Political Polarization Drives Online Conversations About COVID-19 in the United States

Overview

Journal Hum Behav Emerg Technol

Date 2020 Aug 25

PMID 32838229

Citations 54

Authors

Julie Jiang

Emily Chen

Shen Yan

Kristina Lerman

Emilio Ferrara

Affiliations

Soon will be listed here.

Abstract

Since the outbreak in China in late 2019, the novel coronavirus (COVID-19) has spread around the world and has come to dominate online conversations. By linking 2.3 million Twitter users to locations within the United States, we study in aggregate how political characteristics of the locations affect the evolution of online discussions about COVID-19. We show that COVID-19 chatter in the United States is largely shaped by political polarization. Partisanship correlates with sentiment toward government measures and the tendency to share health and prevention messaging. Cross-ideological interactions are modulated by user segregation and polarized network structure. We also observe a correlation between user engagement with topics related to public health and the varying impact of the disease outbreak in different U.S. states. These findings may help inform policies both online and offline. Decision-makers may calibrate their use of online platforms to measure the effectiveness of public health campaigns, and to monitor the reception of national and state-level policies, by tracking in real-time discussions in a highly polarized social media ecosystem.

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References

Conover M, Davis C, Ferrara E, McKelvey K, Menczer F, Flammini A . The geospatial characteristics of a social movement communication network. PLoS One. 2013; 8(3):e55957. PMC: 3590214. DOI: 10.1371/journal.pone.0055957. View

Serrano M, Boguna M, Vespignani A . Extracting the multiscale backbone of complex weighted networks. Proc Natl Acad Sci U S A. 2009; 106(16):6483-8. PMC: 2672499. DOI: 10.1073/pnas.0808904106. View

Pennycook G, McPhetres J, Zhang Y, Lu J, Rand D . Fighting COVID-19 Misinformation on Social Media: Experimental Evidence for a Scalable Accuracy-Nudge Intervention. Psychol Sci. 2020; 31(7):770-780. PMC: 7366427. DOI: 10.1177/0956797620939054. View

Stella M, Ferrara E, De Domenico M . Bots increase exposure to negative and inflammatory content in online social systems. Proc Natl Acad Sci U S A. 2018; 115(49):12435-12440. PMC: 6298098. DOI: 10.1073/pnas.1803470115. View

Jiang J, Chen E, Yan S, Lerman K, Ferrara E . Political polarization drives online conversations about COVID-19 in the United States. Hum Behav Emerg Technol. 2020; 2(3):200-211. PMC: 7323338. DOI: 10.1002/hbe2.202. View

Alshaabi T, Arnold M, Minot J, Adams J, Dewhurst D, Reagan A . How the world's collective attention is being paid to a pandemic: COVID-19 related n-gram time series for 24 languages on Twitter. PLoS One. 2021; 16(1):e0244476. PMC: 7787459. DOI: 10.1371/journal.pone.0244476. View

Broniatowski D, Jamison A, Qi S, AlKulaib L, Chen T, Benton A . Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate. Am J Public Health. 2018; 108(10):1378-1384. PMC: 6137759. DOI: 10.2105/AJPH.2018.304567. View

Cinelli M, Quattrociocchi W, Galeazzi A, Valensise C, Brugnoli E, Schmidt A . The COVID-19 social media infodemic. Sci Rep. 2020; 10(1):16598. PMC: 7538912. DOI: 10.1038/s41598-020-73510-5. View

Chen E, Lerman K, Ferrara E . Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set. JMIR Public Health Surveill. 2020; 6(2):e19273. PMC: 7265654. DOI: 10.2196/19273. View

10.

Jamison A, Broniatowski D, Dredze M, Sangraula A, Smith M, Quinn S . Not just conspiracy theories: Vaccine opponents and proponents add to the COVID-19 'infodemic' on Twitter. Harv Kennedy Sch Misinformation Rev. 2021; 1. PMC: 8345314. DOI: 10.37016/mr-2020-38. View

11.

Gao J, Zheng P, Jia Y, Chen H, Mao Y, Chen S . Mental health problems and social media exposure during COVID-19 outbreak. PLoS One. 2020; 15(4):e0231924. PMC: 7162477. DOI: 10.1371/journal.pone.0231924. View

12.

Jacomy M, Venturini T, Heymann S, Bastian M . ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. PLoS One. 2014; 9(6):e98679. PMC: 4051631. DOI: 10.1371/journal.pone.0098679. View

13.

Gallotti R, Valle F, Castaldo N, Sacco P, De Domenico M . Assessing the risks of 'infodemics' in response to COVID-19 epidemics. Nat Hum Behav. 2020; 4(12):1285-1293. DOI: 10.1038/s41562-020-00994-6. View