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An open automation system for predatory journal detection.

Li-Xian Chen1, Shih-Wen Su2, Chia-Hung Liao2

  • 1School of Big Data, Fuzhou University of International Studies and Trade, Fuzhou, 350202, China.

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|February 22, 2023
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Summary
This summary is machine-generated.

This study introduces an academic journal predatory checking (AJPC) system using machine learning to identify predatory journals. The AJPC system effectively distinguishes legitimate from predatory publications, aiding researchers in navigating scholarly resources.

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Area of Science:

  • Bibliometrics
  • Scholarly Communication
  • Information Science

Background:

  • The proliferation of open-access journals has increased academic exchange but also led to a rise in predatory journals.
  • Predatory journals undermine the integrity of scholarly reporting and publishing.
  • Distinguishing between legitimate and predatory journals is crucial for researchers and institutions.

Purpose of the Study:

  • To develop and evaluate a machine learning-based system for identifying predatory academic journals and publishers.
  • To enhance the accuracy of predatory journal detection through advanced feature extraction and classification methods.
  • To provide a reliable reference tool for the academic community to assess journal legitimacy.

Main Methods:

  • A data collection process extracted information from 833 blacklists and 1213 whitelists.
  • Feature extraction identified indicative words and phrases associated with predatory journals.
  • Eight classification algorithms were employed, with a focus on the bag-of-words model and TF-IDF enhanced by diff scores.

Main Results:

  • The proposed Academic Journal Predatory Checking (AJPC) system effectively identifies predatory journals.
  • Enhancing classification models with diff scores improved the detection of predatory journal features.
  • Performance tests indicated the AJPC system is comparable to or surpasses existing tools in identifying suspect publishers and publications.

Conclusions:

  • The developed machine learning system offers a robust method for detecting predatory academic journals.
  • The AJPC system provides valuable reference results and incorporates user feedback for continuous improvement.
  • This tool supports the academic community in maintaining scholarly publishing standards.