Repulsive Bounded-Confidence Model of Opinion Dynamics in Polarized Communities

Kann, Claudia; Feng, Michelle

doi:10.48550/arxiv.2301.02210

Cited by 2 publications

(2 citation statements)

References 25 publications

(25 reference statements)

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“…There have been numerous studies of BCMs, which have been generalized in many ways. Recent studies have incorporated phenomena such as media outlets with fixed opinions 37 , polyadic interactions (i.e., interactions between three or more agents) 38,39 , noise 40 , asymmetric confidence intervals 41 , cost of opinion change 42 , agents with heterogeneous activity levels 43 , smooth interaction kernels (in the form of sigmoidal functions) to describe how agents influence each other 44 , opinion repulsion 45 , homophilic adaptivity of network structure 46 , preserving connectivity of the interaction network 47 , and adaptive confidence bounds 48 .…”

Section: Introductionmentioning

confidence: 99%

An ``Opinion Reproduction Number'' for Infodemics in a Bounded-Confidence Content-Spreading Process on Networks

Brooks,

Porter

2024

Preprint

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We study the spreading dynamics of content on networks. To do this, we use a model in which content spreads through a bounded-confidence mechanism. In a bounded-confidence model (BCM) of opinion dynamics, the agents of a network have continuous-valued opinions, which they adjust when they interact with agents whose opinions are sufficiently close to theirs. The employed content-spread model introduces a twist into BCMs by using bounded confidence for the content spread itself. To study the spread of content, we define an analogue of the basic reproduction number from disease dynamics that we call an \emph{opinion reproduction number}. A critical value of the opinion reproduction number indicates whether or not there is an ``infodemic'' (i.e., a large content-spreading cascade) of content that reflects a particular opinion. By determining this critical value, one can determine whether or not an opinion will die off or propagate widely as a cascade in a population of agents. Using configuration-model networks, we quantify the size and shape of content dissemination using a variety of summary statistics, and we illustrate how network structure and spreading model parameters affect these statistics. We find that content spreads most widely when the agents have large expected mean degree or large receptiveness to content. When the amount of content spread only slightly exceeds the critical opinion reproduction number (i.e., the infodemic threshold), there can be longer dissemination trees than when the expected mean degree or receptiveness is larger, even though the total number of content shares is smaller.

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Section: Introductionmentioning

confidence: 99%

An ``Opinion Reproduction Number'' for Infodemics in a Bounded-Confidence Content-Spreading Process on Networks

Brooks,

Porter

2024

Preprint

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“…When nodes in our BCMs have a negative interaction, they adapt their dyadic confidence bounds, but their opinions stay the same. Other works have considered BCMs with "repulsion," in which the opinions of interacting nodes with sufficiently different opinions move farther apart from each other [2,25,29].…”

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confidence: 99%

Bounded-Confidence Models of Opinion Dynamics with Adaptive Confidence Bounds

J.¹,

Luo²,

Porter³

2023

Preprint

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People's opinions change with time as they interact with each other. In a boundedconfidence model (BCM) of opinion dynamics, individuals (which are represented by the nodes of a network) have continuous-valued opinions and are influenced only by neighboring nodes whose opinions are within their confidence bound. In this paper, we formulate and analyze discrete-time BCMs with heterogeneous and adaptive confidence bounds. We introduce two new models: (1) a BCM with synchronous opinion updates that generalizes the Hegselmann-Krause (HK) and (2) a BCM with asynchronous opinion updates that generalizes the Deffuant-Weisbuch (DW) model. We analytically and numerically explore our adaptive BCMs' limiting behaviors, including the confidencebound dynamics, the formation of clusters of nodes with similar opinions, and the time evolution of an "effective graph", which is a time-dependent subgraph of a network with edges between nodes that can currently influence each other. For a wide range of parameters that control the increase and decrease of confidence bounds, we demonstrate for a variety of networks that our adaptive BCMs result in fewer major opinion clusters and longer convergence times than the baseline (i.e., nonadaptive) BCMs. We also show that our adaptive BCMs can have pairs of adjacent nodes that converge to the same opinion but are not able to influence each other. This qualitative behavior does not occur in the associated baseline BCMs.

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Repulsive Bounded-Confidence Model of Opinion Dynamics in Polarized Communities

Cited by 2 publications

References 25 publications

An ``Opinion Reproduction Number'' for Infodemics in a Bounded-Confidence Content-Spreading Process on Networks

An ``Opinion Reproduction Number'' for Infodemics in a Bounded-Confidence Content-Spreading Process on Networks

Bounded-Confidence Models of Opinion Dynamics with Adaptive Confidence Bounds

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