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Daniel Acuna

@danielacuna.bsky.social
767 followers 66 following 13 posts

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 · 08/06/2026
AIs are helping automate science, but do they uphold research integrity principles while working autonomously? Most models cave in to questionable research practice request. Paper: arxiv.org/abs/2605.29468 Data & Code: github.com/sciosci/SciI... Website: sciosci.github.io/sciintbench/
arxiv.org
SciIntBench: Measuring LLM Compliance with Research Integrity Norms Under Adversarial Framing
Large language models (LLMs) are increasingly used to support scientific work, but it is unclear whether they uphold responsible conduct of research (RCR) norms or help undermine them. We introduce Sc...
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Daniel Acuna @danielacuna.bsky.social · 27/08/2025
Great piece by jeffreybrainard.bsky.social with comments from others www.science.org/content/arti...
science.org
AI tool labels more than 1000 journals for ‘questionable,’ possibly shady practices
New algorithm could help scientists avoid publishing in shady titles
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Reposted by Daniel Acuna
Jeffrey Brainard @jeffreybrainard.bsky.social · 27/08/2025
Can AI help identify high-volume, low-quality, “questionable” scientific journals (which some, controversially, call #predatoryjournals )? Authors of this new study emphasize aiding not replacing human evaluators of these journals. @science.org www.science.org/content/arti...
science.org
AI tool labels more than 1000 journals for ‘questionable,’ possibly shady practices
New algorithm could help scientists avoid publishing in shady titles
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Daniel Acuna @danielacuna.bsky.social · 27/08/2025
Take a look at the full paper: - science.org/doi/10.1126/... I started this project 5 years ago. Paper with my former students Han Zhuang and Lizhen Liang Disclaimer: I am the founder of ReviewerZero AI (www.reviewerzero.ai), where we help with research integrity issues.
science.org
Estimating the predictability of questionable open-access journals
AI screening of journals identifies over a thousand questionable journals, helping experts review where it is needed most.
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Daniel Acuna @danielacuna.bsky.social · 27/08/2025
A few takeaways: - the AI of course still makes mistakes, but we believe those mistakes are worth it - predictions should be part of a triage system with experts. we make our predictions "interpretable", aligning with DOAJ guidelines - But AI for integrity is here to stay
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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 journalsGraph showing growth in citationsGraph 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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Daniel Acuna @danielacuna.bsky.social · 21/11/2024
that makes sense! 🧠
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Daniel Acuna @danielacuna.bsky.social · 20/11/2024
Who are these hundreds of people who started following me recently? I mean, hi 👋, but how did you find me without any advertisement from my part? 😅 This place is starting to look great.
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Daniel Acuna @danielacuna.bsky.social · 02/10/2024
NotebookLM podcast version of the paper. Pretty good! notebooklm.google.com/notebook/d75... (need a Google account)
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Daniel Acuna @danielacuna.bsky.social · 02/10/2024
Excited to share a new article with Han Zhuang in Quantitative Studies of Science! "Incorporating costs and benefits to the evaluation of uncertain research results," we propose a unified framework for deciding when to continue research, even if it might be wrong 🤯. direct.mit.edu/qss/article/...
direct.mit.edu
Incorporating costs and benefits to the evaluation of uncertain research results: applications to cancer research funding
Abstract. Correctness is a key aspiration of the scientific process, yet recent studies suggest that many high-profile findings may be difficult to replicate or require considerable evidence for verif...
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Daniel Acuna @danielacuna.bsky.social · 25/10/2023
Tenure/tenure-track faculty positions in NLP in CS. Looking for people in science of science! Super interdisciplinary dept, great place to work and grow, amazing location. Learn more jobs.colorado.edu/jobs/JobDeta... Feel free to reach out and ask about the position and details.
jobs.colorado.edu
Tenured/Tenure-Track Faculty Search in Natural Language Processing
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