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Modelling Spirals of Silence and Echo Chambers by Learning from the Feedback of Others
What are the mechanisms by which groups with certain opinions gain public voice and force others holding a different view into silence? Furthermore, how does social media play into this? Drawing on neuroscientific insights into the processing of social feedback, we develop a theoretical model that a...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601760/ https://www.ncbi.nlm.nih.gov/pubmed/37420504 http://dx.doi.org/10.3390/e24101484 |
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author | Banisch, Sven Gaisbauer, Felix Olbrich, Eckehard |
author_facet | Banisch, Sven Gaisbauer, Felix Olbrich, Eckehard |
author_sort | Banisch, Sven |
collection | PubMed |
description | What are the mechanisms by which groups with certain opinions gain public voice and force others holding a different view into silence? Furthermore, how does social media play into this? Drawing on neuroscientific insights into the processing of social feedback, we develop a theoretical model that allows us to address these questions. In repeated interactions, individuals learn whether their opinion meets public approval and refrain from expressing their standpoint if it is socially sanctioned. In a social network sorted around opinions, an agent forms a distorted impression of public opinion enforced by the communicative activity of the different camps. Even strong majorities can be forced into silence if a minority acts as a cohesive whole. On the other hand, the strong social organisation around opinions enabled by digital platforms favours collective regimes in which opposing voices are expressed and compete for primacy in public. This paper highlights the role that the basic mechanisms of social information processing play in massive computer-mediated interactions on opinions. |
format | Online Article Text |
id | pubmed-9601760 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96017602022-10-27 Modelling Spirals of Silence and Echo Chambers by Learning from the Feedback of Others Banisch, Sven Gaisbauer, Felix Olbrich, Eckehard Entropy (Basel) Article What are the mechanisms by which groups with certain opinions gain public voice and force others holding a different view into silence? Furthermore, how does social media play into this? Drawing on neuroscientific insights into the processing of social feedback, we develop a theoretical model that allows us to address these questions. In repeated interactions, individuals learn whether their opinion meets public approval and refrain from expressing their standpoint if it is socially sanctioned. In a social network sorted around opinions, an agent forms a distorted impression of public opinion enforced by the communicative activity of the different camps. Even strong majorities can be forced into silence if a minority acts as a cohesive whole. On the other hand, the strong social organisation around opinions enabled by digital platforms favours collective regimes in which opposing voices are expressed and compete for primacy in public. This paper highlights the role that the basic mechanisms of social information processing play in massive computer-mediated interactions on opinions. MDPI 2022-10-18 /pmc/articles/PMC9601760/ /pubmed/37420504 http://dx.doi.org/10.3390/e24101484 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Banisch, Sven Gaisbauer, Felix Olbrich, Eckehard Modelling Spirals of Silence and Echo Chambers by Learning from the Feedback of Others |
title | Modelling Spirals of Silence and Echo Chambers by Learning from the Feedback of Others |
title_full | Modelling Spirals of Silence and Echo Chambers by Learning from the Feedback of Others |
title_fullStr | Modelling Spirals of Silence and Echo Chambers by Learning from the Feedback of Others |
title_full_unstemmed | Modelling Spirals of Silence and Echo Chambers by Learning from the Feedback of Others |
title_short | Modelling Spirals of Silence and Echo Chambers by Learning from the Feedback of Others |
title_sort | modelling spirals of silence and echo chambers by learning from the feedback of others |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601760/ https://www.ncbi.nlm.nih.gov/pubmed/37420504 http://dx.doi.org/10.3390/e24101484 |
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