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.
Alexander Wasdahl (Wed,) studied this question.