Unpacking public trust in AI-generated news: constructing and validating a three-factor measurement tool

Section: Artificial Intelligence in Media and Communication Studies

This methodological investigation sought to establish and validate the construct structure of a robust psychometric instrument for measuring public trust in AIgenerated news content, addressing a critical gap in digital media studies regarding the impact of automation. The research employed a sequential two-phase validation approach across two distinct, non-probability samples (N1= 850; N2= 900). In Study 1, Exploratory Factor Analysis (EFA) on an initial item pool identified a clear, three-factor configuration, which was subsequently streamlined and tested in Study 2 using Confirmatory Factor Analysis (CFA) on a 10-item scale. The confirmed dimensions were identified as: Trust in Content Reliability/Accuracy, Trust in Impartiality/Objectivity, and Risk Perception of Automation. The CFA model fit indices confirmed the dimensional fidelity of the instrument (x2/df=2.95; CFI=0.95; RMSEA=0.05), which also demonstrated exceptional reliability (α ≥0.79) and strong external evidence through anticipated correlations with media skepticism and technological awareness. Critically, the final scale successfully predicted key behavioral outcomes related to content adoption and sharing. Consequently, the validated Trust in AI News Scale offers a highly reliable and valid tool, essential for future analysis of socio-technical trust dynamics in automated journalism.

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To cite this article: Mazari N. (2025) Doverie auditorii k novostyam, generiruemym II: konstruirovanie i validatsiya trekhfaktornogo instrumenta izmereniya [Unpacking public trust in AI-generated news: constructing and validating a three-factor measurement tool]. Vestnik Moskovskogo Universiteta. Seriya 10. Zhurnalistika 6: 53–79. DOI: 10.55959/msu.vestnik.journ.6.2025.5379