subjectivity.

Method: communities detection on the constructed weighted graph of a social network, psycholinguistic analysis of community content using a list of discourse markers of subjectivity, statistical methods for identifying the relationship between network characteristics and the frequency of discourse markers.

Results: algorithms to construct a graph and to import user attributes were developed, an algorithm for dividing a weighted graph into implicit user communities was implemented, the subjectivity of the content of the selected network communities in the social network Twitter has was assessed, the relationship and directional shift in the connectivity of the graph and various indicators of the subjectivity of the network community were identified.

Research target: Computer Science Psychology
Priority areas: IT and mathematics
Language: Russian
Full text
DOI
Keywords: social subjectivitysocial network analysisnetwork communityвыделение сообществанализ социальной сетиdiscourse markersдискурсивные маркерысоциальный графgraph community detection
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