The 'Second Shift' Is Not Why Women Avoid News
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:
Neural Network Maps as a Method for Constructing Mathematical Models
Scientists from HSE University–Nizhny Novgorod and the Institute of Physics Belgrade, Serbia, are jointly exploring the application of machine learning techniques and neural networks to the study of nonlinear dynamics. Natalya Stankevich, Leading Research Fellow at the Laboratory of Topological Methods in Dynamics of the Faculty of Informatics, Mathematics, and Computer Science at HSE University–Nizhny Novgorod, spoke to the HSE News Service about this international project.
HSE Scientists Develop Method to Compress Large Language Models Without Losing Quality
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed a new compression method for large language models such as GPT and LLaMA that reduces their size by 25–36% without additional training or significant loss of accuracy. This is the first approach to use mathematical transformations—specifically, rotations of model weights—to make models more amenable to compression with structured matrices. The study results have been published in ACL Findings 2025. The code is available on GitHub.
Machine Learning Models Can Help Reduce Volatility and Boost Stock Market Returns
The use of machine learning models makes it possible to achieve greater accuracy in predicting risks in the Russian stock market compared to classical econometric approaches. The predictive power of these models increases by 23%, while the average investor’s return can reach up to 13% per annum. These conclusions were drawn by Nikita Lysenok from the Department of Financial Market Infrastructure at the HSE Faculty of Economic Sciences. The paper has been published in Fundamental and Applied Mathematics.
Pocket Money, Personal Interest, and Family Practices: What Shapes Students’ Economic Literacy?
University students' economic literacy depends not only on their field of study but also on their interest in economics, the learning environment, and family financial practices. For example, students who received pocket money irregularly tend to perform better on economic literacy tests than their peers who received financial support on a regular basis. These findings come from a study conducted by HSE University involving more than 1,100 students from five Russian universities. The findings have been published in Cakrawala Pendidikan.
HSE Study Reveals Imbalance in the Generative AI Market
Researchers at HSE University analysed how effectively the global generative artificial intelligence market converts investment into real revenue, concluding that AI is currently developing faster than it is paying off. The results have been published in the journal Foresight and STI Governance.
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.
Resource Race and Green Transition: Three Unexpected Conclusions from Foresight Centre’s Research on Climate and Poverty
Beneath the surface of green energy—which most people associate with solar panels, electric vehicles, and reduced CO2 emissions—lies a complex web of geopolitical interests, international inequality, and resource constraints. Researchers from the Laboratory for Science and Technology Studies (LST) at the HSE ISSEK Foresight Centre have published a series of articles in leading international journals on hidden and overt conflicts surrounding critically important metals and minerals, as well as related processes in the energy sector.
Immersion in Second Language Environment Influences Bilinguals’ Perception of Emotions
Researchers at the Cognitive Health and Intelligence Centre at the HSE Institute for Cognitive Neuroscience have discovered how bilingual individuals process emotional words in their native (first) and non-native (second) languages. It was found that the link between word meaning and bodily sensations is weaker in a second language than in a first language. However, the more a person is immersed in a language environment, the smaller this difference becomes. The article has been published in Language, Cognition and Neuroscience.
Researchers Find More Effective Approach to Revealing Majorana Zero Modes in Superconductors
An international team of researchers, including physicists from HSE MIEM, has demonstrated that nonmagnetic impurities can help more accurately reveal Majorana zero modes—quantum states considered promising building blocks for quantum computing. The researchers found that these impurities shift the energy levels that typically obscure the Majorana signal, while leaving the mode itself largely unaffected, thereby making its spectral peak more distinct. The study has been published in Research.
New Development by HSE Scientists Helps Design Reliable Electronics Faster at a Lower Cost
Scientists from HSE MIEM have developed a new approach to modelling electrothermal processes in high-power electronic circuits on printed circuit boards (PCB). The method allows engineers to quickly and accurately predict how electronic components heat up during operation, helping prevent overheating and potential failures. The results have been published in Russian Microelectronics.