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Cited by 499

The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.

The Cognitive Mediation Model of Learning From the News: Evidence From Nonelection, Off-Year Election, and Presidential Election Contexts

EVELAND, WILLIAM P.·2001·Communication Research

Abstract

The cognitive mediation model predicts that the impact of a learning gratification for news media use on knowledge of news content is mediated by information processing variables. Specifically, surveillance gratifications seeking should encourage two forms of information processing: news attention and elaboration. These forms of information processing should covary and have a direct and positive impact on learning of news content. The impact of surveillance gratifications seeking on knowledge—expected at the zero-order level—should be approximately zero when these information processing variables are controlled. A secondary analysis of two sample surveys (N = 512 and N = 567) plus analysis of original data specifically designed to test the model (N = 299) provide nearly complete support for hypotheses derived from the model in the context of political learning from the news. Suggestions for expansion of the model are provided.

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Metadata

Authors
EVELAND, W. P.
Venue
Communication Research
Published
2001-10
Volume
28
Issue
5
Pages
571-601
ISSN
  • 0093-6502 (ISSN-L)
  • 1552-3810
OpenAlex
W2138047389
Cited by
499
References
70
FWCI
24.97
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The Cognitive Mediation Model of Learning From the News: Evidence From Nonelection, Off-Year Election, and Presidential Election Contexts. https://app.cochat.ai/papers/10.1177/009365001028005001
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EVELAND, W. P. (2001). The Cognitive Mediation Model of Learning From the News: Evidence From Nonelection, Off-Year Election, and Presidential Election Contexts. Communication Research. https://doi.org/10.1177/009365001028005001

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.