The DOI matches its Crossref record; 63 of the 64 references it cites were found in a registry or an index.
Red Media, Blue Media: Evidence of Ideological Selectivity in Media Use
Abstract
We show that the demand for news varies with the perceived affinity of the news organization to the consumer’s political preferences. In an experimental setting, conservatives and Republicans preferred to read news reports attributed to Fox News and to avoid news from CNN and NPR. Democrats and liberals exhibited exactly the opposite syndrome—dividing their attention equally between CNN and NPR, but avoiding Fox News. This pattern of selective exposure based on partisan affinity held not only for news coverage of controversial issues but also for relatively “soft” subjects such as crime and travel. The tendency to select news based on anticipated agreement was also strengthened among more politically engaged partisans. Overall, these results suggest that the further proliferation of new media and enhanced media choices may contribute to the further polarization of the news audience.
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“The directional (or defensive) motivation leads to selective exposure, encouraging individuals to seek information (or news sources) that they believe is congenial to their existing attitudes (Iyengar & Hahn, 2009;Stroud, 2011).”
From Do Search Algorithms Endanger Democracy? An Experimental Investigation of Algorithm Effects on Political Polarization (2020) · cites this paper as Iyengar & Hahn, 2009
In the citing paper
“As a result of this transformation, individuals are being increasingly exposed to only information that reinforces their existing views, thus avoiding challenging opinions (Sunstein 2001; Garrett 2009; Iyengar and Hahn 2009).”
From Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data (2015) · cites this paper as Iyengar and Hahn 2009
In the citing paper
“This generates a so-called echo-chamber environment that fosters social extremism and political polarization.”
From Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data (2015) · cites this paper as Iyengar and Hahn 2009
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Metadata
- Authors
- Iyengar, S., & Hahn, K. S.
- Affiliations
- Stanford University (US)
- University of California, Los Angeles (US)
- ORCID
- Iyengar, Shanto
- Venue
- Journal of Communication
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-03
- Volume
- 59
- Issue
- 1
- Pages
- 19-39
- ISSN
- 0021-9916 (ISSN-L)
- 1460-2466
- OpenAlex
- W2166155258
- Cited by
- 1,940
- References
- 64
- Citations by year
- 2026: 73 · 2025: 137 · 2024: 116 · 2023: 105 · 2022: 167 · 2021: 156 · 2020: 246 · 2019: 162 · 2018: 148 · 2017: 129 · 2016: 101 · 2015: 92 · 2014: 89 · 2013: 80 · 2012: 64
- FWCI
- 148.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Communication (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Media Influence and Politics
- Social Sciences › Communication › News Media Framing and Political Communication
- Social Sciences › Political Science and International Relations › Electoral Systems and Political Participation
- Keywords
- selective exposure, political affiliation, Fox News, convolutional neural networks, NPR, political polarization, political participation, new media
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