This work is licensed under the Creative Commons Attribution 4.0 International License.
Candal-Pedreira, C., Ross, J. S., Ruano-Ravina, A., Egilman, D. S., Fernández, E., & Pérez-Ríos, M. (2022). Retracted papers originating from Paper mills: cross sectional study. bmj, 379.Candal-PedreiraC.RossJ. S.Ruano-RavinaA.EgilmanD. S.FernándezE.Pérez-RíosM. (2022). Retracted papers originating from Paper mills: cross sectional study. bmj, 379.Search in Google Scholar
Chakraborty, J., Pradhan, D. K., & Nandi, S. (2021). On the identification and analysis of citation pattern irregularities among journals. Expert Systems, 38(4), e12561.ChakrabortyJ.PradhanD. K.NandiS. (2021). On the identification and analysis of citation pattern irregularities among journals. Expert Systems, 38(4), e12561.Search in Google Scholar
Chen, J., Hou, H., Gao, J., Ji, Y., & Bai, T. (2019). RGCN: recurrent graph convolutional networks for targetdependent sentiment analysis. In International Conference on Knowledge Science, Engineering and Management (pp. 667-675). Cham: Springer International Publishing.ChenJ.HouH.GaoJ.JiY.BaiT. (2019). RGCN: recurrent graph convolutional networks for targetdependent sentiment analysis. In International Conference on Knowledge Science, Engineering and Management (pp. 667-675). Cham: Springer International Publishing.Search in Google Scholar
Christopher, J. (2021). The raw truth about Paper mills. FEBS letters, 595(13), 1751-1757.ChristopherJ. (2021). The raw truth about Paper mills. FEBS letters, 595(13), 1751-1757.Search in Google Scholar
da Silva, J. A. T., & Nazarovets, S. (2023). Assessment of retracted papers, and their retraction notices, from a cancer journal associated with “Paper mills”. Journal of Data and Information Science, 8(2), 118-125.da SilvaJ. A. T.NazarovetsS. (2023). Assessment of retracted papers, and their retraction notices, from a cancer journal associated with “Paper mills”. Journal of Data and Information Science, 8(2), 118-125.Search in Google Scholar
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.DevlinJ.ChangM. W.LeeK.ToutanovaK. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.Search in Google Scholar
Else, H., & Van Noorden, R. (2021). The fight against fake-paper factories that churn out sham science. Nature, 591(7851), 516-520.ElseH.Van NoordenR. (2021). The fight against fake-paper factories that churn out sham science. Nature, 591(7851), 516-520.Search in Google Scholar
Else, H. (2022). ‘Papermill alarm’software flags potentially fake papers.ElseH. (2022). ‘Papermill alarm’software flags potentially fake papers.Search in Google Scholar
Hu, Z., Dong, Y., Wang, K., & Sun, Y. (2020). Heterogeneous graph transformer. In Proceedings of the web conference 2020 (pp. 2704-2710).HuZ.DongY.WangK.SunY. (2020). Heterogeneous graph transformer. In Proceedings of the web conference 2020 (pp. 2704-2710).Search in Google Scholar
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., … & Liu, T. Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30.KeG.MengQ.FinleyT.WangT.ChenW.MaW.LiuT. Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30.Search in Google Scholar
Liu, Q., Barhoumi, A., & Labbé, C. (2024). Miscitations in scientific papers: dataset and detection.LiuQ.BarhoumiA.LabbéC. (2024). Miscitations in scientific papers: dataset and detection.Search in Google Scholar
Oransky, I. (2022). Nearing 5,000 retractions: A review of 2022. Retraction Watch. https://retractionwatch. com/2022/12/27/nearing-5000-retractions-a-review-of-2022/OranskyI. (2022). Nearing 5,000 retractions: A review of 2022. Retraction Watch. https://retractionwatch.com/2022/12/27/nearing-5000-retractions-a-review-of-2022/Search in Google Scholar
Rogerson, A. M. (2014). Detecting the work of essay mills and file swapping sites: some clues they leave behind.RogersonA. M. (2014). Detecting the work of essay mills and file swapping sites: some clues they leave behind.Search in Google Scholar
Seifert, R. (2021). How Naunyn-Schmiedeberg’s Archives of Pharmacology deals with fraudulent papers from Paper mills. Naunyn-schmiedeberg’s Archives of Pharmacology, 394, 431-436.SeifertR. (2021). How Naunyn-Schmiedeberg’s Archives of Pharmacology deals with fraudulent papers from Paper mills. Naunyn-schmiedeberg’s Archives of Pharmacology, 394, 431-436.Search in Google Scholar
Van Noorden, R. (2023). How big is science’s fake-paper problem?. Nature, 623(7987), 466-467.Van NoordenR. (2023). How big is science’s fake-paper problem?. Nature, 623(7987), 466-467.Search in Google Scholar
Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., & Yu, P. S. (2019). Heterogeneous graph attention network. In The world wide web conference (pp. 2022-2032).WangX.JiH.ShiC.WangB.YeY.CuiP.YuP. S. (2019). Heterogeneous graph attention network. In The world wide web conference (pp. 2022-2032).Search in Google Scholar
Wang, K., Shen, W., Yang, Y., Quan, X., & Wang, R. (2020). Relational graph attention network for aspect-based sentiment analysis. arXiv preprint arXiv:2004.12362.WangK.ShenW.YangY.QuanX.WangR. (2020). Relational graph attention network for aspect-based sentiment analysis. arXiv preprint arXiv:2004.12362.Search in Google Scholar
Wittau, J., Celik, S., Kacprowski, T., Deserno, T. M., & Seifert, R. (2024). Fake paper identification in the pool of withdrawn and rejected manuscripts submitted to Naunyn–Schmiedeberg’s Archives of Pharmacology. Naunynschmiedeberg’s Archives of Pharmacology, 397(4), 2171-2181.WittauJ.CelikS.KacprowskiT.DesernoT. M.SeifertR. (2024). Fake paper identification in the pool of withdrawn and rejected manuscripts submitted to Naunyn–Schmiedeberg’s Archives of Pharmacology. Naunynschmiedeberg’s Archives of Pharmacology, 397(4), 2171-2181.Search in Google Scholar
Xu, K., Hu, W., Leskovec, J., & Jegelka, S. (2018). How powerful are graph neural networks?. arXiv preprint arXiv:1810.00826.XuK.HuW.LeskovecJ.JegelkaS. (2018). How powerful are graph neural networks?. arXiv preprint arXiv:1810.00826.Search in Google Scholar
Zhang, Y., Jin, R., & Zhou, Z. H. (2010). Understanding bag-of-words model: a statistical framework. International journal of machine learning and cybernetics, 1, 43-52.ZhangY.JinR.ZhouZ. H. (2010). Understanding bag-of-words model: a statistical framework. International journal of machine learning and cybernetics, 1, 43-52Search in Google Scholar