When ChatGPT and other generative artificial intelligence can produce scientific articles that look real — especially to someone outside that field of research — what’s the best way to figure out which ones are fake?
Ahmed Abdeen Hamed, a visiting research fellow at Binghamton University, State University of New York, has created a machine-learning algorithm he calls xFakeSci that can detect up to 94% of bogus papers — nearly twice as successfully as more common data-mining techniques.
“My main research is biomedical informatics, but because I work with medical publications, clinical trials, online resources and mining social media, I’m always concerned about the authenticity of the knowledge somebody is propagating,” said Hamed, who is part of George J. Klir Professor of Systems Science Luis M. Rocha’s Complex Adaptive Systems and Computational Intelligence Lab. “Biomedical articles in particular were hit badly during the global pandemic because some people were publicizing false research.”
In a new paper published in the journal Scientific Reports, Hamed and collaborator Xindong Wu, a professor at Hefei University of Technology in China, created 50 fake articles for each of three popular medical topics — Alzheimer’s, cancer and depression — and compared them to the same number of real articles on the same topics.
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