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This paper presents an analysis of a temporal network that describes the social connections of a large-scale (~ 30,000) sample of online social network users, inhabitants of a fixed city. We tested how the main network formation determinants - transitivity, preferential attachment, and social selection - contribute to network evolution. Among other things, we found that opinion social selection does affect tie appearing whereas its impact on tie removing is rather unclear. We report that transitivity displayed the strongest effect on network dynamics. Surprisingly, a closer look revealed an intriguing and complex interplay between the transitivity, preferential attachment, and anti-preferential attachment mechanisms. For a given pair of unconnected nodes, if they have no mutual connections, then the probability of tie creation goes up with the sum of node degrees - that is exactly what the preferential attachment mechanism is assumed to do. Instead, if the nodes have at least one common friend, then the highest probability of tie appearing is achieved if both the nodes have only a few friends - a phenomenon that is called anti-preferential attachment. We attempted to explain this finding by appealing to the notions of social communities and leaders., Comment: 7 figures and 2 tables in the main part of the manuscript, 11 figures and 1 table in appendix

The heterogeneity of the influence processes is an important feature of social systems: how we perceive social influence and how we influence other individuals is heavily influenced by our opinion and non-opinion attributes. The latter include demographic, cultural, and structural (how we are embedded in social networks) characteristics. Furthermore, the results of the influence processes may also depend on how similar the interacting individuals are in terms of their features. This paper addresses this issue and elaborates on an agent-based model that is sensitive to the individual characteristics, both opinion and non-opinion ones. The model is fortified with a ranking algorithm that mimics the ranking algorithms widely adopted in real-world online social networks. For the resulting model, I elaborate a mean-field approximation that describes the behavior of the model at the macroscopic level via an autonomous system of ordinary differential equations. The properties of this system are thoroughly studied., Comment: 6 pages, conference paper

Understanding how individuals change their opinions is essential to mitigate the harmful effect of fake news and stop opinion polarization. Building upon an extremely flexible opinion dynamics model that can capture the key microscopic mechanisms of social influence, the current paper elaborates a framework to find optimal control, which, thus, can be applied to a broad set of opinion dynamics settings. Using a combination of theoretical and computational approaches, I characterize the properties of optimal control and demonstrates how the elaborated framework can be applied to specific examples, which cover the classical situations that garner remarkable attention in the literature on opinion dynamics models: assimilative influence, bounded confidence, and dissimilative influence., Comment: 58 pages, 2 figures, 15 tables

We introduce a minimal opinion formation model, which is quite flexible and can reproduce a broad variety of the existing micro-influence assumptions and models. At the same time, the model can be easily calibrated on real data, upon which it imposes only a few requirements. From this perspective, our model can be considered as a bridge, connecting theoretical studies on opinion formation models and empirical research on social dynamics. We investigate the model analytically by using mean-field approximation and numerically. Our analysis is exemplified by recently reported empirical data drawn from an online social network. Employing these data for the model calibration, we demonstrate that the model may reproduce fragmented and polarizing social systems. Furthermore, we manage to generate an artificial society that features properties quantitatively and qualitatively similar to those observed empirically at the macro scale. This ability became possible after we had advanced the model with two important communication features: selectivity and personalization algorithms., Comment: 59 pages, 8 figures, 6 tables

This cross-sectional study characterizes users' attitudes towards the face mask requirements introduced by the Russian government as a response to the COVID-19 pandemic. We study how they relate to other users' characteristics such as age, gender, and political attitudes. Our results indicate that men and elder individuals - demographic groups that are most vulnerable to COVID-19 -- underestimate the benefits of wearing face masks. We also discovered that users in opposition to the Russian government highly approve of this anti-COVID-19 measure -- an oppositionist will approve of the face mask requirements with the probability of 0.95. For those who support the Russian government, the odds of approval are merely 0.45., Comment: 6 pages, 2 tables, 3 figures

In this paper, we present an empirical study of the opinion dynamics of a large-scale sample of online social network users. We estimate opinions of users as continuous scalars based on their subscriptions to information sources and analyze how friendship connections affect the dynamics of these estimations. Distinguishing between positive (toward opinions of friends) and negative (away from opinions of friends) opinion shifts, we find that the existence and magnitude of opinion shifts are positively related (largely through a linear form or an inverted U-shaped form) to the degree of divergence in opinions between user and their friends. Additionally, we moderate the balance between positive and negative shifts using opinion divergence: if the opinions of the focal user and their friends are too similar or dissimilar, there is a relatively low chance of a positive shift., Comment: 66 pages. The Journal of Mathematical Sociology (2021)