Redefining media agendas: topic problematization in online reader comments

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

Public Discussion in Russian Social Media: An Introduction

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

Inequality and Communicative Struggles in Digital Times: A Global Report on Communication for Social Progress

Originally the “Media and Communication” chapter of the International Panel on Social Progress, published by Cambridge University Press, we hope this version as a CARGC Press book will expand the reach of the authors’ vision of communication for social progress.https://repository.upenn.edu/cargc_strategicdocuments/1001/thumbnail.jp

Media, communication and the struggle for social progress

This article discusses the role of media and communications in contributing to social progress, as elaborated in a landmark international project ? the International Panel on Social Progress. First, it analyses how media and digital platforms have contributed to global inequality by examining media access and infrastructure across world regions. Second, it looks at media governance and the different mechanisms of corporatized control over media platforms, algorithms and content. Third, the article examines how the democratization of media is a key element in the struggle for social justice. It argues that effective media access ? in terms of distribution of media resources, even relations between spaces of connection and the design and operation of spaces that foster dialogue, free speech and respectful cultural exchange ? is a core component of social progress

Estimating Topic Modeling Performance with Sharma–Mittal Entropy

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

Effects of user behaviors on accumulation of social capital in an online social network

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.Published versio

Predicting Subjective Well-being in a High-risk Sample of Russian Mental Health App Users

[EN] 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. 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Effects of conspiracy thinking style, framing and political Interest on accuracy of fake news recognition by social media users: evidence from Russia, Kazakhstan and Ukraine

This study examines the effect of specific factors (including user features, such as propensity for conspiracy thinking, and news item features, such as news frame and news source) on the accuracy of social media users in fake news recognition. Being a part of a larger research on fake news perception, this study uses the data from an online experiment that asks social media users from three countries (Russia, Ukraine and Kazakhstan) to evaluate a set of news items constructed with specific conditions. Namely, the users receive true and fake news about the neighboring countries framed differently and ascribed to either domestic or foreign sources. We then assess users’ accuracy in detecting fake news. The results of the study confirm the important role of conspiracy thinking style in false news recognition (leading to a decrease in accuracy) and users’ capability for deliberation on social media more broadly. However, the influence of contextual factors is mixed. While news sources exhibit no influence on the accuracy of fake or true news detection, dominant framing tends to increase the accuracy of true news only. More predictors of news recognition accuracy are discussed in the paper. As a result, this research contributes to the theory of fake news susceptibility by revealing a rich set of individual factors and interaction effects that influence human judgment about news truthfulness and impact deliberation possibilities in socially mediated environments