Using Twitter Data to Understand Public Perceptions of Approved versus Off-label Use for COVID-19-related Medications

Abstract

Understanding public discourse on emergency use of unproven therapeutics is essential to monitor safe use and combat misinformation. We developed a natural language processing (NLP)-based pipeline to understand public perceptions of and stances on COVID-19-related drugs on Twitter across time. This retrospective study included 609,189 US-based tweets between January 29th, 2020 and November 30th, 2021 on four 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. 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 two major US political parties was significantly different (p<0.001); 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%). We make all the data, code, and models available at https://github.com/ningkko/COVID-drug.

Contribution highlights

• Understanding public discourse on emergency use of unproven therapeutics is essential to monitor safe use and combat misinformation.
• We developed a natural language processing (NLP)-based pipeline to understand public perceptions of and stances on COVID-19-related drugs on Twitter across time.

Extracted from the public abstract; no claims have been added.

Code and data

Publication metadata

DOI

10.1093/jamia/ocac114

Volume

29

Issue

10

Pages / article

1668-1678

Version relationship

No separate preprint-to-publication relationship is currently recorded.

BibTeX

@article{Hua2022Twitterdata,
  title = {Using Twitter Data to Understand Public Perceptions of Approved versus Off-label Use for COVID-19-related Medications},
  author = {Yining Hua and Hang Jiang and Shixu Lin and Jie Yang and Joseph M Plasek and David W Bates and Li Zhou},
  journal = {Journal of the American Medical Informatics Association},
  year = {2022},
  url = {https://academic.oup.com/jamia/article/29/10/1668/6625661},
  doi = {10.1093/jamia/ocac114},
  volume = {29},
  number = {10},
  pages = {1668--1678}
}