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Modelling opinion dynamics under the impact of influencer and media strategies
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 me...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10632524/ https://www.ncbi.nlm.nih.gov/pubmed/37938634 http://dx.doi.org/10.1038/s41598-023-46187-9 |
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author | Helfmann, Luzie Djurdjevac Conrad, Nataša Lorenz-Spreen, Philipp Schütte, Christof |
author_facet | Helfmann, Luzie Djurdjevac Conrad, Nataša Lorenz-Spreen, Philipp Schütte, Christof |
author_sort | Helfmann, Luzie |
collection | PubMed |
description | 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 framework for opinion dynamics that can accommodate various versions of opinion dynamics as well as account for different roles, namely that of individuals, media and influencers, who change their own opinion positions on different time scales. Numerical simulations of instances of this framework show the importance of their relative influence in creating qualitatively different opinion formation dynamics: with influencers, fragmented but short-lived clusters emerge, which are then counteracted by more stable media positions. The framework allows for mean-field approximations by partial differential equations, which reproduce those dynamics and allow for efficient large-scale simulations when the number of individuals is large. Based on the mean-field approximations, we can 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, our framework allows us to 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 a more flexible modelling approach in opinion dynamics and a better understanding of the different roles and strategies in the increasingly complex information ecosystem. |
format | Online Article Text |
id | pubmed-10632524 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106325242023-11-10 Modelling opinion dynamics under the impact of influencer and media strategies Helfmann, Luzie Djurdjevac Conrad, Nataša Lorenz-Spreen, Philipp Schütte, Christof Sci Rep Article 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 framework for opinion dynamics that can accommodate various versions of opinion dynamics as well as account for different roles, namely that of individuals, media and influencers, who change their own opinion positions on different time scales. Numerical simulations of instances of this framework show the importance of their relative influence in creating qualitatively different opinion formation dynamics: with influencers, fragmented but short-lived clusters emerge, which are then counteracted by more stable media positions. The framework allows for mean-field approximations by partial differential equations, which reproduce those dynamics and allow for efficient large-scale simulations when the number of individuals is large. Based on the mean-field approximations, we can 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, our framework allows us to 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 a more flexible modelling approach in opinion dynamics and a better understanding of the different roles and strategies in the increasingly complex information ecosystem. Nature Publishing Group UK 2023-11-08 /pmc/articles/PMC10632524/ /pubmed/37938634 http://dx.doi.org/10.1038/s41598-023-46187-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Helfmann, Luzie Djurdjevac Conrad, Nataša Lorenz-Spreen, Philipp Schütte, Christof Modelling opinion dynamics under the impact of influencer and media strategies |
title | Modelling opinion dynamics under the impact of influencer and media strategies |
title_full | Modelling opinion dynamics under the impact of influencer and media strategies |
title_fullStr | Modelling opinion dynamics under the impact of influencer and media strategies |
title_full_unstemmed | Modelling opinion dynamics under the impact of influencer and media strategies |
title_short | Modelling opinion dynamics under the impact of influencer and media strategies |
title_sort | modelling opinion dynamics under the impact of influencer and media strategies |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10632524/ https://www.ncbi.nlm.nih.gov/pubmed/37938634 http://dx.doi.org/10.1038/s41598-023-46187-9 |
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