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Cited by 122

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Can an Algorithm Reduce the Perceived Bias of News? Testing the Effect of Machine Attribution on News Readers’ Evaluations of Bias, Anthropomorphism, and Credibility

Waddell, T. Franklin·2019·Journalism & Mass Communication Quarterly
DOI 10.1177/1077699018815891
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Abstract

Although accusations of editorial slant are ubiquitous to the contemporary media environment, recent advances in journalism such as news writing algorithms may hold the potential to reduce readers’ perceptions of media bias. Informed by the Modality-Agency-Interactivity-Navigability (MAIN) model and the principle of similarity attraction, an online experiment ( n = 612) was conducted to test if news attributed to an automated author is perceived as less biased and more credible than news attributed to a human author. Results reveal that perceptions of bias are attenuated when news is attributed to a journalist and algorithm in tandem, with positive downstream consequences for perceived news credibility.

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Metadata

Authors
Waddell, T. F.
Venue
Journalism & Mass Communication Quarterly
Publisher
SAGE Publications
Published
2019-01-25
Volume
96
Issue
1
Pages
82-100
ISSN
  • 1077-6990
  • 2161-430X
DOI
10.1177/1077699018815891
Cited by
122
References
7
Language
en
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)

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CoChat. (2026). Citation check: Can an Algorithm Reduce the Perceived Bias of News? Testing the Effect of Machine Attribution on News Readers’ Evaluations of Bias, Anthropomorphism, and Credibility. https://app.cochat.ai/papers/10.1177/1077699018815891
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Waddell, T. F. (2019). Can an Algorithm Reduce the Perceived Bias of News? Testing the Effect of Machine Attribution on News Readers’ Evaluations of Bias, Anthropomorphism, and Credibility. Journalism & Mass Communication Quarterly. https://doi.org/10.1177/1077699018815891

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