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Related Experiment Videos

Detecting Hijacked Journals by Using Classification Algorithms.

Mona Andoohgin Shahri1, Mohammad Davarpanah Jazi1, Glenn Borchardt2

  • 1Department of Computer and Information Technology, Foolad Institute of Technology, Foolad shahr, Isfahan, 8491663763, Iran.

Science and Engineering Ethics
|April 12, 2017
PubMed
Summary

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A Decade in Hijacked Journals: What Will be the Future Trend?

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Researchers can now detect hijacked journals, which imitate legitimate publications, using a new classification algorithm. This method helps identify these deceptive academic scams, protecting researchers from fraudulent publication schemes.

Area of Science:

  • Academic Publishing
  • Information Science
  • Computer Science

Background:

  • Hijacked journals pose a growing threat to researchers, often impersonating reputable scientific publications.
  • These fraudulent journals exploit researchers by soliciting exorbitant page charges for non-existent publications.
  • Existing detection methods for hijacked journals are limited in scope and effectiveness.

Purpose of the Study:

  • To develop and present a novel classification algorithm for detecting hijacked academic journals.
  • To provide a systematic approach for identifying deceptive imitation journals.
  • To address the increasing challenge of hijacked journals in scholarly communication.

Main Methods:

  • A classification algorithm was developed to analyze journal websites.
Keywords:
Academic ethicsEditorial processHijacked journalsInternet fraudSpam emails

Related Experiment Videos

  • Criteria common to reputable journals were used for classification.
  • The algorithm was applied to inspect the websites of 104 scientific journals.
  • A decision tree was created and tested on known authentic and hijacked journals.

    • Main Results:

    • The study successfully developed a method for detecting hijacked journals.
    • The classification algorithm demonstrated effectiveness in distinguishing between legitimate and hijacked publications.
    • Analysis of 104 journal websites provided valuable data on hijacked journal characteristics.

    Conclusions:

    • The proposed classification algorithm offers a viable tool for identifying hijacked journals.
    • This research contributes to safeguarding the integrity of academic publishing.
    • Further development and application of the algorithm can help mitigate the impact of journal hijacking.