Evaluation and control of opinion polarization and disagreement: a review

Li, Yuejiang; Zhao, Hong

doi:10.1108/ijcs-02-2021-0003

Cited by 5 publications

(1 citation statement)

References 43 publications

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“…Recent attempts to find control strategies of opinion dynamics consist of a variety of seemingly independent approaches. On the one hand, different heuristics have been developed to determine which agents should be targeted by an external controller to maximize their influence [41,43,34]. On the other hand, in [2,31], it is studied how external controllers have to act to bring the opinion distribution of agents close to a specified target distribution, while, in [34], it is established how to control opinion distributions by (externally) interfering with the interaction network between individuals, and, in [1,25], dynamic external controls for mean-field opinion models are considered.…”

Section: Introductionmentioning

confidence: 99%

Modelling opinion dynamics under the impact of influencer and media strategies

Helfmann¹,

Conrad²,

Lorenz-Spreen³

et al. 2023

Preprint

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Digital communication has made the public discourse considerably more complex, and new actors and strategies have emerged as a result of this seismic shift. Aside from the often-studied interactions among individuals during opinion formation, which have been facilitated on a large scale by social media platforms, the changing role of traditional media and the emerging role of "influencers" are not well understood, and the implications of their engagement strategies arising from the incentive structure of the attention economy even less so. Here we propose a novel opinion dynamics model that accounts for these different roles, namely that media and influencers change their own positions on slower time scales than individuals, while influencers dynamically gain and lose followers. Numerical simulations show the importance of their relative influence in creating qualitatively different opinion formation dynamics: with influencers, fragmented but shortlived clusters emerge, which are then counteracted by more stable media positions. Mean-field approximations by partial differential equations reproduce this dynamic. Based on the mean-field model, we study how strategies of influencers to gain more followers can influence the overall opinion distribution. We show that moving towards extreme positions can be a beneficial strategy for influencers to gain followers. Finally, we demonstrate that optimal control strategies allow other influencers or media to counteract such attempts and prevent further fragmentation of the opinion landscape. Our modelling framework contributes to better understanding the different roles and strategies in the increasingly complex information ecosystem and their impact on public opinion formation.

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Section: Introductionmentioning

confidence: 99%

Modelling opinion dynamics under the impact of influencer and media strategies

Helfmann¹,

Conrad²,

Lorenz-Spreen³

et al. 2023

Preprint

View full text Add to dashboard Cite

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The power of social networks and social media’s filter bubble in shaping polarisation: an agent-based model

Chueca Del Cerro

2024

Appl Netw Sci

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Tthe role social media platforms play on the emergence of polarisation is an ongoing debate in the political communication literature. Social media’s filter bubbles and online echo chambers shape people’s opinions by curating the information they have available. However, the extent to which this is the case remains unclear. Social simulation scholars have provided valuable insights into the subject through opinion dynamics models and agent-based modelling approaches. This article proposes a social simulation approach to the topic of opinion dynamics from a political communication perspective to understand how social network configurations and the media environment contribute to the emergence of national identity polarisation. We built an agent-based simulation model of national identity dynamics with a multilayer multiplex network of interacting agents in a hybrid media environment of both, traditional media and social media platforms. We use the Catalan secessionist movement to ground, contextualise and empirically inform parts of our model. We found that the initial social network setup conditions had a large impact on the emergence of polarisation amongst agents. In particular, homophily-based social networks composed of a majority of like-minded individuals produced greater polarisation compared to random networks. This was aggravated in the presence of social media filtering algorithms, selectively exposing agents to supportive information. These results emphasise the importance of both the selective exposure by social media filtering algorithms and one’s social networks (echo chambers) for polarisation to emerge. This interaction reinforces the influence of social media platforms and social networks have on the emergence of polarisation.

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

Kozitsin

2024

Chaos, Solitons & Fractals

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Evaluation and control of opinion polarization and disagreement: a review

Cited by 5 publications

References 43 publications

Modelling opinion dynamics under the impact of influencer and media strategies

Modelling opinion dynamics under the impact of influencer and media strategies

The power of social networks and social media’s filter bubble in shaping polarisation: an agent-based model

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

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