As researchers, we've all encountered suspicious, incomplete, or even hallucinated citations generated by AI tools and literature review systems.
To help address this problem, I've developed a Reference Verifier that cross-checks references against multiple independent scholarly databases and identifies potentially fake, incorrect, or inconsistent citations.
Check-My-Reference: https://reference-verifier.onrender.com/
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Verifies references against 8 academic sources
- Crossref
- Semantic Scholar
- OpenAlex
- PubMed
- arXiv
- CORE
- DataCite
- Google Scholar
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Detects:
- Hallucinated references
- Incorrect metadata
- DOI mismatches
- Publication year inconsistencies
- Missing or incomplete citation information
Recently tested on 7 IoT/Federated Learning references:
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✅ 7 Verified References
Examples of issues detected:
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Blockchained Federated Learning for Internet of Things: A Comprehensive Survey
- Cited as 2023
- Verified sources indicate 2024
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Federated Learning for the Internet of Things: Applications, Challenges, and Opportunities
- Cited as 2021
- Verified sources indicate 2022
- Reference integrity scoring
- DOI validation
- Multi-source verification
- JSON report export
- BibTeX export
- Verification confidence scores
- Researchers
- PhD Scholars
- Faculty Members
- Conference Organizers
- Journal Editors
- Students preparing theses/dissertations
With the growing use of AI-assisted writing, citation hallucinations have become a serious concern in academic publishing. My goal is to provide a simple tool that helps researchers validate references before manuscript submission.
The tool is currently available for free testing.
I'd love feedback on:
- Accuracy of verification results
- User interface and workflow
- Additional features you'd like to see
- Integration with reference managers (Zotero, Mendeley, EndNote, etc.)