  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 which resulted in
polarization of individual behaviors and divergent policy adoption across
regions. Here, to better understand such divide, we characterize and compare
the pandemic narratives of the Democratic and Republican politicians on social
media using novel computational methods including computational framing
analysis and semantic role analysis. By analyzing tweets from the politicians
in the U.S., including the president, members of Congress, and state governors,
we systematically uncover the contrasting narratives in terms of topics,
frames, and agents that shape their narratives. 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. By using contrasting framing and semantic roles, 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. Both parties' narratives also include shout-outs to their followers
and blaming of the other party. 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.
