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Purpose: Academic and other researchers have limited tools with which to address the current proliferation of predatory and hijacked journals. These journals can have negative effects on science, research funding, and the dissemination of information. As most predatory and hijacked journals are not error free, this study used ChatGPT, an artificial intelligence (AI) technology tool, to conduct an evaluation of journal quality. Methods: Predatory and hijacked journals were analyzed for reliability using ChatGPT, and the reliability of result have been discussed. Results: It shows that ChatGPT is an unreliable tool for journal quality evaluation for both hijacked and predatory journals. Conclusion: To show how to address this gap, an early trial version of Journal Checker Chatbot has been developed and is discussed as an alternative chatbot that can assist researchers in detecting hijacked journals. [ABSTRACT FROM AUTHOR]

Paper mills, companies that write scientific papers and gain acceptance for them, then sell authorships of these papers, present a key challenge in medicine and other healthcare fields. This challenge is becoming more acute with artificial intelligence (AI), where AI writes the manuscripts and then the paper mills sell the authorships of these papers. The aim of the current research is to provide a method for detecting fake papers. The method reported in this article uses a machine learning approach to create decision trees to identify fake papers. The data were collected from Web of Science and multiple journals in various fields. The article presents a method to identify fake papers based on the results of decision trees. Use of this method in a case study indicated its effectiveness in identifying a fake paper. This method to identify fake papers is applicable for authors, editors, and publishers across fields to investigate a single paper or to conduct an analysis of a group of manuscripts. Clinicians and others can use this method to evaluate articles they find in a search to ensure they are not fake articles and instead report actual research that was peer reviewed prior to publication in a journal. [ABSTRACT FROM AUTHOR]

There are many misleading scientific metrics that are not known to the scientific community, particularly novice researchers. There is limited research in the area of misleading metrics, particularly related to business journals. Therefore, this research aims to examine the use of misleading metrics by business journals, the most popular misleading metrics, and countries contributing to the website traffic for such metrics. We used Scimago ranking for business journals and examined the website of each for the use of misleading metrics. Further, we used a domain-based approach by gathering data from Search Engine Optimization websites (i.e., Alexa and Ubersuggest). Only a few Scopus-indexed, low-quality business journals used misleading metrics on their website. The most common misleading metrics were International Scientific Institute, Open Academic Journals Index, CiteFactor, IndexCopernicus, and International Scientific Indexing. In addition, Indian authors were the most frequent visitors of the websites of misleading metrics. [ABSTRACT FROM AUTHOR]

At the moment, scholarly publishing is faced with much academic misconduct and threats such as predatory journals, hijacked journals, phishing, and other scams. In response, we have been discussing this misconduct and trying to increase the awareness of researchers, but it seems that there is a lack of research that presents guidelines for editors to help them protect themselves against these threats. It seems that information security is missing in some parts of scholarly publishing that particularly involves medical journals. In this paper, we explain different types of cyber-attacks that especially threaten editors and academic journals. We then explain the details involved in each type of attack. Finally, we present general guidelines for detection and prevention of the attacks. In some cases, we use small experiments to show that our claim is true. Finally, we conclude the paper with a prioritization of these attacks. [ABSTRACT FROM AUTHOR]

The academic world today includes hijacked journals and predatory publishers that operate based on a ‘pay and publish’ model and function for financial reasons only. Here we present lesser known aspects and practices of these journals to researchers, showing the core of the problem. [ABSTRACT FROM AUTHOR]

Hijacked journals are cloned websites that resemble the homepages of legitimate journals, whose aim is to collect processing and publication fees from unwary authors. There is a growing recognition that the recent proliferation of these scam sites poses a threat to the integrity of the scientific process. This study presents an approach intended to prevent the indexing of papers published by hijacked journals in scientific databases by using classification algorithms. We will provide an overview of the problem, define key features of hijacked journals, and present a decision tree that can be used to detect hijacked publications. [ABSTRACT FROM PUBLISHER]

Invalid journals are recent challenges in the academic world and many researchers are unacquainted with the phenomenon. The number of victims appears to be accelerating. Researchers might be suspicious of predatory journals because they have unfamiliar names, but hijacked journals are imitations of well-known, reputable journals whose websites have been hijacked. Hijacked journals issue calls for papers via generally laudatory emails that delude researchers into paying exorbitant page charges for publication in a nonexistent journal. This paper presents a method for detecting hijacked journals by using a classification algorithm. The number of published articles exposing hijacked journals is limited and most of them use simple techniques that are limited to specific journals. Hence we needed to amass Internet addresses and pertinent data for analyzing this type of attack. We inspected the websites of 104 scientific journals by using a classification algorithm that used criteria common to reputable journals. We then prepared a decision tree that we used to test five journals we knew were authentic and five we knew were hijacked. [ABSTRACT FROM AUTHOR]