It would be very rare (and undesirable) for humans to (a) review all the content shared on a site and (b) review content pre-publish – that is, when a user tries to share something, having it "approved" by a human before it goes live on the site/app.
Instead, companies rely upon content review algorithms which do a lot of the heavy lifting. The algorithms attempt to "understand" the content being created and shared. At point of creation there are limited signals – who uploaded it (account history or lack thereof), where it was uploaded from, the content itself and other metadata. As the content exists within the product more data is gained – who is consuming it, is it being flagged by users, is it being shared by users and so on.
These richer signals factor into the algorithm continuing to tune its conclusion about whether a piece of content is appropriate for the site or not. Most of these systems have user flagging tools which factor heavily into the algorithmic scoring of whether content should be elevated for review.
Most broadly, you can think about a piece of content as being Green, Yellow or Red at any given time. Green means the algorithm thinks it's fine to exist on the site. Yellow means it's questionable. And Red, well, red means it shouldn't be on the site. Each of these designations are fluid and not perfect. There are false positives and false negatives all the time.
To think about the effectiveness of a Content Policy as *just* the quality of the technology would be incomplete. It's really a policy question decided by people and enforced at the code level. Management needs to set thresholds for the divisions between Green, Yellow and Red. They determine whether an unknown new user should default to be trusted or not. They conclude how to prioritize human review of items in the Green, Yellow or Red buckets. And that's where humans mostly come into play…