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Purpose: Nowadays, many studies discuss scholarly publishing and associated challenges, but the problem of hijacked journals has been neglected. Hijacked journals are cloned websites that mimic original journals but are managed by cybercriminals. The present study uses a topic modeling approach to analyze published papers in hijacked versions of medical journals. Methods: A total of 3384 papers were downloaded from 21 hijacked journals in the medical domain and analyzed by topic modeling algorithm. Results: Results indicate that hijacked versions of medical journals are published in most fields of the medical domain and typically respect the primary domain of the original journal. Conclusion: The academic world is faced with the third-generation of hijacked journals, and their detection may be more complex than common ones. The usage of artificial intelligence (AI) can be a powerful tool to deal with the phenomenon. [ABSTRACT FROM AUTHOR]

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]

Scholarly publishing currently is faced by an upsurge in low-quality, questionable "predatory/hijacked" journals published by those whose only goal is profit. Although there are discussions in the literature warning about them, most provide only a few suggestions on how to avoid these journals. Most solutions are not generalizable or have other weaknesses. Here, we use a novel information technology, i.e., blockchains, to expose and prevent the problems produced by questionable journals. Thus, this work presented here sheds light on the advantages of blockchain for producing safe, fraud-free scholarly publishing. [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]

In recent years, scholarly publishing has been faced with many distractive phenomena. Generally, most researchers are unaware of fraudulent practices now common to scholarly publishing and are at risk of becoming a victim of them. Editors also need to have sufficient knowledge about these practices. There are papers that try to increase awareness of authors about fraud in scholarly publishing, but it seems that there is no good academic resource to direct editors. In this paper, we try to present a general guideline for increasing journal quality by discussing the emerging threats to scholarly publishing and methods to avoid being victimized. [ABSTRACT FROM AUTHOR]

Predatory journals are a well-known issue for scholarly publishing and they are repositories for bogus research. In recent years, the number of predatory journals has risen and it is necessary to present a solution for this challenge. In this paper, we will discuss about a possible ranking of predatory journals. Our ranking approach is based on Beall's criteria for detection of predatory journals and it can help editors to improve their journals or convert their questionable journals to non-predatory ones. Moreover, our approach could help young editors to protect their journals against predatory practice. Finally, we present a case study to clarify our approach. [ABSTRACT FROM AUTHOR]