Upcoming Events
Characterizing Political Narratives about COVID-19 on Twitter: Elise Jing
Apr 30, 2021, 3:00 - 4:30 PM
Online Forum Link | Join WebEx meeting Or dial 173.243.2.68 and enter the meeting # +1-202-860-2110 United States Toll (Wash., D.C.) Access code: 161 962 8856 |
Date | Friday, April 30, 2021 |
Time | 3:00 pm - 4:30 pm EDT (UTC-4:00) |
Title | Characterizing Partisan Political Narratives about COVID-19 on Twitter |
Authors
| Elise Jing, Scientist at Sirius XM + Pandora |
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 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 former 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. |
Speaker Bio | Elise Jing is a Scientist at Sirius XM + Pandora. Before joining Pandora, she earned her Ph.D. degree in Complex Systems from Indiana University Bloomington. Her research focuses on understanding narrative data through computational methods derived from natural language processing and text mining. The scope of her study includes online communities, social media, non-profit organizations, and podcasts. She is also an affiliate of the NSF program of Interdisciplinary Training in Complex Networks and Systems, and an alumni of the Santa Fe Institute. |