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Paper mills submit 2% of journals, corrupting research. This study introduces a new authorship network model to detect these fraudulent paper-mill networks, improving research integrity.

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Area of Science:

  • Bibliometrics
  • Research Integrity
  • Network Science

Background:

  • Paper mills fabricate 2% of journal submissions, risking research corruption and straining editorial processes.
  • Current detection methods focus on textual analysis of fabricated papers, which will become less effective as paper mill technologies advance.
  • Fabricated papers require networks of authors, often added through transactions, leading to minimal repeat co-authorship.

Purpose of the Study:

  • To develop a robust method for detecting paper mill activity by analyzing authorship networks.
  • To understand the social and technological structures of paper mills to create effective countermeasures.
  • To identify and analyze the 'authorship-for-sale' networks characteristic of paper mill operations.

Main Methods:

  • Constructed a model encoding key characteristics of 'authorship-for-sale' networks.
  • Developed a network fingerprint for statistical detection of paper-mill networks.
  • Validated the model by comparing its detected networks with those identified through textual analysis methods.

Main Results:

  • The proposed model generates a characteristic network fingerprint for robust paper mill network detection.
  • The model shows statistically significant overlap with existing textual analysis detection approaches.
  • Researchers linked to networks identified by this model are associated with 37% of papers flagged by problematic paper screening methods.

Conclusions:

  • The authorship network model provides a robust method for detecting paper mill activity.
  • This network-based approach complements existing textual analysis techniques, enhancing fraud detection capabilities.
  • Strategies, both technological and social, are needed to limit the expansion and propagation of paper mill networks.