Literature DB >> 35775946

Using Twitter data to understand public perceptions of approved versus off-label use for COVID-19-related medications.

Yining Hua1,2, Hang Jiang3, Shixu Lin4, Jie Yang4, Joseph M Plasek1,2, David W Bates1,2, Li Zhou1,2.   

Abstract

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.
© 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.

Entities:  

Keywords:  COVID-19; deep learning; drug safety; natural language processing; public health; social media

Mesh:

Substances:

Year:  2022        PMID: 35775946      PMCID: PMC9278189          DOI: 10.1093/jamia/ocac114

Source DB:  PubMed          Journal:  J Am Med Inform Assoc        ISSN: 1067-5027            Impact factor:   7.942


  24 in total

1.  Ebola, Twitter, and misinformation: a dangerous combination?

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Journal:  BMJ       Date:  2014-10-14

2.  Need for Transparency and Reliable Evidence in Emergency Use Authorizations for Coronavirus Disease 2019 (COVID-19) Therapies.

Authors:  Mike Z Zhai; Carolyn T Lye; Aaron S Kesselheim
Journal:  JAMA Intern Med       Date:  2020-05-19       Impact factor: 21.873

3.  How Partisanship Affected Public Reaction to Potential Treatments for COVID-19.

Authors:  Thomas L Brunell; Sarah P Maxwell
Journal:  World Med Health Policy       Date:  2020-08-20

4.  The anti-scientists bias: The role of feelings about scientists in COVID-19 attitudes and behaviors.

Authors:  Carmen Sanchez; David Dunning
Journal:  J Appl Soc Psychol       Date:  2021-02-16

5.  Surveilling COVID-19 emotional contagion on Twitter.

Authors:  Cristina Crocamo; Marco Viviani; Lorenzo Famiglini; Francesco Bartoli; Gabriella Pasi; Giuseppe Carrà
Journal:  Eur Psychiatry       Date:  2021-02-03       Impact factor: 5.361

6.  An interactive web-based dashboard to track COVID-19 in real time.

Authors:  Ensheng Dong; Hongru Du; Lauren Gardner
Journal:  Lancet Infect Dis       Date:  2020-02-19       Impact factor: 25.071

7.  Revisiting the cardiovascular risk of hydroxychloroquine in RA.

Authors:  Yves-Marie Pers; Guillaume Padern
Journal:  Nat Rev Rheumatol       Date:  2020-12       Impact factor: 20.543

8.  A stance data set on polarized conversations on Twitter about the efficacy of hydroxychloroquine as a treatment for COVID-19.

Authors:  Ece C Mutlu; Toktam Oghaz; Jasser Jasser; Ege Tutunculer; Amirarsalan Rajabi; Aida Tayebi; Ozlem Ozmen; Ivan Garibay
Journal:  Data Brief       Date:  2020-10-15

Review 9.  An augmented multilingual Twitter dataset for studying the COVID-19 infodemic.

Authors:  Christian E Lopez; Caleb Gallemore
Journal:  Soc Netw Anal Min       Date:  2021-10-20

10.  Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention.

Authors:  Zunyou Wu; Jennifer M McGoogan
Journal:  JAMA       Date:  2020-04-07       Impact factor: 56.272

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  1 in total

1.  Tracking the Impact of COVID-19 and Lockdown Policies on Public Mental Health Using Social Media: Infoveillance Study.

Authors:  Minghui Li; Yining Hua; Yanhui Liao; Li Zhou; Xue Li; Ling Wang; Jie Yang
Journal:  J Med Internet Res       Date:  2022-10-13       Impact factor: 7.076

  1 in total

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