Thank you @JAllemandou!

Participants' user data gathering complete: https://github.com/wikimedia-research/cws-historical-metrics/blob/main/02-user_data_gathering.ipynb
(note, the data is not public, as it may contain potential PII)

Regarding random sampling, how balanced should the dataset be across all of the dimensions? If we want to ensure a minimum across a some or all of the dimensions, I'd suggest a stratified random sample.

@Pginer-WMF the Meta-Wiki page is getting quite long with a lot of tables. I would suggest moving tables older than preceding four quarters to an archive sub-page and linking it from the main one. I am happy to that do that if it sounds good to you.

Updated the Meta-Wiki page at https://www.mediawiki.org/wiki/Content_translation/Deletion_statistics_comparison#March_2023_through_April_2023_(Q4_2023)

https://github.com/wikimedia-research/cws-historical-metrics/commit/875293bcd391571a5aeeb7966ff39cad8bce945c

Notebook: https://github.com/wikimedia-research/content-translation-deletion-stats/blob/main/deletion-stats_FY23-Q4.ipynb

@mpopov Yes, as of now, the main use case for this data will be the Quarterly Learning Sessions.

I was looking a bit further into the percentage of the reverts made by ClueBot NG that were reverted back. Although all of these are not necessarily false positives, it's interesting to see that the percentage of reverts reverted back has come down by about 50% over time (~16% at the beginning to ~8% in June 2023). I am wondering if you have any insight into what's causing this (it can be that the model is re-learning consistently, or the decrease in overall revert rate might be a factor, or something else).

@Pginer-WMF the list and the criteria used look good to me. If the languages have high usage of cx despite having no MT support, enabling MT will benefit the editors.

• The bot usually monitors content namespaces and for every revert that was made, a message is posted to the user's talk page whose edit had been reverted.
  • While there were edits made in content namespaces that are not reverts, and reverts in non-content namespaces, their frequency is insigificant, and don't add any value to the analysis.
  • non-content reverts: 0.029% & content edits (excluding reverts): 0.009% of all time edits made by the bot
• From 2010 to early 2013, the bot made between 1500-2500 reverts on average per day.
• From 2013 to 2018, the average daily reverts were between 500-1000.
• From 2019 to mid-2020, the average daily reverts were between 150-250.
• Starting July 2020, the average daily reverts increased to ~500, a trend which continued until the end of 2021.
• There was a sharp drop in the daily reverts made since Jan 2022; it continued to drop until August 2022 when the bot made 65 reverts on average per day.
• From Sep 2022 to Jun 2023 (end of data), the average daily reverts were between 150-200.

Note: As the bot posts a talk page message for each revert (in most cases), the actual edit count would approximately be double of average reverts mentioned above.

Here is the summary of the analysis:

Here's the summary of further analysis.

@nshahquinn-wmf thanks for checking on this.

@BTullis I checked the files. I made a backup of a few data files, and the rest that is required are on GitHub/GitLab - so everything can be removed.

@Hghani (re)joined WMF on the newly formed Movement Insights team, and his access has been reinstated T322145#8862574

• Weekly data is available at https://tr.wikipedia.org/wiki/%C3%96zel:ContentTranslationStats
• Usernames are no longer required

@Samwalton9 I have updated the task description to reflect our discussion. Please add if I missed anything, or change as needed.

@Samwalton9 and I discussed the initial results (as mentioned below) and decided it would be best to expand the scope of this task to investigate further.

@ppelberg Here is the summary of the analysis

Yes, I agree that there is substantial usage of Nuke on unregistered users.

@Samwalton9 Sorry, this took a bit longer than excepted. Although I gathered all-time logs, given the scope of the task i.e. to analyse nuke usage to inform changes required due to IP masking, only recent records (last 3 years) were considered for the analysis, which included ~240000+ nuke actions across wikis. My reasoning is that if there has been no usage of the feature at all during the last three years, it won't be helpful to identify wikis to talk to, and also 3 years is a reasonable amount of time to understand the distributions.

@Samwalton9 I have a clarification question for the second question,

What is this as a percentage of all reverts made within 24 hours of an edit occurring?

I couldn't specifically understand, what "24 hours of an edit occurring" meant.

1.) Because of an issue with EditAttemptStep's sanitization process, the analysis Megan and Mikhail are reviewing includes data from the previous 90 days as opposed to the past year

• Yes, they are reviewing the analysis that considers 90 days of event data, instead of the past year.

Hi, @mforns! Given the recent team changes, we are not exactly sure which team(s) to tag for review. Megan suggested you might be able to help or redirect if needed. Thank you.