Systematic Review of AI Provenance and Disclosure Cues in Journalism

A systematic literature review synthesizing 47 empirical studies on how AI provenance and disclosure cues in journalism affect perceived credibility and trust. The review, authored by Lorena Licenji, was published on figshare on May 5, 2026, following a PRISMA-guided search of 492 records from Scopus and Web of Science. It proposes a Cue–Inference–Target (CIT) framework to explain audience judgments.

Use Cases

• Analyzing the relationship between AI attribution and perceived message credibility based on the review's synthesis of study results.
• Investigating conditional effects of AI disclosure on trust, moderated by topic or baseline trust, as discussed in the results.
• Designing factorial experiments on AI cues in news, informed by the review's proposed CIT framework and research gaps.
• Comparing operationalizations of AI provenance cues across heterogeneous study designs mapped in the review.

Strengths

• Follows PRISMA 2020 and PRISMA-S guidelines for systematic reviews, indicating a structured methodology.
• Synthesizes findings from 47 included studies, providing a consolidated evidence base.
• File is 267.3 KB, making it a compact and easily downloadable resource.

Limitations

• Row count is unknown, which may limit suitability assessment for quantitative meta-analysis.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset's scope is limited to a review of literature; it does not contain primary research data.

Provenance

Source

figshare

Collection Method

Systematic literature review following PRISMA guidelines, searching Scopus and Web of Science Core Collection.

Time Range

Literature search conducted on February 2, 2026; temporal coverage of reviewed studies is not specified.

Freshness

Last updated 2026-05-05 05:24:45