Moderator Tools/Automoderator/Testing

To help communities test and evaluate Automoderator 's accuracy, we are making a test spreadsheet available with data on past edits and whether Automoderator would have reverted them or not.

Diagram demonstrating the Automoderator software decision process.

Automoderator’s decisions result from a mix of a machine learning model score and internal settings. While the model will get better with time through re-training, we’re also looking to enhance its accuracy by defining some additional internal rules. For instance, we’ve observed Automoderator occasionally misidentifying users reverting their own edits as vandalism. To improve, we’re seeking similar examples and appreciate your assistance in identifying them.

Note that this test does not necessarily reflect Automoderator's final form - we will be using the results of this test to make it better!

How to test Automoderator

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Screenshot of the spreadsheet, with example responses filled in.
  • If you have a Google account:
    1. Use the Google Sheet link below and make a copy of it
      • You can do this by clicking File > Make a Copy ... after opening the link.
    2. After your copy has loaded, click Share in the top corner, then give any access to avardhana wikimedia.org (leaving 'Notify' checked), so that we can aggregate your responses to collect data on Automoderator's accuracy.
      • Alternatively, you can change 'General access' to 'Anyone with the link' and share a link with us directly or on-wiki.
  • Alternatively, use the .ods file link to download the file to your computer.
    • After adding your decisions, please send the sheet back to us at avardhana wikimedia.org, so that we can aggregate your responses to collect data on Automoderator's accuracy.

After accessing the spreadsheet...

  1. Follow the instructions in the sheet to select a random dataset, review 30 edits, and then uncover what decisions Automoderator would make for each edit.
    • Feel free to explore the full data in the 'Edit data & scores' tab.
    • If you want to review another dataset please make a new copy of the sheet to avoid conflicting data.
  2. Join the discussion on the talk page.

Alternatively, you can simply dive in to the individual project tabs and start investigating the data directly.


We welcome translations of this sheet - if you would like to submit a translation please make a copy, translate the strings on the 'String translations' tab, and send it back to us at swalton wikimedia.org.

If you want us to add data from another Wikipedia please let us know and we would be happy to do so.

About Automoderator

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Automoderator’s model is trained exclusively on Wikipedia’s main namespace pages, limiting its dataset to edits made to Wikipedia articles. Further details can be found below:

Internal configuration

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In the current version of the spreadsheet, in addition to considering the model score, Automoderator does not take actions on:

  • Edits made by administrators
  • Edits made by bots
  • Edits which are self-reverts
  • New page creations

The datasets contain edits which meet these criteria, but Automoderator should never say it will revert them. This behaviour and the list above will be updated as testing progresses if we add new exclusions or configurations.

Caution levels

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In this test Automoderator has five 'caution' levels, defining the revert likelihood threshold above which Automoderator will revert an edit.

  • At high caution, Automoderator will need to be very confident to revert an edit. This means it will revert fewer edits overall, but do so with a higher accuracy.
  • At low caution, Automoderator will be less strict about its confidence level. It will revert more edits, but be less accurate.

The caution levels in this test have been set by the Moderator Tools team based on our observations of the models accuracy and coverage. To illustrate the number of reverts expected at different caution levels see below:

Daily edits Daily edit reverts Average daily reverts by Automoderator
Very cautious

>0.99

Cautious

>0.985

Somewhat cautious

>0.98

Low caution

>0.975

Not cautious

>0.97

English Wikipedia 140,000 14,600 152 350 680 1,077 1,509
French Wikipedia 23,200 1,400 24 40 66 98 136
German Wikipedia 23,000 1,670 14 25 43 65 89
Spanish Wikipedia 18,500 3,100 57 118 215 327 445
Russian Wikipedia 16,500 2,000 34 57 88 128 175
Japanese Wikipedia 14,500 1,000 27 37 48 61 79
Chinese Wikipedia 13,600 890 9 16 25 37 53
Italian Wikipedia 13,400 1,600 40 61 99 151 211
Polish Wikipedia 5,900 530 10 16 25 35 45
Portuguese Wikipedia 5,700 440 2 7 14 21 30
Hebrew Wikipedia 5,400 710 16 22 30 38 48
Persian Wikipedia 5,200 900 13 26 44 67 92
Korean Wikipedia 4,300 430 12 17 23 30 39
Indonesian Wikipedia 3,900 340 7 11 18 29 42
Turkish Wikipedia 3,800 510 4 7 12 17 24
Arabic Wikipedia 3,600 670 8 12 18 24 31
Czech Wikipedia 2,800 250 5 8 11 15 20
Romanian Wikipedia 1,300 110 2 2 4 6 9
Croatian Wikipedia 500 50 1 2 2 3 4
... ... ... ... ... ... ... ...
All Wikipedia projects 538 984 1,683 2,533 3,483

This data can be viewed for other Wikimedia projects here.

Score an individual edit

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Importing this user script will give you a 'Get revert risk score' button in your Tools menu.

We have created a simple user script to retrieve a Revert Risk score for an individual edit. Simply import User:JSherman (WMF)/revertrisk.js into your commons.js with mw.loader.load( 'https://en.wikipedia.org/wiki/User:JSherman_(WMF)/revertrisk.js?action=raw&ctype=text/javascript' );.

You should then find a 'Get revert risk score' in the Tools menu in your sidebar. Note that this will only display the model score, and does not take into account Automoderator's internal configurations as detailed above. See the table above for the scores above which we are investigating Automoderator's false positive rate.

Initial results

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Quantitative

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22 testing spreadsheets were shared back with us, totalling more than 600 reviewed edits from 6 Wikimedia projects. We have aggregated the data to analyse how accurate Automoderator would be at different caution levels:

Not cautious (0.97) Low caution (0.975) Somewhat cautious (0.98) Cautious (0.985) Very cautious (0.99)
75% 82% 93% 95% 100%

In our Moderator Tools/Automoderator/Measurement plan we said that we wanted the most permissive option Automoderator could be set at to have an accuracy of 90%. The ‘Not cautious’ and ‘Low caution’ levels are clearly below this, which isn’t surprising as we didn’t have clear data from which to select these initial thresholds. We will be removing the ‘Not cautious’ threshold, as a 25% error rate is clearly too low for any communities. We will retain ‘Low caution’ for now, and monitor how its accuracy changes as model and Automoderator improvements occur leading up to deployment. We want to err on the side of Automoderator not removing bad edits, so this is a priority for us to continue reviewing.

When we have real world accuracy data from Automoderator's pilot deployment we can investigate this further and consider changing the available thresholds further.

Qualitative

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On the testing talk page and elsewhere we also received qualitative thoughts from patrollers.

Overall feedback about Automoderator’s accuracy was positive, with editors feeling comfortable at various thresholds, including some on the lower end of the scale.

Some editors raised concerns about the volume of edits Automoderator would actually revert being relatively low. This is something that we’ll continue to discuss with communities. From our analysis (T341857#9054727) we found that Automoderator would be operating at a somewhat similar capacity to existing anti-vandalism bots developed by volunteers, but we’ll continue to investigate ways to increase Automoderator’s coverage while minimising false positives.

Next steps

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Based on the results above, we feel confident in the model’s accuracy and plan to continue our work on Automoderator. We will now start technical work on the software, while exploring designs for the user interface. We expect that the next update we share will contain configuration wireframes for feedback.

In the meantime please feel free to continue testing Automoderator via the process above - more data and insights will continue to have a positive impact on this project.