Abstract
The advent of AI-generated news as a novel form of content demands renewed attention toward modes of understanding reader perceptions. This research sought to answer: What are the evaluative criteria used by readers in their perception of automated news content generated via natural language processing? A series of exploratory factor analyses (EFA) was conducted on results of a survey using items obtained in an initial survey phase to uncover underlying factors contributing to differences in reader article rankings. When compared with the results of factor analyses for human-generated news content, the findings offer new constellations of terms that reflect the dimensions that readers attend to in articles attributed to artificial intelligence.
DOI
10.30658/hmc.11.10
Author ORCID Identifier
Alexander Wasdahl: 0009-0001-8861-0000 
Recommended Citation
Wasdahl, A. (2025). Machine credibility: How news readers evaluate AI-generated content. Human-Machine Communication, 11, 191–211. https://doi.org/10.30658/hmc.11.10
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