Grounded in the MAIN model and multidimensional information-quality frameworks, this research conceptualizes news evaluation through three distinct lenses: credibility, newsworthiness, and readability. Through a 2 (authorship: AI vs. human) × 3 (news domain: finance, weather, sports) mixed experiment (N = 301), participants evaluated identical articles attributed to either an AI system or a human journalist. The results reveal a consistent “credibility penalty” for AI-labeled news across all domains, suggesting that authorship serves as a domain-general source heuristic. However, the effects on newsworthiness and readability were domain-contingent, shifting based on genre-specific expectations and the informational stakes of the topic. These findings demonstrate that audience responses to AI journalism are multidimensional and context-sensitive rather than uniform. This study offers significant implications for communication theory, transparency in disclosure practices, and the strategic adoption of AI in modern newsrooms.
Park et al. (Mon,) studied this question.
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