The public's and journalists' perceptions of news written by algorithms and human journalists
Jaemin Jung, Haeyeop Song, Youngju Kim, yunsuk Im, Sewook Oh
Erstpublikation in: Computers in Human Behavior, Volume 71, June 2017, Pages 291–298
Publikationsdatum:
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Abstracts
This study adopted a two (author: algorithm or journalist) by two (notification of author: real or inverse) between subject design to investigate how the public and journalists perceive the quality of algorithms-written articles compared with human journalist's work. Findings showed that both the public and journalists' evaluations were varied by the manipulation of author notification. That is, the public gave higher scores to the algorithm's work when it was notified as the real author, but they gave lower scores to the algorithm's work when the author was notified as a journalist. It confirmed the public's negative attitude toward journalists' credibility and craving for new information and communication technology (ICT) products/services in Korea. Based on journalists' resistance to change and innovation and the theory of prejudice, it was expected that journalists would be favorable to another journalist's work and unfavorable to an algorithm's work. However, contrary to the hypothetical expectation, journalists also gave higher scores to an algorithm's work and lower scores to a journalist's work. Implications relating to the intrusion of algorithm-written articles into journalism were discussed.
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