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The use of social network sites helps people to make and maintain social ties accumulating social capital, which is increasingly important for individual success. There is a wide variation in the amount and structure of online ties, and to some extent this variation is contingent on specific online user behaviors which are to date under-researched. In this work, we examine an entire city-bounded friendship network (N = 194,601) extracted from VK social network site to explore how specific online user behaviors are related to structural social capital in a network of geographically proximate ties. Social network analysis was used to evaluate individual social capital as a network asset, and multiple regression analysis-to determine and estimate the effects of online user behaviors on social capital. The analysis reveals that the graph is both clustered and highly centralized which suggests the presence of a hierarchical structure: a set of sub-communities united by city-level hubs. Against this background, membership in more online groups is positively associated with user's brokerage in the location-bounded network. Additionally, the share of local friends, the number of received likes and the duration of SNS use are associated with social capital indicators. This contributes to the literature on the formation of online social capital, examined at the level of a large and geographically localized population.

Topic modeling is a popular approach for clustering text documents. However, current tools have a number of unsolved problems such as instability and a lack of criteria for selecting the values of model parameters. In this work, we propose a method to solve partially the problems of optimizing model parameters, simultaneously accounting for semantic stability. Our method is inspired by the concepts from statistical physics and is based on Sharma–Mittal entropy. We test our approach on two models: probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA) with Gibbs sampling, and on two datasets in different languages. We compare our approach against a number of standard metrics, each of which is able to account for just one of the parameters of our interest. We demonstrate that Sharma–Mittal entropy is a convenient tool for selecting both the number of topics and the values of hyper-parameters, simultaneously controlling for semantic stability, which none of the existing metrics can do. Furthermore, we show that concepts from statistical physics can be used to contribute to theory construction for machine learning, a rapidly-developing sphere that currently lacks a consistent theoretical ground.

Although conflict representation in media has been widely studied, few attempts have been made to perform large-scale comparisons of agendas in the media of conflicting parties, especially for armed country-level confrontations. In this article, the authors introduce quantitative evidence of agenda divergence between the media of conflicting parties in the course of the Ukrainian crisis 2013–2014. Using 45,000 messages from the online newsfeeds of a Russian and a Ukrainian TV channel, they perform topic modelling coupled with qualitative analysis to reveal crisis-related topics, assess their salience and map evolution of attention of both channels to each of those topics. They find that the two channels produce fundamentally different agenda sequences. Based on the Ukrainian case, they offer a typology of conflict media coverage stages.

Recent advancements in large language models (LLMs) have opened new possibilities for developing conversational agents (CAs) in various subfields of mental healthcare. However, this progress is hindered by limited access to high-quality training data, often due to privacy concerns and high annotation costs for low-resource languages. A potential solution is to create human-AI annotation systems that utilize extensive public domain user-to-user and user-to-professional discussions on social media. These discussions, however, are extremely noisy, necessitating the adaptation of LLMs for fully automatic cleaning and pre-classification to reduce human annotation effort. To date, research on LLM-based annotation in the mental health domain is extremely scarce. In this article, we explore the potential of zero-shot classification using four LLMs to select and pre-classify texts into topics representing psychiatric disorders, in order to facilitate the future development of CAs for disorder-specific counseling. We use 64,404 Russian-language texts from online discussion threads labeled with seven most commonly discussed disorders: depression, neurosis, paranoia, anxiety disorder, bipolar disorder, obsessive-compulsive disorder, and borderline personality disorder. Our research shows that while preliminary data filtering using zero-shot technology slightly improves classification, LLM fine-tuning makes a far larger contribution to its quality. Both standard and natural language inference (NLI) modes of fine-tuning increase classification accuracy by more than three times compared to non-fine-tuned training with preliminarily filtered data. Although NLI fine-tuning achieves slightly higher accuracy (0.64) than the standard approach, it is six times slower, indicating a need for further experimentation with NLI hypothesis engineering. Additionally, we demonstrate that lemmatization does not affect classification quality and that multilingual models using texts in their original language perform slightly better than English-only models using automatically translated texts. Finally, we introduce our dataset and model as the first openly available Russian-language resource for developing conversational agents in the domain of mental health counseling.

Availability of alternative information through social media, in particular, and digital media, in general, is often said to induce social discontent, especially in states where traditional media are under government control. But does this relation really exist, and is it generalizable? This article explores the relationship between self-reported online news consumption and protest participation across 48 nations in 2010–2014. Based on multilevel regression models and simulations, the analysis provides evidence that those respondents who reported that they had attended a protest at least once read news online daily or weekly. The study also shows that the magnitude of the effect varies depending on the political context: surprisingly, despite supposedly unlimited control of offline and online media, autocratic countries demonstrated higher effects of online news than transitional regimes, where the Internet media are relatively uninhibited.

Despite recent achievements in predicting personality traits and some other human psychological features with digital traces, prediction of subjective well-being (SWB) appears to be a relatively new task with few solutions. COVID-19 pandemic has added both a stronger need for rapid SWB screening and new opportunities for it, with online mental health applications gaining popularity and accumulating large and diverse user data. Nevertheless, the few existing works so far have aimed at predicting SWB, and have done so only in terms of Diener’s Satisfaction with Life Scale. None of them analyzes the scale developed by the World Health Organization, known as WHO-5 – a widely accepted tool for screening mental well-being and, specifically, for depression risk detection. Moreover, existing research is limited to English-speaking populations, and tend to use text, network and app usage types of data separately. In the current work, we cover these gaps by predicting both mentioned SWB scales on a sample of Russian mental health app users who represent a population with high risk of mental health problems. In doing so, we employ a unique combination of phone application usage data with private messaging and networking digital traces from VKontakte, the most popular social media platform in Russia. As a result, we predict Diener’s SWB scale with the state-of-the-art quality, introduce the first predictive models for WHO-5, with similar quality, and reach high accuracy in the prediction of clinically meaningful classes of the latter scale. Moreover, our feature analysis sheds light on the interrelated nature of the two studied scales: they are both characterized by negative sentiment expressed in text messages and by phone application usage in the morning hours, confirming some previous findings on subjective well-being manifestations. At the same time, SWB measured by Diener’s scale is reflected mostly in lexical features referring to social and affective interactions, while mental well-being is characterized by objective features that reflect physiological functioning, circadian rhythms and somatic conditions, thus saliently demonstrating the underlying theoretical differences between the two scales.

Media audiences representing a significant portion of the public in any given country may hold opinions on media-generated definitions of social problems which differ from those of media professionals. The proliferation of online reader comments not only makes such opinions available but also alters the process of agenda formation and problem definition in the public space. Based on a dataset of 33,877 news items and 258,121 comments from a sample of regional Russian newspapers we investigate readers’ perceptions of social problems. We find that the volume of attention paid to issues or topics by the media and the importance of those issues for audiences, as judged by the number of their comments, diverge. Further, while the prevalence of general negative sentiment in comments accompanies such topics as disasters and accidents that are not perceived as social problems, a high level of sentiment polarization in comments does suggest issue problematization. It is also positively related to topic importance for the audience. Thus, instead of finding fixed social problem definitions in the reader comments, we observe the process of problem formation, where different points of view clash. These perceptions are not necessarily those expressed in media texts since the latter are predominantly “hard” news covering separate events, rather than trends or issues. As our research suggests, problematization emerges from readers’ background knowledge, external experience, or values.

Changes in individual and institutional financial behavior leading to shifts in liquidity flows often depend on events reflected in news. However, the task of establishing relationship between financial behavior and news remains challenging and understudied. We propose a news-based feature generation approach that allows accounting for news events in liquidity flow time-series predicting tasks, thereby improving the forecasting quality. These features are constructed as different types of entropies and calculated at different levels of text abstraction based on word counts, TF-IDF values, probabilistic topics, and contextual embeddings. We show that this feature engineering procedure is effective for predicting changes in two types of liquidity flows: stock market trading volume and the volume of ATM cash withdrawals. As the first type, we use our original collection of 651, 208 business news articles from a Russian news agency dating to 2013-2021 to predict abnormal jumps in the trade volume of 32 leading Russian companies. With our approach, 97% of them experience an increase in the quality of predicting the differences in daily trading volumes from their median values. For the ATM withdrawals task, we test the impact of economic news from three leading Russian media sources (N = 55, 712) on withdrawals from 100 ATMs located in Moscow. For 95% of them we improve the quality of prediction of year-to-year weekly withdrawal volume change. Additionally, we find that some news sources have a higher predictive power than others. The approach is potentially generalizable for other domains of financial behavior across the globe.

Russian media have recently (re-)gained attention of the scholarly community, mostly due to the rise of cyber-attacking techniques and computational propaganda efforts. A revived conceptualization of the Russian media as a uniform system driven by a well-coordinated propagandistic state effort, though having evidence thereunder, does not allow seeing the public discussion inside Russia as a more diverse and multifaceted process. This is especially true for the Russian-language mediated discussions online, which, in the recent years, have proven to be efficient enough in raising both social issues and waves of political protest, including on-street spillovers. While, in the recent years, several attempts have been made to demonstrate the complexity of the Russian media system at large, the content and structures of the Russian-language online discussions remain seriously understudied. The thematic issue draws attention to various aspects of online public discussions in Runet; it creates a perspective in studying Russian mediated communication at the level of Internet users. The articles are selected in the way that they not only contribute to the systemic knowledge on the Russian media but also add to the respective subdomains of media research, including the studies on social problem construction, news values, political polarization, and affect in communication.

The rise of videoconferencing (VC) technologies has transformed how individuals collaborate and interact across professional and personal contexts. However, empirical studies comparing VC and face-to-face (FtF) interactions remain fragmented, partly due to a lack of open, multimodal datasets capturing both modalities with rich behavioural, physiological, and self-report measures. To address this gap, we introduce the dataset, comprising approximately 180 hours of audio-visual recordings of unacquainted dyads engaging in structured and creative collaborative tasks under controlled laboratory conditions. Participants were randomly assigned to VC or FtF interaction. The dataset includes six salivary oxytocin measurements, self-reports on affect, personality traits, relevant attitudes, communication outcomes, and a repeated sustained attention task. Behavioural recordings from frontal and side camera views are available for most dyads, with individual-level data for 127–131 participants. The dataset enables research into social bonding, cooperation, behavioural and physiological synchrony, and broader communication dynamics, filling a critical gap in resources for comparative communication studies.