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The growing amplification of social media: measuring temporal and social contagion dynamics for over 150 languages on Twitter for 2009–2020

Working from a dataset of 118 billion messages running from the start of 2009 to the end of 2019, we identify and explore the relative daily use of over 150 languages on Twitter. We find that eight languages comprise 80% of all tweets, with English, Japanese, Spanish, Arabic, and Portuguese being th...

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Autores principales: Alshaabi, Thayer, Dewhurst, David Rushing, Minot, Joshua R., Arnold, Michael V., Adams, Jane L., Danforth, Christopher M., Dodds, Peter Sheridan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8010293/
https://www.ncbi.nlm.nih.gov/pubmed/33816048
http://dx.doi.org/10.1140/epjds/s13688-021-00271-0
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author Alshaabi, Thayer
Dewhurst, David Rushing
Minot, Joshua R.
Arnold, Michael V.
Adams, Jane L.
Danforth, Christopher M.
Dodds, Peter Sheridan
author_facet Alshaabi, Thayer
Dewhurst, David Rushing
Minot, Joshua R.
Arnold, Michael V.
Adams, Jane L.
Danforth, Christopher M.
Dodds, Peter Sheridan
author_sort Alshaabi, Thayer
collection PubMed
description Working from a dataset of 118 billion messages running from the start of 2009 to the end of 2019, we identify and explore the relative daily use of over 150 languages on Twitter. We find that eight languages comprise 80% of all tweets, with English, Japanese, Spanish, Arabic, and Portuguese being the most dominant. To quantify social spreading in each language over time, we compute the ‘contagion ratio’: The balance of retweets to organic messages. We find that for the most common languages on Twitter there is a growing tendency, though not universal, to retweet rather than share new content. By the end of 2019, the contagion ratios for half of the top 30 languages, including English and Spanish, had reached above 1—the naive contagion threshold. In 2019, the top 5 languages with the highest average daily ratios were, in order, Thai (7.3), Hindi, Tamil, Urdu, and Catalan, while the bottom 5 were Russian, Swedish, Esperanto, Cebuano, and Finnish (0.26). Further, we show that over time, the contagion ratios for most common languages are growing more strongly than those of rare languages.
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spelling pubmed-80102932021-03-31 The growing amplification of social media: measuring temporal and social contagion dynamics for over 150 languages on Twitter for 2009–2020 Alshaabi, Thayer Dewhurst, David Rushing Minot, Joshua R. Arnold, Michael V. Adams, Jane L. Danforth, Christopher M. Dodds, Peter Sheridan EPJ Data Sci Regular Article Working from a dataset of 118 billion messages running from the start of 2009 to the end of 2019, we identify and explore the relative daily use of over 150 languages on Twitter. We find that eight languages comprise 80% of all tweets, with English, Japanese, Spanish, Arabic, and Portuguese being the most dominant. To quantify social spreading in each language over time, we compute the ‘contagion ratio’: The balance of retweets to organic messages. We find that for the most common languages on Twitter there is a growing tendency, though not universal, to retweet rather than share new content. By the end of 2019, the contagion ratios for half of the top 30 languages, including English and Spanish, had reached above 1—the naive contagion threshold. In 2019, the top 5 languages with the highest average daily ratios were, in order, Thai (7.3), Hindi, Tamil, Urdu, and Catalan, while the bottom 5 were Russian, Swedish, Esperanto, Cebuano, and Finnish (0.26). Further, we show that over time, the contagion ratios for most common languages are growing more strongly than those of rare languages. Springer Berlin Heidelberg 2021-03-31 2021 /pmc/articles/PMC8010293/ /pubmed/33816048 http://dx.doi.org/10.1140/epjds/s13688-021-00271-0 Text en © The Author(s) 2021 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 Regular Article
Alshaabi, Thayer
Dewhurst, David Rushing
Minot, Joshua R.
Arnold, Michael V.
Adams, Jane L.
Danforth, Christopher M.
Dodds, Peter Sheridan
The growing amplification of social media: measuring temporal and social contagion dynamics for over 150 languages on Twitter for 2009–2020
title The growing amplification of social media: measuring temporal and social contagion dynamics for over 150 languages on Twitter for 2009–2020
title_full The growing amplification of social media: measuring temporal and social contagion dynamics for over 150 languages on Twitter for 2009–2020
title_fullStr The growing amplification of social media: measuring temporal and social contagion dynamics for over 150 languages on Twitter for 2009–2020
title_full_unstemmed The growing amplification of social media: measuring temporal and social contagion dynamics for over 150 languages on Twitter for 2009–2020
title_short The growing amplification of social media: measuring temporal and social contagion dynamics for over 150 languages on Twitter for 2009–2020
title_sort growing amplification of social media: measuring temporal and social contagion dynamics for over 150 languages on twitter for 2009–2020
topic Regular Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8010293/
https://www.ncbi.nlm.nih.gov/pubmed/33816048
http://dx.doi.org/10.1140/epjds/s13688-021-00271-0
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