Opinion Formation Process Research Articles

İNFORMASİYA CƏMİYYƏTİ ŞƏRAİTİNDƏ İCTİMAİ RƏYİN FORMALAŞDIRILMASI

The article is devoted to the study of the phenomenon of public opinion in modern information society. Identification of the main characteristics of public opinion in the information society is carried out by studying mass communications. The process of public opinion formation involves a complex interaction of various factors that affect the interpretation of information

Opinion formation process in a hierarchical society

Opinion formation process in a hierarchical society

Balancing homophily and prejudices in opinion dynamics: An extended Friedkin–Johnsen model

In this paper we propose an extended version of the Friedkin–Johnsen (FJ) model where we analyze the effects that a homophily-based influence matrix has on the opinion formation process. In particular, we assume that the influence matrix varies over time, and its entries (that represent the appraisals that individuals have of the other individuals) update based on the comparison between their opinions and can take both positive and negative values. This leads to a system of two difference equations, where the first one corresponds to the standard FJ model, except that the influence matrix is no longer constant, and the second one models the dynamics of this matrix. We show that a necessary and sufficient condition for the convergence of this modified version of the classical FJ model is that the influence matrix becomes constant in a finite number of steps. Moreover, we provide the explicit expression for the agents’ asymptotic opinions in some special cases: the purely cooperative setting, the case of a structurally balanced network and the case of a single discussion topic. Finally, the case when the topics are correlated and the influence matrix depends on the average opinion vectors is investigated.

Asynchronous opinion dynamics in social networks

Opinion spreading in a society decides the fate of elections, the success of products, and the impact of political or social movements. A prominent model to study opinion formation processes is due to Hegselmann and Krause. It has the distinguishing feature that stable states do not necessarily show consensus, i.e., the population of agents might not agree on the same opinion. We focus on the social variant of the Hegselmann–Krause model. There are n agents, which are connected by a social network. Their opinions evolve in an iterative, asynchronous process, in which agents are activated one after another at random. When activated, an agent adopts the average of the opinions of its neighbors having a similar opinion (where similarity of opinions is defined using a parameter ε\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varepsilon $$\\end{document}). Thus, the set of influencing neighbors of an agent may change over time. We show that such opinion dynamics are guaranteed to converge for any social network. We provide an upper bound of O(n|E|2(ε/δ)2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext {O}}(n|E|^2 (\\varepsilon /\\delta )^2)$$\\end{document} on the expected number of opinion updates until convergence to a stable state, where |E|\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$|E|$$\\end{document} is the number of edges of the social network, and δ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\delta $$\\end{document} is a parameter of the stability concept. For the complete social network we show a bound of O(n3(n2+(ε/δ)2))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext {O}}(n^3(n^2 + (\\varepsilon /\\delta )^2))$$\\end{document} that represents a major improvement over the previously best upper bound of O(n9(ε/δ)2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext {O}}(n^9 (\\varepsilon /\\delta )^2)$$\\end{document}.

Polarization and the Democratic System: Kinds, Reasons, and Sites

It is widely agreed that the increased polarization many countries experience is bad for democracy. However, existing assessments of how polarization affects democracy operate with simplified understandings of both polarization and democracy. Bringing empirical studies and democratic theory into dialogue, I argue that polarization cannot be understood as a single phenomenon that can be evaluated in one way. Moreover, its different kinds affect different parts of the democratic system in distinct ways. First, we must distinguish between the degree of polarization in a given context and the different kinds of polarization at play. Second, we must consider whether people have good reasons for their polarizing behavior or whether it is entirely irrational. If people have good reasons for their polarizing behavior, the problem lies elsewhere than in polarization itself. Third, we must distinguish between the content of polarized opinions and the process of opinion formation. Both can be assessed with democratic criteria, but they raise different questions. Finally, for democratic evaluation it matters where polarization occurs and thus, we must differentiate between different sites of polarization: civil society, election campaigns, and legislatures. I recommend a systemic approach to assessing the democratic implication of polarization, which analyzes both the effects of polarization at different sites and on democracy as a composite whole.

Chaos in Opinion-Driven Disease Dynamics.

During the COVID-19 pandemic, it became evident that the effectiveness of applying intervention measures is significantly influenced by societal acceptance, which, in turn, is affected by the processes of opinion formation. This article explores one among the many possibilities of coupled opinion-epidemic systems. The findings reveal either intricate periodic patterns or chaotic dynamics, leading to substantial fluctuations in opinion distribution and, consequently, significant variations in the total number of infections over time. Interestingly, the model exhibits a protective pattern.

Opinion Models, Election Data, and Political Theory.

A unifying setup for opinion models originating in statistical physics and stochastic opinion dynamics are developed and used to analyze election data. The results are interpreted in the light of political theory. We investigate the connection between Potts (Curie-Weiss) models and stochastic opinion models in the view of the Boltzmann distribution and stochastic Glauber dynamics. We particularly find that the q-voter model can be considered as a natural extension of the Zealot model, which is adapted by Lagrangian parameters. We also discuss weak and strong effects (also called extensive and nonextensive) continuum limits for the models. The results are used to compare the Curie-Weiss model, two q-voter models (weak and strong effects), and a reinforcement model (weak effects) in explaining electoral outcomes in four western democracies (United States, Great Britain, France, and Germany). We find that particularly the weak effects models are able to fit the data (Kolmogorov-Smirnov test) where the weak effects reinforcement model performs best (AIC). Additionally, we show how the institutional structure shapes the process of opinion formation. By focusing on the dynamics of opinion formation preceding the act of voting, the models discussed in this paper give insights both into the empirical explanation of elections as such, as well as important aspects of the theory of democracy. Therefore, this paper shows the usefulness of an interdisciplinary approach in studying real world political outcomes by using mathematical models.

Crisis? What crisis? The risk of fighting disinformation with the DSA's crisis response mechanism

ABSTRACT Since the coronavirus pandemic and the war in Ukraine, the EU has been concerned about the dissemination of disinformation in times of crisis. A mandatory Crisis Response Mechanism in the Digital Services Act is intended to counteract its dissemination via very large providers. However, the mechanism's weaknesses could trigger a far greater crisis for the free opinion-forming process.

A Social Network Model for Analysis of Public Opinion Formation Process

A Social Network Model for Analysis of Public Opinion Formation Process

Opinion evolution and dynamic trust-driven consensus model in large-scale group decision-making under incomplete information

The shift to a new era of dealing with big data has driven continuous progress and development in computer science, artificial intelligence and machine learning. This change has led to the application of advanced techniques in the realm of decision science, particularly in the area of large-scale group decision-making (LSGDM). However, although these existing techniques have become the core of LSGDM methods, they are still limited in solving problems facing incomplete data. In addition, due to the rise of social media platforms such as Weibo, WeChat and Twitter, which build bridges for communication between decision makers (DMs), this brings new opportunities and challenges for consensus research. To address this set of issues, this study develops a consensus architecture that combines dynamic social network and opinion evolution in the context of an incomplete multi-attribute LSGDM. It is worth mentioning that the proposed consensus framework is a novel decision-making system that can be used to complete the estimation of the missing values and the consensus reaching process (CRP) by simulating the realistic decision-making scenarios. Firstly, considering the size of the trust value and the length of the path, a new trust propagation method is designed to achieve a more reliable estimation of the unknown trust value. Secondly, this paper establishes a missing value estimation method by virtue of the improved DeGroot model, which is able to obtain complete evaluation information by simulating the opinion formation process of DMs. Next, a hierarchical clustering algorithm with stronger robustness is constructed, which not only can adaptively complete the clustering process, but also integrally considers two attributes of trust and opinion similarity. In light of the above research, this study designs an opinion evolution and dynamic trust-driven consensus model, referred to as the DSN-DG-LSGDM model. Finally, the sensitivity analysis and experiments on a real dataset verify the significant superiority of the constructed DSN-DG-LSGDM model compared with the extant LSGDM consensus models.

Staged Beefing in the Music Industry in Ghana: Perspectives of Selected Entertainment Bloggers and Music Artistes

This study explores staged beefing in the music industry for entertainment bloggers and music artists in Ghana. This qualitative case study focuses on capturing detailed information from participants using structured interviews. Through a purposive selection technique, ten participants were chosen and interviewed to learn about their personal experiences with the phenomena of staged beefing in the music industry and its ramifications for both bloggers and musicians. The participants' responses were analysed using the thematic analysis approach, which was based on Agenda Setting and Framing Theory. Participants identified implications of staged beefing to both bloggers and musicians as fake feuds, collaboration, and controversy, monetary gains, and clout. The data capture that some musicians and entertainment bloggers collaborate to create ‘fake’ feuding scenarios and that the phenomenon of ‘fake feuds’ is real event that happen. The result also shows that entertainment bloggers and musicians present information in such a way as to market the artiste and connect with the audiences during the process of opinion formation, which subsequently influences attitudes and behaviours. Based on the outcome of the interview, supported by the literature it became obvious that both bloggers and musicians who consent to create fake feuds do so to promote their brand visibility and relevance to attract large corporations for endorsement deals and or boost sales of their music

On evolving network models and their influence on opinion formation

In this paper, we propose a new model for continuous time opinion dynamics on an evolving network. As opposed to existing models, in which the network typically evolves by discretely adding or removing edges, we instead propose a model for opinion formation which is coupled to a network evolving through a system of ordinary differential equations for the edge weights. We interpret each edge weight as the strength of the relationship between a pair of individuals, with edges increasing in weight if pairs continually listen to each other’s opinions and decreasing if not. We investigate the impact of various edge dynamics at different timescales on the opinion dynamics itself. This is done partly through analytic results and partly through extensive numerical simulations of two case studies: one using bounded confidence interaction dynamics in the opinion formation process (as in the classical Hegselmann–Krause model) and one using an exponentially decaying interaction function. We find that the dynamic edge weights can have a significant impact on the opinion formation process, since they may result in consensus formation but can also reinforce polarisation. Overall, the proposed modelling approach allows us to quantify and investigate how the network and opinion dynamics influence each other.

Opinion Dynamics with Limited Information

We study opinion formation games based on the famous model proposed by Friedkin and Johsen (FJ model). In today’s huge social networks the assumption that in each round agents update their opinions by taking into account the opinions of all their friends is unrealistic. So, we are interested in the convergence properties of simple and natural variants of the FJ model that use limited information exchange in each round and converge to the same stable point. As in the FJ model, we assume that each agent i has an intrinsic opinion s_i in [0,1] and maintains an expressed opinion x_i(t) in [0,1] in each round t. To model limited information exchange, we consider an opinion formation process where each agent i meets with one random friend j at each round t and learns only her current opinion x_j(t). The amount of influence j imposes on i is reflected by the probability p_{ij} with which i meets j. Then, agent i suffers a disagreement cost that is a convex combination of (x_i(t) - s_i)^2 and (x_i(t) - x_j(t))^2. An important class of dynamics in this setting are no regret dynamics, i.e. dynamics that ensure vanishing regret against the experienced disagreement cost to the agents. We show an exponential gap between the convergence rate of no regret dynamics and of more general dynamics that do not ensure no regret. We prove that no regret dynamics require roughly varOmega (1/varepsilon ) rounds to be within distance varepsilon from the stable point of the FJ model. On the other hand, we provide an opinion update rule that does not ensure no regret and converges to x^* in tilde{O}(log ^2(1/varepsilon )) rounds. Finally, in our variant of the FJ model, we show that the agents can adopt a simple opinion update rule that ensures no regret to the experienced disagreement cost and results in an opinion vector that converges to the stable point x^* of the FJ model within distance varepsilon in textrm{poly}(1/varepsilon ) rounds. In view of our lower bound for no regret dynamics this rate of convergence is close to best possible.

Modeling communication asymmetry and content personalization in online social networks

The increasing popularity of online social networks (OSNs) attracted growing interest in modeling social interactions. On online social platforms, a few individuals, commonly referred to as influencers, produce the majority of content consumed by users and hegemonize the landscape of the social debate. However, classical opinion models do not capture this communication asymmetry. We develop an opinion model inspired by observations on social media platforms with two main objectives: first, to describe this inherent communication asymmetry in OSNs, and second, to model the effects of content personalization. We derive a Fokker–Planck equation for the temporal evolution of users’ opinion distribution and analytically characterize the stationary system behavior. Analytical results, confirmed by Monte-Carlo simulations, show how strict forms of content personalization tend to radicalize user opinion, leading to the emergence of echo chambers, and favor structurally advantaged influencers. As an example application, we apply our model to Facebook data during the Italian government crisis in 2019. Our work provides a flexible framework to evaluate the impact of content personalization on the opinion formation process, focusing on the interaction between influential individuals and regular users. This framework is interesting in the context of marketing and advertising, misinformation spreading, politics and activism.

P-718 Preimplantation genetic testing in Belgium: recommendations for the genetic centres

Abstract Study question How do genetic centres in Belgium apply international guidelines on preimplantation genetic testing (PGT)? Summary answer We present the national consensus on how to practice PGT, starting from initial patient uptake and embryo selection to follow-up of pregnancies. What is known already PGT includes PGT-M for monogenic disorders, PGT-SR for structural rearrangements and PGT-A for aneuploidy screening. The application of whole-genome technologies to trophectoderm biopsies for PGT-M and PGT-SR/A has evidenced the high prevalence of mosaicism due to chromosome segregation errors during the first mitotic divisions, making biopsy results interpretation and embryo transfer policy more challenging and complex. Guidelines from several international societies have been published in recent years to leverage the standardization of the whole PGT service. Although these are based on expertise and continuously acquiring evidence, it is widely recognized that variations in local and national regulations diversify PGT practices. Study design, size, duration Recommendations regarding PGT indications, patient counselling, embryo transfer policy, reporting, quality and pregnancy follow-up have been built to harmonize PGT services nationally. With a special focus on mosaicism, aiming to balance the highest chance for a pregnancy with the lowest risk for a chromosomally abnormal ongoing pregnancy or child. Participants/materials, setting, methods The Belgian PGT working group consists of laboratory geneticists, clinical geneticists and IVF specialists from eight nationally accredited centres. The group operates under the BeSHG umbrella and meets quarterly to formulate opinions and statements on PGT practice with the main goal of guaranteeing a high standard of care, exchange knowledge and implement a harmonized workflow. The Belgian social security system provides reimbursement of six genetic testing cycles for all PGT-M/SR patients but not for PGT-A. Main results and the role of chance PGT is offered for all monogenic and chromosomal disorders, if the causal genetic variants are known and documented. PGT for HLA-typing is allowed for the therapeutic benefit of an existing child after couple's psychological evaluation. Aneuploidy screening can be offered to specific patient groups to increase IVF success rate. However, social sexing and testing for non-medical or eugenic purposes is prohibited. At intake, the couple is counselled about the genetic aspect of the disease, psychological and financial aspects of PGT, applied methodology, including average success and misdiagnosis rate, and embryo transfer policies. Affected embryos, and embryos without diagnosis for PGT-M/SR are not transferred. For PGT-A (combined or not with PGT-M/SR), embryos are categorized as: (A) Euploid - suitable for transfer, if mosaicism is absent or below the detection threshold. (B) Mosaic transferable - suitable for transfer, but only following ranking and/or counselling and follow up, if mosaicism for any chromosome not described in (C) is detected; (C) Aneuploid or Mosaic - not suitable for transfer, if mosaicism of 8, 9, 13, 16, 18, 21 and X or full aneuploidy of any chromosome is detected. Non-invasive prenatal testing is strongly recommended to all women pregnant after PGT and is nationally reimbursed. Limitations, reasons for caution The practical recommendations, which are supplementary to the recently published ESHRE PGT recommendations, are based on expertise and data from the literature available at the time of preparation. As such, they will need regular reconsideration and an update or extension may follow when new solid scientific evidence arises. Wider implications of the findings As international recommendations represent a greatest common denominator over many local regulations, this work serves as example for drawing country-specific agreements for genetic centres. The process of consensus expert opinion formation on best practice may be applicable in other countries where PGT service is not overseen by a designated agency. Trial registration number Not applicable

The Effects of Individuals’ Opinion and Non-Opinion Characteristics on the Organization of Influence Networks in the Online Domain

The opinion dynamics literature argues that the way people perceive social influence depends not only on the opinions of interacting individuals, but also on the individuals’ non-opinion characteristics, such as age, education, gender, or place of residence. The current paper advances this line of research by studying longitudinal data that describe the opinion dynamics of a large sample (~30,000) of online social network users, all citizens of one city. Using these data, we systematically investigate the effects of users’ demographic (age, gender) and structural (degree centrality, the number of common friends) properties on opinion formation processes. We revealed that females are less easily influenced than males. Next, we found that individuals that are characterized by similar ages have more chances to reach a consensus. Additionally, we report that individuals who have many common peers find an agreement more often. We also demonstrated that the impacts of these effects are virtually the same, and despite being statistically significant, are far less strong than that of opinion-related features: knowing the current opinion of an individual and, what is even more important, the distance in opinions between this individual and the person that attempts to influence the individual is much more valuable. Next, after conducting a series of simulations with an agent-based model, we revealed that accounting for non-opinion characteristics may lead to not very sound but statistically significant changes in the macroscopic predictions of the populations of opinion camps, primarily among the agents with radical opinions (≈3% of all votes). In turn, predictions for the populations of neutral individuals are virtually the same. In addition, we demonstrated that the accumulative effect of non-opinion features on opinion dynamics is seriously moderated by whether the underlying social network correlates with the agents’ characteristics. After applying the procedure of random shuffling (in which the agents and their characteristics were randomly scattered over the network), the macroscopic predictions have changed by ≈9% of all votes. What is interesting is that the population of neutral agents was again not affected by this intervention.

Social conformity or attitude persistence? The bandwagon effect and the spiral of silence in a polarized context

ABSTRACT This study examines whether and to what extent the bandwagon effect and the spiral of silence impact opinion formation in a polarized context, where individuals tend to be persistent in their policy attitudes. Focusing on contentious policy issues at the heart of the culture war in American politics, our aim is to study the relative importance of attitude persistence and social conformity in the opinion-formation process, and how these responses depend on individuals’ ideological commitments. We conducted an experimental study of US citizens, where participants donated money to organizations advocating opposed positions on seven of the most contentious issues in American politics. Utilizing the presentation of opinion polls as a treatment, the findings are threefold. First, we show that polls cause ideologically moderate people to abandon the minority and conform to the majority opinion regardless of the issue at stake. By contrast, we show that attitude persistence prevails among ideologically extreme people. Second, we demonstrate that seeing polls generally demobilizes people with minority views. Third, we find that opinion-conversion and demobilization jointly undermine minority opinions, while only a small minority of extremists repels both mechanisms. These findings have important implications for research on opinion formation in today's polarized political landscape.

THE SECURITY OF SWEDEN AND NATO: ROAD WITH OBSTACLES

The article analyzes a set of problems related to Sweden’s planned accession to NATO. The author examines the impact on this process of various factors, both foreign and domestic, analyzes the processes of public opinion formation (pages dedicated to the annual representative security conference in January 2023 in Stockholm). Among them, Turkish-Swedish relations occupy a special place, since Turkey, as a NATO member, puts forward to Sweden a number of conditions dictated by the interests of the Turkish Republic itself and affecting Sweden’s immigration policy. They concern both the fate of the Kurdish community in Sweden, and the willingness or unwillingness of the Swedish authorities to adjust their actions against Muslim communities in this northern country under pressure from Turkey (in this regard, the immediate and long-term consequences of such provocative acts as the public burning of the Koran in Stockholm are being considered). The article also shows how the topical problems of Sweden’s foreign policy affect domestic political processes. The author comes to the conclusion that it is possible to analyze the problem of Sweden’s accession to NATO only in the context of analyzing the processes in the entire system of modern international relations

A novel method detecting controversial interaction in the multiplex social comment network

With the rise of online social media, users from across the world can participate in opinion formation processes, and some discussions lead to controversial debates around this phenomenon. Controversy detection in social media can help explore public discourse spaces and understand topical issues. Previous controversial detection studies focus more on identifying the opinion or emotional orientation of a comment, while we focus on whether there is a controversial relationship between a comment and its replies. Here, we collect a dataset consisting of 511 news articles, 103,787 comments, and 71,579 users on the Chinese social media platform, Toutiao, and we study the controversial interactions on the subsets of this dataset. Our approach treats news, comments, and users as different types of nodes and constructs multiplex networks connected by user–comment links (i.e., publishing relationship), comment–news links (i.e., comment relationship), and comment–comment links (i.e., replying relationship). Furthermore, we propose a model based on deep learning to detect controversial interactions from these multiplex networks. Our supervised model achieves 83.24% accuracy, with an improvement compared to competitive models. Moreover, we illustrate the applicability of our approach using different ratios of training and testing sets. Our results demonstrate the usefulness of the multiplex networks model for controversial interaction detection and provide a new perspective on controversy detection problems.

Mixed Opinion Dynamics Based on DeGroot Model and Hegselmann–Krause Model in Social Networks

Most existing opinion formation processes apply one opinion dynamics model. However, this article combines opinion formation and complex networks to innovatively develop two new opinion dynamics models to more realistically describe the opinion evolution process: 1) an opinion similarity mixed (OSM) model and 2) a structural similarity mixed (SSM) model, both of which include characteristics from the DeGroot model and the Hegselmann–Krause bounded confidence model. In addition, the strong and weak relations between individuals are considered. The network dynamically changes by two developed network updating algorithms based on opinion similarity and structural similarity. Simulations are then conducted using artificial and real-world networks, which are Erdös-Rényi random networks, random regular networks, scale-free networks, and the Twitter network. It is found that compared with static networks, the opinion evolution in dynamic networks produces fewer opinion clusters and smaller opinion variances. The dynamic network mechanism reduces the weak relations between agents and improves the global clustering coefficient in the ER random networks but not in the Twitter network, which means that the network topology has an impact on results. Therefore, it is concluded that agents’ subjective behaviors significantly influence the outcome of opinion evolution and networks, which is consistent with real life.