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Partisan asymmetries in exposure to misinformation.
Rao, Ashwin; Morstatter, Fred; Lerman, Kristina.
Afiliação
  • Rao A; Department of Computer Science, University of Southern California, Los Angeles, 90007, USA. mohanrao@usc.edu.
  • Morstatter F; Information Sciences Institute, University of Southern California, Marina del Rey, 90292, USA. mohanrao@usc.edu.
  • Lerman K; Information Sciences Institute, University of Southern California, Marina del Rey, 90292, USA.
Sci Rep ; 12(1): 15671, 2022 09 19.
Article em En | MEDLINE | ID: mdl-36123387
Online misinformation is believed to have contributed to vaccine hesitancy during the Covid-19 pandemic, highlighting concerns about social media's destabilizing role in public life. Previous research identified a link between political conservatism and sharing misinformation; however, it is not clear how partisanship affects how much misinformation people see online. As a result, we do not know whether partisanship drives exposure to misinformation or people selectively share misinformation despite being exposed to factual content. To address this question, we study Twitter discussions about the Covid-19 pandemic, classifying users along the political and factual spectrum based on the information sources they share. In addition, we quantify exposure through retweet interactions. We uncover partisan asymmetries in the exposure to misinformation: conservatives are more likely to see and share misinformation, and while users' connections expose them to ideologically congruent content, the interactions between political and factual dimensions create conditions for the highly polarized users-hardline conservatives and liberals-to amplify misinformation. Overall, however, misinformation receives less attention than factual content and political moderates, the bulk of users in our sample, help filter out misinformation. Identifying the extent of polarization and how political ideology exacerbates misinformation can help public health experts and policy makers improve their messaging.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Política / Mídias Sociais / COVID-19 Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Sci Rep Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Política / Mídias Sociais / COVID-19 Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Sci Rep Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos