[throw16180339]

> Just take it down, there will be 15 million new posts in an hour.

I'm a mod at a 150k+ user subreddit. AI can probably handle removing objectionable content, but I spend most of my time on other areas that are much less susceptible to automation.

• Removing requests when there are recent similar posts. We've found that users disengage when the same requests are posted every week.

• Hosting AMAs. An AI isn't going to know who we should invite or whether they'll be a good fit for our subreddit.

• Detecting and banning people deceptively promoting their own products. This is incredibly common and often quite difficult.

[linuxftw]

I'm confident an AI can be utilized for 'remove duplicate content' type of operation.

AMAs don't require a mod to participate, just the community. All mods really do is sticky a thread.

People deceptively promoting their own products, others are free to down vote them and report the post.

[throw16180339]

Exact duplicates are already automatically removed by bots. We remove request posts that lack detail or when there have been several similar active posts within the year. This strikes a balance between allowing every post only once and swamping the subreddit with nearly identical requests. We removed about 75% of the posts reported for this category last month, the remaining 25% either didn't have previous similar requests or had enough to detail to stay up. Many cases are ambiguous and resolved by majority vote.

AMAs don't happen spontaneously in major subs. As with everything else, there's a lot of admin work that goes on behind the scenes. We mostly invite people for AMAs; under 10% of this year's 10+ AMAs were requested by the participant. Someone has to email them, answer their questions, schedule a time, explain how to post, join them in a chat channel, etc.

Deceptive promotion is rarely flagged on our sub. We got ~450 reports last month and none of them were for promotion. Promotion posts are quite rare; they're against our rules and most people don't even try. What we regularly see are comments from users recommending their own products. Some cases are obvious, e.g. new accounts frequently recommending a book without ratings that was released yesterday. Many are much harder to identify; we regularly search user profiles and the Internet to confirm an identity. One person we banned last year had been covertly recommending their book for months. It took numerous searches and several weeks to remove all their promotion comments.