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The spread of low-credibility content by social bots
The massive spread of digital misinformation has been identified as a major threat to democracies. Communication, cognitive, social, and computer scientists are studying the complex causes for the viral diffusion of misinformation, while online platforms are beginning to deploy countermeasures. Litt...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6246561/ https://www.ncbi.nlm.nih.gov/pubmed/30459415 http://dx.doi.org/10.1038/s41467-018-06930-7 |
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author | Shao, Chengcheng Ciampaglia, Giovanni Luca Varol, Onur Yang, Kai-Cheng Flammini, Alessandro Menczer, Filippo |
author_facet | Shao, Chengcheng Ciampaglia, Giovanni Luca Varol, Onur Yang, Kai-Cheng Flammini, Alessandro Menczer, Filippo |
author_sort | Shao, Chengcheng |
collection | PubMed |
description | The massive spread of digital misinformation has been identified as a major threat to democracies. Communication, cognitive, social, and computer scientists are studying the complex causes for the viral diffusion of misinformation, while online platforms are beginning to deploy countermeasures. Little systematic, data-based evidence has been published to guide these efforts. Here we analyze 14 million messages spreading 400 thousand articles on Twitter during ten months in 2016 and 2017. We find evidence that social bots played a disproportionate role in spreading articles from low-credibility sources. Bots amplify such content in the early spreading moments, before an article goes viral. They also target users with many followers through replies and mentions. Humans are vulnerable to this manipulation, resharing content posted by bots. Successful low-credibility sources are heavily supported by social bots. These results suggest that curbing social bots may be an effective strategy for mitigating the spread of online misinformation. |
format | Online Article Text |
id | pubmed-6246561 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-62465612018-11-26 The spread of low-credibility content by social bots Shao, Chengcheng Ciampaglia, Giovanni Luca Varol, Onur Yang, Kai-Cheng Flammini, Alessandro Menczer, Filippo Nat Commun Article The massive spread of digital misinformation has been identified as a major threat to democracies. Communication, cognitive, social, and computer scientists are studying the complex causes for the viral diffusion of misinformation, while online platforms are beginning to deploy countermeasures. Little systematic, data-based evidence has been published to guide these efforts. Here we analyze 14 million messages spreading 400 thousand articles on Twitter during ten months in 2016 and 2017. We find evidence that social bots played a disproportionate role in spreading articles from low-credibility sources. Bots amplify such content in the early spreading moments, before an article goes viral. They also target users with many followers through replies and mentions. Humans are vulnerable to this manipulation, resharing content posted by bots. Successful low-credibility sources are heavily supported by social bots. These results suggest that curbing social bots may be an effective strategy for mitigating the spread of online misinformation. Nature Publishing Group UK 2018-11-20 /pmc/articles/PMC6246561/ /pubmed/30459415 http://dx.doi.org/10.1038/s41467-018-06930-7 Text en © The Author(s) 2018 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Shao, Chengcheng Ciampaglia, Giovanni Luca Varol, Onur Yang, Kai-Cheng Flammini, Alessandro Menczer, Filippo The spread of low-credibility content by social bots |
title | The spread of low-credibility content by social bots |
title_full | The spread of low-credibility content by social bots |
title_fullStr | The spread of low-credibility content by social bots |
title_full_unstemmed | The spread of low-credibility content by social bots |
title_short | The spread of low-credibility content by social bots |
title_sort | spread of low-credibility content by social bots |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6246561/ https://www.ncbi.nlm.nih.gov/pubmed/30459415 http://dx.doi.org/10.1038/s41467-018-06930-7 |
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