In this labeling campaign, we'll label a random sample of TODO edits by the type of change that was made in the edit. This will allow us to train and test a machine learning model that detects these edit types later. See #Why?.
We're labeling edits based on Wikipedia:Labels/Edit_types/Taxonomy. See that page for more information about what each "edit intention" means.
Progress edit
| Labeling of 2.2k revisions: 51.9% complete | ||
List of volunteers edit
- EpochFail (talk · contribs)
- Mdann52 (talk · contribs)
- He7d3r (talk · contribs)
- DarTar (talk · contribs)
- ONUnicorn (talk · contribs)
- Epicgenius (talk · contribs)
- TheMagikCow (talk · contribs)
- Kharkiv07 (talk · contribs)
- Stuartyeates (talk · contribs)
- Noyster (talk · contribs)
- Masssly (talk · contribs)
- Blackmane (talk · contribs)
- Diyiy (talk · contribs)
- Econterms (talk · contribs)
- JMatazzoni (WMF) (talk · contribs)
- Okutodue (talk · contribs)
- Your name here
Why? edit
- rich revision histories
- enrich article revision history pages, user contribution pages, recent changes with tagged edits
- predict contributor roles
- study if wikipedian roles can be predicted from their edit types and design automated recommendations / recruitment strategies for articles in need of specific roles
- article lifecycles
- analyze the evolution of individual articles (by type of activities) and study how the article lifecycle has changed over time and across languages
- edit types and editing interfaces
- study if people make different types of edits as a function of the edit interface they are using
- newbie task recommendations
- Understand what tasks are most likely to be successfully picked up by newbies recommending minimizing reverts or deletions; study the engagement/retention effects of priming new contributors with the expected response to the quality and type of their contribution
- wiki work visualizations
- Make it easy to perform project-level or article-level data analysis / visualization by type of contributions