帮助:新的编辑审阅过滤器/质量和意图过滤器

This page is a translated version of the page Help:New filters for edit review/Quality and Intent Filters and the translation is 32% complete.

Outdated translations are marked like this.

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新的编辑审阅过滤器引入了两个过滤器组——贡献质量、用户意图，两者的工作方式与其他审阅过滤器不同。这两个组中的过滤器分别提供：编辑是否可能有问题以及操作人是否善意的概率预测。了解这些特有工具的运作方式有助您更有效地使用它们。

Additionally, the language-agnostic revert risk model, enabled in 2024, provides a prediction about how likely an edit is to require reverting.

Knowing a bit about how these unique tools work will help you use them more effectively.

这些过滤器目前只在部分wiki上可用。

基于机器学习

质量和意图过滤器的预测由ORES计算，它是一个机器学习程序，基于之前的大量人类编辑训练而成。机器学习是一种强大的计数，可使机器学会有限的人类判断力。

质量和意图过滤器只在支持“破坏”和“诚信”ORES“模型”的wiki上可用。ORES“破坏”模型“damaging”提供质量预测，“诚信”模型提供意图预测。

启用ORES需要志愿者对相关wiki上的修订内容评分。这里介绍了其过程，以及如何在你的wiki上开始。

The language-agnostic revert risk model supports all language Wikipedias and does not require manual training by volunteers.

选择正确的工具

Looking at the Quality, Intent, and Revert Risk filters, you may notice something different about them. Unlike filters in other groups, the various options don’t target different edit properties. Instead, many of them target the same property, but offer different levels of accuracy.

为什么有人要使用不太精确的工具？因为精确有代价。

增加预测概率（较“精确”）

This conceptual diagram illustrates how the Quality filters relate to one another on many wikis (performance varies).

As you can see, the 很可能有问题 filter captures results composed almost entirely of problem edits (high precision). But it captures only a small portion of all problem edits (low recall). Notice how everything in 很可能有问题 (and 可能有问题) is also included in the broader 或有问题, which provides high recall but low precision (because it returns a high percentage of problem-free edits).

You may be surprised to see that 或有问题 overlaps with 非常好. Both filters cover the indeterminate zone between problem and problem-free edits in order to catch more of their targets (broader recall).

因空间原因，例图不能反映其规模。

The more “accurate” filters on the menu return a higher percentage of correct versus incorrect predictions and, consequently, fewer false positives. (In the lingo of pattern recognition, these filters have a higher “precision”.) They achieve this accuracy by being narrower, stricter. When searching, they set a higher bar for probability. The downside of this is that they return a smaller percentage of their target.

Example: The 很可能有问题 filter is the most accurate of the Quality filters. Performance varies from wiki to wiki, but on English Wikipedia its predictions are right more than 90% of the time. The tradeoff is that this filter finds only about 10% of all the problem edits in a given set —because it passes over problems that are harder to detect. The problems this filter finds will often include obvious vandalism.

找到更多目标（更模糊）

If your priority is finding all or most of your target, then you’ll want a broader, less accurate filter. These find more of what they’re looking for by setting the bar for probability lower. The tradeoff here is that they return more false positives. (In technical parlance, these filters have higher “recall”, defined as the percentage of the stuff you’re looking for that your query actually finds.)

Example: The 或有问题 filter is the broadest Quality filter. Performance varies on different wikis, but on English Wikipedia it catches about 82% of problem edits. On the downside, this filter is right only about 15% of the time.

If 15% doesn’t sound very helpful, consider that problem edits actually occur at a rate of fewer than 5 in 100—or 5%. So 15% is a 3x boost over random. And of course, patrollers don’t sample randomly; they’re skilled at using various tools and clues to increase their hit rates. Combined with those techniques, 或有问题 provides a significant edge.

(As noted above, ORES performs differently on different wikis, which means that some are less subject to the tradeoffs just discussed than others. On Polish Wikipedia, for example, the 可能有问题 filter captures 91% of problem edits, compared to 34% with the corresponding filter on English Wikipedia. Because of this, Polish Wikipedia does not need—or have—a broader 或有问题 filter.)

两全其美（高亮功能）

You can get the best of both worlds by filtering broadly but highlighting using more accurate functions. Here, the user casts a wide net for damage by checking to use the broadest Quality filter, 或有问题. At the same time, she identifies the worst or most obvious problems by highlighting (but not filtering with) 可能有问题, 很可能有问题 and 可能有恶意.

The filtering system is designed to let users get around the tradeoffs described above. You can do this by filtering broadly while Highlighting the information that matters most.

To use this strategy, it’s helpful to understand that the more accurate filters, like 很可能有问题, return results that are a subset of the less accurate filters, such as 或有问题. In other words, all “Very likely” results are also included in the broader 或有问题. (The diagram above illustrates this concept.)

Example: Find almost all damage while emphasizing the worst/most likely:

With the default settings loaded,

Check the broadest Quality filter, 或有问题.

At the same time, highlight —without checking the filter boxes— 可能有问题, in yellow, and 很可能有问题, in red.

Because you are using the broadest Quality filter, your results will include most problem edits (high “recall”). But by visually scanning for the yellow, red and orange (i.e., blended red + yellow) bands, you will easily be able to pick out the most likely problem edits and address them first. (Find help on using highlights without filtering.)

找到好编辑（并鼓励）

This reviewer wants to thank new users who are making positive contributions. The 非常好 filter isolates problem-free edits with 99% accuracy. Filtering for 新手 and 初学者 limits the search to these two experience levels, while applying a green highlight to 新手 (only) enables the reviewer to distinguish at a glance between the two levels.

Good faith is easy to find, literally! So are good edits.

The 极可能善意 filter and the 非常好 (Quality) filter give you new ways to find and encourage users who are working to improve the wikis. For example, you might use the 非常好 filter in combination with the 新手 filter to thank new users for their good work.

Example: Thank good-faith new users

Clear the filters by clicking the Trashcan. Then select the 页面编辑 and 人工（非机器人） filters.
Check the Quality filter 非常好.
Check the User Registration and Experience filters 新手 and 初学者 (this has the hidden effect of limiting your results to registered users).
Highlight the 新手 filter, in green.

All edits in your results will be good edits by Newcomers (users with fewer than 10 edits and 4 days of activity) and Learners (users with fewer than 500 edits and 30 days of activity). The green highlight lets you easily distinguish between the two.

Good is everywhere!

The “good” filters mentioned above are both accurate and broad, meaning they aren’t subject to the tradeoffs described in the previous section (they combine high “precision” with high “recall”). These filters are correct about 99% of the time and find well over 90% of their targets. How can they do that?

The happy answer is that the “good” filters perform so well because good is more common than bad. That is, good edits and good faith are much, much more plentiful than their opposites—and therefore easier to find. It may surprise some patrollers to hear this, but on English Wikipedia, for example, one out of every 20 edits has problems, and only about half those problematic edits are intentional vandalism.

^[1]

过滤器列表

On wikis where Quality and Intent Filters are deployed, some filters may be missing due to a better quality of predictions. The better ORES performs on a wiki, the fewer filter levels are needed.

贡献质量预测

非常好: 高精度查找到几乎所有不存在问题的编辑。

或有问题: 以低精度查找到大多数有缺陷或做破坏的编辑。

可能有问题: 以高精度查找出大多数有问题的编辑。; 以中等精度找出有问题的编辑。

很可能有问题: 以极高精度找出最显著的存在缺陷或做破坏的编辑。

用户目的预测

极可能善意: 以高精确度查找出几乎所有善意的编辑。

或为恶意: 以低精度查找到大多数或存恶意的编辑。

可能有恶意: 以中等精度查找到中等概率的存在恶意的编辑。

Revert risk

Filter levels TBD. Uses the Language-agnostic revert risk model.

备注

↑ These figures come from research that went into training the “damaging” and “good faith” ORES models on English Wikipedia. That’s to say, when volunteers scored a large, randomly drawn set of test edits, this is what they found.

[1] These figures come from research that went into training the “damaging” and “good faith” ORES models on English Wikipedia. That’s to say, when volunteers scored a large, randomly drawn set of test edits, this is what they found.

[1]