This article considers the experiences of four groups of young people (from Germany, Estonia, Russia and the UK) whose ‘mode of being’ is reduced or distorted in different ways as a result of misrecognition or stigmatisation. It argues that the responses young people make to this form of social subordination are enabled or constrained by the recognition status - misrecognition, absence of recognition or stigmatisation - they experience. It demonstrates that the experience of misrecognition and stigmatisation, in some cases, may constitute a resource to act and stimulate social change but that the institutional response to it may also work to re-embed the stigma or misrecognition that young people’s action sought to counter. It argues that stigmatisation and misrecognition are more than the accumulation of negative representations of individuals or groups. They reflect the configuration of power relations that underpin the institutionalisation of the labelling of specific groups as unworthy of respect and deny them the opportunity to participate equally in social life. The outcome of the struggle for recognition is thus not a matter of young people ‘choosing’ a positive identity or showing the desire to engage but the willingness of society to open itself to that engagement.
Hierarchical topic modeling is a potentially powerful instrument for determining topical structures of text collections that additionally allows constructing a hierarchy representing the levels of topic abstractness. However, parameter optimization in hierarchical models, which includes finding an appropriate number of topics at each level of hierarchy, remains a challenging task. In this paper, we propose an approach based on Renyi entropy as a partial solution to the above problem. First, we introduce a Renyi entropy-based metric of quality for hierarchical models. Second, we propose a practical approach to obtaining the “correct” number of topics in hierarchical topic models and show how model hyperparameters should be tuned for that purpose. We test this approach on the datasets with the known number of topics, as determined by the human mark-up, three of these datasets being in the English language and one in Russian. In the numerical experiments, we consider three different hierarchical models: hierarchical latent Dirichlet allocation model (hLDA), hierarchical Pachinko allocation model (hPAM), and hierarchical additive regularization of topic models (hARTM). We demonstrate that the hLDA model possesses a significant level of instability and, moreover, the derived numbers of topics are far from the true numbers for the labeled datasets. For the hPAM model, the Renyi entropy approach allows determining only one level of the data structure. For hARTM model, the proposed approach allows us to estimate the number of topics for two levels of hierarchy.
Hydra is regarded as the largest Russian-language online marketplace for illicit goods with no less than four million Euro of annual revenue. In this paper, we pro- vide a qualitative analysis of the unique organization of the Hydra cryptomarket. The theoretical framework for this study draws on information asymmetry and assumptions of signaling theory. Data collected include longitudinal parsing and non-participant observation from 2019 to 2021. Hydra is a monopolistic market- place with no alternatives of the same scale in Russia, and exhibits considerable differences with regard to international cryptomarkets. In the analysis, we present the three themes: physical stashes, formalized rules, and the architecture of the Hydra market. Contrary to the reliance on the postal system for delivering drugs in- ternationally, Hydra introduces a novel delivery method called stashes, which puts buyers at higher risks to get caught by law enforcement agents and makes them more “active” in obtaining drugs. Buyers not only have to choose and pay for drugs; they also need to go out in the streets and personally look for their purchased stuff. While elaborated formalized rules enabled by strong sanctioning mechanisms (e.g., fines) cover every possible aspect of interaction connected to the marketplace us- age, a multilayered platform architecture allows for the emergence of complex hi- erarchies and segregation between users. Hydra also provides automatic services related to drug selling (instant purchases, non-mediated disputes, etc.). In addition, personal communications between actors are confined by the architecture, so that visible public communications give buyers the only substantial grounds for decision- making. These findings raise questions considering the role of credibility signals, asymmetry of information, and formalized rules in resolving issues of product quality, stable exchange processes, and communication processes.
Co-playing, or playing video games together, is a social practice that enriches relationships and game experience by providing the players with informational and social support. This study explores how co-playing integrates into friendship in two small (6–7 people), male communities of adolescent and adult friends. Both communities are local and school-based; both focus on co-playing Dota 2. The study focuses on the leadership in these small networks, compares their co-playing patterns, and the ways in which co-playing affects the relationships in both communities, enhancing their bonding social capital. We apply network analysis and personal interviews to compare and contrast how the co-playing communities emerged, are maintained and evolve along with the friendship. The main conclusion is that such co-playing communities emerge around a single Dota 2 enthusiast in the early secondary school as a common pastime, but co-playing video games increases bonding social capital among the community members. Network analysis demonstrates the differences in leadership in the teen and adult communities. The research shows how video games are embedded in collective everyday friendship and how co-playing communities function in support of such a relationship. The findings could be further tested against female and mixed co-playing communities.
Pereboom and Caruso propose the quarantine model as an alternative to existing models of criminal justice. They appeal to the established public health practice of quarantining people, which is believed to be effective and morally justified, to explain why -in criminal justice- it is also morally acceptable to detain wrongdoers, without assuming the existence of a retrospective moral responsibility. Wrongdoers in their model are treated as carriers of dangerous diseases and as such should be preventively detained (or rehabilitated) until they no longer pose a threat to society. Our main concern in this paper is that Pereboom and Caruso adopt an idiosyncratic meaning of quarantine regulations. We highlight a set of important disanalogies between their quarantine model and the quarantine regulations currently adopted in public health policies. More specifically, we argue that the similarities that Pereboom and Caruso propose to substantiate their analogy are not consistent—despite what they claim—with the regulations underlying quarantine as an epidemiological process. We also notice that certain quarantine procedures adopted in public health systems are inadequate to deal with criminal behaviors. On these grounds, we conclude that Pereboom and Caruso should not appeal to the quarantine analogy to substantiate their view, unless they address the issues and criticism we raise in this paper.
Ethnicity-targeted hate speech has been widely shown to influence on-the-ground inter-ethnic conflict and violence, especially in such multi-ethnic societies as Russia. Therefore, ethnicity-targeted hate speech detection in user texts is becoming an important task. However, it faces a number of unresolved problems: difficulties of reliable mark-up, informal and indirect ways of expressing negativity in user texts (such as irony, false generalization and attribution of unfavored actions to targeted groups), users’ inclination to express opposite attitudes to different ethnic groups in the same text and, finally, lack of research on languages other than English. In this work we address several of these problems in the task of ethnicity-targeted hate speech detection in Russian-language social media texts. This approach allows us to differentiate between attitudes towards different ethnic groups mentioned in the same text – a task that has never been addressed before. We use a dataset of over 2,6M user messages mentioning ethnic groups to construct a representative sample of 12K instances (ethnic group, text) that are further thoroughly annotated via a special procedure. In contrast to many previous collections that usually comprise extreme cases of toxic speech, representativity of our sample secures a realistic and, therefore, much higher proportion of subtle negativity which additionally complicates its automatic detection. We then experiment with four types of machine learning models, from traditional classifiers such as SVM to deep learning approaches, notably the recently introduced BERT architecture, and interpret their predictions in terms of various linguistic phenomena. In addition to hate speech detection with a text-level two-class approach (hate, no hate), we also justify and implement a unique instance-based three-class approach (positive, neutral, negative attitude, the latter implying hate speech). Our best results are achieved by using fine-tuned and pre-trained RuBERT combined with linguistic features, with F1-hate=0.760, F1-macro=0.833 on the text-level two-class problem comparable to previous studies, and F1-hate=0.813, F1-macro=0.824 on our unique instance-based three-class hate speech detection task. Finally, we perform error analysis, and it reveals that further improvement could be achieved by accounting for complex and creative language issues more accurately, i.e., by detecting irony and unconventional forms of obscene lexicon.
Proliferation of misinformation in digital news environments can harm society in a number of ways, but its dangers are most acute when citizens believe that false news is factually accurate. A recent wave of empirical research focuses on factors that explain why people fall for the so-called fake news. In this scoping review, we summarize the results of experimental studies that test different predictors of individuals’ belief in misinformation.
The review is based on a synthetic analysis of 26 scholarly articles. The authors developed and applied a search protocol to two academic databases, Scopus and Web of Science. The sample included experimental studies that test factors influencing users’ ability to recognize fake news, their likelihood to trust it or intention to engage with such content. Relying on scoping review methodology, the authors then collated and summarized the available evidence.
The study identifies three broad groups of factors contributing to individuals’ belief in fake news. Firstly, message characteristics—such as belief consistency and presentation cues—can drive people’s belief in misinformation. Secondly, susceptibility to fake news can be determined by individual factors including people’s cognitive styles, predispositions, and differences in news and information literacy. Finally, accuracy-promoting interventions such as warnings or nudges priming individuals to think about information veracity can impact judgements about fake news credibility. Evidence suggests that inoculation-type interventions can be both scalable and effective. We note that study results could be partly driven by design choices such as selection of stimuli and outcome measurement.
We call for expanding the scope and diversifying designs of empirical investigations of people’s susceptibility to false information online. We recommend examining digital platforms beyond Facebook, using more diverse formats of stimulus material and adding a comparative angle to fake news research.
How are candidates without official party affiliation able to succeed in authoritarian elections? We analyzed 1,101 independents who took part in city council elections in Russia’s regional capitals between 2014 and 2018. We found that independent candidates’ electoral fortunes depended both on their personal resources enabling them to attract voters’ support and pre-electoral deals with the regime. We also discovered that the chances of being elected were higher for those formally independent candidates who were the regime’s hidden representatives. For the latter group, the chances to win the race were boosted mostly by pre-electoral deals, rather than their personal resources.
Rich data from social network sites (SNS) attracts the attention of psychologists and sociologists interested in interpersonal dynamics, friendship networks, and social capital. The presented study explores the effect of network structural features and psychological characteristics of SNS users on changes in their friendship networks. The data from the representative and diverse sample of 375 Russian Vkontakte SNS users from Vologda city was used. Two waves of network data collection allow us to estimate changes in the size of the friendship networks. Regression analysis reveals similarities in the factors responsible for the changes in networks for users who attract or reject friends. We discuss possible explanations of this phenomenon, as well as limitations of the study and further research directions.
The paper explores changes in interpretations and perceptions of masculinity in the context of peripheral and transit societies. Using the qualitative methodology of participant research and semi-structured interviews, I describe this question with the example of youth street workout community in Makhachkala, the capital of the republic of Dagestan (Russia). This republic with a complex ethnic and religious composition is currently going through a socio-economic, political and cultural transformation associated with the transition from socialism to capitalism and inclusion in the globalized world. My thesis is that within the community, young Dagestan men and adolescents solve the problem of successful masculine socialization in conditions of perceived habitual insecurity.
This article offers a gentle introduction to the measurement invariance (MI) literature with a focus on its relevance to comparative political research. It reviews 1) the conceptual foundations of MI; 2) standard procedures of testing for MI in practical applications within the multiple-group confirmatory factor analysis (MGCFA) paradigm; and 3) two novel
approaches to MI, Bayesian approximate measurement invariance, and MGCFA alignment optimization, which are especially suitable for dealing with extremely heterogeneous data from large-scale comparative surveys typical for modern political science. It then provides an empirical illustration of the key concepts and methods from the MGCFA-MI literature by applying them to testing for MI of two recently introduced measures of democracy attitudes, so-called liberal and authoritarian notions of democracy, across 60 countries in the sixth round of the World Values Survey. These analyses show that both measures can be considered reliable comparative measures of democratic attitudes, although for different reasons. Finally, this study emphasizes that some survey-based constructs, e.g., authoritarian notions of democracy, do not follow the reflective (correlation-based) logic of construct development. These alternative measures, known as formative measures, do not assume strong correlations between their
indicators, for which reason it is inappropriate to test their comparability using the reflective MGCFA approach. Instead, their comparability can be tied to their correlations with theoretically relevant external variables.
Online social networks have become an essential communi- cation channel for the broad and rapid sharing of information. Currently, the mechanics of such information-sharing is captured by the notion of cascades, which are tree-like networks comprised of (re)sharing actions. However, it is still unclear what factors drive cascade growth. Moreover, there is a lack of studies outside Western countries and platforms such as Facebook and Twitter. In this work, we aim to investigate what fac- tors contribute to the scope of information cascading and how to predict this variation accurately. We examine six machine learning algorithms for their predictive and interpretative capabilities concerning cascades’ structural metrics (width, mass, and depth). To do so, we use data from a leading Russian-language online social network VKontakte capturing cascades of 4,424 messages posted by 14 news outlets during a year. The results show that the best models in terms of predictive power are Gradient Boosting algorithm for width and depth, and Lasso Regression algorithm for the mass of a cascade, while depth is the least predictable. We find that the most potent factor associated with cascade size is the number of reposts on its origin level. We examine its role along with other factors such as content features and characteristics of sources and their audiences.
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.
To measure the effects of peer influence and peer selection on drinking behavior in adolescence through a rigorous statistical approach designed to unravel these interrelated processes.
We conducted systematic searches of electronic databases, thesis collections and conference proceedings to identify studies that used longitudinal network design and stochastic actor-oriented modeling to analyze drinking behavior in adolescents. Parameter estimates collected from individual studies were analyzed using multilevel random-effects models.
We identified 26 articles eligible for meta-analysis. Meta-analyses for different specifications of the peer influence effect were conducted separately. The peer influence effect was positive for every specification: for average similarity (avSim) mean log odds ratio was 1.27 with 95% confidence interval [0.04; 2.49]; for total similarity (totSim) 0.46 (95% CI = [0.44; 0.48]), and for average alter (avAlt) 0.70 (95% CI = [-0.01; 1.41]). The peer selection effect (simX) was also positive: 0.46 (95% CI = [0.28; 0.63]). Conversion log odds ratio values to Cohen’s d gives estimates from 0.25 to 0.70, which is considered as medium to large effect.
Advances in methodology for social network analysis have made it possible to accurately estimate peer influence effects free from peer selection effects. More research is necessary to clarify the roles of age, gender, and individual susceptibility on the changing behavior of adolescents under the influence of their peers. Understanding the effects of peer influence should inform practitioners and policy makers to design and deliver more effective prevention programs.
A healthy democracy requires trust that people can be impartial in important truth-seeking institutions including journalism, justice, and science. Recently some U.S. elites have adopted alarmingly extreme rhetoric against truth-seekers, denouncing mainstream journalism as fake news, criminal investigations as partisan witch-hunts, climate science as a hoax, and career civil servants as a deep state conspiracy. In response, some news organizations have taken the unusual step of publishing op/eds defending these institutions. Two experiments tested effects of such op/eds. In study 1, participants spent twelve days using a purpose-built news portal containing real, timely news with random assignment to the availability of real, timely op/eds defending impartiality of truth-seekers. These op/eds increased trust in truth-seeking institutions and increased the belief that people can serve as impartial professionals. Study 2 replicated this with a laboratory experiment assigning video op/ed exposure instead of text op/ed availability while adding several outcomes.
Simmel wird in Russland fast gleichzeitig wie in Deutschland bekannt. Zu Simmels Lebenszeit werden in russischer Sprache 27 seiner Werke veröffentlicht.
Rapidly proliferating social media not only serve as a new channel of human communication but also open up research opportunities to ask a wider set of questions about political, sociological and psychological factors that influence interpersonal and group online communication, development and maintenance of personal networks, and the growth or decline of social capital. In this chapter, we discuss the research opportunities provided by the new surveys, observational and experimental data that may be obtained from a social networking site. For doing so, we refer to Russian-language social networking sites (SNS) or SNS segments, notably VKontakte as the most popular SNS in Russia. We demonstrate how the aforementioned types of data may or have already been used to address research tasks from a number of disciplines.
Social tie maintenance has always had cognitive and emotional costs and has always been leading to uneven distribution of communication volume among egos' alters. This distribution, known as a social signature, is assumed to be relatively stable for each individual. Availability of digital traces of human communication allows testing
whether this assumption is true and whether it holds in specific channels of computer-mediated communication. In this paper, we investigate private messaging on a popular social networking website using a sample of 39
egos and 8063 alters over the period of 18 months. We find that this channel of communication does not reduce cognitive costs as the overall number of users' active contacts, on average, does not differ from the cognitive limit known as Dunbar's number. Confirming some previous research, we also find that the volume of communication is unevenly distributed, related to emotional closeness, and that changes in this distribution (that is, the changes in social signature) over time within an individual are smaller than the distances between social signatures of
different individuals. However, as an absolutely novel finding, we demonstrate that the changes within individuals are statistically significant, thus questioning the concept of social signature as a stable phenomenon.
Current research largely tends to ignore the drug-testing model that was developed in the “Second World” as an explicit alternative to the randomized controlled trial. This system can be described as “socialist pharmapolitics,” accounting for the specific features of state socialism that influenced the development and testing of experimental drugs. The clinical trials model employed in the “Second World” was heavily influenced by the Soviet Union, which was by far the most influential player in the socialist bloc during the Cold War. Based on extensive archival research, this article presents an empirical case of a late Soviet clinical trial as a pragmatic alternative to the randomized controlled trial model. It accounts for the divergences between the official model prescribed by the Soviet authorities and the messy realities of healthcare practice. It further outlines different factors that ultimately shaped how clinical trials were organized in Soviet institutions “on the ground.” Accordingly, this article presents a “real-life” history of “socialist pharmapolitics” and outlines the problems that this system faced in practice.
Archival research was conducted at the Russian State Archive of Scientific and Technical Documentation in Moscow. Archival files include scientific, technical, and registration documentation such as biochemical, pharmacological, and clinical descriptions of the experimental drug Meldonium, letters between various hospitals, research institutes and the Soviet regulatory body, as well as 26 reports of completed clinical trials. Manual content analysis was used for the interpretation of results.
This article presents an empirical case of a late Soviet clinical trial as a pragmatic alternative to the randomized controlled trial model. It demonstrates some key differences from the randomized controlled trial model. This article also highlights some of the discrepancies between the model that was officially prescribed by the Soviet authorities and the realities of experimental drug testing in the Soviet Union in the late 1980s and early 1990s. In particular, it notes some elements of randomization, double-blinding, and the use of placebo that were present in Meldonium trials despite being formally denounced by Soviet bioethics.
The Soviet model for testing experimental drugs differed from the Western one substantially in a number of respects. This difference was not only proclaimed officially by the Soviet authorities, but was for the most part enforced in clinical trials in practice. At the same time, our research demonstrates that there were important differences between the official model and the clinical realities on the ground.