ArticleJournal of the American Medical Informatics Association : JAMIA2022
Using Twitter data to understand public perceptions of approved versus off-label use for COVID-19-related medications.
Article in Journal of the American Medical Informatics Association : JAMIA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed.
- Translational stewardship in pandemic therapeutics: ivermectin, evidentiary misalignment, and the propagation of constraints.Journal of translational medicine · 2026Article
- Topic and Sentiment Trends in Semaglutide Discussions on X: Subpopulation-Based Longitudinal Analysis.Online journal of public health informatics · 2026Article
- Characterizing Public Sentiments and Drug Interactions in the COVID-19 Pandemic Using Social Media: Natural Language Processing and Network Analysis.Journal of medical Internet research · 2025Article
- Factors associated with knowledge, attitudes, and behaviors regarding antiviral medications for COVID-19 among US adults.PloS one · 2025Article
- Pharmacovigilance in the digital age: gaining insight from social media data.Experimental biology and medicine (Maywood, N.J.) · 2025Review
- Wellness Influencer Responses to COVID-19 Vaccines on Social Media: A Longitudinal Observational Study.Journal of medical Internet research · 2024Observational
- Streamlining social media information retrieval for public health research with deep learning.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Using Social Media as a Source of Real-World Data for Pharmaceutical Drug Development and Regulatory Decision Making.Drug safety · 2024Article
- Sexual and Gender-Diverse Individuals Face More Health Challenges during COVID-19: A Large-Scale Social Media Analysis with Natural Language Processing.Health data science · 2024Article
- BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices.Frontiers in public health · 2024Article
- Dissemination of Registered COVID-19 Clinical Trials (DIRECCT): a cross-sectional study.BMC medicine · 2023Article
- A deep learning approach for transgender and gender diverse patient identification in electronic health records.Journal of biomedical informatics · 2023Article
- Off-label drug use during the COVID-19 pandemic in Africa: topic modelling and sentiment analysis of ivermectin in South Africa and Nigeria as a case study.Journal of the Royal Society, Interface · 2023Article
- Trend and Co-occurrence Network of COVID-19 Symptoms From Large-Scale Social Media Data: Infoveillance Study.Journal of medical Internet research · 2023Article
- A twisting tale of misinformation: should ivermectin be approved as a treatment for COVID-19 disease?Future virology · 2023Article
- Patient safety discourse in a pandemic: a Twitter hashtag analysis study on #PatientSafety.Frontiers in public health · 2023Article
- Hierarchical Concept Relations Improve Detection of Off-Label Drug Use in Electronic Health Records Data.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- MonkeyPox2022Tweets: A Large-Scale Twitter Dataset on the 2022 Monkeypox Outbreak, Findings from Analysis of Tweets, and Open Research Questions.Infectious disease reports · 2022Article
- Tracking the Impact of COVID-19 and Lockdown Policies on Public Mental Health Using Social Media: Infoveillance Study.Journal of medical Internet research · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveUnderstanding 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.
methodsThis 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.
resultsTime-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%).
conclusionOur 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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.