Many thanks to all who replied on this! Once y'all pointed me in the right 
direction, I was able to go back up the chain a bit and find what I needed in 
SpamAssassin to block the particular spammer in question based on his Bitcoin. 
Thanks again!

________________________________
From: Ethan Lewis <iethanlew...@gmail.com>
Sent: Thursday, May 25, 2023 5:00:40 AM
To: mailman-users@python.org
Subject: [Mailman-Users] Re: Question about content filtering in message body

One popular approach for text filtering is to create a list of "bad words" or 
inappropriate terms and check if any of those words appear in the message body. 
Here's a high-level overview of how you can implement this:

Define a list of inappropriate words or phrases that you want to filter. This 
list can be as extensive as you need it to be, and you can include words 
related to profanity, offensive language, or any other content you wish to 
block.

Tokenize the message body: Split the text into individual words or tokens. The 
exact method for tokenization depends on the programming language or library 
you are using. Common options include using regular expressions or built-in 
string manipulation functions.

Compare the tokens with the list of inappropriate words: Iterate over the 
tokens in the message body and check if each token appears in your list of 
inappropriate words. You may need to consider case sensitivity and handle 
variations of words (e.g., plural forms, alternative spellings) depending on 
your requirements.

Take action based on the results: If you find any matches between the tokens 
and the inappropriate word list, you can decide how to handle those messages. 
Options include flagging the message for manual review, replacing inappropriate 
words with asterisks or other symbols, or rejecting the message altogether.

Please note that implementing a content filtering system involves some level of 
customization based on your specific needs and the programming language or 
platform you are working with. There are also third-party services and 
libraries available that provide more advanced text filtering capabilities, 
such as natural language processing (NLP) algorithms or machine learning 
models, which can help improve the accuracy of the filtering process.
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