# Detecting a network of hijacked journals by its archive

arXiv: https://arxiv.org/abs/2101.01224 · HTML: https://mlsanity.com/paper/2101.01224/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2021.

## Built on by (most cited first, 2 of 2)

- [Tortured phrases: A dubious writing style emerging in science. Evidence of critical issues affecting established journals](https://mlsanity.com/paper/2107.06751/) (2021)
- [Academ-AI: documenting the undisclosed use of generative artificial intelligence in academic publishing](https://mlsanity.com/paper/2411.15218/) (2024)

## Abstract (arXiv)

This study describes a method to detect hijacked journals based on the analysis of the archives of clone journals. This approach is most effective in discovering a network of hijacked journals that have the same organizer(s). Analysis of the archives of clone journals allowed to detect 62 URLs of hijacked journals. It also provided the possibility to predict two clone websites before they became operational. This study shows that most detected hijacked journals represent a network of clone journals organized by one or several fraudulent individuals. The information and content of nine legitimate journals were compromised in international and national scientometric databases.