ArticleJournal of medical Internet research2021
Partisan Differences in Twitter Language Among US Legislators During the COVID-19 Pandemic: Cross-sectional Study.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- SciComm Optimizer for Policy Engagement: a randomized controlled trial of the SCOPE model on state legislators' research use in public discourse.Implementation science : IS · 2023Trial
- Prevalence, Themes, and Partisan Differences in US State Legislator X Posts Mentioning Suicide: Content Analysis.JMIR formative research · 2026Article
- The digitally accountable public representation database: online communication by U.S. officials.Scientific data · 2025Article
- Geosocial Media's Early Warning Capabilities Across US County-Level Political Clusters: Observational Study.JMIR infodemiology · 2025Observational
- How politics affect pandemic forecasting: spatio-temporal early warning capabilities of different geo-social media topics in the context of state-level political leaning.Frontiers in public health · 2025Article
- Self-Reported COVID-19 Vaccine and Booster Acceptance and Hesitancy Among Autistic Adults in Pennsylvania: Cross-Sectional Analysis of Survey Data.JMIR public health and surveillance · 2024Article
- Perceived risk, emotions, and stress in response to COVID-19: The interplay of media use and partisanship.Risk analysis : an official publication of the Society for Risk Analysis · 2023Article
- State and Federal Legislators' Responses on Social Media to the Mental Health and Burnout of Health Care Workers Throughout the COVID-19 Pandemic: Natural Language Processing and Sentiment Analysis.JMIR infodemiology · 2023Article
- A scoping review on the use of natural language processing in research on political polarization: trends and research prospects.Journal of computational social science · 2023Article
- Partisan differences in the effects of economic evidence and local data on legislator engagement with dissemination materials about behavioral health: a dissemination trial.Implementation science : IS · 2022Article
- Public communication about public health where we really need to go.NPJ digital medicine · 2022Article
- Public Opinions toward COVID-19 Vaccine Mandates: A Machine Learning-based Analysis of U.S. Tweets.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Political attitude change over time following COVID-19 lockdown: Rallying effects and differences between left and right voters.Frontiers in psychology · 2022Article
- Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective.Journal of medical Internet research · 2021Article
- Integrating Google Trends Search Engine Query Data Into Adult Emergency Department Volume Forecasting: Infodemiology Study.JMIR infodemiologyArticle
- Partisan Differences in Legislators' Discussion of Vaccination on Twitter During the COVID-19 Era: Natural Language Processing Analysis.JMIR infodemiologyArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundAs policy makers continue to shape the national and local responses to the COVID-19 pandemic, the information they choose to share and how they frame their content provide key insights into the public and health care systems.
objectiveWe examined the language used by the members of the US House and Senate during the first 10 months of the COVID-19 pandemic and measured content and sentiment based on the tweets that they shared.
methodsWe used Quorum (Quorum Analytics Inc) to access more than 300,000 tweets posted by US legislators from January 1 to October 10, 2020. We used differential language analyses to compare the content and sentiment of tweets posted by legislators based on their party affiliation.
resultsWe found that health care-related themes in Democratic legislators' tweets focused on racial disparities in care (odds ratio [OR] 2.24, 95% CI 2.22-2.27; P<.001), health care and insurance (OR 1.74, 95% CI 1.7-1.77; P<.001), COVID-19 testing (OR 1.15, 95% CI 1.12-1.19; P<.001), and public health guidelines (OR 1.25, 95% CI 1.22-1.29; P<.001). The dominant themes in the Republican legislators' discourse included vaccine development (OR 1.51, 95% CI 1.47-1.55; P<.001) and hospital resources and equipment (OR 1.22, 95% CI 1.18-1.25). Nonhealth care-related topics associated with a Democratic affiliation included protections for essential workers (OR 1.55, 95% CI 1.52-1.59), the 2020 election and voting (OR 1.31, 95% CI 1.27-1.35), unemployment and housing (OR 1.27, 95% CI 1.24-1.31), crime and racism (OR 1.22, 95% CI 1.18-1.26), public town halls (OR 1.2, 95% CI 1.16-1.23), the Trump Administration (OR 1.22, 95% CI 1.19-1.26), immigration (OR 1.16, 95% CI 1.12-1.19), and the loss of life (OR 1.38, 95% CI 1.35-1.42). The themes associated with the Republican affiliation included China (OR 1.89, 95% CI 1.85-1.92), small business assistance (OR 1.27, 95% CI 1.23-1.3), congressional relief bills (OR 1.23, 95% CI 1.2-1.27), press briefings (OR 1.22, 95% CI 1.19-1.26), and economic recovery (OR 1.2, 95% CI 1.16-1.23).
conclusionsDivergent language use on social media corresponds to the partisan divide in the first several months of the course of the COVID-19 public health crisis.
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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.