Exposure to ideologically diverse news and opinion on Facebook
Not getting all sides of the news?
People are increasingly turning away from mass media to social media as a way of learning news and civic information. Bakshy et al. examined the news that millions of Facebook users' peers shared, what information these users were presented with, and what they ultimately consumed (see the Perspective by Lazer). Friends shared substantially less cross-cutting news from sources aligned with an opposing ideology. People encountered roughly 15% less cross-cutting content in news feeds due to algorithmic ranking and clicked through to 70% less of this cross-cutting content. Within the domain of political news encountered in social media, selective exposure appears to drive attention.
Abstract
Exposure to news, opinion, and civic information increasingly occurs through social media. How do these online networks influence exposure to perspectives that cut across ideological lines? Using deidentified data, we examined how 10.1 million U.S. Facebook users interact with socially shared news. We directly measured ideological homophily in friend networks and examined the extent to which heterogeneous friends could potentially expose individuals to cross-cutting content. We then quantified the extent to which individuals encounter comparatively more or less diverse content while interacting via Facebook’s algorithmically ranked News Feed and further studied users’ choices to click through to ideologically discordant content. Compared with algorithmic ranking, individuals’ choices played a stronger role in limiting exposure to cross-cutting content.
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Supplementary Material
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Figs. S1 to S10
Tables S1 to S6
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References and Notes
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Science
Volume 348 | Issue 6239
5 June 2015
5 June 2015
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Copyright © 2015, American Association for the Advancement of Science.
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Submission history
Received: 20 October 2014
Accepted: 27 April 2015
Published in print: 5 June 2015
Acknowledgments
We thank J. Bailenson, D. Eckles, A. Franco, K. Garrett, J. Grimmer, S. Iyengar, B. Karrer, C. Nass, A. Peysakhovich, S. Taylor, R. Weiss, S. Westwood, J. M. White, and anonymous reviewers for their valuable feedback. The following code and data are archived in the Harvard Dataverse Network, http://dx.doi.org/10.7910/DVN/LDJ7MS: “Replication Data for: Exposure to Ideologically Diverse News and Opinion on Facebook”; R analysis code and aggregate data for deriving the main results (tables S5 and S6); Python code and dictionaries for training and testing the hard-soft news classifier; aggregate summary statistics of the distribution of ideological homophily in networks; and aggregate summary statistics of the distribution of ideological alignment for hard content shared by the top 500 most shared websites. The authors of this work are employed and funded by Facebook. Facebook did not place any restrictions on the design and publication of this observational study, beyond the requirement that this work was to be done in compliance with Facebook’s Data Policy and research ethics review process (www.facebook.com/policy.php).
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