@inproceedings{burgiss-etal-2024-predatory,
    title = "Predatory Publication of {AI}-Generated Research Papers",
    author = "Burgiss, Lizzie  and
      Tatum, Ben  and
      Henshaw, Christopher  and
      Boswell, Madison  and
      Michaels, Alan",
    editor = "Mitkov, Ruslan  and
      Ezzini, Saad  and
      Ranasinghe, Tharindu  and
      Ezeani, Ignatius  and
      Khallaf, Nouran  and
      Acarturk, Cengiz  and
      Bradbury, Matthew  and
      El-Haj, Mo  and
      Rayson, Paul",
    booktitle = "Proceedings of the First International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security",
    month = jul,
    year = "2024",
    address = "Lancaster, UK",
    publisher = "International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.nlpaics-1.1/",
    pages = "1--9",
    abstract = "In an academic ecosystem where faculty face a ``publish or perish'' mantra, there are distinct openings for predatory publishers. Defined loosely, these are journals who value profits over scholarly cultivation and prey upon unsuspecting authors. Prior research has built lists of suspected predatory publishers to inform colleagues of risks, yet few quantify common characteristics exhibited by these publishers. To test hypotheses around these journals, we probed the behavior of 256 suspected predatory journals drawn from Beall{'}s and Kscien{'}s lists. Using active open source intelligence techniques, we tested the existence and extent of review processes, publication fees, operating location, and communication patterns. We submitted five different ChatGPT4-authored papers to our targeted publishers {--} these papers were accepted and/or published by 55 journals. By characterizing the responses, we developed a journal assessment rubric to aid authors seeking to publish their work. In the process, we also identified a presumptive shadow network of publishing companies using these practices based on analysis of websites, addresses, and shared employees. All underlying data for our study is open sourced for other researchers to draw their own conclusions."
}