SCiMet: Stable, sCalable and reliable Metric-based framework for quality assessment in collaborative content generation systems
作者：
Highlights：
• Existing metrics for evaluating artifacts such as research articles are not comprehensive.
• Existing metrics such as h-index or impact factor mostly rely on citation count.
• Quality of artifacts (articles), contributors (authors) and venues are interrelated.
• Comprehensive, interrelated, iteratively computed metrics are harder to manipulate
• SCiMet (proposed method) is strong against manipulation and also applicable to other collaborative content generation systems.
摘要
•Existing metrics for evaluating artifacts such as research articles are not comprehensive.•Existing metrics such as h-index or impact factor mostly rely on citation count.•Quality of artifacts (articles), contributors (authors) and venues are interrelated.•Comprehensive, interrelated, iteratively computed metrics are harder to manipulate•SCiMet (proposed method) is strong against manipulation and also applicable to other collaborative content generation systems.
论文关键词：Quality assessment,Quality metric,Scientometrics,Collaborative content,Attack-resilient
论文评审过程：Received 9 June 2020, Revised 25 October 2020, Accepted 17 December 2020, Available online 19 January 2021, Version of Record 19 January 2021.
论文官网地址：https://doi.org/10.1016/j.joi.2020.101127
