Predatory Publication of AI -Generated Research Papers (2024)

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.

## experimental design to quantify publisher behaviors, our study submitted fake papers to suspected predatory journals. recognizing kscien's list as a super-set of beall's, we drew our list of publishers and subsequently journals from kscien's list @xcite . kscien's list totaled 1,298 publishers and journals as of 05/18/2023. although this list is extensive, we reduced the list to include only journals with cyber-aligned topics such as computer science, business it, science, or engineering. this reduction led to the targeted 256 publishers. no other criteria or limitations were placed on the selection. a maximum of three journals per publisher were allowed for our submission pool to ensure adequate diversity and minimize impact on legitimate review processes. paper topics were also randomized before submission. a sample of the themes is shown in figure 1 , and a full list of submissions can be found in the appendix content @xcite . to help ensure the integrity of our own results and to offer the research community access to the underlying methodology, we have open sourced all the resulting artifacts (anonymizing some identifying information) @xcite . we would like to note our commitment and intent for ethical experimental design, stressing that fake papers were only sent to publishers appearing on community lists of predatory publishers. our experiences with chatgpt suggest that it would be capable of coauthoring semi-believable papers with more human input and iteration, which would be better suited to penetration testing of non-predatory, albeit low quality, journals that are expected to incorporate expert review. we constructed five ai-generated conferencelength papers that pass superficial scrutiny. the titles include optimizing bubble sort, prompt engineering framework, randomized fake identities, fraud detection with fake identities, and automated clock-in reminder system. the diversity in the papers topics was to avoid a publisher lacking a rigorous peer review process to identify the correlation. our papers were developed using chatgpt and the prompts and response logs are available as part of our open source data @xcite . the results of the abstract and experimental design generation phase varied from extremely convincing to weak and wordy arguments. we initially tested each prompt with chatgpt 3.5, incrementally refining the papers with human-in-the-loop prompts to chatgpt 4. our intent was to create documents with sufficient content that a novice might accept as legitimate, while anyone with a bachelor's level education in the appropriate field would recognize the inherent lack in scholarly value. a key takeaway from our ai prompting is that it is essential to outline the product you wish to create, then further prompt extrapolation until the desired results are achieved. the more technical information provided in the prompt, the more technical the answer became. each ai prompt topic included key words and ideas pertaining to the subject and began by asking for a hypothetical abstract on the topic. in addition, chatgpt responds best when the questions being asked are an incremental rewording of its previous answer. working in this manner, an initially convincing paper can be built entirely with ai generated elements such as code, data, and citations. when read with any attention to detail, however, our fake papers contain verbose rambling with little substance and impossibly positive results. in addition, they contained blatant grammatical errors, formatting issues, and citation problems. most notably, all citations in the paper are entirely falsified, which can be easily verified. a critical element in all papers are the few and fake citations. the prompt for these was provided as follows: "could i have 5 fictitious citations relating to the paper in bibtex format." notably, chatgpt always provides a warning when providing these false citations: "please note that these citations are fictional and generated for illustrative purposes only. make sure to replace them with appropriate and accurate citations based on actual sources when writing your research paper." chat-gpt is aware of its use in falsifying information, as it suggests an effort to avoid such behaviors. as an extension of our overarching research project, use & abuse of personal information, the team built a signup engine for a mock user database @xcite . the database fields were generated to mimic demographics similar to the united states through the use of official government records such as us census data. all traceable information such as addresses are designed to mimic reality, but do not contain any real personal information in order to protect individuals and organizations from accidental identity impersonation as shown in figure 2 . the development and further experimental use of this software are documented in the associated papers @xcite . armed with identities, papers, a signup engine, and data collection tools, we proceeded with controlled distribution of the papers for evaluation by potentially predatory journals. ## results and data analysis our process entailed collecting information on our publishers and journals such as urls, operating locations, and website appearance. we then collected emails received in response. the emails were manually read and assigned to categories based on their primary topic of discussion: acknowledged submission, rejected submission, request to complete more steps accepted submission, and request for payment. the following analysis is the result of statistical significance and qualitative observations made from the collected emails and publisher attributes. submissions were performed according to the request of the journal. 41% were submitted via email and the other 59% were submitted though online portals. an email address was given for all submissions connecting to our email server. every identity asked to be sent all possible communications from the publishers to their respective email address. emails received were evaluated for red flags, domain changes, similar websites, geographic locations, submission outcomes, and other notable occurrences. the relevance of each subsection is intended to be a different identifier for potentially predatory publishers. they have been subsequently compiled into a rubric for use by authors to assess the nature and intent of academic publishers in the following section. ## rubric using the results of this experiment, we sought to construct a quantitative rubric that potential authors can use as a guide to evaluating publishers. we have also evaluated all of the journals used in this study (both those who accepted and rejected our fake papers) as proof of utility as shown in figure 9 . in order to construct the rubric, we consolidated the common traits among predatory publishers as mentioned above and attempted to translate their statistical difference to a point system as seen in figure 7 . after adjustment according to other qualitative observations, this resulted in the overall point distribution as shown in figure 9 . we acknowledge that this study only passively collected emails, so a more thorough experiment might integrate human or machine responses to received communications in order to better identify publisher actions that go beyond autoacceptance.using this preliminary rubric, our suggestion is to expand by incorporating existing qualitative research on predatory publishing behaviors such as those highlighted in the predatory publishing rubric of think, check, submit, as well as loyola marymount university's journal evaluation tool @xcite . a more expansive study could help dial in better quantified scores as described in the future work section. after using the rubric instructions seen in figure 8 , move on to the following score ranges. for scores of 2-10, you may proceed but note the observation of some qualities possessed by predatory journals. for scores of 11-20, proceed with high caution but do not proceed if you observe signs the publisher only cares about fees or that they lack a throughout peer review process. finally, for scores of 20 or higher, do not proceed as you have extensive indicators of predatory practices. ## conclusions in order to investigate the predatory nature of suspected publishers we submitted ai-generated academic papers to suspected predatory publishers that had journals under the cyber umbrella. we collected journals' operating address, all emails received from them, and their url, among other general information. we then started to notice overlaps in locations of these companies which led us to further overlaps in employees of the publishers. in observing urls, we found that almost 30% of url turnover resulted in gambling content that had ties to a formerly charged company, suncity. in addition, we learned that some publishing companies in our study are sub-companies of the publisher and academic conference company omics group inc. which was involved in an $50.1 million federal trade commission lawsuit. after thorough data analysis, and with the knowledge of our experiences in receiving both rejections and acceptances of our fake papers, we put together a rubric for fellow academic writers. our aim is to provide a resource that can guide authors in their personal assessment of academic journals that they may wish to publish with. we then further proved our rubric by assessing the publishers involved in our own study. this research offers concrete insights into the processes of knowledge management underlying scholarly publications, as well as groundwork for more comprehensive indicators of predatory publishing practices.