Fred IS prominently thanked, and with good reason.
In fact, let me thank him extra: thanks, Fred!
P.S. If you're using dChan, especially our frequency-of-a-search-term-over-time tool, you should read the About section bc there's Stuff You Should Know™.
dchan.qorigins.org/about/
In fact, let me thank him extra: thanks, Fred!
P.S. If you're using dChan, especially our frequency-of-a-search-term-over-time tool, you should read the About section bc there's Stuff You Should Know™.
dchan.qorigins.org/about/
https://twitter.com/fr_brennan/status/1439429403109470215
Some might say that the "frequency-of-a-search-term-over-time tool" is actually called the "time series visualizer," but I say to them: shut up and stop being correct, you jerks. >:(
Anyway, here it is if you wanna play around with it, kids.
dchan.qorigins.org/timeseries
Anyway, here it is if you wanna play around with it, kids.
dchan.qorigins.org/timeseries
By default it's set to display how frequently the term is used per 1,000 posts in our dataset.
You CAN switch to a different view, where it measures the absolute frequency of a term, but there are almost no circumstances in which you SHOULD.
You CAN switch to a different view, where it measures the absolute frequency of a term, but there are almost no circumstances in which you SHOULD.
It's SO rare that if you're looking at a query and thinking, "Should I search by the actual # of times this phrase was used?", I STRONGLY encourage you to 1) read the about page & 2) DM me or Robert and talk it over first, bc oh boy are there some hidden pitfalls to that method.
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