Systematic Review of AI Provenance and Disclosure Cues in Journalism, 2026

A 2026 systematic literature review synthesizing 47 empirical studies on how AI provenance and disclosure cues in journalism affect perceived credibility and trust. The dataset, a 69.3 KB Excel file, was created by Lorena Licenji and follows PRISMA 2020 and PRISMA-S guidelines, analyzing results from searches of Scopus and Web of Science. It includes a structured narrative synthesis guided by the Synthesis Without Meta-analysis (SWiM) guideline.

Use Cases

• Analyzing the relationship between AI attribution cues and perceived message credibility based on the review's findings.
• Studying the conditional effects of topic and baseline trust on audience responses to AI-disclosed news as described in the results.
• Examining the proposed Cue–Inference–Target (CIT) framework for epistemic-quality versus normative-legitimacy judgments from the discussion.
• Identifying research gaps for future factorial designs separating provenance from disclosure cues as suggested in the discussion.

Strengths

• Follows PRISMA 2020 and PRISMA-S systematic review standards, indicating a structured methodology.
• Synthesizes findings from 47 included studies after screening 492 initial records.
• Proposes a theoretical Cue–Inference–Target (CIT) framework based on the review's synthesis.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is small (69.3 KB), suggesting limited scope, likely containing summary tables rather than raw study data.

Provenance

Source

figshare, authored by Lorena Licenji.

Collection Method

Systematic literature review following PRISMA 2020/PRISMA-S, with searches in Scopus and Web of Science Core Collection.

Time Range

The review was conducted with a search date of 2 February 2026; the temporal coverage of the included studies is not specified.

Freshness

Last updated 2026-05-05 05:24:44; freshness should be verified.