Systematic Review of AI Provenance and Disclosure Cues in Journalism

47 empirical studies synthesize how AI authorship cues and disclosure transparency affect perceived news credibility and trust. The review, conducted following PRISMA 2020 guidelines, analyzed 290 records screened from Scopus and Web of Science. Lorena Licenji authored this 46.6 KB document, last updated in May 2026.

Use Cases

• Designing experiments on news credibility based on the review's findings about conditional AI penalty effects.
• Developing transparency guidelines for AI in journalism based on the analysis of disclosure cue impacts.
• Framing future research using the proposed Cue–Inference–Target (CIT) theoretical model.
• Comparing study designs and outcome measures across the 47 included empirical studies.

Strengths

• Follows PRISMA 2020 and PRISMA-S systematic review protocols, ensuring a structured methodology.
• Synthesizes findings from 47 empirical studies, providing a substantial evidence base.
• Proposes a novel Cue–Inference–Target (CIT) framework for interpreting results.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is a 46.6 KB document, indicating a limited scope focused on review synthesis rather than primary data.

Provenance

Source

figshare

Collection Method

Systematic literature review of Scopus and Web of Science Core Collection.

Freshness

Last updated 2026-05-05 05:24:42; freshness should be verified.