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Algorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model
The flow of information reaching us via the online media platforms is optimized not by the information content or relevance but by popularity and proximity to the target. This is typically performed in order to maximise platform usage. As a side effect, this introduces an algorithmic bias that is be...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6400382/ https://www.ncbi.nlm.nih.gov/pubmed/30835742 http://dx.doi.org/10.1371/journal.pone.0213246 |
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author | Sîrbu, Alina Pedreschi, Dino Giannotti, Fosca Kertész, János |
author_facet | Sîrbu, Alina Pedreschi, Dino Giannotti, Fosca Kertész, János |
author_sort | Sîrbu, Alina |
collection | PubMed |
description | The flow of information reaching us via the online media platforms is optimized not by the information content or relevance but by popularity and proximity to the target. This is typically performed in order to maximise platform usage. As a side effect, this introduces an algorithmic bias that is believed to enhance fragmentation and polarization of the societal debate. To study this phenomenon, we modify the well-known continuous opinion dynamics model of bounded confidence in order to account for the algorithmic bias and investigate its consequences. In the simplest version of the original model the pairs of discussion participants are chosen at random and their opinions get closer to each other if they are within a fixed tolerance level. We modify the selection rule of the discussion partners: there is an enhanced probability to choose individuals whose opinions are already close to each other, thus mimicking the behavior of online media which suggest interaction with similar peers. As a result we observe: a) an increased tendency towards opinion fragmentation, which emerges also in conditions where the original model would predict consensus, b) increased polarisation of opinions and c) a dramatic slowing down of the speed at which the convergence at the asymptotic state is reached, which makes the system highly unstable. Fragmentation and polarization are augmented by a fragmented initial population. |
format | Online Article Text |
id | pubmed-6400382 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-64003822019-03-17 Algorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model Sîrbu, Alina Pedreschi, Dino Giannotti, Fosca Kertész, János PLoS One Research Article The flow of information reaching us via the online media platforms is optimized not by the information content or relevance but by popularity and proximity to the target. This is typically performed in order to maximise platform usage. As a side effect, this introduces an algorithmic bias that is believed to enhance fragmentation and polarization of the societal debate. To study this phenomenon, we modify the well-known continuous opinion dynamics model of bounded confidence in order to account for the algorithmic bias and investigate its consequences. In the simplest version of the original model the pairs of discussion participants are chosen at random and their opinions get closer to each other if they are within a fixed tolerance level. We modify the selection rule of the discussion partners: there is an enhanced probability to choose individuals whose opinions are already close to each other, thus mimicking the behavior of online media which suggest interaction with similar peers. As a result we observe: a) an increased tendency towards opinion fragmentation, which emerges also in conditions where the original model would predict consensus, b) increased polarisation of opinions and c) a dramatic slowing down of the speed at which the convergence at the asymptotic state is reached, which makes the system highly unstable. Fragmentation and polarization are augmented by a fragmented initial population. Public Library of Science 2019-03-05 /pmc/articles/PMC6400382/ /pubmed/30835742 http://dx.doi.org/10.1371/journal.pone.0213246 Text en © 2019 Sîrbu et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Sîrbu, Alina Pedreschi, Dino Giannotti, Fosca Kertész, János Algorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model |
title | Algorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model |
title_full | Algorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model |
title_fullStr | Algorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model |
title_full_unstemmed | Algorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model |
title_short | Algorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model |
title_sort | algorithmic bias amplifies opinion fragmentation and polarization: a bounded confidence model |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6400382/ https://www.ncbi.nlm.nih.gov/pubmed/30835742 http://dx.doi.org/10.1371/journal.pone.0213246 |
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