Content review: when we use technology and when we use human review
When something goes against the Community Standards, Meta has content enforcement systems in place to respond. For example, we use artificial intelligence and machine learning tools to help us identify and remove a large amount of violating content—often, before anyone sees it. In those instances where our technology misses something or needs more input, we rely on thousands of reviewers around the world to enforce our Community Standards.
As potential content violations get routed to review teams, each reviewer is assigned a queue of posts to individually evaluate. Sometimes, this review means simply looking at a post to determine whether it goes against our policies, such as an image containing adult nudity, in instances when our technology didn’t detect it first.
In other cases, context is key. For example, our technology might be unsure whether a post contains bullying, a policy area that requires extra context and nuance because it often reflects the nature of personal relationships. In this case, we’ll send the post to review teams that have the right subject matter and language expertise for further review. If necessary, they can also escalate it to subject matter experts on the Global Operations or Content Policy teams.
When necessary, we also provide reviewers additional information from the reported content. For example, words that are historically used as racial slurs might be used as hate speech by one person but can also be a form of self-empowerment when shared by another person, in a different context. In some cases, we may provide additional context about such words to reviewers to help them apply our policies and decide whether the post should be left up or taken down.