Help:New filters for edit review/zh

New Filters for Edit Review is a beta option that adds new filtering and other tools as well as an improved filtering interface to Special:RecentChanges and Special:RecentChangesLinked (initially).

These improvements help reviewers to better target their efforts and be more efficient. They also have the potential to particularly benefit new contributors, who require a more supportive edit-review process, according to research.

To learn about the parts of the improved user interface visit the quick tour. To learn how to use the advanced functions provided, explore the pages described below.

The roll out of this new feature starts in March 2017. The “New filters for edit review” beta is not initially available on mobile.

主要函數

 * This page explains how the improved filtering interface works and how to get the most out of the new tools.
 * This page explains how the improved filtering interface works and how to get the most out of the new tools.


 * User-defined Highlighting tools let you use color to emphasize the edits that interest you most. The functions and techniques described on this page will help you to make your Recent Changes results more meaningful.
 * User-defined Highlighting tools let you use color to emphasize the edits that interest you most. The functions and techniques described on this page will help you to make your Recent Changes results more meaningful.


 * "New filters for edit review" introduces two filter groups—Contribution Quality and User Intent—that are powered by machine learning and work differently from other filters. They offer probabilistic predictions about, respectively, whether or not edits are likely to contain problems and whether the user who made them was acting in good faith. Knowing a bit about how these unique tools work will help you use them more effectively.
 * "New filters for edit review" introduces two filter groups—Contribution Quality and User Intent—that are powered by machine learning and work differently from other filters. They offer probabilistic predictions about, respectively, whether or not edits are likely to contain problems and whether the user who made them was acting in good faith. Knowing a bit about how these unique tools work will help you use them more effectively.