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Duration: 2012-12 – 2013-
This page is an incomplete draft of a research project.
Information is incomplete and is likely to change substantially before the project starts.
- Ryan Faulkner
- Aaron Halfaker
- Oliver Keyes
- Dario Taraborelli
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:
- reduce the workload of feedback moderators by prefiltering feedback not worth being moderated:
- automatically hide or decrease the score of bad feedback without the need of hitting AbuseFilter
- increase the score of unmoderated good feedback
- correct the bias towards high-traffic articles produced by the current UI of the FeedbackPage:
- allow moderators to explore the large amount of feedback from low-traffic articles that hardly receives any attention
- surface feedback from articles with an expected higher feedback quality but low moderation activity