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.
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xkcd comics published about sarcasm correlates with...
Variable | Correlation | Years | Has img? |
GMO use in corn grown in South Dakota | r=0.89 | 17yrs | No |
Number of edits to the Wikipedia article for Deloitte | r=0.87 | 17yrs | No |
BP's stock price (BP) | r=0.86 | 17yrs | Yes! |
The number of university biological science teachers in Alabama | r=0.74 | 14yrs | Yes! |
Rain in Berlin | r=0.58 | 16yrs | No |
xkcd comics published about sarcasm also correlates with...
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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.)