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  • Социальные медиа: о чем и кому пишут их пользователи? Некоторые подходы к анализу данных
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Subject
News
September 25, 2026
AI Users Earn Up to 41.8% More Than Non-Users
Research conducted by economists at HSE University has revealed a significant correlation between the regular use of GenAI in the workplace and higher pay among Russian employees. The study found that individuals who frequently use GenAI in their professional activities earn notably more than those who reject these new tools or resort to them occasionally. The salary premium for highly qualified specialists reaches 41.8%. The article was published in the Voprosy Ekonomiki journal.
September 24, 2026
‘Feedback and Constructive Criticism Are Essential in Our Profession
Vincent Fardeau, Associate Professor at HSE ICEF, has reached a major career milestone: he recently published his paper ‘Asymmetric Thin Markets’ in the Journal of Financial Economics, successfully passed his major academic review, and received tenure. In this interview, Vincent discusses the story behind the paper, explains the concept of asymmetric thin markets, and shares his advice for young scholars aiming to publish in top-tier journals.
September 22, 2026
Personal Interest in Doctoral Thesis Topic Most Important for Confidence in Successful Defence
A researcher at HSE University analysed data on 1,539 doctoral students from 161 Russian universities to identify which features of a thesis topic are associated with academic success and engagement. The most important factor was found to be personal interest in the research topic, which was associated with almost all key aspects of doctoral programme experience—from engaging with the academic supervisor to research activity and confidence about successfully defending the thesis. The findings have been published in Higher Education.

 

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Социальные медиа: о чем и кому пишут их пользователи? Некоторые подходы к анализу данных

Прикладная эконометрика. 2017. № 3. С. 74–99.
Kotyrlo E.

Study of users and their segmentation, based on users’ preferred topics of discussion and their networking, is the unique opportunity offered by social networks. Variety of approaches to social media analysis based on social network analysis and text mining is summarized in the paper. It is extended by concentration index application and visualizing of the results of social network analysis.

The study of a model set exhibits that: 1) users can be successfully segmented on the base of their most mentioned topics, which is useful for a product placement and other commercial purposes; 2) distribution of number of posts by authors is highly uneven regardless to the topic of discussion; 3) users connected on-line typically live in the same geographical area; 4) users’ number of posts and centrality indices are correlated.

Research target: Sociology (including Demography and Anthropology Mathematics Philology and Linguistics
Priority areas: sociology IT and mathematics
Language: Russian
Full text
DOI
Text on another site
Keywords: анализ текстовсоциальные медиарегрессионный анализregression analysistext miningиндекс Джиниsocial network analysissocial network sitesGini coefficientсоциально-сетевой анализ
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