Using Twitter Data to Understand Public Perceptions of Approved versus Off-label Use for COVID-19-related Medications
Understanding public discourse on emergency use of unproven therapeutics is crucial for monitoring safe use and combating misinformation. We developed a natural language processing-based pipeline to comprehend public perceptions of and stances on coronavirus disease 2019 (COVID-19)-related drugs on Twitter over time. This retrospective study included 609,189 US-based tweets from January 29, 2020, to November 30, 2021, about four drugs that garnered significant public attention during the COVID-19 pandemic: (1) Hydroxychloroquine and Ivermectin, therapies with anecdotal evidence; and (2) Molnupiravir and Remdesivir, FDA-approved treatments for eligible patients. Time-trend analysis was employed to understand popularity trends and related events. Content and demographic analyses were conducted to explore potential rationales behind people's stances on each drug. Time-trend analysis indicated that Hydroxychloroquine and Ivermectin were discussed more than Molnupiravir and Remdesivir, particularly during COVID-19 surges. Hydroxychloroquine and Ivermectin discussions were highly politicized, related to conspiracy theories, hearsay, and celebrity influences. The distribution of stances between the two major US political parties was significantly different (P < .001); Republicans were 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, while the general population was more likely to support Ivermectin (14%). Our study found that social media users have varying perceptions and stances on off-label versus FDA-authorized drug use at different stages of COVID-19. This indicates that health systems, regulatory agencies, and policymakers should design tailored strategies to monitor and reduce misinformation to promote safe drug use..
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Preprint| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
arXiv.org - (2022) vom: 28. Juni Zur Gesamtaufnahme - year:2022 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Hua, Yining [VerfasserIn] |
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000 |
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| doi: |
http://dx.doi.org/10.1093/jamia/ocac114 |
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| PPN (Katalog-ID): |
XAR036399507 |
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| 520 | |a Understanding public discourse on emergency use of unproven therapeutics is crucial for monitoring safe use and combating misinformation. We developed a natural language processing-based pipeline to comprehend public perceptions of and stances on coronavirus disease 2019 (COVID-19)-related drugs on Twitter over time. This retrospective study included 609,189 US-based tweets from January 29, 2020, to November 30, 2021, about four drugs that garnered significant public attention during the COVID-19 pandemic: (1) Hydroxychloroquine and Ivermectin, therapies with anecdotal evidence; and (2) Molnupiravir and Remdesivir, FDA-approved treatments for eligible patients. Time-trend analysis was employed to understand popularity trends and related events. Content and demographic analyses were conducted to explore potential rationales behind people's stances on each drug. Time-trend analysis indicated that Hydroxychloroquine and Ivermectin were discussed more than Molnupiravir and Remdesivir, particularly during COVID-19 surges. Hydroxychloroquine and Ivermectin discussions were highly politicized, related to conspiracy theories, hearsay, and celebrity influences. The distribution of stances between the two major US political parties was significantly different (P < .001); Republicans were 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, while the general population was more likely to support Ivermectin (14%). Our study found that social media users have varying perceptions and stances on off-label versus FDA-authorized drug use at different stages of COVID-19. This indicates that health systems, regulatory agencies, and policymakers should design tailored strategies to monitor and reduce misinformation to promote safe drug use. | ||
| 650 | 4 | |a Computer Science - Computers and Society |7 (dpeaa)DE-84 | |
| 650 | 4 | |a Computer Science - Computation and Language |7 (dpeaa)DE-84 | |
| 650 | 4 | |a Computer Science - Machine Learning |7 (dpeaa)DE-84 | |
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| 700 | 1 | |a Jiang, Hang |4 aut | |
| 700 | 1 | |a Lin, Shixu |4 aut | |
| 700 | 1 | |a Yang, Jie |4 aut | |
| 700 | 1 | |a Plasek, Joseph M. |4 aut | |
| 700 | 1 | |a Bates, David W. |4 aut | |
| 700 | 1 | |a Zhou, Li |4 aut | |
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