I have some history with Naive Bayes for email filtering[1][2] and it doesn't surprise me that it's still a viable technique. It's incredibly fast to execute and for spam it was fascinating to see that as spammers tried to evade Naive Bayes they were actually increasing the numbers of tokens that made their spam look like spam. And it generalized to multiple categories. To this day I receive messages from people saying they are using POPFile for multi-category email filtering.
As I pointed out in 2004[3], one technique around Naive Bayes (and other machine learning systems) was to pit machine learning against machine learning[4].
[2] https://en.wikipedia.org/wiki/POPFile
[3] https://blog.jgc.org/2023/07/how-to-beat-adaptivebayesian-sp...
[4] https://en.wikipedia.org/wiki/Adversarial_machine_learning