Findings and implications from data mining the IMC review process

  • Authors:
  • Robert Beverly;Mark Allman

  • Affiliations:
  • Naval Postgraduate School, Monterey, CA, USA;International Computer Science Institute, Berkeley, CA, USA

  • Venue:
  • ACM SIGCOMM Computer Communication Review
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

The computer science research paper review process is largely human and time-intensive. More worrisome, review processes are frequently questioned, and often non-transparent. This work advocates applying computer science methods and tools to the computer science review process. As an initial exploration, we data mine the submissions, bids, reviews, and decisions from a recent top-tier computer networking conference. We empirically test several common hypotheses, including the existence of readability, citation, call-for-paper adherence, and topical bias. From our findings, we hypothesize review process methods to improve fairness, efficiency, and transparency.