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Characterizing partisan political narrative frameworks about COVID-19 on Twitter

ABSTRACT: The COVID-19 pandemic is a global crisis that has been testing every society and exposing the critical role of local politics in crisis response. In the United States, there has been a strong partisan divide between the Democratic and Republican party’s narratives about the pandemic which resulted in polarization of individual behaviors and divergent policy adoption across regions. As shown in this case, as well as in most major social issues, strongly polarized narrative frameworks facilitate such narratives. To understand polarization and other social chasms, it is critical to dissect these diverging narratives. Here, taking the Democratic and Republican political social media posts about the pandemic as a case study, we demonstrate that a combination of computational methods can provide useful insights into the different contexts, framing, and characters and relationships that construct their narrative frameworks which individual posts source from. Leveraging a dataset of tweets from the politicians in the U.S., including the ex-president, members of Congress, and state governors, we found that the Democrats’ narrative tends to be more concerned with the pandemic as well as financial and social support, while the Republicans discuss more about other political entities such as China. We then perform an automatic framing analysis to characterize the ways in which they frame their narratives, where we found that the Democrats emphasize the government’s role in responding to the pandemic, and the Republicans emphasize the roles of individuals and support for small businesses. Finally, we present a semantic role analysis that uncovers the important characters and relationships in their narratives as well as how they facilitate a membership categorization process. Our findings concretely expose the gaps in the “elusive consensus” between the two parties. Our methodologies may be applied to computationally study narratives in various domains.

Supplementary Information

The online version contains supplementary material available at 10.1140/epjds/s13688-021-00308-4.

SUBMITTER: Jing E

PROVIDER: S-EPMC8556838 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description: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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Characterizing partisan political narrative frameworks about COVID-19 on Twitter

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