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A Toolkit for Detecting Fallacious Calls for Papers from Potential Predatory Journals.

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Area of Science:

  • Information Science
  • Scholarly Communication
  • Text Mining

Background:

  • Predatory journals exploit authors through deceptive emails.
  • Limited research exists on detecting spam emails from predatory journals.
  • This study addresses the need for better detection methods for unsolicited calls for papers.

Discussion:

  • Analysis of call for paper datasets revealed distinct linguistic patterns.
  • Predatory journals frequently employ self-praise with positive, uncommon terms.
  • Legitimate journals exhibit different communication styles compared to predatory ones.

Key Insights:

  • A lexicon was developed to identify unsolicited calls for papers from predatory journals.
  • Text mining and R programming language were used for analysis.
  • Findings confirm significant differences between predatory and legitimate journal solicitations.

Outlook:

  • Educational initiatives and user-friendly tools can combat predatory publishing.
  • Further research can refine detection algorithms for scholarly communication integrity.
  • Improved detection mechanisms will protect researchers from predatory practices.