More Like this

1. Oscillatory and excitable dynamics in an opinion model with group opinions

   https://doi.org/10.1103/PhysRevE.112.024303  

   Sampson, Corbit R; Restrepo, Juan G; Porter, Mason A (August 2025, Physical Review E)

   In traditional models of opinion dynamics, each agent in a network has an opinion and changes in opinions arise from pairwise (i.e., dyadic) interactions between agents. However, in many situations, groups of individuals possess a collective opinion that can differ from the opinions of their constituent individuals. In this paper, we study the effects of group opinions on opinion dynamics. We formulate a hypergraph model in which both individual agents and groups of three agents have opinions, and we examine how opinions evolve through both dyadic interactions and group memberships. We find for some parameter values that the presence of group opinions can lead to oscillatory and excitable opinion dynamics. In the oscillatory regime, the mean opinion of the agents in a network has self-sustained oscillations. In the excitable regime, finite-size effects create large but short-lived opinion swings (as in social fads). We develop a mean-field approximation of our model and obtain good agreement with direct numerical simulations. We also show—both numerically and via our mean-field description—that oscillatory dynamics occur only when the numbers of dyadic and polyadic interactions of the agents are not completely correlated. Our results illustrate how polyadic structures, such as groups of agents, can have important effects on collective opinion dynamics. 

   more » « less
2. An adaptive bounded-confidence model of opinion dynamics on networks

   https://doi.org/10.1093/comnet/cnac055  

   Kan, Unchitta; Feng, Michelle; Porter, Mason A. (February 2023, Journal of Complex Networks)

   Abstract Individuals who interact with each other in social networks often exchange ideas and influence each other’s opinions. A popular approach to study the spread of opinions on networks is by examining bounded-confidence models (BCMs), in which the nodes of a network have continuous-valued states that encode their opinions and are receptive to other nodes’ opinions when they lie within some confidence bound of their own opinion. In this article, we extend the Deffuant–Weisbuch (DW) model, which is a well-known BCM, by examining the spread of opinions that coevolve with network structure. We propose an adaptive variant of the DW model in which the nodes of a network can (1) alter their opinions when they interact with neighbouring nodes and (2) break connections with neighbours based on an opinion tolerance threshold and then form new connections following the principle of homophily. This opinion tolerance threshold determines whether or not the opinions of adjacent nodes are sufficiently different to be viewed as ‘discordant’. Using numerical simulations, we find that our adaptive DW model requires a larger confidence bound than a baseline DW model for the nodes of a network to achieve a consensus opinion. In one region of parameter space, we observe ‘pseudo-consensus’ steady states, in which there exist multiple subclusters of an opinion cluster with opinions that differ from each other by a small amount. In our simulations, we also examine the roles of early-time dynamics and nodes with initially moderate opinions for achieving consensus. Additionally, we explore the effects of coevolution on the convergence time of our BCM. 

   more » « less
3. Identifying Susceptible Agents in Time Varying Opinion Dynamics through Compressive Measurements

   Wai, Hoi To; Ozgladar, Asuman E.; Scaglione, Anna (April 2018, ICASSP 2018)

   We provide a compressive-measurement based method to detect susceptible agents who may receive misinformation through their contact with ‘stubborn agents’ whose goal is to influence the opinions of agents in the network. We consider a DeGroot-type opinion dynamics model where regular agents revise their opinions by linearly combining their neighbors’ opinions, but stubborn agents, while influencing others, do not change their opinions. Our proposed method hinges on estimating the temporal difference vector of network-wide opinions, computed at time instances when the stubborn agents interact. We show that this temporal difference vector has approximately the same support as the locations of the susceptible agents. Moreover, both the interaction instances and the temporal difference vector can be estimated from a small number of aggregated opinions. The performance of our method is studied both analytically and empirically. We show that the detection error decreases when the social network is better connected, or when the stubborn agents are ‘less talkative’. 

   more » « less
4. On a Networked SIS Epidemic Model with Cooperative and Antagonistic Opinion Dynamics

   https://doi.org/10.1109/TCNS.2022.3145748  

   She, Baike; Liu, Ji; Sundaram, Shreyas; Paré, Philip E. (January 2022, IEEE Transactions on Control of Network Systems)

   We propose a mathematical model to study coupled epidemic and opinion dynamics in a network of communities. Our model captures SIS epidemic dynamics whose evolution is dependent on the opinions of the communities toward the epidemic, and vice versa. In particular, we allow both cooperative and antagonistic interactions, representing similar and opposing perspectives on the severity of the epidemic, respectively. We propose an Opinion-Dependent Reproduction Number to characterize the mutual influence between epidemic spreading and opinion dissemination over the networks. Through stability analysis of the equilibria, we explore the impact of opinions on both epidemic outbreak and eradication, characterized by bounds on the Opinion-Dependent Reproduction Number. We also show how to eradicate epidemics by reshaping the opinions, offering researchers an approach for designing control strategies to reach target audiences to ensure effective epidemic suppression. 

   more » « less
5. A multilayer network model of the coevolution of the spread of a disease and competing opinions

   https://doi.org/10.1142/S0218202521500536  

   Peng, Kaiyan; Lu, Zheng; Lin, Vanessa; Lindstrom, Michael R.; Parkinson, Christian; Wang, Chuntian; Bertozzi, Andrea L.; Porter, Mason A. (November 2021, Mathematical Models and Methods in Applied Sciences)

   During the COVID-19 pandemic, conflicting opinions on physical distancing swept across social media, affecting both human behavior and the spread of COVID-19. Inspired by such phenomena, we construct a two-layer multiplex network for the coupled spread of a disease and conflicting opinions. We model each process as a contagion. On one layer, we consider the concurrent evolution of two opinions — pro-physical-distancing and anti-physical-distancing — that compete with each other and have mutual immunity to each other. The disease evolves on the other layer, and individuals are less likely (respectively, more likely) to become infected when they adopt the pro-physical-distancing (respectively, anti-physical-distancing) opinion. We develop approximations of mean-field type by generalizing monolayer pair approximations to multilayer networks; these approximations agree well with Monte Carlo simulations for a broad range of parameters and several network structures. Through numerical simulations, we illustrate the influence of opinion dynamics on the spread of the disease from complex interactions both between the two conflicting opinions and between the opinions and the disease. We find that lengthening the duration that individuals hold an opinion may help suppress disease transmission, and we demonstrate that increasing the cross-layer correlations or intra-layer correlations of node degrees may lead to fewer individuals becoming infected with the disease. 

   more » « less