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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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Короткая шкала думскроллинга: разработка и апробация

Вестник Московского университета. Серия 10: Журналистика. 2026. Т. 51. № 1. С. 3–35.
Kazun A. D.

This article presents the development and validation of a short doomscrolling scale, conceptualized as excessive consumption of negative news that affects individuals’ emotional state. A five-item scale is proposed, partly based on the components model of behavioral addictions and the diagnostic criteria for addictions (DSM-5). The quality of the instrument is tested using data from a nationally representative Russian survey conducted in spring 2025 (N = 6041). The distribution of responses demonstrates a “stepwise” pattern. There is relatively high agreement with statements about constant consumption of negative content (22%) and maintaining the same level of consumption despite an awareness of its harmful effects (26%). Lower support is observed for items capturing withdrawal-like symptoms (9%) and news-related functional impairments (10%). Accordingly, the scale is appropriate for assessing the severity of doomscrolling. The instrument demonstrates acceptable internal consistency (α = 0.741), which is stable across different socio-demographic groups. All items make a meaningful contribution to the measurement of doomscrolling; removing any individual statement reduces the internal consistency of the scale. The instrument has a unidimensional structure: all items show sufficient communalities and high factor loadings. Three levels of doomscrolling are distinguished (absent, moderate, and pronounced), with higher levels associated with an increased share of respondents reporting nervous disorders, use of tranquilizers and antidepressants, as well as heightened anxiety about the future. The study concludes that the scale is suitable for diagnosing doomscrolling and identifying risk groups.

Research target: Media and Communications Sociology (including Demography and Anthropology Psychology Political Science, International Relations, and Public Administration
Language: Russian
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Keywords: медиаmedianewsновостиаддиктивное поведениеdoomscrollingдумскроллингproblematic news consumptionhigh-choice media environmentпроблемное потребление новостеймедиасреда с большим выборомdoomscrolling scaleшкала думскроллингакомпульсивное потребление
Publication based on the results of:
Consumption and economic behavior of households in Russia, 2025-2027 (2027)
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