Opinion Dynamics Research Articles

Dynamics of Social Influence and Knowledge in Networks: Sociophysics Models and Applications in Social Trading, Behavioral Finance and Business

In this paper we offer a comprehensive review of Sociophysics, focusing on relevant models as well as selected applications in social trading, behavioral finance and business. We discuss three key aspects of social diffusion dynamics, namely Opinion Dynamics (OD), Group Decision-Making (GDM) and Knowledge Dynamics (KD). In the OD case, we highlight special classes of social agents, such as informed agents, contrarians and extremists. As regards GDM, we present state-of-the-art models on various kinds of decision-making processes. In the KD case, we discuss processes of knowledge diffusion and creation via the presence of self-innovating agents. The primary question we wish to address is: to what extent does Sociophysics correspond to social reality? For that purpose, for each social diffusion model category, we present notable Sociophysics applications for real-world socioeconomic phenomena and, additionally, we provide a much-needed critique of the existing Sociophysics literature, so as to raise awareness of certain issues that currently undermine the effective application of Sociophysics, mainly in terms of modelling assumptions and mathematical formulation, on the investigation of key social processes.

Discrete preference games with logic-based agents: Formal framework, complexity, and islands of tractability

Analyzing and predicting the dynamics of opinion formation in the context of social environments are problems that attracted much attention in literature. While grounded in social psychology, these problems are nowadays popular within the artificial intelligence community, where opinion dynamics are often studied via game-theoretic models in which individuals/agents hold opinions taken from a fixed set of discrete alternatives, and where the goal is to find those configurations where the opinions expressed by the agents emerge as a kind of compromise between their innate opinions and the social pressure they receive from the environments. As a matter of facts, however, these studies are based on very high-level and sometimes simplistic formalizations of the social environments, where the mental state of each individual is typically encoded as a variable taking values from a Boolean domain. To overcome these limitations, the paper proposes a framework generalizing such discrete preference games by modeling the reasoning capabilities of agents in terms of weighted propositional logics. It is shown that the framework easily encodes different kinds of earlier approaches and fits more expressive scenarios populated by conformist and dissenter agents. Problems related to the existence and computation of stable configurations are studied, under different theoretical assumptions on the structural shape of the social interactions and on the class of logic formulas that are allowed. Remarkably, during its trip to identify some relevant tractability islands, the paper devises a novel technical machinery whose significance goes beyond the specific application to analyzing opinion formation and diffusion, since it significantly enlarges the class of Integer Linear Programs that were known to be tractable so far.

Aging in Some Opinion Formation Models: A Comparative Study

Changes of mind can become less likely the longer an agent has adopted a given opinion state. This resilience or inertia to change has been called “aging”. We perform a comparative study of the effects of aging on the critical behavior of two standard opinion models with pairwise interactions. One of them is the voter model, which is a two-state model with a dynamic that proceeds via social contagion; another is the so-called kinetic exchange model, which allows a third (neutral) state, and its formed opinion depends on the previous opinions of both interacting agents. Furthermore, in the noisy version of both models, random opinion changes are also allowed, regardless of the interactions. Due to aging, the probability of changing diminishes with the age, and to take this into account, we consider algebraic and exponential kernels. We investigate the situation where aging acts only on pairwise interactions. Analytical predictions for the critical curves of the order parameters are obtained for the opinion dynamics on a complete graph, in good agreement with agent-based simulations. For both models considered, the consensus is optimized via an intermediate value of the parameter that rules the rate of decrease of the aging factor.

Opinion Dynamics on Directed Complex Networks

We propose and analyze a mathematical model for the evolution of opinions on directed complex networks. Our model generalizes the popular DeGroot and Friedkin-Johnsen models by allowing vertices to have attributes that may influence the opinion dynamics. We start by establishing sufficient conditions for the existence of a stationary opinion distribution on any fixed graph, and then provide an increasingly detailed characterization of its behavior by considering a sequence of directed random graphs having a local weak limit. Our most explicit results are obtained for graph sequences whose local weak limit is a marked Galton-Watson tree, in which case our model can be used to explain a variety of phenomena, for example, conditions under which consensus can be achieved, mechanisms in which opinions can become polarized, and the effect of disruptive stubborn agents on the formation of opinions. Funding: This work was supported by the National Science Foundation [Grants NSF-DMS-1929298 and CMMI-2243261].

Effects of correlation between bounded confidences and node degrees on consensus in opinion dynamics on complex networks

In bounded confidence opinion dynamics, agents only consider other agents when their opinions are sufficiently close. In many works, the confidence bound is assumed as a homogeneous one. However, agents in different situations may be with different confidence bounds in real life. In this work, the correlation of degrees and confidence bounds is introduced into modified Hegselmann–Krause model on complex networks and we mainly focus on whether the population can reach consensus, where all agents hold a same opinion. The number of opinion clusters, the relative size of the largest cluster and the probability of reaching consensus are the measures of the opinion cluster profile. The numerical simulation shows that the existence of the correlation promotes the probability of reaching consensus and even in the case where some agents are with very small confidence bounds. On scale-free and Erdös–Rényi random networks, the negative correlation supports the consensus much more than the positive correlation case. However, two correlations show the same effect on small-world networks, and this effect is also observed in scale-free and Erdös–Rényi random networks when the average degree becomes very large.

Phase transition and universality of the majority-rule model on complex networks

In this paper, we investigate the phenomena of order-disorder phase transition and the universality of the majority-rule model defined on three complex networks, namely the Barabási–Albert, Watts–Strogatz and Erdős–Rényi networks. Assume each agent holds two possible opinions randomly distributed across the networks’ nodes. Agents adopt anticonformity and independence behaviors, represented by the probability p, where with a probability p, agents adopt anticonformity or independence behavior. Based on our numerical simulation results and finite-size scaling analysis, it is found that the model undergoes a continuous phase transition for all networks, with critical points for the independence model greater than those for the anticonformity model in all three networks. We obtain critical exponents identical to the opinion dynamics model defined on a complete graph, indicating that the model exhibits the same universality class as the mean-field Ising model.

Evaluating the Impact of Internet Users' Emotional Tendencies on Opinion Dynamics through Computer-Assisted Sentiment Analysis of Texts

- Internet communication is characterised by "non-linear flow" and "unorganised aggregation,"  and computer-assisted text sentiment analysis is an important means of evaluating the impact of netizens' emotions on the dynamics of public opinion. The uncertainty, anonymity, disorder, and blindness inherent in fast-gathering and fast-dispersing Internet communication drive the continuous generation of texts, which has an important impact on public opinion. In order to study this issue in depth, this paper chooses two text types of "popular Weibo" and "popular topics" in Sina Weibo as the object of study and analyses the issue in detail from the two dimensions of agenda construction and agenda setting. In the process of the study, the required text data were collected through Python, and the theoretical assumptions were verified with text analysis methods to explore the influence of text sentiment bias on opinion diffusion, with a view to providing new insights and references for the application of the theory of opinion diffusion and the field of computational diffusion.

Artificial Intelligence-Powered Sentiment Analysis Tool to Assess the Popularity of Political Leaders in Informal Communities

This study investigates the utility of Artificial Intelligence sentiment analysis tools in assessing the popularity of political leaders within informal communities. In contemporary socio-political landscapes, understanding public sentiment towards political figures is paramount for gauging societal perceptions and predicting electoral outcomes. Leveraging sentiment analysis techniques, this research delves into the nuanced expressions and sentiments prevalent in informal online communities regarding political leaders. By analyzing textual data from social media platforms, forums, and other digital sources, the study aims to discern patterns and trends indicative of public opinion dynamics. Through natural language processing and machine learning algorithms, sentiment analysis tools enable the automated extraction of sentiments, emotions, and attitudes towards political leaders, providing valuable insights into their perceived popularity and public reception. The findings contribute to our understanding of the intersection between technology, politics, and public opinion, shedding light on the evolving landscape of political discourse in digital spaces. The study underscores the significance of sentiment analysis for political analysts, policymakers, and researchers in comprehending and navigating the complexities of contemporary political environments.

A data-driven kinetic model for opinion dynamics with social network contacts – ADDENDUM

A data-driven kinetic model for opinion dynamics with social network contacts – ADDENDUM

Conformity and Mass Media Influence in the Sznajd Model on Regular Lattices

The polarization of opinions and difficulties in reaching a consensus are central problems of many modern societies. Understanding the dynamics governing those processes is, therefore, one of the main aims of sociophysics. In this work, the Sznajd model of opinion dynamics is investigated with Monte Carlo simulations performed on four different regular lattices: triangular, honeycomb, and square with von Neumann or Moore neighborhood. The main objective is to discuss the interplay of the probability of convincing (conformity) and mass media (external) influence and to provide the details of the possible phase transitions. The results indicate that, while stronger bonds and openness to discussion and argumentation may help in reaching a consensus, external influence becomes destructive at different levels depending on the lattice.

Modeling the opinion dynamics of superstars in the film industry

One of the most challenging questions in the film industry is to rank superstars, which ultimately affects some performance indicators like movie success. In this work, we address this question by means of opinion dynamics models, where the evolution of opinions in a population is analyzed. We apply a model of this kind to study the evolution of opinions about a set of well-known movie superstars in a real-world population. Also, we use real-world data from a specialized cinema website to model mass communication processes (representing film releases and their related news and marketing campaigns), and to measure the performance of our model. Our results show that the proposed model is able to accurately represent this complex system, where the opinion dynamics of superstars are mostly driven by emotional mechanisms, and reveal that film releases and their corresponding marketing campaigns only have a short term effect on those opinions. To the best of our knowledge, this is the first work that applies opinion dynamics models to the study of opinions about superstars in the film industry.

Polyadic Opinion Formation: The Adaptive Voter Model on a Hypergraph

AbstractThe adaptive voter model is widely used to model opinion dynamics in social complex networks. However, existing adaptive voter models are limited to only pairwise interactions and fail to capture the intricate social dynamics that arises in groups. This paper extends the adaptive voter model to hypergraphs to explore how forces of peer pressure influence collective decision‐making. The model consists of two processes: individuals can either consult the group and change their opinion or leave the group and join a different one. The interplay between those two processes gives rise to a two‐phase dynamics. In the initial phase, the topology of the hypergraph quickly reaches a new stable state. In the subsequent phase, opinion dynamics plays out on the new topology depending on the mechanism by which opinions spread. If the group always follows the majority, the network rapidly converges to fragmented communities. In contrast, if individuals choose an opinion proportionally to its representation in the group, the system remains in a metastable state for an extended period of time. The results are supported both by stochastic simulations and an analytical mean‐field description in terms of hypergraph moments with a moment closure at the pair level.

Expressive and Flexible Simulation of Information Spread Strategies in Social Networks Using Planning

In the digital age, understanding the dynamics of information spread and opinion formation within networks is paramount. This research introduces an innovative framework that combines the principles of opinion dynamics with the strategic capabilities of Automated Planning. We have developed, to the best of our knowledge, the first-ever numeric PDDL tailored for opinion dynamics. Our tool empowers users to visualize intricate networks, simulate the evolution of opinions, and strategically influence that evolution to achieve specific outcomes. By harnessing Automated Planning techniques, our framework offers a nuanced approach to devise sequences of actions tailored to transition a network from its current opinion landscape to a desired state. This holistic approach provides insights into the intricate interplay of individual nodes within a network and paves the way for targeted interventions. Furthermore, the tool facilitates human-AI collaboration, enabling users to not only understand information spread but also devise practical strategies to mitigate potential harmful outcomes arising from it. Demo Video link - https://tinyurl.com/3k7bp99h

A novel higher-order Deffuant–Weisbuch networks model incorporating the Susceptible Infected Recovered framework

The literature on opinion dynamics lacks consideration of the changing states of nodes during the process of opinion interaction, as observed in the literature on social networks. To address this gap, this paper proposes an adapted three-dimensional Deffuant–Weisbuch model based on the Susceptible Infected Recovered (SIR) model, which explores the impact of node state changes on opinion interaction. We incorporate the SIR model to describe changes in node state and improve the trust threshold by taking into account nodes’ emotional factor, cognitive ability, and state of the arts. The model is also modified using the opinion natural evolution coefficient and accounts for dynamic changes in social network structure. We validate our model with different network types and structures, and verify our results using the Louvain algorithm and index of centrality. Our model reveals that under different network types and structures, multiple opinion cliques are formed, and the opinions of ordinary nodes tend to converge around those of opinion leaders, indicating the guiding role of opinion leaders in shaping public opinion.

A Novel Influence Analysis-Based University Major Similarity Study

In the field of education, investigating the relationships between different majors in universities is an important topic in current educational research. The application of social networks from informatics provides new opportunities and potentials for the field of education. Due to the complexity of social interactions, the social network connections surrounding individuals exert a significant influence on their daily decision-making processes. This paper aims to introduce the social network and influence analysis theories from informatics into the field of education, regarding major as a variable, and comparing and analyzing the influence relationships between majors. An empirical study was conducted, involving the collection of questionnaire data on graduates’ evaluations of various aspects of their university experiences across different majors. The evolution of this model follows the DeGroot opinion dynamics with the inclusion of stubborn nodes. By defining leader majors and general majors based on the data and modeling the questionnaire data as the outcome of a discrete random process, an influence matrix is ultimately generated through the opinion dynamic model. Through this modeling approach, we revealed the underlying influence relationships between different disciplines (majors). These findings provide schools with insights to adjust the directions of discipline cultivation, and offer new perspectives and methods for the study of majors in higher education.

How individuals’ opinions influence society’s resistance to epidemics: an agent-based model approach

BackgroundProtecting public health from infectious diseases often relies on the cooperation of citizens, especially when self-care interventions are the only viable tools for disease mitigation. Accordingly, social aspects related to public opinion have been studied in the context of the recent COVID-19 pandemic. However, a comprehensive understanding of the effects of opinion-related factors on disease spread still requires further exploration.MethodsWe propose an agent-based simulation framework incorporating opinion dynamics within an epidemic model based on the assumption that mass media channels play a leading role in opinion dynamics. The model simulates how opinions about preventive interventions change over time and how these changes affect the cumulative number of cases. We calibrated our simulation model using YouGov survey data and WHO COVID-19 new cases data from 15 different countries. Based on the calibrated models, we examine how different opinion-related factors change the consequences of the epidemic. We track the number of total new infections for analysis.ResultsOur results reveal that the initial level of public opinion on preventive interventions has the greatest impact on the cumulative number of cases. Its normalized permutation importance varies between 69.67% and 96.65% in 15 models. The patterns shown in the partial dependence plots indicate that other factors, such as the usage of the pro-intervention channel and the response time of media channels, can also bring about substantial changes in disease dynamics, but only within specific ranges of the dominant factor.ConclusionsOur results reveal the importance of public opinion on intervention during the early stage of the pandemic in protecting public health. The findings suggest that persuading the public to take actions they may be hesitant about in the early stages of epidemics is very costly because taking early action is critical for mitigating infectious diseases. Other opinion-related factors can also lead to significant changes in epidemics, depending on the average level of public opinion in the initial stage. These findings underscore the importance of media channels and authorities in delivering accurate information and persuading community members to cooperate with public health policies.

Quantum-Mechanical Modelling of Asymmetric Opinion Polarisation in Social Networks

We propose a quantum-mechanical model that represents a human system of beliefs as the quantised energy levels of a physical system. This model represents a novel perspective on opinion dynamics, recreating a broad range of experimental and real-world data that exhibit an asymmetry of opinion radicalisation. In particular, the model demonstrates the phenomena of pronounced conservatism versus mild liberalism when individuals are exposed to opposing views, mirroring recent findings on opinion polarisation via social media exposure. Advancing this model, we establish a robust framework that integrates elements from physics, psychology, behavioural science, decision-making theory, and philosophy. We also emphasise the inherent advantages of the quantum approach over traditional models, suggesting a number of new directions for future research work on quantum-mechanical models of human cognition and decision-making.

Opinion dynamics in communities with major influencers and implicit social influence via mean-field approximation

Opinion dynamics in communities with major influencers and implicit social influence via mean-field approximation

Asymmetric games on networks: Mapping to Ising models and bounded rationality

We investigate the dynamics of coordination and consensus in an agent population. Considering agents endowed with bounded rationality, we study asymmetric coordination games using a mapping to random field Ising models. In doing so, we investigate the relationship between group coordination and agent rationality. Analytical calculations and numerical simulations of the proposed model lead to novel insight into opinion dynamics. For instance, we find that bounded rationality and preference intensity can determine a series of possible scenarios with different levels of opinion polarization. To conclude, we deem our investigation opens a new avenue for studying game dynamics through methods of statistical physics.

Optimal control in opinion dynamics models: diversity of influence mechanisms and complex influence hierarchies

Understanding how individuals change their opinions is essential to mitigating the harmful effect of fake news and stopping opinion polarization. The current situation in the field of opinion dynamics models is that we still do not know exactly how social influence changes individuals’ opinions and behavior. The question of which model best describes real social processes remains debatable. The current paper attempts to reconcile this problem. Building upon a relatively simple opinion dynamics model that can capture the key microscopic mechanisms of social influence and affords a tractable description of various complex influence hierarchies of social systems whereby agents may differ in how they are open to influence and how influential they are, we elaborate 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, we characterize the properties of the optimal control and systematically demonstrate how these algorithms capture stylized examples, in which the key microscopic mechanisms of social influence as well as various influence hierarchies are covered.