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Like-minded sources on Facebook are prevalent but not polarizing.
Nyhan, Brendan; Settle, Jaime; Thorson, Emily; Wojcieszak, Magdalena; Barberá, Pablo; Chen, Annie Y; Allcott, Hunt; Brown, Taylor; Crespo-Tenorio, Adriana; Dimmery, Drew; Freelon, Deen; Gentzkow, Matthew; González-Bailón, Sandra; Guess, Andrew M; Kennedy, Edward; Kim, Young Mie; Lazer, David; Malhotra, Neil; Moehler, Devra; Pan, Jennifer; Thomas, Daniel Robert; Tromble, Rebekah; Rivera, Carlos Velasco; Wilkins, Arjun; Xiong, Beixian; de Jonge, Chad Kiewiet; Franco, Annie; Mason, Winter; Stroud, Natalie Jomini; Tucker, Joshua A.
Afiliação
  • Nyhan B; Department of Government, Dartmouth College, Hanover, NH, USA. nyhan@dartmouth.edu.
  • Settle J; Department of Government and Data Science, William and Mary, Williamsburg, VA, USA.
  • Thorson E; Maxwell School of Citizenship and Public Affairs, Syracuse University, Syracuse, NY, USA.
  • Wojcieszak M; Department of Communication, University of California, Davis, CA, USA.
  • Barberá P; Amsterdam School of Communication Research, University of Amsterdam, Amsterdam, The Netherlands.
  • Chen AY; Meta, Menlo Park, CA, USA.
  • Allcott H; CUNY Institute for State and Local Governance, New York, NY, USA.
  • Brown T; Environmental and Energy Policy Analysis Center, Stanford University, Stanford, CA, USA.
  • Crespo-Tenorio A; Meta, Menlo Park, CA, USA.
  • Dimmery D; Meta, Menlo Park, CA, USA.
  • Freelon D; Meta, Menlo Park, CA, USA.
  • Gentzkow M; Research Network Data Science, University of Vienna, Vienna, Austria.
  • González-Bailón S; Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, USA.
  • Guess AM; Department of Economics, Stanford University, Stanford, CA, USA.
  • Kennedy E; Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, USA.
  • Kim YM; Department of Politics, Princeton University, Princeton, NJ, USA.
  • Lazer D; School of Public and International Affairs, Princeton University, Princeton, NJ, USA.
  • Malhotra N; Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA.
  • Moehler D; School of Journalism and Mass Communication, University of Wisconsin-Madison, Madison, WI, USA.
  • Pan J; Network Science Institute, Northeastern University, Boston, MA, USA.
  • Thomas DR; Graduate School of Business, Stanford University, Stanford, CA, USA.
  • Tromble R; Meta, Menlo Park, CA, USA.
  • Rivera CV; Department of Communication, Stanford University, Stanford, CA, USA.
  • Wilkins A; Meta, Menlo Park, CA, USA.
  • Xiong B; School of Media and Public Affairs, The George Washington University, Washington, DC, USA.
  • de Jonge CK; Institute for Data, Democracy, and Politics, The George Washington University, Washington, DC, USA.
  • Franco A; Meta, Menlo Park, CA, USA.
  • Mason W; Meta, Menlo Park, CA, USA.
  • Stroud NJ; Meta, Menlo Park, CA, USA.
  • Tucker JA; Meta, Menlo Park, CA, USA.
Nature ; 620(7972): 137-144, 2023 Aug.
Article em En | MEDLINE | ID: mdl-37500978
ABSTRACT
Many critics raise concerns about the prevalence of 'echo chambers' on social media and their potential role in increasing political polarization. However, the lack of available data and the challenges of conducting large-scale field experiments have made it difficult to assess the scope of the problem1,2. Here we present data from 2020 for the entire population of active adult Facebook users in the USA showing that content from 'like-minded' sources constitutes the majority of what people see on the platform, although political information and news represent only a small fraction of these exposures. To evaluate a potential response to concerns about the effects of echo chambers, we conducted a multi-wave field experiment on Facebook among 23,377 users for whom we reduced exposure to content from like-minded sources during the 2020 US presidential election by about one-third. We found that the intervention increased their exposure to content from cross-cutting sources and decreased exposure to uncivil language, but had no measurable effects on eight preregistered attitudinal measures such as affective polarization, ideological extremity, candidate evaluations and belief in false claims. These precisely estimated results suggest that although exposure to content from like-minded sources on social media is common, reducing its prevalence during the 2020 US presidential election did not correspondingly reduce polarization in beliefs or attitudes.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Política / Atitude / Mídias Sociais Tipo de estudo: Risk_factors_studies Limite: Adult / Humans País como assunto: America do norte Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Política / Atitude / Mídias Sociais Tipo de estudo: Risk_factors_studies Limite: Adult / Humans País como assunto: America do norte Idioma: En Ano de publicação: 2023 Tipo de documento: Article