Five ways to spot when a paper is a fraud

"A growing number of AI tools can detect fraudulent elements in papers, but they can be expensive to use. Such tools are probably better deployed by journal publishers rather than individual reviewers, says Elisabeth Bik, a science-integrity consultant in San Francisco, California, especially because feeding unpublished content into AI tools can compromise confidentiality and is generally frowned on during peer review."

"The good news is that recognizing a problematic manuscript is 'way easier than you would believe', says Reese Richardson, a metascientist at Northwestern University in Evanston, Illinois. But the work is time-consuming, especially for beginners, he says. 'We know the fraudsters are going to [commit] fraud,' Bik says. By teaching people how to spot bad papers, however, 'we're gonna make it a little bit harder for them'."

"For example, if several references seem unrelated to the research in the article, it could indicate that the author is being paid to include those citations, or that a paper mill (a business that produces fake or low-quality research articles) is citing its own papers, says Ozturk. Or the citations could be fake - a consequence of fabricated responses, known as hallucinations, from large language models or attempts to evade plagiarism detectors."

Peer reviewers face increasing challenges from paper mill output and AI-generated content. While AI detection tools exist, they are expensive and better deployed by publishers due to confidentiality concerns. Recognizing fraudulent manuscripts is achievable but requires time and training. Experts recommend teaching reviewers detection strategies to make fraud harder. Key approaches include examining references for unrelated citations, signs of paid inclusion, self-citations from paper mills, or fabricated citations from AI hallucinations or plagiarism evasion attempts. Systematic vetting methods help identify potentially untrustworthy papers.

Read at Nature