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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
Published on: October 24, 2019
Estimating the predictability of questionable open-access journals.
Han Zhuang1, Lizhen Liang2, Daniel E Acuna3
1Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, China.
Artificial intelligence (AI) can systematically identify questionable journals by analyzing website data. This scalable approach aids research integrity by flagging thousands of suspect publications, complementing expert review.
Area of Science:
- Bibliometrics
- Scholarly Communication
- Research Integrity
Background:
- Questionable journals pose a significant threat to global research integrity.
- Manual vetting of journals is often slow and lacks scalability.
- Identifying predatory or unreliable academic publishing venues is a growing concern.
Purpose of the Study:
- To explore the potential of artificial intelligence (AI) for systematically identifying questionable journals.
- To develop and evaluate an AI-driven method for assessing journal legitimacy.
- To provide a scalable solution for detecting unreliable academic publishing venues.
Main Methods:
- Utilized artificial intelligence (AI) to analyze journal website design, content, and publication metadata.
- Trained and evaluated the AI model against extensive human-annotated datasets.
- Adjusted decision thresholds to balance comprehensive screening with precise identification.
Main Results:
- The AI method achieved practical accuracy in identifying questionable journals.
- Over 1000 suspect journals were flagged at a balanced threshold.
- These flagged journals publish a substantial number of articles and receive significant citations.
Conclusions:
- AI offers a powerful, scalable tool for enhancing research integrity checks.
- Automated triage using AI can effectively identify potentially problematic journals.
- Integrating AI with expert review is crucial for robust vetting of academic venues.

