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SCiMet: Stable, sCalable and reliable Metric-based framework for quality assessment in collaborative content generation systems

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  • Allahbakhsh, Mohammad
  • Amintoosi, Haleh
  • Behkamal, Behshid
  • Beheshti, Amin
  • Bertino, Elisa

Abstract

In collaborative content generation (CCG), such as publishing scientific articles, a group of contributors collaboratively generates artifacts available through a venue. The main concern in such systems is the quality. A remarkable range of research considers quality metrics partially when dealing with the quality of artifacts, contributors, and venues. However, such approaches have several drawbacks. One of the most notable ones is that they are not comprehensive in terms of the metrics to evaluate all entities, including artifacts, contributors, and venues. Also, they are vulnerable to potential attacks.

Suggested Citation

  • Allahbakhsh, Mohammad & Amintoosi, Haleh & Behkamal, Behshid & Beheshti, Amin & Bertino, Elisa, 2021. "SCiMet: Stable, sCalable and reliable Metric-based framework for quality assessment in collaborative content generation systems," Journal of Informetrics, Elsevier, vol. 15(2).
    • Handle: RePEc:eee:infome:v:15:y:2021:i:2:s1751157720306441
    DOI: 10.1016/j.joi.2020.101127
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    References listed on IDEAS

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