Partisan Differences in Twitter Language Among US Legislators During the COVID-19 Pandemic: Cross-sectional Study

Abstract

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.

Keywords: COVID-19; Twitter; US legislators; content; cross-sectional; digital health; infodemiology; infoveillance; language; natural language processing; policy; policy makers; politics; sentiment; social media.