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|Title:||Man vs. Machine? The impact of algorithm authorship on news credibility||Authors:||Tandoc, Edson C.
Yao, Lim Jia
|Keywords:||Social sciences::Communication||Issue Date:||2020||Source:||Tandoc, E. C., Yao, L. J. & Wu, S. (2020). Man vs. Machine? The impact of algorithm authorship on news credibility. Digital Journalism, 8(4), 548-562. https://dx.doi.org/10.1080/21670811.2020.1762102||Project:||T1-002-125-05||Journal:||Digital Journalism||Abstract:||Facing budget constraints, many traditional news organizations are turning their eyes on automation to streamline manpower, cut down on costs, and improve efficiency. But how does automation fit into traditional values of journalism and how does it affect perceptions of credibility, an important currency valued by the journalistic field? This study explores this question using a 3 (declared author: human vs. machine vs. combined) × 2 (objectivity: objective vs. not objective) between-subjects experimental design involving 420 participants drawn from the national population of Singapore. The analysis found no main differences in perceived source credibility between algorithm, human, and mixed authors. Similarly, news articles attributed to an algorithm, a human journalist, and a combination of both showed no differences in message credibility. However, the study found an interaction effect between type of declared author and news objectivity. When the article is presented to be written by a human journalist, source and message credibility remain stable regardless of whether the article was objective or not objective. However, when the article is presented to be written by an algorithm, source and message credibility are higher when the article is objective than when the article is not objective. Findings for combined authorship are split: there were no differences between objective and non-objective articles when it comes to message credibility. However, combined authorship is rated higher in source credibility when the article is not objective than when the article is objective.||URI:||https://hdl.handle.net/10356/154737||ISSN:||2167-0811||DOI:||10.1080/21670811.2020.1762102||Rights:||© 2020 Informa UK Limited, trading as Taylor & Francis Group. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||WKWSCI Journal Articles|
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