Opinion polarization in social networks

Loy, Nadia; Raviola, Matteo; Tosin, Andrea

doi:10.1098/rsta.2021.0158

Cited by 15 publications

(11 citation statements)

References 37 publications

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“…Focusing on changing the beliefs of key members, such as INFLs, of a polarized group may trigger a snowball effect in the beliefs of all members of the given community. The results in [123] suggest that this may be the case as they showed that most individuals from a network over time switch to opposite sentiments about the preconceived belief. The results obtained in [111] suggest that another possible way to revert polarization is to shield the members from their corresponding echo chambers, allowing them to access the ideas of members outside these chambers freely.…”

Section: Polarization Dimension As Shown In Tablementioning

confidence: 95%

Online Fake News Opinion Spread and Belief Change: A Systematic Review

Altoe,

Moreira,

Pinto

et al. 2024

Human Behavior and Emerging Technologies

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Fake news has been linked to the rise of psychological disorders, the increased disbelief in science, and the erosion of democracy and freedom of speech. Online social networks are arguably the main vehicle of fake news spread. Educating online users with explanations is one way of preventing this spread. Understanding how online belief is formed and changed may offer a roadmap for such education. The literature includes surveys addressing online opinion formation and polarization; however, they usually address a single domain, such as politics, online marketing, health, and education, and do not make online belief change their primary focus. Unlike other studies, this work is the first to present a cross-domain systematic literature review of user studies, methodologies, and opinion model dimensions. It also includes the orthogonal polarization dimension, focusing on online belief change. We include peer-reviewed works published in 2020 and later found in four relevant scientific databases, excluding theoretical publications that did not offer validation through dataset experimentation or simulation. Bibliometric networks were constructed for better visualization, leading to the organization of the papers that passed the review criteria into a comprehensive taxonomy. Our findings show that a person’s individuality is the most significant influential force in online belief change. We show that online arguments that balance facts with emotionally evoking content are more efficient in changing their beliefs. Polarization was shown to be cross-correlated among multiple subjects, with politics being the central polarization pole. Polarized online networks start as networks with high opinion segregation, evolve into subnetworks of consensus, and achieve polarization around social network influencers. Trust in the information source was demonstrated to be the chief psychological construct that drives online users to polarization. This shows that changing the beliefs of influencers may create a positive snowball effect in changing the beliefs of polarized online social network users. These findings lay the groundwork for further research on using personalized explanations to reduce the harmful effects of online fake news on social networks.

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Section: Polarization Dimension As Shown In Tablementioning

confidence: 95%

Online Fake News Opinion Spread and Belief Change: A Systematic Review

Altoe,

Moreira,

Pinto

et al. 2024

Human Behavior and Emerging Technologies

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“…Even though, in general, the collision kernel can be given as a unique function of (x, x * , c, c * ), here we assume that it can be factorized in such a way that B(x, x * ) describes the influence of the neuron location on the interactions, while G(c, c * ) refers to their degree of connections. Substituting (11) into (12) we get…”

Section: Spatially Inhomogeneous Casementioning

confidence: 99%

“…This means that, unlike gas molecules, the interactions among the considered particles are mediated by a background graph structure discriminating which interactions may actually take place and at which rate. Examples of kinetic descriptions of networked interactions can be found, for instance, in opinion formation problems [7,12,19] or in the modeling of the spread of infectious diseases [13]. In these problems, the background network may enter the big picture in essentially two ways.…”

Section: Introductionmentioning

confidence: 99%

Kinetic and macroscopic equations for action potential in neural networks

Conte¹,

Groppi²,

Tosin³

2023

Preprint

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Starting from the concept of binary interactions between pairs of particles, a kinetic framework for the description of the action potential dynamics on a neural network is proposed. It consists of two coupled levels: the description of a single brain region dynamics and the interactions among different regions. On one side, the pairwise interaction between neurons exchanging membrane potential is statistically described to account for the unmanageable number of neuron synapses within a single brain region. On the other, the network connections accounting for the brain region topology are represented and studied using concepts of the graph theory. Equilibrium and stability of the obtained macroscopic systems are analyzed as well as numerical simulations of the system dynamics are performed in different scenarios. In particular, the latter allows us to observe the influence of the discrete network topology on the membrane potential propagation and synchronization through the different regions, in terms of its spiking characteristics.

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“…Loy et al [ 25 ] propose a Boltzmann-type kinetic description of opinion formation on social networks, which takes into account a general connectivity distribution of the individuals. The opinion exchange processes considered here were inspired by the Sznajd model and related simplifications but the individuals are not placed on a regular lattice, nor it is the case of mean field interaction as was the case for few of the above-mentioned studies.…”

Section: Kinetic Exchange Models Of Opinion Formationmentioning

confidence: 99%

Kinetic exchange models of societies and economies

Toscani

Sen

Biswas

2022

Phil. Trans. R. Soc. A.

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The statistical nature of collective human behaviour in a society is a topic of broad current interest. From formation of consensus through exchange of ideas, distributing wealth through exchanges of money, traffic flows, growth of cities to spread of infectious diseases, the application range of such collective responses cuts across multiple disciplines. Kinetic models have been an elegant and powerful tool to explain such collective phenomena in a myriad of human interaction-based problems, where an energy consideration for dynamics is generally inaccessible. Nonetheless, in this age of Big Data, seeking empirical regularities emerging out of collective responses is a prominent and essential approach, much like the empirical thermodynamic principles preceding quantitative foundations of statistical mechanics. In this introductory article of the theme issue, we will provide an overview of the field of applications of kinetic theories in different socio-economic contexts and its recent boosting topics. Moreover, we will put the contributions to the theme issue in an appropriate perspective. This article is part of the theme issue ‘Kinetic exchange models of societies and economies’.

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Opinion polarization in social networks

Cited by 15 publications

References 37 publications

Online Fake News Opinion Spread and Belief Change: A Systematic Review

Online Fake News Opinion Spread and Belief Change: A Systematic Review

Kinetic and macroscopic equations for action potential in neural networks

Kinetic exchange models of societies and economies

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