# of daily editors seen on English #Wikipedia, January 2001 to December 2017—anonymous users, logged in users, then two kinds of bots.
So, lazyfediverse:
What happened 2006/2007? Was the financial collapse presaged by collapsing Wiki edit activity? Did Peak Oil trigger Peak Wiki?
(I've spent a month getting a metric ton [~1 GB JSON, converting into ~2 GB NetCDFs] of historic data from Wikimedia REST API and this is the first visualization I made. Won't be the last. https://github.com/fasiha/wikiatrisk)
Daily #Wikipedia editors active on #English, #French, #Japanese, #Russian, #Chinese, #Arabic, and #Hebrew Wikipedias.
So.
Many.
Questions.
(Um, the SVG is at https://gist.github.com/fasiha/8c6f0f0814687fdddcee43c10b5d4a8c with the full curve information, what the hell Mastodon, SVG > PNG.)
Maybe the cutest thing is how English, French, and Russian (the European~) lines have the periodic dips at year-end as people celebrate Xmas/New Years by not editing Wikipedia.
Meanwhile, the Japanese, Chinese, Arabic, and Hebrew lines lol dgaf about holidays, they edit Wikipedia without any serious break (at this resolution at least).
Well color me purple, I'd have thought more #Wikipedia editing happens during the weekend—imaging hardworking Wiki editors wrapping up a week of work work, looking forward to the weekend to do some Real Wiki Work—but nop.
Here's the last year+ of human editors (anon+registered) seen on English each day, by day of week.
Mon–Thurs dominate, some slacking on Friday, tons of slacking on Saturday. Sunday is between Saturday and Friday.
O hai Japan.
Japanese editors match my mental model, logging in in bigger droves on the weekend to contribute.
@22 i would like to see the plot with the y-axis running all yhe way to 0 though
@Maltimore Here it is (ignore the drop to 0 at the right, missing data). So ~200 fewer Japanese editors log in during the work-week than the weekends, around 9% (which is similar to the drop between Mon–Thurs and Friday for the English Wiki).
I'll be predicting daily editing activity, so I'm really interested in finding reliable trends to remove from the data before training prediction models, and the day--of-week is a great predictor. I'll post some other cycle analysis in a few hours.
