• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

The 'Second Shift' Is Not Why Women Avoid News

The 'Second Shift' Is Not Why Women Avoid News

© HSE University

Women are more likely than men to avoid political and economic news, but the reasons for this behaviour are linked less to structural inequality or family-related stress than to personal attitudes and the emotional perception of news content. This conclusion was reached by HSE researchers after analysing data from a large-scale survey of more than 10,000 residents across 61 regions of Russia. The study findings have been published in Woman in Russian Society.

News consumption is regarded as an important indicator of how people perceive the world around them. For some, regularly following the news is a way to stay informed about public life and better understand political and economic processes. Others believe it is sufficient to receive information through social media, where major news stories spread automatically. There is also a third strategy: deliberate avoidance of news, particularly coverage related to politics and economics.

The researchers hypothesised that women might be more likely to distance themselves from the news agenda because of structural factors. Academic literature often suggests that childcare, housekeeping, and providing emotional support to family members—the so-called 'second shift'—can limit the time and resources women have available for engaging with social and political information.

To test this hypothesis, researchers from HSE University analysed data from the 2024 survey ‘Research on COVID-19 in Russia’s Regions’ (RoCiRR). Although the survey primarily focused on the effects of the pandemic, the questionnaire also included a large section devoted to news consumption. The survey involved more than 10,000 respondents from 61 Russian regions and included questions about how often respondents read the news, their screen time, and a self-assessment of their media consumption habits. It also covered respondents’ marital status, employment, education, values, and levels of anxiety.

The analysis shows that women are indeed more likely to avoid political and economic news, but this is not directly linked to having children. Instead, the emotional perception of news content proves to be a much more significant factor: women are more likely to associate such news with anxiety and negative experiences. Having a partner, regardless of status, is associated with higher levels of news consumption, whereas full-time employment reduces the amount of time people generally devote to following the news.

Additional analysis reveals further patterns. Both men and women who adhere to more conservative values are, on average, more likely to distance themselves from the news agenda. In addition, individuals with higher levels of education are less likely to spend a large amount of time on consuming news. Anxiety shows a dual relationship: it is associated both with more frequent news exposure and with news avoidance.

Anastasia Kazun

'We had expected to find confirmation of the hypothesis regarding the impact of structural inequality, but no such effect was observed in the current Russian data. Moreover, we had assumed that individuals with higher education would follow the news more actively, as previous studies suggest that education can reduce the emotional costs of engaging with news content. However, the findings indicate that news avoidance is more of an individual strategy than a behaviour associated with any single social characteristic,' notes one of the study authors, Anastasia Kazun, Senior Research Fellow at the Laboratory for Studies in Economic Sociology.

The findings demonstrate that news consumption practices are shaped by a range of factors, from emotional state to personal values. According to the researchers, this suggests that media communication and public awareness strategies should take into account not only the socio-demographic characteristics of audiences, but also the psychological aspects of their engagement with news.

The study was conducted within the framework of the Basic Research Programme at HSE University.

See also:

HSE University to Develop Predictive Analytics System for Icebreaker Motors

Industrial automation is one of the key applications of artificial intelligence. A predictive analytics system for large electric motors is among the solutions being developed for the industry as part of HSE University’s Strategic Technological Project ‘Multi-Agent Platform of AI Solutions for Industry-Specific Tasks.’ What is predictive analytics, how can it improve the operation of electric motors, and what specialists joined forces to develop this technology? Anton Zarubin, Dean of the School of Computer Science, Physics, and Technology at HSE University–St Petersburg and the project development coordinator, explains in this interview with the HSE News Service.

How to Assess Students’ Knowledge in the Age of AI

A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.

Scientists Train Neural Network to Generate Process Plans from 3D Models

Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.

Biologists Discover 'Molecular Fingerprint' of Preeclampsia

Researchers at HSE University employed a new method to model hypoxia in placental cells during pregnancies complicated by preeclampsia and identified molecular markers of tissue hypoxia. Since hypoxia is one of the key mechanisms underlying preeclampsia, these findings are important for a more accurate and timely diagnosis of the disease and for the development of effective treatment methods. The paper has been published in Placenta.

‘Hedgehog’ Versus ‘Relatives’: Researchers Measure How the Brain Responds to Unexpected Words During Natural Speech

Russian neurophysiologists, including researchers from HSE University, have demonstrated the feasibility of using event-related fields (ERFs) to study brain activity during natural speech perception. The researchers showed that this approach can be applied not only to individual words but also to continuous speech. Their findings indicate that words whose meanings differ significantly from the preceding context require longer processing times. The study also reveals that the brain processes function words in two stages: first, it identifies their grammatical role and then uses this information to predict the next word. The study has been published in Frontiers in Human Neuroscience.

HSE Researchers Create New Corpus of Early Child Speech in Russian

Researchers at the HSE Centre for Language and Brain have presented RusLan-M, an open multimedia corpus that makes it possible to trace the development of early child speech in Russian from first words to the emergence of complex grammatical constructions. The database contains around 41 hours of video recordings and more than 35,000 child utterances. The new resource will help researchers study more precisely how children acquire Russian and, in the longer term, develop more reliable tools for assessing speech development. The study has been published in Language Resources and Evaluation.

Scientists Develop Algorithm for More Reliable Processors in Data Centres

Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.

Researchers Rank Recommendation Algorithms Using Sports Tournament Model

Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed an approach for selecting recommendation algorithms more effectively. Their approach uses pairwise comparisons of algorithms to create a tournament table, with the overall ranking based on their performance across all datasets in the tournament. This can reduce the number of algorithms that need to be tested when developing new services, saving both time and money. The study was presented at the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026).

Researchers Develop Method for Direct Generation of Regulatory DNA

Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’

Researchers at HSE University and Sber Train Neural Networks to Better Predict User Preferences

The HSE FCS AI and Digital Science Institute and Sber have introduced a new architecture for recommendation systems that combines two classes of models, enabling algorithms to better predict users’ interests and needs. A preprint of the paper has been published on arxiv.org and presented at Urban ML.