@inproceedings{bless-etal-2025-analyzing,
    title = "Analyzing the Evolution of Scientific Misconduct Based on the Language of Retracted Papers",
    author = "Bless, Christof  and
      Waldis, Andreas  and
      Parfenova, Angelina  and
      A. Rodriguez, Maria  and
      Marfurt, Andreas",
    editor = "Ghosal, Tirthankar  and
      Mayr, Philipp  and
      Singh, Amanpreet  and
      Naik, Aakanksha  and
      Rehm, Georg  and
      Freitag, Dayne  and
      Li, Dan  and
      Schimmler, Sonja  and
      De Waard, Anita",
    booktitle = "Proceedings of the Fifth Workshop on Scholarly Document Processing (SDP 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.sdp-1.6/",
    doi = "10.18653/v1/2025.sdp-1.6",
    pages = "57--71",
    ISBN = "979-8-89176-265-7",
    abstract = "Amid rising numbers of organizations producing counterfeit scholarly articles, it is important to quantify the prevalence of scientific misconduct.We assess the feasibility of automated text-based methods to determine the rate of scientific misconduct by analyzing linguistic differences between retracted and non-retracted papers.We find that retracted works show distinct phrase patterns and higher word repetition.Motivated by this, we evaluatetwo misconduct detection methods, a mixture distribution approach and a Transformer-based one.The best models achieve high accuracy ({\ensuremath{>}}0.9 F1) on detection of paper mill articles and automatically generated content, making them viable tools for flagging papers for closer review.We apply the classifiers to more than 300,000 paper abstracts, to quantify misconduct over time and find that our estimation methods accurately reproduce trends observed in the real data."
}