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Cited by 336Media Influence and PoliticsNews Media Framing and Political CommunicationComputational and Text Analysis Methods

The DOI matches its Crossref record; 51 of the 52 references it cites were found in a registry or an index.

Fair and Balanced? Quantifying Media Bias through Crowdsourced Content Analysis

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Budak, Ceren, Goel, Sharad, Rao, Justin M.·2016·Public Opinion Quarterly
DOI 10.1093/poq/nfw007
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Abstract

It is widely thought that news organizations exhibit ideological bias, but rigorously quantifying such slant has proven methodologically challenging. Through a combination of machine learning and crowdsourcing techniques, we investigate the selection and framing of political issues in 15 major U.S. news outlets. Starting with 803,146 news stories published over 12 months, we first used supervised learning algorithms to identify the 14% of articles pertaining to political events. We then recruited 749 online human judges to classify a random subset of 10,950 of these political articles according to topic and ideological position. Our analysis yields an ideological ordering of outlets consistent with prior work. We find, however, that news outlets are considerably more similar than generally believed. Specifically, with the exception of political scandals, we find that major news organizations present topics in a largely non-partisan manner, casting neither Democrats nor Republicans in a particularly favorable or unfavorable light. Moreover, again with the exception of political scandals, there is little evidence of systematic differences in story selection, with all major news outlets covering a wide variety of topics with frequency largely unrelated to the outlet's ideological position. Finally, we find that news organizations express their ideological bias not by directly advocating for a preferred political party, but rather by disproportionately criticizing one side, a con…

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Research works printed but not linked: 6

  1. 10.1162/jmlr.2003.3.4–5.993 · DOI
  2. Flaxman Seth Goel Sharad Rao Justin M. . 2015. “Ideological Segregation and the Effects of Social Media on News Consumption.” Social Science Research Network 2363701. Available at http://ssrn.com/abstract=2363701.
  3. Gamson William A . 1992. Talking Politics . Cambridge: Cambridge University Press.
  4. Kohut Andrew Doherty Carroll Dimock Michael Keeter Scott . 2012. “In CNews Landscape, Even Television Is Vulnerable.” Pew Center for the People and the Press. Available at http://www.people-press.org/2012/09/27/section-4-demographics-and-political-views-of-news-audiences/.
  5. Sutter, 2001, “Can the Media Be So Liberal? The Economics of Media Bias.”, Cato Journal, 20, 431
  6. Wais Paul Lingamneni Shivaram Cook Duncan Fennell Jason Goldenberg Benjamin Lubarov Daniel Marin David Simons Hari . 2010. “Towards Building a High-Quality Workforce with Mechanical Turk.” Proceedings of Computational Social Science and the Wisdom of Crowds, NIPS, 1–5.

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Metadata

Authors
Budak, C., Goel, S., & Rao, J. M.
Affiliations
Microsoft (United States) (US)
Corresponding author
Budak, Ceren
ORCID
Venue
Public Opinion Quarterly
Publisher
Oxford University Press (OUP)
Published
2016
Volume
80
Issue
S1
Pages
250-271
ISSN
  • 0033-362X (ISSN-L)
  • 1537-5331
DOI
10.1093/poq/nfw007
OpenAlex
W1119807432
Cited by
336
References
52
Citations by year
2026: 18 · 2025: 40 · 2024: 28 · 2023: 38 · 2022: 30 · 2021: 55 · 2020: 38 · 2019: 39 · 2018: 32 · 2017: 9 · 2016: 5
FWCI
81.00
Type
journal article
Language
en
Open access
Locations
  • Public Opinion Quarterly (publishedVersion)
  • SSRN Electronic Journal (acceptedVersion)
  • HighWire Press Open Archive (submittedVersion)
Topics
  • Social Sciences › Sociology and Political Science › Media Influence and Politics
  • Social Sciences › Communication › News Media Framing and Political Communication
  • Social Sciences › General Social Sciences › Computational and Text Analysis Methods
Keywords
media bias, ideological bias, news framing, media coverage of politics, partisan bias, political scandal, news selection

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