%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 23
%N 6
%P e27300
%T Partisan Differences in Twitter Language Among US Legislators During the COVID-19 Pandemic: Cross-sectional Study
%A Guntuku,Sharath Chandra
%A Purtle,Jonathan
%A Meisel,Zachary F
%A Merchant,Raina M
%A Agarwal,Anish
%+ Department of Computer and Information Science, University of Pennsylvania, 3300 Walnut St, Philadelphia, PA, 19103, United States, 1 215 615 3211, sharathg@cis.upenn.edu
%K Twitter
%K COVID-19
%K digital health
%K US legislators
%K natural language processing
%K policy makers
%K social media
%K policy
%K politics
%K language
%K cross-sectional
%K content
%K sentiment
%K infodemiology
%K infoveillance
%D 2021
%7 3.6.2021
%9 Original Paper
%J J Med Internet Res
%G English
%X Background: As 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. Objective: We 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. Methods: We 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. Results: We 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). Conclusions: Divergent 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. 
%M 33939620
%R 10.2196/27300
%U https://www.jmir.org/2021/6/e27300
%U https://doi.org/10.2196/27300
%U http://www.ncbi.nlm.nih.gov/pubmed/33939620