ArticleJournal of healthcare informatics research2021
Investigating Public Discourses Around Gender and COVID-19: a Social Media Analysis of Twitter Data.
Article in Journal of healthcare informatics research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Dataset on public engagement with the world health organization on X (Twitter) from 2008 to 2021.Data in brief · 2026Article
- How Glucagon-Like Peptide-1 Medications Are Depicted in Instagram Posts Regarding Women's Health, Nontraditional Access, and Barriers to Access: Content Analysis.Journal of medical Internet research · 2025Article
- Media portrayals and social stigma surrounding COVID-19 survivors: a content analysis of Chinese media coverage.BMC public health · 2025Article
- Twitter Misinformation Discourses About Vaping: Systematic Content Analysis.Journal of medical Internet research · 2023Article
- Stigmatization in the context of the COVID-19 pandemic: a survey experiment using attribution theory and the familiarity hypothesis.BMC public health · 2023Article
- #WuhanDiary and #WuhanLockdown: gendered posting patterns and behaviours on Weibo during the COVID-19 pandemic.BMJ global health · 2022Review
- Advances of Sentiment Analysis Applications in Obstetrics/Gynecology and Midwifery.Materia socio-medica · 2021Review
- The hybrid political role of feminism on Twitter during COVID-19: SISMA Mujer in Colombia.Women's studies international forumArticle
- #DomesticViolence During the COVID-19 Global Pandemic: An Analysis of Public Commentary via Twitter.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We collected over 50 million tweets referencing COVID-19 to understand the public's gendered discourses and concerns during the pandemic. We filtered the tweets based on English language and among three gender categories: men, women, and sexual and gender minorities. We used a mixed-method approach that included topic modelling, sentiment analysis, and text mining extraction procedures including words' mapping, proximity plots, top hashtags and mentions, and most retweeted posts. Our findings show stark differences among the different genders. In relation to women, we found a salient discussion on the risks of domestic violence due to the lockdown especially towards women and girls, while emphasizing financial challenges. The public discourses around SGM mostly revolved around blood donation concerns, which is a reminder of the discrimination against some of these communities during the early days of the HIV/AIDS epidemic. Finally, the discourses around men were focused on the high death rates and the sentiment analysis results showed more negative tweets than among the other genders. The study concludes that Twitter influencers can drive major online discussions which can be useful in addressing communication needs during pandemics.
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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.