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Statistical Physics of Opinion Formation and Social Phenomena

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Statistical Physics".

Deadline for manuscript submissions: closed (15 April 2023) | Viewed by 27622

Special Issue Editor

Instituto de Cálculo, FCEyN, Universidad de Buenos Aires and Conicet, Intendente Guiraldes 2160, Cero + Infinito, Buenos Aires C1428EGA, Argentina
Interests: statistical physics; sociophysics; biophysics; networks; interacting particle systems; epidemic spreading; agent-based models

Special Issue Information

Dear Colleagues,

The mathematical modelling of social phenomena has grown enormously during the last two decades, motivated by the availability of large-scale data of human behavior, attracting social and computer scientists as well as theoretical physicists and applied mathematicians.  In particular, the field of physics, which uses mathematical methods from statistical and nonlinear physics to study human collective behavior, so-called sociophysics, has developed to investigate social phenomena such as opinion formation, political polarization, language evolution, cultural dissemination, rumor spreading, collective action, and crowd behavior.  Among these, the modelling of opinion dynamics in a society has gained special attention, and several agent-based models have been introduced to capture different aspects of the opinion formation process from a theoretical viewpoint.  Lately, opinion models have started to include more realistic features of social interactions, such as individuals' emotions and persuasion rooted in social psychology, as well as different types of individuals within a society, such as contrarians, extremists, moderates, nonconformists, and inflexibles.

The aim of this Special Issue is to provide original and recent investigations on sociophysics—in particular, on opinion dynamics.  We encourage the submission of articles on emerging topics such as high-order interactions in networks (simplicial complexes), coupled social and disease processes applied to the COVID-19 pandemic, the inclusion of emotional arousal, data analysis based on social networks, and comparisons and/or validations of models with real data.  Works may implement tools such as agent-based models, Monte Carlo simulations, information theory, entropy concepts, and machine learning techniques.  Both review papers and regular articles are welcome.

Dr. Federico Vazquez
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sociophysics
  • opinion dynamics
  • stochastic processes
  • agent-based models
  • networks
  • simplicial complex
  • computational methods for social sciences
  • statistical physics approaches for social dynamics

Published Papers (19 papers)

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Editorial

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4 pages, 188 KiB  
Editorial
Modeling and Analysis of Social Phenomena: Challenges and Possible Research Directions
Entropy 2022, 24(4), 491; https://doi.org/10.3390/e24040491 - 31 Mar 2022
Cited by 6 | Viewed by 1662
Abstract
This opening editorial aims to interest researchers and encourage novel research in the closely related fields of sociophysics and computational social science. We briefly discuss challenges and possible research directions in the study of social phenomena, with a particular focus on opinion dynamics. [...] Read more.
This opening editorial aims to interest researchers and encourage novel research in the closely related fields of sociophysics and computational social science. We briefly discuss challenges and possible research directions in the study of social phenomena, with a particular focus on opinion dynamics. The aim of this Special Issue is to allow physicists, mathematicians, engineers and social scientists to show their current research interests in social dynamics, as well as to collect recent advances and new techniques in the analysis of social systems. Full article
(This article belongs to the Special Issue Statistical Physics of Opinion Formation and Social Phenomena)

Research

Jump to: Editorial

13 pages, 526 KiB  
Article
Contrarian Majority Rule Model with External Oscillating Propaganda and Individual Inertias
Entropy 2023, 25(10), 1402; https://doi.org/10.3390/e25101402 - 30 Sep 2023
Cited by 1 | Viewed by 669
Abstract
We study the Galam majority rule dynamics with contrarian behavior and an oscillating external propaganda in a population of agents that can adopt one of two possible opinions. In an iteration step, a random agent interacts with three other random agents and takes [...] Read more.
We study the Galam majority rule dynamics with contrarian behavior and an oscillating external propaganda in a population of agents that can adopt one of two possible opinions. In an iteration step, a random agent interacts with three other random agents and takes the majority opinion among the agents with probability p(t) (majority behavior) or the opposite opinion with probability 1p(t) (contrarian behavior). The probability of following the majority rule p(t) varies with the temperature T and is coupled to a time-dependent oscillating field that mimics a mass media propaganda, in a way that agents are more likely to adopt the majority opinion when it is aligned with the sign of the field. We investigate the dynamics of this model on a complete graph and find various regimes as T is varied. A transition temperature Tc separates a bimodal oscillatory regime for T<Tc, where the population’s mean opinion m oscillates around a positive or a negative value from a unimodal oscillatory regime for T>Tc in which m oscillates around zero. These regimes are characterized by the distribution of residence times that exhibit a unique peak for a resonance temperature T*, where the response of the system is maximum. An insight into these results is given by a mean-field approach, which also shows that T* and Tc are closely related. Full article
(This article belongs to the Special Issue Statistical Physics of Opinion Formation and Social Phenomena)
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51 pages, 7033 KiB  
Article
Bounded Confidence and Cohesion-Moderated Pressure: A General Model for the Large-Scale Dynamics of Ordered Opinion
Entropy 2023, 25(8), 1219; https://doi.org/10.3390/e25081219 - 16 Aug 2023
Viewed by 1103
Abstract
Due to the development of social media, the mechanisms underlying consensus and chaos in opinion dynamics have become open questions and have been extensively researched in disciplines such as sociology, statistical physics, and nonlinear mathematics. In this regard, our paper establishes a general [...] Read more.
Due to the development of social media, the mechanisms underlying consensus and chaos in opinion dynamics have become open questions and have been extensively researched in disciplines such as sociology, statistical physics, and nonlinear mathematics. In this regard, our paper establishes a general model of opinion evolution based on micro-mechanisms such as bounded confidence, out-group pressure, and in-group cohesion. Several core conclusions are derived through theorems and simulation results in the model: (1) assimilation and high reachability in social networks lead to global consensus; (2) assimilation and low reachability result in local consensus; (3) exclusion and high reachability cause chaos; and (4) a strong “cocoon room effect” can sustain the existence of local consensus. These conclusions collectively form the “ideal synchronization theory”, which also includes findings related to convergence rates, consensus bifurcation, and other exploratory conclusions. Additionally, to address questions about consensus and chaos, we develop a series of mathematical and statistical methods, including the “energy decrease method”, the “cross-d search method”, and the statistical test method for the dynamical models, contributing to a broader understanding of stochastic dynamics. Full article
(This article belongs to the Special Issue Statistical Physics of Opinion Formation and Social Phenomena)
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13 pages, 956 KiB  
Article
An Agent-Based Statistical Physics Model for Political Polarization: A Monte Carlo Study
Entropy 2023, 25(7), 981; https://doi.org/10.3390/e25070981 - 27 Jun 2023
Cited by 1 | Viewed by 949
Abstract
World-wide, political polarization continues unabated, undermining collective decision-making ability. In this issue, we have examined polarization dynamics using a (mean-field) model borrowed from statistical physics, assuming that each individual interacted with each of the others. We use the model to generate scenarios of [...] Read more.
World-wide, political polarization continues unabated, undermining collective decision-making ability. In this issue, we have examined polarization dynamics using a (mean-field) model borrowed from statistical physics, assuming that each individual interacted with each of the others. We use the model to generate scenarios of polarization trends in time in the USA and explore ways to reduce it, as measured by a polarization index that we propose. Here, we extend our work using a more realistic assumption that individuals interact only with “neighbors” (short-range interactions). We use agent-based Monte Carlo simulations to generate polarization scenarios, considering again three USA political groups: Democrats, Republicans, and Independents. We find that mean-field and Monte Carlo simulation results are quite similar. The model can be applied to other political systems with similar polarization dynamics. Full article
(This article belongs to the Special Issue Statistical Physics of Opinion Formation and Social Phenomena)
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33 pages, 12109 KiB  
Article
Inference on a Multi-Patch Epidemic Model with Partial Mobility, Residency, and Demography: Case of the 2020 COVID-19 Outbreak in Hermosillo, Mexico
Entropy 2023, 25(7), 968; https://doi.org/10.3390/e25070968 - 22 Jun 2023
Viewed by 1425
Abstract
Most studies modeling population mobility and the spread of infectious diseases, particularly those using meta-population multi-patch models, tend to focus on the theoretical properties and numerical simulation of such models. As such, there is relatively scant literature focused on numerical fit, inference, and [...] Read more.
Most studies modeling population mobility and the spread of infectious diseases, particularly those using meta-population multi-patch models, tend to focus on the theoretical properties and numerical simulation of such models. As such, there is relatively scant literature focused on numerical fit, inference, and uncertainty quantification of epidemic models with population mobility. In this research, we use three estimation techniques to solve an inverse problem and quantify its uncertainty for a human-mobility-based multi-patch epidemic model using mobile phone sensing data and confirmed COVID-19-positive cases in Hermosillo, Mexico. First, we utilize a Brownian bridge model using mobile phone GPS data to estimate the residence and mobility parameters of the epidemic model. In the second step, we estimate the optimal model epidemiological parameters by deterministically inverting the model using a Darwinian-inspired evolutionary algorithm (EA)—that is, a genetic algorithm (GA). The third part of the analysis involves performing inference and uncertainty quantification in the epidemic model using two Bayesian Monte Carlo sampling methods: t-walk and Hamiltonian Monte Carlo (HMC). The results demonstrate that the estimated model parameters and incidence adequately fit the observed daily COVID-19 incidence in Hermosillo. Moreover, the estimated parameters from the HMC method yield large credible intervals, improving their coverage for the observed and predicted daily incidences. Furthermore, we observe that the use of a multi-patch model with mobility yields improved predictions when compared to a single-patch model. Full article
(This article belongs to the Special Issue Statistical Physics of Opinion Formation and Social Phenomena)
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11 pages, 587 KiB  
Article
Threshold Cascade Dynamics in Coevolving Networks
Entropy 2023, 25(6), 929; https://doi.org/10.3390/e25060929 - 13 Jun 2023
Cited by 2 | Viewed by 833
Abstract
We study the coevolutionary dynamics of network topology and social complex contagion using a threshold cascade model. Our coevolving threshold model incorporates two mechanisms: the threshold mechanism for the spreading of a minority state such as a new opinion, idea, or innovation and [...] Read more.
We study the coevolutionary dynamics of network topology and social complex contagion using a threshold cascade model. Our coevolving threshold model incorporates two mechanisms: the threshold mechanism for the spreading of a minority state such as a new opinion, idea, or innovation and the network plasticity, implemented as the rewiring of links to cut the connections between nodes in different states. Using numerical simulations and a mean-field theoretical analysis, we demonstrate that the coevolutionary dynamics can significantly affect the cascade dynamics. The domain of parameters, i.e., the threshold and mean degree, for which global cascades occur shrinks with an increasing network plasticity, indicating that the rewiring process suppresses the onset of global cascades. We also found that during evolution, non-adopting nodes form denser connections, resulting in a wider degree distribution and a non-monotonous dependence of cascades sizes on plasticity. Full article
(This article belongs to the Special Issue Statistical Physics of Opinion Formation and Social Phenomena)
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22 pages, 7283 KiB  
Article
Voter-like Dynamics with Conflicting Preferences on Modular Networks
Entropy 2023, 25(6), 838; https://doi.org/10.3390/e25060838 - 24 May 2023
Cited by 1 | Viewed by 867
Abstract
Two of the main factors shaping an individual’s opinion are social coordination and personal preferences, or personal biases. To understand the role of those and that of the topology of the network of interactions, we study an extension of the voter model proposed [...] Read more.
Two of the main factors shaping an individual’s opinion are social coordination and personal preferences, or personal biases. To understand the role of those and that of the topology of the network of interactions, we study an extension of the voter model proposed by Masuda and Redner (2011), where the agents are divided into two populations with opposite preferences. We consider a modular graph with two communities that reflect the bias assignment, modeling the phenomenon of epistemic bubbles. We analyze the models by approximate analytical methods and by simulations. Depending on the network and the biases’ strengths, the system can either reach a consensus or a polarized state, in which the two populations stabilize to different average opinions. The modular structure generally has the effect of increasing both the degree of polarization and its range in the space of parameters. When the difference in the bias strengths between the populations is large, the success of the very committed group in imposing its preferred opinion onto the other one depends largely on the level of segregation of the latter population, while the dependency on the topological structure of the former is negligible. We compare the simple mean-field approach with the pair approximation and test the goodness of the mean-field predictions on a real network. Full article
(This article belongs to the Special Issue Statistical Physics of Opinion Formation and Social Phenomena)
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15 pages, 1039 KiB  
Article
Three-State Opinion Model on Complex Topologies
Entropy 2022, 24(11), 1627; https://doi.org/10.3390/e24111627 - 10 Nov 2022
Cited by 1 | Viewed by 1147
Abstract
We investigate opinion diffusion on complex networks and the interplay between the existence of neutral opinion states and non-trivial network structures. For this purpose, we apply a three-state opinion model based on magnetic-like interactions to modular complex networks, both synthetic and real networks [...] Read more.
We investigate opinion diffusion on complex networks and the interplay between the existence of neutral opinion states and non-trivial network structures. For this purpose, we apply a three-state opinion model based on magnetic-like interactions to modular complex networks, both synthetic and real networks extracted from Twitter. The model allows for tuning the contribution of neutral agents using a neutrality parameter. We also consider social agitation, encoded as a temperature, that accounts for random opinion changes that are beyond the agent neighborhood opinion state. Using this model, we study which topological features influence the formation of consensus, bipartidism, or fragmentation of opinions in three parties, and how the neutrality parameter and the temperature interplay with the network structure. Full article
(This article belongs to the Special Issue Statistical Physics of Opinion Formation and Social Phenomena)
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12 pages, 1503 KiB  
Article
Modelling Spirals of Silence and Echo Chambers by Learning from the Feedback of Others
Entropy 2022, 24(10), 1484; https://doi.org/10.3390/e24101484 - 18 Oct 2022
Cited by 3 | Viewed by 1691
Abstract
What are the mechanisms by which groups with certain opinions gain public voice and force others holding a different view into silence? Furthermore, how does social media play into this? Drawing on neuroscientific insights into the processing of social feedback, we develop a [...] Read more.
What are the mechanisms by which groups with certain opinions gain public voice and force others holding a different view into silence? Furthermore, how does social media play into this? Drawing on neuroscientific insights into the processing of social feedback, we develop a theoretical model that allows us to address these questions. In repeated interactions, individuals learn whether their opinion meets public approval and refrain from expressing their standpoint if it is socially sanctioned. In a social network sorted around opinions, an agent forms a distorted impression of public opinion enforced by the communicative activity of the different camps. Even strong majorities can be forced into silence if a minority acts as a cohesive whole. On the other hand, the strong social organisation around opinions enabled by digital platforms favours collective regimes in which opposing voices are expressed and compete for primacy in public. This paper highlights the role that the basic mechanisms of social information processing play in massive computer-mediated interactions on opinions. Full article
(This article belongs to the Special Issue Statistical Physics of Opinion Formation and Social Phenomena)
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23 pages, 1156 KiB  
Article
Feedback Loops in Opinion Dynamics of Agent-Based Models with Multiplicative Noise
Entropy 2022, 24(10), 1352; https://doi.org/10.3390/e24101352 - 24 Sep 2022
Cited by 5 | Viewed by 1193
Abstract
We introduce an agent-based model for co-evolving opinions and social dynamics, under the influence of multiplicative noise. In this model, every agent is characterized by a position in a social space and a continuous opinion state variable. Agents’ movements are governed by the [...] Read more.
We introduce an agent-based model for co-evolving opinions and social dynamics, under the influence of multiplicative noise. In this model, every agent is characterized by a position in a social space and a continuous opinion state variable. Agents’ movements are governed by the positions and opinions of other agents and similarly, the opinion dynamics are influenced by agents’ spatial proximity and their opinion similarity. Using numerical simulations and formal analyses, we study this feedback loop between opinion dynamics and the mobility of agents in a social space. We investigate the behaviour of this ABM in different regimes and explore the influence of various factors on the appearance of emerging phenomena such as group formation and opinion consensus. We study the empirical distribution, and, in the limit of infinite number of agents, we derive a corresponding reduced model given by a partial differential equation (PDE). Finally, using numerical examples, we show that a resulting PDE model is a good approximation of the original ABM. Full article
(This article belongs to the Special Issue Statistical Physics of Opinion Formation and Social Phenomena)
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21 pages, 692 KiB  
Article
Analytical and Numerical Treatment of Continuous Ageing in the Voter Model
Entropy 2022, 24(10), 1331; https://doi.org/10.3390/e24101331 - 21 Sep 2022
Cited by 3 | Viewed by 1057
Abstract
The conventional voter model is modified so that an agent’s switching rate depends on the ‘age’ of the agent—that is, the time since the agent last switched opinion. In contrast to previous work, age is continuous in the present model. We show how [...] Read more.
The conventional voter model is modified so that an agent’s switching rate depends on the ‘age’ of the agent—that is, the time since the agent last switched opinion. In contrast to previous work, age is continuous in the present model. We show how the resulting individual-based system with non-Markovian dynamics and concentration-dependent rates can be handled both computationally and analytically. The thinning algorithm of Lewis and Shedler can be modified in order to provide an efficient simulation method. Analytically, we demonstrate how the asymptotic approach to an absorbing state (consensus) can be deduced. We discuss three special cases of the age-dependent switching rate: one in which the concentration of voters can be approximated by a fractional differential equation, another for which the approach to consensus is exponential in time, and a third case in which the system reaches a frozen state instead of consensus. Finally, we include the effects of a spontaneous change of opinion, i.e., we study a noisy voter model with continuous ageing. We demonstrate that this can give rise to a continuous transition between coexistence and consensus phases. We also show how the stationary probability distribution can be approximated, despite the fact that the system cannot be described by a conventional master equation. Full article
(This article belongs to the Special Issue Statistical Physics of Opinion Formation and Social Phenomena)
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15 pages, 3598 KiB  
Article
Opinion Polarization in Human Communities Can Emerge as a Natural Consequence of Beliefs Being Interrelated
Entropy 2022, 24(9), 1320; https://doi.org/10.3390/e24091320 - 19 Sep 2022
Cited by 5 | Viewed by 1921
Abstract
The emergence of opinion polarization within human communities—the phenomenon that individuals within a society tend to develop conflicting attitudes related to the greatest diversity of topics—has been a focus of interest for decades, both from theoretical and modelling points of view. Regarding modelling [...] Read more.
The emergence of opinion polarization within human communities—the phenomenon that individuals within a society tend to develop conflicting attitudes related to the greatest diversity of topics—has been a focus of interest for decades, both from theoretical and modelling points of view. Regarding modelling attempts, an entire scientific field—opinion dynamics—has emerged in order to study this and related phenomena. Within this framework, agents’ opinions are usually represented by a scalar value which undergoes modification due to interaction with other agents. Under certain conditions, these models are able to reproduce polarization—a state increasingly familiar to our everyday experience. In the present paper, an alternative explanation is suggested along with its corresponding model. More specifically, we demonstrate that by incorporating the following two well-known human characteristics into the representation of agents: (1) in the human brain beliefs are interconnected, and (2) people strive to maintain a coherent belief system; polarization immediately occurs under exposure to news and information. Furthermore, the model accounts for the proliferation of fake news, and shows how opinion polarization is related to various cognitive biases. Full article
(This article belongs to the Special Issue Statistical Physics of Opinion Formation and Social Phenomena)
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12 pages, 2414 KiB  
Article
Opinion Dynamics with Higher-Order Bounded Confidence
Entropy 2022, 24(9), 1300; https://doi.org/10.3390/e24091300 - 14 Sep 2022
Cited by 6 | Viewed by 1465
Abstract
The higher-order interactions in complex systems are gaining attention. Extending the classic bounded confidence model where an agent’s opinion update is the average opinion of its peers, this paper proposes a higher-order version of the bounded confidence model. Each agent organizes a group [...] Read more.
The higher-order interactions in complex systems are gaining attention. Extending the classic bounded confidence model where an agent’s opinion update is the average opinion of its peers, this paper proposes a higher-order version of the bounded confidence model. Each agent organizes a group opinion discussion among its peers. Then, the discussion’s result influences all participants’ opinions. Since an agent is also the peer of its peers, the agent actually participates in multiple group discussions. We assume the agent’s opinion update is the average over multiple group discussions. The opinion dynamics rules can be arbitrary in each discussion. In this work, we experiment with two discussion rules: centralized and decentralized. We show that the centralized rule is equivalent to the classic bounded confidence model. The decentralized rule, however, can promote opinion consensus. In need of modeling specific real-life scenarios, the higher-order bounded confidence is more convenient to combine with other higher-order interactions, from the contagion process to evolutionary dynamics. Full article
(This article belongs to the Special Issue Statistical Physics of Opinion Formation and Social Phenomena)
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13 pages, 7415 KiB  
Article
Authority or Autonomy? Exploring Interactions between Central and Peer Punishments in Risk-Resistant Scenarios
Entropy 2022, 24(9), 1289; https://doi.org/10.3390/e24091289 - 13 Sep 2022
Cited by 1 | Viewed by 1018
Abstract
Game theory provides a powerful means to study human cooperation and better understand cooperation-facilitating mechanisms in general. In classical game-theoretic models, an increase in group cooperation constantly increases people’s gains, implying that individual gains are a continuously varying function of the cooperation rate. [...] Read more.
Game theory provides a powerful means to study human cooperation and better understand cooperation-facilitating mechanisms in general. In classical game-theoretic models, an increase in group cooperation constantly increases people’s gains, implying that individual gains are a continuously varying function of the cooperation rate. However, this is inconsistent with the increasing number of risk-resistant scenarios in reality. A risk-resistant scenario means once a group does not successfully resist the risk, all individuals lose their resources, such as a community coping with COVID-19 and a village resisting a flood. In other words, individuals’ gains are segmented about the collaboration rate. This paper builds a risk-resistant model to explore whether punishment still promotes collaboration when people resist risk. The results show that central and peer punishments can both encourage collaboration but with different characteristics under different risk-resistant scenarios. Specifically, central punishment constrains the collaboration motivated by peer punishment regardless of risk, while peer punishment limits the collaboration induced by central punishment only when the risk is high. Our findings provide insights into the balance between peer punishment from public autonomy and central punishment from central governance, and the proposed model paves the way for the development of richer risk-resistant models. Full article
(This article belongs to the Special Issue Statistical Physics of Opinion Formation and Social Phenomena)
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15 pages, 828 KiB  
Article
Local-Forest Method for Superspreaders Identification in Online Social Networks
Entropy 2022, 24(9), 1279; https://doi.org/10.3390/e24091279 - 11 Sep 2022
Cited by 1 | Viewed by 1290
Abstract
Identifying the most influential spreaders in online social networks plays a prominent role in affecting information dissemination and public opinions. Researchers propose many effective identification methods, such as k-shell. However, these methods are usually validated by simulating propagation models, such as epidemic-like models, [...] Read more.
Identifying the most influential spreaders in online social networks plays a prominent role in affecting information dissemination and public opinions. Researchers propose many effective identification methods, such as k-shell. However, these methods are usually validated by simulating propagation models, such as epidemic-like models, which rarely consider the Push-Republish mechanism with attenuation characteristic, the unique and widely-existing spreading mechanism in online social media. To address this issue, we first adopt the Push-Republish (PR) model as the underlying spreading process to check the performance of identification methods. Then, we find that the performance of classical identification methods significantly decreases in the PR model compared to epidemic-like models, especially when identifying the top 10% of superspreaders. Furthermore, inspired by the local tree-like structure caused by the PR model, we propose a new identification method, namely the Local-Forest (LF) method, and conduct extensive experiments in four real large networks to evaluate it. Results highlight that the Local-Forest method has the best performance in accurately identifying superspreaders compared with the classical methods. Full article
(This article belongs to the Special Issue Statistical Physics of Opinion Formation and Social Phenomena)
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10 pages, 2358 KiB  
Article
Statistical Mechanics of Political Polarization
Entropy 2022, 24(9), 1262; https://doi.org/10.3390/e24091262 - 08 Sep 2022
Cited by 7 | Viewed by 2227
Abstract
Rapidly increasing political polarization threatens democracies around the world. Scholars from several disciplines are assessing and modeling polarization antecedents, processes, and consequences. Social systems are complex and networked. Their constant shifting hinders attempts to trace causes of observed trends, predict their consequences, or [...] Read more.
Rapidly increasing political polarization threatens democracies around the world. Scholars from several disciplines are assessing and modeling polarization antecedents, processes, and consequences. Social systems are complex and networked. Their constant shifting hinders attempts to trace causes of observed trends, predict their consequences, or mitigate them. We propose an equivalent-neighbor model of polarization dynamics. Using statistical physics techniques, we generate anticipatory scenarios and examine whether leadership and/or external events alleviate or exacerbate polarization. We consider three highly polarized USA groups: Democrats, Republicans, and Independents. We assume that in each group, each individual has a political stance s ranging between left and right. We quantify the noise in this system as a “social temperature” T. Using energy E, we describe individuals’ interactions in time within their own group and with individuals of the other groups. It depends on the stance s as well as on three intra-group and six inter-group coupling parameters. We compute the probability distributions of stances at any time using the Boltzmann probability weight exp(−E/T). We generate average group-stance scenarios in time and explore whether concerted interventions or unexpected shocks can alter them. The results inform on the perils of continuing the current polarization trends, as well as on possibilities of changing course. Full article
(This article belongs to the Special Issue Statistical Physics of Opinion Formation and Social Phenomena)
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14 pages, 604 KiB  
Article
Opinion Dynamics and Unifying Principles: A Global Unifying Frame
Entropy 2022, 24(9), 1201; https://doi.org/10.3390/e24091201 - 27 Aug 2022
Cited by 8 | Viewed by 2266
Abstract
I review and extend the set of unifying principles that allow comparing all models of opinion dynamics within one single frame. Within the Global Unifying Frame (GUF), any specific update rule chosen to study opinion dynamics for discrete individual choices is recast into [...] Read more.
I review and extend the set of unifying principles that allow comparing all models of opinion dynamics within one single frame. Within the Global Unifying Frame (GUF), any specific update rule chosen to study opinion dynamics for discrete individual choices is recast into a probabilistic update formula. The associated dynamics is deployed using a general probabilistic sequential process, which is iterated via the repeated reshuffling of agents between successive rounds of local updates. The related driving attractors and tipping points are obtained with non-conservative regimes featuring both threshold and threshold-less dynamics. Most stationary states are symmetry broken, but fifty–fifty coexistence may also occur. A practical procedure is exhibited for several versions of Galam and Sznajd models when restricted to the use of three agents for the local updates. Comparing these various models, some are found to be identical within the GUF. Possible discrepancies with numerical simulations are discussed together with the difference between the GUF procedure and a mean field approach. Full article
(This article belongs to the Special Issue Statistical Physics of Opinion Formation and Social Phenomena)
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17 pages, 1236 KiB  
Article
Contrarian Voter Model under the Influence of an Oscillating Propaganda: Consensus, Bimodal Behavior and Stochastic Resonance
Entropy 2022, 24(8), 1140; https://doi.org/10.3390/e24081140 - 17 Aug 2022
Cited by 2 | Viewed by 1218
Abstract
We study the contrarian voter model for opinion formation in a society under the influence of an external oscillating propaganda and stochastic noise. Each agent of the population can hold one of two possible opinions on a given issue—against or in favor—and interacts [...] Read more.
We study the contrarian voter model for opinion formation in a society under the influence of an external oscillating propaganda and stochastic noise. Each agent of the population can hold one of two possible opinions on a given issue—against or in favor—and interacts with its neighbors following either an imitation dynamics (voter behavior) or an anti-alignment dynamics (contrarian behavior): each agent adopts the opinion of a random neighbor with a time-dependent probability p(t), or takes the opposite opinion with probability 1p(t). The imitation probability p(t) is controlled by the social temperature T, and varies in time according to a periodic field that mimics the influence of an external propaganda, so that a voter is more prone to adopt an opinion aligned with the field. We simulate the model in complete graph and in lattices, and find that the system exhibits a rich variety of behaviors as T is varied: opinion consensus for T=0, a bimodal behavior for T<Tc, an oscillatory behavior where the mean opinion oscillates in time with the field for T>Tc, and full disorder for T1. The transition temperature Tc vanishes with the population size N as Tc2/lnN in complete graph. In addition, the distribution of residence times tr in the bimodal phase decays approximately as tr3/2. Within the oscillatory regime, we find a stochastic resonance-like phenomenon at a given temperature T*. Furthermore, mean-field analytical results show that the opinion oscillations reach a maximum amplitude at an intermediate temperature, and that exhibit a lag with respect to the field that decreases with T. Full article
(This article belongs to the Special Issue Statistical Physics of Opinion Formation and Social Phenomena)
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16 pages, 2314 KiB  
Article
Dynamic Parameter Calibration Framework for Opinion Dynamics Models
Entropy 2022, 24(8), 1112; https://doi.org/10.3390/e24081112 - 12 Aug 2022
Cited by 2 | Viewed by 1389
Abstract
In the past decade, various opinion dynamics models have been built to depict the evolutionary mechanism of opinions and use them to predict trends in public opinion. However, model-based predictions alone cannot eliminate the deviation caused by unforeseeable external factors, nor can they [...] Read more.
In the past decade, various opinion dynamics models have been built to depict the evolutionary mechanism of opinions and use them to predict trends in public opinion. However, model-based predictions alone cannot eliminate the deviation caused by unforeseeable external factors, nor can they reduce the impact of the accumulated random error over time. To solve this problem, we propose a dynamic framework that combines a genetic algorithm and a particle filter algorithm to dynamically calibrate the parameters of the opinion dynamics model. First, we design a fitness function in accordance with public opinion and search for a set of model parameters that best match the initial observation. Second, with successive observations, we tracked the state of the opinion dynamic system by the average distribution of particles. We tested the framework by using several typical opinion dynamics models. The results demonstrate that the proposed method can dynamically calibrate the parameters of the opinion dynamics model to predict public opinion more accurately. Full article
(This article belongs to the Special Issue Statistical Physics of Opinion Formation and Social Phenomena)
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