Help:New filters for edit review/Quality and Intent Filters/pt-br

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

Estes filtros estão disponíveis somente em certas wikis.

Baseado em aprendizado de máquina
The predictions that make the Quality and Intent filters possible are calculated by ORES, a machine learning program trained on a large set of edits previously scored by human editors. Machine learning is a powerful technology that lets machines replicate some limited aspects of human judgement.

The Quality and Intent filters are available only on wikis where the “damaging” and “good faith” ORES “models” are supported. The ORES “damaging” model powers Quality predictions, while its “good-faith” model powers Intent.

Enabling ORES requires volunteers to score edits on the relevant wiki. This page explains the process and how you can get it started on your wiki.

Escolhendo a ferramenta certa
Vendo os filtros de Qualidade e Intenção, você pode notar algo diferente sobre eles. Diferente dos filtros de outros grupos, as várias opções não focam em diferentes propriedades de edição. Ao invés, muitas delas focam na mesma propriedade, mas oferecem diferentes níveis de precisão.

Por que alguém escolheria usar uma ferramenta que é menos precisa? Porque tal precisão tem um custo.

Aumentar a probabilidade de previsão (maior 'precisão')
[[File:RC-quality-filters-diagram.png|alt=This conceptual diagram illustrates how the Quality filters relate to one another.|thumb|400x400px|Este diagrama conceitual ilustra como os filtros de Qualidade relacionam uns com os outros.

As you can see, the  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 ' (and ') is also included in the broader , which provides high recall but low precision (because it returns a high percentage of problem-free edits).

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

Por questões de espaço, o diagrama não reflete escala com precisão. ]] 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  filter is the most accurate of the Quality filters. Its predictions are right about 90% of the time. The tradeoff is that it finds less than 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.

Find more of your target (higher ‘recall’)
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.)


 * Exemplo: O filtro  é o filtro de Qualidade mais amplo. Ele captura cerca de 90% das edições problemáticas. No lado negativo, este filtro está certo apenas cerca de 15% das vezes.

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,  provides a significant edge.

Obtenha o melhor dos dois mundos (com destaques)


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 ', return results that are a subset of the less accurate filters, such as '. In other words, all “Very likely” results are also included in the broader  set—like the bullseye of a target contained within the outer rings. (The diagram at right illustrates this concept.)


 * Example: Find almost all damage while emphasizing the worst/most likely:
 * With the default settings loaded,
 * Check the broadest Quality filter, .
 * At the same time, highlight —without checking the filter boxes— ', in yellow, and ', in red.* Because you are using the broadest Quality filter, your results will include 90% of problem edits (high “recall”). But by scanning for the yellow and orange (i.e., blended red + yellow) bands, you will easily be able to pick out the most likely problem edits. (Find help on using highlights without filtering.)

Re-use your settings
Use the above example as a jumping-off place for your own experiments. Find setting combinations that work for you. When you do, you can save your settings and re-use them. To do so, simply set everything as you want it, then copy the page URL and save it in a document someplace. Clicking on the URL later will reinstate all the settings that were in effect when it was copied.

This technique works on mobile browsers, too, even though the new user interface for filtering doesn’t display on mobile currently. Even without the interface, all your settings will be activated.

Find the good (and reward it)


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

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

Or, since research shows that new users are particularly vulnerable to having their edits reverted, you might use the settings below to find new users who are making mistakes but who are, nonetheless, working in good-faith—and then offer constructive comments and support.


 * Example: Find problem edits by good-faith new users
 * Clear the filters by clicking the Trashcan. Then select the ' and ' filters.
 * Check the medium-level Quality filter, .
 * Check the Experience Level filter  (this has the hidden effect of limiting your results to registered users).
 * Highlight—without checking the filter boxes—the User Intent filter ', in green, and the Quality filter '," in yellow. * All edits in your results will be the ones done by Newcomers (users with fewer than 10 edits and 4 days of activity). The  filter has a medium accuracy, so a little less than half of the results should have some kind of problem. The edits in green or yellow-green are your good faith newcomers who are struggling.

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.

Filters list
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.