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Daniel Acuna

@danielacuna.bsky.social

Assoc. Prof. Computer Science at the University of Colorado, Boulder. Prev iSchool, Syracuse University. Postdoc Northwestern University and Ability Lab. PhD Computer Science UMN, Twin Cities

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Daniel Acuna @danielacuna.bsky.social · 27/08/2025

Applied to journals that haven't been vetted before , we predict more than 1000 new potentially questionable journals. They have collectively published over 500K articles, cited millions of times, and acknowledged major funders in US, China, and Japan

graph showing large grow of publications in questionable journals

Graph showing distribution of countries in our prediction

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Daniel Acuna @danielacuna.bsky.social · 27/08/2025

We tried several methods, including simple regression, random forest and deep learning. - Bibliometric features alone: PRC AUC ≈0.64 - Combined model: PRC AUC ≈0.79 Agreement with DOAJ guidelines and expert reviewers was strong, though false positives remain.

Table that shows good agreement between experts, DOAJ guidelines, and algorithmic predictions

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Daniel Acuna @danielacuna.bsky.social · 27/08/2025

We trained models on ~15,000 journals labeled by DOAJ (12,869 legitimate vs 2,536 removed). Features included: - Website content (editorial boards, policies) - Website design (HTML structure, screenshots) - Bibliometrics (citations, author metrics)

Overview of the system, which combines multiple kinds of signals including the website, visual features, and bibliometric features

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Daniel Acuna @danielacuna.bsky.social · 27/08/2025

🚨New paper🚨 Open access has expanded science’s reach but also fueled the rise of "questionable" journals. Manual vetting can’t keep pace with thousands of titles and bad actors who adapt quickly. Wrong incentives too strong. In a new Science Advances paper we ask: can AI help?

Article front page: https://www.science.org/doi/10.1126/sciadv.adt2792

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