The difficult part is finding a few hundred mods willing to work for you for free, filtering all the filth that tries to be posted.
Only if they have a solution for that can try going their own way.
and yes, I've used content moderation AIs in the past (like Google's Perspective API) and they're not really usable. OpenAI moderation endpoint, embeddings classification, or even just gpt3.5-turbo would work marvelously.
Even $1 per month is enough to keep a lot of the outrage junkies out and you can use the revenue to pay for moderation of the smaller group of power users that remain.
The removal of r/waterniggas (a sub about staying hydrated) seems absurd ideologically, even with that name.
The difficult part is finding a few hundred mods willing to work for you for free, filtering all submissions and comments that conflict with the narrative being pushed by the establishment.
Hacker News is great because many of the comments are substantive, thought-provoking and don't read like State Department press releases or obvious corporate astroturfing.
Most web development is downloading. Social networks have massive uploading and frequent changes.
Twitter literally invented microservices because of this.
The hard thing early on might be getting getting started with good training data. But chatgpt might already be good enough to make reasonable choices today with a good system prompt.
Service-oriented architecture: Am I just a joke to you?
First, I parsed the posts for the most common phrases at varying lengths, hand identifying 3,730 individual strings that I felt indicated spam within the post title, post body, reddit username, reddit user description or comment bodies.
These strings are then checked against new or updated records and things are flagged as spam as needed.
It's been weeks since I've had to manually intervene and identify more spam strings - that's not to say I won't need to eventually as trends and techniques change (or, as it happens - reddit's api changes), but this was a fantastically successful means for identifying and analyzing obvious spam.
Beyond the above, I used what was a relatively simple approach to identify similar post titles to those that were determined to be spam for a "if you thought that was spam then you'll probably think this is too..." type feature that was very effective.
If reddit's api changes weren't happening I'd have already started training an ML model/NN or whatever chatGPT told me was the best one to use in order to classify these objects from the existing data.
Ironically, all of this was in order to offer moderation bots to subreddits to help handle the spam problem.
I started with scraping the API to play with meilisearch as a search engine but was just awestruck at the amount of _obvious_ spam that was getting through automod/reddit's own spam filtering (if there is any?) before being published/available via the API. I just didn't want to store all the metadata I was generating for all the spam posts and couldn't depend on reddit to police the issues on their end.
Now they're still unable to get a handle on spam - but also cutting off the developers trying to help them.