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

被引:11
|
作者
Guntuku, Sharath Chandra [1 ,2 ,3 ]
Purtle, Jonathan [4 ]
Meisel, Zachary F. [3 ,5 ,6 ]
Merchant, Raina M. [2 ,3 ,5 ,6 ]
Agarwal, Anish [2 ,3 ,5 ,6 ]
机构
[1] Univ Penn, Dept Comp & Informat Sci, 3300 Walnut St, Philadelphia, PA 19103 USA
[2] Univ Penn, Penn Med Ctr Digital Hlth, Philadelphia, PA 19103 USA
[3] Univ Penn, Leonard Davis Inst Hlth Econ, Philadelphia, PA 19103 USA
[4] Drexel Univ, Dept Hlth Management & Policy, Dornsife Sch Publ Hlth, Philadelphia, PA 19104 USA
[5] Univ Penn, Dept Emergency Med, Philadelphia, PA 19103 USA
[6] Univ Penn, Ctr Emergency Care Res & Policy, Philadelphia, PA 19103 USA
关键词
Twitter; COVID-19; digital health; US legislators; natural language processing; policy makers; social media; policy; politics; language; cross-sectional; content; sentiment; infodemiology; infoveillance;
D O I
10.2196/27300
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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.
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页数:10
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