A Digital Nudge to Counter Confirmation Bias

Fake news is increasingly an issue on social media platforms. In this work, rather than detect misinformation, we propose the use of nudges to help steer internet users into fact checking the news they read online. We discuss two types of nudging strategies, by presentation and by information. We pr...

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Detalles Bibliográficos
Autores principales: Thornhill, Calum, Meeus, Quentin, Peperkamp, Jeroen, Berendt, Bettina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Big Data
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7931917/
https://www.ncbi.nlm.nih.gov/pubmed/33693334
http://dx.doi.org/10.3389/fdata.2019.00011
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author Thornhill, Calum
Meeus, Quentin
Peperkamp, Jeroen
Berendt, Bettina
author_facet Thornhill, Calum
Meeus, Quentin
Peperkamp, Jeroen
Berendt, Bettina
author_sort Thornhill, Calum
collection PubMed
description Fake news is increasingly an issue on social media platforms. In this work, rather than detect misinformation, we propose the use of nudges to help steer internet users into fact checking the news they read online. We discuss two types of nudging strategies, by presentation and by information. We present the tool BalancedView, a proof-of-concept that shows news stories relevant to a tweet. The method presents the user with a selection of articles from a range of reputable news sources providing alternative opinions from the whole political spectrum, with these alternative articles identified as matching the original one by a combination of natural language processing and search. The results of an initial user study of BalancedView suggest that nudging by information may change the behavior of users towards that of informed news readers.
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spelling pubmed-79319172021-03-09 A Digital Nudge to Counter Confirmation Bias Thornhill, Calum Meeus, Quentin Peperkamp, Jeroen Berendt, Bettina Front Big Data Big Data Fake news is increasingly an issue on social media platforms. In this work, rather than detect misinformation, we propose the use of nudges to help steer internet users into fact checking the news they read online. We discuss two types of nudging strategies, by presentation and by information. We present the tool BalancedView, a proof-of-concept that shows news stories relevant to a tweet. The method presents the user with a selection of articles from a range of reputable news sources providing alternative opinions from the whole political spectrum, with these alternative articles identified as matching the original one by a combination of natural language processing and search. The results of an initial user study of BalancedView suggest that nudging by information may change the behavior of users towards that of informed news readers. Frontiers Media S.A. 2019-06-06 /pmc/articles/PMC7931917/ /pubmed/33693334 http://dx.doi.org/10.3389/fdata.2019.00011 Text en Copyright © 2019 Thornhill, Meeus, Peperkamp and Berendt. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Big Data
Thornhill, Calum
Meeus, Quentin
Peperkamp, Jeroen
Berendt, Bettina
A Digital Nudge to Counter Confirmation Bias
title A Digital Nudge to Counter Confirmation Bias
title_full A Digital Nudge to Counter Confirmation Bias
title_fullStr A Digital Nudge to Counter Confirmation Bias
title_full_unstemmed A Digital Nudge to Counter Confirmation Bias
title_short A Digital Nudge to Counter Confirmation Bias
title_sort digital nudge to counter confirmation bias
topic Big Data
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7931917/
https://www.ncbi.nlm.nih.gov/pubmed/33693334
http://dx.doi.org/10.3389/fdata.2019.00011
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