Wikipedia:Labels/Edit types

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History page mockup. A mockup of edit type category icons is presented on top of an article history page.

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]

  1. EpochFail (talk · contribs)
  2. Mdann52 (talk · contribs)
  3. He7d3r (talk · contribs)
  4. DarTar (talk · contribs)
  5. ONUnicorn (talk · contribs)
  6. Epicgenius (talk · contribs)
  7. TheMagikCow (talk · contribs)
  8. Kharkiv07 (talk · contribs)
  9. Stuartyeates (talk · contribs)
  10. Noyster (talk · contribs)
  11. Masssly (talk · contribs)
  12. Blackmane (talk · contribs)
  13. Diyiy (talk · contribs)
  14. Econterms (talk · contribs)
  15. JMatazzoni (WMF) (talk · contribs)
  16. 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