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Fake news on Twitter during the 2016 U.S. presidential election
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Finding facts about fake news
There was a proliferation of fake news during the 2016 election cycle. Grinberg et al. analyzed Twitter data by matching Twitter accounts to specific voters to determine who was exposed to fake news, who spread fake news, and how fake news interacted with factual news (see the Perspective by Ruths). Fake news accounted for nearly 6% of all news consumption, but it was heavily concentrated—only 1% of users were exposed to 80% of fake news, and 0.1% of users were responsible for sharing 80% of fake news. Interestingly, fake news was most concentrated among conservative voters.
Abstract
The spread of fake news on social media became a public concern in the United States after the 2016 presidential election. We examined exposure to and sharing of fake news by registered voters on Twitter and found that engagement with fake news sources was extremely concentrated. Only 1% of individuals accounted for 80% of fake news source exposures, and 0.1% accounted for nearly 80% of fake news sources shared. Individuals most likely to engage with fake news sources were conservative leaning, older, and highly engaged with political news. A cluster of fake news sources shared overlapping audiences on the extreme right, but for people across the political spectrum, most political news exposure still came from mainstream media outlets.
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Published In
Science
Volume 363 | Issue 6425
25 January 2019
25 January 2019
Copyright
Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.
This is an article distributed under the terms of the Science Journals Default License.
Submission history
Received: 23 May 2018
Accepted: 2 January 2019
Published in print: 25 January 2019
Acknowledgments
We thank L. Adamic, Y. Benkler, S. McCabe, and the three anonymous reviewers for thoughtful feedback on the manuscript and TargetSmart for access to voter data. The research was approved by Northeastern University’s Institutional Review Board. All opinions expressed in this article are those of the authors alone. Funding: D.L. acknowledges support by the ESRC ES N012283/1 and ARO W911NF-12-1-0556. Author contributions: D.L. conceived of the study. N.G., K.J., and L.F. collected and processed data, carried out statistical modeling, and produced visualizations. N.G., K.J., L.F., and B.S.-T. performed literature review and annotated data. All authors devised analyses and wrote and revised the paper. Competing interests: The authors declare no competing interests. Data and materials availability: Aggregate data and code from this study are freely available at Zenodo (26). Deidentified individual-level data are also available at Zenodo (27) upon signing a usage agreement stating that: (i) you shall not attempt to identify, reidentify, or otherwise deanonymize the dataset and (ii) you shall not further share, distribute, publish, or otherwise disseminate the dataset without Northeastern University’s prior written approval.
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