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News
May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Политические категории в обсуждениях соседских онлайн-сообществ: методика эмпирического исследования на примере г. Волгограда

Вестник Российского университета дружбы народов. Серия: Политология. 2025. Т. 27. № 3. С. 507–519.
Бабкин А. А.

In the context of digitalization of society, neighborhood communication is increasingly moving online. The communication of neighbors on public social media platforms creates extensive archives of data that can be used for political science research. These data open up new opportunities for the study of grassroots politics, group identities, political activism, etc. Nevertheless, there is currently a gap in research related to the lack of comprehensive methods that allow analyzing neighborhood online communities on a city scale, not limited to individual cases. The purpose of this work is to eliminate the existing gap by proposing a methods for identifying neighborhood online communities on a city scale using data from the VKontakte social network. Based on this methodology, an attempt is being made to identify in the discussions of the neighboring communities of the city of Volgograd an array of publications in which political categories occur. The paper describes the process of searching for neighborhood online communities, and also describes the preprocessing of text data. A mechanism is proposed for cleaning the corpus of texts from advertisements, which represented a wide proportion of publications, but acted as noise that hindered the main task. Publications mentioning political categories are identified by automatically encoding the cleared corpus into groups of keywords, followed by rechecking. The resulting archive, covering 17 years of neighborhood interaction, made it possible to determine, through the use of thematic modeling tools, the main periods of development of neighborhood communities. Starting from short non-political messages in groups of neighboring youth, ending with an established political repertoire: the work of homeowners associations; interaction with representatives of authorities, mainly executive ones, to solve local problems; organization and participation in local patriotic events. The most active types of neighborhood online-communities were identified: territorial public self-governments, communities of shareholders and microdistrict communities.

Language: Russian
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Keywords: социальные сетиsocial networksгражданский активизмполитическая коммуникацияpolitical activismполитический активизмpolitical communicationметодика исследованияcivic activismresearch methodsцифровые следыneighborhood online communitiesdigital fingerprintsсоседские онлайн-сообщества
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