Hijacked medical journals and the risk to scholarly integrity: a web analytics study of prevalence, traffic channels, and geographic origins of traffic
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Edit Ilona Pallás
,
Mehdi Dadkhah
and
Lóránt Dénes Dávid
Abstract
Objectives
The integrity of research and science is increasingly under threat from questionable journals. In particular, hijacked journals rapidly expand, propagating scam-based, non-peer-reviewed publications. While the academic literature offers discussions, developed methodologies, and block lists to combat these fraudulent journals, the most prevalent hijacked journals and their primary distribution channels remain ambiguous.
Methods
To bridge this knowledge gap, the current study utilized a list of 380 previously detected hijacked journals and the web analytics platform Semrush to identify the most visited hijacked journals and their primary channels for attracting web traffic. This research first analyzes hijacked journals across various fields and then focuses specifically on hijacked medical journals.
Results
Our findings demonstrate that over 50 % of previously detected hijacked journals are active, primarily attracting researchers via email, search engines, and artificial intelligence (AI) tools. Furthermore, the majority of visitors to these journal websites originate from India. The results for medical journals align with these overall trends.
Conclusions
Addressing the significant problem these questionable journals pose necessitates implementing legal action and technological solutions.
Funding source: Hungarian University of Agriculture and Life Sciences (MATE)
Award Identifier / Grant number: This work was supported by the Flagship Research G
Acknowledgments
The authors would like to thank the Flagship Research Groups Programme of the Hungarian University of Agriculture and Life Sciences (MATE).
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: AI technologies (Gemini 2.5 flash and Grammarly) were utilized to increase readability in some sections of the text. The additional conceivable applications of AI tools have been stated in the method section. The AI has been used to improve readability and fluency not writing from scratch.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: This work was supported by the Flagship Research Groups Programme of the Hungarian University of Agriculture and Life Sciences (MATE).
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Data availability: Data available on request from the authors.
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