Research:AFTRank

Contact

Ryan Faulkner

Wikimedia Foundation

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Research:Projects

This page is an incomplete draft of a research project.
Information is incomplete and is likely to change substantially before the project starts.

Key Personnel

• Ryan Faulkner
• Aaron Halfaker
• Oliver Keyes
• Dario Taraborelli

Project Summary

This project aims to develop and test a classifier to identify good feedback from a corpus of unmoderated/non-featured article feedback (v.5) posts. The design goals of AFTRank are the following:

1. reduce the workload of feedback moderators by prefiltering feedback not worth being moderated:
   1. automatically hide or decrease the score of bad feedback without the need of hitting AbuseFilter
   2. increase the score of unmoderated good feedback
2. correct the bias towards high-traffic articles produced by the current UI of the FeedbackPage:
   1. allow moderators to explore the large amount of feedback from low-traffic articles that hardly receives any attention
   2. surface feedback from articles with an expected higher feedback quality but low moderation activity