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README.md
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wordfreq is a Python library for looking up the frequencies of words in many wordfreq is a Python library for looking up the frequencies of words in many
languages, based on many sources of data. languages, based on many sources of data.
The word frequencies are a snapshot of language usage through about 2021. I may
continue to make packaging updates, but the data is unlikely to be updated again.
The world where I had a reasonable way to collect reliable word frequencies is
not the world we live in now. See [SUNSET.md](./SUNSET.md) for more information.
Author: Robyn Speer Author: Robyn Speer
## Installation ## Installation
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Policy. This software gives statistics about words that are commonly used on Policy. This software gives statistics about words that are commonly used on
Twitter; it does not display or republish any Twitter content. Twitter; it does not display or republish any Twitter content.
## Can I convert wordfreq to a more convenient form for my purposes, like a CSV file?
No. The CSV format does not have any space for attribution or license
information, and therefore does not follow the CC-By-SA license. Even if you
tried to include the proper attribution in a header or in another file, someone
would likely just strip it out.
wordfreq isn't particularly separable from its code, anyway. It depends on its
normalization and word segmentation process, which is implemented in Python
code, to give appropriate results.
A reasonable way to transform wordfreq would be to port the library to another
programming language, with all credits included and packaged in the usual way
for that language.
## Citing wordfreq ## Citing wordfreq
If you use wordfreq in your research, please cite it! We publish the code If you use wordfreq in your research, please cite it! We publish the code

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# Why wordfreq will not be updated
The wordfreq data is a snapshot of language that could be found in various
online sources up through 2021. There are several reasons why it will not be
updated anymore.
## Generative AI has polluted the data
I don't think anyone has reliable information about post-2021 language usage by
humans.
The open Web (via OSCAR) was one of wordfreq's data sources. Now the Web at
large is full of slop generated by large language models, written by no one to
communicate nothing. Including this slop in the data skews the word
frequencies.
Sure, there was spam in the wordfreq data sources, but it was manageable and
often identifiable. Large language models generate text that masquerades as
real language with intention behind it, even though there is none, and their
output crops up everywhere.
As one example, [Philip Shapira
reports](https://pshapira.net/2024/03/31/delving-into-delve/) that ChatGPT
(OpenAI's popular brand of language model circa 2024) is obsessed with the word
"delve" in a way that people never have been, and caused its overall frequency
to increase by an order of magnitude.
## Information that used to be free became expensive
wordfreq is not just concerned with formal printed words. It collected more
conversational language usage from two sources in particular: Twitter and
Reddit.
The Twitter data was always built on sand. Even when Twitter allowed free
access to a portion of their "firehose", the terms of use did not allow me to
distribute that data outside of the company where I collected it (Luminoso).
wordfreq has the frequencies that were built with that data as input, but the
collected data didn't belong to me and I don't have it anymore.
Now Twitter is gone anyway, its public APIs have shut down, and the site has
been replaced with an oligarch's plaything, a spam-infested right-wing cesspool
called X. Even if X made its raw data feed available (which it doesn't), there
would be no valuable information to be found there.
Reddit also stopped providing public data archives, and now they sell their
archives at a price that only OpenAI will pay.
And given what's happening to the field, I don't blame them.
## I don't want to be part of this scene anymore
wordfreq used to be at the intersection of my interests. I was doing corpus
linguistics in a way that could also benefit natural language processing tools.
The field I know as "natural language processing" is hard to find these days.
It's all being devoured by generative AI. Other techniques still exist but
generative AI sucks up all the air in the room and gets all the money. It's
rare to see NLP research that doesn't have a dependency on closed data
controlled by OpenAI and Google, two companies that I already despise.
I don't want to work on anything that could be confused with generative AI,
or that could benefit generative AI.
OpenAI and Google can collect their own damn data. I hope they have to pay a
very high price for it, and I hope they're constantly cursing the mess that
they made themselves.
— Robyn Speer