%0 Journal Article
%T Can an Algorithm Reduce the Perceived Bias of News? Testing the Effect of Machine Attribution on News Readers¡¯ Evaluations of Bias, Anthropomorphism, and Credibility
%A T. Franklin Waddell
%J Journalism & Mass Communication Quarterly
%@ 2161-430X
%D 2019
%R 10.1177/1077699018815891
%X 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
%K computational journalism
%K media bias
%K message credibility
%K MAIN model
%K similarity attraction
%U https://journals.sagepub.com/doi/full/10.1177/1077699018815891