Automated Journalism: A Meta-Analysis of Readers’ Perceptions of Human-Written in Comparison to Automated News
| Autor: | Graefe, Andreas, Bohlken, Nina |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Publizistische Medien
Journalismus Verlagswesen News media journalism publishing automated news computational journalism meta-analysis review robot journalism Kommunikatorforschung Journalismus Wirkungsforschung Rezipientenforschung Communicator Research Journalism Impact Research Recipient Research Journalismus Nachrichten Automatisierung Qualität Rezipient Wahrnehmung Glaubwürdigkeit journalism news automation quality recipient perception credibility |
| Zdroj: | Media and Communication, 8, 3, 50-59, Algorithms and Journalism: Exploring (Re)Configurations |
| Druh dokumentu: | Zeitschriftenartikel<br />journal article |
| ISSN: | 2183-2439 |
| DOI: | 10.17645/mac.v8i3.3019 |
| Popis: | This meta-analysis summarizes evidence on how readers perceive the credibility, quality, and readability of automated news in comparison to human-written news. Overall, the results, which are based on experimental and descriptive evidence from 12 studies with a total of 4,473 participants, showed no difference in readers’ perceptions of credibility, a small advantage for human-written news in terms of quality, and a huge advantage for human-written news with respect to readability. Experimental comparisons further suggest that participants provided higher ratings for credibility, quality, and readability simply when they were told that they were reading a human-written article. These findings may lead news organizations to refrain from disclosing that a story was automatically generated, and thus underscore ethical challenges that arise from automated journalism. |
| Databáze: | SSOAR – Social Science Open Access Repository |
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