I love this tool. Congratulations to everyone who was involved!
The more I use it, the more I wonder about some questions that have been raised in the Telegram group:
1)  are there some stats on the amount of vandalism that comes from IPs? 
2) related to (1) most of the clear spam edits I've found using the tool, come from unregistered users changing descriptions and "instance of" an item to silly claims. If this trend is backed up by the data you get from the annotation tool, would it make sense to at least protect those fields to be edited only by registered users?
3) is there (or will there be) an easier* way to report vandalism from an IP other than writing a report at Administrator's noticeboard (ca. 3 min for each report)?
I imagine something like looking at this IP's "contributions" (all vandalism) & having a button under "User" where I could report this person's contributions as vandalism.

Again, thank you for letting us know about this awesome tool!

On Fri, Aug 11, 2023 at 7:14 PM Seth Deegan <jayandseth@gmail.com> wrote:
Awesome tool! We should make this a banner on Wikidata to get as many people to annotate as possible! The potential for reverting edits like the ones shown in the tool is very exciting!



El jue, 10 ago 2023 a la(s) 02:28, Mohammed Sadat Abdulai (mohammed.abdulai@wikimedia.de) escribió:

Hi everyone,


As you're likely aware, the Wikimedia Foundation's Machine Learning and Research teams have been working on migrating from ORES to Lift Wing — a new open-source machine learning infrastructure. This shift brings a host of new capabilities and simplifies the process of retraining models over time. (For more details, see the previous announcement)


Lift Wing has already been trained using a dataset comprising reverted and patrolled edits. However, it would be extremely helpful to have additional new training data to help the model get even better at detecting problematic edits on Wikidata. Therefore, we need your help.


How You Can Contribute

The Research team built a tool to make your involvement easy and effective. This tool allows you to label new training data quickly and efficiently. You can find the tool here: Annotation Tool. It will show you an edit and ask you if you would keep or revert the edit. You can skip any you are not sure about.


By participating in this process, you're helping enhance the accuracy of the bad edits detection system on Wikidata, making it more robust and reliable.


If you encounter any issues or want to provide general feedback, feel free to leave us a note on this ticket phab:T341820.


Cheers,

--
Mohammed S. Abdulai
Community Communications Manager, Wikidata

Wikimedia Deutschland e. V. | Tempelhofer Ufer 23-24 | 10963 Berlin
Phone: +49 (0) 30 577 116 2466
https://wikimedia.de

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