Man vs. Machine? The Impact of Algorithm Authorship on News Credibility

Edson C. Tandoc, Lim Jia Yao, Shangyuan Wu

Research output: Contribution to journalArticlepeer-review

45 Scopus citations


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.

Original languageEnglish
Pages (from-to)548-562
Number of pages15
JournalDigital Journalism
Issue number4
StatePublished - 20 Apr 2020

Bibliographical note

Funding Information:
This research is supported by the corresponding author’s Tier 1 Grant (T1-002-125-05) awarded by the Singapore’s Ministry of Education. The authors wish to thank Zheng Ruoming, an undergraduate student at Nanyang Technological University (NTU) during the time of data collection, for assisting with this project.

Publisher Copyright:
© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group.


  • Automation
  • journalism
  • news
  • objectivity
  • robot news


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