Blockchain-Based Content Provenance in News Recommendation: A Scoping Review
Blockchain has been proposed to establish the origin and integrity of news content, and separately to support recommender systems, yet whether provenance information is actually used inside news recommendation and ranking pipelines remains unclear. This scoping review maps the literature on blockchain-based content provenance for news and determines how, and with what evidence, it connects to news recommendation or ranking. The review followed the Arksey and O'Malley framework and its refinements and is reported according to the PRISMA-ScR guideline (Tricco et al., 2018). A systematic search of Scopus, Web of Science, IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and arXiv was performed on 25 October 2026. Records were charted with an a priori five-level scheme (L0 to L4) describing how provenance status enters a ranking pipeline, and a five-stage scheme for evaluation maturity. The search identified 1,247 records; after removing 312 duplicates, 935 records were screened and 42 assessed in full text, with 17 studies meeting the eligibility criteria. The included proposals were conceptual, framework-level, prototype-stage, or reported technical experiments. None of the included blockchain-based studies used provenance status as an input to ranking, filtering, or personalisation (level L0). Adjacent user studies of provenance and disclosure labels (samples of 160 to 6,114 participants) concerned non-blockchain provenance or labels, and one placed labels inside a news-recommender prototype. Recommender-side misinformation research relied on fact-check labels, account reliability, or network position rather than cryptographic provenance. No study from an African setting was identified. The review confirms that provenance and recommendation research have developed in parallel; we propose six design questions to guide future work at their intersection and argue that evaluating provenance-aware ranking is a critical and unfilled research space.
Authors
- Augustine Chidiebere Onuora (ORCID: https://orcid.org/0000-0001-6723-6323)
- Taiwo Adisa Oyeniran (ORCID: https://orcid.org/0000-0002-4347-5650)
- George Elechi Christopher
Institutions
- Akanu Ibiam, Federal Polytechnic Unwana (NG)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-10-30
- DOI
- https://doi.org/10.5281/zenodo.23800063
- Primary Topic
- Recommender Systems and Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00