Mehdi Dadkhah
1* 
, Mihály Hegedűs
2 , Prema Nedungadi
3 , Raghu Raman
4 , Lóránt Dénes Dávid
5,6,7 1 Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, India.
2 Tomori Pál College, Chamber of Hungarian Auditors, Budapest, Hungary.
3 Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, India.
4 Amrita School of Business, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, India.
5 John von Neumann University, Faculty of Economics and Business, Department of Tourism and Hospitality, HU-6000 Kecskemét, Hungary.
6 Hungarian University of Agriculture and Life Sciences (MATE), Institute of Rural Development and Sustainable Economy, Department of Sustainable Tourism, HU-2100 Gödöllő, Hungary.
7 Eötvös Loránd University, Faculty of Social Sciences, Savaria University Centre, Savaria Department of Business Economics, HU-9700 Szombathely, Hungary.
Abstract
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.