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Using Twitter data to understand public perceptions of approved versus off-label use for COVID-19-related medications
OBJECTIVE: Understanding public discourse on emergency use of unproven therapeutics is essential to monitor safe use and combat misinformation. We developed a natural language processing-based pipeline to understand public perceptions of and stances on coronavirus disease 2019 (COVID-19)-related dru...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9278189/ https://www.ncbi.nlm.nih.gov/pubmed/35775946 http://dx.doi.org/10.1093/jamia/ocac114 |
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author | Hua, Yining Jiang, Hang Lin, Shixu Yang, Jie Plasek, Joseph M Bates, David W Zhou, Li |
author_facet | Hua, Yining Jiang, Hang Lin, Shixu Yang, Jie Plasek, Joseph M Bates, David W Zhou, Li |
author_sort | Hua, Yining |
collection | PubMed |
description | OBJECTIVE: Understanding public discourse on emergency use of unproven therapeutics is essential to monitor safe use and combat misinformation. We developed a natural language processing-based pipeline to understand public perceptions of and stances on coronavirus disease 2019 (COVID-19)-related drugs on Twitter across time. METHODS: This retrospective study included 609 189 US-based tweets between January 29, 2020 and November 30, 2021 on 4 drugs that gained wide public attention during the COVID-19 pandemic: (1) Hydroxychloroquine and Ivermectin, drug therapies with anecdotal evidence; and (2) Molnupiravir and Remdesivir, FDA-approved treatment options for eligible patients. Time-trend analysis was used to understand the popularity and related events. Content and demographic analyses were conducted to explore potential rationales of people’s stances on each drug. RESULTS: Time-trend analysis revealed that Hydroxychloroquine and Ivermectin received much more discussion than Molnupiravir and Remdesivir, particularly during COVID-19 surges. Hydroxychloroquine and Ivermectin were highly politicized, related to conspiracy theories, hearsay, celebrity effects, etc. The distribution of stance between the 2 major US political parties was significantly different (P < .001); Republicans were much more likely to support Hydroxychloroquine (+55%) and Ivermectin (+30%) than Democrats. People with healthcare backgrounds tended to oppose Hydroxychloroquine (+7%) more than the general population; in contrast, the general population was more likely to support Ivermectin (+14%). CONCLUSION: Our study found that social media users with have different perceptions and stances on off-label versus FDA-authorized drug use across different stages of COVID-19, indicating that health systems, regulatory agencies, and policymakers should design tailored strategies to monitor and reduce misinformation for promoting safe drug use. Our analysis pipeline and stance detection models are made public at https://github.com/ningkko/COVID-drug. |
format | Online Article Text |
id | pubmed-9278189 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-92781892022-07-18 Using Twitter data to understand public perceptions of approved versus off-label use for COVID-19-related medications Hua, Yining Jiang, Hang Lin, Shixu Yang, Jie Plasek, Joseph M Bates, David W Zhou, Li J Am Med Inform Assoc Research and Applications OBJECTIVE: Understanding public discourse on emergency use of unproven therapeutics is essential to monitor safe use and combat misinformation. We developed a natural language processing-based pipeline to understand public perceptions of and stances on coronavirus disease 2019 (COVID-19)-related drugs on Twitter across time. METHODS: This retrospective study included 609 189 US-based tweets between January 29, 2020 and November 30, 2021 on 4 drugs that gained wide public attention during the COVID-19 pandemic: (1) Hydroxychloroquine and Ivermectin, drug therapies with anecdotal evidence; and (2) Molnupiravir and Remdesivir, FDA-approved treatment options for eligible patients. Time-trend analysis was used to understand the popularity and related events. Content and demographic analyses were conducted to explore potential rationales of people’s stances on each drug. RESULTS: Time-trend analysis revealed that Hydroxychloroquine and Ivermectin received much more discussion than Molnupiravir and Remdesivir, particularly during COVID-19 surges. Hydroxychloroquine and Ivermectin were highly politicized, related to conspiracy theories, hearsay, celebrity effects, etc. The distribution of stance between the 2 major US political parties was significantly different (P < .001); Republicans were much more likely to support Hydroxychloroquine (+55%) and Ivermectin (+30%) than Democrats. People with healthcare backgrounds tended to oppose Hydroxychloroquine (+7%) more than the general population; in contrast, the general population was more likely to support Ivermectin (+14%). CONCLUSION: Our study found that social media users with have different perceptions and stances on off-label versus FDA-authorized drug use across different stages of COVID-19, indicating that health systems, regulatory agencies, and policymakers should design tailored strategies to monitor and reduce misinformation for promoting safe drug use. Our analysis pipeline and stance detection models are made public at https://github.com/ningkko/COVID-drug. Oxford University Press 2022-07-18 /pmc/articles/PMC9278189/ /pubmed/35775946 http://dx.doi.org/10.1093/jamia/ocac114 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_modelThis article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) |
spellingShingle | Research and Applications Hua, Yining Jiang, Hang Lin, Shixu Yang, Jie Plasek, Joseph M Bates, David W Zhou, Li Using Twitter data to understand public perceptions of approved versus off-label use for COVID-19-related medications |
title | Using Twitter data to understand public perceptions of approved versus off-label use for COVID-19-related medications |
title_full | Using Twitter data to understand public perceptions of approved versus off-label use for COVID-19-related medications |
title_fullStr | Using Twitter data to understand public perceptions of approved versus off-label use for COVID-19-related medications |
title_full_unstemmed | Using Twitter data to understand public perceptions of approved versus off-label use for COVID-19-related medications |
title_short | Using Twitter data to understand public perceptions of approved versus off-label use for COVID-19-related medications |
title_sort | using twitter data to understand public perceptions of approved versus off-label use for covid-19-related medications |
topic | Research and Applications |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9278189/ https://www.ncbi.nlm.nih.gov/pubmed/35775946 http://dx.doi.org/10.1093/jamia/ocac114 |
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