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
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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