It would be extremely rare (and undesirable) for humans to (a) analysis all the articles shared on a site and (b) analysis content pre-publish – that is, whenever a user tries to share something, having it “approved” by a people before it goes go on the site/app.
Instead, companies rely after content analysis algorithms which execute a lot of the major lifting. The algorithms try to “understand” this content being created and shared. At point of creation there are limited signals – who uploaded it (bill history or lack thereof), where it had been uploaded from, this content itself and additional metadata. As this content exists within the product more info is gained – who is consuming it, is it being flagged by users, is it being shared by users etc.
These richer signals factor into the algorithm continuing to tune its conclusion about whether a bit 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 ought to be elevated for review.
Most broadly, you may look at a piece of articles as being Green, Yellow or Red at any given time. Green means the algorithm thinks it’s excellent to are present on the website. Yellow means it’s questionable. And Red, well, crimson means it must not be on the internet site. Each of these designations are fluid rather than perfect. There are false positives and false negatives all the time.
To think about the potency of a Content Policy as *just* the quality of the technology would be incomplete. It’s really a policy dilemma decided by persons and enforced at the code level. Management needs to set thresholds for the divisions between Green, Yellow and Red. They determine whether an unidentified new user should default to come to be trusted or certainly not. They conclude how exactly to prioritize human review of things in the Green, Yellow or Red buckets. And that’s where humans mostly come into play…