Feature recognition of retracted papers:Based on papers questioned by PubPeer platform
[Purposes]This study aims to scientifically and accurately identify and predict the characteristics of retracted papers,better correct scientific misconduct,reduce academic fraud,and protect the integrity of science.[Methods]We collected data and information related to questioned papers from the PubPeer platform.After data processing and screening,a dataset of 1792 retracted papers with complete data was established.The natural and comment attributes of these retracted papers were analyzed,and a PubCancel model was developed to identify the features and assess the accuracy of the retracted papers.[Findings]The PubCancel model developed in this study accurately and effectively identifies the retracted papers,with an accuracy rate of 98.24%.This method has significant practical implications for evaluating paper quality and provides researchers with a fast way to assess paper credibility.[Conclusions]Studying the situation of retracted papers in questioned papers on the PubPeer platform is of great significance for academic warning research.The application of the feature recognition model of retracted papers can assist journals and editors in detecting abnormal papers promptly,standardizing paper publication processes,and promptly retracting problematic papers,thereby enhancing the integrity of scientific research.
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