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Political polarization drives online conversations about COVID-19 in the United States.
Jiang, Julie; Chen, Emily; Yan, Shen; Lerman, Kristina; Ferrara, Emilio.
  • Jiang J; USC Information Sciences Institute University of Southern California Los Angeles California USA.
  • Chen E; Department of Computer Science University of Southern California Los Angeles California USA.
  • Yan S; USC Information Sciences Institute University of Southern California Los Angeles California USA.
  • Lerman K; Department of Computer Science University of Southern California Los Angeles California USA.
  • Ferrara E; USC Information Sciences Institute University of Southern California Los Angeles California USA.
Hum Behav Emerg Technol ; 2(3): 200-211, 2020 Jul.
Article in English | MEDLINE | ID: covidwho-1898740
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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Full text: Available Collection: International databases Database: MEDLINE Type of study: Randomized controlled trials Language: English Journal: Hum Behav Emerg Technol Year: 2020 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Randomized controlled trials Language: English Journal: Hum Behav Emerg Technol Year: 2020 Document Type: Article