Additional Info: I designed a Python workflow to perform OCR on every xkcd comic, feed that text into a large language model, and ask the model whether this comic was about the category named in the title.
Report an error
xkcd comics published about social media correlates with...
Variable | Correlation | Years | Has img? |
Biomass power generated in Malta | r=0.9 | 11yrs | Yes! |
Total length of LockPickingLawyer YouTube videos | r=0.88 | 9yrs | No |
Popularity of the first name Neil | r=0.72 | 16yrs | Yes! |
xkcd comics published about social media also correlates with...
<< Back to discover a correlation
You caught me! While it would be intuitive to sort only by "correlation," I have a big, weird database. If I sort only by correlation, often all the top results are from some one or two very large datasets (like the weather or labor statistics), and it overwhelms the page.
I can't show you *all* the correlations, because my database would get too large and this page would take a very long time to load. Instead I opt to show you a subset, and I sort them by a magic system score. It starts with the correlation, but penalizes variables that repeat from the same dataset. (It also gives a bonus to variables I happen to find interesting.)