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Dynamics and characteristics of misinformation related to earthquake predictions on Twitter
The spread of misinformation on social media can lead to inappropriate behaviors that can make disasters worse. In our study, we focused on tweets containing misinformation about earthquake predictions and analyzed their dynamics. To this end, we retrieved 82,129 tweets over a period of 2 years (Mar...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10435459/ https://www.ncbi.nlm.nih.gov/pubmed/37592002 http://dx.doi.org/10.1038/s41598-023-40399-9 |
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author | Dallo, Irina Elroy, Or Fallou, Laure Komendantova, Nadejda Yosipof, Abraham |
author_facet | Dallo, Irina Elroy, Or Fallou, Laure Komendantova, Nadejda Yosipof, Abraham |
author_sort | Dallo, Irina |
collection | PubMed |
description | The spread of misinformation on social media can lead to inappropriate behaviors that can make disasters worse. In our study, we focused on tweets containing misinformation about earthquake predictions and analyzed their dynamics. To this end, we retrieved 82,129 tweets over a period of 2 years (March 2020–March 2022) and hand-labeled 4157 tweets. We used RoBERTa to classify the complete dataset and analyzed the results. We found that (1) there are significantly more not-misinformation than misinformation tweets; (2) earthquake predictions are continuously present on Twitter with peaks after felt events; and (3) prediction misinformation tweets sometimes link or tag official earthquake notifications from credible sources. These insights indicate that official institutions present on social media should continuously address misinformation (even in quiet times when no event occurred), check that their institution is not tagged/linked in misinformation tweets, and provide authoritative sources that can be used to support their arguments against unfounded earthquake predictions. |
format | Online Article Text |
id | pubmed-10435459 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104354592023-08-19 Dynamics and characteristics of misinformation related to earthquake predictions on Twitter Dallo, Irina Elroy, Or Fallou, Laure Komendantova, Nadejda Yosipof, Abraham Sci Rep Article The spread of misinformation on social media can lead to inappropriate behaviors that can make disasters worse. In our study, we focused on tweets containing misinformation about earthquake predictions and analyzed their dynamics. To this end, we retrieved 82,129 tweets over a period of 2 years (March 2020–March 2022) and hand-labeled 4157 tweets. We used RoBERTa to classify the complete dataset and analyzed the results. We found that (1) there are significantly more not-misinformation than misinformation tweets; (2) earthquake predictions are continuously present on Twitter with peaks after felt events; and (3) prediction misinformation tweets sometimes link or tag official earthquake notifications from credible sources. These insights indicate that official institutions present on social media should continuously address misinformation (even in quiet times when no event occurred), check that their institution is not tagged/linked in misinformation tweets, and provide authoritative sources that can be used to support their arguments against unfounded earthquake predictions. Nature Publishing Group UK 2023-08-17 /pmc/articles/PMC10435459/ /pubmed/37592002 http://dx.doi.org/10.1038/s41598-023-40399-9 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 Dallo, Irina Elroy, Or Fallou, Laure Komendantova, Nadejda Yosipof, Abraham Dynamics and characteristics of misinformation related to earthquake predictions on Twitter |
title | Dynamics and characteristics of misinformation related to earthquake predictions on Twitter |
title_full | Dynamics and characteristics of misinformation related to earthquake predictions on Twitter |
title_fullStr | Dynamics and characteristics of misinformation related to earthquake predictions on Twitter |
title_full_unstemmed | Dynamics and characteristics of misinformation related to earthquake predictions on Twitter |
title_short | Dynamics and characteristics of misinformation related to earthquake predictions on Twitter |
title_sort | dynamics and characteristics of misinformation related to earthquake predictions on twitter |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10435459/ https://www.ncbi.nlm.nih.gov/pubmed/37592002 http://dx.doi.org/10.1038/s41598-023-40399-9 |
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