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

06/29/2022
by   Yining Hua, et al.
3

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 healthcare backgrounds tended to oppose Hydroxychloroquine (+7 general population; in contrast, the general population was more likely to support Ivermectin (+14 https://github.com/ningkko/COVID-drug.

READ FULL TEXT

page 7

page 8

page 10

page 11

research
03/04/2021

MP Twitter Engagement and Abuse Post-first COVID-19 Lockdown in the UK: White Paper

The UK has had a volatile political environment for some years now, with...
research
07/23/2022

Vaccine Discourse on Twitter During the COVID-19 Pandemic

Since the onset of the COVID-19 pandemic, vaccines have been an importan...
research
10/03/2022

Understanding the illicit drug distribution in England: a data-centric approach to the County Lines Model

The County Lines Model (CLM) is a relatively new illicit drugs distribut...
research
04/15/2020

Framing COVID-19: How we conceptualize and discuss the pandemic on Twitter

Doctors and nurses in these weeks are busy in the trenches, fighting aga...
research
03/24/2020

Covid-19 Tweeting in English: Gender Differences

At the start of 2020, COVID-19 became the most urgent threat to global p...
research
05/05/2021

ExcavatorCovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for COVID-19

Timely responses from policy makers to mitigate the impact of the COVID-...

Please sign up or login with your details

Forgot password? Click here to reset