Edit Review Improvements/lzh

編輯審核優化是Collaboration團隊的專案，目的在研究出可行辦法，以降低現在編輯審核流程中，對於各wiki站的新手編輯所造成之負面效應. 大部份的「編輯—審核」以及「巡查」工具的設計，是為了要保障內容的品質、並防範惡意的編寫者——這兩項都是極端重要的任務. A body of research, however, suggests that these processes can, particularly when they involve automated or semi-automated tools, have the unintended consequence of discouraging and even driving away good-faith new editors.

To solve this problem, Collaboration Team is investigating ways to separate good-faith new users from current edit-review workflows and, ultimately, to provide a supportive review process that helps new users become productive contributors.

Problem

 * Research shows that for new wiki editors in particular, “being reverted predicts both a decrease in activity and a reduction in the probability of survival” as editors.
 * At the same time, the increasing use of automated and semi-automated edit-review tools has brought about an increase in rejection of good-faith newcomers. The use of these tools “significantly increases the negative effect of rejection on desirable newcomer retention.”
 * The above notwithstanding, edit-review tools are essential for vandalism fighters and others working to maintain wiki integrity and quality. How can we help and retain new users while maintaining the productivity of vandalism fighters and other edit reviewers?

Goals
Ultimately this project aims to have an effect on editor retention, an objective that aligns well with the overall goals of the Wikimedia Foundation 2016-17 Annual Plan, developed in close consultation with the user community.
 * Ensure good-faith new editors have more constructive, less discouraging experiences of edit and article review.
 * By providing richer data about recent changes, enable patrollers and edit-reviewers of all types to work more efficiently and to pursue diverse interests (e.g., fighting vandalism, supporting new users) in a more effective and targeted way.

The approach tracks in particular with the goals the Annual Plan lays out for the Product Team, which promise, among other things, to “Invest in new types of content…curation and collaboration tools.”

Solutions
To begin to address the problems of struggling but good-faith newcomers, a good first step will be to ensure that reviewers can find them. To make this possible, we propose to analyze recent changes using data from a variety of sources, including and most notably the machine-learning program ORES (Objective Revision Evaluation Service). ORES’s good faith model, trained on human judgement, can find 95% of good-faith edits with 98% accuracy. ORES can also predict edits that will be reverted and those that are damaging to the wikis.

While research shows that new editors are particularly vulnerable to rejection, there’s also evidence that edit-review and even rejection can be a powerful learning experience for newcomers. For reviewers interested in supporting new users, then, a stream of edits that are a) likely to be reverted but which were b) made in good faith will, we hope, represent a string of teachable moments.

The edit analysis described above will be made available initially to users in two ways :


 * On the Special:Recent Changes page, where a suite of new filters will be provided as a beta feature (read a description of the planned new filters for edit review)
 * In a new machine-readable feed dubbed ReviewStream (ReviewStream Product Description), designed to be ingested by downstream edit-review tools.

Current activity

 * To visualize possible product directions, the Collaboration Team is exploring design concepts while continuing to research the issues.
 * To better gauge the size of the problem and be able to track progress, we’re working to define and measure new-editor retention.
 * Design Research is organizing and conducting interviews with users touched by this issue in various ways, to better understand their motivations and workflows. Groups who will be interviewed in the near term include: anti-vandalism patrollers, recent changes patrollers, Teahouse hosts, Welcoming Committee members, and AfC reviewers.
 * The Research and Data team is working to make predictions better by refining the accuracy of prediction models.
 * There was a discussion of the project at Wikimania 2016, in June

Improving filtering in Recent Changes page
More information



In order to help reviewers to easily find the contributions they look for, we plan to improve the way filtering works on the Special:Recent Changes page. The goal is to make the list of contributions easy to filter, allow for more filter criteria (especially those relevant for helping newcomers) and facilitate combining multiple filters for different purposes.

This interactive prototype illustrates the filtering concept proposed. For additional context, you can check.

Before reaching there, this will be done in multiple steps inside a beta feature. More details below.

Initial steps
Initially, namespaces and tags won't be integrated into the filtering system. Filters related to ORES will be supported. These filters include:
 * Review. Filters that allow reviewers to focus on those contributions not reviewed yet, or those already processed by other reviewers.
 * Contribution quality. Filters that allow to identify contributions that are good or damaging.
 * User intent. Filters that allow to identify contributions that were made in good or bad faith.
 * User experience level. Filters that allow to target edits depending on the expertise of their author.

Future plans
Creating the streams/pages of “teachable moments” described above has the potential to establish edit-review as a new space for instructing and supporting new editors.

The mere existence of such a platform, however, won’t in itself ensure that this new practice will take root. To truly have an impact on newcomer retention, interventions may be required at multiple points in the editing and review cycles: before publication, to spot problems and enable authors to seek help; during review, to facilitate a constructive process; and even after review, to help new users overcome rejection and learn from from their experiences.

In addition to exploring ideas for intervening at various points, we’re pursuing answers to questions such as these:


 * How can we bring reviewers to this new activity?
 * What would make reviewers most effective in the job of supporting newcomers during edit review?
 * How can we make the process rewarding for reviewers, so that they stay involved?

The counter-vandalism community also has an important role to play in this arena. Richer data about edits and editors should make patrollers of all types not only more discriminating about which edits might be in good faith, but also more efficient at their job of combating harm. It will be important to work closely with vandalism fighters and others to understand how their processes and tools might best be adapted to realize these potential gains.

Principles
As we pursue this project, the following principles will guide our planning.


 * Smart but human. Use technology to support rather than replace human interaction. Artificial intelligence can provide analysis, but humans should make decisions.
 * Cross-community. Find solutions that will work across language groups and projects, rather than building wiki-specific tools.
 * Platform not feature. Seek solutions that are extensible and reusable by current and future community-created and WMF tools.
 * Mobile. Although edit-review is not currently popular on mobile, consider mobile users carefully in our plans.
 * Adoption. In addition to creating new technology, focus on finding ways to encourage reviewers to adopt and continue to use the new tools.
 * Integration. In seeking new solutions, build on and integrate with existing practices whenever possible.
 * Incremental approach. As we move into this new area, proceed incrementally to each milestone and then evaluate where to go next.
 * Participatory design. Collaborate with editors and tool developers already working in this space.

Related documents

 * Grants:IdeaLab/Fast and slow new article review
 * Research:Newcomer survival models