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Quantifying and contextualizing the impact of bioRxiv preprints through automated social media audience segmentation

Engagement with scientific manuscripts is frequently facilitated by Twitter and other social media platforms. As such, the demographics of a paper's social media audience provide a wealth of information about how scholarly research is transmitted, consumed, and interpreted by online communities...

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Detalles Bibliográficos
Autores principales: Carlson, Jedidiah, Harris, Kelley
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7508356/
https://www.ncbi.nlm.nih.gov/pubmed/32960891
http://dx.doi.org/10.1371/journal.pbio.3000860
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author Carlson, Jedidiah
Harris, Kelley
author_facet Carlson, Jedidiah
Harris, Kelley
author_sort Carlson, Jedidiah
collection PubMed
description Engagement with scientific manuscripts is frequently facilitated by Twitter and other social media platforms. As such, the demographics of a paper's social media audience provide a wealth of information about how scholarly research is transmitted, consumed, and interpreted by online communities. By paying attention to public perceptions of their publications, scientists can learn whether their research is stimulating positive scholarly and public thought. They can also become aware of potentially negative patterns of interest from groups that misinterpret their work in harmful ways, either willfully or unintentionally, and devise strategies for altering their messaging to mitigate these impacts. In this study, we collected 331,696 Twitter posts referencing 1,800 highly tweeted bioRxiv preprints and leveraged topic modeling to infer the characteristics of various communities engaging with each preprint on Twitter. We agnostically learned the characteristics of these audience sectors from keywords each user’s followers provide in their Twitter biographies. We estimate that 96% of the preprints analyzed are dominated by academic audiences on Twitter, suggesting that social media attention does not always correspond to greater public exposure. We further demonstrate how our audience segmentation method can quantify the level of interest from nonspecialist audience sectors such as mental health advocates, dog lovers, video game developers, vegans, bitcoin investors, conspiracy theorists, journalists, religious groups, and political constituencies. Surprisingly, we also found that 10% of the preprints analyzed have sizable (>5%) audience sectors that are associated with right-wing white nationalist communities. Although none of these preprints appear to intentionally espouse any right-wing extremist messages, cases exist in which extremist appropriation comprises more than 50% of the tweets referencing a given preprint. These results present unique opportunities for improving and contextualizing the public discourse surrounding scientific research.
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spelling pubmed-75083562020-09-30 Quantifying and contextualizing the impact of bioRxiv preprints through automated social media audience segmentation Carlson, Jedidiah Harris, Kelley PLoS Biol Meta-Research Article Engagement with scientific manuscripts is frequently facilitated by Twitter and other social media platforms. As such, the demographics of a paper's social media audience provide a wealth of information about how scholarly research is transmitted, consumed, and interpreted by online communities. By paying attention to public perceptions of their publications, scientists can learn whether their research is stimulating positive scholarly and public thought. They can also become aware of potentially negative patterns of interest from groups that misinterpret their work in harmful ways, either willfully or unintentionally, and devise strategies for altering their messaging to mitigate these impacts. In this study, we collected 331,696 Twitter posts referencing 1,800 highly tweeted bioRxiv preprints and leveraged topic modeling to infer the characteristics of various communities engaging with each preprint on Twitter. We agnostically learned the characteristics of these audience sectors from keywords each user’s followers provide in their Twitter biographies. We estimate that 96% of the preprints analyzed are dominated by academic audiences on Twitter, suggesting that social media attention does not always correspond to greater public exposure. We further demonstrate how our audience segmentation method can quantify the level of interest from nonspecialist audience sectors such as mental health advocates, dog lovers, video game developers, vegans, bitcoin investors, conspiracy theorists, journalists, religious groups, and political constituencies. Surprisingly, we also found that 10% of the preprints analyzed have sizable (>5%) audience sectors that are associated with right-wing white nationalist communities. Although none of these preprints appear to intentionally espouse any right-wing extremist messages, cases exist in which extremist appropriation comprises more than 50% of the tweets referencing a given preprint. These results present unique opportunities for improving and contextualizing the public discourse surrounding scientific research. Public Library of Science 2020-09-22 /pmc/articles/PMC7508356/ /pubmed/32960891 http://dx.doi.org/10.1371/journal.pbio.3000860 Text en © 2020 Carlson, Harris 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 Meta-Research Article
Carlson, Jedidiah
Harris, Kelley
Quantifying and contextualizing the impact of bioRxiv preprints through automated social media audience segmentation
title Quantifying and contextualizing the impact of bioRxiv preprints through automated social media audience segmentation
title_full Quantifying and contextualizing the impact of bioRxiv preprints through automated social media audience segmentation
title_fullStr Quantifying and contextualizing the impact of bioRxiv preprints through automated social media audience segmentation
title_full_unstemmed Quantifying and contextualizing the impact of bioRxiv preprints through automated social media audience segmentation
title_short Quantifying and contextualizing the impact of bioRxiv preprints through automated social media audience segmentation
title_sort quantifying and contextualizing the impact of biorxiv preprints through automated social media audience segmentation
topic Meta-Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7508356/
https://www.ncbi.nlm.nih.gov/pubmed/32960891
http://dx.doi.org/10.1371/journal.pbio.3000860
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