@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Predatory Publication of AI-Generated Research Papers
%A Burgiss, Lizzie
%A Tatum, Ben
%A Henshaw, Christopher
%A Boswell, Madison
%A Michaels, Alan
%Y Mitkov, Ruslan
%Y Ezzini, Saad
%Y Ranasinghe, Tharindu
%Y Ezeani, Ignatius
%Y Khallaf, Nouran
%Y Acarturk, Cengiz
%Y Bradbury, Matthew
%Y El-Haj, Mo
%Y Rayson, Paul
%S Proceedings of the First International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security
%D 2024
%8 July
%I International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security
%C Lancaster, UK
%F burgiss-etal-2024-predatory
%X 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.
%U https://aclanthology.org/2024.nlpaics-1.1/
%P 1-9
Markdown (Informal)
[Predatory Publication of AI-Generated Research Papers](https://aclanthology.org/2024.nlpaics-1.1/) (Burgiss et al., NLPAICS 2024)
ACL
- Lizzie Burgiss, Ben Tatum, Christopher Henshaw, Madison Boswell, and Alan Michaels. 2024. Predatory Publication of AI-Generated Research Papers. In Proceedings of the First International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security, pages 1–9, Lancaster, UK. International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security.